Human Capital Estimates in China: New Panel
Data on China by Provinces 1985-2010
Haizheng Li
School of Economics
Georgia Institute of Technology
Email: haizheng.li@econ.gatech.edu
Qinyi Liu
School of Economics and Trade
Hunan University
Bo Li
China Center for Human Capital and Labor Market Research
Central University of Finance and Economics
Barbara Fraumeni
Muskie School of Public Service
University of Southern Maine
Xiaobei Zhang
School of Economics
Zhejiang University of Finance and Economics
中国人力资本与劳动经济研究中心
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Human capital
 “The knowledge, skills, competencies and attributes
embodied in individuals that facilitate the creation of
personal, social and economic well-being”
--OECD, 2001
 Importance of Human Capital
 A central determinant of economic growth

Significant contributions to 30 years’ economic growth in
China
 Enhances the ability in

Developing technological innovations

Adapting and implementing technologies developed
 Reduce poverty and inequality
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Human capital
 Importance of Human Capital
Measurement
 Trace the distribution and dynamics of human
capital
 Aid empirical studies and policy analysis
 Promote the creation of human capital satellite
account
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Human capital measurement
 Challenges
 The unique characteristics make it difficult to estimate its value

Partial measurements like education are commonly used

Lack of data existing method cannot be applied to China

Arduous work for data collection, processing and calculation, especially
at provincial level

Empirical researches desire panel data of human capital
o
Panel data of comprehensive measures of human capital at a state or province level
are lacking for most countries including China
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Research team and sponsors
 Research Team (starting in 2008)
 China Center for Human Capital and Labor Market
Research (CHLR) special-term faculty, full-time faculty,
doctoral and Master’s student, and staff
 Sponsors
 National Natural Science Foundation of China
 Central University of Finance and Economics
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Methodology
 Jorgenson-Fraumeni Lifetime Income-based Approach
 Include all aspects of human capital services measured by
market value
 Widely used in measuring a nation’s total human capital stock
 Argentina (Coremberg, 2010), Australia (Wei, 2007, 2008), Canada
(Gu and Wong, 2009), India (Gundimeda et al., 2007), New
Zealand (Le, Gibson, and Oxley, 2005), Norway (Liu and Greaker,
2009), Sweden (Ahlroth, Bjorkland, and Forslund, 1997), United
Kingdom (O’Mahony and Stevens, 2004 and Jones and
Chirpanhura, 2010), and the United States (Christian, 2010)
 The OECD human capital consortium
 OECD, 2010; Mira and Liu, 2010; Liu, 2011
 China’s national level human capital
 Li et al., 2010; Li, Liang et al., 2013
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Methodology
 Jorgenson-Fraumeni lifetime income-based approach
 Calculate human capital stock for each individual as the estimated
present value of expected future lifetime earnings


Backward recursive estimation beginning with the oldest covered age
Divide life cycle into five stages

Retirement, Work-only, Work-school, School-only, Pre-school
 Modifications
 Incorporate the Mincer model (National level)
 Expand Mincer model to include macro-variables (Provincial level)
 Derive total human capital stock separated by urban and rural in
estimation
 Adjustment for cross-province comparison with a living cost
adjustment index
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Augmented Mincer model
 Lack of data in China
 use the Mincer model to estimate earnings
 Extended model for earnings in provinces
 ln(inc)= β0 + β1 · ln(Avwage)+ β2 · Sch+ β3 · Sch · Avgdp+ β4
· Sch · Ratio+ β5 · Exp+ β6 · Exp2 +u







ln(inc): the logarithm of earnings
Sch: years of schooling
Exp: years of work experience
Avwage: average wage of a province
Avgdp: provincial GDP per capita
Ratio: provincial primary industry employment proportion of the
total labor force
u: random error
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Augmented Mincer model
 Extended model
 Avwage reflects the income gap among provinces

Reflect provincial differences in earnings of those with no
schooling and no labor-market experience
 Avgdp and Ratio capture the provincial economic
development stage and labor market structure

Return to schooling is affected by the development stage and
labor market structure (Li 2003, Zhang et al., 2005, and Yang,
2005)
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Data
 Imputing population by cohort
 Population data into 4-dimensions



National Censuses (1982, 1990, 2000 and 2010)
1% sample of national population survey (1987, 1995 and 2005)
Provincial Statistical Yearbooks (1982-2010)
 Age distribution & Enrollment rates



The China Health and Nutrition Survey (CHNS 1989, 1991,
1993, 1997 and 2000)
The Chinese Household Income Project (CHIP 1995)
The China Education Statistical Yearbook (2003-2010)
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Data
 Estimating Mincer parameters
 Micro:





Annual Urban Household Survey (UHS 1986-1997)
China Health and Nutrition Survey (CHNS 1989, 1991, 1993, 1997, 2000,
2004, 2006 and 2009)
Chinese Household Income Project (CHIP 1988, 1995, 1999, 2002 and
2007)
Chinese Family Panel Studies (CFPS 2009)
China Household Finance Survey (CHFS 2010)
 Macro: provincial statistical yearbooks (1982-2010)
 Growth rate & Employment rate
 Provincial statistical yearbooks (1982-2010)
 Discount rate
 4.58% --used by Jorgenson and Fraumeni (1992a) and the OECD
consortium (OECD 2010)
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Results--Human Capital panel data
 Provide provincial human capital panel data
 22 provinces/cities

Guangdong, Jiangsu, Shandong, Henan, Hubei, Hunan, Anhui,
Shanghai, Liaoning, Beijing, Guizhou, Gansu, Tianjin, Heilongjiang,
Zhejiang, Guangxi, Shaanxi, Hainan, Jiangxi, Jilin, Chongqing, Sichuan
 1985-2010cover most of the reform era
 Total human capital(HC), Per capita HC, Labor force
human capital(LFHC), Average LFHC

Urban/rural, Education, Age, Gender
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 Total human capital(HC)
 Human capital reserve
 population aged 0-15 (have not entered the labor market)
 full-time students aged 16+ (not in the labor force)
 Human capital in use
 non-retired population aged 16+ and in the labor force
 The average annual growth rate
1985-2010
1985-1994
1995-2010
6.4%
1.2%
9.3%
  lower than the growth rate of the Chinese economy
  much faster in the latter period
 Similar change happens at urban/rural, education, age, gender
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Table 1 Descriptive Statistics for Human Capital
Sample Mean
1985
1995
2005
2010
1,602
1,722
4,723
8,177
626
765
3,127
5,954
Rural
1,023
1,003
1,673
2,328
Male
987
1,085
3,040
5,354
Female
615
638
1,683
2,822
Age 0-15 (human capital reserve)
860
876
2,160
3,273
Age 16-59 (labor force human
capital including students)
742
846
2,563
4,903
Variable
Total human capital
(Billion RMB)
Of: Urban
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 Total human capital(HC)
 Urban-rural


Urban increase by 8.5 times; rural 1.3 times from 1985 to 2010
Urban/rural
o 60-76% in 1985-1995  2.6 times in 2010
  Fast urbanization in China
  Increasing educational attainment gap
 Gender


Annual growth: male 6.6% ; female 6.1%
Male/total: 61.6% in 1985 65.5% in 2010


Rising gender ratio of male for China’s one-child policy
Rising gender inequality in educational attainment
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 Total human capital(HC)
 Age (Figure 1)

Human capital reserve (aged 0-15) :
 Its ratio in total: 54% in 198540% in 2010
 Its population share in total: 39% in 198523% in 2010

Labor force age human capital(aged 16-59, including
students):
 Its ratio in total: 46% in 198560% in 2010
 Its population share in total: 61% in 1985 77% in 2010
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Figure 1
Human Capital for Different Cohorts-Provincial Average
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 Total human capital(HC)
 Age (Figure 2)

Labor force human capital(16-59, excluding students)
o The share in total declined since 2000, increase starts at
2005
o
Full-time students(aged 16-59) in school increase

Its share in labor force:


grows from 2.9% in 1985 to 6.9% in 2010
Due to expanded education opportunities since 1999
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Figure 2
Population & Human Capital Share-Provincial Average
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 Labor force human capital(LFHC)
 Trend:
 Average annual growth in 1985-2010

%
HC
LFHC
Labor force
Total
Urban
Rural
6.4
8.8
3.4
6.8
9.2
4.3
1.6
4.3
0.0
LFHC grows faster than total HC (fewer young people)
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 Labor force human capital(LFHC)
 Education(Figure 4)
 Human capital share in education:
 Illiterate and primary: declined rapidly
 Junior school remains the largest, and started to decline
since 2006
 Senior: flat
 College or above
 The highest increase (close to 20% annual growth)
 Increased from 1.7% in 1985 to 26.3% in 2010
 much higher than its population share, 12.3% in 2010
o
A rising return to college education documented by Zhang et al. (2005)
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Figure 4 Education Share in Labor Force Human Capital (LFHC)
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 Per capita human capital (PCHC)
 Measure of the intensity of human capital
 Compare the annual average growth rates

Since 1995 they grew at a similar annual growth rate

Average annual growth rates of population
 1.2% in 1985-1994; 0.6% in 1995-2010
  much slower than the growth of HC
 HC growth is not driven solely by population growth
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Table 2 Descriptive Statistics for Per Capita Human Capital
Sample Mean
1985
1995
2005
2010
49
46
121
202
Of: Urban
73
65
165
267
Rural
40
36
77
116
Male
58
55
148
247
Female
40
36
90
150
Age 0-15 (human capital reserve)
69
71
221
382
Age 0 (Average lifelong income
for new-born)
76
78
247
407
Variable
Per capita human capital
(Thousand RMB)
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 Per capita human capital (PCHC) (Table 2)
 Urban/rural
 Gap enlarged
 Rural/Urban: 54% in 198543% in 2010
 Education (Figure 3)
 Average years of schooling
 5.5 years in 1985 8.8 years in 2010
 Gender

Female/male: 69% in 1985 61% in 2010
 Age 0-15
 Average annual growth
 PCHC: 6.6%; Population: -1.5%
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Figure 3
Average Years of Schooling (Avsch) and Per Capita Human Capital (PCHC)
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Top 5 Provinces for Human Capital in 2010
Total human capital
(Billion RMB)
1
2
3
4
5
Per capita human
capital
(Thousand RMB)
1
2
3
4
5
Total population
Guangdong
Jiangsu
Shandong
Zhejiang
Henan
Guangdong
Henan
Shandong
Sichuan
Jiangsu
21,974
21,029
18,386
15,335
13,888
Per capita GDP
Shanghai
Beijing
Zhejiang
Tianjin
Jiangsu
378
345
337
332
329
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Shanghai
Beijing
Tianjin
Liaoning
Heilongjiang
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 Average labor force human capital(ALFHC)
 Urban/rural:



Urban: RMB 167 thousand; Rural: RMB 90 thousand in 2010
Average annual growth
%
1985-2010
1985-1994
1995-2010
Urban
4.9
-1.0
8.2
Rural
4.3
-0.2
6.8
Since 1995, urban ALFHC has grown much faster than rural
 Education gap between urban and rural areas
 Higher labor quality and thus higher productivity in urban
areas, if the age structure in urban and rural areas were
identical  urban/rural gap will continue to rise.
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Top 5 Provinces for Labor Force Human Capital in 2010
Labor force human
capital (Billion RMB)
1
Guangdong
12,220
2
Jiangsu
8,552
3
Zhejiang
7,122
4
Shandong
6,498
5
Henan
5,796
Average labor force
human capital
(Thousand RMB)
1
Shanghai
263
2
Beijing
255
3
Tianjin
222
4
Zhejiang
206
5
Jiangsu
184
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Human capital as a measure of social development--beyond GDP
measures
 “…beyond Gross Domestic Product…”  measure of economic and
social progress
--Stiglitz Commission report
 Expected average lifetime income for newborns rises
rapidly
 It Annual growth rate of 6.7% faster than PCHC (5.6%)
 Provincial difference:
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Figure 5
Ratios of Labor Force Human Capital (LFHC), Physical Capital (PC), and GDP
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Human capital and physical capital
 LFHC/physical capital
 Decreases rapidly across time

12 times in 1985  5 times in 2010
 Possible reason:

High physical capital investment in China
 Increased at an average annual growth of 18.9% during 19921997
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Human capital and GDP
 GDP/LFHC
 Measure of labor use efficiency


Provincial ratio is between 6-11% on average
Shows a slow and stable growth
 Compared with GDP/physical capital:



In the range of 49-66%
Decline since 1995
Human capital rises faster than physical capital in
efficiency
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Illustration of Data Applications
 Human capital as an input in a production function
 Traditional production function
𝛽
𝑌𝑡 = 𝐴𝐾𝑡𝛼 𝐿𝑡
 Augmented production function
𝛽
𝑌𝑡 = 𝐴𝐾𝑡𝛼 𝐻𝑡
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Table 4 Production function estimation
Pooled OLS
Human
Labor
capital
Var.
Definition
lnK
log(physical capital)
0.647***
log(labor)
(0.007)
0.366***
lnL
Labor
Human
capital
0.449***
0.603***
0.533***
(0.012)
(0.015)
0.185
(0.037)
(0.021)
lnH
Cons
log(labor force
human capital)
FE
(0.183)
0.551***
0.221***
(0.023)
(0.063)
-5.376***
-13.224***
-2.058
-4.623***
(0.348)
(0.583)
(3.056)
(1.532)
Obs.
546
546
546
546
R2
0.954
0.966
0.981
0.986
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Illustration of Data Applications
 Pooled OLS (Table 4)



LFHC has much higher output elasticity than labor
When human capital is included
 Smaller elasticity of physical capital, even smaller than that for
human capital
Constant returns to scale
 Fixed Effects



LFHC still has higher elasticity than that of labor
Elasticity of labor and LHFC become much smaller than OLS
Decreasing returns to scale
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Illustration of Data Application
 Growth accounting based on human capital measure
 TFP growth/the Solow residual
∧
•
𝑔=𝑌

•
•
𝑌 − 𝑠𝐾 ⋅ (𝐾 𝐾) − 𝑠𝐻 (𝐻 𝐻
Shares of physical capital, 𝑠𝐾 = 0.33, Shares of human capital
and labor, 𝑠𝐻 =𝑠𝐿 =0.67
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Table 5
Sources of China’s Economic Growth using Solow Growth Accounting
Growth rate (% per year)
Output
Physical capital
Labor
TFP (with labor)
Labor force human capital
TFP (with labor force human capital)
1986-2010
1986-2010
10.29
16.48
1.64
3.76
10.29
16.48
7.03
0.14
Contribution to GDP growth (%)
Physical capital
53.32
Labor
TFP (with labor)
Labor force human capital
TFP (with labor force human capital)
10.69
35.99
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53.32
45.87
0.81
38
Illustration of Data Application
 Contributions to economic growth (Table 5)
 With traditional labor input
 Physical capital accounts for 53.3% of growth
 Economic growth in China has been mainly driven by
physical capital
 Labor input: 10.7%
TFP: 36%
 With human capital input
 LFHC: 45.9%
TFP: 0.8%
 Consistent with studies that use two-step procedure to
estimate how human capital affect TFP growth
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Summary
 The data provide very rich information in studying China’s
human capital
 A comprehensive picture of China’s human capital distribution and
dynamics
 Demonstrations of applications of the new data
 Estimating a production function
 Conducting the growth decomposition exercise using the data
 Compared the results with the traditional estimation based on labor
inputs
 The new panel data on China’s provincial human capital are
quite reliable; and should be a valuable new resource for related
studies and policy analysis
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Questions?
41