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Foreign language professors

Official title Foreign Language and Literature Teachers, Postsecondary (SOC 25-1124)

Also called Spanish professor, French professor, German professor, Chinese professor, Mandarin professor

In brief

In May 2025, BLS counted 19,830 US foreign language professors, with median pay of $79,350 a year. Of the BLS counts since 2021, the highest was 21,170, in May 2024. The earliest figure here is an estimate of 200 from 1883, not measured the same way as today's count. The last full technology era for foreign language professors: LMS integration, video-conferencing, and neural machine translation, 2015 to 2023.

What the sources say

Official outlook

BLS projects little or no change in employment from 2025 to 2035 (+0.2%).

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Foreign language professors: jobs and pay since 1883 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/25-1124

Chicago

Future History. "Foreign language professors: jobs and pay since 1883." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/25-1124.

BibTeX

@misc{futurehistory_25_1124, author = {{Future History}}, title = {Foreign language professors: jobs and pay since 1883}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/25-1124}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
180018251850187519001925195019752000now
2026
Known then as Professor of Modern Languages (antebellum and early collegiate era)
Known then as Modern Language Professor / Instructor in Modern Languages (MLA era, Grammar-Translation dominant)
Known then as Foreign Language Instructor / Language Laboratory Director (NDEA Cold War era, Audio-Lingual Method)
Known then as Foreign Language and Literature Faculty / Lecturer (communicative turn and tenure-track consolidation)
Known then as Foreign Language and Literature Teachers, Postsecondary (BLS SOC 25-1124)
Known today as Foreign Language and Literature Teachers, Postsecondary (BLS SOC 25-1124)
Projected to still be called Foreign Language and Literature Teachers, Postsecondary (BLS SOC 25-1124)
Data from the May 2025 survey
US Employment
20K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$79,350
Est.

≈ $77,316 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1883

December 27-28, 1883: forty college language professors gather at Columbia College in New York City to found the Modern Language Association of America. Franklin Carter of Williams College serves as first president; A. Marshall Elliott of Johns Hopkins is elected secretary. The MLA's stated purpose is to encourage the study of modern languages at a time when the collegiate curriculum is dominated by classical languages. The association will become the primary professional home of foreign language and literature faculty for the next 140 years.

Beat · 1917

US entry into World War I triggers a wave of anti-German sentiment that effectively destroys German-language instruction at American schools and universities. Indiana University German enrollment collapses by mid-1918. South Dakota's Council of Defense bans German instruction at all educational levels. By 1923, 23 states have passed laws restricting or prohibiting foreign-language instruction in elementary grades; universities are more sheltered but not immune. French and Spanish programs absorb some of the displaced enrollment but cannot fully replace the lost demand. The German-language professorial workforce shrinks dramatically and never fully recovers to its pre-war relative position.

Beat · 1958

September 2, 1958: President Eisenhower signs the National Defense Education Act, passed in direct response to the Soviet Sputnik launch (October 1957). Title VI provides federal funding for language and area studies programs at US colleges and universities; Title IV funds graduate fellowships to create more college instructors. The NDEA transforms foreign language departments from modestly funded humanities programs into national-security infrastructure. Language laboratories are built or expanded with federal dollars at hundreds of universities. The act directly grows the foreign-language faculty workforce through both supply (graduate fellowships) and demand (new program funding). For twenty years after 1958, Cold War strategic interest in critical languages -- Russian, Arabic, Chinese, Japanese -- provides a federal rationale for language programs that supplements (and sometimes substitutes for) enrollment-driven demand.

Beat · 2009

The MLA enrollment census for fall 2009 records 1,673,566 students enrolled in languages other than English in US colleges and universities -- the all-time peak. From 1980 to 2009, language enrollment had grown from 924,337 to this high, a 81 percent increase over three decades. The peak coincides with the worst year of the Great Recession, when many students returned to higher education and enrollment broadly spiked. It is the last MLA census to show growth. Every subsequent census through 2021 records decline.

Beat · 2021

The 2021-2022 MLA enrollment survey finds that total language enrollments have fallen 29.3 percent from the 2009 peak. The 2016-2021 period alone saw a 16.6 percent decline -- the steepest five-year drop ever recorded in the MLA census. German fell 33.6 percent, French more than 23 percent, Arabic 27 percent, Spanish 18 percent. Korean is the exception at +38.3 percent. The report documents that language enrollments declined at more than twice the rate of overall college enrollment during 2016-2021, confirming that the decline is field-specific, not just demographic. The MLA calls on institutions to invest in language study as a response; several regional universities have begun closing language departments entirely.

Beat · 2024

January 2024: Duolingo lays off 10 percent of its contract content creators, explicitly citing AI replacement. Duolingo CEO states that AI will handle "all the content that humans are doing today" (Bloomberg, January 2024). The announcement is widely covered as a signal moment in language education: the world's largest consumer language-learning platform has committed to replacing human content creators with AI. For university foreign language faculty, the symbolic resonance is significant: if Duolingo -- the platform that already made the case against formal language instruction in many students' minds -- is also replacing its human workforce with AI, the argument for institutional language programs as the site of essential human instruction becomes narrower and more specific.

Beat · 2025

October 2025: the MLA Executive Council issues its Statement on Educational Technologies and AI Agents, the field's most authoritative response to the AI challenge. The statement asserts that "AI tools cannot develop the intercultural competence, critical humanistic understanding, or embodied linguistic intuition that language education cultivates." The field's emerging consensus positions AI as augmenting lower-level drilling and materials-generation tasks while humans remain irreplaceable for oral proficiency assessment, literary interpretation in the target language, and heritage learner mentorship.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace foreign language professors? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+0.2%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 25-1124 (Foreign language and literature teachers, postsecondary): 24.6 thousand jobs in 2025, projected 24.7 thousand in 2035, a change of +0.1 thousand (+0.2%). Matrix employment counts all jobs in the occupation, including self-employed workers (0.2% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
56.7%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 25-1124.00 Foreign Language and Literature Teachers, Postsecondary. Shown: human-rated β = 0.567, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.289 and γ (all LLM-assisted) 0.844; GPT-4-rated β 0.533. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for foreign language professors

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereProvide formative feedback on student writing in the target language: using DeepL Pro to generate a reference machine translation of student drafts for diagnostic comparison, then applying native or near-native linguistic expertise to identify errors the machine translation cannot characterize (pragmatic miscalibration, register violations, false cognates, cultural inappropriateness, and idiomatic failures that score correctly by MT but read as non-native), producing developmental feedback that advances acquisition rather than merely correcting surface form.

Provide formative feedback on student writing in the target language: using DeepL Pro to generate a reference machine translation of student drafts for diagnostic comparison, then applying native or near-native linguistic expertise to identify errors the machine translation cannot characterize (pragmatic miscalibration, register violations, false cognates, cultural inappropriateness, and idiomatic failures that score correctly by MT but read as non-native), producing developmental feedback that advances acquisition rather than merely correcting surface form.[11]

Where your edge is

DeepL Pro (2025) provides professional-quality translation feedback for European language pairs that exceeds what most intermediate-level language students can generate: a student comparing their own draft Spanish email against DeepL's rendering can self-correct surface errors before submitting. Integrate this explicitly into your writing workflow: assign the DeepL comparison as a required pre-submission step ("identify three places where DeepL's version differs from yours and explain which is more appropriate for this audience/register"), then focus your instructor feedback on the errors that neither the student nor DeepL caught, and on pragmatic and cultural dimensions of language use that MT handles poorly. Turnitin's non-English AI detection is less reliable than for English (Turnitin's own 2026 roadmap flags this as in development); use it as a conversation prompt rather than standalone evidence. Gradescope's answer-grouping handles structured grammar and translation exercises efficiently, saving a large share of mechanical grading time for composition-intensive feedback.

AI is sitting alongside you hereDesign and deliver grammar and language-structure instruction: using AI tools (ChatGPT Edu) to generate differentiated grammar exercise sets, reading comprehension scaffolds, and vocabulary practice materials efficiently, then ensuring that classroom time is invested in the communicative practice and cultural contextualization tasks that AI tools cannot substitute, including explicit instruction on how to use and critically evaluate AI translation tools as part of language learners' professional linguistic toolkit.

Design and deliver grammar and language-structure instruction: using AI tools (ChatGPT Edu) to generate differentiated grammar exercise sets, reading comprehension scaffolds, and vocabulary practice materials efficiently, then ensuring that classroom time is invested in the communicative practice and cultural contextualization tasks that AI tools cannot substitute, including explicit instruction on how to use and critically evaluate AI translation tools as part of language learners' professional linguistic toolkit.[7],[12]

Tools picking this up
Where your edge is

Grammar exercise generation and vocabulary drilling are the language-instruction tasks most directly exposed to AI automation: ChatGPT Edu generates a well-scaffolded conjugation exercise set or subjunctive usage drill in under two minutes. Treat this as time saved from mechanical preparation, not as a threat to your pedagogical value. The value reallocation: spend the recovered prep time on communicative task design (role-plays, simulated cultural encounters, debate exercises on contemporary issues in the target culture) and on explicit AI-literacy instruction for language learners, teaching students to use DeepL and ChatGPT as translation aids while understanding their failure modes (DeepL' register errors, ChatGPT's hallucinated idioms, Google Translate's dated phrasing in less-common language pairs). Students who leave your course knowing how to critically use AI translation tools alongside genuine communicative competence are far more professionally valuable than those who learned only the language structure. Assign Duolingo Max Roleplay exercises as outside-class vocabulary and speaking reinforcement: its GPT-4-powered conversations are effective for Novice through Intermediate range drilling.

AI is sitting alongside you herePrepare course materials including syllabi, reading lists, assignment scaffolds, and pronunciation and listening exercises: using ChatGPT Edu to generate first-draft grammar handouts, reading comprehension questions, and cultural background summaries

Prepare course materials including syllabi, reading lists, assignment scaffolds, and pronunciation and listening exercises: using ChatGPT Edu to generate first-draft grammar handouts, reading comprehension questions, and cultural background summaries; using ELSA Speak to recommend self-paced pronunciation modules as outside-class resources; and using NotebookLM to synthesize secondary literary criticism for target-language literature courses, then applying expert linguistic and literary judgment to ensure pedagogical coherence, cultural accuracy, and alignment with ACTFL proficiency guidelines.[7],[13]

Where your edge is

Use ChatGPT Edu to generate a week's worth of differentiated grammar exercises (Novice, Intermediate, and Advanced levels) from a single prompt specifying the structural target and the course's thematic context: this converts a prep task into a review-and-refine pass. Use NotebookLM to synthesize critical essays on course literary texts: upload six PDFs of García Márquez scholarship and generate a thematic overview with inline source references as your lecture starting point. Recommend ELSA Speak to students for between-class pronunciation drilling: it provides phoneme-level feedback that would require individual faculty coaching time at scale, but is now available as a self-paced supplement that frees your class time for discourse-level pronunciation coaching (intonation, rhythm, linking sounds in connected speech) that ELSA does not yet handle well. The ACTFL (2025) consensus is that AI materials generation is now a baseline professional competency for language faculty; not using it is a competitive disadvantage, not a principled stance.

Where foreign language professors go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Education Administrators, Postsecondary

Foreign language faculty who have served as department chairs, language program directors, study abroad coordinators, or international programs administrators are well-positioned for postsecondary education administrator roles. In 2025–2026, there is specific demand for language-proficient administrators who can lead internationalization strategy, manage Title VI National Flagship Language programs, develop AI policy for language departments, and negotiate study abroad partnerships with target-culture institutions. The enrollment crisis in language departments has also created demand for faculty who can lead program restructuring, articulate the value of language education to budget committees, and design AI-literate curricula that make the institutional case for the language department's survival. The CRI delta is positive because postsecondary administration is growing (+7% BLS) with stronger job stability than the declining language faculty market at many institutions, and administration roles have meaningful AI augmentation for data analytics and reporting without the existential threat that AI translation poses to language instruction rationale.

What you'd add
  • · Language program enrollment management: using institutional research data to analyze language enrollment trends, develop evidence-based enrollment projections, and design targeted recruitment strategies for heritage learner and study-abroad-bound student populations
  • · Grant writing for international and language programs: NEA, Title VI NRC and FLAS grants, Fulbright Scholar Programs, and state humanities council mechanisms that fund language and international education initiatives
  • · International partnership development: negotiating exchange agreements with target-culture universities, managing SEVIS compliance for international student programs, and designing dual-degree or certificate programs with overseas partner institutions
  • · Curriculum design for AI-era language programs: developing the department's pedagogical response to AI translation, integrating communicative competence and intercultural education goals into accreditation documentation and general education requirements
  • · Higher education budget and enrollment analytics: reading and producing institutional research data, building program-level budget models for language sequences, and communicating enrollment and financial data to deans and provosts in terms of institutional strategy rather than disciplinary advocacy
What it takesSome new skills to pick up
Tools of the era

How has the work of foreign language professors changed since 1800?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Grammar-Translation Method (textbooks, lexicons, literary texts)

    The dominant pedagogy of the 19th-century modern-language classroom was borrowed wholesale from how Latin and Greek had been taught for centuries: the instructor would explain grammatical rules in English, students would memorize vocabulary lists and conjugation paradigms, and the main exercise would be translating passages from the target language into English (or vice versa). No technology beyond the printed textbook was involved. The leading texts -- Karl Plötz's German reader (widely used in American colleges) and similar French and Spanish grammars -- were the primary tools. The method privileged reading and translation over speaking; a student who had spent four semesters on French was expected to read Racine, not order a croissant.

    Effect on the work

    The Grammar-Translation Method required no technology investment beyond textbooks and limited class sizes, making it scalable at low cost. It also required instructors who were primarily literary scholars rather than trained pedagogues.

  • The Direct Method and early phonograph recordings

    The Reform Movement of the 1880s-1900s, led by linguists including Wilhelm Viëtor in Germany and Henry Sweet in England, produced the Direct Method: instruction conducted entirely in the target language, emphasizing oral communication and correct pronunciation from the start. The Direct Method reached American universities gradually, carried by the MLA's debates about pedagogical reform in its early decades. At the same time, the invention of the phonograph (Edison, 1877) created the first non-human language-teaching tool: recorded speech in the target language, initially used for pronunciation demonstration. Victor Talking Machine Company produced foreign-language records in the 1900s-1920s for classroom use. The Direct Method required instructors with genuine native or near-native oral fluency -- not just literacy -- in the target language, beginning to shift hiring criteria.

  • Language laboratory (reel-to-reel tape, headset booths)

    The language laboratory -- rows of individual student booths, each with a reel-to-reel tape recorder and headset -- became the dominant technology of postwar language instruction, catalyzed by two forces. First, the Army Specialized Training Program of WWII demonstrated that intensive, audio-based immersion could produce functional speakers quickly; second, the National Defense Education Act of 1958 poured federal money into language programs and explicitly funded language laboratory equipment as a national-security investment following Sputnik. The Audio-Lingual Method that dominated language instruction from the late 1950s through the 1960s was built around the language lab: students would listen to recorded dialogues, repeat phrases in drills (the "pattern drill"), and receive corrective feedback from the tape. Language labs were expensive capital investments that required both a faculty practitioner and a dedicated laboratory director or technician. Every major American university built or expanded a language laboratory between 1958 and 1968 using NDEA Title VI funds.

    Effect on the work

    The language lab era created a new support position (laboratory director / language lab coordinator) alongside the traditional teaching faculty. It also cemented the Audio-Lingual Method as the dominant pedagogy, briefly displacing the Grammar-Translation approach. By the late 1960s academic research began showing that Audio-Lingual drilling produced mechanical repetition but not genuine communicative ability, setting up the communicative turn of the 1970s. Language labs became expensive investments that were already obsolescent by the time most were fully equipped.

  • Communicative Language Teaching + video (VHS, satellite TV)

    The communicative turn in language pedagogy, which began in Europe in the early 1970s and reached mainstream American practice by the late 1970s and 1980s, rejected both Grammar-Translation memorization and Audio-Lingual drilling in favor of communicative competence: the ability to use language appropriately in real social contexts. The key pedagogical tools became role-play activities, task-based exercises, and authentic input -- real materials from the target culture rather than scripted textbook dialogues. VHS video cassettes (widely available in the US by 1980) allowed language instructors to show films, news broadcasts, and documentaries in the target language in the classroom without the cost and complexity of satellite reception. Satellite dishes for direct reception of French, Spanish, and German television were installed at many universities in the 1980s. The communicative approach also elevated the status of oral communication and listening comprehension relative to the literary text, further reshaping the language faculty's role.

  • Internet, CD-ROM courseware, and Google Translate (2006)

    The internet transformed the availability of authentic target-language materials: by the late 1990s, any student with web access could read Le Monde in French, watch telenovelas in Spanish via streaming, or access Goethe-Institut German resources. CD-ROM language courseware (Rosetta Stone launched in 1992; Tell Me More launched in the late 1990s) offered self-paced interactive practice for the first time outside institutional settings. The deepest disruption of this era came on April 28, 2006, when Google launched Google Translate. At launch it supported Arabic, Chinese, French, German, Korean, Russian, and Spanish. It was statistical machine translation -- poor by modern standards -- but it immediately generated the question that has shadowed language departments ever since: "If I can just paste this into Google Translate, why do I need to learn the language?" Language enrollment in US higher education peaked in 2009 at 1.67 million students, then began a decade-long structural decline. The Google Translate era was when the instrumental argument for language learning -- "you need this to communicate with people" -- started losing the debate.

    Effect on the work

    Language enrollment in US higher education peaked at 1,673,566 in 2009 (MLA census) and had fallen to approximately 1,185,000 by 2021 -- a 29 percent decline over twelve years. Faculty positions began contracting in 2010-2015, with the contraction concentrated in tenure-track lines while adjunct and contingent positions remained more stable. Department closures began at mid-tier regional universities.

  • LMS integration (Canvas, Blackboard), video-conferencing, and neural machine translation

    The shift to Learning Management System-based instruction (Canvas, Blackboard, Moodle) reshaped how language faculty designed courses: recorded grammar explanations moved to asynchronous video, writing assignments were submitted digitally, and formative feedback was delivered via audio comments on uploaded voice recordings. The COVID-19 pandemic (2020-2021) forced all language instruction online abruptly; faculty who had resisted synchronous video teaching had to adopt Zoom overnight. The parallel development of neural machine translation -- Google Translate adopted neural MT in November 2016; DeepL launched in August 2017 and immediately outperformed Google on European language pairs -- elevated the "why do I need to learn this" question from a student rationalization to a near-unanswerable challenge for general language instruction. By 2021, DeepL's translation of standard prose was demonstrably better than what most intermediate students could produce, making traditional written translation exercises functionally non-discriminating as assessments.

  • AI conversation tutors, LLM translation, and AI academic tools (Duolingo Max, DeepL, ChatGPT Edu)

    The GPT-4-powered generation of AI language tools, arriving at scale in 2023, marked a qualitative shift from earlier machine translation: Duolingo Max (launched March 2023) offered unlimited conversational AI practice in scenario-based contexts; DeepL Pro was used by 94 percent of Fortune 500 companies by 2025; ChatGPT Edu was deployed across California State University's 460,000-student system in February 2025, including foreign language departments. For language faculty, the shift has two dimensions. As a threat: students now have a plausible, low-cost substitute for much of what lower-level grammar and vocabulary instruction produces, and the MLA enrollment decline -- already 29 percent from peak before these tools arrived -- has continued. As an opportunity: many instructors have integrated AI conversation practice tools into their courses to extend student speaking-practice hours beyond what classroom contact time allows. The faculty members navigating this moment most successfully are those redesigning assessments around oral proficiency, cultural encounter, and the literary interpretation that AI demonstrably cannot substitute -- and those explicitly teaching students to be critical users and evaluators of AI translation as a professional skill.

    Effect on the work

    Eloundou et al. (2023) explicitly names Foreign Language and Literature Teachers, Postsecondary in the highest-LLM-exposure tier of postsecondary occupations, alongside English and History teachers. The exposure is real: grammar instruction, written-assignment feedback, and materials generation are all substantially automatable. The durably human core -- oral proficiency assessment, target-language literary seminar, heritage learner mentorship -- is genuine but narrower than the full job description.

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Foreign Language and Literature Teachers, Postsecondary employment and pay by year

On record since1800
Latest US employment19,830 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$79,350 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+0.2% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 29 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1883200n/aEst., Modern Language Association Researched contextReport
19182,500n/aEst., JSTOR Daily Researched contextReport
196012,000$6,500Est., National Defense Education Act (employment) Researched contextEst., AAUP (American Association of… (pay) Researched contextReport
197522,000n/aEst., MLA Researched contextReport
1980n/a$19,500Est., AAUP Researched contextReport
200021,000$44,380Est., O*NET 25-1124.00 (employment) Researched contextBLS OEWS 2000 (pay) Official statisticReport
200319,710$46,780BLS OEWS May 2003 Official statisticReport
200422,460$47,600BLS OEWS May 2004 Official statisticReport
200523,830$49,570BLS OEWS May 2005 Official statisticReport
200624,680$51,900BLS OEWS May 2006 Official statisticReport
200725,100$53,610BLS OEWS May 2007 Official statisticReport
200826,400$55,570BLS OEWS May 2008 Official statisticReport
200927,020$56,740BLS OEWS May 2009 Official statisticReport
201028,100$59,080BLS OEWS May 2010 Official statisticReport
201129,010$59,630BLS OEWS May 2011 Official statisticReport
201229,810$58,670BLS OEWS May 2012 Official statisticReport
201330,590$58,620BLS OEWS May 2013 Official statisticReport
201430,880$59,490BLS OEWS May 2014 Official statisticReport
201530,120$61,380BLS OEWS May 2015 Official statisticReport
201628,720$63,500BLS OEWS May 2016 Official statisticReport
201727,240$65,010BLS OEWS May 2017 Official statisticReport
201825,590$67,640BLS OEWS May 2018 Official statisticReport
201924,860$69,990BLS OEWS May 2019 Official statisticReport
202022,790$69,920BLS OEWS May 2020 Official statisticReport
202119,640$77,030BLS OEWS May 2021 Official statisticReport
202219,520$76,030BLS OEWS May 2022 Official statisticReport
202320,820$78,760BLS OEWS May 2023 Official statisticReport
202421,170$77,010BLS OEWS May 2024 Official statisticReport
202519,830$79,350BLS OEWS May 2025 Official statisticReport
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