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Economics Teachers, Postsecondary

Scrub through 212years of this role's history, from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.

2026drag to travel through time
18251850187519001925195019752000now
2026
Known today as Economics Teachers, Postsecondary (BLS SOC 25-1063)
Latest actual · 2024
16K
BLS OEWS May 2024 -- present-day anchor for the projection cone. Employment of 15,800 with median annual wage of $119,980, sourced from O*NET which reflects the same BLS establishment-survey figure. Employment is modestly below the pre-COVID level, reflecting pandemic-era hiring freezes at many universities and the ongoing shift toward adjunct and non-tenure-track instruction. The vast majority of positions (99.7%) are in educational services: state, local, and private institutions, split roughly 41.6% private and 58.2% state/local. Economics teachers are a small but highly compensated specialization within the 1.95 million total postsecondary teachers.
Latest actual · 2024
$119,980
BLS OEWS May 2024 median annual wage, sourced from O*NET which reflects the same BLS establishment-survey figure. At $119,980, economics teachers earn roughly double the median for all postsecondary teachers ($84,380 in May 2024) and are among the top three highest-earning postsecondary disciplines alongside business and engineering faculty. The wage premium reflects the strong outside-academia demand for economics PhDs: Federal Reserve, IMF, World Bank, economic consulting firms, financial institutions, and tech companies all compete for the same talent pool.
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Beat · 2025

The Royal Economic Society, University College London CTaLE, and the Stone Centre issue "Rethinking Economics Assessments for a GenAI World" (October 2025), a sector-wide call to action with ten recommendations for economics programme directors. The report documents that 88% of students now use generative AI for assessments (HEPI/Kortext, February 2025), up from 53% the prior year, and that UK economics departments are scrambling to redesign assessment formats. The AEA Committee on Economic Education features dedicated AI-in-teaching workshops at the ASSA 2026 meetings. The AEA-CEE frames AI-resilient assessment design and causal-inference pedagogy as the core competencies that economics education must protect.

Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Lecture, blackboard, and classical text (Smith, Ricardo, Mill)

    The founding technology of economics teaching was the treatise and the public lecture. Adam Smith delivered his political economy in lectures at Glasgow from 1751 before publishing "The Wealth of Nations" in 1776; the text circulated as the foundational reading in American political economy courses from the 1820s onward. Teaching was entirely oral and textual: the professor lectured from notes or from a primary text, students copied by hand, and examinations were oral. The blackboard, introduced into American classrooms around 1800-1820, gave economics instructors the first visual-spatial tool for drawing supply-demand diagrams and production frontiers. Mathematical notation was minimal until the late 19th century; the dominant mode was verbal and historical reasoning.

    Work toolChanging equipment

  • Mathematical formalism + the graduate seminar (Marshall, Walras, Keynes)

    Alfred Marshall's "Principles of Economics" (1890) introduced systematic mathematical formalism to English-language economics teaching -- supply and demand curves, marginal utility, elasticity -- transforming the undergraduate curriculum from historical political economy into analytical science. The graduate seminar, imported from German universities by the generation of American economists who studied in Berlin and Vienna in the 1880s, became the primary vehicle for PhD training. Blackboard mathematics replaced oral disputation as the credential-conferring ritual. The first generation of American PhD economists -- trained at Johns Hopkins, Harvard, and Columbia -- installed the seminar culture in every research university by 1920. The Keynesian revolution of the late 1930s added macroeconomic modeling to the curriculum, requiring instructors to teach IS-LM diagrams, multiplier effects, and the liquidity preference theory for the first time.

    Work toolChanging equipment

  • Samuelson's "Economics" textbook + Keynesian synthesis (mass undergraduate era)

    Paul Samuelson published "Economics: An Introductory Analysis" in 1948 at the request of his MIT department chair, who wanted a text capable of engaging undergraduates in a compulsory two-semester course. The book sold more than 300,000 copies of each edition from 1961 through 1976 and was translated into 41 languages. Its adoption across American universities created for the first time a standardized two-semester introductory economics sequence -- micro then macro, or the reverse -- that every economics department in the country could recognize as their own curriculum. The "neoclassical synthesis" Samuelson popularized gave economics instructors a shared language: perfect competition, IS-LM, the Phillips curve, indifference curves. The GI Bill drove college enrollment from 1.5 million in 1940 to 2.7 million by 1950, and Samuelson's text was the pedagogical infrastructure that allowed small departments to serve hundreds of students simultaneously.

    Effect on the work

    The standardized Samuelson curriculum allowed economics departments to scale their introductory teaching through large-lecture formats, effectively reducing the labor intensity per enrolled student and concentrating faculty hiring in upper-division and graduate teaching. This is the formative moment when "introductory economics" became a distinct lower-division product separable from the more labor-intensive upper-division and graduate curriculum.

    Work toolChanging equipment

  • Statistical computing (SPSS, SAS, and later Stata and EViews)

    The availability of statistical software on university mainframes and then personal computers transformed the teaching of econometrics from a mathematical exercise into an empirical practice. SPSS (released 1968) and SAS (released 1972) gave faculty and graduate students the ability to run regressions on real datasets without coding from scratch in Fortran. Stata (released 1985) became the standard econometrics teaching platform through the 1990s and 2000s. The practical consequence for economics instructors was significant: econometrics, previously a graduate-level specialization, began migrating into upper-division undergraduate courses as the computational barrier dropped. An economics faculty member in 1975 taught econometrics largely through algebraic derivation; by 1995, the same course required students to run OLS, probit, and instrumental-variables estimations on real data. The instructor's job shifted from transmitting mathematical technique to teaching empirical judgment: whether results are credible, whether standard errors are correctly estimated, whether the data limitations invalidate the conclusions.

    Work toolChanging equipment

  • Internet, JSTOR, and online problem sets (course management systems)

    The web transformed the research and teaching infrastructure of economics faculty in two waves. The first, through 2000, was access: JSTOR (launched 1995) put decades of journal archives on every faculty member's desktop; NBER working papers became freely downloadable; the AEA began hosting an electronic job-market clearinghouse. The second wave, from 2000-2015, was course delivery: Blackboard, Moodle, and Canvas allowed problem sets, readings, and discussion boards to move online, and clicker systems and online homework platforms (like MyEconLab, launched by Pearson in the early 2000s) automated grading of principles-level problem sets for the first time. The combination reduced routine grading labor for large intro courses and gave faculty access to real-time data on student performance, but it also created the expectation that economics courses would have online components regardless of format.

    Work toolChanging equipment

  • R and Python for empirical economics (reproducible research era)

    The replication crisis in social sciences and a wave of influential empirical papers using difference-in-differences, regression discontinuity, and synthetic control methods drove a transition in economics research tools from Stata to R and Python through the 2010s. Graduate econometrics courses began incorporating R (open-source, with a rich ecosystem for causal inference via the "econometrics" and "fixest" packages) and Python (for machine learning applications and large-dataset work). For economics faculty, this created a new pedagogical challenge: students arrived in graduate school with heterogeneous technical backgrounds ranging from no coding experience to Python fluency, and course design had to accommodate both. The transition also opened a path toward machine learning applications in economics -- Melissa Dell's "Deep Learning for Economists" (Journal of Economic Literature, 2025) became the capstone of this era.

    Work toolChanging equipment

  • Generative AI in economics teaching (ChatGPT 99th pct TUCE, Gradescope, Elicit)

    The November 2022 release of ChatGPT, followed by Geerling, Mateer, Wooten and Damodaran's 2023 finding that ChatGPT scored in the 99th percentile on the Test of Understanding in College Economics (Macroeconomics section), marked a rupture in economics teaching that has not yet resolved. By 2025, 88% of students used generative AI for assessments (HEPI/Kortext, February 2025), up from 53% the prior year; the Royal Economic Society and University College London's CTaLE issued a sector-wide call to action in October 2025 with ten recommendations for programme redesign. The tools that have materially changed the instructor's daily work include: Gradescope (AI-assisted grading deployed at 3,000+ institutions, handling structured problem sets that used to consume hours of TA time), Elicit (138M+ papers, up to 80% reduction in literature review time), Consensus AI (200M+ peer-reviewed papers categorized as yes/no/mixed findings), and Stata-MCP and GitHub Copilot (allowing econometrics code to be generated from plain-English descriptions in real time during lecture). The effect on the instructor's role is not replacement but fundamental redesign: the questions that used to distinguish an educated economics student from an uneducated one -- defining price elasticity, computing comparative advantage, solving for IS-LM equilibrium -- are now trivial for AI. The questions that require expert judgment -- whether a proposed instrument satisfies the exclusion restriction, whether a DiD parallel-trends assumption is plausible for a specific policy context, whether a student's causal story survives Socratic challenge -- are exactly those that AI systems consistently fail on, and are now the instructor's primary pedagogical territory.

    Effect on the work

    AI tools recover an estimated 6-10 hours per week for economics faculty who adopt Gradescope for structured grading, Packback for discussion facilitation, and Elicit/Consensus for literature synthesis -- time that can be reinvested in research and high-leverage teaching. Korinek (2023) identifies six categories of AI use for economic researchers (ideation, writing, background research, data analysis, coding, and mathematical derivations) and finds LLMs have crossed the threshold to become useful across a wide range of cognitive tasks for economists.

    AI audit toolsPattern detection
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS Occupational Outlook Handbook -- Postsecondary Teachers (2024-2034)
→ 2034
+7%
BLS Occupational Outlook Handbook projects the broader "postsecondary teachers" category (25-1000) at +7% growth from 2024-2034, faster than the 4% all-occupations average. This sector-level figure is more optimistic than the occupation-specific projection for economics teachers (25-1063, +2.1%) because the broader postsecondary teachers category is lifted by faster-growing STEM and health sciences disciplines. The divergence reflects the structural constraint on economics faculty demand: economics major enrollment has grown modestly while STEM enrollment has grown substantially. Reported here as an upper-bound context for the sector trend; the occupation-specific BLS matrix figure (+2.1%) is the more precise estimate.
BLS National Employment Matrix 2024-2034
→ 2034
+2.1%
BLS Employment Projections 2024-2034: industry-occupation matrix modeling. The matrix projects economics teachers, postsecondary at 15,800 (2024) growing to 16,100 (2034), an increase of approximately 300 positions or 2.1% over the decade. This is classified as "little or no change" against the all-occupations projected growth of 4%. Employment is split roughly 41.6% private institutions and 58.2% state and local institutions; four-year colleges and universities represent 46.6% of positions. The projected growth at public four-year colleges (4.7%) modestly outpaces private institutions. The BLS methodology models replacement demand from retirements as the primary driver of openings, not net new positions.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Federal Reserve FEDS Notes -- Educational Exposure to Generative AI (Timmerman and Xiao, 2026)
→ 2030
89%
of tasks
The Federal Reserve Board FEDS Notes paper "Educational Exposure to Generative Artificial Intelligence" (Timmerman and Xiao, February 2026) scored academic fields by language-model z-score, with Economics/Business at 0.89 -- moderately high exposure, above-average across academic disciplines. The measure captures how strongly a field's instructional content aligns with generative AI capability. A score of 0.89 (on a normalized distribution, approximately top 20% of all fields) reflects that economics courses involve substantial text generation, quantitative problem-solving, and conceptual explanation -- all tasks where LLMs now perform at or above typical undergraduate level. The 89% reported here represents the normalized exposure score (0.89 mapped to 89th percentile) as a task-exposure indicator, not a prediction of employment change.
Eloundou et al. -- "GPTs are GPTs" (2023)
→ 2028
80%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for economics teachers, postsecondary. The curated file for 25-1063.00 documents beta_withTool = 0.805 -- top-quartile LLM exposure across all 923 occupations in the Eloundou dataset -- reflecting that the dominant tasks (lecture preparation, assessment design, grading, and literature synthesis) are all language-model-compatible at the workflow level. The Federal Reserve FEDS Notes (Timmerman and Xiao, February 2026) independently scored Economics/Business fields at a language-model z-score of 0.89, above average across all academic fields. This exposure score should be read as a measure of task-level LLM compatibility, not as a prediction of job loss: the 80% exposure reflects that a large share of the tasks an economics teacher performs daily can be significantly augmented or accelerated by LLMs, not that the role will disappear. The durable human core -- causal-inference assessment design, dissertation mentorship, and Socratic oral examination -- is the fraction not captured by this exposure estimate.
Today, in this role

What's shifting in the work right now

The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.

What's changing in your day

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

AI is sitting alongside you hereGrade and provide feedback on economics assignments — problem sets, short analytical essays, and econometric project reports — using Gradescope's AI-assisted answer grouping for structured problem sets (multiple-choice and numerical questions with deterministic answers), then applying expert economic judgment to evaluate whether a student's interpretation of a regression coefficient correctly identifies a causal or correlational relationship, and whether a proposed IV strategy is conceptually sound.

Grade and provide feedback on economics assignments — problem sets, short analytical essays, and econometric project reports — using Gradescope's AI-assisted answer grouping for structured problem sets (multiple-choice and numerical questions with deterministic answers), then applying expert economic judgment to evaluate whether a student's interpretation of a regression coefficient correctly identifies a causal or correlational relationship, and whether a proposed IV strategy is conceptually sound.[12],[8]

Tools picking this up
Where your edge is

Deploy Gradescope for all economics assessments where the answer is deterministic — optimal bundle calculation, IS-LM intersection, game-theory Nash equilibria, elasticity calculations. The AI-assisted answer grouping handles high-volume structured grading efficiently and is deployed at 3,000+ institutions. Reserve expert grading effort for the qualitative judgment that only an economics expert can make: is this student's interpretation of a coefficient causally justified given the identification strategy described? Does this proposed instrument plausibly satisfy the exclusion restriction? Is this claim about equilibrium selection defended correctly? Use Turnitin AI detection as a flag that triggers closer human scrutiny, not as standalone evidence of academic dishonesty — the German study (Feb 2026) shows AI-using students are faster but not better, suggesting detection should focus on whether the submitted reasoning is genuinely the student's.

AI is sitting alongside you herePrepare course materials — syllabi, problem sets, lecture notes, and handouts — using ChatGPT Edu and Claude to generate first-draft content on standard micro/macro topics, then editing with economic expertise to correct causal-language overstatements, ensure that AI-generated problem sets present novel numbers and institutional contexts AI cannot have memorized, and that syllabus AI-use policies are current with the RES/AEA guidelines emerging in 2025–2026.

Prepare course materials — syllabi, problem sets, lecture notes, and handouts — using ChatGPT Edu and Claude to generate first-draft content on standard micro/macro topics, then editing with economic expertise to correct causal-language overstatements, ensure that AI-generated problem sets present novel numbers and institutional contexts AI cannot have memorized, and that syllabus AI-use policies are current with the RES/AEA guidelines emerging in 2025–2026.[5],[1]

Where your edge is

ChatGPT and Claude can draft a reasonable 12-week economics syllabus, generate 20 principles-level problem-set questions, and produce a readable explainer on comparative advantage in minutes — tasks that previously consumed hours of faculty prep time. The expert editing step remains critical: AI-generated economics content reliably conflates correlation with causation in its explanatory text, uses memorized numbers and contexts that students may have seen in training-data examples, and misrepresents model assumptions in edge cases. Use AI as a scaffolding accelerator; invest your preparation time in the editorial pass for precision, in designing problems that require institutional context AI cannot reproduce, and in ensuring your syllabus AI-use policy reflects the institution's actual 2026 stance rather than 2023 defaults.

AI is sitting alongside you hereHold office hours and respond to student questions on economic concepts, model applications, and problem-set solutions — using Khanmigo or ChatGPT Edu to handle routine principles-level conceptual questions (supply-demand shifts, comparative advantage, basic game theory) asynchronously, and reserving direct faculty engagement for the student who cannot reconcile a model's predictions with their own economic intuition — the moment that requires Socratic dialogue, not a lookup.

Hold office hours and respond to student questions on economic concepts, model applications, and problem-set solutions — using Khanmigo or ChatGPT Edu to handle routine principles-level conceptual questions (supply-demand shifts, comparative advantage, basic game theory) asynchronously, and reserving direct faculty engagement for the student who cannot reconcile a model's predictions with their own economic intuition — the moment that requires Socratic dialogue, not a lookup.[4],[8]

Tools picking this up
Where your edge is

The Economics Network (UK, 2025) documents widespread student use of ChatGPT for "explaining concepts" as the most common assessment-adjacent AI use — a task that maps directly onto what office hours have historically provided for principles-level courses. Redirect standard conceptual questions ("how does the substitution effect work?") to Khanmigo or ChatGPT Edu, which handle these competently. Reserve your office-hour engagement for the student who has genuinely engaged with the AI explanation and is still confused — that gap between the AI's correct-but-abstract answer and the student's intuition is the highest-leverage teaching moment. Geerling et al. (2023) specifically recommends augmenting learning with chatbots as one of three strategic responses to AI capability in economics; implementing this systematically rather than ad hoc is the faculty expert's responsibility.

Where this role is heading

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

A direction you could grow

Education Administrators, Postsecondary

Economics faculty frequently move into department chair, director of undergraduate studies, associate dean for academic affairs, or provost-track roles — particularly those who have led curriculum redesign efforts, chaired hiring committees, managed the AEA CSWEP or CSMGEP equity programs, or served on AI governance task forces. Economics departments are under significant pressure to redesign curricula in response to AI (the RES/CTaLE call to action, Oct 2025), respond to declining economics major enrollment trends at some institutions, and modernize data science and computational content. Faculty with economic credibility and governance experience are highly valued for academic administration roles. The CRI increase reflects that postsecondary education administration is moderately AI-augmented for data analytics and reporting tasks, while the strategic judgment and faculty-relations core is durable.

What you'd add
  • · Higher education budget management: faculty line planning, research overhead cost recovery, endowment income projections, and departmental capital planning
  • · AEA career resources: CSWEP mentoring programs, CSMGEP diversity initiatives, and CTREE conference program committee administration — governance experience valued in academic admin searches
  • · Accreditation and program review: AACSB or regional accreditor self-study documentation for economics programs housed within business schools; HLC continuous improvement processes
  • · Faculty personnel processes: promotion-and-tenure committee leadership, academic hiring (job-market flyout logistics, offer management, start-up package negotiation)
  • · AI governance for economics departments: developing institutional policy on student AI use, evaluating Gradescope/Packback/ChatGPT Edu for departmental deployment, faculty development planning for AI pedagogy
What it takesSome new skills to pick up
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The data behind this timeline

On record since1824
Latest tracked employment15,800 (US, 2024)
Latest median pay$119,980 (2024)
Outlook+2.1% by 2034 (BLS National Employment Matrix 2024-2034)
View all 28 cited data points
YearUS employmentMedian annual paySource
1870200n/aESTIMATE
1900800n/aESTIMATE
19505,000n/aESTIMATE
1965n/a$17,500ESTIMATE
197511,000n/aESTIMATE
2000n/a$62,000ESTIMATE
200313,200$64,950BLS-OEWS
200412,230$67,520BLS-OEWS
200512,670$68,910BLS-OEWS
200612,330$71,850BLS-OEWS
200712,840$75,300BLS-OEWS
200812,540$80,130BLS-OEWS
200912,860$81,170BLS-OEWS
201013,020$83,370BLS-OEWS
201113,300$85,310BLS-OEWS
201213,390$87,950BLS-OEWS
201313,600$90,070BLS-OEWS
201413,710$90,870BLS-OEWS
201513,580$94,000BLS-OEWS
201613,060$95,770BLS-OEWS
201712,770$98,350BLS-OEWS
201812,750$101,480BLS-OEWS
201916,300$107,000BLS-OEWS
202013,080$107,260BLS-OEWS
202111,790$104,940BLS-OEWS
202211,640$103,930BLS-OEWS
202312,210$115,300BLS-OEWS
202415,800$119,980BLS-OEWS
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