Presentation at Research Seminar for The ESADE Group for Research in Economics and Finance by Dr. Octasiano M. Valerio Mendoza. This dissemination activity was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838534. https://chinequaljustice.iqs.edu/

1. Human capital dynamics in China:
Evidence from a club convergence
approach
Octasiano Miguel VALERIO MENDOZA
Assistant Professor of Quantitative Methods
Marie Skłodowska-Curie Fellow
GREF Research Seminar
February 25, 2021
This research has received funding from the European Union’s Horizon 2020
research and innovation program under the Marie Sklodowska-Curie grant
agreement No. 838534.

2. Outline
I. Introduction
II. Literature Review
III. Research Question
IV. Data
V. Methodology
VI. Results
VII.Conclusions
2

3. Motivations
3
Introduction
China’s Economic ‘Miracle’
Preferential
Policies

4. Preferential Policies in Urban China
4
1980
4 SEZs.
1980
1984
Extended preferential policies
and autonomy of SEZs to 14
coastal cities. (Open Coastal
Cities)
1984
1988
Extended to 8 Cities
along the Yangtze
River. (Open
Riverside Cities)
1988
1992
There were
60 Open
Cities
1992
Introduction
Source: Created by Author using and Wikimedia Commons. (2016)

5. Shenzhen SEZ’s GDP per capita grew at an
annual growth rate of 26% over 40 years.
5

6. Shenzhen’s GDP per capita is
comparable to higher income nations.
6
Source: Author’s creation using Shenzhen Statistical Yearbook 2020; Shenzhen Economic and Social Development Report 2020; World Bank Data

7. Motivations
7
Introduction
China’s Economic ‘Miracle’
Preferential
Policies
Considered the Engine of Chinese Growth
Effects on Economic Growth, FDI, Trade
No Research on Wellbeing (Inequality, Education, Health)
Income
Inequality
Education
Health
 Preferential policies and income inequality.
 Preferential policies and education inequality.
 Income Inequality in China's Economic and Technological Development Zones and High-Tech Industrial
Development Zones, 1995-2002.
 Infrastructure Development, Income Inequality and Urban Sustainability in the People’s Republic of China.
 Preferential policies and health inequality.

8. There is little variance in educational attainment
and inequality
8
Variance
Gini: < 1%
AYS: < 1

9. The variance increases between provinces.
9
Variance
Gini: < 6%
AYS: < 3
W C E
Gini
(%)
2 3 5
AYS 1 1 2

10. There is a large variance between cities.
10
Variance
Gini: < 13%
AYS: < 6
W C E
Gini
(%)
10 12 13
AYS 6 5 6

11. Motivations
11
Introduction
China’s Economic ‘Miracle’
Preferential
Policies
Considered the Engine of Chinese Growth
Effects on Economic Growth, FDI, Trade
No Research on Wellbeing (Inequality, Education, Health)
Income
Inequality
Education
Health
 Preferential policies and income inequality.
 Preferential policies and education inequality.
 Income Inequality in China's Economic and Technological Development Zones and High-Tech Industrial
Development Zones, 1995-2002.
 Infrastructure Development, Income Inequality and Urban Sustainability in the People’s Republic of China.
 Heterogeneous determinants of educational achievement and inequality across urban China.
 Human capital dynamics in China: Evidence from a club convergence approach
 Preferential policies and health inequality.
H2020-MSCA-IF
CHINEQUALJUSTICE
Testing the Chinese Development Model
under the Capabilities Approach:
The Effects of Preferential Policies and
Special Economic Zones on Inequality
and Social Justice (2020-2022).

12. After 4 decades of reform, China is at an important juncture
12
Environmental Protection
Education and skills development
Reducing Inequality was placed as a top policy priority.

13. China Modernization 2035-49
Consumption led-growth
• Strong middle class
• Dominant service sector
(70-80%)
• Increase in Education and Skills
Middle-Income Trap
• Declining demographic dividend
(Two-child policy)
• Increase in innovation and
productivity
• Human Capital
13

14. Human Capital
 Human capital has been acknowledged as a development objective, which acts as a main contributor to
economic growth , poverty alleviation, and other development goals
 (Romer, 1986; Ravallion & Chen, 1997; Baldacci, Clements, Gupta, & Cui, 2008; Kosack & Tobin, 2015; Manca, 2012; Ramos, Surinach,
& Artís, 2013; Poelhekke, 2013; Männasoo, Hein, & Ruubel, 2018; Benos & Zotou, 2014)
 Poverty, and human development, traps may result from an undersupply of human capital,
 (Mayer-Foulkes, 2008; Kosack & Tobin, 2015; (Coulombe & Tremblay, 2001; Coulombe, 2003; Villarroya, 2007; Khor, y otros, 2016).
 More alarmingly, even though human capital is increasing, the stock of human capital in China remains low
compared to other countries, jeopardizing future prosperity, leading towards a possible ¨middle-income
trap”
 (Khor, et al., 2016)
14

15. The complexity and difficulty of measuring human capital is reflected
in the variation of variables used in the literature.
literacy rates (Romer, 1986; Ranis, Stewart, & Ramirez, 2000),
 lack consistent definitions across countries and omit components of human capital
enrollment figures (Barro, 1991; Chakraborty, 2004; Baldacci, Clements, Gupta, & Cui, 2008),
 reflect future human capital stock, but not the present human capital stock
schooling years (Collins, Bosworth, & Rodrik, 1996; Barro, 2001; Papageorgiou, 2003; Hanushek &
Woessmann, 2008)
 effects weaken considerably, or become insignificant, when controlling for quality indicators
student-teacher ratios (Barro, 1991),
educational expenditure (Daniels, 1996; Bose, Haque, & Osborn, 2007),
 reflect future human capital stock, but not the present human capital stock
scores (Bosworth & Collins, 2003).
These measures fail to consider the human capital acquired outside school, such as on-the-job training.
15

16. Jorgenson-Fraumeni Lifetime Income-based
Aproach of Human Capital
estimates human capital for an individual as the present value of
expected future lifetime earnings and is the most widely used method
in constructing human capital accounts
(Jorgenson & Fraumeni, 1989; 1992a; 1992b; Fraumeni, Christian, & Samuels,
2017; Li, Liu, Li, Fraumeni, & Zhang, 2014).
16

17. Jorgenson-Fraumeni Lifetime Income-based
Aproach of Human Capital
𝑚𝑖𝑦,𝑠,𝑎,𝑒
= 𝑦𝑚𝑖𝑦,𝑠,𝑎,𝑒 ∙ 𝑒𝑝𝑦,𝑠,𝑎,𝑒 + 𝑠𝑟𝑦+1,𝑠,𝑎+1 ∙ 𝑒𝑟𝑦+1,𝑠,𝑎+1,𝑒+1 ∙ 𝑚𝑖𝑦,𝑠,𝑎+1,𝑒+1 + 1 − 𝑒𝑟𝑦+1,𝑠,𝑎+1,𝑒+1 ∙ 𝑚𝑖𝑦,𝑠,𝑎+1,𝑒 ∙
1 + 𝐺
1 + 𝑅
𝑚𝑖: lifetime market labor income per capita
𝑦𝑚𝑖: average annual market labor income
ep: employment rate
er: enrollment rate
sr: survival rate
G: real income growth rate
R: discount rate
17

18. Jorgenson-Fraumeni Lifetime Income-based
Aproach of Human Capital
Total Human Capital Stock
𝐻𝐶 𝑦 =
𝑠 𝑎 𝑒 𝑟
𝑚𝑖𝑦,𝑠,𝑎,𝑒,𝑟𝐿𝑦,𝑠,𝑎,𝑒,𝑟
Labor Force Human Capital Stock
18

19. Significance and Objective
Research Gaps:
oNo studies on convergence of human capital in China at the provincial level.
oFew studies use the J-F lifetime income human capital measure in China.
oNo empirical studies have analyzed human capital patterns using
sophisticated panel convergence techniques.
Objective:
oTo examine human capital development across, and within, Chinese provinces
from a club convergence perspective.
19

20. Data
J-F Human Capital Index (CHLR) for 31 provinces from 1985 to 2016.
oHuman Capital per capita
Province Level (CPI & LCI)
o Urban & Rural (CPI & LCI)
oLabor Force Human Capital per capita
Province Level (CPI & LCI)
o Urban & Rural (CPI & LCI)
20

21. Methodology I
Convergence test and clustering algorithm
(Phillips and Sul, 2007, Econometrica)
o no particular assumptions about stationarity
o transitional behavior
o measures the degree and speed of convergence
o identifies convergence clubs (and diverging regions) in the panel
The framework allows us to distinguish between
o overall convergence
o full divergence
o subgroup convergence
21

22. Methodology II
Time-varying factor model:
𝑦𝑖𝑡 = 𝛿𝑖𝑡𝜇𝑡 where 𝛿𝑖𝑡= 𝛿𝑖 +
𝜎𝑖
log 𝑡 𝑡𝛼 𝜉𝑖𝑡 (1)
𝑦𝑖𝑡 : human capital growth for province i, year t
𝜇𝑡 : common trend
𝛿𝑖𝑡 : province-specific, time-varying loading
𝜉𝑖𝑡 : idiosyncratic term
𝛼 : speed of convergence
22

23. Methodology III
Hypotheses:
𝐻0: lim
𝑡→∞
𝛿𝑖𝑡 = 𝛿
𝐻𝐴: lim
𝑡→∞
𝛿𝑖𝑡 ≠ 𝛿
23

24. Methodology IV
Relative transition path
ℎ𝑖𝑡 =
𝑦𝑖𝑡
𝑁−1
𝑖=1
𝑁
𝑦𝑖𝑡
=
𝛿𝑖𝑡
𝑁−1
𝑖=1
𝑁
𝛿𝑖𝑡
Cross-sectional variance
𝐻𝑡 =
𝑖=1
𝑁
ℎ𝑖𝑡 − 1 2 → 0 𝑎𝑠 𝑡 → ∞
Convergence test:
log
𝐻1
𝐻2
− 2 log log 𝑡 = 𝑎 + 𝑏 log 𝑡 + 𝑢𝑡
Clustering Algorithm
24

25. Methodology V
Clustering Algorithm
1. Cross-section ordering by final observation
2. Core group formation
3. Sieve individuals for club membership
4. Recursion and stopping rule
25

26. Descriptive Statistics
26

27. Empirical results
Human Capital per capita
27

28. Relative Transition Paths all provinces in China, 1985-2016 (hit)
28

29. Average Relative Transition path for each convergence club as well as diverging provinces, 1985-
2016
29

30. Human Capital Convergence Clubs
30

31. Labor Force Human Capital
31

32. Labor Force Human Capital
32

33. Labor Force Human Capital
33

34. Urban Rural Human Capital LCI
34

35. Urban Rural Human Capital
35

36. Urban Rural Human Capital
36

37. Comparison with education-based measures
37

38. Comparison with education-based measures
38

39. Discussion
CPI vs LCI
• LCI: Adjusting for province
purchasing power parity reveals
less divergence and decreases
the variation in HCPC and
LFHPC.
Total Human Capital vs Labor Force
Human Capital
• Labor Force reflects the
productive capacity of the
current population’s labor force.
• Total Human Capital includes
those aged zero to 16, and those
who are still students, which are
the reserve human capital.
39

40. Conclusions
No overall human capital convergence in China.
Beijing, Tianjin and Shanghai are converging towards the highest
levels of human capital.
Almost all other provinces are failing to catch up, forming separate
clusters at lower levels.
oMajority of provinces converge into Club 2,
Diverging provinces and clubs at the lower bound are at greatest risk
of entering development traps.
oare not only failing to catch up to the lowest subgroup, Club 3, but are even
diverging away from it.
41

41. Conclusions
Khor et al. (2016) points out, in order to avoid the middle-income
trap, the Chinese labor force needs to achieve a signicantly higher
share of upper secondary school attainment level, comparable to the
OECD average (80%).
The findings of this paper highlight the magnitude of the challenge
for most provinces in generating the necessary growth in human
capital that enables China to continue on a prosperous development
path.
42

42. Thank you for your attention!
Questions and comments are welcome.
43
Octasiano.Valerio@iqs.url.edu
This research has received funding from the European Union’s Horizon 2020
research and innovation program under the Marie Sklodowska-Curie grant
agreement No. 838534.