Political scientists
Also called Polisci researcher, Policy researcher, Government researcher, International relations scholar, Comparative politics scholar
In brief
In May 2025, BLS counted 5,540 US political scientists, with median pay of $142,080 a year. Of the BLS counts since 2003, the highest was 6,350, in May 2016. The earliest figure here is an estimate of 204 from 1904, not measured the same way as today's count. The last full technology era for political scientists: Big data and text-as-data methods, 2010 to 2020. Text-as-data methods created a new subspecialty (computational political science) and increased the value of programming skills for junior researchers.
What the sources say
Official outlook
BLS projects employment to decline 2% from 2025 to 2035.
AI exposure studies
- 54.2% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
- 0.039 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013
Since the 2013 prediction
In 2013, Frey and Osborne put this job's probability of computerisation at 0.039. BLS counted 5,570 of these jobs in May 2013 and 5,540 in May 2025 (-1%, a change derived by Future History).
Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.
Last updated .
Cite this page
Future History. (2026, October 11). Political scientists: jobs and pay since 1904 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-3094
Future History. "Political scientists: jobs and pay since 1904." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-3094.
@misc{futurehistory_19_3094, author = {{Future History}}, title = {Political scientists: jobs and pay since 1904}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-3094}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }
The access date is today's date on your device.
≈ $138,437 in 2024 prices (derived by us, CPI-adjusted)
John W. Burgess, a Civil War veteran who had studied at the Ecole Libre des Sciences Politiques in Paris and at German universities, convinces Columbia's trustees to let him open a graduate school of political science. It awards the first PhDs in political science in the Western Hemisphere. Burgess believed that scientific training in the state would produce a civil-service class that could advise governments on the basis of expertise rather than patronage. The experiment succeeded as an institution-builder: within 25 years, most major American research universities had established separate political science departments.
Woodrow Wilson, who would be elected 28th President of the United States in 1912, submits his doctoral dissertation "Congressional Government" to Johns Hopkins University, completing his PhD in political science under Herbert Baxter Adams. The dissertation analyzes Congressional committee government as the actual operating system of American politics, arguing that formal constitutional structures were largely window dressing. Wilson becomes president of Princeton in 1902, governor of New Jersey in 1910, and President in 1912, the first American president to hold a political science doctorate and perhaps the discipline's most consequential advertisement for its policy relevance.
December 30, 1903: the American Political Science Association is founded at Tulane University in New Orleans, primarily to distinguish the "science of politics" from the study of history and economics. Founding membership: 204. The association's formation reflects both the maturation of a distinct academic identity and the Progressive Era conviction that scientific expertise in government could cure the corruption and inefficiency of machine politics. Woodrow Wilson attends the founding meeting; Frank Goodnow of Columbia, the most prominent American administrative law scholar of the era, becomes the association's first president.
The post-World War II expansion of American higher education, driven by the GI Bill (1944) and subsequently by Cold War federal investment in social science research, transforms the scale of the political science profession. The National Science Foundation, established in 1950, begins funding social science research including survey studies and international relations research. University enrollments double and then triple over the following 25 years, creating sustained demand for political science faculty. APSA membership grows from approximately 2,857 in 1940 toward 7,963 by 1970. The behavioral revolution, with its emphasis on empirical, quantitative measurement of political behavior, aligns the discipline with the scientific-method ethos that justified federal funding.
The academic job market for political scientists collapses. Graduate programs across the social sciences had tripled PhD production over the preceding decade, reaching nearly 30,000 new doctoral degrees per year in all disciplines by 1970. The baby boom cohort that had filled universities was aging through; major departments began freezing hiring. Real faculty wages began what would become a decade of decline. Political scientists who graduated in the late 1970s and early 1980s entered a labor market structurally unable to absorb them on the tenure track. Many moved into government agencies, think tanks, and consulting, diversifying the occupation's employer base beyond the academy for the first time.
The Varieties of Democracy (V-Dem) project begins its long-term data collection effort at what will become the V-Dem Institute at the University of Gothenburg. V-Dem eventually builds a dataset tracking over 400 political indicators across 180+ countries from 1789 to the present, using thousands of country experts as coders and Bayesian item-response models to aggregate expert disagreement. By 2025, V-Dem's methodology is replicated and extended by machine learning classifiers that predict democratic backsliding. The dataset is the most ambitious data-collection project in political science history and represents the field's most durable contribution to AI-era comparative politics research.
The September 11, 2001 attacks produce a significant, if temporary, increase in federal demand for political scientists with area studies expertise: Middle East, Central Asia, and South Asian specialists are recruited by the CIA, DIA, State Department, and the newly created Department of Homeland Security (2002). The post-9/11 security buildup is one of the two largest federal demand shocks for political science expertise in the occupation's history, the other being the Cold War expansion of the 1950s-60s. RAND, Brookings, and Carnegie see significant growth in their international security research programs. Political scientists with language competency in Arabic, Dari, and Pashto command salary premiums unprecedented in the discipline.
The APSA Presidential Task Force on AI, commissioned by APSA President Taeku Lee and led by Joshua Tucker and Nathaniel Persily, publishes its report "Artificial Intelligence, Politics, and Political Science." The task force finds that work using generative AI in political science research has "dramatically increased" and that the field lacks agreed-upon benchmarks for validating AI-assisted research quality. The report identifies five domains where AI raises fundamental challenges for democratic governance: power concentration in AI corporations, information ecosystems and epistemic fragmentation, national security and algorithmic warfare, public administration and bureaucratic automation, and electoral integrity. Political scientists who can navigate both AI systems and democratic institutions are identified as occupying a unique and valuable position in the emerging AI governance landscape.
Will AI replace political scientists? What the research says
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.
What's changing day to day for political scientists
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereUse GPT-4-class LLMs via prompt-engineered pipelines to extract structured data from unstructured political documents (coding administrative records, federal advisory committee meeting minutes, archival newspaper articles, biographical databases, legislative committee reports, or diplomatic correspondence) converting raw text into analyzable datasets with specified fields, then validating extraction accuracy with human spot-checks and documenting prompt specifications, model versions, and error rates for methodological transparency in peer review.
Use GPT-4-class LLMs via prompt-engineered pipelines to extract structured data from unstructured political documents (coding administrative records, federal advisory committee meeting minutes, archival newspaper articles, biographical databases, legislative committee reports, or diplomatic correspondence) converting raw text into analyzable datasets with specified fields, then validating extraction accuracy with human spot-checks and documenting prompt specifications, model versions, and error rates for methodological transparency in peer review.[4],[15]
GPT-4-class LLMs have compressed multi-week RA coding projects into hours: PS:PSP 2024 documents <5% error rates on news source extraction and 94% character accuracy on OCR cleanup of archival materials. The displacement of RA labor is real and acknowledged in the literature. The researcher's value-add shifts to: (1) designing the extraction schema that operationalizes the theoretical concept; (2) iterative prompt engineering to reduce edge-case failures; (3) validation discipline (LLMs hallucinate on ambiguous cases and perform worse on long documents; [4] APSA Task Force 2026 notes the field lacks agreed-upon benchmarks for validating AI extraction) researchers who document extraction validity rigorously will differentiate their work in peer review. Use batch API mode for cost efficiency on large corpora and always report model version + full prompt in supplementary materials.
AI is sitting alongside you hereConduct systematic literature reviews and rapid research synthesis using Elicit and NotebookLM, uploading working paper corpora, journal article PDFs, and policy reports to identify the frontier consensus on a political science research question, map which empirical strategies have been used on similar identification problems, surface methodological gaps and theoretical debates, and generate a structured annotated bibliography before committing to a new research design or policy report.
Conduct systematic literature reviews and rapid research synthesis using Elicit and NotebookLM, uploading working paper corpora, journal article PDFs, and policy reports to identify the frontier consensus on a political science research question, map which empirical strategies have been used on similar identification problems, surface methodological gaps and theoretical debates, and generate a structured annotated bibliography before committing to a new research design or policy report.[16],[17]
AI literature synthesis tools (Elicit, NotebookLM) now compress political science literature review from weeks to hours: Elicit's Research Agents (late 2025) screen up to 1,000 relevant papers and extract structured data from 20,000 data points; NotebookLM enables Q&A across a loaded PDF corpus in minutes. For political science specifically, these tools are strong at mapping what prior studies have done and what the findings are. The residual human judgment is in critically evaluating research design quality: whether a cited paper's identification strategy actually establishes causality given its political context, a judgment that requires theoretical and methodological depth AI tools cannot yet provide.
AI is sitting alongside you hereTrain a HuggingFace transformer model (fine-tuned BERT, RoBERTa, or domain-specific PolitBERT) on a labeled corpus of political texts (congressional floor speeches, party manifestos, treaty language, or social media posts) to perform automated classification tasks such as ideological position estimation, sentiment scoring, topic labeling, or toxicity detection at corpus scale (tens of thousands to millions of documents), then validate classifier performance against gold-standard human-coded ground truth and assess out-of-sample generalizability across time periods and political contexts.
Train a HuggingFace transformer model (fine-tuned BERT, RoBERTa, or domain-specific PolitBERT) on a labeled corpus of political texts (congressional floor speeches, party manifestos, treaty language, or social media posts) to perform automated classification tasks such as ideological position estimation, sentiment scoring, topic labeling, or toxicity detection at corpus scale (tens of thousands to millions of documents), then validate classifier performance against gold-standard human-coded ground truth and assess out-of-sample generalizability across time periods and political contexts.[5],[9]
Transformer-based text classifiers now enable a single political scientist to analyze document corpora that previously required multi-year RA coding teams: BERTopic modeling of more than a hundred thousand congressional speeches, scaling party manifestos across many countries, or classifying millions of social media posts. The irreplaceable researcher contribution is the classification schema design (what theoretical concept does the label operationalize?), the gold-standard coding that trains the model, and the validity assessment: does classifier performance generalize beyond the training context? PSRM 2024 explicitly notes that "fine-tuning provides a scientifically defensible starting point" while warning against treating LLM outputs as ground truth without validation. Develop expertise in transformer fine-tuning workflows (HuggingFace Trainer API, PEFT methods) and model card documentation for reproducibility.
Where political scientists go next
Natural next steps for someone with your foundation: not exits, evolutions.
- Compliance Officers
- US median pay
- $80,730 (May 2025)
- BLS outlook 2025-35
- +3.8%
- Statisticians
- US median pay
- $105,650 (May 2025)
- BLS outlook 2025-35
- +11%
- Economists
- US median pay
- $124,720 (May 2025)
- BLS outlook 2025-35
- +4.7%
What each move takes
Compliance Officers
Political scientists who specialize in regulation, administrative law, legislative process, or government affairs have well-developed skills for compliance and regulatory affairs roles. Knowledge of regulatory frameworks, legislative history, rulemaking process (notice-and-comment, APA), and political risk assessment maps directly onto the core Compliance Officer toolkit. AI governance is an emerging compliance domain where political scientists have a distinctive advantage, understanding how AI regulation is being designed (EU AI Act, NIST AI RMF, executive orders) requires exactly the mix of technical-policy literacy and regulatory knowledge that political scientists develop. Think tanks, lobbying firms, and technology companies are actively hiring political scientists for AI regulatory compliance and government affairs roles. BLS projects employment growth for Compliance Officers through 2034, positive, and compensation is competitive with academic research roles without the tenure-track uncertainty.
- · Regulatory compliance frameworks: NIST AI RMF (AI Risk Management Framework), EU AI Act compliance requirements, FTC consumer protection enforcement, CFPB regulatory standards
- · Enterprise risk management (ERM): risk identification, risk appetite frameworks, internal controls, audit methodology
- · Contract and administrative law basics: APA rulemaking, Federal Register navigation, state-level regulatory equivalents
- · Compliance management systems: GRC (Governance, Risk, Compliance) platforms (Riskonnect, LogicGate, ServiceNow GRC)
- · AI governance and ethics frameworks: OECD AI Principles, IEEE Ethically Aligned Design, algorithmic impact assessment methodologies
How has the work of political scientists changed since 1880?
The tools that defined the work, one line per era. Select an era to see how it reshaped the work.
Library, primary documents, and institutional fieldwork (pre-quantitative era)
The first generation of political scientists worked with the same tools as historians: the library, the archive, the government document collection, and personal observation. Woodrow Wilson wrote his 1885 "Congressional Government" at Princeton without ever visiting the halls of Congress; he analyzed American politics entirely from committee reports and published documents. Charles Merriam at the University of Chicago began pushing in the 1920s for more systematic data collection, but the dominant method was careful reading of constitutions, statutes, judicial decisions, and official records. The political scientist's technological environment was a good research library and a typewriter.
Survey research infrastructure: IBM punch cards, census microdata, Gallup/NORC data (behavioral revolution)
The behavioral revolution in political science, which dominated the discipline from the late 1940s through the early 1970s, was made possible by a new technological infrastructure: the sample survey, the IBM punch card sorter, and the emergence of national polling organizations. Paul Lazarsfeld at Columbia and Angus Campbell at Michigan's Survey Research Center developed the tools for measuring actual voter behavior through random sample surveys. The 1948 election studies, the 1952 American National Election Study, and the Michigan tradition of survey-based voting research defined what "rigorous" political science meant for a generation. Cold War federal funding, especially from the National Science Foundation (established 1950), supported the infrastructure. The IBM 650 computer (1954) and subsequent mainframes made cross-tabulation of large survey datasets practical for the first time.
Effect on the workThe behavioral turn expanded the occupation by creating a new class of quantitative political scientists whose skills were legible to government funders. NSF social science funding grew substantially during the 1950s-1960s Cold War expansion. APSA membership grew between 1940 and 1970.
SPSS, SAS, and departmental mainframe computing (quantitative infrastructure matures)
SPSS (Statistical Package for the Social Sciences) was first released in 1968 and quickly became the standard statistical environment for political scientists at universities. It made regression analysis, crosstabulation, and factor analysis accessible without custom programming. The combination of SPSS with the codified survey data archives maintained at the Inter-university Consortium for Political and Social Research (ICPSR, founded 1962) gave political scientists a shared data infrastructure: you could order magnetic tape copies of election studies, congressional voting records, or cross-national survey data and analyze them on your department's mainframe. This infrastructure standardized what counted as a publishable quantitative study and reinforced the discipline's turn toward journal-article research as the primary currency of professional recognition.
Personal computing, Stata, R, and internet-sourced data (quantitative standards tighten)
The shift from mainframe to personal computing and the emergence of Stata (1985) and R (1993) as cross-platform statistical environments transformed the daily workflow of political scientists. Regression tables and graphical data analysis that once required booking time on a departmental mainframe could now be done on a laptop. The internet enabled new data collection methods: automated retrieval of legislative records, vote tracking databases, and international conflict datasets. Journals began requiring data and code replication archives. The Comparative Manifesto Project, the Correlates of War Project, and eventually the Varieties of Democracy (V-Dem) dataset built the large-N comparative infrastructure that made quantitative international relations and comparative politics possible. The 1990s also saw the rise of formal game-theoretic modeling, enabled by mathematical software like Mathematica and Maple.
Big data and text-as-data methods: Twitter corpora, congressional text, NLP pipelines
The "text-as-data" revolution gave political scientists access to an entirely new class of evidence: the full text of legislative proceedings, party manifestos, diplomatic cables (Wikileaks, 2010), social media archives, and digitized historical newspapers. Researchers at Princeton, Columbia, and Stanford developed Python-based NLP pipelines for scaling political positions from text, estimating ideology from legislative speech, and measuring sentiment in political discourse at corpus scale. Twitter and Facebook data became primary sources for studying political behavior, polarization, and misinformation. The Congressional Record going back to 1873 was digitized. JSTOR made hundreds of thousands of academic articles machine-readable. The tools were available in Python and R; the bottleneck was the research question and the theoretical interpretation.
Effect on the workText-as-data methods created a new subspecialty (computational political science) and increased the value of programming skills for junior researchers. Demand for political scientists with Python and R expertise grew at think tanks, government agencies, and technology companies' policy teams.
Large language models, AI annotation tools, and transformer-based analysis
GPT-4-class LLMs and transformer-based models (BERT, RoBERTa, domain-specific PolitBERT) arrived with measurable effects on political science research workflows by 2023-24. The most documented change is in annotation and coding tasks: Tornberg 2025, published in Social Science Computer Review, found that LLMs outperform both traditional supervised classifiers and human expert coders on political social media annotation tasks, drawing on "capacities akin to human interpretation." GPT-4 achieves approximately 94% character accuracy on archival OCR cleanup (Lee et al., PS:PSP 2024) and under 5% error on structured data extraction from news articles. Tools like Elicit and NotebookLM compress systematic literature review from weeks to hours. ATLAS.ti AI and NVivo AI reduce qualitative coding cycles from weeks to days. The 2026 APSA Presidential Task Force flagged that the field lacks agreed-upon benchmarks for validating AI-assisted research, creating methodological uncertainty. The AI governance policy domain is simultaneously generating demand: political scientists with computational methods expertise are sought by DHS, CISA, NIST, and think tanks working on AI policy.
Effect on the workDirect displacement of RA-level annotation, literature review, and OCR tasks is documented and ongoing. The field's total employment has not yet declined measurably on these grounds (citing funding constraints more than AI displacement). The occupation is small enough that even moderate RA-task displacement may reduce demand for junior researchers without appearing in the headline BLS employment figure for the occupation code.
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Browse all rolesPolitical Scientists employment and pay by year
View all 31 years of cited US data, each linked to its source
| Year | US employment | Annual pay (median unless marked) | Source | Report |
|---|---|---|---|---|
| 1904 | 204 | n/a | Est., Tulane School of Liberal Arts Researched context | Report |
| 1915 | 1,462 | n/a | Est., Wikipedia Researched context | Report |
| 1940 | 2,857 | n/a | Est., Grokipedia Researched context | Report |
| 1965 | n/a | $12,000 | Est., American Historical Association / AAUP Researched context | Report |
| 1968 | 5,176 | n/a | Est., PS Researched context | Report |
| 1970 | 6,493 | n/a | Est., PS Researched context | Report |
| 1980 | n/a | $30,000 | Est., AHA / AAUP 1991 Faculty Salary Update Researched context | Report |
| 2000 | 4,250 | $81,040 | BLS OEWS 2000 Official statistic | Report |
| 2003 | 4,840 | $81,370 | BLS OEWS May 2003 Official statistic | Report |
| 2004 | 4,370 | $86,750 | BLS OEWS May 2004 Official statistic | Report |
| 2005 | 5,010 | $84,100 | BLS OEWS May 2005 Official statistic | Report |
| 2006 | 3,970 | $90,140 | BLS OEWS May 2006 Official statistic | Report |
| 2007 | 3,940 | $91,580 | BLS OEWS May 2007 Official statistic | Report |
| 2008 | 3,530 | $104,130 | BLS OEWS May 2008 Official statistic | Report |
| 2009 | 3,970 | $104,090 | BLS OEWS May 2009 Official statistic | Report |
| 2010 | 4,470 | $107,420 | BLS OEWS May 2010 Official statistic | Report |
| 2011 | 5,340 | $103,860 | BLS OEWS May 2011 Official statistic | Report |
| 2012 | 5,750 | $102,000 | BLS OEWS May 2012 Official statistic | Report |
| 2013 | 5,570 | $100,920 | BLS OEWS May 2013 Official statistic | Report |
| 2014 | 5,640 | $104,920 | BLS OEWS May 2014 Official statistic | Report |
| 2015 | 3,910 | $99,730 | BLS OEWS May 2015 Official statistic | Report |
| 2016 | 6,350 | $114,290 | BLS OEWS May 2016 Official statistic | Report |
| 2017 | 6,320 | $115,110 | BLS OEWS May 2017 Official statistic | Report |
| 2018 | 5,660 | $117,570 | BLS OEWS May 2018 Official statistic | Report |
| 2019 | 6,010 | $122,220 | BLS OEWS May 2019 Official statistic | Report |
| 2020 | 6,010 | $125,350 | BLS OEWS May 2020 Official statistic | Report |
| 2021 | 5,650 | $122,510 | BLS OEWS May 2021 Official statistic | Report |
| 2022 | 5,660 | $128,020 | BLS OEWS May 2022 Official statistic | Report |
| 2023 | 5,580 | $132,350 | BLS OEWS May 2023 Official statistic | Report |
| 2024 | 5,950 | $139,380 | BLS OEWS May 2024 Official statistic | Report |
| 2025 | 5,540 | $142,080 | BLS OEWS May 2025 Official statistic | Report |
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