一国教育人力资本究竟应如何测量？——一个相关前沿测量方法与转换技术的系统评述

doi:10.16382/j.cnki.1000-5560.2025.10.002

摘要/Abstract

摘要：

人力资本是宏观教育与经济政策研究中经常使用的核心概念。要完成一项宏观教育政策计量研究，必须先要实现对一国人力资本的精确测量。通过一定的测量方法与数据转换技术形成国际可比的教育人力资本数据库，是学者开展宏观教育政策研究的基础性工作。本文沿着教育人力资本测量的“演变史”，系统介绍受教育年限、学生认知技能、经质量调整后的受教育年限和成人认知技能四种常用的教育人力资本测量指标，包括它们的基本原理、测算步骤、转换技术与常用国际数据库等，并依据人力资本理论与教育生产函数理论，对这四个测量指标的优缺点进行比较分析，提出评判教育人力资本测量指标优劣的四个基本原则，即“直接测量优于间接测量”、“存量测量优于流量测量”、“产出测量优于投入测量”和“质量测量优于数量测量”，最后对未来国家教育人力资本研究的发展方向进行了讨论。

关键词: 教育人力资本, 宏观教育政策, 受教育年限, 认知技能

Abstract:

Human capital is a core concept commonly used in macro education and economic policy research. To complete a macro-econometric study of education policy, it is essential to achieve an accurate measurement of a country’s human capital. Forming an internationally comparable database of educational human capital through certain measurement methods and data conversion technology is the basic work of scholars to carry out macro education policy research. Following the “evolutionary history” of educational human capital measurement, this paper systematically introduces four commonly used measurement indicators of educational human capital, including years of education, students’ cognitive skills, quality-adjusted years of education and adult cognitive skills, including their basic principles, measurement steps, conversion techniques and commonly used international databases. Based on the theory of human capital and educational production function, a comparative analysis was conducted on the advantages and disadvantages of these indicators. Four basic principles for evaluating these indicators were proposed, namely “direct measurement is superior to indirect measurement”, “stock measurement is superior to flow measurement”, “output measurement is superior to input measurement” and “quality measurement is superior to quantity measurement”. Finally, we discuss the development direction of future national education human capital research.

Key words: educational human capital, macro education policies, years of education, cognitive skills

云如先, 黄斌, 祝雅汶. 一国教育人力资本究竟应如何测量？——一个相关前沿测量方法与转换技术的系统评述[J]. 华东师范大学学报(教育科学版), 2025, 43(10): 10-32.

Ruxian Yun, Bin Huang, Yawen Zhu. How to Measure the Educational Human Capital of A Country： A Systematic Review on Frontier Measurement Methods and International Data Conversion Technology[J]. Journal of East China Normal University(Educationa, 2025, 43(10): 10-32.

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	Barro-Lee 教育成就数据库	维特根斯坦中心人力资本数据
使用方法	趋势外推法（前推+后推）	迭代后推法
主要文献	Barro & Lee (2013)	Speringer et al. (2019)
覆盖国家和地区数量	146个	185个
覆盖年份	1950–2015年	1950–2015年
最近更新年份	2021年	2024年
包含内容	平均受教育年限	教育获得分布、平均受教育年限
人口年龄跨度	15-24、25-34、…55-64岁	15-24、25-34、…100+岁
人口队列分类	15-64岁；25-64岁	15+；25+等
获取网址	http://barrolee.com/	https://dataexplorer.wittgensteincentre.org

	HLO数据库（新版）	全球人力资本估计（Global Human Capital Estimates）	统一尺度下的学生获得数据
方法文献	Angrist et al. (2021)	Lim et al. (2018)	Gust et al. (2024)
转换方法	线性转换辅以均值标准差转换	线性转换辅以高拟合补值	均值标准差转换辅以高拟合补值
元数据来源	TIMSS、PIRLS、PISA、SACMEQ、PASEC、SERCE、TERCE、EGRA	TIMSS、PIRLS、PISA、SACMEQ、PASEC、PERCE、SERCE、TERCE、NAEP、NAS、智商（Intelligence Quotient，IQ）、 Barro–Lee数据	TIMSS、PISA、SACMEQ、PASEC、SERCE、TERCE
学段	小学和初中学生	5-9岁，10-14岁和15-19岁学生	以初中学生为主
测试科目	数学、科学、阅读	学习指数	数学和科学的得分均值
数据类型	国家均值	国家均值	国家均值
数据跨期与结构	2000-2017年非平衡面板	1990-2016年平衡面板	2018/2019年横截面
国家和地区数量	164	195	159
数据获取	https://datacatalog.worldbank.org/search/dataset/0038001	https://ghdx.healthdata.org/record/ihme-data/global-human-capital-estimates-1990-2016	Gust et al.（2024）文章附表

方法文献		Filmer et al. (2020)	Altinok & Diebolt (2024)
采用的函数形式		公式（27）	公式（27）
元数据来源	学生认知技能	文章中：TIMSS或PISA 后续更新：教育质量全球数据库（Global Dataset on Education Quality (2020 Update)）	TIMSS、PIRLS、PISA、SACMEQ、PASEC、PERCE、SERCE、TERCE、EGRA、ASER
元数据来源	受教育年限	Barro–Lee 数据（25-29岁）	Barro–Lee 数据（15-64岁）
时间跨度		2010、2017、2018、2020年的非平衡面板数据	1970-2020年的平衡面板数据（5年间隔）
覆盖国家和地区数目		174个	120个
数据获取		https://datacatalog.worldbank.org/search/dataset/0038030/Human-Capital-Index	https://link.springer.com/article/10.1007/s11698-023-00276-x

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