How to Measure the Educational Human Capital of A Country： A Systematic Review on Frontier Measurement Methods and International Data Conversion Technology

doi:10.16382/j.cnki.1000-5560.2025.10.002

Abstract

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

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.

Figures/Tables 10

References

	黄斌, 何沛芸, 朱宇, 魏易. (2022). 基于父母教育背景的中国家庭校外教育支出分化. 中国教育学刊, (04), 19- 28.
	黄斌, 云如先. (2023). 教育发展何以强国——基于1960—2020年认知技能国际可比数据的实证分析. 教育研究, 44 (10), 125- 136.
	黄斌, 云如先, 吴凯霖. (2024). 认知技能分布对国民经济增长的影响: 教育强国的新证据. 华东师范大学学报(教育科学版), 42 (9), 13- 32.
	魏易. (2020). 校内还是校外: 中国基础教育阶段家庭教育支出现状研究. 华东师范大学学报(教育科学版), 38 (05), 103- 116.
	周金燕. (2024). 探索非认知能力的投资规律: 跨学科的理论和证据. 教育经济评论, 9 (01), 21- 43.
	Altinok, N., Angrist, N., & Patrinos, H. A. (2018). Global Data Set on Education Quality (1965–2015). Policy Research working paper, Washington, D. C. : World Bank Group.
	Altinok, N., & Diebolt, C. (2024). Cliometrics of Learning-Adjusted Years of Schooling: Evidence from a New Dataset. Cliometrica, 18 (3), 691- 764.
	Altinok, N., Diebolt, C., & Demeulemeester, J. L. (2014). A New International Database on Education Quality: 1965–2010. Applied Economics, 46 (11), 1212- 1247.
	Altinok, N., & Murseli, H. (2007). International Database on Human Capital Quality. Economics Letters, 96 (2), 237- 244.
	Angrist, N., Djankov, S., Goldberg, P. K., & Patrinos, H. A. (2021). Measuring Human Capital Using Global Learning Data. Nature, 592 (7854), 403- 408.
	Angrist, N., Evans, D., Filmer, D. P., Glennerster, R., Rogers, F. H., & Sabarwal, S. (2020). How to Improve Education Outcomes Most Efficiently? A Comparison of 150 Interventions Using the New Learning-Adjusted Years of Schooling Metric. CDG Working Paper.
	Balaj, M., Henson, C. A., Aronsson, A., et al. (2024). Effects of Education on Adult Mortality: A Global Systematic Review and Meta-Analysis. The Lancet Public Health, 9 (3), e155- e165.
	Barro, R. J., & Lee, J. W.(1993). International Comparisons of Educational Attainment, Journal of Monetary Economics, 32(3), 363—394.
	Barro, R. J., & Lee, J.W. (2001). International Data on Educational Attainment: Updates and Implications. Oxford Economic Papers, 53 (3), 541- 563.
	Barro, R. J., & Lee, J.W. (2013). A New Data Set of Educational Attainment in the World, 1950–2010. Journal of Development Economics, 104 (September), 184- 198.
	Barro, R. J., & Lee, J. W.(2015). Education Matters: Global Schooling Gains from the 19th to the 21st Century. Oxford, UK: Oxford University Press.
	Bauer, R., Potančoková, M., Goujon, A., & K. C., S. (2012). Populations for 171 Countries by Age, Sex, and Level of Education around 2010: Harmonized Estimates of the Baseline Data for the Wittgenstein Centre Projections. IIASA Interim Report IR-12-016.
	Braun, H. I., & Holland, P. W. (1982). Observed-score Test Equating: A Mathematical Analysis of Some ETS Equating Procedures. In P. W. Holland & D. B. Rubin (Eds. ), Test equating (pp. 9-49). New York: Academic.
	Cohen, D., & Soto, M. (2007). Growth and Human Capital: Good Data, Good Results. Journal of Economic Growth, 12 (1), 51- 76.
	Das, J., & Zajonc, T. (2010). India Shining and Bharat Drowning: Comparing Two Indian States to the Worldwide Distribution in Mathematics Achievement. Journal of Development Economics, 92 (2), 175- 187.
	De La Fuente, Á., & Doménech, R. (2006). Human Capital in Growth Regressions: How Much Difference Does Data Quality Make? Journal of the European Economic Association, 4(1), 1—36.
	De La Fuente, Á., & Doménech, R. (2015). Educational Attainment in the OECD, 1960–2010. Updated Series and a Comparison with Other Sources. Economics of Education Review, 48 (October), 56- 74.
	De La Fuente, Á., & Doménech, R. (2024). Cross-Country Data on Skills and the Quality of Schooling: A Selective Survey. Journal of Economic Surveys, 38 (1), 3- 26.
	Égert, B., De La Maisonneuve, C. & Turner, D. (2024). A New Macroeconomic Measure of Human Capital Exploiting PISA and PIAAC: Linking Education Policies to Productivity. Education Economics, 32(6),7451—761.
	Filmer, D., Rogers, H., Angrist, N., & Sabarwal, S. (2020). Learning-Adjusted Years of Schooling (LAYS): Defining a New Macro Measure of Education. Economics of Education Review, 77 (August), 101971.
	Gethin. (2023). Distributional Growth Accounting: Education and the Reduction of Global Poverty, 1980-2022. Job Market Paper.
	Glawe, L., & Wagner, H. (2022). Is Schooling the Same as Learning? – The Impact of the Learning-Adjusted Years of Schooling on Growth in a Dynamic Panel Data Framework. World Development, 151 (March), 105773.
	Goujon, A., K. C., S., Speringer, M., et al. (2016). A Harmonized Dataset on Global Educational Attainment Between 1970 and 2060 – an Analytical Window into Recent Trends and Future Prospects in Human Capital Development. Journal of Demographic Economics, 82 (3), 315- 363.
	Gust, S., Hanushek, E. A., & Woessmann, L. (2024). Global Universal Basic Skills: Current Deficits and Implications for World Development. Journal of Development Economics, 166 (January), 103205.
	Hanushek, E. A. (2003). The Failure of Input-Based Schooling Policies. Economic Journal, 113 (485), 64- 98.
	Hanushek, E. A., & Kimko, D. D. (2000). Schooling, Labor-Force Quality, and the Growth of Nations. American Economic Review, 90 (5), 1184- 1208.
	Hanushek, E. A., & Woessmann, L. (2008). The Role of Cognitive Skills in Economic Development. Journal of Economic Literature, 46 (3), 607- 668.
	Hanushek, E. A., & Woessmann, L. (2012a). Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation. Journal of Economic Growth, 17 (4), 267- 321.
	Hanushek, E. A., & Woessmann, L. (2012b). Schooling, Educational Achievement, and the Latin American Growth Puzzle. Journal of Development Economics, 99 (2), 497- 512.
	Hanushek, E. A., & Woessmann, L. (2015). Universal Basic Skills: What Countries Stand to Gain. Organisation for Economic Co-operation and Development, Paris.
	Hanushek, E. A., & Zhang, L. (2009). Quality‐Consistent Estimates of International Schooling and Skill Gradients. Journal of Human Capital, 3 (2), 107- 143.
	Hastedt, D., & Desa, D. (2015). Linking Errors Between Two Populations and Tests: A Case Study in International Surveys in Education. Practical Assessment, Research, and Evaluation, 20(1), 14.
	Holland, P. W., & Thayer, D. T. (2000). Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions. Journal of Educational and Behavioral Statistics, 25 (2), 133- 183.
	Kaarsen, N. (2014). Cross-Country Differences in the Quality of Schooling. Journal of Development Economics, 107 (March), 215- 224.
	Kolen, M. J. (1984). Effectiveness of Analytic Smoothing in Equipercentile Equating. Journal of Educational Statistics, 9 (1), 25- 44.
	Kolen, M. J., & Brennan, R. L. (2014). Test Equating, Scaling, and Linking: Methods and Practices. New York: Springer.
	Krueger, A. B., & Lindahl, M. (2001). Education for Growth: Why and for Whom? Journal of Economic Literature, 39 (4), 1101—1136.
	Lim, S. S., Updike, R. L., Kaldjian, A. S., et al. (2018). Measuring Human Capital: A Systematic Analysis of 195 Countries and Territories, 1990–2016. The Lancet, 392 (10154), 1217- 1234.
	Lucas, R. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22 (1), 3- 42.
	Lutz, W., Goujon, A., K. C., S., & Sanderson, W. C. (2007). Reconstruction of Populations by Age, Sex and Level of Educational Attainment for 120 Countries for 1970-2000. Vienna Yearbook of Population Research, 193—235.
	Martellini, P., Schoellman, T. & Sockin, J. (2024). The Global Distributionof College Graduate Quality. Journal of Political Economy, 132 (2), 434- 483.
	Patel, D., & Sandefur, J. (2020). A Rosetta Stone for Human Capital. CGD Working Paper.
	Patrinos, H. A., & Angrist, N. (2018). Global Dataset on Education Quality: A Review and Update (2000-2017). Policy Research working paper, Washington, D. C. : World Bank Group.
	Pritchett, L. (2013). The Rebirth of Education: Schooling ain't Learning. Washington, D. C. : CGD Books.
	Reardon, S. F., Kalogrides, D., & Ho, A. D. (2021). Validation Methods for Aggregate-Level Test Scale Linking: A Case Study Mapping School District Test Score Distributions to a Common Scale. Journal of Educational and Behavioral Statistics, 46 (2), 138- 167.
	Reiter, C., Özdemir, C., Yildiz, D., Goujon, A., Guimaraes, R., & Lutz, W. (2020). The Demography of Skills-Adjusted Human Capital. Working Paper.
	Romer, P. (1986). Increasing Returns and Long-run Growth. Journal of Political Economy, 96 (5), 1002- 1037.
	Sandefur, J. (2018). Internationally Comparable Mathematics Dcores for Fourteen African Countries. Economics of Education Review, 62 (February), 267- 286.
	Schoellman, T. (2012). Education Quality and Development Accounting. The Review of Economic Studies, 79 (1), 388- 417.
	Schultz, T. M. (1961). Investment in Human Capital. American Economic Review, 51 (1), 1- 17.
	Singh, A. (2014). Emergence and Evolution of Learning Gaps across Countries: Linked Panel Evidence from Ethiopia, India, Peru and Vietnam. CSAE working paper.
	Speringer, M., Goujon, A., K. C., S., Potančoková, M., Reiter, C., Jurasszovich, S., & Eder, J. (2019). Global Reconstruction of Educational Attainment, 1950 to 2015: Methodology and Assessment. VID Working Paper.

	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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