1. What’s the Role of Demographic Dividend in
China’s Economic Development?
Sirui Zhang
Abstract
The effect of changes in age structure on economic growth has been
widely studied in the demography and population economics literature. In
this paper, I first test the relationship among China’s economics growth,
changes in age structure, labor force participation, education. Then I plug
some other factors, such as, international trade, life expectancy, urbaniza-
tion and legal progress, into my model. Using the time series data from
1978 to 2013 in China, I find that the demographic dividend has a posi-
tive effect on China. However, other elements such as international trade,
urbanization, legal progress may play a more important role in China’s
last 30 years economic development.
1 Introduction
Demographic dividend refers to a period-usually 20 to 30 years-when fer-
tility rates fall due to significant reductions in child and infant mortality
rates. As women and families realize that fewer children will die dur-
ing infancy or childhood, they will begin to have fewer children to reach
their desired number of offspring, further reducing the proportion of non-
productive dependents. This fall is often accompanied by an extension
in average life expectancy that increases the portion of the population
that is in the working-age-group. It would further spur economic growth
since more working people will produce and consume more products and
service.
Bloom and Williamson focused on the relationship between age struc-
ture change and economic growth and thus explicitly introduced age as a
relevant source of population heterogeneity into the analysis [1]. Recently,
studies of the effect of changes in educational attainment showed that in-
deed improvements in education seem to be a key driver of economic
growth[4] and have predictive power for future income developments[3].
In Cuaresma, Lutz and Sanderson’s paper, they plug the human capital
into the Cobb-Douglas production model and the human capital is con-
nected to the people’s avearge years of schooling. Using a global panel
of countries, they find that after the effect of human capital dynamics
is controlled for, no evidence exists that changes in age structure affect
labor productivity. Their results imply that improvements in educational
attainment are the key to explaining productivity and income growth
1

2. and that a substantial portion of the demographic dividend is an educa-
tion dividend. [2]. As a Chinese, I am very curious about the relation-
ship among demographic element(such as age-structure, education) and
China’s development after the Chinese Economic Reform. The reason I
choose this period is because it’s very hard to find data before the reform.
Also, China is experiencing a huge change and a rapid economics devel-
opment during these 30 years. The model I used in my paper is followed
by Cuaresma,Lutz and Sanderson’s paper, i.e., the model incorporating
human capital. In my regressions, the effect of age-structrue is relatively
remarkable in China. However it seems that education’s progress has no
big effect on China’s development. Also, according to the regression, I
found that the adjusted R2
is not big in my basic model. So I introduce
some other explanatory variables and it turns out that some of them are
the key factors in spurring the China’s development. At the end of my
paper, I do a forecast of annual average growth rate of China between
2014 an 2050 using the regression (7) in section 3.2.
The structure of the paper is as follows. Section 2 is the set-up of
model. In section 3.1, I do the regression according to the model which
has been set-up in section 2. In section 3.2, I do new regressions after
inserting some new variables.In section 3.3, I do the forecast about China’s
future development. In section 4, I conclude the results.
2 Modeling Set-Up
I start my analysis by considering an aggregate production function given
by
Yt = AtKα
t H1−α
t (1)
where Yt is total output at time t, At is total factor productivity(TFP),
Kt is the capital stock, Ht is human capital. Also, Ht = htLt where Lt is
total labor input and human capital per worker is denoted by ht, which
in turn is defined as
ht = expθst (2)
where θ refers to the returns to schooling, and st are the average years of
schooling of the labor force. Then, I get
Yt = AtKα
t (htLt)1−α
(3)
Considering variables per worker, I divide Lt at both sides of equation 3
yt = Atkα
t h1−α
t (4)
where yt = Yt
Lt
is GDP per worker, and kt = Kt
Lt
is capital per worker. In
growth rates, equation 4 can be written as
∆ ln yt = ∆ ln At + α∆ ln kt + (1 − α) ln ht (5)
Because income per capita instead of income per worker is typically used
for growth regressions, the relationship between total population, working-
age population, and labor force needs to be taken into account in order to
2

3. differentiate pure accounting effects from causal links among employment,
age structure, and income growth. Notice that
yt =
Yt
Lt
=
Yt
Nt
Nt
Lt
= ˜yt
Nt
Lt
(6)
where ˜yt denotes GDP per capita, and Nt refers to total population.
Combining equation 5 and equation 6 to obtain an expression for income
per capita
∆ ln ˜yt = ∆ ln yt + ∆ ln Lt − ∆ ln Nt = ∆ ln At + α∆ ln kt + (1 − α)∆ ln ht
+ ∆ ln Lt − ∆ ln Nt
(7)
Plug in equation 2
∆ ln ˜yt = ∆ ln yt + ∆ ln Lt − ∆ ln Nt = ∆ ln At + α∆ ln kt + (1 − α)θ∆st
+ ∆ ln Lt − ∆ ln Nt
(8)
If we assume that, because of technology adoption and income convergence
dynamics, the growth rate of TFP depends on the distance to the global
technology frontier as proxied by the level of labor productivity.
∆ ln At = δ + µ ln yt−1 (9)
This specification implies thus that the growth rate of TFP can be decom-
posed into a secular trend, captured by the parameter δ, and (assuming
a negative µ parameter)conditional convergence dynamics, which make
TFP growth linearly dependent on the (lagged)income per worker of the
country.
In addition, the overall human capital stock (average years of school-
ing) is often assumed to affect the growth rate of TFP by acting as a
catalyst of technology creation and technology adoption. This view of
the role of human capital leads to an econometric specification where, in
addition to the change in average years of schooling, the level of education
also enters the model as a determinant of TFP growth; thus
∆ ln At = δ + ρst−1 + µ ln yt−1 (10)
Using the fact that
ln yt = ln ˜yt + ln
Nt
Wt
+ ln
Wt
Lt
= ln ˜yt − ln
Wt
Nt
− ln
Lt
Wt
(11)
where Wt denotes working-age population
∆ ln ˜yt = δ + ρst−1 + µ ln ˜yt−1 − µ ln
Wt−1
Nt−1
− µ ln
Lt−1
Wt−1
+ α∆ ln kt
+ (1 − α)θ∆st + ∆ ln Lt − ∆ ln Nt
(12)
3

4. 3 Regression Analysis and Forecast
3.1 Regression based on the model
Before using the model to do the regression, I need to explain the data
source. I get the data such as GDP,population, working age popula-
tion(age from 20 to 64 population), labor,capital from the website of China
Government. Also, I found the mean years of schooling(age 15+) from
the World Bank. The problem is that I can only get this data every five
years. So I use the matlab to interpolate the data and get the every year’s
data. This method is not definite and might lack of precision.
When we talk about the demographic dividend, I think the ratio of
working-age population(age from 20 to 64 population) to the total pop-
ulation is a very important term. Let’s check the changing of this term
during last 30 years in China.
Figure 1: working age population to total population
From the figure 1, we can easily find that W
N
increases tremendously in
last 30 years. Intuitionly, it will promote the economic development since
there are more people working and people in their working age always
consuming more. The big increments in W
N
in China is because of the
one-child policy. In 1960s and 1970s, Chinese government encouraged
family to have more children since our leader thought that more people
means more power. Then the rapid increase of population had induced
many bad effects. So starting from around 1980, the Chiniese government
started to implement the one-child policy. From figure 2, we can clearly
feel the enormous impact of the one-child policy. Lots of people who
were born in 1960s and 1970s became adults after 1980 and they had
fewer children. So theses makes working age population ratio after 1980
4

5. become very huge.
Figure 2: population growth
Let’s review the model that we built in section 2. Here,we should
notice that µ is assumed to be negative. So the coefficient of ln ˜yt−1 is
negative which means the previous year’s GDP per capita has the negative
effect on the growth rate of GDP per capita this year. Also the parameters
of ln(
Wt−1
Nt−1
) and ln(
Lt−1
Wt−1
) are positive and this makes sense. Since
Wt−1
Nt−1
and
Lt−1
Wt−1
are less than 1, ln(
Wt−1
Nt−1
) and ln(
Lt−1
Wt−1
) are negative. Multi-
plying the positive parameters, these two terms have the negative effect
on ln ˜yt−1. However, we can find when both
Wt−1
Nt−1
and
Lt−1
Wt−1
approach
to 1, ln(
Wt−1
Nt−1
) and ln(
Lt−1
Wt−1
) also become larger and approach to 0. This
means when
Wt−1
Nt−1
and
Lt−1
Wt−1
become larger, they will alleviate negative
effect on ln ˜yt−1. Also, since α is smaller than 1 and θ,ρ is positive, the
∆ ln kt, ∆st, st−1 have the positive effect on ln ˜yt−1.
The table 1 shows the regression result. Here I use OLS to do the
regression. Since my data covers only a span of 30 years, it is too few
to use other complex regression methods. I did many regressions on the
different combinations of explanatory variables. Here I choose 6 regression
results.
At first glance, we can find that ln kt, ln ˜yt−1, ln(
Wt−1
Nt−1
) have the sig-
nificant effects on ∆ ln ˜yt. To compare the first two regressions, after I
add ∆ ln Lt and ∆ ln Nt into the regression, there is no big change. The
adjusted R2
just increases a little bit and ∆ ln Lt and ∆ ln Nt are not sig-
nificant statistically. Then in the third regression, I continue to plug in
ln ˜yt−1, ln(
Wt−1
Nt−1
) and ln(
Lt−1
Wt−1
), the results are consistent with my expec-
tation. But there is still some problems which need to explain. First, the
5

6. Table 1: Regression results 1
Dependent variable:
∆ ln ˜yt
(1) (2) (3) (4) (5) (6)
ln kt 0.32∗∗∗
0.36∗∗∗
0.20∗
0.19.
0.19.
0.20∗
(0.07) (0.09) (0.09) (0.10) (0.10) (0.10 )
∆ ln Lt 0.13 -0.18 -0.21 -0.24 -0.16
(0.42) (0.38) (0.41) (0.41 ) 0.40
∆ ln Nt 2.76 -5.10 -4.58 -4.26 -5.50
(2.51) (7.82) (8.31) (8.37) (8.19)
ln ˜yt−1 -0.21∗∗
-0.21∗
-0.09 -0.20∗
(0.07) (0.08) (0.16) (0.09)
ln Wt−1
Nt−1
2.89∗∗
2.80∗∗
2.29.
2.90∗∗
(0.82) (0.93 ) (1.11) ( 0.84)
ln Lt−1
Wt−1
-0.17 -0.17 -0.10 -0.17
(0.20 ) (0.21) (0.23) (0.21 )
st−1 -0.17 -0.02
(0.20) (0.10)
∆st -1.09 -8.31
(4.82) ( 9.83)
Constant 0.08∗∗∗
0.05∗∗
3.45∗∗
3.38∗∗
3.39∗∗
3.52∗∗
(0.02) (0.03) (1.09) (1.16 ) (1.17) (1.15 )
Observations 33 33 30 30 30 30
R2
0.37 0.41 0.65 0.65 0.67 0.65
Adjusted R2
0.35 0.35 0.56 0.54 0.54 0.54
Residual Error 0.049 0.049 0.041 0.042 0.042 0.042
F Statistic 18.11 6.829 7.212 5.934 5.214 5.933
Note: .
p<0.05;∗
p<0.01; ∗∗
p<0.001; ∗∗∗
p<0.0001
coefficient of ∆Lt is negative. Here the coefficient is very close to 0 which
means this variable’s effect on the dependent variables is almost negligi-
ble. Also, the variable is not significant according to the corresponding
6

7. standard error and the t-value of this variable is -0.476, very close to 0,
which means it is highly possible that this variable don’t need to exsit in
this regression. Then, we find that ln(
Lt−1
Wt−1
) has the similar problem as
∆Lt. Also, I can use the similar reason to explain. Here, the t-value is
-0.850 for ln(
Lt−1
Wt−1
).
Figure 3: labor to working age population
From figure 3, we can find that L
W
is quite stable and close to 1 after
1978. It leads that ln(
Lt−1
Wt−1
) is quite stable and close to 0. This might also
be the reason why ln(
Lt−1
Wt−1
) has no big effect in the regression. Continue
to study on the regression 3, we find that ln kt and ln(
Wt−1
Nt−1
) have a great
impact on ∆˜yt. If we assume that ln kt is 0.16(this value is the average
ln kt from 1978 to 2013.), using the parameter 0.19640, we can calculate
the contribution of ln kt is 0.0314. This value is around 23 percent of the
average growth rate of GDP per capita(0.1342) from 1978 to 2013. Also,
if ln(
Wt−1
Nt−1
) increases 0.1(according to the data, ln(
Wt−1
Nt−1
) has increased
around 0.35 in the past 30 years), the negative effect on ∆˜yt will decrease
2.88707 ∗ 0.1 = 0.288707. This is very impressive. It means that ∆˜yt
will increase 28.8 % when ln(
Wt−1
Nt−1
) increases 0.1 and the other variables
hold constant. This result hugely confirms that the demographic dividend
is existing in China. Before we study on regression (4),(5),(6) to test
the education’s effect, let’s review the changing of the education level in
different countries.
In the figure 4, schooling years means the average schooling years
among 15+ years old people in a country. China, India and Vietnam have
the similar situation. These three countries have the similar increase rate
and their education are all at a low level. USA’s education level keeps
7

8. almost constant and high. South Korea’s education increment is very
impressive. It has almost same education level as USA does now, however
30 years ago, it just had a level which is a little higher than China. If
we just focus on China, we will find that China’s education level didn’t
increase much, just from 5 years to 8 years through 35 years.
Now, we will study on regression (4),(5),(6). In Cuaresma,Lutz and
Sanderson’s paper, when they plug into the education variables, ln(
Wt−1
Nt−1
)
became less significant and st−1, ∆st are significant. So they get the ev-
idence that demographic dividend partly is an education dividend. But
when I do the same thing, I don’t get the same result. In all three regres-
sion (4),(5),(6), st−1, ∆st are not significant statitically. Also, the signs
of the parameters are also strange. At the beginning of this section, I
claimed that the signs should be positive accorrding to the formula. But
in my result, the signs are all negative which makes no sense. The first
thing I need to say is these parameters are not significant, so these vari-
ables seem useless to us. Moreover, I found that the coefficients of st−1 are
relatively close to 0 and the corresponding t-values are -0.845 and -0.223,
so we can say that st−1 has no explanation power in the regression. Recall
that this term is plugged into the TFP by force. This result seems tell us
that human capital stock has very small effect on China’s TFP during the
past 30 years. In regression (5), I find ln(
Wt−1
Nt−1
) becomes less significant
after plugging st−1,∆st in. But since st−1, ∆st are still not significant.
I cannot get any result. The regression (5) seems to give us some hint
about the education’s effect. But we need other methods to test more.
At last, when we compare the regression (3) and (4),(5),(6), I find that
adjusted R2
become smaller when I plug into the education variables. As
Figure 4: mean years of schooling for age 15+
8

9. we all know, in most of times, the R2
will increase only if you plug into
more variables. But here, I find that adjusted R2
become smaller when
I plug into more variables. This phenomenon can partly confirm that
education has no big effect on ∆˜yt. And we can get the rough result that
China has a big demography dividend in past 30 years but this dividend
has not much connection with education.
3.2 Regression based on the extension model
In Alexia Prskawetz, Tomas Kogel, Warren C.Sanderson and Sergei Scher-
bov’s paper, they did the regression on annual average growth with ln(output
per working age person), growth of the working age population, growth
of the total population, ln(life expectancy), ln(youth dependency ratio),
ln(openness to trade) and RULE which is a composite indicator that mea-
sures protection afforded to property rights and the strength of the rule
of law[5]. Here,I can also use some other variables to regress ∆˜yt.
First, I find the ln(life expectancy), youth dependence ratio( popu-
lation under 20 to population from 20 to 64), elderly dependence ra-
tio(population older than 64 to population from 20 to 64). These three
variables has direct connection with ln(W
N
). As people’s expectancy age
become longer, I assume that people still retire after 64, so longer ex-
pectancy age means bigger elderly dependence ratio. The youth and el-
derly dependence ratio, life expectancy from 1950 to 2009 are from Chi-
nese Government website. Life expectancy from 2010 to 2050 is from UN,
Department of Economic and Social Affairs. Here I only get the data for
male and female. So I calculate the mean. Figure 5 is the expectancy age
from 1950 to 2050.
Figure 5: expectancy age in China
9

10. Moreover, after China’s open since 1978, China gradually becomes the
world’s manufacture center. The international trade also has a big effect
on China’s economic’s development. So I use the openness(the ratio of sum
of import and export to GDP) to represent the trade level. Also, in past 30
years, China government deciphering the hukou system and a large rural
population migrate to the urban to seek jobs. This migration contributes
the large growth in industry meanwhile, the agriculture also makes the
progress since the new skills and technologies are implemented.Figure 6
is the changing of ratio of urban to rural population from 1949 to 2050.
The data from 1949 to 2013 is from Chinese Government website. I get
the forecast data of 2050 from UN, Deparment of Economics and Social
Affairs. Then I use matlab to do the interpolation to get the data from
2014 to 2049.
Figure 6: urban to rural population
At last, China also experiencing a gradually politics reform in past 30
years. This reform also spurs the economic development since the reform
makes Chinese institution more suitable for market economics. Here I use
the number of lawyers(from Chinese Government website) to represent
the level of legalization in China. The reason I choose this varible is first
it’s easy to get, secondly the number of lawyers has the direct connection
with the Chinese legal construction.
The table 2&3 is the regression result. I choose 7 regression results in
the table.
In the first regression, I just pick elderly and youth dependence ra-
tios, ln(life expectancy), ln(kt) and ln(
Wt−1
Nt−1
). The result is not good,
no variables except ln(kt) is significant. I expect that the coefficient of
ln(life expectancy) and youth dependence ratio negative since they both
10

11. have the negative effect on economics development. The reason is these
variables has the similar effect on ∆˜yt. So when I use them together to
Table 2: Regression results 2
Dependent variable:
∆ ln ˜yt
(1) (2) (3) (4) (5) (6) (7)
ln(kt) 0.33∗∗∗
0.31∗∗∗
0.37∗∗
0.43∗
0.24∗
0.25∗∗∗
0.27∗∗∗
(0.08) (0.06) (0.12) (0.15) (0.08) (0.06) (0.05)
ln(˜yt−1) 0.19 0.56 -0.09
(0.21) (0.39) (0.06)
ln(Wt−1
Nt−1
) 3.60 -9.47 -7.67 -13.48.
-1.17
(3.23 ) (8.29) (4.86) (6.60) 1.05
∆ ln(Lt) 0.52 0.67.
0.28
(0.36) (0.38) (0.32)
∆ ln(Nt) -28.44∗∗
-32.06∗∗
-22.33∗
-7.85
(9.20) ( 9.98) (8.26) (6.82)
st−1 0.17
(0.87)
∆st -23.72
(21.21)
ln(expage) 1.64 -6.83.
-10.90∗
-21.37 -14.81∗∗∗
-9.41∗∗
-7.52∗∗
(3.53 ) (3.70) (4.52) (28.02 ) (3.59) (2.57) (1.99)
ln(lawyer) 0.10 0.23∗∗
0.24∗∗
0.21∗∗
0.06∗
0.06∗
(0.06) (0.07) (0.07) (0.07) (0.02) (0.02)
Openness 0.69∗∗∗
0.89∗∗∗
1.03∗∗
0.67∗∗∗
0.63∗∗∗
0.58 ∗∗∗
( 0.16) ( 0.20 ) (0.27) (0.12) (0.12) (0.11)
Youth 2.28 -4.85
(2.04) (4.65 )
Note: .
p<0.05;∗
p<0.01; ∗∗
p<0.001; ∗∗∗
p<0.0001
11

12. Table 3: Regression results 2-Continued
Dependent variable:
∆ ln ˜yt
(1) (2) (3) (4) (5) (6) (7)
Elderly -2.69 -33.35 -45.22 -66.36
(9.17 ) (29.96) (33.12 ) (47.99 )
Urban
Rural 1.52.
2.86∗
4.06∗
1.47∗∗
0.61∗
0.50∗
(0.81 ) (1.10) (1.45) (0.43) (0.24 ) (0.22)
Constant -6.07 28.83.
41.87∗
81.07 60.21∗∗∗
39.14∗∗
31.07∗∗
(14.64) (14.44) (19.19) (111.49) (14.07) (10.73) (8.19)
Observations 29 26 26 26 26 26 26
R2
0.57 0.83 0.89 0.90 0.87 0.82 0.81
Adjusted R2
0.48 0.74 0.81 0.81 0.80 0.76 0.76
Residual Error 0.046 0.032 0.027 0.027 0.027 0.031 0.031
F Statistic 6.22 10.02 11.92 10.04 12.37 14.23 16.54
Note: .
p<0.05;∗
p<0.01; ∗∗
p<0.001; ∗∗∗
p<0.0001
regress, they will be weaken and affected by each other and leads to a
bad result. In the regression (2), I continue to plug ln(number of lawyer),
openness and the ratio of urban to rural population into the regression.
The result is much better. The regression (2)’s adjusted R2
is much bigger
than regression (1)’s which means the variables I plug in are meaningful!
The ’openness’ is very significant. However, I also found that the coef-
ficient of ln(
Wt−1
Nt−1
) becomes negative. This is a huge change and we can
also find this phenomenon in the latter regrssions. Also I notice ln(
Wt−1
Nt−1
)
is not significant anymore when I plug into these extra variables. Maybe
the reason is these extra variables have contained the effect that ln(
Wt−1
Nt−1
)
used to have. In the regression (3),(4),(5), I plug ∆ ln Lt and ∆ ln Nt in
and erase the youth dependence ratio since I think ln(life expectancy) and
elderly dependence ratio have more direct effect on ∆˜yt. The regression
results confirm my point. In these three regressions, the ratio of urban
to rural population, ln(life expectancy), ln(number of lawyers),openness
have a huge effect on dependent variable. Also I notice the adjusted R2
s
are largest among all regressions I have. In these three regressions, the
sign of the coefficients of ∆ ln Lt and ∆ ln Nt satisfies the model in section
2. In regression (6) and (7), I just picked out the significant variables in
the previous regressions and do the new regressions. I got a very nice
regression result in regression(7) where every varible is significant. Also,
the number of variables are becoming smaller while the R2
is still high.
12

13. This is also the evidence that these variables have a very big effect on
∆˜yt.
3.3 forecast
After doing so many regressions, I want to use the regression to forecast
the average ∆˜yt from 2014 to 2050. I choose the regression (7) in the table
2&3.The reasons are as follows. Firstly, there is not many variables in
regression (7), Secondly, every variable is significant, Thirdly, the adjusted
R2
is high enough.
∆ ln ˜yt = 0.27400 ∗ ln(kt) − 7.51744 ∗ ln(expage) + 0.05507 ∗ ln(lawyer)
+ 0.57924 ∗ Openness + 0.496 ∗
Urban
Rural
+ 31.066
(13)
Here, I already have the forecast data for ln(life expectancy) and the
ratio of urban population to rural population. For the rest of variables,
I use two ways to deal with. The first way is to keep these variables,
i.e.’ln(kt)’, ’ln(lawyer)’, ’openness’ constant. The second way is use the
auto-regression to simulate these variables in the future.
Ut = β1 ∗ Ut−1 + β2 ∗ Ut−2 (14)
Here I choose 2 lags. Since these variables are time series, the auto-
regression will have some sense of forecastibility. Then I use the equa-
tion 15 to forecast the average ∆˜yt from 2014 to 2050.
∆ ln ˜y = 0.27400 ∗ 0.5 ∗ (ln(k2014) + ln(k2050)) − 7.51744 ∗ 0.5∗
(ln(expage)2014 + ln(expage)2050) + 0.05507 ∗ 0.5∗
(ln(lawyer)2014 + ln(lawyer)2050) + 0.57924 ∗ 0.5∗
(Openness2014 + Openness2050) + 0.496 ∗ 0.5∗
((
Urban
Rural
)
2014
+ (
Urban
Rural
)2050) + 31.066
(15)
Then the result of first way is 0.2714 and the result of second way is
0.2754. The reason of these two are similar is the data calculated by
auto-regression has no much difference compared to the constant ones.
Also, we need to notice the average of ∆˜yt from 1978 to 2013 is 0.1343
and 0.2754 is much bigger than 0.1343.
Actually this kind of forecast is very rough and the result I got has low
reliability. But still we can get some useful information from this forecast.
No matter which way I use to forecast, ln(kt), ln(lawyer) and Openness
keep constant or relatively constant. The only things matter here are
ln(expage) and Urban
Rural
. From the figure 5 and figure 6, we know the latter
one’s increment is much faster than the former one. So the population
aging’s nagetive effect will be over-compensated by the urbanization pro-
cess. This intuitively tells us that perhaps China’s aging problem will not
be that serious since China have other incentives to spur the economy to
grow constantly.
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14. 4 Conclusion
From above, we can find that in the past 30 years, China has a big de-
mographic dividend which spurs the economic development and education
progress doesn’t have much effect on economic development. Also,China’s
economy is also affected by other factors like urbanization, openness and
legal construction. It seems these factors stand on more important postion
than demographic dividend does. If Chinese government keeps perfecting
and optimizing the institution, using the correct policy, I think China will
become richer even though the aging problem is becoming more serious.
References
Bloom, D. E. and J. G. Williamson (1998). Demographic transitions
and economic miracles in emerging asia. The World Bank Economic
Review 12(3), 419–455.
Cuaresma, J. C., W. Lutz, and W. Sanderson (2014). Is the demographic
dividend an education dividend? Demography 51(1), 299–315.
Cuaresma, J. C. and T. Mishra (2011). The role of age-structured educa-
tion data for economic growth forecasts. Journal of Forecasting 30(2),
249–267.
Lutz, W., J. C. Cuaresma, and W. Sanderson (2008). The demography
of educational attainment and economic growth. Science 319(5866),
1047–1048.
Prskawetz, A., T. K¨ogel, W. C. Sanderson, and S. Scherbov (2007). The
effects of age structure on economic growth: An application of proba-
bilistic forecasting to india. International Journal of Forecasting 23(4),
587–602.
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