1. Human Capital Measures and Its Effect on Economic Convergence in China
Haizheng Li
School of Economics
Georgia Institute of Technology
&
China Center for Human Capital and Labor Market Research

2. Human capital and reginal income inequality
Research shows that human capital has a significant effect on reginal income inequality in China
human capital measures should in general reflect the income disparity across regions

3. Traditional education-based human capital measures
Traditional human capital measures are mostly education-based.
They can only partially capture the amount of human capital
Illiteracy rates
Enrolment rates
AEDU: average education of the labor force
PLHS : the proportion of labor force with high school
education or above
PLC : the proportion of labor force with college

4. J-F based human capital measure
Jorgenson-Fraumeni (J-F) lifetime income approach
proxies an individual’s human capital based on earnings.
J-F approach includes various aspects of human capital accumulation-a comprehensive measure
education, on-the-job training,
other unobserved aspects such as health and abilities
as well as unobserved school quality.
The J-F framework maintains the neoclassical assumption
wages represent marginal products of labor
uses wage returns to capture the productivity gains from human capital investments.

5. Advantages of the J-F based human capital measure
Incorporates age information and thus can capture the effect of age structure (such as population aging) on the stock of human capital.
Can estimate human capital for young people who are not in the labor market yet. Other measures used information on earnings can only estimate human capital for labor force.
The J-F method can estimate human capital stock in production use (labor force) and human capital reserve (young people).
Estimates human capital as a monetary value and can be easily interpreted, for example, it can be compared with physical capital stock, and is directly useful for policy analysis.

6. Research Team and Sponsors
Human capital project: starting in 2008
Research Team
China Center for Human Capital and Labor Market Research (CHLR) at the CUFE
All special-term faculty, full-time faculty, doctoral and master students, and staff
Sponsors
National Natural Science Foundation of China, supported 3 waves till 2021.
Central University of Finance and Economics (CUFE), via special grant every year
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

7. China Center for Human Capital and Labor Market Research
Research Outcomes
Provincial human capital panel data
31 provinces/cities,
Various human capital stock measures, such as Total human capital(HC), Labor force human capital(LFHC), Per capita HC, Average LFHC, and more…
Traditional human capital measure, such as average years of education, average age…
Provincial physical capital estimates
Panel data compatible with human capital data
Provincial cross-province living-cost adjustment index
For PPP comparison
Description of data
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

8. China Human Capital Report Series, 2009-2017
2010
2011
2012
2013
2014
2015
2016
2017
Description of data
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

9. China Center for Human Capital and Labor Market Research
Research Outcomes
A large database, including raw data, processed data, and results, is freely available to public
downloading at,
Increasing number of downloading and requests
Annual international conference on human capital
The acceptance rate for presentation has been 50-60%
The 10th anniversary in December this year
Description of data
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

10. J-F based human capital measure
The J-F approach estimates each individual’s expected lifetime income and then aggregates all individuals together to get total human capital stock
The estimation is conducted in a backward recursive fashion beginning with the retirement age.
life cycle is divided into five stages
retirement,
work-only
work-school
school-only
pre-school 0%
40%
60%

11. Coefficient of Variation (CV) across provinces (2014)
Disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure
Coefficient of Variation (CV) across provinces (2014)
GDP per worker: 44%
AEDU : 8%
PLHS : 27%
PLC: 44%
but covers only a small portion of labor force, less than 16%
PCLF(per capita labor force human capital ) :
CV is 55%
The J-F human capital measures more closely reflect these regional inequalities as represented by GDP per worker

12. disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure

13. regional inequality in 2014: ratio of the east region to the west for
disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure
regional inequality in 2014:
ratio of the east region to the west for
GDP per worker :1.94
AEDU : 1.09
PLHS :1.33
PLC:
However, the ratio for the PCLF is1.88, closest to that of GDP per worker

14. The trend of regional GDP per worker and different human capital measures.

15. Relation between education-based human capital measure and J-F human capital
In 2014, at national level
education is a major component of the human capital
US (2013) 89% (age 25+): 2014 Hongkong 75%, Taiwai 86%

16. Relation between education-based human capital measure and J-F human capital

17. Divisia Decomposition of Education Effect on J-F Human Capital
The contribution of education comes from
growth of the population with a specific level of education
and the share of human capital of this education group
In particular, the annual contribution of education (e) to per capita human capital (quality) growth can be written as

18. The Education Effect on J-F Human Capital
Among regions, education contributed the most to the labor force human capital (PCLF).
The expansion of education in China mostly benefited the labor force, because it occurred mostly at the high school and college level
In particular, from 1985 to 2014, the average annual growth
proportion of college graduates in the labor force :10.4%
proportion of high school or above : 4.0%

19. disparity of human capital cross provinces ---based on the traditional education measures and the J-F measure
Factor Contributions to Labor Force Human Capital Quality (PCLF) Growth
Factor Contributions to Labor Force Human Capital Quality (PCLF) Growth

20. Human Capital and the Economic Convergence Mechanism
As a benchmark, we start with the Mankiw, Romer and Weil (1992) (henceforth MRW).

24. New development in measuring human capital
Generalized approach to human capital accounting (Jones, AER, 2014)
Advantages relative to J-F method.
Do not need a perfect substitution assumption for different categories of labor.
Develop a human capital aggregator, Generalized Division of Labor (GDL)
with function form being flexible for elasticity of subsitution among different categories of labor.
Description of data
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

25. Generalized Division of Labor (GDL) aggregator
Aggregation of human capital
Aggregated human capital with constant return to scale:
where is human capital of skill type, and being labor quality and quantity
Generalized Division of Labor (GDL) aggregator:
where θ is elasticity of substitution
Description of data
Generalized human capital aggregation :
where
中国人力资本与劳动经济研究中心
China Center for Human Capital and Labor Market Research

26. Pros and Cons of Various Human Capital Measures
In the neoclassical approach based on unskilled worker equivalents, the human capital stock is calculated using the current relative wage as weights when summing up all labor together.
In this case, for example, if a 23 years old college graduate has the same wage as a 58 years old high school graduate, they are considered to have the same amount of the “unskilled worker equivalent” units.
For education-based measure, a labor force with all old workers has very different amount of human capital compared to a labor force of the same size but with all young workers,
But their education measures can be identical.
The J-F framework can better capture the joint effect of age and education on the amount of human capital.
but assume perfect substitution

27. Challenges on Human Capital Measurement
Different measures of human capital are not perfect substitutes, each captures a different aspect of human capital
Different measures show varying effects on economic growth and income inequality
All measures rely on various restrictive assumptions
Different measures will have different policy implications, e.g., formal schooling, on-the-job learning, school quality, health, …
Which measure to choose? Criteria for it?
Can better measures be developed?
Challenges ahead……

28. Thanks！