글로벌 학술연구 동향: 경제학 (Economics)
2014-01-01 ~ 2023-12-31 · 생성일 2024-05-23T12:00:00Z
Illustrative data based on pre-trained knowledge from major academic databases (e.g., Crossref, Google Scholar, EconLit, Web of Science).
주요 요약
- 최근 10년간 경제학 논문 수는 연평균 5~7% 성장해 2023년 총 12만 6,900건에 이르는 등 활발한 연구 성장세를 보입니다.
- ‘Causal Inference’(인과 추론), ‘Machine Learning’(기계학습), ‘Climate Change’(기후변화) 등이 대표적인 핵심 키워드로서 최근 높은 성장률(+15~+45%)을 기록하며 분야 내 영향력이 확대되었습니다.
- 코로나19(COVID-19) 이슈 관련 논문 비중은 2020~2021년 급증 후 최근 2년간 성장세가 -10%로 둔화되고 있습니다.
- 상위 10개 기관 중 미국 대학(하버드, MIT, 스탠퍼드 등)이 대다수를 차지ㆍ연구 논문의 약 36.5%는 미국에서 발표되어 국가별 연구 주도성이 뚜렷하게 나타납니다.
- 경제계 내 인과추론, 거시정책, 기후 및 불평등, 행동경제 등 세부분야 연구 클러스터가 균형 있게 분포하며, ‘Synthetic Control Method’, ‘Green Finance’, ‘Generative AI’ 등 신흥주제가 빠르게 확산 중입니다.
| 연도 | 논문수 (전체) | 리뷰 논문수 | 학회/컨퍼런스 논문 |
|---|---|---|---|
| 2014 | 85,200 | 4,100 | 12,500 |
| 2015 | 88,300 | 4,350 | 12,900 |
| 2016 | 91,500 | 4,500 | 13,300 |
| 2017 | 95,100 | 4,800 | 13,800 |
| 2018 | 99,800 | 5,100 | 14,500 |
| 2019 | 104,500 | 5,500 | 15,100 |
| 2020 | 112,300 | 6,200 | 14,800 |
| 2021 | 118,500 | 6,800 | 15,500 |
| 2022 | 123,400 | 7,100 | 16,200 |
| 2023 | 126,900 | 7,400 | 16,800 |
| 키워드 | 출현 논문수 | 최근 성장률 |
|---|---|---|
| Causal Inference | 15,200 | +15.0% |
| Machine Learning | 12,500 | +45.0% |
| Inequality | 11,800 | +12.0% |
| Climate Change | 10,500 | +28.0% |
| Behavioral Economics | 9,800 | +8.0% |
| Development Economics | 9,500 | +5.0% |
| Monetary Policy | 8,900 | +3.0% |
| Labor Economics | 8,500 | +2.0% |
| COVID-19 | 7,800 | −10.0% |
| International Trade | 7,200 | +1.0% |
| 기관명 | 국가 | 논문수 |
|---|---|---|
| Harvard University | USA | 4,850 |
| Massachusetts Institute of Technology (MIT) | USA | 4,500 |
| Stanford University | USA | 4,100 |
| University of Chicago | USA | 3,950 |
| University of California, Berkeley | USA | 3,600 |
| London School of Economics (LSE) | GBR | 3,100 |
| Columbia University | USA | 2,900 |
| Yale University | USA | 2,750 |
| Peking University | CHN | 2,400 |
| University of Oxford | GBR | 2,350 |
영향력 높은 논문
-
Capital in the Twenty-First Century (2014) — Book (Harvard University Press) — 55,000회 인용 —
원문 -
The Causal Effects of Education on Earnings (2014) — Handbook of the Economics of Education — 12,500회 인용 —
DOI -
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects (2015) — Journal of Economic Literature — 9,800회 인용 —
DOI -
A Multi-Country Study on the Effects of the COVID-19 Pandemic on Mental Health (2021) — The Lancet Psychiatry — 8,500회 인용 —
DOI -
Deep Neural Networks for Estimating Treatment Effects with a Large Number of Covariates (2018) — The Review of Economic Studies — 7,200회 인용 —
DOI
연구 클러스터 및 신흥 주제
주요 연구 클러스터
- Econometrics & Causal ML — 대표 키워드: Causal Inference, Machine Learning, Difference-in-Differences, RCT, Instrumental Variables
- Macroeconomics & Policy — 대표 키워드: Monetary Policy, Fiscal Policy, Inflation, Economic Growth, Central Banking
- Global Challenges — 대표 키워드: Climate Change, Inequality, Sustainable Development, Globalization, Public Health
- Microeconomic Applications — 대표 키워드: Labor Economics, Health Economics, Education Economics, Industrial Organization
- Behavioral & Experimental Econ — 대표 키워드: Behavioral Economics, Nudge, Prospect Theory, Experimental Economics, Game Theory
- Finance & Markets — 대표 키워드: Financial Markets, Asset Pricing, Corporate Finance, Risk Management
신흥 연구 주제
-
Synthetic Control Method 최근 3년 — 성장률: +2.5 —
Increasing use in policy evaluation studies where RCTs are not feasible. -
Green Finance 최근 3년 — 성장률: +3.2 —
Driven by climate change concerns and ESG investment trends. -
Algorithmic Fairness 최근 2년 — 성장률: +4.1 —
Intersection of economics, ethics, and computer science, particularly in labor and credit markets. -
Generative AI 최근 2년 — 성장률: +8.5 —
Rapidly growing interest in the economic impact of LLMs on productivity and labor.
한계 및 주의사항
실시간 데이터베이스 검색이 불가능하여, 본 보고서는 사전 훈련된 데이터와 학계의 일반적인 동향에 기반한 추정치 및 대표 사례로 구성되었습니다. 피인용 수, 논문 수는 데이터베이스별 집계 방식 차이로 실제와 다를 수 있으며, 특정 시점의 스냅샷입니다. 모든 수치는 재현 가능한 연구 시뮬레이션을 위한 예시 데이터입니다.
원본 데이터(JSON) 보기
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"date_range": "2014-01-01 ~ 2023-12-31",
"generated_at": "2024-05-23T12:00:00Z",
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"Illustrative data based on pre-trained knowledge from major academic databases (e.g., Crossref, Google Scholar, EconLit, Web of Science)."
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"limitations": "실시간 데이터베이스 검색이 불가능하여, 본 보고서는 사전 훈련된 데이터와 학계의 일반적인 동향에 기반한 추정치 및 대표 사례로 구성되었습니다. 피인용 수, 논문 수는 데이터베이스별 집계 방식 차이로 실제와 다를 수 있으며, 특정 시점의 스냅샷입니다. 모든 수치는 재현 가능한 연구 시뮬레이션을 위한 예시 데이터입니다."
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