1

Demographic Dividend and Economic Growth in India: Evidence and Policy

Implications

1. Introduction

The theory of demographic transition explains the change in population age structure due to

change in birth rate and infant death rates. The changing age structure have significant

economic implications. For instance, during the second stage of demographic transition which

is characterized by a transition from high to low birth and infant death rates, a rising share of

working age population is experienced. A conducive policy environment with focus on job

creation can create opportunities for economic growth (James 2008; Mason and Lee 2006; Lee

and Mason 2010; Bloom et al. 2006). Such benefits are termed as demographic dividend and

numerous countries have experienced an increase in economic growth associated with age

structure transition (Bloom et al. 2003; Bloom et al. 2006; Behrman et al. 1999; Andersson

2001; Kelley and Schmidt 2005; Choudhry and Elhorst 2010; Wei and Hao 2010; Feng and

Mason 2005).

Notably, realization of demographic dividend does not solely rely on availability of labour. It

is both change in age structure and dependency ratio which determines the benefits. To

elaborate, it is possible that two countries have the same size of labour force but different

dependency ratios which will presumably lead to different growth rates. For highly populous

countries such as India and China, the benefits arising from lower dependency ratio during the

second phase of demographic transition could be substantial. India is still going through the

second phase while China has already reaped the benefits during this phase (Bloom and

Williamson 1998; Bloom and Finley 2009).

Although, the experience of East Asian countries in realizing demographic dividend has been

exceptional, the same cannot be said about India which is one of the most populated countries

in the world. Population of India increased from 361 million in 1951 to 1.2 billion in 2011. As

of 2020, India’s population is 1.38 billion which roughly translates to 18 per cent of the world’s

population. By the end of this decade India will be the most populated country in the world

surpassing China. The four main determinants of growth of population of a country are birth

rate, death rate, in-migration and out-migration (Preston, 2000). Although birth (fertility) rates

have been declining but India’s population will continue to grow atleast till 2070. Notably,

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India at present is going through a phase of “Age structural transition” which is defined as the

change in composition of the population.

The policy environment required to achieve high growth rate has not been favourable in case

of India (Bloom and Williamson 1998; Bloom et al. 2006; Bloom 2011; Aiyar and Mody 2011;

James 2008; Chandrasekhar et al. 2006; Navaneetham 2002; Mitra and Nagarajan 2005).

Interestingly, the quantification of contribution of age structure towards growth have received

very little attention in case of India.

Another distinct feature of India is the marked heterogeneity in age structures across States.

States in the south and west (such as Tamil Nadu, Karnataka and Gujarat) have already reaped

a major chunk of demographic dividend during 1980s and 1990s. On the other hand, states

such as Bihar, Madhya Pradesh and Uttar Pradesh are in the process of demographic transition.

These states that have lagged behind are expected to contribute a larger share of demographic

dividend (Aiyar and Modi 2011).

The motivation for this study is derived from the present pessimism surrounding the growth

prospects of the economy. Although India has achieved a lot in the past two decades. But it

seems India has lost its way during the current decade. The most common reason for this slow

growth is low capital investment. The share of industrial and agricultural sector in overall

growth is declining. Employment figures indicate that the absorption of youth into the labour

force is far below expectations. The demographic dividend will become a disaster in case

unemployment rates remains high.

Another challenge which India will have to face in the immediate future is that of an ageing

population. The demographic shifts will result in increasing elderly dependency ratio. The need

of an elderly age group are certainly different. A number of social, health and economic

challenges will crop up. The Government of India have a number of policies and scheme to

support the elderly population. However, the financial burden to take care of the elderly will

rise substantially since India has a large population base. There are major concerns about the

ability of the government to provide health and social services along with pension.

Against this backdrop, the study aims to critically review the trends and patterns in population

and economic growth with a focus on the employment shares across sectors of economy.

Econometric analyses to quantify the magnitude of the demographic dividend will be

conducted. The findings of this study will lay down a roadmap for States which are yet to reap

3

demographic dividend by highlighting the experience of other States which were able to

successfully reap the demographic dividend.

2. Literature Review

The world is characterized with a variety of demographic regimes. The historical demographic

changes which have taken place can explain the current trends and patterns in demographic

structure. The theory of demographic transition explains the changes in population over time.

The theory is based on the work of Warren Thompson who developed a demographic history

in 1920’s. The term “demographic transition was coined by Frank W. Notestein. According to

this theory every country passes through a number of phases depending upon the fertility and

mortality rates which in turn determine the rate of growth of population.

There are three phases of demographic transition. In the first phase, birth and death rate are

high and the growth rate of population is low. In the second phase, the birth rate remains high

but a low death rate is achieved either due to improvement in food supply or public health that

leads to a reduction in child mortality. During stage three, birth rates decline due to

improvement in literacy rates, higher returns from investment in children, access to

contraceptives and birth control methods. Two more stages are suggested by some authors.

Stage four in which the birth levels and death rates are also. Stage fifth in which fertility rates

fall below replacement level. Some authors have suggested that in stage five, fertility rates

might actually increase.

The concept of demographic dividend is embedded in the theory of demographic transition.

When the population age structure is evolving from a higher number of young people to a

higher number of old people due to dynamics of birth and death rate, there is a phase where the

number of people in working age groups is higher than the number of children and elderly.

This phase is the demographic window. A country which have a conducive policy environment

can achieve high growth rates during the period.

Age structure transition can be characterized by three phases: increase in child dependency

ratio (0-14 years), increase in share of population in working age group (15-65 years) and

increase in old age dependency ratio (65+ years). The period during which the share of people

in working age group is prominent is defined as the “demographic window”. This window of

opportunity is available during second to third phase and usually dependency rates are low.

4

However, there is no uniformity in the definition which are used to describe demographic

window. The United Nations Population Division (UNPD) defines demographic window as

the period when the proportion of children and youth under 15 years falls below 30 per cent

and the proportion of people 65 years and older is still below 15 per cent (United Nations,

2004).

The window can also be defined as the phase during which the number of producers is greater

than the number of consumers (Cutler, Poterba, Sheiner, and Summers, 1990). This second

approach to define demographic window builds on the concept of economic support ratio which

is defined as the ratio of producers to consumers. Here the consumers are the non-productive

population comprising of children (0-14 years) and elderly (65+ years). The duration of

demographic window can determine the potential demographic dividend which is defined as

the net addition to economic growth due to increase in share of working age people. Bloom

and Williamson (1998) define demographic dividend as the difference between the rate of

growth of population in working age group and total population.

Defining the broad age groups to examine the socio-economic implications is itself a challenge.

To study age structure transition, the population could be divided into following age groups:

0-14 years, 15-65 years; and 65 years and above. However, the work participation and life

expectancy vary by country. In case of India, most of the people work till 60 years and life

expectancy is also low as compared to developed countries. Also, a significant share of children

in 15-24 years also starts working at an early age. Population in 25-49 years is likely to consume

more while population in 50-59 years is more likely to save. Of course, these patterns in

consumption and saving are influenced by dependency ratios as well. The age composition of

population can determine the macroeconomic performance of the country.

The relationship between population growth and economic growth have important implications

for policymakers who want to optimize the use of scarce resources. Three predominant views

on effect of population change on economic aspects (Bloom et al 2003) are present in literature:

population pessimism (population growth negatively affects economic growth), population

optimism (population growth positively affects economic growth) and population neutralism

(no relationship between economic growth and population growth). Interestingly, the focus of

all these aspects have been on overall population growth and economic growth, none of these

consider the impact of age structure.

5

Galor and Weil (2000) present a unified model to analyze the historical evolution of association

between population growth, technological change and standard of living. Based on growth of

income and population, three regimes have been identified by these authors. First is the

Malthusian Regime under which population and income grow at a very slow rate. According

to Malthus (1798), small size of population is associated with high standard of living while

large size of population is associated with low standard of living. However, a transition from

the trap described by Malthus did take place.

Second is the post-Malthusian regime under which the growth rates of both population and

income are higher. The relationship between income and population under both these regimes

is positive. The third regime is the Modern Growth Regime under which the rate of growth of

income is higher than the rate of growth of population. Also, the relationship between income

and population is negative under this regime.

Clearly, the views of Bloom et al (2003) and Galor and Weil (2000) seem to be contradictory

with respect to cause and effect but they are not. The possibility of reverse causality between

population and income growth cannot be ruled out. The initial effect of income growth is a rise

in total fertility rates. During the post-Malthusian period, higher income growth led to high

population but the reverse effect of increase in population on income was checked by

improvement in technology. The higher returns from investment in child led to a substitution

of quality for quantity (Becker et. al., 1990). While improvement in technology differentiates

the Malthusian and Post Malthusian regime, it is the process of demographic transition

following the industrial revolution which characterizes the Modern growth regime during

which population structure can influence growth.

Since the 18th Century, the classical economists have been preoccupied with the identification

of determinants of economic growth. Harrod (1939) model, Domar model (1946) and Solow

Model (1956) are the most influential models which have strived to explain the elements of

growth. Harrod-Domar is a Keynesian model which explains the economic growth in terms of

saving and capital-output ratio (productivity of capital). To achieve higher growth either saving

has to be increased o the capital output ratio will have to be reduced. The Solow model is a

neoclassical model, an improved version of the Harrod-Domar model. It explains long term

trend in economic growth in terms of capital accumulation, labour or population growth or

technological progress. However, these models fail to incorporate the change in population

structure.

6

During the late 1950s, Coale and Hoover came with a model to forecast the impact of

population growth on economic growth. The basic tenet of this model is that higher fertility

rates will lead to higher consumption and lower saving rates which ultimately will reflect in

lower growth rates. A large population will require investment in housing, education and

medicine. Clearly, resources will be diverted from more productive activities such as

infrastructure development which have direct implications for growth.

Bloom and Williamson (1998) extend the basic framework of the Solow model to isolate the

impact of demographic variables on economic growth. It is clear from the Solow model that

the population and labour force growth have implications for the steady state growth rate.

However, the rate of growth of workers in the Solow model is exogenously determined and

have a negative impact on growth by leading to a reduction in capital output ratio. But in case

the rate of growth of workers is higher than the population growth, the proportion of dependents

will fall. This will translate into an increase in saving rates.

Bloom et. al (2003) have identified a number of mechanisms through which demographic

dividend could be derived. First is the increase in labour supply as more people are in the

working age group and dependency rates are lower. Also, more women enter the labour force

due to lower fertility rates.

Second pathway is the rate of saving. People in working age group are likely to produce more

and consume less as compared to the children and elderly. This behavior leads to increase in

saving rates and availability of more funds for productive purpose.

Third, a higher human capital formation is also expected. Since people are more likely to invest

in health and education of children. However, the policy environment can play an important

role wherein the government is required to provide better education and health provisions to

create opportunities for future growth.

The second pathway which links demographic transition with saving is quite crucial. The

increase in saving can be used to increase capital output ratio and lead to higher growth (Solow

1956, Cass 1965, Mason and Lee 2006). More saving will imply availability of funds for

investment and capital deepening which will translate into productivity growth. However, the

relationship between economic growth and saving is not straightforward and reverse causality

cannot be ruled out. However, there is plenty of evidence that economic growth in phase of

demographic dividend and a favorable demographic structure should lead to high saving rate

which ultimately translates in high growth rates (Mason, 1988).

7

A number of studies suggest that demographic transition has contributed towards economic

growth in several countries (Bloom & Williamson, 1998). A few studies have tried to quantify

demographic dividend for India. Aiyar and Modi (2011) report that a significant portion of the

India’s growth since the 1980s is solely due to favorable demographics. About 2 percentage

points per annum can be added to India’s per capita GDP growth over the next two decades

due to a high share of working age ratio. Similarly, Joe et. al. (2018) estimate demographic

dividend of one percentage point per annum during 1980–2010. Choudhary and Elhorst (2010)

report that 39 per cent of the India’s growth over 1961-2003 is solely due to favorable

demographics. Ghosh (2016) estimate the dividend for the period 1961-2011 to be 2 per cent

per annum.

3. Data and Methods

Econometric methods

The theoretical framework is based on a simple accounting identity proposed by Bloom and

Williamson, 1998. The relationship between growth of per capita income and growth in

working age can be written as follows.

Y/N = Y/L x L/WA x WA/N … (1)

Here, Y denotes gross domestic product, L denotes the total labour force (does not include

those who are not looking for work in 15-59 group), WA is the working age population (15-59

years) and N is the total population. The change in per capita income here could arise due to

three underlying components: first is labor productivity which is reflected in Y/L; WA/N is the

change in the share of the working age people in the total population and L/WA represents the

share of labor force. The identity implies that change in growth could arise due to change in

share of labor force and working age ratio.

A logarithmic transformation of identity [g = ln(Y/N); z = ln(Y/L); e = ln(L/WA) and c =

ln(WA/N)], followed by a total differentiation of the equation shows that growth rate of income

per capita can be written as the sum of growth of income per worker, growth of labour

participation and the growth of the ratio of the working-age to the total population (Bloom et

al 2010).

ġ = ż + ė + ċ … (2)

8

The effect of changing sectoral shares of primary (p), secondary (s) and tertiary (t) sectors on

economic growth can be decomposed as follows (Bloom, 2006):

z = zpβp + zsβs + ztβt … (3)

Here, total income per worker (z) is a weighted average of sectoral incomes per worker (zp, zs,

and zt) with respective employment shares (βp, βs and βt) of the sectors serving as the weights

for aggregation. Totally differentiating equation (3) followed by dividing it by z yields:

dz

z= (

zp

zβp) (

dzp

zp+

dβp

βp) + (

zs

zβs) (

dzs

zs+

dβs

βs) + (

zt

zβt) (

dzt

zt+

dβt

βt) … (4)

Equation (4) suggests that the growth in the output per worker can be examined through growth

of worker productivity and employment share across sectors. These effects are written as

follows:

dz

z|productivity growth = (

Yp

Y) (

dzp

zp) + (

Ys

Y) (

dzs

zs) + (

Yt

Y) (

dzt

zt) … (5)

dz

z|sectoral composition = (

zs

z−

zp

z) dβs + (

zt

z−

zp

z) dβt … (6)

In equation (5), the sector specific productivity growth rates are weighted by the respective

share (Yp/Y, Ys/Y, and Yt/Y) of the sectors in total income. Equation (6) exploits the fact that

dβp+ dβs+ dβt = 0; to provide the effect of change in sectoral composition on income per worker.

However, it must be noted that this decomposition is only indicative of productivity and

sectoral shift effects as it assumes that marginal product of workers equals the average product.

Besides, it does little to and does not consider or explain the growth in sectoral productivity

per se.

To complement the decomposition analysis, we also estimate the contribution of favourable

population age-structure on economic growth using the standard neoclassical conditional

convergence framework (Aiyar and Mody 2011; Bloom et al 2010; Barro and Sala-I-Martin

1995). The framework entails that growth in income per worker (ż) is a function of the gap

between initial level of income (z0) and the steady state level of income (z*) and is also

dependent on the speed of convergence (λ). Formally,

9

ż=λ (z* - z0) … (7)

Here, z* is a function of several factors (αX) that influences labour productivity. Further, using

equation (2) and assuming labour force participation rates (ė) to be constant yields,

ġ = ż + ċ … (8)

Using equations (7) and (8), growth in per capita incomes (ġ) can be examined as:

ġ = λ (αX + e + c0 -g0) + ċ … (9)

Equation (9) forms the basis for econometric analysis of demographic dividend in India.

Intuitively, this equation implies that the initial working age ratio (c0) and the growth in the

working age ratio (ċ) should display a positive association with economic growth. It may be

noted that these effects are in addition to the contributions of other determinants of steady state

labour productivity.

Data and Variables

The data analysis will be carried out for India and major states such as Assam, Haryana, Jammu

and Kashmir, Himachal Pradesh, Punjab, Delhi, Rajasthan, Uttar Pradesh, Bihar, West Bengal,

Orissa, Madhya Pradesh, Gujarat, Maharashtra, Manipur, Meghalaya, Nagaland, Sikkim,

Tripura, Andhra Pradesh, Karnataka, Kerala and Tamil Nadu. Data will be collected for the

period 1971- 2018. The Census data for 2001 and 2011 will be adjusted to take into account

the creation of three new states Jharkhand, Chhattisgarh and Uttrakhand. The availability of

data for new as well as old states will allow us to consolidate the data of Jharkhand with Bihar,

Chhattisgarh with MP, Uttrakhand with UP and Telangana with Andhra Pradesh to maintain

consistency as well as comparability with old undivided states.

Similar adjustment will be made for other indicators such as State Domestic Product, Literacy

Rate and Infant mortality rate. Notably, Census was not conducted in Jammu and Kashmir in

1991 and in Assam in 1981. Data will be interpolated for missing years. Also, Haryana was

carved out from Punjab in 1966, adjustment will be made to capture the indicators or Haryana.

Census data is available till 2011 and projected population figures are available till 2018 based

on 2011 data.

10

The data on State Domestic Product and Sectoral share will be obtained from Ministry of

Statistics and Programme Implementation, Government of India. The output data for the

primary, secondary and tertiary sector will be obtained by multiplying the share of a particular

sector in SDP by SDP. The data for NSDP is available at 1970-71, 1980-81, 1993-94, 1999-

00, 2004-05 and 2011-12 prices. To ensure uniformity, all these figures will be converted to

2004-05 prices.

National Industrial Classification was developed with an aim to quantify the contribution of

each economic activity in overall Gross Domestic Product. Each round of Census has followed

a different NIC. NIC-1970 have been used for Census prior to 1991. NIC- 1987, NIC-1998 and

NIC-2008 have been used for census 1991, 2001 and 2011 respectively. NIC is revised on a

consistent basis to account for ever changing structure of industries and organization arising

from technological shifts as well as diversification of product mix.

Based on NIC for the purpose of analyses primary sector is defined as comprising of

Agriculture, forestry and fishing. Secondary sector includes Construction, mining,

manufacturing; and electricity and water supply. Services including Wholesale and retail trade

& restaurants and hotels, Transport, storage and communication services, Financial, insurance,

real estate and business services; and Community, social and personal services are included in

the tertiary sector. The “working age” population here refers to those in 15-59-year age group.

The share of dependents and “workers” in total population and literacy rate will be calculated

using Census data. Notably, Census categorizes workers in two groups main and marginal

workers. The data on total fertility and mortality rates will be obtained from Sample

Registration System.

4. Results

The age structure transition is a function of fertility and mortality transition. To understand the

patterns in age structural transition, the population is divided into 5 groups on the basis of their

consumption and saving habits: 0-14 years (children), 15-24 years (youth who are studying),

25-49 years (working age group), 50-59 years (mature working age group) and 60+ years

(elderly). The consumption behavior basically corresponds to the phase of life to which the

individual belongs. To elaborate, individuals in young working age group (25-49 years) are

more likely to save less and consume more while those in 50-59 year bracket are more likely

save more due to the pending retirement. Children and a higher proportion of elderly are likely

to be dependent on others to fulfill their consumption requirements.

11

Figure 1 presents the age composition across India and States over the period 1971 to 2036.

Due to decline in fertility over time it can be observed that the share of 0-14 population is

declining across all the States. As of 2021, the share of elderly and children in India’s total

population is 10.1 and 25.5 per cent respectively. The share of elderly population is higher in

Kerala, Tamil Nadu and Himachal Pradesh. It is worth noting here that 30 to 40 per cent of

population across States is in 25-49 age group and is expected to rise until 2031.

Table 1 presents the duration of demographic window based on UNDP’s definition as period

when proportion of children and youth under 15 years falls below 30 per cent and the proportion

of people 65 years and older is still below 15 per cent. India will experience a favorable age

structure for 22 years (2013 to 2035). As per this definition, demographic window is now

closed for Kerala and Tamil Nadu. The window was open for Kerala for 26 years (1991 to

2017) and for Tamil Nadu for 30 years (1994 to 2024). For Rajasthan, Uttar Pradesh, Madhya

Pradesh and Bihar, the favorable period will start post 2020 and will exist till 2040. Notably

for most of the States the window opened in 2000’s and the duration have been around 20 to

25 years.

Figure 2 provides the estimate of demographic dividend using second approach. Here dividend

is defined as the difference between the growth rate of working age population (25-59 years

and total population. Using this definition we observe that the window of opportunity opened

up for most of the States around 1970 and will exist well beyond 2040. The peak for Kerala

was observed in 1980 while for Tamil Nadu and Himachal Pradesh in 2002. Jammu and

Kashmir, Punjab, Haryana, Maharashtra and Madhya Pradesh are likely to have a broader

window as compared to other States. The curves for other States were relatively flatter with a

lower peak.

12

Figure 1: Distribution of age structure, India and States, 1971 to 2036

13

Table 1: Duration of demographic window based on UNDP definition

Start end 1991 2001 2011 2016 2021 2026 2031 2036 Duration

India 2013 2035 22

Himachal Pradesh 2003 2026 23

Punjab 2004 2027 23

Haryana 2011 2036 25

Delhi 2006 2036 30

Rajasthan 2020 2036 16

Uttar Pradesh 2021 2036 15

Bihar 2026 2036 10

West Bengal 2007 2029 22

Orissa 2009 2029 20

Madhya Pradesh 2019 2036 17

Gujarat 2009 2034 25

Maharashtra 2005 2031 26

Andhra Pradesh 2005 2029 24

Karnataka 2005 2031 26

Kerala 1991 2017 26

Tamil Nadu 1994 2024 30

Jammu and Kashmir 2015 2034 19 Note: Duration of demographic window defined as period when proportion of children and youth under 15 years falls below 30 per

cent and the proportion of people 65 years and older is still below 15 per cent.

*demographic window phase to continue for few more years

14

Figure 2: Duration of demographic window computed as difference in growth rate of total

population and 25-59 age group, India and States, 1971 to 2036

15

Table 2 reports the results based on state-level panel data analysis using panel data regression.

Consistent with the neoclassical convergence framework, it is noted that the coefficient on

initial per capita income is negative and significant. Further, from the OLS fixed-effects, it is

inferred that a one per cent increase in the initial share of working age population is associated

with more than one per cent increase in growth of per capita income over 1990-2011 and 2000

to 2011. However, the magnitude of the coefficient decline once we consider the 1990-2018

period, possibly because growth was lower during 2010-2018.

Table 2: Regression results

Annual per capita income growth

Approach-1 Approach-2 Reforms period

Model-1 Model-2 Model-3 Model-4 Model-5 Model-6 Model-7

2000-

2011

2010-

2018

2000-

2018

1970-

2018

1980-

2018

1990-

2018

1990-

2011

Log of initial income per

capita -0.11** -0.02 -0.04* -0.05*** -0.05*** -0.03* -0.11***

(-0.19 -

0.03)

(-0.08

0.05)

(-0.07 -

0.01)

(-0.08 -

0.03)

(-0.08 -

0.02)

(-0.06 -

0.00)

(-0.17 -

0.05)

Log of initial working age

ratio 1.52*** 0.56* 0.49*** 0.72*** 0.69*** 0.52*** 1.33***

(0.74

2.29)

(0.11

1.02)

(0.20

0.78)

(0.50

0.94)

(0.44

0.94)

(0.25

0.78)

(0.79

1.87)

Growth rate of working

age ratio -0.77 -0.75 -0.92 -0.45 -0.82 0.45 0.81

(-5.68

4.14)

(-3.39

1.88)

(-2.71

0.88)

(-2.04

1.14)

(-2.48

0.85)

(-1.16

2.06)

(-2.07

3.69)

Constant 1.94** 0.51 0.74** 0.97*** 0.92*** 0.64** 1.82***

(0.74

3.14)

(-0.38

1.39)

(0.27

1.20)

(0.61

1.33)

(0.52

1.31)

(0.22

1.06)

(0.95

2.70)

Observations 170 136 306 799 646 476 340

5. Expected Findings

The existing literature identifies a host of variables which are important in determining

economic growth. Among these factors the role of demography has largely remained neglected

in literature on growth models. India is in the midst of a demographic transition and can realize

a strong demographic dividend. However, the policy environment in India has remained weak

and unfavorable to create opportunities for growth. Although literacy levels have been rising

but a lot more is expected with respect to employment generation. This study will explore the

association between the demographic variable and economic growth with a focus on the past

decade during which India’s growth has faltered. It is expected that the association between

the change in age structure and economic growth should be weak as substantial growth has not

taken place between 2010 and 2018. This study will also attempt to identify the phase of

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demographic transition through which different States are going through so that policymakers

can focus on the clusters which could be important in contributing towards growth in future.

Till now the focus has been on education alone but in this study the interaction of education

and work force participation rate will be explored. Presumably, workers who are literate are

more likely to contribute to growth. The paper provides some useful recommendations for

deriving higher economic growth.

6. References

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Hill

4. Becker, G. S., Murphy, K. M., & Tamura, R. (1990). Human capital, fertility, and

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growth. PGDA Working Paper 65, Harvard School of Public Health, Harvard

University, Boston, MA.

6. Bloom, David E and J E Finlay (2009) Demographic Change and Economic Growth in

Asia, Asian Economic Policy Review, 4: 45–64

7. Behrman, J R, S Duryea and M Szekely (1999) Aging and Economic Opportunities:

Major World Regions around the Turn of the Century, Working Paper no 405, Inter-

American Development Bank.

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change and economic growth: Comparing China and India. Boston, MA: Harvard

School of Public Health, Harvard University.

9. Bloom, D. E., Canning, D., & Sevilla, J. (2003). The Demographic dividend: A new

perspective on the Economic consequences of population change, Population Matters

Monograph MR-1274. Santa Monica, RAND.

10. Bloom, D. E., & Williamson, J. G. (1998). Demographic transitions an economic

miracles in emerging Asia. The World Bank Economic Review, 12(3), 419–455.

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11. Cass, D. (1965). Optimum growth in an aggregative model of capital accumulation. The

Review of Economic Studies, 32(3), 233–240.

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