Lund University
Master in Economic Demography
Department of Economic History
Demographic Dividend in East and South Asia,
The Economic Impact of Changes Age Structure in Population.
Md. Mazedur Rahman Khan
Master’s Thesis
27 May 2010
Supervisor:
Martin Dribe
EKHR 01
Master’s Thesis (15 Credits ECTS)
Spring 2010
Abstract.
The demographic transition of declining of mortality and fertility, causes changes in age
structure of the population, provide countries a generation that is proportionately bigger than
the predecessors and their successors. A greater share of population that is in the working
age, gives the opportunities of economic growth in a country is called demographic dividend.
The bulk working age population is expected to faster economic growth, boost labor supply,
increase physical capital and expand human development due to transitional favorable
dependency ratio. The aim of the paper is to examine, the nature of the effects of changes age
structure on the Gross Domestic Products in the pre dividend period and dividend periods of
the ten countries of East and South Asia. The changes age structure directly associated with
the labor force and there is a good association between labor supply (support ratio) and
human capital as well as physical capital in our data. We have evaluated the impact of these
three mechanisms on the economic growth of the countries of the East and South Asia, 19602006.The results show that the effect of labor supply (support ratio), human capital (life
expectation and schooling) and physical capital (total trade) is significant and positive on the
GDP per capita in the demographic dividend periods and pre dividend period. The results
also show that the effect of changes age structure on the GDP is higher in the demographic
dividend periods than pre dividend period.
Key words: changes of age structure, demographic dividend, growth of GDP per capita, East
and South Asia.
Acknowledgement.
I would like to give special thanks to my supervisor Dr. Martin Dribe from the Department of
Economic History, Lund University, whose suggestions and encouragement highly motivated
me to write the Master Thesis.
I would also like to express my deep appreciation to my mother Syadunnesa, my wife Tuhina
and my daughter Mohsina who always have been inspired me during my study in Sweden.
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Table of Contents
Page
Abstracts
02
Introduction
04
1.1 Research Problem
05
1.2 Aim and Scope
06
1.3 Outline of the Thesis
06
2.
08
1.
Background
2.1 The Demographic Transition
08-12
2.2 The Support Ratio and Dependency Ratio
13-14
2.4 The Demographic Dividend
15-20
2.5 Theoretical Background
20-21
2.6 The Hypothesis
21-22
3.
22
Data
3.1 Sources of Data
22
3.2 Sample
22-23
4.
23
Methodology
4.1 Statistical Model
23-25
4.2 Definition of Variables
25-26
5.
27
Empirical Analysis
5.1 Statistical Results
27-33
5.2 Discussion
39-41
6.
Conclusions
41
References
42-43
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1. Introduction.
1.1 Research Problem:
The demographic dividend has been known as a window of opportunity for economic
development. It is the optimistic concern of demography. There are many success stories of
economic development in the world, have been driven partly due to a rising share of working
age people in the population. Any human population consist of three broad age groups: (1)
children or young (ages 0-14) who are mainly consumers and economically depend on prime
age adults,(2) those of working age (ages 15-64),whose efforts are primarily responsible for
economic production, (3) the elderly (ages 65 and older ),who are economically dependent on
prime age adults.1The consequences of mainly declining fertility and reduced mortality among
the young, change the structure of the population, the young makes the generation bigger than
its predecessors and following decline in fertility makes it bigger also than its successors. 2
When these young enter in the working age, the greater share of population that is in the
working age gives the opportunity of demographic dividend. According to Andrew Mason,
“The demographic dividend refers to a one-time feature of the demographic transition.
Fertility decline produces a period during which the working-age population grows much
more rapidly than the child population. Thus, a larger share of the population becomes
concentrated in the highly productive working ages.”
It is the first demographic dividend where decreasing young dependents and increasing
working age population create opportunity for rapid economic growth. When this great share
of working age population enters the age of retirement by declining share of working age
population and increasing the share of older age population, it is the beginning of second
demographic dividend. The first demographic dividend creates an opportunity for economic
development through high rates of savings, investment and it gives more favorable condition
for human resource investment.
The relation between demographic variables and the economy is not deterministic rather the
economic outcome from demographic change is policy dependent (Andrew Mason, July,
2005).The demographic dividend does not automatically ensure the economic growth of a
country. It mainly depends on the activities of the respective countries before and during
demographic transition period. There are various factors that are important to maximize the
opportunity of demographic dividend. For example the development of strong institutions,
effective development of policies that encourage research, innovation and savings, efficient
allocation of labor, business friendly capital markets, strengthening of the global trading
system, quality educational and health care systems, rapid growth in industrial and
manufacturing employment, investment in human resources, sound political environment and
the elimination of gender bias. While the first demographic dividend gives the opportunity to
accumulate assets by extending period of retirement, high savings and lowering the burden of
youth dependency, it is a good opportunity to deepening human and physical capital in the
period of second demographic dividend as well as investing additional assets domestically or
Mason, A. and Feeney, G.(2001) “Population in East Asia”, Population change and economic development in
East Asia, challenges met, opportunities seized, Mason, A. ed. Stanford University Press,P-79.
1
Lee, Ronald. (2003) “Demographic Transition: Three centuries of fundamental change”, Journal of Economic
Prospect, vol. 17(4).P- 182.
2
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internationally to boost national income.3 Thus the first demographic dividend produces a
transitory bonus, and the second demographic dividend transforms that bonus into greater
assets thus sustainable development. It depends on how effective the policies have been
implemented. Thus the demographic dividend period is a window of opportunity rather than
a guarantee of economic development. The dividends are sequential, the first dividend begins
first and comes to an end, and the second dividend begins somewhat later and continues
indefinitely. They certainly overlap. The first and second dividends both have positive effect
on economy.4
The countries of East Asia have utilized the opportunity of first demographic dividend
successfully. The countries were poor in 1960, income of Japan was well below than United
States and other Western countries but higher than the rest of the countries of East Asia. The
countries were largely agrarian and traditional; there were no promising prospects of
economic growth. The political and economic institutions were weak, rates of savings and
investment were low but one of the bright characteristics was relatively high level of literacy.
From 1960 to 1990 the economic success of the region was remarkable; Japan became the
world’s second largest economy. The per capita income in Singapore exceeded the United
Kingdom, Canada, Italy, and Australia. The standards of living had greatly improved in
South Korea, Taiwan, Thailand and Indonesia.5 On the other hand the countries of South Asia
are about to pass through the favorable stages of their demographic transition. The high youth
dependency rates is decreasing in South Asian countries, they might have chance to utilize
the opportunity of demographic dividend.
The changes age structure deliver demographic dividend through the three main factors of
economic growth, labor supply, physical capital and human capital (Andrew Mason,
2002).The direct effects of changes of age structure is labor supply. We have calculated labor
supply by support ratio; it is the ratio of working age population to dependent population. The
physical capital of an economy influenced by many factors, the changes age structure is one
of them. There are association between age structure variable and physical capital variables in
our data. The correlation between support ratio and total trade (physical capital) is 0.50 and
the correlation between support ratio and investment is 0.62.There are many factors that are
associated with human capital ,the changes of age structure is one of among them. The
correlation between support ratio and life expectancy (human capital) is 0.81 and the
correlation between support ratio and years of schooling (human capital) is 0.70. Thus we can
examine the physical capital variables and human capital variables as the product of changes
age structure.
The study countries are five from East Asia namely Japan, Singapore, South Korea, Indonesia
and Thailand and five countries from South Asia namely India, Bangladesh, Pakistan, Sri
Lanka and Nepal.
Mason, A. (2001) “Population Change and Economic Development in East Asia, Challenges met,
opportunities seized”, Mason, A. ed. Stanford University Press,P-4-5.
3
Lee, R. and Mason, A. “What is the demographic dividend?” Finance and Development, A magazine of the
International Monitory Fund , Sep 2006,vol-43(3).
4
Mason, A. (2001) “Population Change and Economic Development in East Asia, Challenges met,
opportunities seized”, Mason, A. ed. Stanford University Press, P-6-7.
5
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1.2 Aim and Scope:
The aim of this paper is to investigate the nature of the effects of changes of age structure of
population on the economic growth in the pre demographic dividend period and the
demographic dividend periods of the countries of East and South Asia. The analysis will
focus on labor supply, physical capital and human capital as determinants of economic
growth. As changes of age distribution of population, direct effect the labor supply and
indirectly affect the physical and human capital. The focus of this paper is to estimate how
the human capital, physical capital and labor supply factors impact on economic growth
between the pre demographic dividend period, demographic dividend periods. We will divide
the total study period into three distinct time periods for each country such as pre
demographic dividend period, first demographic dividend period and second demographic
period. The pre dividend and dividend periods vary from one country to another and it
depends on the dynamics of support ratio. We will also divide the total study period into four
time periods such as time period1960-1970, period 1971-1980, period 1981-1990 and period
1991-2006 to compare the dividend periods with time trend.
The causes of demographic dividend are the combined effect of declining fertility and
mortality. We will explain this process as a background that how fertility transition and
mortality transition produce boom generation and change age structure of population to the
favorable for economic growth. We will not investigate the process empirically. We will
investigate empirically the impact of the human capital, physical capital and labor supply
factors on the GDP per capita. We have considered the health variable life expectancy and
education variable average years of schooling as human capital and total trade (openness) and
investment as physical capital and finally support ratio as labor supply. The data was
available from 1960 to 2006. This time frame of the data may not cover the three study
periods because some countries first dividend period begins earlier than the study time and
some countries first dividend periods end later than the study time period. But all the study
countries begin the first dividend within the time frame of our study and most of them
continuing it after 2006.Hence our main focus on the study will be pre dividend period and
first dividend period. We will also examine the impact of second dividend period, though the
study data range will cover very few countries that experienced second dividend.
1.3 Outline of the Thesis:
In chapter 1 the background of the research with a brief introduction of the research problem
will be discussed to understand the demographic dividend. The aim and scope of the paper
will also be explained in this chapter. In chapter 2 the previous research on demographic
dividend and theories behind the demographic dividend and hypothesis of the study will be
discussed. In addition the prerequisites of taking advantage of the demographic dividend and
examples of some countries of the world that benefitted from the demographic dividend will
be discussed. Finally theoretical foundation of the impact of the changes age structure on
economic development will be presented in this chapter. The procedure of data collection and
the sources will be discussed in chapter 3.Chapter 4 defines the regression variables and
statistical model while chapter 5 presents the statistical results and discussion of the outcomes
of the empirical analysis related to hypothesis and theoretical considerations. Chapter 6 ends
the thesis with a conclusion.
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2. Background.
This chapter, we will present the previous research on demographic dividend and theories
behind the demographic dividend and other issues related to the hypothesis from various
sources of literature. The prerequisites of taking advantage of the demographic dividend will
also be discussed. Examples of some countries of the world will be discussed that benefitted
from the demographic dividend. Finally theoretical foundation of the impact of the changes
age structure on economic growth will be presented in this chapter.
2.1 The Demographic transition.
The demographic transition is a set of interrelated transitions. The mortality usually ( but not
always) begins to decline in the first phase of demographic transition while fertility remains
high. In the first phase of demographic transition mortality declines mostly among infant and
children, its result is bulge in the young age of population and increase young dependency
ratio. The decline in mortality and the decline in fertility are not synchronized; usually
fertility begins to decline some times after mortality (exceptional is France, fertility transition
begins without a decline in mortality)6.
The lag between the two causes population growth. This growth at the beginning of the
demographic transition creates a boom generation; this generation changes the shape of the
age structure of the population. Bloom and Canning (2003) explain the growth of population
by the following graphs.
The boom generation is unique as fertility decline and mortality decline the result is bulge in
the age structure. The result in age structure of population has changed with a bulge in the
early age, the cohort of this large numbers of children in the age structure is the prime asset
6
Weeks, J. R. (2008) “Population, An Introduction to Concepts and Issues” Tenth Edition, P-99.
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of a nation and they may contribute to unprecedented economic development. First, there are
many young people, who need proper food, cloths, houses, health care and education.
The bulge wave moves through the population in their life cycle and produce a large working
age population. This working age population will contribute in the growth of economy.
Finally they will be elderly people and dependent again.
The initial spurt of population growth happened between the beginning of the mortality
decline and end of the fertility decline. Interesting is when the boom generation becomes
adult, it creates its own echo, another new boom generation. They produce further boom, it is
the population momentum, and its effects will be felt perhaps 50 to 100 years before the
population age structure settled down. When the age structure remains constant the effect of
population growth is neutral but when the proportion of workers rises or falls make the
opportunities for economic growth.7
The systematic change in the age structure is the integral part of demographic transition and
that continue long time after other rates have stabilized. The case of India as an example can
give explanation of shifts in age distribution. The pre transitional total fertility rate and life
expectancy of India was 6 births per woman and 25 years respectively (panel A and B). The
mortality decline begins 1900 but it leads fertility decline after 50 years. The fertility
transition of India is slow relative to East Asia but similar to Latin America’s. The population
growth rate rose from less than 0.5 percent in 1900 to more than 2 percent per year by 1950.
The result is bulge population and total population quadrupled in the twentieth century and
will increase another 60 percent in twenty first century and population leveling out to near
zero by 2100 (panel C and D).
7
Bloom, E, D. and Canning, D. et al (2003) “The Demographic Dividend: A New Perspective on Economic
Consequences of population Change”, Santa Monica, RAND, P-30-34.
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The shifts in age distribution can be seen in the dependency ratios. In the first phase of the
transition, when the mortality begins to decline but fertility remains high, mortality declined
most at young ages, increase the proportion of children in the population and raising child
dependency ratio. The mortality decline makes populations majority of younger’s that can
last many decades, for India the period was about 70 years. This stage of demographic
transition is the foundation of demographic dividend. Family find themselves with increasing
numbers of surviving children. Both family and governments may struggle to fight for high
number of children. The nature of benefits from demographic dividend depends on the
performance of this stage with the high number of children by the respective countries.
Source: Lee, Ronald.(2003“Demographic Transition: Three centuries of fundamental change”, Journal of
Economic Prospect,vol.17(4),P -181.
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The second phase as fertility decline the working age population grows faster than population
as a whole, thus the total dependency ratio decline. The rapidly growing a relatively large
share of working age population contribute economic growth is the demographic dividend or
gift or bonus. In India bonus occurs between 1970 and 2015. In third phase increasing
longevity leads to rapid increase elderly and low fertility slows the working age population
causes the old dependency ratio raises rapidly, thus total dependency ratio increase. It is the
second demographic dividend. In India this stage occurs about between 2015 and 2060 (Lee,
Ronald. 2003).
The shift of age distribution in the process of demographic transition is not automatic. There
are various pre conditions to have the process favorable to economic growth. The decline of
mortality is the initial steps of changes age structure. The beginning of the classical
demographic transition starts with mortality decline around 1800 in northwest Europe (Lee,
Ronald. 2003). The decline of the mortality of the world began early twentieth century and
decline dramatically after the Second World War. The causes of first stage of mortality
decline are control of contagious and infectious diseases. These diseases are dangerous
because they spread by air or water.
The invention of smallpox vaccine and preventive medicine in the late eighteenth century
started the declining mortality in Europe. Then public health, quarantine measures, improved
personal hygiene and germ theory of disease becomes more widely accepted. Improvement in
nutrition played an important role in the early stage of demographic transition to increasing
life expectancy. The famine mortality was reduced by improvement in storage and
transportation for regional and international food markets. The people could provide better
food to the children due to increasing income. Healthy people with stronger organs can better
capable to prevent disease. The high income countries of the world reduced mortality sharply
due to reduction of infectious disease and increases nutritious food to the young. Many low
income countries did not begin the mortality transition until twentieth century but they gain in
life expectancy quite rapidly (Lee, Ronald. 2003).
The life expectancy of India rose from 24 years in 1920 to 62 years in 2000 and in China rose
from 41 years in 1950-1955 to 70 years in 1995-1999.8 The policy of the government is very
important to harness the demographic dividend of a country. The magnitude of the benefit of
the demographic dividend mainly depends on the policy of the respective country. In the
second demographic dividend period although have low savings rate and labor force growing
slowly, capital/labor ratio may rise.
This pattern stimulates growth in labor productivity due to capital deepening. 9 Thus, the
changes of the age structure in the population promoting economy of a society in many ways.
Lee, Ronald. (2003) “Demographic Transition: Three centuries of fundamental change”, Journal of Economic
Prospect, vol. 17(4),P 170-173.
9
Lee, Ronald. (2003) “Demographic Transition: Three centuries of fundamental change”, Journal of Economic
Prospect, vol. 17(4), P 182-183.
8
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Source: Mason, A. and W.Feng, (200)“Demographic Dividend and Prospects for Economic Development in
China”, Paper presented in the United Nations Expert Group Meeting on Social and Economic Implications of
Changing Population Age Structures, Mexico City, August 31-September 2, 2005,P-11.
China has witnessed rapid demographic transition since the mid twentieth century. The speed
of demographic transition in China is the fastest in the world. Within less than 40 years China
finished its demographic transition. The changes in population age structure have allowed
China to yield its demographic dividend since the mid 1960s. A productive population age
structure with a rich supply of labor force has contributed to 15-25 percent of economic
growth in the reform period of China.10
The changing age structure has the potential to yield benefits to a country for two reasons.
First it lowers the dependency ratio which means that more resources can be invested in the
economy. Second, the increase in life expectancy improves the savings behavior population,
thus influence positively on income levels. The aging process begins at the end of the
demographic transition more specifically sustained levels of low fertility rates and high
longevity increase the proportion of elderly in the population. Population aging can increase
the amount of savings because the elderly have to finance their longer inactive lives.
Fang, C. and Dewen, W. “Demographic Transition and Economic Growth in China”, Institute of Population
and Labor Economics, Chinese Academy of Social Sciences, Beijing, China ,P-1.
10
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It is the second demographic dividend and is related to accumulation of wealth that arises in
response to population aging. The magnitude of these effects depends on the how this wealth
is created. If the elderly rely on large transfer from younger generations, private or public
sources, it will mean a lower increase in capital accumulation. If the elderly rely on large
transfer from their own earnings in the previous savings, it will mean a higher increase in
capital accumulation.11
Spain is an example of rapid growing aged population, not only the aging process started later
but also the baby boom in Spain was ten years later (around the sixties) than the rest of the
occidental countries. In 1975, last cohorts of baby boomers were being born and in 1970,
Spain had the second highest fertility in Europe. After quite dramatic decline in fertility, now
a day having one of the lowest fertility rates. It reached 1.16 births per women in 1996.Spain
has one of the highest life expectancy in the world for a woman in 2005 and it was 83.5
years. The combination of fast fertility decline and higher life expectancy causes a rapid
aging process. The proportion of population aged 65 and over to jump from 7 to 14, Spain
took only 45 years, where as in France it took 115 years.12
Both the developed and developing countries have been experiencing substantial change in
their age distribution. The developing countries are experiencing late demographic transition
where as the developed countries experienced early demographic transition. The countries
those experienced early demographic transition they have experienced early first
demographic dividend and early second demographic dividend. Most developed countries are
about to cross the first demographic dividend and experiencing second demographic
dividend. On the other hand developing countries are starting and some countries started the
first demographic dividend. The timing of the changes of age structure varies from country to
country and the nature of behaves of age structure is different for that reason the benefits
from the changing age distribution varies from country to country.
The economic performance of working age population is mainly depends on their skills,
physical and mental capability, environment, living standards. Usually an experienced person
contribute more than an inexperienced person but it has a limit according to increase of age.
Thus changes of age structure of population are an important factor of consideration for the
economic growth.
2.2 Support and dependency ratio dynamics.
The support ratio is the relationship between producers and consumers within an economy.
There are age specific differences among consumers and producers. The population of
different age group are not demand the same consumptions and the people of all working
group are not same level of producers. The young, adults and elderly have different weight as
effective consumers. The productivity level of producer depends on the production
characteristics of the producer. For example, a highly skilled producer always produce higher
than a non skilled producer. The support ratio is reciprocal of the total dependency ratio.The
real support ratio considers the ratio of economically active workers compared to inactive.
The effective support ratio is not considering just age distribution but it considers the people
C, Patxotan. And Renitaria, E.et al “The Impact of Changes in Population Age Structure on the Economic
Growth of Spain”, XXVI IUSSP 27 Sept-2 October ,P-2.
12
C, Patxotan. And Renitaria, E.et al “The Impact of Changes in Population Age Structure on the Economic
Growth of Spain”, XXVI IUSSP 27 Sept-2 October,P-9.
11
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are economically active or not. For example, many people aged 15-65 are effectively
economically inactive such as students, people on sickness and disable, long term
unemployed ,early retirement and mothers (or fathers) looking after children at home.
Similarly some people over 65 may still be working. The support ratio is important because
economically active people may pay much more income tax, corporation tax, more sales and
VAT taxes but the economically inactive below 16 and over 65 also tend to be bigger
recipients of government expenditure for education, pensions and health care. The decrease in
the support ratio (increase in the total dependency ratio) can cause fiscal problems for the
government. Old people require more health care spending than young that means the people
over 65 require higher government spending than children under 15. It should have to keep in
mind to consider more weight for elderly than children. When dependency ration is
increasing to keep the economy balance there may be some solutions, rising retirement age in
line with longer life spans, encouraging immigration of people in early 20s and 30s and
reducing real value of state pension and encouraging private pension system.13
Source: Mason, A (2003) “Capitalizing on the Demographic Dividend”, Population and Poverty, Population and
Development Strategies, New York, United Nations Population Fund, P 3.
The low dependency rates are associated with high savings and the effects of dependency rate
changed on Asian savings, investment, and thus net capital flows. A revolutionary decline in
East Asian dependency rates from 1960 to 1990.The youth dependency rates were much
higher in developing East Asia than they were in developed countries during their period of
demographic transition. The industrialized countries youth shares averaged about 26 percent
during the baby boom in 1950s where as South Korea 42.8 percent, Taiwan 45.2 percent,
Singapore 43.5 percent and Thailand 46.2 percent.
13
Web site,“ www.economicshelp.org and dependency-ratio+sr.html” date 28.04.2010
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This period of dependency during the 1950s and 1960s must have created an economic
burden on the shrinking working population of East Asia. In 1990-1992, an enormous decline
in East Asian youth dependency rate and the decline were matched by a rise in the share of
working peoples. The youth dependency rates were in South Korea 24.8 percent, Taiwan 26.4
percent, Singapore 23.3 percent and Thailand 31.5 percent in 1990-1992.These decline in
youth dependency creating demographic gift in the means of working and saving adults and
the gift was large enough to ensuring economic growth. In 1990-1992, the prime age group
(25-59) of Hong Kong and Singapore were slightly more than half of their population and the
Japan, South Korea and Taiwan were very close behind that and in 2005, South Korea 53.6
percent, Singapore 53.4 percent, Taiwan 51.8 percent and Thailand 48.8 percent.
The demographic transition has had a profound impact on East Asian savings, investment and
foreign capital dependency since 1950, the rise in savings was greater than investment. The
youth dependency has fallen and the share working people has risen most dramatically and
they kicked the habit of foreign capital dependency and they are the capital exporter.
Dependency rates have played an important role for the economic success in East Asia since
1960s and probably will work over the next quarter century. To some extent it is clear that the
demographic transition had driven a significance part of economic transformation of the
region. The East Asian experience offer some unambiguous lesson for two important region
of the world, Africa and South Asia, that are about to pass through the favorable stages of
their demographic transition. If they can harness their falling dependency and rising working
force activity rates, they may be able to achieve an economic miracle too.14
2.4 Demographic dividend.
1. Labor Supply:
In economics, Labor is a measure of work performed by human; it is conventionally contrast
with the other factors of production as land and capital. The labor force usually defined as the
number of people aged between 15 and 64, excluding prisoners, psychiatric wards, stay at
home spouse, children and those serving in the military. The changes in the labor force
happen due to the population growth, net immigration, new entrants, and retirements from the
labor force. Population growth in the labor market means increase of working age population.
The Neo-classical economist’s concept is that the labor market works like other market that is
the force of supply and demand jointly determine the wage rate and number of people
employed. The nature of labor market is not as like as the other market such as markets for
goods or money, in various reasons. The most important is the function of supply and
demand in determining price and quantity. When the price is high the tendency of goods
market is to produce more goods until the satisfaction of demand. The supply can’t
effectively manufacture by labor due to people have limited amount of time in a day, we
can’t manufacture more people. The labor supply comes from household.15
To development of economy of a country, labor force always plays a key role. The
demographic transition effects labor supply in two ways. First, when the boom generation
Williamson, J, G. and Higgins, M.(2001). “The Accumulation and Demography Connection in East Asia”,
Population Change and Economic Development in East Asia, Challenges met, opportunities seized ,Mason, A.
ed. Stanford University Press, P 124-127,153-154.
14
15
Web page-http://en.wikipedia.org/wiki/Labour_economics,date 6May 2010.
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aged between 15 and 64, working age, enter the labor market, lowering total dependency
ratio, they could have strong effects on economy by increasing per capita production.
Second, As family size become smaller women are more likely to enter labor force and also
more likely to be educated. The education increases their participation as well as productivity
in labor market.16East Asia was able to produce rapid growth in employment and labor
productivity. Between 1960 and 1990, the labor forces increased by 2.7 percent annually,
where as the population growth rate was 1.9 percent. The gap was 0.8 percent per year, the
figure was twice of the Latin America during the same period. East Asia was not only able to
ensure high growth in its labor productivity but also in wages at the same time. The
successful research for enormous gain in agricultural productivity, feeding large populations
with fewer farmers, was important factor for productive labor market (Hayami, Y. 2001).
Third, Creating new industries and new jobs in the service and manufacturing sector was very
successful, effective export promotion policies, favorable trading environment and prudent
microeconomic policy ensured the friendly investment environment in East Asia. The
available capital was from foreign sources as well as from domestic sources to meet up the
expanding manufacturing sector (Bauer, John .2001). Demographic dividend played a vital
role for promoting employment, labor market productivity growth and human resources
investment.17
The proportion of representation of Women in the formal labor market was very low at the
beginning of the demographic transition of East Asia and wage gap between man and women
was quite large, women had less education than man but they had made remarkable
improvement since 1960.They contributed large share of the labor force due to the removal of
the barriers of gender bias and significantly declined educational differences and wage gap
between male and female. The smaller gender gap and increasing numbers of women in the
labor force had effectively capitalized the economy of the region was a vital features of the
demographic dividend (Mason, A .2003).The lower population growth contributes positively
to the economic growth. The principle mechanism here is the effects of the time required for
child rearing on the labor input of their parents. Fewer children imply less time input from
parents thereby freeing up labor time.18
Ireland is another example of strong impact on economic growth by utilizing the opportunity
of demographic dividend. The crude birth rate of Ireland dropped dramatically from 21.0 per
thousand to 14.2 per thousand between 1980 and 1990, because of the availability of
contraceptives; it was banned before 1979 by law. The declining fertility causes declining
youth dependency and creates a large share of working-age population. The result was rapid
economic growth. The growth rate of income per capita was about 3.5 percent yearly from
1960-1990 and in 1990s, it peaked at 3.5 percent that was more than any other European
Bloom, E, D. and Canning, D. et al (2003) “The Demographic Dividend: A New Perspective on Economic
Consequences of population Change”, Santa Monica, RAND,P-39,40.
16
Mason, A. (2003) “Capitalizing on the Demographic Dividend”, Population and Poverty, Population and
Development Strategies, New York, United Nations Population Fund, P-8 .
17
Ross, G.; John, B. and Grant,S.(2003) “Population Ageing In New Zealand: Implications for Living Standards
and the Optimal Rate of Saving” ,Newzeland Treasury Working Paper,P-12,13.
18
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economy, for this reasons the country was known as the “Irish Tiger.” The increased labor
supply, particularly female labor force participation was remarkable to boost rapid economic
growth. The availability of contraceptive and freedom of women, made it possible.19
2. Physical Capital:
Physical capital generally refers to any non-human asset made by humans and then used in
production.20 Conceptually Physical capital means the plant and equipment used in the
further production. As a proxy for the amount of physical things the money value of plant and
equipment can’t be used. Without measuring the physical capital unambiguously it is not
possible to determine its marginal product.21 The Harrod–Domar model of growth implies
that growth depends on the quantity of labor and capital and more investment leads to capital
accumulation that generates economic growth. In addition the model also had implications
that less economically developed countries have plentiful supply of labor but they have
shortage of physical capital thus slowing economic progress.
The economic growth depends on the favorable policies for increase investment through
increasing saving to use that investment more efficiently through technological advances.22
The demographic dividend through the growth of savings influences the investment and trade
of a country. The young and old consume more than they produce, where as working age
population tend to have higher level of economic output and high level of savings. The
tendency of people to save more between the ages 40 and 65, when they are less burdened
for children and prepare for retirement. So when boom generation enters into their 40s,
national savings will tend to rise.23
Sound health and longevity make savings easier and more attractive. A very effective feature
of demographic dividend in East Asia was the role of saving and investment. The savings rate
were near to zero and severe capital shortage were the characteristics of East Asia in the late
1950s and early 1960s ,while during the 1960s and 1970s domestic saving rates rise
dramatically to very high level.
Thus, under the effective conditions demographic dividend should provide substantial
increase in savings rates. The high rates of savings can not ensure high rates of economic
growth, there are many factors associated with this such as political instability, corruption,
imprudent fiscal policy and weak institutions can destroy confidence of investors of the
global capital markets. , The East Asia was very successful to utilize the opportunity of
Bloom, E, D. and Canning, D. et al (2003) “The Demographic Dividend: A New Perspective on Economic
Consequences of population Change”, Santa Monica, RAND, P-34,35.
19
20
Web site, “ http://en.wikipedia.org/wiki/Physical_capital”,date 06/05/ 2010.
21
Web site, “ http://www.encyclopedia.com/doc/1G2-3045301946.html”,date 10/05/2010.
22
Web site, “http://en.wikipedia.org/wiki/Harrod%E2%80%93Domar_model”, date 10/05/2010.
23
Studies examining the relationship between age structure and savings include Leff (1969); Mason (1981,
1987); Webb and Zia (1990); Kelley and Schmidt (1996); Higgins and Williamson (1997); and Bloom,
Canning, and Graham (2002).
Master’s Thesis
Page 16
demographic dividend for development of economy by creating a friendly economic and
political environment.24
The changes in the population age structure have reduced the population dependency and
enhanced the productiveness of population. The increasing levels of economically active
population and employment have yield an economic surplus and high savings. High savings
rate is helpful for capital formation and encourage economic growth. This situation
influences greatly in the second demographic dividend period. Over an individual’s lifespan
savings will increase upon entering the working force and decrease in retirement. For
example savings rates in East Asian economies were higher than both the world and the
developed economies. The savings rate in Japan was more than 36.4 percent in the 1960s,
while the savings rates in Hong Kong, Thailand and Malaysian were 20 to 30 percent in
1970s and continued to increase during the subsequent two decades. The savings rate in
China has continued to increase since the 1960s and it picked 1990s at 39.5percent. A high
savings rate has been played as an important factor of the rapid economic growth of Chain.
The increase in working age population will rise the national savings rates and implies overall
national capital formation.25
3. Human Capital:
Human capital theory suggests that education or training increase the productivity of workers
through imparting useful knowledge and skills thus raising workers’ future income due to
increasing their lifetime earnings (Becker, 1964, Becker, 1964 and Mincer (1974).Human
capital is the combinations of those characteristics of human beings that increase production
of an economy. It usually includes people’s knowledge and skills acquired partly through
education and it also include people’s strength and vitality that depend on their health and
nutrition. The human capital theory considers the health and education as the inputs of
economic production.26
Many economic theories considered human capital simply as workforce, one of three factors
of production and consider it to be a fungible resource, homogeneous and easily
interchangeable. Adam Smith comment on human capital as, “Fourthly, of the acquired and
useful abilities of all the inhabitants or members of the society. The acquisition of such
talents, by the maintenance of the acquirer during his education, study, or apprenticeship,
always costs a real expense, which is a capital fixed and realized, as it were, in his person.
Those talents, as they make a part of his fortune, so do them likewise that of the society to
which he belongs. The improved dexterity of a workman may be considered in the same light
as a machine or instrument of trade which facilitates and abridges labor, and which, though it
costs a certain expense, repays that expense with a profit.”27
Mason, A. (2003) “Capitalizing on the Demographic Dividend”, Population and Poverty, Population and
Development Strategies, New York, United Nations Population Fund,P-10-11.
24
Fang, C. and Dewen, W. “Demographic Transition and Economic Growth in China”, Institute of Population
and Labor Economics, Chinese Academy of Social Sciences, Beijing, China,P-12-15.
25
Web site “ http://www.evancarmichael.com/African-Accounts/1665/Human-and-Physical-Capital-TheEffects-of-Human-Capital-on-Economic-Development.html”date:06/05/2010.
27
Web site, “ http://en.wikipedia.org/wiki/Human_capital”,date 10/10/2010.
26
Master’s Thesis
Page 17
Becker G. (1964) in his book “Human capital” says that human capital is similar to physical
means of production. In addition he says one can invest in human capital through education
training, medical treatment like factories and machines and outputs depend partly on the rate
of return on the human capital one owns. Thus human capital is a means of production by
which additional investment produce additional output. Human capital is sustainable but not
transferable like land, labor, and fixed capital. The modern economic growth theory sees
human capital an essential growth factor. Human capital became considerably more valuable
in the United States during nineteenth and early twentieth century due to the advancement of
technology. Claudia Goldin revered the twentieth century as “the human capital century”. To
satisfy the demand of the new advancement of technology, high tech human capital requires
jumping in productivity and economic prosperity in a society.28
The demographic transition has strong effect on investment in human capital though the
return is not expected to receive shortly but effect on economic development is significant. A
long life expectancy gives people confidence to make fundamental change throughout their
live. When a society expects to get full advantages of demographic dividend, it is needed to
changes attitude and culture regarding education, family, retirement and role of women
because people of the society become more valuable assets. The positive relationship
between education and earnings is well known. In Latin America, for example, a worker who
has 6 years education can earn average more 50 percent than the person who does not has
education. The rate increases to 120 percent when 12 years of education and exceeds 200
percent for 17 years of education (Inter-American Development Bank, 1999). As life
expectancy increases, parents could realize that investing time and money on education and
health care may benefit a lot through the long working life of their children. The result of
educational investment is great; the labor force will be more productive, promoting higher
wages and a better standard of living. Obviously, this potential labor force will contribute a
significant improvement in the economy. After educated more advanced levels by taking
longer period, they enter the work force later but they are likely to be more productive once
they start working (International Labor Office, 1996; Bloom, Canning, and Sevilla, 2001).29
For example, East Asia had the high literacy rate, high educational attainment, advanced
health care, low rates of mortality, gives them a healthier and more productive workforce,
stronger incentives to invest in human capital, and stronger incentives to reduce fertility.
The productive human resource played an important role for economic development of the
region. The effect of demographic dividend, acting indirectly such as the school-age
population stopped growing on the other hand the working-age population and the tax-base
growing rapidly. This scenario had a very favorable effect on fiscal policy because spending
per student could be increased without increasing taxes per worker. When children are fewer
than the previous then parents could invest more money on education or on health care per
child without increasing the budget. For this reason, human resource investment per child
increased rapidly and East Asia had given more attention on primary and then secondary
education. Thus the countries of East Asia not only achieved higher return but also very
28
Web site , “ http://en.wikipedia.org/wiki/Human_capital”,date. 10/05/2010.
Bloom, E, D. and Canning, D. et al (2003) “The Demographic Dividend: A New Perspective on Economic
Consequences of population Change”, Santa Monica, RAND,P-39-40.
29
Master’s Thesis
Page 18
favorable age distribution effects (Ahlburg and Jensen 2001).30 South Korea is good example
of preparing human capital, as its birth rate fell in the mid-1960s, elementary school
enrolments declined and funds previously allocated for elementary education were used to
improve the quality of education at higher levels.
Small family, fewer children enhance the health of women, increase their participation in the
labor force in turn increase the social status and personal freedom. Thus they led to have
more energy to contribute both to their families and to the society in South Korea. Parents are
less burdened for few children, family income can be focused more upon better food for
infants and incomes can go toward prolonged education for babies to improve their life
prospects.31
4. The demographic situation:
The duration of first dividend period of South Asia is more than 60 years and it is very slow
in nature. There is a clear relationship between the average gain and the duration of dividend
period of the developing countries (Masson, A. 2005).
Figure:The GDP per capita of South Asian countries,1960-2006.
7000
GDP per capita(USD)
6000
5000
BD
4000
IND
Nep
3000
PAK
2000
SRI
1000
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
0
Source: Authors own calculation from data WDI
Mason, A. (2003) “Capitalizing on the Demographic Dividend”, Population and Poverty, Population and
Development Strategies, New York, United Nations Population Fund,P-9,10 .
30
Ross, J. (2004)“Understanding the Demographic Dividend”. Policy Project, Future Group from the web site “
http://www.policyproject.com/pubs/generalreport/Demo_Div.pdf” date 24.05.2010, P-1,3.
31
Master’s Thesis
Page 19
The GDP per capita of all the countries of South Asia was less than 400 USD in 1960.The
increase of GDP per capita of India and Pakistan was almost same; in 2006 the GDP per
capita of both the country is just more than 3500 USD. Sri-Lanka is only one country of the
South Asia that has shown the rapid increase of GDP per capita peaked at about 6000 USD in
2006.
The first demographic dividend is the rate of growth of support ratio (Mason, A. 2005).
The support ratio of all the countries of South Asia was between about 70 percent and 150
percent point in 1960.The total gain of support ratio of Nepal was 13.63 percent point from
1960 to 2006 it was the lowest total gain of support ratio of the South Asia ( table 3A). The
situation of India and Bangladesh was almost same but India was in the advantage position.
From 1960 to 2006 the total gain of support ratio of India was 43 percent point and it peaked
166 percent point in 2006 but the increase of the support ratio of Bangladesh was about 48
percent and it peaked at 161 percent point in 2006. The increase of Bangladesh was rapid in
the last two decades. The nature of the support ratio of Sri-Lanka was completely different
from the rest of the South Asian countries. In 1960 the support ratio of Sri-Lanka was 116
percent point and increased gradually with the total gain of 98 percent point and peaked at
230 percent point in 2006. There a similarity between the increasing pattern of the GDP per
capita and the increasing pattern of the support ratio of the South Asian countries. In both the
figure the lowest increase was for Nepal and highest increase was for Sri-Lanka. The rate of
increase of support ratio implies the pattern of the economic improvement of a country.
Figure: The dynamics of support ratio of the countries of South Asia
240
support ratio(%)
220
200
Bangladesh
180
India
160
Nepal
140
Pakistan
120
Sri-Lanka
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
100
Source: Authors own calculation from data WDI
The GDP per capita of Japan was about 1000 USD and South Korea was about 800 USD and
the GDP per capita of rest of the countries was less than about 300 USD in 1960. From 1960
to 2006, all the countries dramatically increased the GDP per capita. The most dramatic
increase happened for the countries Singapore, Japan and South Korea. The increase of GDP
per capita of Singapore was very sharp; it increased 5473 percent point to peaked at 43167
USD in 2006. The second highest point increase was for Japan and the GDP per capita of
Japan was highest in 1960 among the countries of the East Asia. The increase of South Korea
was 7313 percent point, it is the highest increase of GDP in respect to percentage change but
in 2006 it was the third highest GDP per capita.
Master’s Thesis
Page 20
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
South Korea
Indonesia
Japan
Singapore
Thailand
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
GDP per capita USD
Figure: The GDP per capita of the East Asian countries.
Source: Authors own calculation from data WDI
The increase of GDP of Indonesia was lowest and Thailand closely replicated Indonesia. An
interesting finding is Japan joined in the 5000 USD club of GDP per capita in 1973 and
Singapore 1978, South Korea 1985, Thailand 1991 and Indonesia 2005. Japan crossed 10000
USD by 7 years but Singapore crossed the same line by 5 years and finally in 1994 Singapore
crossed Japan peaked at 43167 USD in 2006.
The falling youth dependency ratio contributes to the economic growth miracle in East Asia
(Mason, A. 2005).The first demographic dividend of Japan begins before 1960 and continued
up to 1992 and in 1960 the support ratio of Japan was 178 percent point and picked 231
percent point in 1992. The support ratio of Singapore increased dramatically from 116
percent point in 1960 to 263 percent point in 2006. The increase of the support ratio of
Thailand was also very fast in 1960 it was 108 percent point and in 2006 it reached at 240
percent point.
Figure: The dynamics of support ratio of East Asia.
280
260
support ratio (%)
240
220
South Korea
200
Indonesia
180
Japan
160
Sigapore
140
Thailand
120
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
100
Source: Authors own calculation from data WDI
Master’s Thesis
Page 21
The increase of the support ratio of East Asia was very fast from 1970 to 2006 and
dramatically increased the support ratio and GDP per capita simultaneously.
Table.1 A: Distribution of the population by age in Asia during the second half of the
twentieth century
Country
Bangladesh
India
Pakistan
Nepal
Sri Lanka
China
Hong Kong
South Korea
Indonesia
Japan
Philippines
Singapore
Thailand
Peak years for youth
dependency and 1990-92
1975-79
1990-92
1965-69
1990-92
1965-69
1990-92
1975-79
1990-92
1955-59
1990-92
1965-69
1990-92
1960-64
1990-92
1965-69
1990-92
1970-74
1990-92
1950-54
1990-92
1965-69
1990-92
1960-64
1990-92
1965-69
1990-92
Young:
0-14
46.0
43.3
40.4
36.3
46.3
45.9
42.2
41.9
41.7
31.8
40.0
26.4
40.7
20.1
42.4
24.8
42.4
34.9
34.7
18.0
45.2
39.6
43.5
23.3
46.2
31.5
Prime:
25-59
29.6
31.4
35.8
37.3
31.1
31.3
34.9
34.1
34.3
41.1
35.5
43.7
40.9
50.8
34.6
47.3
34.4
37.7
38.1
48.9
30.7
35.3
35.1
51.8
31.5
40.6
Old:
65+
3.5
2.9
3.6
4.6
3.4
2.7
3.2
3.2
3.7
5.4
4.4
6.0
3.0
9.3
3.3
5.0
3.1
4.1
5.1
12.4
2.8
3.4
2.4
5.9
3.0
4.1
Δ from
peak
-2.7
-4.1
-0.4
-0.3
-9.9
-13.6
-20.4
-18.0
-7.3
-16.7
-5.6
-20.2
-14.7
Note: Δ from peak refers to the percentage point change between the peak and 1990-92.Source: United Nations
(1992).
Source: Higgins, M. and Williamson, Jeffrey G. (1997) “Age Structure Dynamics in Asia and Dependence on
Foreign Capital”, Population and Development Review, Vol. 23( 2 ), P-264.
The Hong Kong and Singapore were economically most favorable position due to over half
of their populations was in prime ages in 1990-1992 (table.1 A). Japan, South Korea, and
Taiwan were close behind them. On the other extreme Pakistan and Bangladesh were the
least favored with less than a third of their populations was in prime ages. Nepal and the
Philippines just crossed one-third, and the rest of the countries fall somewhere in between
them. In the poorest part of Asia, the prime age (25-59) share in the population is expected to
rise, for example the prime age share of Bangladesh would rise from 31.4 to 46.1 percent
from 1990 to 2025.The prime age share is expected to fall in the richest parts of Asia, for
Master’s Thesis
Page 22
example the prime age share of Japan would fall by 5.3 percentage points in the same
period.32
Table 1 B. Percentage of the population of Asian countries in the prime ages, 25-59, 19902025
Country
1990-92
2005
2025
Δ 2025-1990
Bangladesh
31.4
36.2
46.1
+14.7
India
37.3
40.2
47.5
+10.2
Pakistan
31.3
33.4
45.0
+13.7
Nepal
34.1
35.9
46.9
+12.8
Sri Lanka
41.1
46.2
47.5
+6.4
China
43.7
50.0
50.4
+6.7
Hong Kong
50.8
55.4
45.0
-5.8
South Korea
47.3
53.6
49.5
+2.2
Indonesia
37.6
44.1
49.5
+11.9
Japan
48.9
47.6
43.6
-5.3
Singapore
51.8
53.4
45.0
-6.8
Thailand
40.6
48.8
48.9
+8.3
Note: refers to the percentage point change between 2025 and 1990-92 in the percent aged 25-59 years, source:
United Nations (1991).Source: Higgins, M. and Williamson, Jeffrey G. (1997) “Age Structure Dynamics in Asia
and Dependence on Foreign Capital”, Population and Development Review, Vol. 23( 2) , P-266.
2.5 Theoretical Background:
The theoretical perspective of the research of the effects of the changes of age structure on
economic growth is based on the Neo classical theory. The theory focus that the economic
growth is caused by the increase in the labor quantity (population growth), improvements in
the quality of labor through education , increase in capital (through higher savings and
investment) and improvements in technology.33 The neoclassical growth model suggests that
population growth reduces economic growth because of capital dilution. The various
empirical study suggested that the effects of the growth rate of total population on the growth
of GDP per capita is negative but the effects of the growth of the working age population is
significant and positive.34
Higgins, M. and Williamson, Jeffrey G. (1997) “Age Structure Dynamics in Asia and Dependence on Foreign
Capital”, Population and Development Review, Vol. 23( 2) ,P- 265-266.
32
33
Web site, “ http://www.bized.co.uk/virtual/dc/ copper/theory/th12.htm”, date: 09/0572010.
Prskawetz, A. and Kogel ,T.et al.(2007) “ The effects of age structure on economic growth: An application of
probabilistic forecasting to India”, International Journal of Forecasting ,Vol-23,P-588-589.
34
Master’s Thesis
Page 23
Let us Consider the neo-classical growth model or Solow–Swan growth model based
production function: Y t=F (Kt Et Lt) …………… (3)
Where Y represents the total production in an economy, E t represents the technology
parameter and Kt is capital and Lt is labor. We can formulate the function as a Cobb
Douglas production function35 and divide both by the population N t .we can get:
Yt
𝑁𝑡
=
𝐾𝑡∝ (𝐸𝑡 𝐿𝑡 ) 1−∝
𝑁𝑡
……………… ………………………… (4)
If we do the natural logarithm of equation 4 we can rewrite the equation as:
In Yt − In Yt−1 = ∝ (In k t −In k t−1 ) + (1−∝)(Inet – In et−1 )(Inlt -Inlt−1) ) … (5)
= {𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙} + {𝐻𝑢𝑚𝑎𝑛 𝑐𝑎𝑝𝑖𝑡𝑎𝑙} + {𝐿𝑎𝑏𝑜𝑟 𝑠𝑢𝑝𝑝𝑙𝑦}
The equation (5) is the production function of the three main factors of economic
development, physical capital, human capital and labor supply.36 The production function (5)
is an appropriate model to examine the effect of changes age structure on economic growth.
The changes age structure directly effect labor supply and labor supply (support ratio)
strongly associated with human capital and physical capital in our data. The output of the
production function (5) is the combination of human capital, physical capital and labor
supply.
2.6 The Hypothesis:
The developed and developing countries of the world are experiencing substantial changes in
their age structure. The every country of the world has experienced or will experience a
substantial increase of working age population. A rise in the proportion of working age
population has direct favorable effects on per capita income ( Mason, A. 2005).The effects of
changes age structure of population mainly work through the working age people for
economic growth. Physical capital and human capital also perform as the marginal
byproducts of the changes of age structure of population. There are various factors those are
responsible for promote physical capital but for our research we have used total trade and
investment. Human capital usually measured by the health and education. To calculate human
capital effects we have used life expectancy at birth and years of schooling and support ratio
for labor supply.
Thus our hypotheses are based on human capital, physical capital and labor supply theory.
Hypothesis 1:
We expect there should be a positive relationship between the life expectancy and GDP and
the effects in the dividend periods should be higher than the pre dividend period.
Hypothesis 2:
We expect the relationship between the years of schooling and GDP should be positive and
the impact in the dividend periods should be more than the pre dividend period.
Mas-Colell, A. and Michael , D.et al.(1995)“ Microeconomic Theory”, Oxford student edition, Oxford
University Press ,P-130-134.
35
Munoz, S, E. (2009) “Demographic Windfalls in South America, The Impact of Age Structural Changes on
Economic Growth”, Master Thesis, Master Programme in Economic Demography, Lund University, P-2324,36-40.
36
Master’s Thesis
Page 24
Hypothesis 3:
We expect the increase of support ratio leads increase of the GDP and the impact of support
ratio should be more in the demographic dividend periods compared to pre dividend period.
Hypothesis 4:
We expect the effects of total trade (openness) and investment should be different between
the pre dividend period and dividend periods.
3. Data.
This chapter will present an overview on the data used for the analysis and its sources as well
as the sample of the study population.
3.1. Sources of Data:
The data used in this study is panel data of 47 years from 1960 to 2006 for 10 countries. The
data is quantitative in nature and aggregated on country level.
Support Ratio (SR): The data on support ratio comes from the World Development
Indicators (WDI) data base. We have given some assumptions that the people in working age
are net producers and all of them have same level of productivity and all the people of the
population have the same consumption. The support ratio is the ratio of working age
population ages 15-64 to the dependent population younger than 15 or older than 64. The data
was available of the population ages 15-64 (% of total). We have done some mathematical
calculation to get support ratio.
Life expectancy: The data of life expectancy comes from the World Development Indicators
(WDI) data base. The data was available for each year as unit in years.
Education: The data of education was available in the average years of schooling of adults
population aged 15 and over. The data was estimated by Robert Barro and Jong- Wah Lee, as
the part of Research at the World Bank. The data was measured in 5 years span we made
interpolations to get the data for the full period.
GDP (gross domestic products) per capita: The data of GDP per capita comes from the
Penn World Tables (PWT) data base. It is measured at current price in USD.
Total trade (Openness): The measure consists of imports plus exports divided by GDP is the
total trade as a percentage share of GDP. The data was available by the Penn World Tables.
Investment: The data made available by the Penn World Tables (PWT). It was in the form of
investment share of Real GDP per capita in percentage.
Master’s Thesis
Page 25
3.2 Sample:
We have used six variables and we have also used natural log of all the variables for
regression analysis to convert them in percentage.
Table 2: The descriptive statistics of the variables.
Variables
In GDP
In Life expectancy
In total trade
(Openness)
In Investment
In support ratio
In years of schooling
Number of
observations
470
470
470
Mean
Minimum
Maximum
7.439
4.101
-1.033
Standard
deviation
1.380
0.180
1.078
5.018
3.655
0-3.912
10.67
4.41
1.519
470
470
420
-1.549
.418
1.155
0.546
0.260
0.917
0-3.402
0.076
-2.12
-.532
0.991
2.383
We have used 5 years lag of the variable average years of schooling, hence the variable has
50 missing values.
4. Methods.
4.1 Statistical Model:
The fixed-effects OLS regression model:
To analyze Panel data, we have used Fixed-Effects Ordinary Least Square (OLS) Model.
Fixed-effects model explore the relationship between predictor and outcome variables within
the country. The each country has own individual characteristics that may or may not effect
the dependent variable. For using fixed effects we assume that within the individual may
impact the dependent variable and we need to control for this. Fixed effects remove the effect
of time invariant characteristics from the dependent as well as independent variables; hence
we can assess the predictor’s net effect. The time invariant characteristics of the fixed effects
model are unique to the individual and should not be correlated with other individual
characteristics. Each country is different and the countries error term and the constant should
not be correlated with the others.37 Fixed-Effects OLS model is appropriate for analyzing our
panel data. We have to use dummy variables to evaluate nature of the impact of the growth of
the GDP per capita for the changes in age structure during the pre dividend period, first
dividend period and second dividend period. The Fixed-effects OLS regression model is:
Y i, j= β1i+β 2 X 2i, j+………. + βk Xk,ij+γj+e i,j
Where Y i,j = the natural log of the GDP per capita for the country j (j=1,2,3,4,…10) and
year i (i= 1960-2006).
Xi, j are the independent variables.
γj is the coefficient denoting the fixed effects of country.
e i,j =error term.
β k´ s are the coefficients of the respective variables.
37
Web site,“ http://dss.princeton.edu/training” date: 06/05/2010.
Master’s Thesis
Page 26
The model has the following assumptions and scope:
1. The expectation of the error term must be equal to zero. That is E (ei,j)=0.
2. The variance of the GDP per capita of the model is constant and equal to the variance of
error term. That means Var (GDP per capita) = Var (ei,j) =σ2.
3. The two independent variables must be uncorrelated.
That means Cov (X2 ij, X3ij) = Cov (ei.ej).
4. All the independent variables are normally distributed about their mean and error term is
normally distributed. That means eij~N (0, σ2).
5. The explanatory variables are not linear function of other explanatory variables.
We have used various regression models for our analysis to evaluate the effects of changes
age structure on the GDP between pre dividend period and dividend periods. As the proxy of
age structure human capital, physical capital and labor supply factors should be examined
through the statistical model. We have three periods for study namely pre dividend period,
first dividend period and second dividend period. Most of our study countries have pre
dividend period and first dividend period but few countries have second dividend period. To
compare the dividend periods we have also divided our total study period into four time
periods for our study such as time period 1960-1970 (base period), period 1971-1980,period
1981-1990 and period 1991-2006.
1. Mode.1(Human capital with dividend periods): We have used the human
capital theory considering the health and education as the inputs of economic production.
Thus we have considered the life expectancy as the health variable. The are some others
factors those are also represent the status of the health of a nation those are TFR (total
fertility rates), CDR (crude deaths rates) , IMR (infant mortality rates) , Maternal mortality
rates. We could not use them because of lack of data. The most valuable factors of human
capital are knowledge and skills. There are various means of approaches to acquire those
qualities. There are no shortcut methods to convert those qualities to the quantities one for
our analysis. Most of the scholars of social science used education as the proxy of knowledge
and skills. We have considered the average years of schooling of aged 15 and over to
evaluate the human capital of working age population. Thus our HC model is: GDP per capita
=F (Life expectancy + average years of schooling+ pre dividend period (reference category
period)+first dividend period +second dividend period).
2. Model.2(Human capital with time periods): We have used human capital
variables life expectancy and years of schooling along with time periods 1960-1970
(reference period), period 1971-1980, period 1981-1990 and the period 1991-2006 to
compare dividend period with the time trend. Thus our model is: GDP per capita = F (Life
expectancy + average years of schooling+ period 1971-1980 +period 1981-1990 + 1991-2006
period).
3. Model. 3 (The interaction model of human capital): We have used interaction
between human capital variables and dividend periods in this model.Thus the model is: GDP
percapita =F (expectancy + average years of schooling+ total trade+ investment+ support
ratio +interaction of life expectancy and pre dividend period + interaction of life expectancy
and first dividend period + interaction of life expectancy and second dividend + interaction of
years of schooling and pre dividend period + interaction of years of schooling and first
dividend period + interaction of years of schooling and second dividend).
Master’s Thesis
Page 27
4. Model.4 (Combined model without dividend periods): We have used all
factors of human capital, physical capital and labor supply to evaluate the effects of the
demographic dividend combined by them. Thus the model is: GDP per capita =F
(expectancy + average years of schooling+ total trade+ investment+ support ratio).
5.Model.5 ( Combined modelwith dividend periods): We have used human
capital, physical capital and labor supply variables with dividend periods to evaluate the
effects of the demographic dividend combined by them between the pre dividend ,first
dividend and second dividend period. Thus the model is: GDP per capita =F (expectancy +
average years of schooling+ total trade+ investment+ support ratio +pre dividend period
+first dividend period +second dividend period).
6. Model.6 (Combined model with time periods): We have used human capital,
physical capital and labor supply variables with time periods to compare the effects of
dividend periods and time periods. Thus the model is: GDP per capita =F (expectancy +
average years of schooling+ total trade+ investment+ support ratio +period 19601970(reference period) +period 1971-1980 +period 1981-1990+period 1991-2006).
7. Model.7 (physical capital with dividend periods): To capture demographic
dividend of physical capital we have used the total trade and investment of a country. Thus
PC model is: GDP per capita=F (total trade+ investment+ pre dividend period (reference
period)+ first dividend period +second dividend period).
8. Model.8 (Physical capital with time periods): We have used physical capital
variables total trade and investment with time periods 1960-1970 (reference period), period
1971-1980, period 1981-1990 and the period 1991-2006 to compare dividend period with the
time trend. Thus our model is: GDP per capita = F (total trade + investment+ period 19601970+period 1971-1980 +period 1981-1990 + 1991-2006 period).
9. Model. 9 (The interaction model of physical capital): We have used interaction
between physical capital variables and dividend periods in this model.
Thus the model is: GDP per capita =F (expectancy + average years of schooling+ total trade+
investment+ support ratio +interaction of total trade and pre dividend period + interaction of
total trade and first dividend period + interaction of total trade and second dividend +
interaction of investment and pre dividend period + interaction of investment and first
dividend period + interaction of investment and second dividend)
10. Model.10 (labor supply with dividend periods): To capture demographic
dividend of labor supply we have used the support ratio of population. Thus the LS model is:
GDP per capita=F (support ratio+ pre dividend period (reference period) + first dividend
period +second dividend period).
11. Model. 11 (labor supply with time periods ): To compare between dividend
periods and time periods we have used the model. Thus the model is: GDP per capita =F
(support ratio+ period 1960-1970(reference category) +period 1971-1980 +period 19811990+period 1991-2006).
Master’s Thesis
Page 28
12. Model. 12(The interaction model of labor supply): We have used interaction
between support ratio and dividend periods in this model. Thus the model is: GDP per capita
=F (expectancy + average years of schooling+ total trade+ investment+ support ratio
+interaction of support ratio and pre dividend period + interaction of support ratio and first
dividend period + interaction of support ratio and second dividend).
4.2 Definition of variables.
Support Ratio (SR): Support ratio is the relationship between producers (working age
people) and consumers (dependent people). We can measure the demographic dividend by
evaluating the support ratio. The SR is the ratio of the population of aged 15 to 64 to the
population below aged 15 and over 64.
Support Ratio =
number of people aged 15−64
number of people aged 0 to 14 and those aged 65 and over
100
The support ratio provides information regarding the effect of changes age structure on
economic output.38 A common way to see the economy is in the demographic dividend or not
is to look at the dynamics support ratio. We have used support ratio as an independent
variable to measure the demographic dividend.
Dividend Period:We can get dividend period from support ratio. The first difference of SR
is:
Δ SR = SR t –SR t-1.
When ΔSR > 0 , the dividend is positive. The time span during the positive dividend is the
first dividend period.
GDP (gross domestic products) per capita: It is a measure of a country's overall economic
output can be treated as a best economic development parameter of country. We have used
GDP as a dependent variable of the regression analysis.
Life expectancy: Health is an indispensible influencing factor to boost the aggregate
productivity of a country. The life expectancy is the most appropriate proxy for the health
status of the population. We have used the life expectancy as an independent variable to
explain human capital.
Years of schooling: To measure the human capital of society education is the most
appropriate one. Average years of schooling of adults have been used to measure the
education. We have used education as an independent variable. A higher educated workforce
is more productive and contributes more in the economy.
Total trade (Openness): It is representing the international trade as the physical capital of a
country. The openness is the measures of exports and imports. It has been measured as
exports plus imports divided by the GDP. We have used it as an independent variable in the
regression.
Munoz,S,E.(May,2009).“Demographic Windfalls in South America,The Impact of Age Structural Changes on
Economic Growth”, Master Thesis, Master Programme in Economic Demography, Lund University,P-22,23.
38
Master’s Thesis
Page 29
Investment: Investment is always an influencing measure to boost the aggregate productivity
of a country. We have used it as an independent variable in the regression.39
In our regression analysis we have used dividend periods as the dummy variables denoted as
“pre dividend period”, “first dividend period” and “second dividend period”.40 We have used
time period (year) as the dummy variables denoted as “period 1960-1970” as preference time
period, “period 1960-1970” ,“period is 1971-1980”, “period 1981-1990” and “period is 19912006”.
5. Empirical analysis.
In this chapter we will test the theories presented earlier through empirical analysis of the
data of study. In addition we will test our hypothesis of the research by our regression
outputs.
5.1 Statistical Results:
In this part, results of statistical analysis will be discussed and interpreted, and in the
following, these outcomes will be compared with theory and previous researches to measure
the reliability of this research. To analyze the effects of changes age structure on the growth
of GDP per capita, we used Fixed-effects OLS regression model. We have used twelve
models, the results presented in the table 4, 5, 6 and 7. We have examined the individual
effects of human capital, physical capital and labor supply on the growth of GDP per capita.
Then we looked at the combined effects of all the three determinants of GDP per capita and
compared them with individual outcomes. We have measured demographic dividend periods
for each country to examine the impact of age structure on economic growth more in the
demographic dividend periods.
5.1.1 The demographic dividend periods of South Asia.
Table 3A: The dividend period and dividend gain of support ratio of South Asia.
Country
First
Second
Support Ratio of first Total
Annual
Dividend
Dividend
dividend period (%)
Gain(AG)
Period
Period
Begin
gain(TG)
End
Simon Veckten “The Demographic Dividend and International Capital Flows”,Master thesis,spring
2009,Master in Economic Demography,Department of Economic History,Lund University.P-19-21.
39
40
In Table 3 A and Table 3 B ,we have measured the pre dividend pried, first dividend period and second
dividend period for each and every country of our study. We have considered pre dividend period as the
reference category period in the dummy.
Master’s Thesis
Page 30
Bangladesh
1968-2006+
-
109.0052
160.9256
47.63%
1.22%
-
123.8078
166.0707
34.16%
0.83%
-
120.8071
137.1905
13.63%
1.05%
-
116.2723
148.1032
27.38%
0.68%
-
116.3257
229.8371
97.58%
2.08%
39+ Years
India
1966-2006+
41+ Years
Nepal
1994-2006+
13+ Years
Pakistan
1967-2006+
40+ Years
Sri-Lanka
1960-2006+
47+ Years
Source: WDI data and calculated by author.*Total gain, Annual gain. 41
The first demographic dividend period begins in Sri-Lanka in 1960, India 1966, Pakistan
1967, Bangladesh 1968 and Nepal 1994. All the countries of South Asia continued their first
dividend after 2006.
5.1.2 The demographic dividend periods of East Asia.
Table 3B: The dividend period and dividend gain of support ratio of East Asia.
Country
South
Korea
First Dividend Second
Period
Dividend
Period
1972-2006+
35+ Years
-
Support Ratio of first Total
dividend period (%)
gain(TG)
Begin
142.5159
End
208.1199
46.03%
Annual
Gain(A
G)
1.34%
41
Total gain is the difference between the SR of end period and beginning period divided by the value of the
beginning period and annual gain is total gain divided by dividend period.
Master’s Thesis
Page 31
Indonesia
1971-2006+
36+ Years
-
119.8653
197.0568
63.4%
1.79%
Japan
1960-1992
33 Years
1993-2006+
14+ Years
178.2013
230.3601
29.27%
0.89%
Singapore
1965-2006+
42+ Years
-
115.78
262.6842
126.88%
3.02%
Thailand
1968-2006+
39+ Years
-
108.1837
240.2831
122.11%
3.13%
Source: Authors own calculation from data WDI
The first dividend period begins in Japan in 1960, in Singapore 1965, Thailand 1968, South
Korea 1972 and Indonesia 1971. Japan crossed first dividend period in 1992 and entered into
second dividend in 1993.All other countries of East Asia will continue first dividend after
2006.
5.1.3: Regression results.
1. The human capital as the determinant of gross domestic products.
Table 4 presents the results of fixed effects OLS regressions including the dividend periods
dummies and human capital variables (model, 1). The dividend dummy variables would
capture the effect of changes age structure on GDP per capita for different dividend period
caused by the life expectancy and years of schooling. The model, 2 of table 4 present the
results of the fixed effects OLS regressions of variables life expectancy and years of
schooling including the time period (year) dummies. The time period (year) dummy variables
capture the trend of the effect of age structure on GDP per capita over time. The model,3 of
the table 4 present the results of the regressions including the combined variables of human
capital, physical capital and labor supply with the dividend period dummy and the
interactions of dividend periods and human capital variables. From the results of the model,3
we want to look at the total effect of life expectancy and years of schooling in the different
dividend periods.
The estimated results from OLS regressions, presented in Table 4 shows that the model
1(human capital with dividend periods) fits the data well. Within the countries, data
explained 69% variation of the independent variables and between the countries data
explained 85% variation of the independent variables. Overall the model explained 75%
variation of the explanatory variables. But model 2 (human capital with time periods dummy)
does not fit the data well. The model explained between the countries only 11 % and overall
49 % variation of the independent variables but within the countries it explained 89 %
variation. The other models associated with human capital fit the data well.
Master’s Thesis
Page 32
We note that the coefficient of life expectancy is positive and statistically significant (Pvalue < 0.05) in all the estimates of table 4. Thus the impact of life expectancy on GDP per
capita is positively significant. Higher life expectancy has a positive growth of GDP per
capita. The 1 percent increased of life expectancy contributed 8.39% on the GDP per capita
(model.1). When we control the physical capital and labor supply, then the contribution of
life expectancy decreases by 5.82% (model.5). The average year of schooling is not
significant as a human capital indicator as well as not showing expected sign (model.1).
The GDP per capita is 0.094% higher in the first dividend period than the pre dividend period
and 0.77% higher in the second demographic dividend period of the East and South Asian
countries (model.1). The GDP per capita is 0.69 percent higher in the time period 1971-1980
, 1.61 percent higher in the time period 1981-1990 and 2.33 percent higher in the time period
1991-2006 than the time period 1960-1970 (model.2). The results indicate that the trend of
the effect of human capital variables on GDP increasing over the time. The effect of having 1
% higher life expectancy and being first dividend period is to increase GDP by 9 % and the
corresponding effect of a 1 % higher life expectancy and being in the second dividend period
is a 24 % lower GDP compared to the base line life expectancy in the pre dividend period. A
possible explanation of the negative effect of life expectancy in the second dividend period is
that only one country out of the ten providing the data of second dividend period (13 years
data of Japan). Thus results concerning second dividend period could not represent the ten
countries of the study. We should focus mainly on the first dividend period and pre dividend
period to analyses the data.
Thus the model 1 show that the effect of human capital on GDP is higher in the dividend
periods than the pre dividend period and the model 2 also indicates the same conclusion that
the effect in the time periods from 1971 to 2006 is higher than the time period 1960-1970.
The model 3 suggested that the effect is higher in the first dividend period than pre dividend
period but not second dividend period. Hence from the results of the regressions,we can say
that the effect of human capital is higher in the first dividend period than the pre dividend
period and the effect is positively significant.
Table 4: Fixed effects OLS regression results of GDP per capita of human
capital.
Dependent variable
In(GDP per capita)
Master’s Thesis
Model.1.
Model.2.
Model.3.
Human capital With
dividend periods
Human capital With
time periods
Human capital, physical
capital, labor supply With
dividend periods and
interaction of dividend
periods and human capital.
Page 33
Explanatory variables
coefficients
P>|t|
Constant
-26.921
0.000
coefficie
nts
0.613
In (Life expectancy)
8.389
0.000
1.421
In (total trade)
-
-
0.228
0.005
In(Investment)
-
-
-0.151
0.040
In(support ratio)
-
-
2.538
0.000
In(years of schooling)
-.112
0.330
0.147
0.097
First dividend period
0.094
0.444
7.793
0.009
Second dividend period
0.775
0.001
-33.637
0.632
Inter (lif* first div)
-2.037
0.007
Inter(lif*second div)
6.585
0.747
Inter(Ys*first div)
0.882
0.747
Inter(Ys*second div)
2.907
0.764
-0.238
P>|t|
Coefficients
P>|t|
0.710
-7.411
0.013
0.001
3.216
0.000
0.000
Period 1971-1980
0.693
0.000
Period 1981-1990
1.608
0.000
Period 1991-2006
2.332
0.000
R-square(within)
0.688
0.891
0.889
R-square(between)
0.85
0.111
0.84
R-square(overall)
0.752
0.488
0.811
Number of observations
420
420
420
Number of countries
10
10
10
Table 5: Fixed effects OLS regression results of GDP per capita of
combined factors.
Dependent variable
Model.4
Model.5.
Model.6.
In(GDP per capita)
Explanatory variables
Constant
Master’s Thesis
Human capital, physical
capital, labor supply.
Human capital, physical
capital, labor supply
With dividend periods
Human capital, physical
capital, labor supply
With time periods
coefficient
s
-6.785
coefficients
Coefficients
P>|t|
0.001
-4.841
P>|t|
0.013
5.367
P>|t|
0.001
Page 34
In (Life expectancy)
3.158
0.000
2.567
0.000
0.152
0.690
In (total trade)
0.519
0.000
0.341
0.000
0.254
0.000
In(Investment)
-0.299
0.000
-0.228
0.005
-0.075
0.228
In(support ratio)
2.563
0.000
2.812
0.000
1.44
0.000
In(years of schooling)
0.287
0.001
0.341
0.000
0.027
0.667
First dividend period
0.259
0.002
Second dividend period
1.117
0.000
Period 1971-1980
0.585
0.000
Period 1981-1990
1.274
0.000
Period 1991-2006
1.753
0.000
R-square(within)
0.84
0.859
0.923
R-square(between)
0.757
0.836
0.693
R-square(overall)
0.746
0.805
0.777
Number of observations
420
420
420
Number of countries
10
10
10
2. The physical capital as the determinant of gross domestic products.
Table 6 presents the results of fixed effects OLS regressions including the dividend periods
dummies and physical capital variables (model, 7). The dividend dummy variables would
capture the effect of changes age structure on GDP per capita for different dividend period
caused by the physical capital variables. The model, 8 of table 6 present the results of the
regressions of variables total trade and investment including the time period (year) dummies.
The time period (year) dummy variables capture the trend of the effect of age structure on
Master’s Thesis
Page 35
GDP per capita over time. The model, 9 of the table 6 present the results of the regressions
including the combined variables of human capital, physical capital and labor supply with the
dividend period dummy and the interactions of dividend period and physical capital
variables. We want to look at the total effect of total trade and investment in the different
dividend periods by the model, 9.
Table 6 shows that the model 7 does not fit the data well. The model explained 64% of
variation of the independent variables within the countries and between the countries model
explained 21% variation of the independent variables. Overall the model explained only 29%
variation of the explanatory variables. But model 8 (physical capital with time periods
dummy) and model 9 (human capital, physical capital, labor supply with dividend periods
and the interaction of dividend periods and physical capital) fits data well. The effects of the
physical capital variables total trade are positive and significant in all the model of table 6 (Pvalue <0.05).
The coefficients of another physical capital variable investment in the table 6 show that the
effect of investment is negative. In model 7 it is not also significant but another 2 models of
the table 6 shows that its effect is significant (P- value <0.05). The GDP is 0.84 percent
higher in the first dividend period and 1.34 percent higher in the second dividend period than
the pre dividend period due to the effects of the physical capital variables (model, 7). Because
of the effect of the physical capital the GDP is 0.82 percent higher in the time period 19711980, 1.71 percent higher in the time period 1981-1990 and 2.35 percent higher in the time
period 1991-2006 compared to the time period 1960-1970 (model, 8).The interaction model
of physical capital (model, 9) also shows that the GDP is 1.04 percent higher in the first
dividend period and 2.54 percent higher in the second dividend period than pre dividend
period. Thus we can conclude that the effect of physical capital on GDP per capita is higher
in the dividend periods than pre dividend period of the countries of East and South Asia.
Table 6: Fixed effects OLS regression results of GDP per capita of physical
capital.
Dependent variable
In(GDP per capita)
Explanatory variables
Master’s Thesis
Model.7.
Model.8.
Model.9.
Physical capital With
dividend periods
Physical capital With time
periods
Human capital, physical
capital, labor supply With
dividend periods and
interaction of dividend
periods and physical
capital.
coefficients
coefficients
P>|t|
P>|t|
Coefficients
Page 36
P>|t|
Constant
8.165
0.000
In (Life expectancy)
-
-
In (total trade)
1.712
0.000
0.328
In(Investment)
-0.227
0.055
-0.124
In(support ratio)
-
In(years of schooling)
-5.463
0.005
2.538
0.000
0.000
0.035
0.795
0.049
-0.368
0.010
-
2.715
0.000
-
-
0.431
0.000
First dividend period
0.839
0.000
1.041
0.000
Second dividend period
1.342
0.000
2.539
0.605
trade
0.301
0.007
trade
1.057
0.404
Inter(Invest *first div)
0.221
0.155
Inter(Invest*second div)
-.263
0.930
Period 1971-1980
Period 1981-1990
Period 1991-2006
Interaction (total
6.250
0.815
1.707
2.348
0.000
0.000
0.000
0.000
*first div)
Interaction
(total
*second div)
R-square(within)
0.644
0.90
0.865
R-square(between)
0.209
0.093
0.842
R-square(overall)
0.293
0.574
0.805
Number of observations
470
470
420
Number of countries
10
10
10
3. The labor supply as the determinant of gross domestic products.
Table 7 presents the results of fixed effects OLS regressions including the dividend periods
dummies and support ratio variable (model, 10). The dividend dummy variables would
capture the individual effect of support ratio on GDP per capita. The model, 11 of table 7
present the results of the regressions of variables support ratio with the time period (year)
dummies. The time period (year) dummy variables will provide the nature of the effect of
support ratio on GDP per capita over the changes the time period from one to another. The
time periods and dividend periods results will provide pattern of the effect of support ratio on
GDP according to the progress of time. The model, 12 of the table 7 present the results of the
regressions including the combined variables of human capital, physical capital and labor
Master’s Thesis
Page 37
supply with the dividend period dummy and the interactions of dividend period and support
ratio. The results of the model, 12 will provide us the total effect of support ratio in the
different dividend period.
The results of Table 7 show that the model 10(labor supply with dividend periods dummy),
model 11(labor supply with time periods dummy) and model 12(human capital, physical
capital, labor supply with dividend periods and interactions of dividend period and support
ratio) fits the data well. All the models of table 7, the data explained more than 77 percent
variation of independent variables within the countries and more than 84 percent between the
countries and overall data explained more than 76 percent variation.
The coefficients of support ratio of the model 10 suggest that the effect of support ratio on
GDP per capita is positive and significant. The model, 11 also support the result of the model
10.Having a 1 percent increase of support ratio the contribution of GDP per capita is 3.93
percent. When we control human capital and physical capital the effect of support ratio on
GDP decreased by 1.12 percent (model, 10 and 5). The effect of having 1 % higher support
ratio and being first dividend period is to increase GDP by 2.14 % and the corresponding
effect of a 1 % higher support ratio and having in the second dividend period is a 2.36 %
higher GDP compared to the base support ratio of pre dividend period (model, 12). The GDP
is 1.14 % higher in the first dividend period and 2.62 % higher in the second dividend period
than pre dividend period due to the effect of the support ratio (model, 10). The trend of the
effect of the support ratio on GDP per capita is positively increased with time. The GDP is
0.79 % higher in the time period 1971-1980 than the period 1960-1970 and 1.56 % higher in
the time period 1981-1990 and 2.14 % higher in the time period 1991-2006 (model, 11). The
above results suggest that the effect of support ratio on GDP per capita is higher in the first as
well as second dividend period than the pre dividend period.
Table 7: Fixed effects OLS regression results of GDP per capita of labor
supply.
Dependent variable
In(GDP per capita)
Explanatory variables
Master’s Thesis
Model.10.
Model.11.
Model.12.
Labor supply With
dividend periods
Labor supply With time
periods
Human capital, physical
capital, labor supply With
dividend periods and
interactions of labor
supply.
coefficients
P>|t|
coefficients
P>|t|
Coefficients
P>|t|
Page 38
Constant
4.828
0.000
In (Life expectancy)
-
In (total trade)
5.626
-5.688
0.002
-
2.951
0.000
-
-
0.269
0.001
In(Investment)
-
-
-0.307
0.000
In(support ratio)
3.934
0.000
-1.452
0.011
In(years of schooling)
-
-
0.341
0.000
First dividend period
1.143
0.000
-0.667
0.000
Second dividend period
2.616
0.000
2.249
0.081
4.259
0.000
1.558
0.373
1.396
0.000
0.000
Period 1971-1980
0.795
0.000
Period 1981-1990
1.557
0.000
Period 1991-2006
2.138
0.000
Interaction(SR*first
dividend)
Interaction
(SR*second
dividend)
R-square(within)
0.769
0.92
0.878
R-square(between)
0.887
0.908
0.836
R-square(overall)
0.812
0.758
0.82
Number of observations
470
470
420
Number of countries
10
10
10
5.2 Discussion.
As the part of the demographic transition all the countries of the world had experienced or are
experiencing or will experience substantial changes of age structure of the population with
potential important implication for economic growth (Mason, A. 2005). The aim of the paper
was to examine, the nature of the effects of changes age structure on the GDP in the pre
dividend period and dividend periods of the ten countries of East and South Asia. We have
used fixed-effects OLS regression models to estimate the effects of the changes age structure
of population. The changes age structure, gives the opportunity of demographic dividend and
demographic dividend directly associated with labor supply and indirectly associated with
human capital and physical capital. Our study data shows good association between labor
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supply (support ratio) and human capital as well as physical capital. Thus we have examined
the effect of labor supply, human capital and physical capital variables in various ways to
indentify the nature of the effects on GDP per capita in the demographic dividend periods.
We have used five demographic and economic variables to capture the economic growth to
measure the effects of human capital, physical capital and labor supply as the proxy of
changes age structure. The results of regression analysis presented in the table 4-7 show that
the impact of all the variables except investment is positively significant and the effect is
higher in the demographic dividend periods than the pre dividend period.
a) Human Capital.
1. Life expectancy.
The results suggested that there is a positive relationship between the life expectancy and
GDP and the effect is higher in the first dividend period than pre dividend period. The
increase of life expectancy will effect to increase the per capita Gross Domestic products of
the countries of East and South Asia. When the age structure changes from the pre
demographic dividend period to first demographic dividend period the effect of the life
expectancy is different between the two periods and first dividend period is higher.Thus,
these findings verify our hypothesis that life expectancy as a proxy of human capital has
positive relationship with GDP and the effect on the first dividend period is higher than the
pre dividend period. The effect on first dividend period is higher due to the increased
proportion of the working age people in the population. The increased proportion of GDP per
capita is the first demographic dividend of the countries of East and South Asia in 1960-2006
due to increase of life expectancy.
2. Years of schooling.
The regression results suggested that there is a mixed relationship between the average years
of schooling and GDP and the effect is higher in the dividend period than pre dividend
period. The individual effect of human capital indicates the effect of years schooling is
negative but in the combined effect of human capital, physical capital and labor supply as
well as with dividend periods (model 4 and model 5) show that the effect of years of
schooling is not only positive but also statistically significant (P-value <0.05).The increase of
average years of education will effect to increase the per capita Gross Domestic products.
When the age structure of the population changes from the pre demographic dividend period
to first demographic dividend period the impact of education changes between the two
periods. Thus, the results verify our hypothesis that education as a proxy of human capital has
positive relationship with GDP and the effect on the pre dividend period and dividend period
is completely different and also the effect of dividend period is higher than pre dividend
period. Human capital theory suggests that education increase the productivity of workers
thus rising workers future income due to increasing their life time earnings (Becker, (1964)
and Mincer (1974)). Our findings support the human capital theory that the effect of
education on GDP is positive and higher in the dividend periods than pre dividend period.
b) Physical Capital.
1. Total trade (openness).
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It is the international trade deals with exports and imports of a country as the physical capital
of production. The change of the proportion of working age people in the population affect
the imports and exports thus also effect the production. The regression results suggested that
there is a positive relationship between the total trade (openness) and GDP and the impact is
different between the pre dividend period and dividend periods. The increase of total trade
will effect to increase the per capita Gross Domestic products. The results of individual
model (model, 7) and combined model with and without dividend periods (model 4 and 5)
show that the effect of total trade is positively significant (P-value <0.05). The GDP is higher
in the dividend periods than the pre dividend period and the trend of GDP is positively up
ward over the time period from 1971-1980 to 1991-2006 compared to the time period 19601970 (model 8).
Hence we can conclude that there is a positive relationship between total trade and GDP and
its impact on the dividend periods is bigger than the pre dividend period. So the results verify
our hypothesis that the effect of total trade is different between the pre dividend period and
dividend periods. International trade is the exchange of capital, goods and service across the
international borders. A good share of GDP comes from international trade. Its economic,
social and political importance is increasing day by day.42 Theory of physical capital
suggests that the proxy of physical capital total trade contribute economy positively. Our
findings from analysis support the physical capital theory that total trade (openness) is
significant for economic growth and the effect is higher in the dividend periods than pre
dividend period due to the increase of the proportion of working people in the dividend
period.
2. Investment.
Investment is very much related with savings and consumptions as well as production. It
represents the physical capital of the economic growth. The regression results suggested that
funding in investment does not increase GDP and the impact is not same between the pre
dividend period and dividend periods. The result of the coefficient of the OLS regression
(model, 7, 8, 9) implies that the effect of investment is negative and not statistically
significant (P-value > 0.05). The results also show that the GDP is higher in the dividend
periods than the pre dividend period. The trend of the effect of investment is positive from
the beginning time period 1960-1970 to the ending time period 1991-2006(model 8).Thus our
results of regression verify our hypothesis that the effect of investment is different in between
the pre dividend period and dividend periods.
c) Labor Supply.
1. Support ratio:
Support ratio is the proportion of labor force. The changes of the structure of working age
people in the population direct reflect through the support ratio. The OLS regression results
suggested that there is a positive relationship between the support ratio and GDP and the
effect is higher in the dividend periods than in the pre dividend period. The increase of
support ratio will effect to increase the per capita Gross Domestic products. When the age
structure changes from the pre demographic dividend period to first demographic dividend
42
http://en. wikipedia.org/wiki/International trade, date 12/05/2010.
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period the effect of the support ratio is changes between the two periods and the nature of
change is higher in the first dividend period than the pre dividend period.When the age
structure of the population changes from the first demographic dividend period to second
demographic dividend period again the effect of the support ratio is influencing positively in
the second dividend period compared to the first dividend period (model, 11).
Thus, these findings verify our hypothesis that there is a positive relationship between
support ratio and GDP and the effect on the dividend periods is higher than the pre dividend
period. The effect on the first dividend period is higher due to the increased proportion of the
working age people in the population.
Thus we can conclude that the effect of the changes of age structure of people in the
population is higher in the dividend periods than pre dividend period. The amount of higher
contribution on economic growth by the age structure is nothing but the demographic
dividend.
6. Conclusion.
We began this paper with the modest goal to test the hypothesis that whether the effects of
changes of age structure of the population on GDP is significant or not. In addition we
wanted to evaluate the nature of the effect between the pre dividend period and dividend
periods. All model estimates confirmed our hypothesis that the effect of age structure through
human capital, physical capital and labor supply is positive and statistically significant and
the effect is higher in the dividend periods than pre dividend period of the countries of the
East and South Asia, 1960-2006.
To conclude we can say that the changes of age structure created the opportunity for the
economic growth of the East and South Asian. The countries of East and South Asia have
experienced and are experiencing the demographic dividend due to the changes of age
structure of the population.
Further studies should focus on trying to identify the factors for specific countries why they
were able to utilize the demographic dividend. It is essential to consider some factors for
further research such as policy regarding economic growth of the country, political situation,
fertility, mortality. Another important issue is to consider the age specific producers and
consumers weight. If anybody can use the survey data then the empirical research will
represent the population consistently.
Finally we should consider that the demographic dividend is still now present in the East and
South Asia. They should invest more on education and health to prepare their future
generation to achieve the goal of dividend. They should also consider policy regarding
economic growth and political stability of the country.
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