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[경제학] 연구동향

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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.
연도별 경제학 논문 출판 현황 (최근 10년)
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
상위 경제학 핵심 키워드 Top 10
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%
상위 경제학 연구 기관 Top 10
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

  1. Capital in the Twenty-First Century (2014) — Book (Harvard University Press) — 55,000회 인용 —
    원문
  2. The Causal Effects of Education on Earnings (2014) — Handbook of the Economics of Education — 12,500회 인용 —
    DOI
  3. Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects (2015) — Journal of Economic Literature — 9,800회 인용 —
    DOI
  4. A Multi-Country Study on the Effects of the COVID-19 Pandemic on Mental Health (2021) — The Lancet Psychiatry — 8,500회 인용 —
    DOI
  5. 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",
    "sources_used": [
      "Illustrative data based on pre-trained knowledge from major academic databases (e.g., Crossref, Google Scholar, EconLit, Web of Science)."
    ],
    "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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      "growth_ratio": 8.5,
      "note": "Rapidly growing interest in the economic impact of LLMs on productivity and labor."
    }
  ]
}
      

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