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SOC 19-3011 · O*NET 19-3011.00
Use data, models, and economic theory to explain phenomena, evaluate policy or market problems, and write the assumptions, uncertainty, and recommendations into a reviewable report.
Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.
Positive signal: defines the question and definitions first, then checks the data, method, and alternative explanations.
First judge whether you are willing to work long term with definitions, data quality, model assumptions, uncertainty, and rebuttals, rather than only pursuing attractive conclusions.
Practice reviewing the seven fields of three fictional research cards: question, data source, sample/scope, period, method, result, review arrangement (uncertainty + escalation target). A: question = change in indicator A; source = dataset A; scope = sample A; period = period A; method = unknown; result = estimate A; review arrangement = record uncertainty and refer the unknown to the research lead, 1 missing. B: question = change in indicator B; source = dataset B; scope = sample B; period = period B; method = unknown; result = estimate B; review arrangement = unknown, 2 missing. C: question = change in indicator C; source = dataset C; scope = sample C; period = period C; method = descriptive comparison; result = estimate C; review arrangement = record uncertainty and refer anomalies to the designated reviewer, 0 missing. Answers 1, 2, 0.
Occupational definition: research economic and statistical data, prepare reports or formulate plans, and address issues such as production and distribution, monetary, or fiscal policy. 19-3011.00 centers on research into economic problems, reports, and policy/market interpretation; it is classified with market research and securities valuation positions by actual main duties. Economists may also analyze consumer demand and sales, so adjacent codes cannot be interpreted as tasks that never overlap.
Economists deliver not only models but also explainable conclusions, limitations, and verifiable materials.
Career direction comparison
Compare the research object, data and model basis, responsibility boundaries, and purpose of recommendations, and do not interchange because the tools are the same.
| Career direction | Core work and output | Key differences | Best-fit choice |
|---|---|---|---|
| Investment fund managers | Economists: explain macroeconomic, policy, or cross-domain economic phenomena and produce research, forecasts, or recommendations. | Manage fund investment portfolios and are responsible for investment decisions on entrusted funds. | The decision object and fiduciary responsibility differ, and portfolio management and compliance requirements must be added. |
| Market research analysts and marketing specialists | Market research analysts: study consumers, competition, prices, and marketing conditions to judge the potential sales of a product or service. | Study consumers, competition, and marketing conditions to judge the potential sales of a product or service. |
AI impact on this career
Editorial candidate, adoption or efficiency for this occupation not verified: test data cleaning code, variable notes, charts, and report drafts on approved materials and check item by item against the raw data. AI Impact Score 67% is an editorial estimate. Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.The three tasks, ratings and sources are listed in the FermatMind method source record on this page.
FermatMind AI Impact Score estimation method v1
FermatMind / 费马测试 · 2026-10-18
Chinese mainland salary reference
The exact Chinese economist wage distribution was not obtained. 2025 scientific research and technical services urban non-private 182,064 yuan per year and private 83,560 yuan per year are matching pre-tax industry averages for research/consulting directions and do not cover all government or financial directions; they cover urban legal entity units with 5 or more people and exclude self-employment/freelance work. The industry includes many occupations and cannot be used as the economist median, percentiles, starting pay, or monthly wage.
When comparing positions, break down the institution type, research field, degree, region, project responsibility, and output requirements.
Insufficient evidence: no national single-occupation definition obtained
U.S. salary reference
The 25th percentile is 89,330 USD, and the median is 124,720 USD.
| Definition | Value | Period | Limitation |
|---|---|---|---|
| Low end | 67,360 USD per year | 10th percentile | The low end of the U.S. 2025 wage data distribution; not entry-level starting pay. |
| Median | 124,720 USD per year | 50th percentile | Half of practitioners are below and half above this value; the hourly median is 59.96 USD. |
| High end | 238,060 USD per year |
Career fit guide
Positive signal: able to analyze data patterns and proactively look for contrary evidence.
Fit signals： Positive signal: able to explain estimates, charts, and uncertainty to non-specialist readers.
Caution signals： Positive signal: willing to maintain code, a data dictionary, and reproduction records.
Verification actions： Caution signal: chasing only significance without checking the sample, definitions, or model specification.
Work pressure and occupational boundaries
First identify five categories of risk: data definitions, sample and model, causal statements, tool output, and authorization boundaries.
BLS/O*NET support the task background; the following consequences and controls are editorial risk analysis, not occurrence rates, a statutory process, or uniform responsible positions.
Typical scenario：Data definitions or versions change without being recorded.
Roles most affected：Economists
Possible consequence：May make estimates and conclusions incomparable and affect downstream decisions.
How to reduce risk：Editorial suggestion: verify the data definition, period, and version and explain the differences, and verify the actual data owner and acceptance standards separately.
Entry, qualifications, and career development
Business, research, or international organization positions may require a master's or doctorate and work experience.
Career outlook and transitions to related occupations
U.S. economist employment and openings correspond only to the U.S. national definition and do not represent China or a specific institution or an individual's hiring probability.
United States · Economists (19-3011.00)
U.S. 2025 employment 18,600, projected 19,500 in 2035, a ten-year net increase of about 900; 2025—2035 growth 5% (BLS rounding), with about 1,100 annual average openings including growth and replacement demand, not annual net additions or a ten-year total. Government budget changes can affect employment; this is not Chinese demand, real-time institutional positions, or individual hiring probability.
Employment growth
Growth rate, employment size, and openings are not the same indicator.
Does not establish：Projections are not a guarantee of individual hiring.
Core work includes researching data, using mathematical models and statistical techniques to explain phenomena and predict trends, assessing policy impacts, providing consulting, and producing reports or recommendations.
Economists explain economic phenomena and policy; market research analysts focus on the potential sales of products and services and the marketing market; financial and investment analysts focus on investment projects and institutional financial data and valuation.
Choose a real task from this page for a small practice project or a conversation with someone in the field. Use the planning guides to set a date, an output and feedback questions before deciding whether to continue.
This is work sample and data analysis exercise verification, not personality diagnosis or a hiring conclusion.
Caution signal: treating correlation as causation, treating a single significant result as the final answer, or being unwilling to record limitations.
Editorial deliverable verification suggestion: the research question, data sources and definitions, method and code, verifiable results, robustness and limitations, and interpretation and recommendations should form a traceable chain, and the real institutional review and release authorization should then be verified; model estimates and final policy value judgments must still be kept separate. This page did not obtain a uniform institutional closed-loop system and is not a general approval process.
| Dimension | Primary track | Comparison track |
|---|---|---|
| Loop | Propose the economic question | |
| Step one | Collect and process data | |
| Step two | Model and estimate | |
| Step three | Test and check robustness | |
| Step four | Interpret and recommend | |
| Step five | Report and review | |
| Step six | Form the review |
Research data in fields such as finance, labor, and agriculture.；Use mathematical models and statistical techniques to explain phenomena and predict trends.；Assess policy impacts and provide consulting on economic relationships.
Inputs: survey, administrative, market, or enterprise data and the institutional context.；Processing: defining indicators, modeling, estimation, validation, and interpretation.；Outputs: reports, charts, forecasts, recommendations, or policy options.
Editorial deliverable suggestion: distinguish descriptive, predictive, and causal questions.；Keep the data dictionary, code, assumptions, and versions.；Deliver uncertainty and limitations together with the conclusions; actual institutional standards are verified separately.
In the United States usually at least a master's degree (some government entry positions accept a bachelor's and some positions require a doctorate); data permissions, external release, and policy signing are verified by institution and position, and a research report is not the same as individual investment or legal advice.
Work objects and AI boundaries for economists Editorial trial candidate: whether data cleaning, variable notes, retrieval, charts, and report drafts are suited to generative AI must be verified; the O*NET statistical software recruitment signal cannot prove GenAI capability, occupational adoption, or efficiency. No samples of economist AI deployment and effects were obtained this round. Data definitions and representativeness, causal identification, assumptions, and robustness require human review, and release and policy recommendations follow the actual institutional permissions and responsibility arrangements.
| Oriented to business decisions, requiring added market research and marketing analysis. |
| Financial and investment analysts | Financial and investment analysts: analyze investment projects or institutional financial data and make quantitative judgments such as business valuation. | Analyze investment projects or institutional financial data and make quantitative judgments such as valuation. | Oriented to investment targets, requiring added valuation and industry analysis. |
|---|
| Personal financial advisors | All three use data, but the research object, end consumer, risk responsibility, and purpose of the recommendations differ, and they cannot be interchanged just because the tools are the same. | Provide financial planning and investment advice to individual clients and are subject to the corresponding regulation. | Oriented to individual clients and regulated, requiring added compliance and client responsibility. |
|---|
| Financial risk specialists | The BLS states that the typical entry requires at least a master's degree, and some government entry positions may accept a relevant bachelor's background. | Assess institutional financial risk and design risk control and compliance arrangements. | Oriented to institutional risk, requiring added risk management frameworks and compliance. |
|---|
| Statisticians | Business, research, or international organization positions may require a master's or doctorate and work experience. | Design statistical methods and experiments and perform inference and uncertainty quantification. | Similar methods but a different object, requiring added statistical theory and experimental design. |
|---|
Adjacent occupations differ in research object, basis, and authorization, and method, qualification, and responsibility evidence must be supplemented separately; they cannot be interchanged just because the tools are the same.
Next, verify the direction using a verifiable analysis portfolio or shadowing observation.
Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
Distinguish editorial assistance candidates from judgments the researcher must review; no permanent non-substitutability prediction is made.
A software or model demonstration is not institutional adoption; testing, validation, and human review are required.
A layoff date or substitution rate cannot be derived from task exposure.
| Work area | Task | Current change | Human control point |
|---|---|---|---|
| Data and information | Organize the data dictionary and descriptive statistics | Quality looks at reproducibility, robustness, error, consistency of interpretation, and decision consequences, not only generation speed. | Return to the raw data to verify definitions and versions. |
| Execution and collaboration | Perform the core modeling and estimation tasks | Editorial suggestion to compare errors, reproduction, and review time on the same task; no causal evidence of efficiency gains or net employment for this occupation was obtained. Tools do not automatically assume research integrity or policy responsibility. | The identification strategy, model specification, and robustness are the responsibility of the researcher. |
| Safety and authorization | External release and policy recommendations | Not automatically authorized | Escalate through institutional data permissions and release authorization. |
| Review and improvement | Form reproduction and review records | Editorial candidate, adoption or effect not verified: may assist with organization | Editorial suggestion to have authorized research or review personnel check it; no uniform review system was obtained. |
| Inputs and versions | Verify the data source and variable definitions | Proportions of unique U.S. 2025 postings for this occupation that mention software skills: Excel/R/Python each 27%, PowerPoint18%, SQL15%, and SAS14%; the denominator is all unique postings linked to this O*NET occupation. The 24 skill rows include items without proportions, and not given does not mean 0; this does not represent GenAI adoption, efficiency, or substitution rates. | Return to the raw data and documents for verification. |
| Boundaries and anomalies | Identify definition conflicts and gaps | Editorial candidate, adoption or effect not verified: can assist with data cleaning code, variable notes, charts, and draft report structures. | Editorial suggestion to mark definition or permission anomalies and stop relying on unverified results; the actual reporting subject, suspension, and resumption procedures are verified separately. |
| Adoption and responsibility | Form a traceable analysis deliverable | Editorial candidate, adoption or effect not verified: can assist with listing alternative models, robustness checks, and a reproduction checklist. | Editorial suggestion to verify the responsible person, data permissions, and release conditions; the actual systems are verified separately. |
Editorial candidate, effect not verified: test alternative models, robustness checks, and reproduction checklist drafting; the researcher still verifies the model's applicable assumptions and the code.
The correspondence between variable definitions and the theoretical question requires researcher review; this is a responsibility and quality suggestion, and the task description cannot be used to assert that the technology will never be substitutable.
AI primarily changesEditorial candidate, effect not verified: data organization and draft charts
Human responsibility remainsQuestion definition, identification strategy, and causal interpretation
AI primarily changesEditorial candidate, effect not verified: retrieval and draft reproduction checklists
Human responsibility remainsRaw data verification and release authorization
Causal identification, policy trade-offs, and interest impacts must be reviewed according to the position authority; AI output cannot obtain policy decision-making authority by itself, and no prediction that cannot be automated is made.
Confirm the data source, variable definitions, period, and version.
Use tool assistance only within the approved scope.
Editorial suggestion to have authorized researchers review the model, estimates, and conclusions; the actual responsible positions are verified separately.
Editorial suggestion to mark anomalies and report per the actual systems; the suspension/resumption conditions and subjects were not obtained, and no uniform reporting sequence is set.
Will bring wrong definitions or data into the conclusions.
Control: Verify the data source, variable definitions, and version.
Will change the responsibility boundaries for data and release.
Control: Handle through the institutional authorization chain.
May treat correlation as causation and affect policy or investment judgments.
Control: Keep the identification assumptions and robustness checks.
First build data and reproduction habits with verifiable analysis exercises.
Bring tool output into the existing review and audit procedures.
First define data permissions, release authorization, and stop conditions.
A substitution date cannot be derived from task exposure; question definition, identification strategy, causal interpretation, and release responsibility still remain with people.
Source: BLS OOH — Economists dutiesOriginal source ↗
Editorial candidate: test cleaning, variable notes, retrieval, charts, and drafts on approved data, then check errors and review cost; no deployment rate was verified.
Source: NIST AI RMF official framework descriptionOriginal source ↗
Data permissions, causal identification, external release, policy recommendations, and signing responsibility.
Source: BLS OOH — Economists education and qualitiesOriginal source ↗
Review should return to the raw data, sampling, code, assumptions, estimates, and uncertainty.
2025 industry annual average 182,064/83,560 yuan (non-private/private); not converted into an occupational monthly wage
Insufficient evidence: a single quote is not used to substitute for occupational wages
Insufficient evidence: no national single-occupation definition obtained
| Position or scope | Pay and disclosure status | Scope of application and gaps |
|---|---|---|
| Entry requirements | Research-type positions | Education requirements for target positions in China were not obtained; the U.S. BLS typical master's and some bachelor's entry points cannot be extrapolated to China. |
| On-the-job training | Methods and tools | Statistical and econometric methods and tools such as R/Python/SQL deepen with the position. |
| Data and release | Institutional authorization | Data permissions and external release authorization are verified by institution and position. |
| Occupation-level wage definition | National single occupation | Insufficient evidence: no national single-occupation definition obtained. |
Insufficient evidence: no national single-occupation definition obtained
Industry averages are not used to substitute for occupational wages.
2025 industry annual average 182,064/83,560 yuan
The exact Chinese economist wage distribution was not obtained. 2025 scientific research and technical services urban non-private 182,064 yuan per year and private 83,560 yuan per year are matching pre-tax industry averages for research/consulting directions and do not cover all government or financial directions; they cover urban legal entity units with 5 or more people and exclude self-employment/freelance work. The industry includes many occupations and cannot be used as the economist median, percentiles, starting pay, or monthly wage.
Insufficient evidence: no national single-occupation definition obtained
Must be verified by the specific position.
When exact occupational percentiles are missing, keep the data range explanation and verify the real position pay structure directly.
| 90th percentile |
| The 90th percentile for this U.S. occupation is not a guarantee of higher education/rank; specific contracts are verified separately. |
U.S. wage median：124,720 USD per year (59.96 USD per hour, May 2025)；O*NET OnLine — National wages for Economists
U.S. wage low end：67,360 USD per year (10th percentile, May 2025)；O*NET OnLine — National wages for Economists
U.S. wage high end：238,060 USD per year (90th percentile, May 2025)；O*NET OnLine — National wages for Economists
U.S. wage unit：USD per year; USD per hour；O*NET OnLine — National wages for Economists
U.S. wage period：May 2025；O*NET OnLine — National wages for Economists
U.S. exact occupation 19-3011.00, with the definition being the BLS May 2025 wages and the 2025—2035 projection.
No; Chinese and U.S. occupational definitions are not automatically equivalent.
No; the median is not starting pay, and openings are not equal to net additions.
Use it together with the institution, research field, degree, and region.
| Industry | Original source values | How to interpret |
|---|---|---|
| Federal government (excluding postal) | 144,680 USD per year | BLS2025 median wage for selected major industries, not an all-government average |
| Management, scientific, and technical consulting services | 141,010 USD per year | BLS2025 median wage for selected major industries, not Chinese consulting wages |
| Scientific research and development services | 139,880 USD per year | BLS2025 median wage for selected major industries; contract authority is verified separately |
| Period | May 2025 wage data | Time definitions must not be ignored. |
Verified against the exact occupation code 19-3011.00.
Verified against the source region.
Verified against the 2025 wage data and the 2025—2035 projection.
O*NET U.S. occupational interest I100/C59/E49/A24/S23/R6; ICE is a summary of the top three interests and is not individual capability or a diagnosis.
Interests only indicate directions for exploration; actual fit is verified through a verifiable analysis portfolio and shadowing observation.
Can you connect question definition, data, model, and limitations into a reviewable chain?
Caution signal: using complex terminology to cover questions that cannot be answered.
Do not use it this way: Not used for personality diagnosis or hiring conclusions.
Start the career interest testCan you maintain code, a data dictionary, and reproduction records over the long term?
BLS emphasizes analytical, communication, computer, and critical thinking skills.
Do not use it this way: Not used for personality diagnosis or hiring conclusions.
Start the career interest testWould you rather do data and modeling, or policy and communication and explanation?
Editorial observation suggestion: compare whether analysis results can be reproduced and whether limitations can be explained; this round has no evidence that a personality scale predicts work performance, and no judgment of character strengths or weaknesses is made.
Do not use it this way: Preference descriptions are not ability conclusions.
Start the career interest testCan you accept the rhythm of data cycles, review, and external release?
Check against actual project or course cycle experiences.
Do not use it this way: Not used as a hiring basis.
Start the career interest testWhen definition conflicts or data gaps are found, can you stop and report?
Observe the handling order and reporting method through a simulated scenario.
Do not use it this way: Not used as an ability score.
Start the career interest testCan you hand over clearly with data, policy, business, and review parties?
Verify through handover records and feedback from counterparties.
Do not use it this way: Not used as a hiring conclusion.
Start the career interest testFits better when: Can you turn the question into testable hypotheses and an identification strategy.
Watch for: Do not treat correlation as causation.
Explore related career: investment-fund-managersFits better when: Can you maintain a data dictionary and a reproducible cleaning process.
Watch for: Do not skip definition and version verification.
Explore related career: market-research-analysts-and-marketing-specialistsFits better when: Can you perform estimation and robustness checks according to the method and data.
Watch for: Do not conceal failed specifications and limitations.
Explore related career: financial-and-investment-analystsFits better when: Can you explain estimates and uncertainty to non-specialist readers.
Watch for: Do not use terminology to cover questions that cannot be answered.
Explore related career: personal-financial-advisorsFits better when: Can you release externally according to institutional permissions and procedures.
Watch for: Do not release or sign beyond authorization.
Explore related career: financial-risk-specialistsFits better when: Can you keep filling in methods and field and tools.
Watch for: Qualifications and authorization are verified by institution.
Verify with a reproducible data analysis exercise.
Save and reproduce results according to the procedure.
Set stop and escalation conditions.
State the data source, method, and limitations.
Revise per the review comments and keep records.
Verified by institution and position.
Tools cannot replace the identification strategy and release responsibility.
Complete one reproducible paper or public data analysis exercise.
Evidence：Editorial note on economist AI candidates, risk control, and transfers
Typical scenario：Sample selection bias or improper coverage.
Roles most affected：Economists
Possible consequence：May make conclusions non-generalizable or misleading.
How to reduce risk：Editorial suggestion: record the inclusion and exclusion rules, coverage, and key gaps; whether to continue the analysis is determined by the actual project systems.
Evidence：Editorial note on economist AI candidates, risk control, and transfers
Typical scenario：Unstable model specification or missing robustness checks.
Roles most affected：Economists
Possible consequence：May make conclusions highly sensitive to specification and unreliable.
How to reduce risk：Editorial suggestion: compare alternative specifications and sensitivity, and when conclusions are unstable mark the limitations and review per the actual systems.
Evidence：Editorial note on economist AI candidates, risk control, and transfers
Typical scenario：Writing correlation or prediction as causation.
Roles most affected：Economists
Possible consequence：May mislead policy or investment judgments.
How to reduce risk：Editorial suggestion: verify the identification assumptions and strength of evidence, and do not write correlation directly as causation; the actual publishing personnel are verified separately.
Evidence：Editorial note on economist AI candidates, risk control, and transfers
Typical scenario：Erroneous code or citations generated by tools are not detected.
Roles most affected：Economists
Possible consequence：May produce non-reproducible or wrong conclusions.
How to reduce risk：Editorial suggestion: reproduce the tool code and verify the original citations; the tool cannot obtain release or policy decision-making authority by itself.
Evidence：Editorial note on economist AI candidates, risk control, and transfers
Typical scenario：Encountering data or release tasks beyond the scope of authorization.
Roles most affected：Economists
Possible consequence：May produce irreversible compliance or reputational consequences.
How to reduce risk：Editorial suggestion to first verify data and release permissions; the specific reporting subject, suspension/resumption procedures are verified separately. For surveys, administrative, or client data involving identifiable individuals, verify the lawful basis, purpose, minimum necessity, and security measures under the Personal Information Protection Law; sensitive information is judged separately by the Article 28 categories and risk, and economic data is not necessarily all sensitive personal information. The editorial suggestion is to verify the tool recipient, purpose, retention, permissions, and training use, and institutional approval does not replace legal obligations.
Evidence：Editorial note on economist AI candidates, risk control, and transfers · Personal Information Protection Law: boundaries of identifiable personal information in economics research, surveys, and client materials
| Position direction | Initial tasks | Boundaries | Observation evidence | Subsequent development |
|---|---|---|---|---|
| Supervised research assistant | Clean data, organize literature and descriptive statistics, and reproduce routine analyses under supervision. | The supervised scope and release authority are verified against the actual position systems; this is not a uniform prohibition | Data dictionary and reproducible code records, and mentor comments. | Independently complete routine analyses and review them. |
| Economists (government direction) | Produce statistical and policy impact analyses and write reports. | Must comply with institutional data permissions and release authorization. | Reports and reproduction records, and review comments. | Improve policy evaluation and communication capability. |
| Economists (corporate or consulting direction) | Conduct market, pricing, and competition analyses and provide advice to clients. | Corporate economists may also study demand and sales; the job title does not automatically grant individual investment/legal practice authority | Project reports and assumption and limitation notes. | Take on project leader responsibilities. |
| Economists (research or international organization direction) | Conduct econometric and causal research and write papers and technical reports. | Must comply with research ethics and data use and publication norms. | Papers and projects and peer review records. | Expand methods and research fields. |
| Combined teaching and research direction | Teach and supervise student research while conducting personal research. | Economist tasks may include teaching; full-time postsecondary economics teachers have a separate 25-1063 classification, judged by actual main duties | Course and research records, and supervision records. | Follow the degree and academic development path. |
| Stage | Observed evidence | Possible check | escalation |
|---|---|---|---|
| Interview | Can you explain the research question, data source, method, and identification limitations. | Verify the identification strategy, robustness, and data reputation. | When conditions do not match, the institution and position decide, not the individual. |
| Technical assessment or portfolio review | Can you reproduce the results and explain the failed specifications and limitations. | Verify the code, data dictionary, and reproduction steps. | If not passed, no external release or independent responsibility is allowed. |
| Probation and mentoring period | Complete routine analyses under supervision and record the process. | Verify data permissions, citations, and assumption records. | Exceeding authorization or errors should stop work immediately and require retraining. |
| Independent responsibility period | Independently complete analyses and deliver externally per the process. | Verify review, record keeping, and release authorization. | Compliance or unauthorized release problems are handled per institutional systems. |
| Qualification or training | Applicable conditions | Whether it is an entry requirement | Verification materials | Regional and position boundaries | Next step | Subject to verification |
|---|---|---|---|---|---|---|
| Master's degree (typical entry) | Master's in economics or a related field | Yes: the BLS states that the typical entry requires at least a master's degree | Degree materials and course records | U.S. BLS typical entry; Chinese positions are verified separately | Verify the target position's education requirements | Confirmed by the institution |
| Bachelor's degree (some government entry positions) | A bachelor's with sufficient economics, statistics, or mathematics coursework | Depends on the position: mainly government entry positions | Transcripts and relevant course records | Some U.S. government entry points; does not represent China | Verify whether the target position accepts a bachelor's degree | Confirmed by the institution |
| Doctorate (some research or academic positions) | Doctorate in economics or a related field | Depends on the position: common for research or academic positions | Degree and research records | Required by some U.S. positions, but not everyone needs a doctorate | Verify whether the target position requires a doctorate | Confirmed by the institution |
| Econometric and statistical method training | Econometrics, statistics, and causal inference | Direction-related: capability for impact analysis positions | Coursework and project portfolio | Verified by the institution and position | Verify the method requirements of the target project | Confirmed by the institution and project |
| Analysis tool training (R/Python/SQL and so on) | Data processing and visualization tools | 2025 U.S. recruitment software skill signal, not a uniform software certificate requirement | Reproducible portfolio and code | Verified by the institution and position | Verify the tool requirements of the target position | Confirmed by the institution and project |
| Institutional data permissions and release authorization | Data access, external release, and signing authority | No: this is internal institutional authorization | Authorization and approval records | Verified by the institution and jurisdiction | Verify the target position's scope of authorization | Confirmed by the institution |
Typical scope：The seven-day exercise uses only public or fictional data: define the question, write the data dictionary, reproduce results, and perform robustness checks.
Readiness evidence：Keep a verifiable analysis portfolio and reproduction records.
Credential boundary：Verified by institutional and position rules.
Next moves：Fill in method and tool evidence.
Typical scope：The portfolio should include the question, data, method, code, results, limitations, and reproduction steps.
Readiness evidence：Review against the raw data and record review comments.
Credential boundary：Verified by institutional and position rules.
Next moves：Practice anomaly identification and robustness.
Typical scope：Degrees and software certificates do not automatically grant institutional release, policy signing, or investment advice authority.
Readiness evidence：Keep version and change records.
Credential boundary：Verified by institutional and position rules.
Next moves：Escalate through the authorization chain.
Typical scope：When job seeking, verify the research field, data permissions, method requirements, publication or delivery standards, and the final responsible person.
Readiness evidence：Prove stable delivery with actual projects.
Credential boundary：Verified by institutional and position rules.
Next moves：Verify the responsibility boundaries of the next level.
Typical scope：Turn the economic question into a testable research design.
Readiness evidence：Able to explain the inputs and identification limitations.
Credential boundary：Verified by institutional and position rules.
Next moves：Fill in method and data evidence.
Typical scope：Maintain code, the data dictionary, and reproduction per the procedures.
Readiness evidence：Keep original records and versions.
Credential boundary：Verified by institutional and position rules.
Next moves：Review output and robustness.
Typical scope：Editorial suggestion to identify definition and data anomalies and mark the limitations; the actual suspension procedures are verified separately
Readiness evidence：Has escalation and handling records.
Credential boundary：Verified by institutional and position rules.
Next moves：Handle per the actual position systems; no uniform authorization chain was verified
Typical scope：Arrange analysis and data tasks within the scope of authorization.
Readiness evidence：Boundaries and responsible persons are clear.
Credential boundary：Verified by institutional and position rules.
Next moves：Check deliverables and records.
Typical scope：Review data, method, and conclusions.
Readiness evidence：Record deviations and rectification.
Credential boundary：Verified by institutional and position rules.
Next moves：Handle exceptions and disputes.
Typical scope：Editorial suggestion to verify the actual suspension, reporting, and resumption conditions; not a uniform occupational procedure
Readiness evidence：Report anomalies promptly.
Credential boundary：Verified by institutional and position rules.
Next moves：Keep the basis for decisions.
Clarify the research object, indicators, and definitions.
Maintain a data dictionary and reproducible cleaning.
Complete estimation, robustness, and error statements.
Explain externally within the scope of authorization and keep records.
United States · Economists (19-3011.00)
About 18,600 people in 2025; projected 19,500 in 2035, a ten-year net increase of about 900
Employment size
Employment size and openings are not the same indicator.
Does not establish：Projections are not a guarantee of individual hiring.
United States · Economists (19-3011.00)
About 1,100 per year (2025—2035)
Job openings
Openings include replacement demand and are not equal to net additions.
Does not establish：Projections are not a guarantee of individual hiring.
These are capability relationships, not automatic promotions or credential waivers.
Transfers：Transferable abilities: economic and financial data analysis, report writing, and quantitative methods.
Build next：Needs filling in: portfolio management, fiduciary responsibility, and compliance and regulatory requirements.
View career profileTransfers：Transferable abilities: data and statistical analysis and report writing.
Build next：Needs filling in: market research design and marketing analysis.
View career profileTransfers：Transferable abilities: data processing and quantitative analysis.
Build next：Needs filling in: valuation methods and industry analysis.
View career profileTransfers：Transferable abilities: economic and financial analysis and communication.
Build next：Needs filling in: compliance and fiduciary responsibility for individual clients.
View career profileTransfers：Transferable abilities: data analysis and risk assessment thinking.
Build next：Needs filling in: institutional risk management frameworks and compliance.
View career profileTransfers：Transferable abilities: statistical inference and data modeling.
Build next：Needs filling in: statistical theory and experimental design.
View career profileBLS notes that economists need to analyze data, observe patterns, interpret research, use software, and solve complex problems with careful reasoning.
Digital tools can assist with data cleaning, code drafts, robustness checks, and draft charts, but they cannot automatically prove causality or assume responsibility for policy recommendations.
Analysts must look back at the data definition, sample, code, assumptions, estimates, and uncertainty, and have a clearly designated person review external conclusions.
The National Bureau of Statistics wage material verified on this page is aggregated by industry and broad personnel category and does not provide a Chinese exact occupational wage corresponding one-to-one with 19-3011.00; that source cannot be used to generate this occupation's exact percentiles.
The 2025 U.S. economist annual 10/25/50/75/90 percentiles are 67,360, 89,330, 124,720, 174,720, and 238,060 USD.
The BLS states that the typical entry requires at least a master's degree; some entry positions mainly in government may accept a bachelor's with sufficient economics, statistics, or mathematics coursework, and other positions require a doctorate.
Economic analysis must distinguish data observation, model assumptions, estimation results, causal interpretation, and policy judgment, and record review and escalation responsibility.
U.S. economists are projected to grow 5% from 2025 to 2035, faster than 3% for all occupations.
U.S. economists are projected to have about 1,100 job openings per year over the decade, many from replacement of workers who transfer or leave the labor force.
FermatMind research workflow
current page; accessed 2026-09-06
BLS OOH — Economists duties
Limitations：What They Do / Work Environment: office work, surveys, modeling and forecasting, policy evaluation, and consulting; government/corporate tasks may overlap with business demand analysis.
Limitations：U.S. 2025 employment 18,600, projected 19,500 in 2035, a ten-year net increase of about 900; 2025—2035 growth 5% (BLS rounding), with about 1,100 annual average openings including growth and replacement demand, not annual net additions or a ten-year total. Government budget changes can affect employment; this is not Chinese demand, real-time institutional positions, or individual hiring probability.
Limitations：The BLS U.S. typical entry requires at least a master's degree, and some government entry positions accept a bachelor's with sufficient economics/statistics/mathematics coursework; business, research, or international organizations may require a master's or doctorate plus work experience. The O*NET new-hire education feedback is 50% master's, 25% doctorate, and 21% bachelor's (the three listed total 96%), which is not a general Chinese education level or recruitment proportion; the Job Zone Five preparation level does not mean that everyone in this occupation has more than five years of experience.
Limitations：U.S. 19-3011, May 2025 annual 10/25/50/75/90 percentiles 67,360/89,330/124,720/174,720/238,060 USD; hourly percentiles under the same definition 32.38/42.95/59.96/84.00/114.45 USD. The 50th percentile is the median, not the mean; these are employed pre-tax straight-time wages excluding self-employment, and percentiles do not correspond to education, rank, or individual starting pay.
Limitations：Supports only fictional research review and does not replace institutional review, policy decisions, or professional responsibility.
Limitations：Keep the original registered URL; the actual published text is registered separately at r5-nbs-current. The exact Chinese economist wage distribution was not obtained. 2025 scientific research and technical services urban non-private 182,064 yuan per year and private 83,560 yuan per year are matching pre-tax industry averages for research/consulting directions and do not cover all government or financial directions; they use the 5+ urban legal entity unit definition and exclude self-employment/freelance work. The industry includes many occupations and cannot be used as the economist median, percentiles, starting pay, or monthly wage.
Limitations：2026 profile: 16 tasks, 57 context items; interest I100/C59/E49/A24/S23/R6; education 50/25/21 and 20 work styles, all from the U.S. occupation profile.
Limitations：Only for the listed occupation, market, period, and definition.
Limitations：Only for the listed occupation, market, period, and definition.
Limitations：Proportions of unique U.S. 2025 postings for this occupation that mention software skills: Excel/R/Python each 27%, PowerPoint18%, SQL15%, and SAS14%; the denominator is all unique postings linked to this O*NET occupation. The 24 skill rows include items without proportions, and not given does not mean 0; this does not represent GenAI adoption, efficiency, or substitution rates.
Limitations：U.S. 19-3011, May 2025 annual 10/25/50/75/90 percentiles 67,360/89,330/124,720/174,720/238,060 USD; hourly percentiles under the same definition 32.38/42.95/59.96/84.00/114.45 USD. The 50th percentile is the median, not the mean; these are employed pre-tax straight-time wages excluding self-employment, and percentiles do not correspond to education, rank, or individual starting pay.
Limitations：All part-time and full-time employees, straight-time pre-tax, excluding self-employment; includes applicable piece rates / commissions / production bonuses and some allowances, excludes overtime premiums / shift differentials and so on, and does not exclude performance pay across the board. Individual annual hours and take-home pay must be verified separately.
Limitations：The Personal Information Protection Law applies to identifiable natural person information, lawful basis, clear purpose, minimum necessity, and secure handling; general organizational documents, business data, and user information cannot all be called sensitive personal information, and health and financial accounts and similar require separate verification of the sensitive scope under Article 28 and necessity, applicable separate consent, and impact assessment. State secrets, trade secrets, and personal information are separate legal concepts; de-identification is not anonymization, and an approved account does not automatically permit uploads to external AI. Other specific clauses on confidentiality, cybersecurity, and data were not verified item by item this round; verify against the real functions and supervisory rules, and do not falsely claim a full compliance review.
Limitations：Voluntary AI governance recommendations are not industry technical specifications or position authorization for economics research, surveys, and client materials, nor a measurement of tool adoption or efficiency.
Limitations：This batch actually read the ILO2025 summary; for this occupation the reliable mapping and scoring between SOC19-3011 and ISCO were not verified, so it is not claimed that the full study has no score; exposure is not adoption, effect, or substitution.
Limitations：The exact Chinese economist wage distribution was not obtained. 2025 scientific research and technical services urban non-private 182,064 yuan per year and private 83,560 yuan per year are matching pre-tax industry averages for research/consulting directions and do not cover all government or financial directions; they cover urban legal entity units with 5 or more people and exclude self-employment/freelance work. The industry includes many occupations and cannot be used as the economist median, percentiles, starting pay, or monthly wage.
Limitations：Editorial candidates, risk analysis, and capability comparison are not measured effects, a statutory uniform process, recruitment samples, or individual success rates.
Limitations：U.S. 2025 employment 18,600, projected 19,500 in 2035, a ten-year net increase of about 900; 2025—2035 growth 5% (BLS rounding), with about 1,100 annual average openings including growth and replacement demand, not annual net additions or a ten-year total. Government budget changes can affect employment; this is not Chinese demand, real-time institutional positions, or individual hiring probability.
Limitations：Supports only the comparison of main work objects and duties of adjacent U.S. occupations; the transfer distance is an editorial judgment and does not guarantee mutual recognition of skills, practicing qualifications, or success rates.
Limitations：Supports only the comparison of main work objects and duties of adjacent U.S. occupations; the transfer distance is an editorial judgment and does not guarantee mutual recognition of skills, practicing qualifications, or success rates.
Limitations：Supports only the comparison of main work objects and duties of adjacent U.S. occupations; the transfer distance is an editorial judgment and does not guarantee mutual recognition of skills, practicing qualifications, or success rates.
Limitations：Supports only the comparison of main work objects and duties of adjacent U.S. occupations; the transfer distance is an editorial judgment and does not guarantee mutual recognition of skills, practicing qualifications, or success rates.
Limitations：Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
Source scopes, limitations, and statistical periods are verified separately against the original evidence; data not obtained is visibly marked in the corresponding place.
Typical scope：Form reusable method and conclusion reviews.
Readiness evidence：Conclusions are traceable.
Credential boundary：Verified by institutional and position rules.
Next moves：Re-verify before expanding responsibility.
Typical scope：Bear data and release responsibility per the systems.
Readiness evidence：The evidence chain is complete.
Credential boundary：Verified by institutional and position rules.
Next moves：Develop toward research or policy management.
