Population Ageing and Economic Growth in China
Emiel Mijnen
Master Thesis
Stud. Nr: 10000208
Email: [email protected]
Field: International Economics and Globalization.
Thesis supervisor: Drs. N. J. Leefmans.
Second reader: Dr. D. Veestraeten.
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Statement of Originality
This thesis belongs to:
Emiel Mijnen
Please do not print/copy without prior permission.
All information is a product to be used to demonstrate
my work experience and related skills.
This statement certifies that the thesis is based upon
original research undertaken by the author and that the thesis
was conceived and written by the author alone and has not
been published elsewhere. All information and ideas from
others is referenced.
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Table of Contents 1. Introduction ............................................................................................................................ 4
2. Literature Review ................................................................................................................... 7
2.1 General views ................................................................................................................... 7
2.2 China ................................................................................................................................ 8
2.3 Relevant variables ............................................................................................................ 9
2.3.1 Economic growth ....................................................................................................... 9
2.3.2 Population ageing ................................................................................................... 11
2.4 Brief Conclusion ............................................................................................................ 11
3. Variables and Data ............................................................................................................... 13
4. Methodology ........................................................................................................................ 20
5. Empirical Research Results and Interpretation .................................................................... 23
5.1 Hausman Test ................................................................................................................. 23
5.2 Regression results using fixed-effects ............................................................................ 24
5.3 Estimating future effects of the ageing problem in China.............................................. 27
6. Conclusion ............................................................................................................................ 29
References ................................................................................................................................ 31
Appendices ............................................................................................................................... 34
Appendix 1: Descriptive Statistics ....................................................................................... 34
Appendix 2: Projections ....................................................................................................... 34
Appendix 3: DemProj projections ........................................................................................ 35
Appendix 4: Linear Polarisation .......................................................................................... 35
Appendix 5: Data description ............................................................................................... 36
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1. Introduction
China has experienced an increase in population growth during the 1950s. The so-called
‘baby boom’ period after World War II was stimulated by the Ministry of Health through introducing
abortion restrictions (Yao et al., 2014). In 1953, the total population of China was approximately at
582 million, but the population continued growing rapidly (see Graph 1) until the Chinese authorities
decided to intervene. In 1979, when the population was at 975 million, the ‘one-child’ policy was
implemented and led to a continuous decline in population growth. Despite this, the total population
reached 1.340 billion in 2010. Recent developments show that the policy has become more flexible
in November 2013. People were only allowed to have a second child if both parents were a single
child. This has been relaxed to parents having a second child if one of the parents was a single child.
The United Nations predict that the growth will continue until 2030 when the population is expected
to be at its peak level, 1.460 billion. After this, the population will start to reduce (Yao et al., 2014).
Graph 1: Total Population China 1960-2013 (in billions).
Data source: Graph constructed by author based on data from The World Bank (2015).
Besides changes in the total population level, the age structure of the population changed as
well. The United Nations (2015) stated that the population of ages 0-14 has entered a long-term
decline (see Graph 2). From 39,7% in 1960, it is expected to hit 18.8% in 2020 and 15.2% in 2050.
They also believe that the working-age population (currently about 73%) will begin to shrink in 2020
and will be around 60% of total population in 2050. The 65+ population is expected to increase from
7% in 2000 to 15.3% in 2030 and eventually 23.3% in 2050 (Yao et al., 2014). Scholars and policy
makers have become more concerned about the rapid ageing of the population of China. They
predict that it could potentially have adverse effects on sustainable economic growth (Peng, 2008).
China hasn’t constructed a social protection system to support the elderly yet. The changing age
structure is not favorable to continued economic growth. Potential growth rates are therefore
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decreasing, which make it even more difficult to deal with the consequences of population ageing
(Du & Yang, 2014).
Graph 2: Age-structure population China 1960-2013.
Data source: Graph constructed by author based on data from The World Bank (2015).
It is therefore in the interest of China to know to what extent the population ageing problem
would have an effect on economic growth. Doing a quantitative research could potentially contribute
to the current literature concerning the population ageing problem. The aim of this thesis is to
estimate the future effect of population ageing on economic growth for the next 30 years. I chose to
use 30 years because it is useful to know the upcoming 15 years as the United Nations predicts that
China will hit the peak level of their population in 2030. After that it might be interesting to see what
will happen in the next 15 years to the economic growth of China, when the population level is
decreasing. Increasing the prediction range to 50 years or more might produce less accurate
estimates since the estimates of the UN become less accurate as time increases. This is confirmed by
a study of Prskawetz et al. (2007) which found that the ageing structure of a country can be
forecasted accurately for at most two to three decades. The research question will therefore be: ‘To
what extent will population ageing in China affect its economic growth for the next 30 years?’.
The methodology used for this thesis is based on other relevant empirical studies concerning
population ageing and economic growth. First, a cross-country regression will be done to estimate
the effect of population ageing on economic growth using panel data. According to Bloom, Canning &
Fink (2010), the use of panel data is essential in cross-country regression as it accounts for
differences in timing which may also mitigate the negative impacts of ageing. For instance, as rich
countries age faster than poor countries, a rich country can use immigrants from a poor country to
compensate for the ageing problem. This could theoretically slow the ageing process in the
developed country, but increase social pressure and unrest in a country. Therefore, to account for
these effects, it is relevant for this research to use panel data. The methodology in this thesis is based
on the research of Lee et al. (2013) and Bloom & Finlay (2009), that both perform a cross-country
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Population ages 0-14 (% of total)
Population ages 15-64 (% oftotal)
Population ages 65 and above(% of total)
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regression to study the impact of population ageing on economic growth. However, it will include
more economic growth factors in the regression. The dependent variable will be economic growth
and the explanatory variables will be the level of initial income (measured in real GDP per capita),
political instability, population growth, youth & old dependency ratio’s, human capital (life
expectancy & primary school enrollment), working-age population growth rate, fertility rate and
government consumption. The dependency ratio’s, fertility rate and government consumption are
neither included in the research of Bloom & Finlay (2009), nor in the regression of Lee et al. (2013).
They also used data from 80 countries for every 5 years, while this research will use 30 countries and
annual data. This country selection consists of 13 countries which are located in East Asia, Southeast
Asia and South Asia from the selection of Bloom & Finlay (2009), since this is our area of interest
(China). However, these countries have experienced similar demographic changes over time and
therefore another 17 countries were added to the dataset, to increase variation in the data.
Next, the estimated coefficient of the old dependency variable will be used to produce a
forecast for the next 30 years for China for the effect of population ageing on economic growth in
China. This methodology allows us to only examine the effect of the old dependency variable on
economic growth. The U.N. forecasts for the old dependency ratio for China until 2045 will be
inserted into the dependency ratio variable to give an estimate of the effect of population ageing on
economic growth in China for the next 30 years. This method is based on the study of Bloom,
Canning and Fink (2010) and Bloom & Finlay (2009), where they also made projections. However, the
study of Bloom, Canning and Fink (2010) used data until 2009 (and Bloom & Finlay (2009) until 2005),
while I use more recent data (until 2013). Bloom, Canning and Fink (2010) also didn’t do a cross-
country regression first to determine the effect. They focused mainly on the OECD-countries in
general while this research focuses on China specifically. Bloom & Finlay (2009) included China in
their projections but focused more on Asia in general. At last, both studies didn’t account for the
effect of the relaxed one-child policy in their projections (this happened in 2013), while this study
uses DemProj to forecast different scenarios to account for this.
The structure of this thesis is as follows. First there will be a literature review in Chapter 2 to
search for relevant variables and to analyze different views on the effect of population ageing on
economic growth. This should provide a sufficient background of information which can be used for
the rest of the paper. In Chapter 3 the variables used for the regression and the data will be
explained. After this, the methodology of the regression will be explained in Chapter 4, whereas the
results will be presented and analyzed in Chapter 5. Based on the literature review and the empirical
research, the main conclusions will be presented in Chapter 6 along with some possibilities for
further research.
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2. Literature Review
In this chapter the existing literature concerning the effect of population ageing on economic
growth in China will be discussed and analyzed. First, the results of the effects in general (not China
specific) will be presented. Their findings will be linked and compared to each other. The studies
related to China will be viewed and interpreted next. Subsequently, the methodologies used will be
described. Different research methods will be analyzed first to see what variables are useful for
determining the effect of population ageing on economic growth. These variables will be split into
two groups: ‘economic growth variables’ and ‘population ageing variables’. At last, a brief conclusion
will be drawn based on the findings of the literature review.
2.1 General views
Recent studies have investigated what effect population ageing has on economic growth in
general and have found similar results, namely that ageing has a negative effect on economic
growth. Most studies used a cross-country regression (Sala-I-Martin (1997); Barro (1991); Barro &
Lee (1994); etc.), one used a theoretical approach (Prettner, 2013) whereas only the study of Yao,
Kinugasa & Hamori (2014) did a time-series regression for China using an ARDL model. The empirical
studies of Bloom, Canning and Fink (2010) and Bloom et al. (2007) also used a cross-country
regression with panel data and showed that the age structure might be influential on economic
growth (measured by income per capita growth). They state that large youth and elderly cohorts
slow the pace of economic growth, while a large share of working-age workers speeds it. This is in
line with the research of Lee et al. (2013), which found that a larger share of workforce within a
population increases economic growth. The latter statement is based on the studies of Park and Rhee
(2007) and Bloom, Canning and Fink (2008) which state that labor supply and savings are higher
among working-age people (15-65 years). Lee et al (2013) also found that an increase in the young
population (0-14) relative to the elderly population (65+) inhibits economic growth in the short- and
long run. However, the theoretical research of Prettner (2013) shows a controversial result. He finds
that increasing population ageing does not slow down the technological progress of a country and
therefore economic growth. One thing to mention here is that he assumes that technological
progress is the main driver of economic growth. The benefits of private investments into knowledge
creation that individuals make increases with ageing as their created knowledge might pay off more
when they have a longer time horizon. This is also in line with the study of Prskawetz et al. (2007),
where they found a significant positive effect of life expectancy on economic growth. They also found
a significant negative effect of the youth dependency ratio (which is total population aged 0-14
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divided by workforce), which confirms the results of Bloom, Canning and Fink (2010) and Lee et al.
(2013).
There were also some studies that focused more on the population ageing in Asia (Bloom &
Williamson (1998); Bloom, Canning & Malaney (2000); Bloom & Finlay (2009)). The study of Bloom &
Williamson (1998) found a significant negative effect for the growth rate of the young aged
population (0-14), a significant positive effect of the working-age population level growth (same as
Bloom, Canning & Malaney (2000); Bloom & Finlay (2009)) and a positive (but insignificant) effect of
the elderly population growth (65+). The latter result contradicts earlier results but their explanation
is that elderly people take care of younger people, save more, sometimes work in part-time and have
a smaller negative impact when compared to younger people on the economic growth. This is due to
the labor participation and savings-rate for younger people which are very low. Combining all these
effects might explain the positive coefficient.
2.2 China
Several studies focused on the population ageing problem specifically in China. The empirical
study of Yao, Kinugasa and Hamori (2014) shows that the share of workforce (age 15-65) in total
population has a positive impact on economic growth in China (same result has been found by the
research of Hsu, Liao & Zhao (2011)). This means that an increase in the share of potential workers
would increase the economic growth. The research of Peng (2008) shows that the decline in the
growth rate of the workforce level negatively affects the growth rate of the economy, which will
reduce the demand for new investments. Similar results have been found by an empirical study of Du
& Yang (2014). They found that when the productive population (workforce) increases relatively to
the total population, the growth rate of income per capita will increase in China. This is mainly
because economic growth is affected by production and consumption lifecycles. People aged
between 24 and 60 are more productive in comparison with their consumption level and in China it
can be seen that a large group of the total population is in this age-region. They also state that
population ageing leads to rapid accumulation of capital in China. The ageing process can stimulate
saving rates to support investments. When this occurs, the capital-intensity will raise the productivity
of the labor force (output per worker) which will also stimulate economic growth. They also stated
that the age structure is correlated with the consumption pattern. The study of Du & Wang (2011)
shows that people aged 60+ tend to reduce their work-related costs but increase their overall
expenditure on health care. The latter is proven not to be favorable for economic growth, since the
health expenditure could crowd out household consumption on other products.
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2.3 Relevant variables
To determine what factors might be influential on economic growth, several articles will be
analyzed in paragraph 2.3.1 and linked to each other. The resulting factors will be used as control
variables in the regression. More articles will be analyzed in paragraph 2.3.2 to determine what
variables can be used to proxy for population ageing.
2.3.1 Economic growth
Several studies tried to determine what factors are key to use for determining economic
growth. One of the most commonly used explanatory variables was the level of initial income. This
factor was found by a research of Sala-I-Martin (1997) where he did two million cross-sectional
regressions to determine what factors are useful in determining economic growth. One way to
measure the initial income is by taking the logarithm of the real GDP per capita in the initial year
(Barro (1991); Barro & Lee (1994); Bloom & Sachs (1998); Sachs (1997)). The research of Barro & Lee
(1994) explains that the use of an earlier value of Log GDP as an instrument diminishes
overestimating the convergence effect because of measurement error in GDP.
Another variable that was extensively used was human capital (Barro (1991); Barro & Lee
(1994); Bloom & Sachs (1998)). The research of Romer (1989) showed that an increase in human
capital would increase the innovation rate concerning new products due to technological progress. In
this way countries with a higher amount of human capital tend to innovate faster and experience
more economic growth. An earlier research of Nelson & Phelps (1966) showed that the
‘technological spillovers’ is another channel through which human capital affects economic growth. If
the human capital increases, the chance of absorbing ideas that where discovered elsewhere
increases as well. In this way a follower country catches up faster with the technological leader if its
amount of human capital is larger. To proxy for human capital, the research of Barro (1991) used
primary and secondary school enrollment. Bloom & Sachs (1998) used the natural logarithm of the
years of secondary schooling to account for this effect. They also used public health in their
regression to measure human capital. This is in line with the study of Barro & Lee (1994) which
showed that human capital can be measured through education and health. To account for health,
Barro & Lee (1994) and Sachs and Warner (1997) used the life expectancy which they obtained from
the U.N. database. The life expectancy variable and the primary school enrollment variable can both
be found in the significant variable list in the research of Sala-I-Martin (1997).
The third important factor that I came across was government consumption (Barro (1991);
Barro & Lee (1994); Bloom & Sachs; Sachs & Warner (1997)). The study of Barro (1991) showed that
the government consumption had no direct effect on the private productivity or private property
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rights. However, the distorting effect of taxation and government expenditures affects the economic
growth in a negative way. To proxy for government consumption, they used the ratio of real
government consumption expenditure to GDP. Later on, the research of Barro & Lee (1994) showed
that the government consumption variable should be lagged because if the real GDP goes up, the
real government consumption expenditure to GDP goes down. The lag in the variable would deal
with this problem. The study of Sachs & Warner (1997) used central government saving as a variable
(calculated as current government expenditures minus current government revenues) and Bloom &
Sachs (1998) used the government deficit as a variable to proxy for government consumption.
Fourth, many studies used population growth in their regressions (Barro & Lee (1994); Bloom
& Sachs (1998); Sachs & Warner (1997)). The research of Barro & Lee (1994) showed that population
growth would negatively affect economic growth. However, including fertility rates made the
coefficient positive but insignificant. They explain that for a given fertility, the higher population
growth reflects net immigration or lower mortality (and these elements are plausible to be positively
affecting economic growth). Including all variables might give biased results since fertility and
population growth might be influenced by life expectancy (Barro & Lee, 1994). The population
growth variable is not included in the list of significant explanatory variables for economic growth by
Sala-I-Martin (1997).
The fifth factor that seems important is the fertility rate (Barro (1991); Barro & Lee (1994)).
The study of Barro & Lee (1994) shows that an increase in fertility rates involves an increase in the
value of a parent’s time. In this way, raising a child would cost more. Any change that tends to
increase the cost of raising a child would trigger the adult to save more and results in lower fertility.
This means that people shift from saving in the form of children to saving in the form of physical
capital and human capital. The increase in savings reduces the per capita growth rate. The fertility
rate is however not included in the list of significant explanatory variables for economic growth by
Sala-I-Martin (1997).
Another channel through which economic growth is affected is through political instability
(Sala-I-Martin (1997); Barro (1991); Barro & Lee (1994)). One explanation for this is given by Barro &
Lee (1994), namely that an instable government has an adverse effect on property rights and thereby
negatively influences investment and growth. Barro (1991) measured the political instability using
‘Number of political assassinations per year per million of population’ and ‘Number of revolutions
and coups per year’. The latter was also used in the study of Barro & Lee (1994). The research of Sala-
I-Martin (1997) showed some other variables which positively affect economic growth, like: ‘Rule of
Law’, ‘Political Rights’ and ‘Civil Liberties’. He also states that the amount of revolutions and a war
dummy show negative impact on economic growth.
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The last channels which were commonly used were the youth dependency ratio (Barro & Lee
(1994); Bloom & Sachs (1998)) and the growth rate of the working-age population level (Bloom &
Sachs (1998); Sachs & Warner (1997)). Barro & Lee (1994) explains that the youth dependency ratio
(which is the absolute number of people below 15 years divided by the amount of total population)
affects economic growth in two ways. First, the share of non-workers increases which tends to lower
per capita GDP growth. Second, if the youth dependency ratio increases, the work effort of adults
will focus more on raising children instead of working. This will lower GDP growth as well.
2.3.2 Population ageing
To account for population ageing, many studies seem to use the growth rate of the working-
age population (Bloom, Canning & Malaney (2000); Bloom & Finlay (2009); Yao, Kinugasa & Hamori
(2014); Bloom, Canning & Fink (2010); Przkawets et al. (2007)). The study of Przkawets et al. (2007)
found a significant positive coefficient for the working-age population growth rate. Studies also used
the workforce share of total population (Bloom, Canning & Malaney (2000); Bloom & Finlay (2009);
Hsu, Liao & Zhao (2011); Peng (2008)). The study of Bloom & Finlay (2009) states that a decline in the
working-age share will tend to depress economic performance in the region. In fact, all studies seem
to find a significant positive coefficient on the share of workforce of total population when regressed
on economic growth.
Another commonly used variable is the share of younger- and older people in the total
population, called the young- and old-dependency ratios respectively (Du & Yang, 2014; Lee et al.
(2013); Barro & Lee (1994); Przkawets et al. (2007)). Bloom & Williamson (1998) used the growth of
these shares to measure population ageing. The dependency ratios are calculated by dividing the
amount of younger people aged 0-14 (or elderly people, 65+) by the workforce population ( aged 15-
65). The study of Lee et al. (2013) shows that the coefficient for these variables is negative, thus an
increase in the share of younger- or older people would reduce economic growth. However, the
share of workforce might be highly correlated with the dependency ratio variables since these
variables can be linearly predicted from the share of workforce with a substantial degree of accuracy.
In this way we might encounter a multicollinearity bias and this variable will therefore be left out of
the equations.
2.4 Brief Conclusion
Most studies examining the impact of ageing on economic growth use cross-country
regressions. There are four most commonly used variables to proxy for population ageing (assuming
workforce is 15-65 years): young- and old-dependency ratios, the share of workforce in total
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population and the level of workforce growth. For the young dependency ratio, all the studies find a
significant negative coefficient. However, for the old-dependency ratio, the research of Bloom &
Williamson (1998) found a positive (but insignificant) effect which contradicts the results of other
studies (who found a negative coefficient). Moreover, all studies find a significant positive effect of
the share- and the growth rate of the level of workforce within a population on economic growth.
For the cross-country regression, across the studies I found 10 variables of interest. These are as
follows (same as in the introduction/section 2.3): Real initial GDP per capita, population growth,
youth & old dependency ratio’s, political instability, life expectancy, primary school enrollment,
growth rate of the working-age population level, fertility rate and government consumption. To
proxy for population ageing, the dependency ratios and the working-age population growth rate are
used.
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3. Variables and Data
Based on the literature review, I chose to
use 10 variables which might affect economic
growth (listed on the right). The most interesting
variable for this research is the old dependency
ratio, whose coefficient will be used to project
future effects of the population ageing problem in
China with respect to economic growth. As
mentioned earlier, there are 30 countries of interest
which I used in the dataset. However, to present a
graph in this chapter for every variable for all countries would require a lot of space and might make
things cluttered. Therefore, the data summary description can be found in Appendix 1. In this
chapter, each paragraph describes one variable that will be used for the regression equations. The
paragraph will consist of a brief description, followed by the expected effect that it will have on
economic growth. At last, the source of the data will be given and some interesting features in the
data will be described shortly.
The dependent variable for the regression equations is economic growth. This variable is
measured as the GDP per capita percentage growth rate based on constant local currency,
aggregates are based on constant 2005 U.S. dollars. It is calculated as the gross domestic product
divided by the midyear population level. This data is obtained from The World Bank (2015). It is
interesting to see what the economic development regarding growth was for the countries in the
past years (from 1960 to 2013). The data show that the economic growth patterns of the sample
countries are rather similar. It seems relatively constant with some exceptional peaks. One
remarkable peak is the -26% growth of China in 1962. However, this can be explained as there was a
war between China and India in 1962. Since wars cost money and can hurt economic growth, it is
logical to assume that the war caused the sudden drop for obvious reasons. However, after the drop,
China has experienced higher growth rates than any other country in the sample. From the data it
can be seen that sometimes South Korea and Singapore seem to overrule this statement (one time
even Japan) but overall it seems that China is the leader when it comes to economic growth.
The old dependency ratio will be the only explanatory variable of interest in the regression
equations since the aim of this research is to isolate the effect of population ageing on economic
growth. The definition of dependency ratio is as follows: ‘The dependency ratio measures the % of
dependent people (not of working age) / number of people of working age (economically active)’.
This variable is therefore the variable of interest since this variable shows the amount of elderly
Variables of interest
Old dependency ratio Initial Real GDP per capita Population growth Young dependency ratio Political instability Life expectancy Primary school enrollment Working-age population growth rate Fertility rate Government consumption
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people as a percentage relative to the working-age population. The higher this value will be, the
higher the ageing level within a population will be. One might expect a negative coefficient here
since most studies find a negative relationship here due to the fact that an increase in the share of
older people decreases the share of working-age people (except when the inflow of younger people
is from the same magnitude) which negatively affects economic growth. Explanations are: there are
more elderly to support (Bloom & Williamson, 1998), work-related consumption goes down (Du &
Wang, 2011) and it will slow the economic growth (Bloom, Canning & Fink, 2010; Lee et al., 2013).
However, we might also find a positive coefficient as elderly can teach the young, work part-time and
save more (Bloom & Williamson, 1998). The data for this variable until 2013 was obtained from The
World Bank (2015), while the data for the projections were obtained from the United Nations (2015)
database. From the data we can see that most of the Asian countries have a similar pattern with
respect to the old dependency ratio, remaining relatively constant and low over time compared to
the rest. The other non-Asian countries seem to have a slight upward trend in the data. However,
Japan is the only country which seems to deviate from the ‘trending’ pattern from these countries.
With an increase of 31,6% of the old dependency ratio over 54 years (1960-2013) it clearly deviates
from the rest of the Asian countries since the number two of Asia (South Korea) only had an increase
of 9,9% over 54 years. Graph 4 in Appendix 2 presents the projections for the old dependency ratio
for China from the U.N. until 2045. These values will be used to project the future effects of this
variable on economic growth later on. As we can see from the graph, there is a continuous increase
in the old dependency ratio in China for the next 30 years. This refers to the ‘population ageing
problem’ in China. With a difference of 25,0% over 35 years it might be interesting to see to what
extent this will affect China’s economic growth. From The World Bank (2015) data it can be seen that
there was a slight increase of 5,1% over the 54 years in the period 1960-2013. However, the
projected impact is almost 5 times higher in a shorter time period.
To account for effects of other factors on economic growth (which might influence our
coefficient estimation) some control variables will be added to the regression equations. The initial
real GDP per capita variable will be used as a control variable since this variable is directly correlated
with the dependent variable economic growth. It is calculated as the gross domestic product divided
by the midyear population level using 2005 as a base year assuming a constant dollar exchange rate.
However, the natural logarithm of this variable will be used to take the growth of the real GDP per
capita into account (Bloom & Sachs (1998); Barro & Lee (1994); Przkawets et al. (2007); Yao et al.
(2014)). This variable will be lagged one period as according to Barro & Lee (1994) this is essential to
deal with overestimated effects due to measurement errors in GDP (see literature review for more
info). One might expect a negative coefficient here since this variable accounts for the ‘convergence
effect’ (also known as the catch-up effect) according to Sala-I-Martin (1997). This effect states that
15
poorer countries in terms of GDP per capita tend to catch up with ‘richer’ countries since diminishing
returns in countries with lower capital stocks are not as strong as in countries with higher capital
stocks. This means that an increase in this variable should negatively affect economic growth. The
data for this variable has been gathered from The World Bank (2015). This data seems to have an
upward trend. However, the countries South Korea, Japan and Singapore, which were marked as East
Asia by Bloom & Finlay (2009), have experienced a booming period compared to the countries which
were marked as South-East Asia and South Asia (with an exception for China) as these countries
seem to remain at a constant low level. This effect might be explained by the research of Nelson and
Phelps (1966) suggesting that poor countries tend to catch up with rich countries if the poor
countries have a higher amount of human capital per person. Norway is the clear leader in real GDP
per capita since it experiences higher levels throughout the time-period of 54 years.
Another control variable will be population growth. This is measured as the growth rate in
year t of the midyear population level from time t-1 to time t, given as a percentage. According to
Bloom & Williamson (1998) the population growth should affect the economic growth through the
channel that it affects the ratio of the working-age population to dependent population. If the
population growth increases the elderly share of the population due to a higher life expectancy, it
should have a negative direct effect as more elderly need more support. However, population
growth due to a diminishing mortality rate would have no effect since the level of workforce-to-
dependent-population ratio won’t be affected. It might also occur that population growth goes in
line with an increasing fertility rate. Bloom & Williamson explain that then the effect would be
negative on economic growth since there are more mouths to feed (this effect should also occur if
there is a fall in infant mortality). However, this effect will have a delayed positive effect as the
workforce population will be booming two decades later. Therefore we might expect a negative
coefficient here, since the variable is lagged for one period. From a research of Bloom & Canning
(2000) it can be shown that including the population growth will increase the explanatory power of
the regression. One important remark from the research of Barro & Lee (1994) is that the population
growth might be influenced by life expectancy. The data consists of the population growth numbers
from 1960-2013, obtained from The World Bank (2015). From the data it shows that there is a
general, slight decline in population growth for all countries (downward trend). China shows a steep
decline in population growth in 1962, which indicates the war of China with India. Furthermore,
Singapore is highly fluctuating whereas the rest is relatively comparable with each other.
Another effect that needs to be taken into account is the effect of young-aged people on
economic growth, the younger dependency ratio. This ratio works the same as the old dependency
ratio as it is calculated as the level of young-aged people divided by the workforce population
(people aged 0-14 / people aged 15-65). One might expect that the coefficient of the variable will be
16
negative since studies of Bloom & Williamson (1998), Lee et al. (2013) and others show that it will
negatively affect the economic growth. From the research of Barro & Lee (1994) it follows that an
increase in this share will affect the economic growth negatively in two channels. First, it will reduce
the per capita growth since the amount of non-working age people increases. Second, younger
people demand effort of working-age people as they need care. In this way working-age people will
have less time available to be productive to increase economic growth. The data is from the U.N.
(2015) for all 30 countries and it includes projections until 2045 for China. From the data it can be
seen that there seems to be a downward trend. One important remark here is that all the countries
experienced this. In graph 3 (in Appendix 2) we can see the projections of the young dependency
ratio made for China until 2045 using the U.N. database. The dependency ratio will hit its peak in
2020 whereas it will have its lowest point in 2035. These projections show that in the long-run (here
2045) the difference with the starting point is a decline of 1,3 over 35 years. I chose not to include
the growth rate of the youth dependency ratio since several studies showed that the growth rates
have no influence on the level of savings, which ultimately affects the growth of output per working
age person (Bloom et al., (2003); Kelley & Schmidt, (2005); Kögel, (2003)).
To account for the stability of a given government/country, a political instability variable is
included. Of course, there is no data available that consists of ‘political instability’ since this is hard to
measure. However, to estimate this effect I will differ from Barro (1991), who used ‘Number of
political assassinations per year per million of population’ and ‘Number of revolutions and coups per
year’ to proxy for this. Instead, I will introduce two dummy variables. The first variable indicates
whether there was at least one minor armed conflict, which is defined as a conflict with between 25
and 999 battle-related deaths in a given year for each country. If the battle-related deaths in a given
year were 1000 or higher, the second dummy variable (war dummy) will be at value one. All other
cases will give a value of 0 for both dummies. The war dummy is on the Sala-I-Martin (1997) list,
however the other dummy differs since it can separate another ‘middle’ value between a war and
non-war called the ‘minor armed conflict’ value. The research of Sala-I-Martin (1997) shows that the
war dummy has a negative effect on economic growth, therefore we might expect to find negative
coefficients here. The data for this variable is obtained from Themnér and Wallensteen (2014). The
data showed the battle-related events from 1946-2013 for all the countries in the world (with at least
25 deaths). I transformed the data in useful panel data, indicating for every country in my sample
whether there was a minor armed conflict, a war, or nothing at a given time.
Another factor that seems important is the amount of years that people are expected to live
in a given country, their life expectancy. This life expectancy variable is used to proxy for health as
according to Barro & Lee (1994) the human capital of a country can be estimated by using health and
education. The life expectancy variable is a combined variable for male & female, since females are
17
expected to live longer this might be relevant. The variable shows the amount of years that a
newborn would live if the mortality patterns remain the same throughout its life. It reflects the
overall mortality level of a population, and summarizes the mortality pattern that prevails across all
age groups in a given year. It is calculated in a period life table which reflects a snapshot of a
mortality pattern of a population at a given time. One might expect the life expectancy variable to
have a positive coefficient since the research of Kulish et al. (2006) showed that a higher life
expectancy will reduce the demand for health care and many might be able to work and contribute
to the economy longer. However, according to Barro & Lee (1994) the life expectancy might be
biased when including fertility and population growth as well. Greater life expectancy might increase
the survival rate of mothers, which might stimulate families to have more children (which increases
the fertility rate). However, it might also reduce the fertility rate as fewer births are needed to obtain
a desired amount of children reaching adulthood. Higher life expectancy might influence population
growth directly since people are expected to live longer, which increases total population (and thus
growth). The data is obtained from The World Bank (2015). From the data it can be seen that there is
an upward trend. All countries have experienced an increased life expectancy over time. However,
there are some countries which experienced a drawback, for example Malaysia (1965) and Sri Lanka
(1985). But after the drawback these countries also continued to increase their life expectancy
further. In 1960 the difference between the lowest life expectancy (38,456) from Nepal and the
highest life expectancy (73,550) from Norway is a little more than 35 years. This difference has been
reduced in 2013 with India being the lowest (66,456) and Japan being the highest (83,332). The
difference is now approximately 17 years (reduced by 18 years). One interesting booming period is
from China in 1965, which increased its life expectancy with approx. 14 years in a time period of 8
years (1965-1973).
To account for the effects of education on economic growth, one might think of the primary
school enrollment variable. This variable can be used to proxy for human capital based on the study
of Barro & Lee (1994). The primary school enrollment variable is on the list of significant explanatory
variables for economic growth by Sala-I-Martin (1997). Unfortunately, no complete dataset was
available from 1960-2013. Using the data from the World Bank (2015) would reduce my observations
from 1499 to 667, which is more than 50% of my data. Therefore I chose to use a different variable.
The study of Collins and Bosworth (1996) used the average schooling years as a measure of human
capital. This variable was calculated using seven rankings from no-schooling to postsecondary
schooling in combination with the adult’s estimation of the average years of schooling. The study of
Barro and Lee (1994) found a significant positive effect of secondary school enrollment on economic
growth. However, the research of Collins and Bosworth (1996) couldn’t find a significant coefficient
for the average years of schooling variable. For secondary school enrollment no significant coefficient
18
was found by Bloom & Sachs (1998). Therefore the expected effect of this variable is ambiguous.
They used a dataset constructed by Barro & Lee (2010) which contained data from 1960-1990 which
is now updated to a version of 1950-2010 data. This data contains the educational attainment of
people aged 15 years or older, both male and female combined. However, this data is available for 5-
year intervals (1950, 1955, 1960 etc.). To interpolate between the census years, I used a linear
approach to fill in the missing blanks. In Appendix 4 one can find a brief explanation how the linear
interpolation was performed. From the data it can be seen that there is an upward trend in average
years of schooling for the sample countries. Nepal experienced the lowest average years of schooling
with 0,13 years in 1960 and 4,64 years in 2013, whereas the USA experienced the highest with 9,17
years in 1960 and 13,37 years in 2013. One remarkable country here is Canada, as it experienced the
slowest growth in average years of schooling (increase of 0,58 years over 54 years). Another
remarkable country was Germany, which experienced a reduction period of 15 years (from 1960
onwards) before it started increasing.
Another factor that should be accounted for is the growth rate of the level of the working-
age population. However, no data was available for this variable but instead I obtained the share of
workforce of each country from 1960-2013. This share was calculated by taking all people aged 15-65
and divide them by the total population. To obtain the level of workforce in year t, I multiplied the
share of workforce in year t with the total population in year t for each country. However, to
calculate the growth rates of these levels the following adjustment had been made:
𝑌𝑡 =𝑋𝑡 − 𝑋𝑡−1
𝑋𝑡−1
Where Yt is the growth rate at time t and Xr is the level of workforce at time t (so Xt-1 is the share at
time t-1). In this way the difference between the current period and one period before is calculated
and divided by the period before. This should give us the growth rate of the level of the working-age
population. One might expect a positive coefficient here since the research of Bloom & Sachs (1998)
showed that when the size of working-age people increases relatively to the total population, the per
capita productive capacity of the economy expands. This expansion can either be a result of an
increase in employment or an increase in savings, which creates more potential for rapid growth in
an economy. The research of Bloom & Williamson (1998) also showed that when the working-age
population growth rate is higher than the population growth, the economic growth of the country
increases. The data for this variable was obtained from The World Bank (2015). The data shows that
the growth rates have roughly been the same for every country in the sample for the period 1960-
2013.
The fertility rate also seems to be affecting economic growth. The definition for this variable
is: ‘Total fertility rate represents the number of children that would be born to a woman if she were
19
to live to the end of her childbearing years and bear children in accordance with current age-specific
fertility rates’ (The World Bank, 2015). One might find a negative coefficient here since Bloom &
Williamson (1998) state that in case of high fertility there are more children to feed and raise which
costs work-time. The negative coefficient is also found in the research of Barro & Lee (1994). One
important remark is from Bloom et al. (2010), namely that the projections of the U.N. for the fertility
rates until 2050 show substantial declines which would stimulate the female labor-force
participation. For this variable I found two databases, the U.N. (2015) database and The World Bank
(2015) database. I chose to use The World Bank data instead of the U.N. data as the U.N. calculated
the average of each 5 year period based on different scenarios, whereas The World Bank provided
annual data interpolated from the United Nations data (which will not reduce my observations).
From the data we can see a clear downward pattern. The highest fertility rate in 1960 was in the
Philippines (7,148) which dropped over time, hitting 3,043 in 2013. The difference between countries
seems somewhat bigger in 1960 than in 2014. In 1960 the biggest difference was between the
Philippines and Japan (5,147) and in 2014 the biggest difference was between the Philippines and
South Korea (1,856). There seems to be a more common trend towards having fewer children.
The last interesting control variable is the government consumption. However, this variable
will not be included in the regression. The most complete data for this variable has been obtained
from The World Bank (2015). This data only covered the timeline from 1990-2013. In this way, all the
observations before 1990 would be lost (which is 30 years). This will reduce precision and therefore
this variable will be left out of the regression equations.
20
4. Methodology
From the ‘variables and data’ part it is clear that all data for each variable is panel data and
therefore a panel data analysis will be implemented. The old-dependency ratio is the most important
variable since this will be our indicator for population ageing. To estimate the impact of this variable
on economic growth, a cross-country regression will be done first. The effect of 10 variables on the
economic growth of 30 countries will be estimated through six equations. The abbreviations used in
this chapter are described in table 1 below.
Table 1: Short descriptions of the abbreviations used.
Abbreviations
GROWTHit-1 The GDP per capita growth rate for country i at time t-1.
RGDPit-1 The Real GDP per capita for country i at time t-1.
POPit-1 The population growth for country i at time t-1.
YOUNGit-1 The young dependency ratio for country i at time t-1.
OLDit-1 The old dependency for country i ratio at time t-1.
ARMCit Amount of battle-related deaths between 25 and 999 for country i at time t.
WARit Amount of battle-related deaths at 1000+ for country i at time t.
LIFEit-1 The life expectancy for country i at time t-1.
AVGSCHOOLit-1 The average years of schooling for people aged 15+ for country i at time t-1.
WORKGRit-1 The working-age population growth rate for country i at time t-1.
FERTit-1 The fertility ratio for country i at time t-1.
The methodology that will be used is a similar regression method as the research of Bloom &
Finlay (2009) where they used seven different regression equations. However, this research will only
capture four of these models since three models include geographic variables. As we account for
fixed-effects in this regression, the geographic variables aren’t included. Instead, I will add two more
regressions to account for the problem of Barro & Lee (1994) where they showed that both fertility
and population growth might be influenced by life expectancy. The complete model for this research
looks like this:
𝐺𝑅𝑂𝑊𝑇𝐻𝑡 = 𝛼 + 𝛽1𝑅𝐺𝐷𝑃𝑡−1 + β2𝑃𝑂𝑃𝑡−1 + 𝛽3𝑌𝑂𝑈𝑁𝐺𝑡−1 + 𝛽4𝑂𝐿𝐷𝑡−1 + 𝛽5𝐴𝑅𝑀𝐶𝑡
+ 𝛽6𝑊𝐴𝑅𝑡 + 𝛽7𝐿𝐼𝐹𝐸𝑡−1 + 𝛽8𝐴𝑉𝐺𝑆𝐶𝐻𝑂𝑂𝐿𝑡−1 + 𝛽9𝑊𝑂𝑅𝐾𝐺𝑅𝑡−1
+ 𝛽10𝐹𝐸𝑅𝑇𝑡−1 + 𝜀
All equations will be regressed using country-fixed effects to counter possible omitted variable bias
by taking the variables that are country-specific as constant over time (Wei and Hao, 2010).
According to Wei and Hao (2010), the use of panel data for variables counters the small sample bias
21
since the within-variation can be multiplied using time series. Using panel data also makes it possible
to use variables that vary mostly across countries and vary in time and provinces (government
expenditure for example). At last, using lagged variables allows us to counter endogeneity problems
since variables of one period ago are exogenous in the present.
The first model will consist of the real GDP per capita variable, war- and armed conflict
dummies, average years of education and life expectancy. This model is similar to the first model of
the research of Bloom & Finlay (2009) where they used a basic growth model. In the second model,
the population growth will be added to the basic growth model (Bloom & Finlay, 2009). This is similar
to a model proposed by The World Bank (2015). In this model The World Bank accounted for
demographic changes by adding population growth. The full model will be used in the third equation
including the old- and young dependency ratios, the fertility rate and the growth of the working-age
population. Bloom & Finlay (2009) also accounted for other demographic variables in their third
equation by including age differentials. The fourth equation will use the same model as the third
equation but will be regressed using IV-regression. Bloom & Finlay (2009) included the IV to counter
a possible reversed causality bias since the growth of the population and the working-age population
might affect the growth of the real GDP per capita. These variables will be estimated by lagging the
growth rates one period and including the fertility rate as instruments. To check whether the
instruments perform statistically well, a Wald-test will be performed. To test whether the
instruments are consistent, a Durbin-Wu-Hausman test will be used. After this regression, the
complete model will be estimated in the fifth equation including two interaction variables between
life expectancy and population growth/fertility rate. These will be added to see whether the latter
two are interacting with life expectancy. At last, the complete model minus the life expectancy
variable will be regressed using two-stage least-squares (Bloom & Finlay, 2009). The exact regression
equation can be seen in Table 3.
After performing the above regressions, the coefficient of the old-dependency ratio will be
used to make projections for the estimated effect of ageing on economic growth in China. The
projections equation will look like this:
𝐸𝐹𝐹𝐸𝐶𝑇𝑡 =< 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 >∗ 𝑂𝐿𝐷𝑡
The coefficient obtained from the regression will be filled in the ‘<estimated coefficient>’ fragment.
The data from the U.N. is forecasted in 5-year intervals. To obtain yearly estimates, I used the
DemProj program from the Futures Group website (which can be found at Futures Group (2015) in
the references) to estimate yearly projections based on the U.N. data. I chose to do this as the
projected estimates of the U.N. are based on the assumption that the one-child policy applies. But as
mentioned earlier, the one-child policy was relaxed in 2013. The DemProj program allows filling in
and making different projections for the old dependency ratio to account for this. One important
22
development is that Chinese families prefer not to have another child, since it’s more costly. This
situation is called the ‘low fertility’ situation, estimated by the DemProj program assuming that the
fertility rate will be around 1.3 children per woman throughout the period. There might also be a
high case scenario where more people choose to have children, assuming a fertility ratio of 1.8
children per woman. The last scenario will be the one where the relaxed one-child policy is
eliminated, assuming a fertility ratio of 2.3 children per woman (these values are the three different
scenario’s available at the DemProj program). The table of the data produced by the DemProj
program can be found in Appendix 3. After that, the projections from the DemProj program for the
old-dependency ratio until 2045 will be inserted in the above equation. In this way we only look at
the effect of the old-dependency ratio on the economic growth for the next 30 years which will
answer the main research question of this thesis.
23
5. Empirical Research Results and Interpretation
In this chapter I will describe the regression results. For this research it is useful to know
(since we’re using panel data) whether we should use country-fixed effects in our regression, since
earlier studies on the determinants of economic growth also used country-fixed effects when they
used panel data. Therefore a Hausman test will be performed first. After that, it is crucial to get an
unbiased estimate of the old-dependency ratio coefficient. Therefore several regressions will be
performed to find the most accurate estimate following the research of Bloom & Finlay (2009). After
doing the regressions, the obtained coefficient will be used to estimate predictions using the U.N.
data until 2045 of the old-dependency ratio.
5.1 Hausman Test
Before we start the regressions, it is useful to know whether we should take into account
country fixed effects or not. The country fixed effects are useful to assume with panel data because
in that case you assume that omitted variables that differ between countries, are taken as constants
over time. However, it might be the case that some omitted variables are constant between
countries but differ over time. When it is unclear what the case is, one might use ‘random-effects’.
The latter one might be more efficient since it will give better P-values as they are a more efficient
estimator. To determine what is best for our model, I did a Hausman test. The Hausman test
compares a more efficient model (our case: random-effects) with a less efficient model, which
provides more consistent results (fixed-effects). It will check whether the more efficient model also
still produces consistent coefficients. The null hypothesis for this test is formulated as follows: the
more efficient model produces coefficients which are just as consistent as the one produced by the
less efficient model. This means that when we find a significant p-vale (below .05), the null
hypothesis will be rejected. Hence, the more efficient model doesn’t produce coefficients with the
same consistency. The results are given in table 2.
The variables which have a ‘1’ behind their name mean that they are lagged for 1 period (t-
1). The bottom two variables are the interaction variables. POPLIFE is the variable which shows the
population growth times the life expectancy, whereas FERTLIFE is the variable of fertility times the
life expectancy. The ‘Prob>chi2’ shows the P-value for which the null-hypothesis is not rejected. Since
this value is below the 0.05 border (significance of 5%), the null-hypothesis is rejected. Hence, one
might assume that in our case the fixed-effects regression is preferred over a random-effects
regression since this will produce more consistent estimates.
24
Table 2: Hausman test results using the complete model.
Variable: (b) fixed (B) random Difference (b-B) S.E.
LRGDP1 -1,187056 -0,158884 -1,028172 0,450333
POP1 -1,601160 -1,641031 0,039871 0,044370
YOUNG1 -0,069651 -0,044184 -0,025467 0,012940
OLD1 -0,092608 -0,130283 0,037674 0,029361
ARMC -0,767605 -0,973281 0,205676 0,065883
WAR -1,591849 -1,721259 0,129410 0,077511
LIFE1 -0,321054 -0,245319 -0,075735 0,059962
AVGSCHOOL1 0,148990 -0,037892 0,186882 0,097569
WORKGR1 116,609300 118,794000 -2,184717 4,024967
FERT1 -4,604977 -3,979959 -0,625017 0,503434
POPLIFE -0,010113 -0,009448 -0,000665 0,000394
FERTLIFE 0,083157 0,072508 0,010649 0,007534
b = consistent under Ho and Ha B = inconsistent under Ha, efficient under Ho
Test: Ho: difference in coefficients not systematic chi2(10) = 29,75
Prob>chi2 = 0.0009
5.2 Regression results using fixed-effects
In this section, the results of the regression will be discussed and analyzed. The earlier
mentioned equations are regressed in the order in which they were presented. The first column in
table 3 shows equation 1, the second column shows equation 2 etc. until in the last column equation
6 is presented. All regressions are done using country fixed-effects as the Hausman test showed that
it was relevant to do so (see section 5.1).
The first column shows the basic growth model estimated using OLS. The real GDP per capita
term was lagged one period to be an exogenous estimator for the base year growth and to deal with
other biases from Barro & Lee (1994) mentioned earlier in Chapter 3. It shows a significant negative
coefficient which confirms the ‘convergence effect’ which was mentioned in the paper of Sala-I-
Martin (1997). Furthermore, the political variables also show a negative significant impact on
economic growth (war at 5% significance, armed conflict at 10%) which also confirms the findings in
the research of Sala-I-Martin (1997). The study of Barro & Lee (1994) explains that an instable
government has an adverse effect on property rights and therefore negatively influences investment
and economic growth. The life expectancy variable shows a positive significant effect on economic
growth. This also confirms the findings of Kulish et al. (2006) which showed that health costs per
person are lower when their life expectancy is larger and that people may contribute longer to the
economy which stimulates economic growth. At last, the average school variable shows a negative
25
significant coefficient which contradicts the findings of Barro & Lee (1994) which found a positive
coefficient at the secondary school enrollment. However, our variable is considered as the ‘average
years of schooling’ which consists of both primary and secondary school enrollment. For that reason,
comparing these results might not be sufficient enough to draw conclusions.
Table 3: Results using fixed-effect regressions, dependent variable: real GDP per capita growth.
VARIABLES\COLUMN 1 2 3 4 5 6
OLS OLS OLS IV OLS IV
LRGDP1 -1.813*** -2.113*** -2.091*** -0.189 -1.187** -0.0842
(0.391) (0.389) (0.481) (0.130) (0.499) (0.126)
ARMC -0.517* -0.606** -0.688** -1.088*** -0.768*** -1.124***
(0.301) (0.297) (0.285) (0.254) (0.281) (0.254)
WAR -1.033** -1.147** -1.422*** -1.787*** -1.592*** -1.800***
(0.487) (0.482) (0.465) (0.452) (0.460) (0.453)
LIFE1 0.223*** 0.206*** 0.104** 0.0907*** -0.321***
(0.0389) (0.0386) (0.0517) (0.0352) (0.0871)
AVGSCHOOL1 -0.343*** -0.390*** -0.00906 -0.0433 0.149 0.00148
(0.128) (0.127) (0.133) (0.0519) (0.134) (0.0490)
POP1
-1.070*** -1.986*** -2.777*** -1.601*** -2.495***
(0.176) (0.345) (0.379) (0.378) (0.353)
YOUNG1
-0.0430 -0.0275 -0.0697** -0.0169
(0.0278) (0.0219) (0.0276) (0.0215)
OLD1
-0.222*** -0.239*** -0.0926* -0.208***
(0.0442) (0.0347) (0.0492) (0.0317)
WORKGR1
109.7*** 141.8*** 116.6*** 140.8***
(28.37) (28.94) (28.03) (28.98)
FERT1
0.198 0.534 -4.605*** 0.0842
(0.359) (0.328) (0.854) (0.270)
POPLIFE
-0.0101***
(0.00370)
FERTLIFE
0.0832***
(0.0132)
Constant 5.371** 10.85*** 19.31*** 3.323 38.08*** 8.447***
(2.638) (2.754) (4.486) (2.355) (5.398) (1.222)
Observations 1,499 1,498 1,472 1,447 1,47 1,447
#Countries 30 30 30 30 30 30
R-squared 0.039 0.063 0.109 0.146 0.136 0.144
Wald test p-value
0.0000
0.0000
Wu-Hausman test p-value
0.4806
0.5280 Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.
In the second column the variable population growth was added. This variable shows a
significant negative coefficient which means that an increase of population growth would reduce
economic growth within a country. This confirms the findings of Bloom & Williamson (1998) which
26
stated that population growth would negatively affect the economic growth in the short run. There
might be a delayed positive effect two decades later when the younger-aged people enter the
working-age group. However, this variable is only lagged one period and therefore shows a negative
coefficient. In the third column, all demographic variables concerning age groups were added to the
equation. The variable of interest, the old-dependency ratio, shows a significant negative coefficient
which means that an increase of this variable (so an increase in elderly people relative to working-
age people) would negatively affect economic growth. This confirms earlier findings of Du & Wang
(2011), Bloom, Canning & Fink (2010), Lee et al. (2013) and Bloom & Williamson (1998) which are all
explained in chapter 3. However, the research of Bloom & Williamson (1998) stated that the
coefficient for the old-dependency ratio might be positive. This is not shown in the regression for any
scenario, as the coefficients are all negative and highly significant. Furthermore, the growth of the
working-age population positively affects economic growth which would confirm earlier findings of
Bloom & Sachs (1998) and Bloom & Williamson (1998). This also confirms the channel that an
increase in the old-dependency ratio (thus the elderly relative to working-age population) negatively
affects economic growth through the decrease of workforce compared to elderly, which is stated as
the problem of population ageing. At last, the young dependency ratio and fertility rate don’t seem
to have a significant impact on economic growth according to equation three. In the fourth column,
the population growth and working-age population growth were estimated using their lags and the
fertility rate as instruments through an IV-regression. This was done to eliminate a possible reversed
causality of both growth variables on the real GDP per capita variable, since these variables might
influence the growth of the real GDP per capita variable (Bloom & Finlay, 2009). The IV regression
increases the R² value from 0.109 to 0.146 using the same equation, which means that a higher part
of the variance in the data used for the dependent variable is explained by the IV model. By
estimating the variables using instruments, both variables increased in impact and significance which
might confirm the reversed causality problem discussed by Bloom & Finlay (2009). The Wald test
shows that the coefficients from the variables used in this model statistically differ from zero, which
means that they are relevant. Next, the Durbin-Wu-Hausman test evaluates whether and estimator
(in our case the instruments) is consistent when compared to another estimator that is less efficient
but known to be consistent. The p-value is not below the 10% significance border which means that
the instruments which were used are consistent. One remark for equation three and four is that the
average years of schooling variable now shows the same result as the research of Collins and
Bosworth (1996), where they found a positive but insignificant coefficient for this variable. In the
fifth equation interaction variables were added to see whether the life expectancy variable was
interacting with fertility and population growth. Both variables significantly interact with life
expectancy which confirms the problem of a biased life expectancy variable according to Barro & Lee
27
(1994) when including population growth, fertility rate and life expectancy in the same equation
(explained earlier in chapter 3). Therefore in the last equation the life expectancy variable was
excluded from the equation and again the IV-regression was done. The Wald test and the Wu-
Hausman test show the same results when compared to equation four. From the last equation we
can see that the old-dependency ratio is still significantly negative (-0.208) which shows that
population ageing negatively affects economic growth. So this means that when the amount of
elderly increases with 1% relatively to the working-age population, the economic growth would be
reduced with a little more than 0.2%. This is interesting for our research since the old-dependency
ratio keeps increasing for at least the next 30 years in China, which means that economic growth in
China would significantly decrease in this period.
5.3 Estimating future effects of the ageing problem in China
From table 3, one can see the significant estimated effect of the old dependency ratio on
economic growth. In the sixth equation, the old-dependency coefficient was -0.208. I used this
equation since this is the most complete IV-regression without the life expectancy bias from Barro &
Lee (1994). In this chapter I will use the coefficient of this variable to estimate what will happen to
the economic growth in China for the next 30 years, by multiplying the coefficient with the projected
old-dependency ratio for each year. This can be illustrated in this formula:
𝐸𝐹𝐹𝐸𝐶𝑇𝑡 = −0.208 ∗ 𝑂𝐿𝐷𝑡
In the formula, EFFECTt shows the effect that the old-dependency has on economic growth in that
year, according to the old-dependency ratio in that year. So from year to year one can see the
pattern illustrated in graph 5 which shows the estimated effect of population ageing on the
economic growth in China for the next 30 years.
Graph 6: The effect of population ageing on economic growth (in %) in China for the next 30 days,
Data source: graph based on the regression and the projections of the DemProj program.
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
20
14
20
16
20
18
20
20
20
22
20
24
20
26
20
28
20
30
20
32
20
34
20
36
20
38
20
40
20
42
20
44
Low fert.
High fert.
Elimination
28
In the graph, OLDt-1 starts at t=2014 such that the first estimate for EFFECTt will be at t=2014. One
can interpret the percentages in the graph as ‘the amount of reduction of the economic growth
percentage due to the population ageing effect’. The graph shows three different scenarios. From
the graph it can be seen that the scenarios initially align with each other as differences are small until
2030 and then start to deviate. This might be explained as the total population is expected to grow
until 2030 where it will hit its peak level and after that will start to decline (United Nations, 2015).
The first scenario is the case when the relaxed one-child policy would be completely abolished (green
line), assuming a fertility ratio of 2.3. This situation would result in an estimated reduction of 7.31%
economic growth over 30 years for China. However, if they choose to keep the relaxed one-child
policy, economic growth would reduce even more. In the ‘high fertility’ scenario (assuming a fertility
rate of 1.8), economic growth is estimated to be reduced with 7.85%. In the current scenario where
many people choose not to have another child as it might be more costly (the low fertility scenario,
1.3), the economic growth is estimated to be reduced with 8.49% over 30 years for China. So
depending on what the government of China will do concerning the relaxed one-child policy, the
economic growth is estimated to suffer between 7.31% and 8.49% over the next 30 years due to the
population ageing problem. The numbers are given in table 4.
Table 4: Effect of population ageing on future GDP per capita growth in China,
YEAR 2020 2025 2030 2035 2040 2045
Elimination -3,55% -4,16% -4,90% -6,18% -7,59% -8,49%
High fertility -3,55% -4,16% -4.89% -6,04% -7,23% -7,85%
Low fertility -3,55% -4,16% -4,88% -5,91% -6,89% -7,31%
Data source: table based on the regression result and projections from the DemProj program.
29
6. Conclusion
Since the one-child policy was implemented by the government of China in 1979, the supply
of younger people has been reduced. People were allowed to have a second child only this was
conditional, namely that both parents were single child. The policy has been relaxed in 2013 by
stating that parents are allowed to have a second child when only one of the parents was a single
child. The decline of the supply of younger people implies that the share of the elderly is increasing.
Until 2045, the U.N. estimates that the share of elderly in the total population of China will almost
triple. Policy makers expect that the population ageing might have adverse effects on economic
growth (Peng, 2008). The aim of this research was to estimate the effect of the ageing problem on
economic growth in China. To estimate the effect, a regression has been performed. The coefficient
estimate has then been used in combination with projections of the U.N. (2015) for the old
dependency ratio using the DemProj program to determine the impact of population ageing on
economic growth.
Following the regression methodology of Bloom & Finlay (2009), the results show a negative
and significant coefficient for the old-dependency variable. This confirms earlier studies that an
increase in the share of elderly people in a total population has a negative effect on economic growth
for a given country. There are several explanations for this negative coefficient, namely that there
are more elderly to support (Bloom & Williamson, 1998), work-related consumption goes down (Du
& Wang, 2011) and it will thus slow economic growth (Bloom, Canning & Fink, 2010; Lee et al., 2013).
The study of Bloom & Williamson (1998) also stated that the coefficient might be positive since
elderly can teach the young, work part-time and save more. The regressions however showed a
negative significant impact of this variable in every equation and therefore do not align with this
statement from Bloom & Williamson (1998). The continuous increase in the old-dependency ratio
(amount of elderly relative to workforce) in China is estimated to reduce economic growth between
7.31% and 8.49% over the next 30 years, depending on what the government decides to do with the
relaxed one-child policy. If it decides to abolish the policy, the population ageing problem will be
estimated to reduce economic growth with 7.31% over 30 years. However, if the government decides
to keep the relaxed one-child policy, the economic growth is estimated to be reduced between
7.85% and 8.49% (depending on fertility). One interesting finding is that the variable life expectancy
interacts with population growth and fertility rate, which confirms the interaction found in the
research of (Barro & Lee, 1994). The reversed causality bias from (Bloom & Finlay, 2009) which stated
that the growth of population and workforce might influence the real GDP per capita variable has
also been confirmed.
30
Future research on this subject might try to obtain more complete data for the education
variable and government consumption. One might try to experiment with other explanatory
variables for economic growth such as capital stock, trade openness, domestic investment and total
population to get a more accurate estimate for the old-dependency ratio coefficient, which might
create different projections.
31
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34
Appendices
Appendix 1: Descriptive Statistics
Table 5: Descriptive statistics summary
Variable Obs Mean Std. Dev. Min Max
GROWTH 1499 2,91 3,68 -26,56 17,56
LRGDP1 1528 8,57 1,78 4,42 11,12
POP1 1618 1,26 0,95 -1,69 6,36
YOUNG1 1619 46,24 19,51 19,90 96,12
OLD1 1619 14,27 7,20 3,74 40,53
ARMC 1620 0,17 0,37 0,00 1,00
WAR 1620 0,04 0,20 0,00 1,00
LIFE1 1619 69,86 8,52 38,46 83,33
AVGSCHOOL1 1619 6,97 2,96 0,13 13,37
WORKGR1 1589 0,01 0,01 -0,03 0,06
FERT1 1618 2,82 1,50 1,08 7,15 Data source: constructed by author based on the complete dataset.
Appendix 2: Projections
Graph 3: Young dependency ratio projections for China.
Data source: graph constructed by author based on data from the United Nations (2015).
Graph 4: Old dependency ratio projections for China.
Data source: graph constructed by author based on data from the United Nations (2015).
20,0
21,0
22,0
23,0
24,0
25,0
26,0
27,0
2010 2015 2020 2025 2030 2035 2040 2045
Young dependency ratio
0,0
10,0
20,0
30,0
40,0
2010 2015 2020 2025 2030 2035 2040 2045
Old dependency ratio
35
Appendix 3: DemProj projections
Graph 5: DemProj projections for the old-dependency ratio for China.
Data source: graph constructed by author based on data from the United Nations (2015) and DemProj.
Table 6: Projections from DemProj on the old-dependency ratio showed for each 5-years.
PROJECTIONS 2015 2020 2025 2030 2035 2040 2045
Low fert. 15,0739 17,6521 20,5967 24,6436 31,1416 37,5392 41,4744
High fert. 15,0739 17,6521 20,5967 24,4928 30,2922 35,5267 38,1544
Elimination 15,0739 17,6521 20,5967 24,3438 29,4879 33,7190 35,3257
Appendix 4: Linear Polarisation
To do this, I used the following formula for each 5-year interval:
𝑌𝑡 = (𝑋𝑡+5 − 𝑋𝑡) ∗ (𝑣
5) + 𝑋𝑡
The actual value in a given year (Yt) will be estimated by first taking the difference between two 5-
year intervals Xt+5 and Xt (say 1960 and 1965). The difference will be split evenly over the time
periods by multiplying it with the (v/5) part, where v will have the value 1 if it is 1961, value 2 if it is
1962 etc. so eventually you will end up with v = 5 (so, 1965). The (v/5) part will equal 1 and you will
remain with Xt+5 - Xt + Xt = Xt+5. Hence, the formula is correct. The data, however, only consists of
observations until 2010. To retrieve the last three years (to get until 2013), I used the following
formula:
𝑌𝑡 = (𝑋𝑡−1 − 𝑋𝑡−2) + 𝑋𝑡−1
So let’s say I wanted to retrieve the data for a given country in 2011, the difference between the
values of 2010 (= Xt-1) and 2009 (=Xt-2) will be calculated and will be added to the last year (Xt-1) to
estimate the expectancy for the next year Yt (2011).
0
5
10
15
20
25
30
35
40
45
Low fert.
High fert.
Elimination
36
Appendix 5: Data description
Table 7: Variables, definitions and the source of the data for each variable.
Variable Variable description Source
GDP per capita growth
rate
The GDP per capita percentage growth rate
based on constant local currency, aggregates are
based on constant 2005 U.S. dollars.
The World Bank
Old-dependency ratio The dependency ratio measures the % of
dependent people (not of working age) / number
of people of working age (economically active).
The World Bank,
projections from
the U.N. database
and DemProj
Initial Real GDP per capita The gross domestic product divided by the
midyear population level using 2005 as a base
year assuming a constant dollar exchange rate.
The World Bank
Population growth The exponential growth rate in year t of the
midyear population level from time t-1 to time t,
given as a percentage.
The World Bank
Young dependency ratio The level of young-aged people divided by the
workforce population (people aged 0-14 / people
aged 15-65).
The World Bank,
projections from
the U.N. database
and DemProj
Political instability Two dummies, defined as a conflict with between
25 and 999 battle-related deaths in a given year
for each country and for 1000 or higher battle-
related death in a given year for each country.
Themnér and
Wallensteen
Life expectancy The amount of years that a newborn would live if
the mortality patterns remain the same
throughout its life, male and female combined.
The World Bank
Average years of schooling An allocation of each country's population
among seven schooling levels (ranging from
no schooling-illiterate to completed
postsecondary schooling) and an estimate of
average years of schooling of the adult
population constructed from the categorical
data.
Collins and
Bosworth
37
Working-age population
growth rate
The growth rate of the level of the working-age
population (aged 15-65).
The World Bank
Fertility rate Representation of the number of children that
would be born to a woman if she were to live to
the end of her childbearing years and bear
children in accordance with current age-specific
fertility rates.
The World Bank
Table 8: Countries used for the regression.
Countries
Argentinia France Malaysia Russia UK
Australia Germany Nepal Singapore USA
Bangladesh India Norway South Korea
Belgium Indonesia Pakistan Spain
Brazil Ireland Philippines Sri Lanka
Canada Italy Poland Sweden
China Japan Portugal Thailand
