$346,200top of the range in Massachusetts · middle $123,920 / yr
AI augments this role
Economics Professors in the United States earn a median of $123,920 a year. Pay starts near $64,060. Pay reaches $346,200 at the top of the range in Massachusetts, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Economics Teachers, Postsecondary, SOC 25-1063). Last checked 9 September 2026.
Entry level
$64,060
Top of the range · Massachusetts
$346,200
Education
Doctoral degree in Economics
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Economics Teachers, Postsecondary). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Economics ProfessorReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Economics Professor work right now.
MagicSchool AINEWFree / $8.33 mo
All-in-one teacher toolkit with 80+ tools for planning, IEPs, and communication.
How an Economics Professor uses it: generate lesson plans, rubrics, and parent messages in minutes
Brisk TeachingNEWFree / school pricing
AI assistant that works right inside Google Docs, Slides, and the browser.
How an Economics Professor uses it: give feedback, differentiate readings, and build lessons in the tools you already use
CoGraderNEWFree / $19 mo
AI grading tool for writing assignments with rubric-based feedback.
How an Economics Professor uses it: grade a class set of essays in a fraction of the time, with consistent feedback
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How an Economics Professor uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
KhanmigoFree for teachers
Khan Academy's AI tutor and lesson-planning assistant.
How an Economics Professor uses it: plan standards-aligned lessons and give students a safe AI tutor
CuripodFree / $7.50 mo
Generates interactive, slide-based lessons from a topic or standard.
How an Economics Professor uses it: turn a topic into an interactive lesson students respond to live
DiffitFree / paid
Adapts any text or topic to the right reading level with questions.
How an Economics Professor uses it: level a reading for every student and auto-build comprehension questions
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How an Economics Professor uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How an Economics Professor uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
A seminar table, then a stack of referee reports
The whiteboard still has last hour's graph on it, and a student is waiting with a problem set that went sideways on the third exercise. You talk them through the idea without doing the homework for them, then you close the door and open a paper you owe a journal. An economics professor lives in that swing. The public picture is a lecture. The appointment is three jobs braided together: teaching, research, and the service a department needs in order to function. On a good week the three cooperate. On a bad week the lecture is at nine, the referee report is late, and a committee wants a decision about next year's hiring before lunch.
Teaching is design as much as performance. You choose what a course can honestly cover, you build problem sets that reveal whether students followed the argument, and you grade in a way the next instructor can understand. Introductory courses ask for clarity and stamina. Field courses and graduate seminars ask for depth and for the nerve to say when a comment in the room is wrong. Office hours are part of the teaching load even when they feel like interruptions. Students remember whether you were precise and whether you were decent. Colleagues remember whether your grades and your syllabus matched the course the catalog advertised.
Research, teaching, and department service
Research is the other half of a tenure-track life, and at many colleges it is the half that decides promotion. You pick a problem that other economists recognize as real, you build or borrow a way to study it, and you write a paper that can survive a stranger's review. Some of that work is theoretical. Some of it is empirical, with data you are allowed to use and methods you can explain. Collaboration is normal. So is rejection. A professor who can only teach will struggle on a research campus. A professor who can only write will struggle anywhere students are the reason the building is open. The balance differs by institution, and you should learn the balance before you fall in love with a campus photo.
Service is the work that keeps a department from becoming a hallway of closed doors. You sit on hiring committees, you advise students on programs of study, you help revise a curriculum, and you take a turn at the chores nobody lists in a glamorous job ad. Early in a career the chair should protect you from an unfair pile of that work. Later, leadership is part of the job: directing a graduate program, chairing the department, or representing economics to the dean. None of this is optional flavor. A faculty member who dodges every meeting eventually spends the goodwill the research earned. A faculty member who does nothing except meetings never finishes the paper the tenure file needs.
The week has a texture that outsiders miss. There is class, preparation that takes longer than the class, email from students who are panicking before an assessment, a seminar where you comment on a colleague's draft, and a quiet block for your own paper that the quiet block rarely survives intact. Summers, where the contract allows, are when many people push research, advise, or teach an extra course for extra pay. That extra course is a choice with a cost. Ask what the institution expects in the months classes are out of session, because "summer" can mean research time, a required presence, or a pay gap. The answer belongs in the offer conversation, not in a guess.
The doctorate on the posting
A doctorate is the typical preparation for a tenure-track job. The degree, almost always a Ph.D. in economics or a very close field, shows that you can produce original research and defend it. People prepare through graduate coursework, a dissertation, and the seminar circuit that teaches them to take criticism in public. A finished doctorate is what search committees expect to see, or to see arriving by a stated date, before they hire a permanent colleague. Master's-level teaching happens in some institutions and in some lecturer or instructor seats. If the posting says tenure-track assistant professor, read it as a doctoral job unless the text clearly says otherwise.
A doctorate is not a licence. No national board issues it as permission to teach economics, and a college does not treat the diploma as a professional card you can carry from a clinic or a jobsite. The institution appoints you. Accreditation and the school's own rules govern the faculty role. Moving to another college means another search or another appointment, not a simple transfer of a licence. Keep that straight when you compare this work with licensed professions. Your credential is the degree, the papers, and the teaching record. The employer is the one who decides you may stand in its classrooms.
A degree, then an appointment
Tenure-track hiring typically expects a doctorate. The college appoints you. The diploma does not function as a national teaching licence.
How a department actually hires
Most tenure-track searches are national, slow, and paper-heavy. You send a record of research, a teaching statement, letters from people who know the work, and evidence that you can teach the courses the department listed. A committee reads more files than it can interview. A short list leads to conversations, then to a campus visit: a job talk, meetings with faculty, often a meeting with students or the dean. They are listening for a research program that will still be interesting in several years, and for a colleague who can carry a course without drama. Charm without a paper will not survive the job talk. A brilliant paper delivered with contempt for the audience often fails too.
Lecturer, visiting, and adjunct appointments are real jobs with different security and, often, different pay. Do not let a title flatter you into comparing a one-course hire with a full faculty median. Ask the teaching load, the research expectation, whether the line is renewable, and who decides renewal. For a first tenure-track offer, ask about mentoring, the tenure clock, and what a successful file has looked like in that department lately. You are allowed to want those answers before you move a household. An applied economist inside a government office or a consulting firm has a different day, built around briefs and client work rather than a catalog of courses. If that is the life you want, this faculty search is the wrong door.
From the first appointment toward a longer one
A typical tenure-track start is assistant professor. You teach the load you were promised, you publish from the dissertation and from newer projects, and you do enough service to be a citizen without disappearing into committees. Review points arrive on the institution's schedule. Tenure, where the system uses it, is a judgment on research, teaching, and service together, with the mix written in that college's rules rather than in a national script. Associate professor comes with that longer security at many schools, and professor or an equivalent senior title comes later. Some people become chairs. Some leave for a research institute, a central bank, or a firm. Those exits are careers, not failures, and they use the doctorate in a non-classroom way.
Teaching-focused colleges promote on a different mix than research universities. A candidate who ignores that mix will be miserable and, often, unsuccessful. Read recent faculty profiles before you apply. If everyone tenure-track has a stream of journal articles, believe them. If the college celebrates advising and course design, believe that instead. Your preparation is still the doctorate and a record you can document. Letters, syllabi, and student comments you are allowed to share matter because the committee cannot sit in your current classroom. Build that file while the work is happening, not the month a review begins.
What May 2025 shows for this faculty seat
Economics Teachers, Postsecondary is the matching title in the May 2025 Occupational Employment and Wage Statistics. It lines up with a college economics faculty seat. Entry pay is $64,060. The national median is $123,920. The top figure in the published range is $346,200 in Massachusetts, where the Bureau counted enough postsecondary economics teachers to show a high end. Massachusetts also has a median, and it is not that range figure. The state median in Massachusetts is $164,410. The $346,200 number is the high end of the range. Use both, and never as synonyms.
Massachusetts leads the published state medians at $164,410, which is $40,490 above the national median. California's median is $137,360. New York's is $127,500. Texas is $118,530. Pennsylvania is $117,970, and that Pennsylvania figure is also the lowest state median the Bureau published for this occupation. The gap between Massachusetts and Pennsylvania on the median is $46,440. That spread is the geographic story in the medians. It is a different story from the jump between the national median and the Massachusetts range high end, which is $222,280. One describes how state middles differ. The other describes how far the published range extends at the top in one state.
From entry to the national median is $59,860. That is a large step, and it is a reminder not to treat $64,060 as the going rate for a full tenure-track assistant professor at a research department. Entry can reflect shorter or less senior appointments inside a broad faculty series. A tenure-track conversation belongs next to $123,920 unless the institution's own offer and the state median give you a better local anchor. California at $137,360 and New York at $127,500 sit above the national median. Texas at $118,530 and Pennsylvania at $117,970 sit a little below it. None of those medians is $346,200. That Massachusetts figure stays labeled as the high end of the range.
Negotiating the appointment you were actually offered
Separate the base salary from everything else before you celebrate or protest. Startup support, a computer, data purchases, moving costs, a course release, and summer pay can be valuable, and they are not inside the Bureau median. Write the academic-year or annual base in one number. Set it beside $64,060 if the job is truly an entry-shaped, limited appointment. Set it beside $123,920 if it is a regular faculty role at the national middle. If the campus is in Massachusetts, California, New York, Texas, or Pennsylvania, set the state median beside the national one. A Massachusetts median comparison uses $164,410. A Massachusetts conversation about the far end of published pay uses $346,200 and should sound different in your mouth, because it is the high end of the range, not the middle of the state's faculty wages.
The $59,860 between entry and the national median is the gap to discuss when an offer looks like entry pay but the duties look like a full assistant professor: a complete teaching load, a research expectation, and service. The $40,490 between the national median and the Massachusetts median is a geographic comparison, relevant if you are choosing that state, not a raise you can demand in Texas by mentioning Boston. Texas has its own median, $118,530. Pennsylvania's $117,970 is the low end of the published medians and still close to the national median, which tells you the middle of this occupation does not swing as wildly across those states as the range top might suggest. The $222,280 from the national median to the range high end is context for senior distinction, scarce fields, or pay at the far published edge. It is a poor script for a first assistant-professor negotiation.
Ask about the teaching load in courses, not in vibes, and ask whether new course preparations count the same as courses you have already built. Ask who approves outside consulting, if you want to keep a research relationship with an agency or a firm, and whether that work is encouraged or barely tolerated. Ask when you will be reviewed and what evidence the file wants. Then return to the salary line. National entry $64,060. National median $123,920. Massachusetts median $164,410. Massachusetts high end of the range $346,200. The doctorate got you into the conversation. It did not license you, and it did not set the dollar. The appointment and these figures do that work, if you keep the statistics from collapsing into one impressive number.
The top of Economics Professor pay — and how to get there with AI
$346,200what Economics Professor pay reaches in Massachusetts
Highest state-level top-of-range annual wage for Economics Teachers, Postsecondary, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
$64,060entry$123,920middle$346,200top end
Economics professors at the top of this range are rarely teaching more sections than anyone else; they are attached to something that brings money in, whether a funded research programme, an executive course, or the consulting practice their school permits.
The teaching load is fixed and largely identical across a department: prepare syllabi, homework assignments and handouts, deliver lectures on econometrics, price theory and macroeconomics, hold office hours, keep attendance records and grades, serve on committees. Those duties fill a week without producing a second income line. What is scarce is capacity, hours you can aim at a grant application, a professional short course, a policy contract or expert testimony. Capacity comes from compressing the course machinery: generated problem variants with worked solutions, a deep question bank in Blackboard Learn, and analysis code that regenerates every figure when the underlying data updates.
Your playbook, by where you are now
Just startingBuild courses that need no rebuilding
Write each course once as a versioned set of files, syllabi, problem sets, solutions and slides, so next year is an edit rather than a rewrite.
Generate empirical examples in R or Python from live series, so the macroeconomics lectures refresh themselves each term.
Move assessment into Blackboard Learn with a bank deep enough to randomise, and let iParadigms Turnitin carry the integrity checks.
Ask Claude for a first draft of problem variants and worked solutions, then solve every one yourself before students see it.
Protect two mornings a week for research and defend them the way you defend office hours.
What proves it: A full course that runs from a file set you could hand to a colleague tomorrow.
Realistic span: your first three years
A few years inAttach yourself to funded work
Write one external grant or contract application a year whatever the outcome, reusing the framing across attempts.
Turn a course you already teach into a short professional or executive programme the school can charge for.
Take the applied contracts your department allows, policy evaluation, forecasting, litigation support, and schedule them rather than squeezing them in.
Publish where practitioners read, since that is what generates the calls that pay.
Run co-authors and clients through collaborative editing software and a shared calendar, so your week stops disappearing into email.
What proves it: An external funding line or a paid programme with your name attached to it.
Realistic span: years four to eight
ExperiencedDirect something, do not merely teach in it
Take the directorship of a centre, an executive programme or a graduate concentration, and negotiate the release time in writing.
Serve on the committees that set curriculum and hiring, because salary structure is decided there and nowhere else.
Build a research group and bring in the funding that supports doctoral students, which is what named appointments reward.
Be deliberate about institution and region, since Connecticut pays this occupation the most and its private research universities are the reason.
What proves it: A named programme or centre that you direct, with a budget attached.
Realistic span: nine years and beyond
The next 90 days
Open the last month of your calendar and mark every hour as either fixed teaching duty or discretionary. Most economics professors discover the discretionary share is small and unprotected: office hours overrun, committee work spreads, and the proposal never starts. Choose one mechanical piece of the fixed side and industrialise it within ninety days, whether a problem set generator, a randomised question bank, or code that redraws every figure in your lecture notes from current data. Then book the reclaimed block on the calendar under a name, the proposal, the executive course, the contract, because time without a name on it gets taken.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Start with an AI literature tool, not a general chatbot, for research. Open Elicit or Consensus and ask your research question — they surface real papers with citations and extract findings across dozens at once, so you map a literature in an afternoon instead of a month. Always click through to the actual paper before you cite it.
For coding and writing, use ChatGPT or Claude to debug Stata/R and tighten drafts, and NotebookLM to interrogate a corpus of PDFs you trust. All have free or academic tiers. The literature tool confirms what exists; the general models help you build on it — but you verify every number and every reference.
The one rule, forever: Never let AI fabricate or paraphrase scholarship you haven't verified — LLMs invent realistic-looking citations, so confirm every reference exists and says what you claim. Disclose AI use per each journal's and your institution's policy, never paste a manuscript you're peer-reviewing into a chatbot (it's confidential), keep student records FERPA-protected, and re-run every AI-suggested econometric result yourself — the scholarly contribution and its accuracy must be yours.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Map a literature in an afternoon with AI research tools
Why this pays: Research output drives tenure, raises, endowed chairs, and outside offers. Cutting a literature review from weeks to days means more papers in the pipeline — and publication count and quality are the engine behind the $346k professor.
ElicitConsensusResearch Rabbit
1
Pose your question in Elicit to get a table of relevant papers with extracted methods, data, and findings, then map the citation network in Research Rabbit to find the seminal and the newest work you'd otherwise miss.
2
Turn a pile of papers into a structured synthesis you can build on.
Copy-paste this prompt
I'm surveying the literature on [the labor-market effects of minimum wage increases]. From these papers [list or upload], build a synthesis matrix: for each, the identification strategy, data and setting, key elasticity estimate, and main limitation. Then tell me where the literature disagrees, what identification approaches are considered strongest, and the three open questions a new paper could credibly address.
Verify every paper and every number against the original — AI tools misattribute findings and invent citations. Use the synthesis to orient, never as a source you cite blind.
3
Identify the genuine gap and write the contribution statement yourself — the tool finds the map, but the research question is your judgment.
What you'll haveA verified literature map and a defensible research gap in days — the front end of a faster publication pipeline that drives academic pay.
2
Debug and accelerate your econometric code
Why this pays: Empirical work stalls on code. A professor who fixes a Stata or R problem in minutes instead of days spends that time on more analyses and more papers — throughput that compounds into the publication record top pay requires.
ChatGPT Advanced Data AnalysisClaudeStata
1
Paste a Stata or R error (no confidential data) into Claude or ChatGPT to diagnose it, and use the model to translate code between Stata, R, and Python when a coauthor uses a different stack.
2
Get a specification implemented correctly and check the assumptions behind it.
Copy-paste this prompt
I'm estimating [a difference-in-differences model of a state policy change] in Stata. Write the code for a two-way fixed-effects specification with clustered standard errors, then implement a modern robust estimator (e.g. Callaway-Sant'Anna) as a check given staggered adoption. Explain the identifying assumptions, the pre-trends test I should run, and how to interpret divergence between the estimators. Use simulated variable names, not real data.
AI writes plausible but sometimes wrong econometrics — confirm the estimator fits your design and re-derive the assumptions. Never upload confidential or restricted-use microdata to a consumer tool.
3
Re-run every AI-suggested specification yourself on your data and reconcile it with theory before it enters a draft — the results must be yours to defend.
What you'll haveFaster, better-specified empirical work with the assumptions checked — more completed analyses feeding the publication record that pays.
3
Draft, tighten, and referee-proof papers
Why this pays: Clearer papers get accepted at better journals, and journal quality is what tenure committees and deans weigh. A professor who turns drafts around faster and pre-empts referee objections publishes more, higher — the core of top academic pay.
ClaudeOverleafWritefull
1
Draft in Overleaf (LaTeX) and use Claude to tighten your own prose — clarity, structure, and framing — and to turn dense results tables into readable narrative you then verify.
2
Anticipate the referees before you submit.
Copy-paste this prompt
Act as a skeptical referee at a top field journal reviewing my paper on [topic]. Here is my abstract and identification strategy [paste your own text]. Give me the five hardest objections a referee would raise (endogeneity, external validity, mechanism, robustness, contribution), and for each, the specific additional analysis or argument that would address it. Be tough — I'd rather hear it now than in the report.
Use AI to stress-test and polish YOUR writing and ideas, not to generate scholarship. Disclose AI assistance per the journal's policy, and never paste a manuscript you are reviewing for a journal into any tool — that's a confidentiality breach.
3
Write a point-by-point response to a real referee report with AI help on tone and structure, then verify every claim and citation before you send it.
What you'll haveSharper, referee-proofed papers submitted faster — more acceptances at better journals, the record that earns promotion and endowed pay.
4
Win grants and manage the funding pipeline
Why this pays: Grants pay summer salary, buy out teaching, and fund research assistants — often the difference between the median and $346k. A professor who submits more, better proposals directly increases both income and research capacity.
ClaudeChatGPTPerplexity
1
Use Perplexity to find fitting funding calls (NSF, foundations, agencies) and Claude to reverse-engineer a successful past proposal's structure into a template for yours.
2
Turn your research idea into reviewer-ready proposal sections.
Copy-paste this prompt
Help me draft the [broader impacts and project description outline] for a grant proposal on [my research question]. Here is my core idea and preliminary results [paste your own]. Structure it to a reviewer's rubric: significance, innovation, approach/feasibility, and impact. Flag where reviewers will be skeptical and where I need preliminary evidence. Keep the intellectual contribution mine — draft structure and clarity only.
The research idea and feasibility must be genuinely yours; AI helps with structure and clarity, not the science. Follow the funder's AI-use policy and never include unpublished collaborator data you don't have rights to share.
3
Track deadlines and reuse a proposal library so each new submission is faster, submitting to more calls per cycle to raise your hit rate.
What you'll haveMore competitive proposals submitted per cycle — the grant income and research funding that separate top-of-range professors from the median.
5
Build courses and problem sets in a fraction of the time
Why this pays: Teaching well and efficiently protects the scarce resource — research time — that drives pay, and strong teaching ratings support promotion and retention. AI turns course prep from a time sink into an afternoon.
NotebookLMChatGPTClaude
1
Load your syllabus and readings into NotebookLM to generate a study guide, discussion questions, and a briefing your TAs can use — grounded only in your chosen materials.
2
Generate rigorous, original problem sets with worked solutions fast.
Copy-paste this prompt
Create a problem set for [intermediate microeconomics] covering [consumer choice and Slutsky decomposition] at the level of [Nicholson & Snyder]. Include 6 problems escalating in difficulty, at least two requiring a numerical solution and one graphical, plus a full worked solution key and the common student mistakes to flag. Make the numbers clean and the scenarios fresh so they can't be found online.
Verify every solution yourself — AI makes algebra and sign errors that will propagate to students. Keep any student grades or identifiable data out of AI tools (FERPA).
3
Reuse and refine the materials each term, freeing the reclaimed hours for research and outside work — where the real income upside sits.
What you'll haveStrong courses prepped in hours instead of weeks — protecting the research time and ratings that underpin academic advancement.
6
Scale expertise into consulting and expert-witness income
Why this pays: The $346k figure is often base plus lucrative outside work — economic consulting, litigation expert testimony, and textbooks pay well above salary. AI accelerates the research and reporting behind that work, letting a professor take on more of it.
ClaudePerplexityChatGPT Advanced Data Analysis
1
Use Perplexity and Claude to rapidly build the background research and market/industry context for a consulting or litigation engagement, always verifying against primary sources.
2
Draft the structure of an expert analysis, keeping the opinion your own.
Copy-paste this prompt
I'm preparing an economic analysis on [damages from a contract breach in a two-sided market]. Outline the analytical framework an economist would use: the relevant economic theory, the counterfactual/but-for approach, the data I'd need, and the standard methods for quantifying damages. List the assumptions opposing counsel would attack. This is framework only — the opinion and calculations will be mine.
Expert opinions must be your independent, defensible analysis — AI drafts framework and prose, never the conclusion. Never paste confidential case materials or protected data into a consumer tool; use engagement-appropriate, agreed tooling.
3
Turn a course or research area into a book or paid content, using AI to accelerate drafting while you own every argument and number.
What you'll haveMore capacity for high-paying consulting, testimony, and authorship — the outside income that lifts total compensation into the top of the range.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $346,200 tier.
Month 1
Adopt an AI literature tool (Elicit/Consensus) for your active project and verify every paper it surfaces. Make it your default first step on any new question.
Months 2-3
Fold AI into your empirical workflow for debugging and specification checks — re-running every result yourself — and into tightening a paper draft.
Months 3-6
Referee-proof and submit a paper faster with AI stress-testing, and draft a grant proposal from a reverse-engineered template.
Months 6-9
Systematize course prep with NotebookLM and AI-generated problem sets, reclaiming hours for research and outside work.
Months 9-12
Direct the reclaimed time at a high-value output — a top-journal submission, a bigger grant, or a consulting engagement.
Year 2
Compound research productivity and add outside income (consulting, testimony, a textbook) — the mix behind top-of-range total pay.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly 3rd already on data-scientist / python-developer / market-research-analyst / economist. This leftover page says Generate empirical examples in R or Python from live series and use the model to translate code between Stata, R, and Python; Months 2–3 is Fold AI into your empirical workflow for debugging and specification checks. Not leftover 94 CFP and not CFA Level I as the lead (that is financial-analyst / credit-analyst). Confirm 109810403X. Live page HTTP 200, no PC_GEAR / amazon.com/dp / tag=paycrunch-20 at 2026-09-17 4:36 PM PT.
Next steps for an Economics Professor
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Economics Professor work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Economics Teachers, Postsecondary (SOC 25-1063). O*NET Job Zone 5 is typical: graduate or professional school, so the honest next credential is a graduate-level or professional certificate — not a random catalog dump.
The occupation's listed knowledge area is Economics and Accounting, which is what the course searches below actually query.
Economics Professors in this dataset list Facebook among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for teaching and education — a graduate-level or professional certificate that lines up with education, not a generic professional-development aisle.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Economics Professor work, not a claim that they list a counted SOC 25-1063 inventory.
A an Economics Professor resume you can submit beats a blank page. Resume Now is a resume builder — we are not claiming an occupation-specific template library for SOC 25-1063.
An Economics Professor resume that names the actual tasks on this page beats a blank template when you apply.
What Economics Professors earn by state
These are the Bureau of Labor Statistics’ own figures for Economics Teachers, Postsecondary, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.
Massachusetts
$164,410
highest of them · +33% vs the national median
Pennsylvania
$117,970
lowest of the 5 states that qualify · -5% vs the national median
The same job pays $46,440 more a year at the median in Massachusetts than in Pennsylvania — 39% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. Massachusetts also carries the top of this job’s range, $346,200 — the figure quoted at the head of this page.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 25-1063. 5 states clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.
Free data. Use any of it.
PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.
No. AI can survey literature, debug code, and draft prose, but it cannot form an original research question, own the identification strategy, mentor a doctoral student, or stake a scholarly reputation on a result. Teaching and advising are deeply human. What AI changes is throughput: professors who use it publish and win grants faster, freeing time for the research and outside work that drive pay. The scholar's judgment is exactly what stays essential.
Is it academically honest to use AI in research and writing?
Yes, within disclosed limits. Using AI to survey literature, debug code, or polish your own prose is legitimate and increasingly normal — but you must disclose it per each journal's and your institution's policy, never present AI text as original scholarship, and never let it fabricate citations or results. The intellectual contribution and its accuracy must be yours. Transparency is the line.
Can I trust AI for literature reviews and citations?
Trust it to find and organize, never to be the source. Even citation-grounded tools like Elicit misattribute findings, and general chatbots invent realistic-looking references wholesale. Use AI to map a field fast, then read and verify every paper you cite against the original. A fabricated citation in a published paper is a career-damaging error the AI won't answer for.
How does AI actually increase a professor's pay?
Academic pay rises with research productivity, grants, and outside income. AI compresses the time-consuming parts — literature review, coding, drafting, course prep — so you produce more papers, submit more grants, and free hours for lucrative consulting or expert-witness work. More publications and funding drive promotion, endowed positions, and retention offers toward $346k; the outside work adds to it.
Is it safe to put my data or a manuscript I'm reviewing into AI?
No to both without care. Never upload confidential or restricted-use microdata to a consumer tool, and never paste a manuscript you're peer-reviewing into any chatbot — peer review is confidential and doing so breaches that duty. Keep student records out entirely (FERPA). Use AI on your own public or simulated materials, and follow the data-use agreements attached to any restricted dataset.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.