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全球人才竞争力指数Global Talent Competitiveness Index

规模 + 质量 + 创新 + 贡献 四维自有算法 · 排名更新日期:2026-10-20Four-dimensional proprietary algorithm (Scale + Quality + Innovation + Contribution) · Last updated: 2026-10-20

综述Overview

Talent is the source of innovation and the foundation of development. In the era of the knowledge economy and artificial intelligence, the rise and fall of a country (or region) increasingly depends on whether it can cultivate, attract, retain and effectively use high-quality talent. For this reason, "talent competitiveness" has become a key indicator for measuring a country's comprehensive strength and future potential.
人才是创新之源、发展之本。在知识经济与人工智能时代,一国(地区)的兴衰,越来越取决于其能否培养、吸引、留住并有效使用高素质人才。正因如此,"人才竞争力"成为衡量国家综合实力与未来潜力的关键指标。
Internationally there are various talent-related evaluations (such as INSEAD's Global Talent Competitiveness Index GTCI and the World Economic Forum's Human Capital Index), whose perspectives mostly focus on quality dimensions such as "institutional environment, per-capita quality and openness". Such frameworks structurally underestimate late-developing countries with large populations—for example, an economy with the world's largest cohort of engineers and STEM (science, technology, engineering and mathematics) graduates finds it hard to have its "talent dividend" (a large-scale, cost-effective supply of engineers supporting high-intensity industrial innovation) fully reflected in per-capita- and institution-oriented indicators.
国际上有多种人才相关评价(如欧洲工商管理学院 INSEAD 的"全球人才竞争力指数 GTCI"、世界经济论坛的人力资本指数等),其视角多聚焦于"制度环境、人均素质、开放程度"等质量维度。此类框架对人口规模庞大的后发国家存在结构性低估——例如拥有全球最大规模工程师与 STEM(科学、技术、工程、数学)毕业生队伍的经济体,其"人才红利"(大规模、高性价比的工程师供给支撑高强度产业创新)往往难以在"人均"与"制度"导向的指标中得到充分体现。
This index follows the CPPPS proprietary evaluation-algorithm specification and does not directly reproduce any public ranking; instead it builds its own model, sets its own weights and computes its own ranking from underlying public data (higher-education population, total R&D personnel, top scientists / highly cited scholars, R&D investment, patent output, talent-cultivation output, etc.). The model evaluates comprehensively across four dimensions—"Scale, Quality, Innovation, Contribution"—of which "Contribution" is a distinctive CPPPS dimension specifically measuring talent-cultivation output and the talent dividend.
本指数遵循 CPPPS 自有评价算法规范,不直接转载任何公开榜单名次,而是以底层公开数据(高等教育人口、研发人员总数、顶尖科学家/高被引学者、研发投入、专利产出、人才培养输出等)自建模型、自设权重、自算排名。模型从"规模、质量、创新、贡献"四个维度综合评价,其中"贡献"为 CPPPS 特色维度,专门衡量人才培养输出与人才红利。
Evaluation results: China, the United States, Japan, Germany and South Korea rank in the top five. Overall, economies that combine talent scale with research quality, innovation input and talent output rank ahead; economies with a small population or a weak R&D base rank lower. The ranking data are based on publicly known approximate values, marked "subject to official figures", permanently free and public, and are provided for research and reference only.
评价结果:中国、美国、日本、德国、韩国 位列前五。整体看,兼具人才规模与科研质量、创新投入与人才输出的经济体居前;人口体量小或研发基础薄弱的经济体靠后。本排名数据以公开常识近似值为基础、标注"以官方为准",永久免费公开,仅供研究与参考。

分层梯队Tiers

A++ 前三Top 3 A+ 四至十4th–10th A 十一至二十11th–20th B 其余Rest

30 个主要经济体人才竞争力排名Talent Competitiveness Ranking of 30 Major Economies

排名国家/地区人才竞争力指数规模质量创新贡献梯队
1中国China88.2620.019.728.620.0A++
2美国United States81.9914.923.129.514.5A++
3日本Japan58.8511.813.724.39.1A++
4德国Germany57.439.917.521.48.6A+
5韩国South Korea55.769.117.121.18.4A+
6英国United Kingdom54.539.419.916.78.5A+
7法国France47.989.013.717.28.0A+
8印度India47.3813.02.114.118.2A+
9加拿大Canada44.358.416.912.16.8A+
10俄罗斯Russia39.6612.05.99.312.4A+
11澳大利亚Australia38.025.616.110.36.1A
12意大利Italy37.627.710.312.86.8A
13瑞士Switzerland37.253.620.612.50.6A
14荷兰Netherlands36.034.615.612.03.8A
15西班牙Spain34.677.510.79.76.7A
16瑞典Sweden34.063.816.811.22.3A
17巴西Brazil29.298.12.18.210.9A
18土耳其Turkey29.217.52.09.410.2A
19以色列Israel28.272.414.810.60.6A
20新加坡Singapore27.151.316.88.80.3A
21丹麦Denmark24.651.315.56.61.2B
22比利时Belgium24.273.111.97.61.7B
23芬兰Finland24.231.314.87.01.2B
24波兰Poland23.896.64.86.16.4B
25奥地利Austria21.952.211.17.80.9B
26挪威Norway19.610.812.85.30.8B
27墨西哥Mexico18.276.10.42.98.9B
28捷克Czechia13.692.86.53.11.2B
29葡萄牙Portugal13.382.89.10.01.5B
30爱尔兰Ireland12.930.28.93.60.3B

算法详细论述Methodology

一、评价依据

Talent competitiveness is hard to characterize with a single indicator. This index synthesizes the four dimensions of "Scale, Quality, Innovation and Contribution", covering the complete chain of talent from cultivation and agglomeration to output:
人才竞争力难以用单一指标刻画。本指数综合"规模、质量、创新、贡献"四个维度,覆盖人才从培养、集聚到产出的完整链条:
  1. 规模:人才队伍的"家底"——高等教育人口(存量)与研发人员总数(活跃科研力量),衡量人才资源的绝对体量。
  2. 质量:人才队伍的"含金量"——顶尖科学家/高被引学者(头部科研精英)与研发人员密度(人均科研强度),衡量人才的高度与密集度。
  3. 创新:人才转化为生产力的"产出"——研发投入(创新资源强度)与发明专利(PCT 国际专利)产出,衡量人才驱动的创新成果。
  4. 贡献(CPPPS 特色):人才培养输出与"人才红利"——STEM 毕业生规模(每年新增工程师供给)与高校在校生规模(人才后备军),衡量其为全球产业链持续输送高素质人才的贡献能力。
By employing all four dimensions together, it avoids "scale-centrism" (where populous countries necessarily rank first) and also avoids "per-capita-centrism" (where small countries necessarily rank first), and further restores, through the "Contribution" dimension, the "engineer dividend" of populous countries—a competitiveness ignored by most international rankings.
四维并举,既避免"唯规模论"(人口大国必然居前),也避免"唯人均论"(小国必然居前),更通过"贡献"维度还原人口大国"工程师红利"这一被多数国际榜单忽视的竞争力。

二、数学模型

Each indicator is first range-standardized to [0,1]; large-span scale indicators (higher-education population, R&D personnel, R&D expenditure, patents, STEM graduates, enrolled students, highly cited scholars) first take the logarithm and are then standardized to avoid monopoly by head economies; R&D personnel density is a ratio indicator and is directly linearly standardized. After weighted summation, the result is converted into a percentage score:
各指标先做极差标准化至 [0,1],规模类大跨度指标(高等教育人口、研发人员、研发经费、专利、STEM 毕业生、在校生、高被引学者)先取对数再标准化,避免头部经济体垄断;研发人员密度为比率指标,直接线性标准化。加权求和后换算为百分制:
x′ = (x − x_min) / (x_max − x_min),规模类先取 log₁₀(1+x) 指数 = 100 × ( 0.10·高等教育人口′ + 0.10·研发人员′ + 0.18·高被引学者′ + 0.12·研发密度′ + 0.15·研发经费′ + 0.15·专利′ + 0.12·STEM毕业生′ + 0.08·在校生′ )
Each of the four sub-scores (Scale/Quality/Innovation/Contribution) is 100 times the weighted sum of its constituent indicators, for ease of horizontal comparison.
四项分项得分(规模/质量/创新/贡献)各自为所含指标加权和的 100 倍,便于横向比较。

三、指标与权重依据

维度指标方向权重处理依据
规模 0.20高等教育人口正向0.10对数人才存量
研发人员总数正向0.10对数活跃科研力量
质量 0.30高被引学者正向0.18对数头部科研精英
研发人员密度正向0.12线性人均科研强度
创新 0.30研发经费正向0.15对数创新资源投入
PCT 专利正向0.15对数创新成果产出
贡献 0.20STEM 毕业生正向0.12对数人才培养输出
高校在校生正向0.08对数人才后备军/红利
Weight-design logic: Quality and Innovation each account for 30%, highlighting that the essence of "talent competitiveness" is "a high level of research and innovation capability" rather than mere population size; Scale accounts for 20%, retaining the basic stock of talent; Contribution accounts for 20% and is a distinctive CPPPS dimension that specifically supplements the "talent-cultivation output and engineer dividend" perspective generally missing from international rankings.
权重设计逻辑:质量与创新各占 30%,突出"人才竞争力"的本质是"高水平的科研与创新能力",而非单纯的人口规模;规模占 20%,保留人才家底的基本盘;贡献占 20%,为 CPPPS 特色维度,专门补足国际榜单普遍缺失的"人才培养输出与工程师红利"视角。

四、数据来源与局限性

The higher-education population, R&D personnel, R&D expenditure and patents are approximate values from the public data of the UNESCO Institute for Statistics, the World Intellectual Property Organization (WIPO) and national statistical departments; highly cited scholars are approximate values from Clarivate's annual "Highly Cited Researchers" list; STEM graduates and enrolled university students are approximate values from public statistics of national ministries of education. All of the above data are publicly known approximate values, marked "subject to official figures". This model does not include hard-to-quantify soft factors such as talent mobility (immigration attractiveness), institutional environment and salary competitiveness, and the approximate values contain errors; the ranking is provided for research and reference only.
高等教育人口、研发人员、研发经费、专利等为联合国教科文组织(UNESCO)统计所、世界知识产权组织(WIPO)、各国统计部门公开数据的近似值;高被引学者为科睿唯安(Clarivate)"高被引科学家"年度榜单的近似值;STEM 毕业生与高校在校生为各国教育部公开统计的近似值。上述数据均为公开常识近似值,标注"以官方为准"。本模型不包含人才流动(移民吸引力)、制度环境、薪酬竞争力等难以量化的软性因素,且近似值存在误差,排名仅供研究与参考。

五、参考文献

  1. UNESCO Institute for Statistics (UIS):研究与开发(R&D)统计数据。
  2. 世界知识产权组织(WIPO):PCT 国际专利申请年度统计。
  3. 科睿唯安(Clarivate):高被引科学家(Highly Cited Researchers)年度榜单。
  4. OECD:《Main Science and Technology Indicators(MSTI)》。
  5. 各国统计部门:高等教育、在校生、毕业生年度统计。
  6. CPPPS《自有评价算法规范》:规模 + 质量 + 创新 + 贡献 四维框架。

说明Notes

本版采用 CPPPS 自有四维模型(规模 20% + 质量 30% + 创新 30% + 贡献 20%),不直接转载 INSEAD GTCI 等公开榜单名次:①各指标极差标准化至 [0,1] 后加权求和;②规模类大跨度指标(高等教育人口、研发人员、研发经费、专利、STEM 毕业生、在校生、高被引学者)先取对数,避免头部经济体垄断;③研发人员密度为比率指标,线性标准化;④"贡献"为 CPPPS 特色维度,衡量人才培养输出与"人才红利"(大规模高性价比工程师供给),补足国际榜单以"人均/制度"为导向、低估人口大国人才规模优势的偏差。数据:底层指标为公开常识近似值,一律标注"以官方为准"。排名永久免费公开,永不卖榜。
This edition adopts the CPPPS proprietary four-dimension model (scale 20% + quality 30% + innovation 30% + contribution 20%), and does not directly reproduce the rankings of public lists such as INSEAD GTCI: ① each indicator is min-max normalized to [0,1] and then weight-summed; ② wide-span scale indicators (higher-education population, R&D personnel, R&D expenditure, patents, STEM graduates, enrolled students, highly cited scholars) are log-transformed first to avoid monopoly by leading economies; ③ R&D personnel density is a ratio indicator and is linearly normalized; ④ "contribution" is a CPPPS signature dimension, measuring talent-cultivation output and the "talent dividend" (large-scale, cost-effective engineer supply), correcting the bias of international lists that are oriented toward "per-capita/institutional" measures and underestimate the talent-scale advantage of populous countries. Data: underlying indicators are public approximate values, uniformly marked "subject to official figures". The ranking is permanently free and public, and rankings are never sold.