An Analysis of Population Polices in China
Eng Seng CHIA, Yan JIA, Chenxi LI, Yulu LI, Manbo LIAO, Xiaoqi, Zhang
National University of Singapore
1 Engineering Drive 2 $117576 Singapore
Tel: +65 65166431
Email: aaron_chia@nus.edu.sg
Abstract
This paper examines the impact of various population policies in China, in particular, how to
increase the fertility rate and reduce the age dependency ratio. The investigation carried out
involved the identification of relevant factors, the establishment of causal relationships
between factors and the building of a causal loop diagram, and subsequent conversion into a
stock and flow diagram for running simulations. Data collected were from newspapers,
published reports, and official government websites. The results obtained from the
simulations revealed that the newly implemented two-child policy would most likely be
ineffective in alleviating the issue of ageing population in China. Sensitivity analysis carried
out to identify potential points of intervention revealed two possible measures. Both
measures were shown to be effective in increasing the Actual Fertility Rate, and also in
slowing down the increase in Aged Dependency Ratio. Hence, it is recommended that the
Chinese government introduce alternative measures, such as giving cash bonus to families
that give birth to a second child, or implementing policies that reduce the cost of healthcare
services, in order to ensure that its population is productive, and continues to be so in the
future.
Introduction
1.1 Background Information
First implemented in 1980 and enforced at the provincial level by the "Population and Family
Planning Commissions", China’s one-child policy is one of the most extreme population
growth control measures in world’s history. Even before its implementation, fertility levels
have dropped from about 5.8 children per woman to 2.8 in 1979, due to the government’s
less coercive efforts to encourage fewer births [1]. Three decades down the road, the effect
of the policy in contributing to China’s spectacular economic development is evident and it
succeeded in slowing down the population growth in China further, cutting down the fertility
rate from 2.8 in 1980 to 1.5 in 2010 [2]. The growth is primarily due to resources diverted to
support economic growth rather than population.
However, only 35% of Chinese are subject to the one-child policy due to the numerous
exceptions to law. Exceptions are made to couples with "practical difficulties" such as cases
in which the father is a disabled serviceman or who are both single children themselves.
Other exceptions include rural families with their first-born being a girl and ethnic minorities
due to their already limited population [3].
Despite the government’s continuous attempt to adjust and loosen the enforcement level over
time by allowing couples of certain categories to have more than one child, the costs
associated with the policy are glaring and rising. Currently, China is facing challenges of a
declining fertility rate, shrinking labor force, and a growing proportion of elderly with
inadequate government or family support. The real fertility rate is however difficult to
determine because of under-reporting of the number of children, and varying survey
methodologies. A more convincing estimate would be 1.5 children per woman as cited by
various sources [4]. Ifno changes are made to the current family planning policy, a slowdown
in China’s economic and social development due to a shortage in productive labor force
seems impending. This necessitates a re-consideration of the population policies made by the
Chinese government.
1.2 Motivation
In 2015, the Chinese government officially proposed the implementation of a two-child
policy, that is, all couples would be allowed to have up to two children, with the intention to
create a productive workforce in meeting current and future demands, so as to mitigate the
problem of ageing population.
This paper attempts to investigate the impact of introducing the two-child policy, and to
determine its effectiveness in alleviating the problem of ageing population in China. It hopes
to provide more insights on how the Chinese government can adjust its policies to better
address the issue of ageing population.
2 Problem Description
2.1 Key Assumptions
The analysis of China's population policy is based on the following assumptions:
1) Immigration and emigration are excluded from the analysis, since they constitute an
insignificant part to the net change in China’s population. China’s net migration rate
is -0.44 migrant(s) per 1000 population as reported in 2015 [5].
2) Allresidents are registered in hukou (Household Registration System), since all data
available is based on the number of registered population.
3) Equal proportion of females and males constitutes the Mature Population.
4) Exchange rate between China RMB and US Dollars will be set at 1 US dollar to 6.5
RMB throughout the period of study.
5) The quality of education on fertility is not considered. It is assumed that more money
spent on education leads to higher education which leads to lower fertility rates.
6) In the simulation of the two-child policy, it is assumed that all graphical functions
obtained by historical data are valid for current trends.
2.2 Approaches
The investigation was carried out in three stages:
1) Using the One-Child Policy Model developed after rigorous research, a simulation
investigating the effect of the one-child policy with a time span from 1980 to 2015
(35 years) was conducted. Validation of the model was carried out by comparing the
simulation results with the official statistics for China’s Total Population, and Aged
Dependency Ratio (ratio of aged dependents - people above 64 - to the working-age
population - those aged 15 to 64 [6]) during those years.
2
LS
The study simulated, observed and analyzed the two-child policy’s impact on China’s
demographic structure by looking specifically at the trend of Total Population, Actual
Fertility Rate as well as Aged Dependency Ratio. The time span for this simulation
constituted two parts, 1980 to 2015, and 2015 to 2050 (35 years) with 2015 being the
year when the two-child policy was introduced into the model, and 35 years was the
period for one generation to mature and have children. The effectiveness of the two-
child policy in alleviating the issue of ageing population in China would be evaluated.
3
S
Sensitivity analysis was performed on two factors, namely, Percentage of GDP
(Gross Domestic Product) invested in Education, and Average per Capita Income of
Household, in an attempt to identify the more significant factor that contributes to the
worsening situation of ageing population in China. Finally, based on the sensitive
factors identified, other measures that the Chinese government might consider
adopting to better mitigate the current problems would be proposed and tested.
3 Base Case Causal Loop
The causal loop depicted in Figure 1 shows how the Country’s Desired Fertility Rate, the
Aged Dependency Ratio and the Education Level of Population affect the Actual Fertility
Rate.
When the Country’s Desired Fertility Rate increases, Individual’s Desired Fertility Rate will
increase, causing the Actual Fertility Rate to increase. It can be seen from the diagram that
as Actual Fertility Rate increases, Young Population will increase. This will lead to an
increase in both Mature Population and Aged Population in the future. In a shorter period of
study, more mature population will lead to reduced Aged Dependency Ratio and hence
increase Individuals’ Desired Fertility Rate through a reinforcing loop. More mature
population will also lead to an increase in Labor Force and hence GDP. Higher GDP rises up
the Education Level of Population and people become less willing to have more children. In
a longer period of study, more Mature Population will lead to more Aged Population. This
will cause an increase in Aged Dependency Ratio that can undo the reinforcing loop effects.
Therefore, this calls for attention to find a balance between short term and long term effects.
Country's Desired
Fernility Rate
Indo *
Desired Fertility
Rate Pes
Percentage of Money
Education = + Available for Children
Level of Actual Fertility per Household
Population cake:
=
+ Percentage of Money
Young Spent on Elderly per
Investment in
Education Population
\ ee
ae ai
Aged
Mature EE Dependency
ato
Gop Population Re
ww KA
Population
Figure 1 Causal Loop Diagram of Base Model
3.1 Feedback Loops
Three feedback loops were identified in the causal loop diagram. In this section, a detailed
analysis of the relationships between different factors in each feedback loop is provided.
3.1.1 Education Level Erosion Loop
BI is a balancing loop that highlights the relationship between the Education Level of
Population and Individual’s Desired Fertility Rate. As shown in the diagram, an increase in
Actual Fertility Rate would lead to a rise in Young Population. With a delay of 14 years, this
young population will become mature population. An increase in Mature Population will
expand the Labor Force in the country, since more people are available for working. The
country hence becomes more productive, and this leads to an increase in GDP.
rr Aca Ferity
—— Rote
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ae 3) firol
&
\
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Population
_ :
& A
ee
Figure 2 Education Level Loop
With higher GDP, more money will be invested in education and this causes a rise in the
Education Level of Population. Recent studies have noted that the more educated the
population is, the less they are willing to have children [7]. Hence it will lead to a decrease
in Individual’s Desired Fertility Rate, and therefore lowers the Actual Fertility Rate. This
generates a balancing effect on Actual Fertility Rate.
3.2.2 Aged Dependency Ratio Loop (Short Run)
Figure 3 is a reinforcing loop that illustrates the relationship between Aged Dependency
Ratio and Individual’s Desired Fertility Rate in the short run. As shown in the diagram, an
increase in Actual Fertility Rate leads to a rise Young Population. With a delay of 14 years,
this young population becomes mature population.
An increase in Mature Population decreases the Aged Dependency Ratio, since more mature
people are available to support the aged population. A lower Aged Dependency Ratio will
result in a smaller Percentage of Money Spent on Elderly per Household, leaving a larger
Percentage of Money Available for Children per Household. Families are then more willing
to have children, as they are more able to afford the costs of child-raising. This hence leads
to an increase in Individual’s Desired Fertility Rate as well as the Actual Fertility Rate.
a Desde ~~
Percentage of Money
‘cual Fetty ‘voltae for Children
Rote per Hoviehold
{ » |
Yeung
Population
% /
Manure Dependency
Popdoticn os
Figure 3 Aged Dependency Ratio Loop (Short Run)
3.3.3 Aged Dependency Ratio Loop (Long Run)
B2 in Figure 4 is a balancing loop that describes the relationship between Aged Dependency
Ratio and Individual’s Desired Fertility Rate in the long run. As shown in the diagram, an
increase in Actual Fertility Rate will lead to a rise in Young Population. With a delay of 14
years, this young population will become mature population. With another delay of 51 years,
the mature population will become aged population. Therefore, both Mature Population and
Aged Population will increase in the long run. However, the growth rate in Mature Population
is much lower than the growth rate in Aged
‘Actol Fetity
| ( ry) a
Young
Population
is Zt
aaa
Figure 4 Aged Dependency Ratio Loop (Long Run)
Population is restricted due to the government’s one-child policy. As a result, Aged
Dependency Ratio will increase. A higher Aged Dependency Ratio will result in a larger
Percentage of Money Spent on Elderly per Household, leaving a smaller Percentage of
Money Available for Children per Household. Families are then less willing to have children,
as they are now less able to afford the costs of child-raising. This hence leads to a decrease
in Individual’s Desired Fertility Rate as well as the Actual Fertility Rate, thus generating a
balancing effect on the actual fertility rate in the long run.
4 Policies and Analysis Based on Systemic Dynamics Model
4.1 One-Child Policy
Based on the causal loop established, a stock and flow diagram was built (Figure 5), to
simulate the dynamic system that modeled the implementation of the one-child policy. The
simulation is run for 35 years, from 1980 to 2015, which was the period the one-child policy
was implemented in China.
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Figure 5 Stock and Flow Diagram
4.1.1 Model Description
The values used in the simulation can be found in Appendix A [8][9][10].
The following factors were defined with graphical functions obtained using historical data:
Employment Rate [11], GDP Contribution per Labor [12], Average Number of Elderly per
Household, Average Household Income Spent on One Elderly [13], Average per Capita
Income of Household [14], and Percentage of GDP Invested in Education [15]. The graphical
functions is found in Appendix A.
The following graphical functions were defined after thorough research and analysis, so as
to best model the real situation.
Education level is defined on a scale of 0 to 1 as shown in Figure 6. The x-axis of the graph
is the ratio of current Investment in Education to the Investment in Education in 1980. As the
ratio increases, that is, investment in the education sector grows, the general Education Level
of Population increases. The graph is concave in shape, as there exists a ceiling to the increase
in education level, that is, the overall Education Level of Population cannot exceed certain
limit (e.g. postgraduate level).
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Figure 6 Education Level of Population
The relationship between Education Level of Population and Individual’s Desired Fertility
Rate Affected by Education is a negative linear one, and it was confirmed by research. As
education level of an individual advances, the individual’s desired fertility rate decreases
from around 5 to below | in Figure 7 [16].
6.000 Individuats
Educaion Level yay
sa Rate Affected by
1000 4890
Individuate 0.100 4470
Desied 0.200 4.050
0.300 3570
Afected by 400 3030
Education 0.500 2610
0.600 2250
0.700 4.800
0.800 1.440
0.300 1.020
8.000 1.000 0.690
Sas eS
Zz 0.000 1.000
Education_Level_of Population DataPoits: 11
Figure 7 Individual's Desired Fertility Rate Affected by Education
The Effect of Percentage of Household Income Available for Children on Fertility Rate is
defined on a scale of 0 to 2 (Figure 8). It is a multiplier for Country’s Desired Fertility Rate,
and the product of the two is Individual’s Desired Fertility Rate Affected by Household
Income. The rationale for the multiplication is that, having a sound economic foundation is
the prerequisite for Chinese couples to turn the “want” to have a child into action [17]. It is
thus the factor that either limits, or amplifies, the effect of Country’s Desired Fertility Rate —
a reflection of national population policy. With more available income for child-bearing,
individuals are more willing to have children, the multiplier thus increases.
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Figure 8 Effect of Percentage of Household Income Available for Children on Fertility Rate
The graph of China’s Desired Fertility Rate is defined on a scale of 1 to 2 (Figure 9), and it
is a reflection of the continuous adjustment and loosening of the one-child policy made by
the Chinese government over 35 years.
2000 ¥. Counys Dested
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4.000 41000
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Countu's 8.386 1.055
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Fatily Be tet Asie
19.08 1155
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24 1.380
3122 1585
00 ee 3800 cca
1000 36.00
Yeas DataPoits 10
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Figure 9 Country’s Desired Fertility Rate
4.1.2 Result Analyses
Figure 10 shows Total Population and Aged Dependency Ratio obtained from the simulation.
The simulation results reflected the effect of the one-child policy on China’s demographic
structure over a course of 35 years of its implementation. Validation of the model was carried
out by comparing the simulation results with official data from the China Statistical Yearbook
2014 [18].
Figure 10 One-Child Policy Graph
In comparison, Figures 11 and 12 are graphs plotted using official data from the China
Statistical Yearbook 2014 (note the difference in units and scale):
Total Population
150000
2
iS oe
= 100000
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sé
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Figure 11 Official Data of Total Population
Aged Dependency Ratio
20.0
=
< 10.0
G
EE 00
2 1 3 5 7 9 Al 13:35 17 19 21 23 25
af
aaa Data Point
&
Figure 12 Official Data of Aged Dependency Ratio
It could be observed that the graphs of both Total Population and Aged dependency ratio
obtained from the group’s simulation followed similar patterns to that of the actual data.
Moreover, in terms of absolute values obtained from the simulation table (shown in Figure
13), Total Population obtained from the group’s simulation is 1.390 billion people in 2013.
The actual value from the China Statistical Yearbook 2014 is 1.361 billion people at the end
of 2013. The difference between the two is a negligible 2.13%. This further testified that the
model built is valid to a large extent.
3 0.18 1,386.18
32} 0.18 1,390.22
33) 0.18 1,392.80
24] 0.18 1,394.23
Figure 13 Section of the Table from the One-Child Policy Model
4.2 Two-child Policy
Based on the validated model, the group incorporated the recently announced two-child
policy into the dynamic system by extending the simulation time period from 35 years to 70
years. This initial model was used as the base case model. Since the two-child policy took
effect only after 2015, the simulation of the two-child policy was run for 35 years from 2015
to 2050, which was the period for one generation to mature and have children. The first part
(first 35 years) of all graphical functions in this section has the same pattern as the graphical
functions defined in Section 4.1 as the one-child policy has already happened with the impact
on the society discussed earlier. The effect induced by the introduction of two-child policy
would be reflected in the graphical function from the 35" year to the end of the study time.
4.2.1 Model description
The following graphical functions were modified as shown below, while keeping the rest of
the model unchanged.
With the implementation of two-child policy in 2015, the Country’s Desired Fertility Rate
would increase and remained at 2 from the 35" year to the end in this simulation. No
adjustment or loosening of this policy was assumed for the next 35 years, hence the shape of
the graph remained flat (Figure 14).
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Figure 14 Country’s Desired Fertility Rate
GDP Contribution per Labor increased in an increasing rate in the first 35 years and
eventually converged in the long term as shown in Figure 15. This was also the prediction
made by other researchers [19].
2000000 GDP Contibution,
Yeas res
71000 1475000
8657 20450.00
oP 163 23860.00
Contribution 2400 ‘Ses
per Labor 3187 119250.00
BB 154400.00
4700 175300.00
5467 196700.00
233 189550.00
orate] 70.00 190600.00
=p 1S
a 100 7ao
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Figure 15 GDP Contribution per Labor
The Percentage Invested in Education over the next 35 years increased in a slower rate as
compared to the first 35 years (Figure 16). Besides, it has a positive linear relationship with
GDP Contribution per Labor, hence when GDP Contribution per Labor converges, it tends
to converge as well.
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Figure 16 Percentage of GDP Invested in Education
In the long run, more people in China would be able to receive higher education and hence
the Education Level of Population increased and eventually reached a maximum level that
was defined as | as shown in Figure 17.
1.000 Investment in
Ehevorime Sdzvented!
ein” SPoitn
0000 010
50 0450
Education 17000 0610
Level of 0 070
Population m0 086
45.0 0885
‘51000 05
00 0985
$2000 0965
765.00 03%
a 000 0985
6 ‘oem ery
incre afd nfdcsn 180 DanPanc i
Edt Oupt
Figure 17 Education Level of Population
Employment rate in the first 35 years followed the same trend as defined by historical data
in the previous section. It was projected to remain relatively constant in the next 35 years
(Figure 18) by comparing unemployment rate projection results given by other researchers.
They predicted the unemployment rate in China in 2020 to be 4.09% and claimed that it
would stay relatively unchanged till 2050 [20].
Figure 18 Employment Rate
The Average per Capita Income of Household followed the trend shown in the first 35 years
and would increase almost linearly from the 35" year to the 70" year. Consequently, the
Average Household Income Spent on One Elderly would also increase steadily in the next
35 years. The highest average household income per capita was set at 60,000 RMB per year
and the maximum income spent on one elderly was set at 40,000 RMB per year. These were
illustrated in Figures 19 and 20 respectively.
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Figure 20 Average per Capita Income of Household
4.2.2 Result Analysis
The Two-Child Policy Graph | (Figure 21) showed the projected trend in Total Population
and Aged Dependency Ratio given the introduction of the two-child policy. As seen from the
graph, Total Population peaked before 2050 and started to decrease gradually afterwards.
This predicted trend from the simulation was in line with the findings made by other
academic research [21].
Figure 21 Two-Child Policy Graph 1
Hence, the results generated from the simulation were deemed to be valid to a large extent.
As observed from the graph, Aged Dependency Ratio was still increasing even with the
launch of the two-child policy, indicating that the policy has limited impact on alleviating
the problem of ageing population (Figure 21).
Figure 22 Two-Child Policy Graph 2
On top of that, the Country’s Actual Fertility Rate stayed below the replacement level of 2.1,
and decreased even further after the implementation of the two-child policy (Figure 22). As
such, the simulation results revealed another issue that China would be facing - unsustainable
level of fertility rate.
4.3 Sensitivity Analysis
4,3.1 Sensitivity Analysis on Percentage of GDP invested in Education
A sensitivity analysis on Percentage of GDP Invested in Education was performed by the
group to observe the impact of varying education levels of population on the Actual Fertility
Rate in the country. Six simulations were carried out, each with a new graphical function
being defined for the Percentage of GDP Invested in Education. The variations took place in
2015, the year when the two-child policy was introduced. The scenario in each simulation is
summarized in the table below (Table 1):
Table | Simulation Scenarios on Percentage of GDP invested in Education
Simulation Scenario
1 Percentage of GDP Invested in Education decreases drastically after 2015,
and continues to decrease thereafter.
2 Percentage of GDP Invested in Education follows a trend similar to that in
the case for the two-child policy simulation, but with lower absolute values.
3-6 Percentage of GDP Invested in Education follows a trend similar to that in
the case for the two-child policy simulation, but with higher absolute
values.
The simulation results (Appendix B) showed that with drastically decreasing investments in
the education sector (Simulation 1) there would be an obvious increase in Total Population.
However, with increasing investments in the education sector (Simulations 2 to 6), the
simulation results on the trends of Total Population showed no significant deviation from
that in the base case, regardless of the degree of variation introduced in the Percentage of
GDP Invested in Education. This might be due to the fact that the general education level
was already saturated, and more investment in the education sector would not bring any
significant change to the education level, and thus to Total Population. The Percentage of
GDP Invested in Education was thus, a factor that would not be sensitive and hence likely to
have little impact on the system.
4.3.2 Sensitivity Analysis on Average per Capita Income of Household
A sensitivity analysis was performed on Average Per Capita Income of Household by varying
the graphical functions. The scenario in each simulation is summarized in the Table 2 below:
Table 2 Simulation Scenarios on Average per Capita Income of Household
Simulation Scenario
1 Average per Capita Income of Household increases slowly since 2015, and
it increases at a decreasing rate. The income reaches 36,000 RMB in 2050.
2 Average per Capita Income of Household increases slowly since 2015, and
it increases at a decreasing rate. The income reaches around 42,000 RMB in
2050.
3 Average per Capita Income of Household increases since 2015. It reaches
60,000 RMB in the year 2045 and stays at that level till 2050.
4 Average per Capita Income of Household increases significantly since
2015, the rate of increase decreases over time, and the income reaches
70,000 RMB in 2050.
5 Average per Capita Income of Household increases very rapidly since 2015.
It reaches 70,000 RMB in 2035 and stays at that level till 2050.
6 Average per Capita Income of Household increases significantly since
2015, and it increases at a decreasing rate. The income reaches 80,000 RMB
in 2050.
The simulation results (Appendix B) showed that with significant amount of increase in
Average per Capita Income of Household, there would be an obvious increase in the size of
Total Population. The higher the household income, the greater the increase in population
size. As such, Average per Capita Income of Household is a sensitive factor in this system.
4.4 Policy Recommendations
After the Chinese government announced the introduction of the two-child policy, critics
suggested that economic factors are the main concerns for families to have a second child
[21]. From the sensitivity analysis carried out in Section 4.3, it was also found out that
Average per Capita Income of Household is a more sensitive factor affecting the actual
fertility rate of population in this system, as compared to Percentage of GDP Invested in
Education. Hence the next step was to focus on designing and introducing policies that would
reduce the cost of child-raising in China. There are two possible measures that aim firstly, to
ease the pressing problem of low Actual Fertility Rate, and secondly, to slow down the rise
in Aged Dependency Ratio in China. The two measures are giving cash bonus to families
that give birth to a second child and introducing a healthcare scheme that aims to cut down
the cost of healthcare in China. To validate the effectiveness of these policies, the two
measures were incorporated into the two-child policy model, one after the other, while
keeping the other factors unchanged.
4.4.1 Cash Bonus
One proposal is for the Chinese government to give cash incentives to families that give birth
to a second child. The cash bonus may include:
e A one-time-off cash bonus of 10,000 RMB.
e Annual family bonus of 20,000 RMB to support the second child’s living expenses.
The policy was introduced into the model as an additional converter named Cash Bonus,
which is an input to Average Total Household Income.
As seen from the simulation tables obtained (Appendix C), the implementation of the cash
bonus policy successfully increased the projected Total Population in 2050 from 1,496
million to 1,510 million. In addition, the policy also lead to a slight increase in the Actual
Fertility Rate from 1.44 (without cash bonus) to 1.50 (with cash bonus) in year 2050.
However, the policy showed limited effect on slowing down the increase in Aged
Dependency Ratio, as the ratio remained relatively unchanged.
4.4.2 Healthcare Scheme
Since the elderly is prone to diseases, a large amount of household income is spent on
covering their healthcare expenses [22]. It was proposed that the Chinese government
introduce a healthcare scheme that aims to cut down the cost of healthcare in China, so as to
indirectly increase the household income that is available for children. Some possible
measures under this scheme include providing subsidies for health care services, waiving the
medical bills for certain medicines, and expanding the medical insurance coverage. The
policy manifested itself in the form of a new graphical function for Average Household
Income Spent on One Elderly (Figure 23), with a decreased slope as compared to that in the
base case, taking effect from the 35" year.
4000.00 Average
Years Household
Income Spent on
1.000 2180.00
Average 8.867 3782.00
Household ee 1633 568.00
Income 24.00 10747,00
Spent on 31.67 19931.00,
One Elderly 33.33 1731400
47.00 19702.00
5467 21234.00
62.33 2289.00
200.00 70.00 224
ZB 4.000 70.00
} Years DataPoints 10
Edt Output
Figure 23 Income Spent on One Elderly with the Introduction of Healthcare Scheme
As seen from the simulation tables obtained (Appendix C), the implementation of the policy
significantly increased the Actual Fertility Rate from 1.44 (without the Healthcare Scheme)
to 1.60 (with the Healthcare Scheme) in 2050. Moreover, the scheme successfully slowed
down the increase in Aged Dependency Ratio from the 35" year to the 70" year.
5 Conclusion
A dynamic model was designed to realistically translate the circumstances pertaining to the
population policies in China, and offered analysis on the effectiveness of the various state
policies in addressing the problems of low fertility rate and ageing population. The results
obtained from the group’s simulation on the base case model revealed that the newly
implemented two-child policy would most likely be ineffective in alleviating the pressing
issue of ageing population. Through performing sensitivity analysis, the group identified that
Average per Capita Income of Household is a sensitive factor in the systemic model. Based
on this finding, two other measures were recommended. Both measures, giving cash bonus
and introducing a healthcare scheme that reduces the household income spent on elderly,
were shown to be effective in increasing the Actual Fertility Rate, and also in slowing down
the increase in the Aged Dependency Ratio. The Chinese government may wish to consider
introducing these measures. However, the Chinese government also needs to watch out for
the efficacy of these measures. With the rising affluence of its citizens, the suggested
monetary bonuses or health care relief may not be sufficient to induce Chinese to have more
children. Moreover, if people becomes more materialistic and values quality of life over
children, such recommendations may actually backfire. Hence the Chinese government will
need to calibrate its policies based on expectations of its people.
5.1 Limitations
There are several limitations in this model:
1.
Since most of the variables used in the stock and flow diagram were graphical
functions, the build-in sensitivity analysis function was not used. Hence, sensitivity
analysis was done manually by manipulating the graphical functions. Inaccuracy
might have been introduced into the system.
The duration for simulation, which was 70 years, might be too short to observe
significant changes in the demographic structure in China. Hence, the simulation
results, in particular, the trends and values of Total Population, Actual Fertility Rate,
and Aged Dependency Ratio might not be accurate indicators of the true effect of
various population policies being introduced.
The statistics for graphical functions, such as Average per Capita Income of
Household and Percentage of GDP invested in Education, from the year 2015 to the
year 2050, were values projected based on current trends. Their values might not
coincide with that in reality and hence the simulation results might not accurately
reflect the future situation.
In proposing the measures, it is not known whether the Chinese government has
sufficient funds to implement these policies. As such, to ensure the feasibility of the
proposed policies, these policies should be implemented with careful and appropriate
government budgeting.
Despite the above mentioned limitations, this paper offers a relatively comprehensive
investigation of the effects of various population policies in China. In addition, its analysis
and policy recommendations provide new insights to relevant stakeholders.
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