Relationship between GDP, Life Expectancy 
and Growth Rate of G7 Countries
Rafia Shafi, Samreen Fatima
Abstract: Increase in life expectancy is a key indicator to gauge the economic development of a country. Enormous 
studies have been done to test this hypothesis, and the conclusion is still un-decided. This study aims to explore the 
impact of life expectancy on economic growth in G7 countries via regression approach. Keeping in view the unique 
population structure of each of these G7 countries, the trend of life expectancy for each country is also observed. 
Findings of the study indicate that life expectancy in G7 countries increases with constant rate. The increase in life 
expectancy is accompanied with the increase in Gross Domestic Product (GDP) per capita income. We have also 
included the population growth rate as another important factor contributing towards GDP. It is worth mentioning 
here that increase in life expectancy directly affects per capita real income due to higher expenditure on health.
Moreover, it is also found that increase in GDP lessens the population growth.
Keywords: G7 Countries, GDP
Introduction
In recent years, the increase in life expectancy has 
become a critical topic in population studies, as it is 
conditionally dependent on the economic growth and 
the expenditure on health improvement. According to 
World Bank report 1998, improvement in life 
expectancy is strongly linked with per capita income.
It is expected that a prosperous country has a strong
impact on the life expectancy of its inhabitants. An 
increase in GDP, normally decrease mortality rates, 
however, less developed countries experience 
mortality reduction in clusters of different age groups 
such as younger or working ages. Kelley and Schmidt 
(1995) explored that increase in population is neither 
all good nor all bad for economic growth, both 
elements coexist. Rodgers (1979) investigated that 
there exist a relationship between life expectancy,
income and income distribution. On contrary, Becker, 
Philipson, and Soares (2003) suggested no such 
relationship. 
On the other hand, economic growth is a key factor in 
raising standards of living worldwide and the role of 
population growth in the enhancement of living 
standards is a substantial part of it (see Heady & 
Hodge, 2009). There are abundant literature available 
which discusses the relationship between economic 
growth and population growth (Heady & Hodge, 
2009). Past research shows that high income 
countries have relatively low population growth rate 
(Baker, Delong,& Krugman, 2005). However, 
significant effects of population growth on economic 
disparity and on life expectancy are observed.
Various research analysts have investigated empirical 
evidence which showed that robust population 
growth enhances economic growth. In contrast, few 
researchers found reverse evidence to this conclusion.
Moreover, there are literatures which reveal that the 
effects vary with the level of a country’s 
development, the source or nature of the population 
growth. The other factors that lead to non-uniform
impacts on economic growth still need to be probed.
The main objectives of the study are many fold, (1) to 
observe the dependency of economic growth on 
population growth and life expectancy. (2) to explore 
the type of relationship between life expectancy , 
population growth and GDP in G7 countries which 
include US, UK, Canada, Italy, France, Germany and 
Japan.
Literature Review
E. Wesley F. Peterson (2017) studied the relationship 
between population growth and economic growth of 
high income and low income countries on globe and 
reviewed the related literature in this context. In their 
study they found that in low income group, the rapid 
increase in population will increase the demographic 
dividend in these countries as the young people 
become productive adults in future. On contrary, 
growth rate is low in high income group of countries. 
However, few countries show negative growth rate 
indicating that a high percentage of the population 
consists of elderly people. They investigated 
relationship between growth rate, growth in per 
capita output, and overall economic growth using
past 200 years data. Their results reveal that low 
growth rate in high income countries and high growth 
rate in low income countries may create social and 
problems. 
Relationship between GDP, Life Expectancy and Growth Rate of G7 Countries
http://www.ijSciences.com Volume 8 – June 2019 (06)
75
In the same year Linden, M., & Ray, D. (2017), 
analyzed health-income relationship spanning period 
from 1970 to 2010 of 148 countries. They used 
quantile regression method to find association
between health and different income groups. They 
concluded that in low-income countries’ income 
gradient is quite larger than that of rich countries. 
Income disparity is measured by Gini criterion which 
showed that the effect of inequality on health is still 
remarkable in the least income group of countries. On 
the other hand became insignificant among highincome group of countries after the year 2000.
Cervellati,&Sunde (2011) tested that the effect of life 
expectancy on income per capita growth is nonmonotonic. In order to test the hypothesis they used 
47 countries data taken from literatures (UN 
Demographic Yearbook, Maddison(2003)). Their 
result supports the previous findings on causal effect 
of life expectancy on income per capita growth. 
Furthermore, they concluded that improvement in life 
expectancy might affect the income growth indirectly 
as well as increases the probability of observing the 
demographic transition. 
In 2002, Hasan explored long-run association
between Growth rate and Per Capita Income of
Bangladesh. His result exhibited that growth rate and 
GDP were cointegarted in long-run. Furthermore, a 
bidirectional relationship also exists between growth 
rate and GDP (Hasan,2002). In another study (Hasan, 
2010) examined the relationship between population 
and per capita income of China using Granger 
causality method. Empirical analysis, shows the 
existence of negative long-run causal relationship of
per capita income with population growth and shortterm association between growth and per capita 
income. In addition to this, he used neoclassical and 
endogenous growth models which indicated that per 
capita income growth tends to lower the population 
growth. 
Schnabel &Eilers (2009) explored that the life 
expectancy has a nonlinear influence on wealth. They 
followed research of Preston’s study, in which life 
expectancy and GDP had a curvilinear relationship. 
They also used least asymmetrically weighted 
squares which led to combine P-spline curves.
Different smoothers were applied on a large data set 
of different countries. Furthermore, their developed 
models were used to estimate changes in life 
expectancy of individual countries with the passage 
of time.
Model Selection and Data Analysis
The data of GDP (per capita income) and Life 
Expectancy of G7 countries are taken from World 
Bank web site www.world bank.com. The data spans 
a period from 1960 to 2017. All countries GDP are 
taken into USD. All the GDP’s are in billion (13 or 
more digits) so each country’s GDP is divided by 
billion to ease the analysis procedure.
The model we have used for the analysis is a multiple 
linear regression model in two variables. The general 
form of the regression equation is described as;
GDP C b life ectency b growthrate 1 2   exp 
……….(1)
log( ) log( exp ) log( ) GDP  C  b1
life ectency  b2 growthrate
……..(2)
Where C is a constant, ‘b1’is the coefficient 
measuring the effect of life expectancy on GDP and 
‘b2’ is the coefficient measuring the effect of 
population growth rate on GDP. The model (1) is 
further modified for the two group ofG7 countries. 
One group, in which population growth is positive 
and another in which population growth is negative 
for some period of time. Log-Log Regression 
equation(2) is used for Canada, USA and Franc
(having positive growth rate), whereas for the 
remaining countries standard regression equation(1)
is considered. 
The GDP, Life Expectancy and Population Growth 
rates are plotted separately for all G7 countries and 
are shown in Figure1 to figure 3.
Relationship between GDP, Life Expectancy and Growth Rate of G7 Countries
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76
Figure 1: Annual GDP of G7 countries (in billions)
The Figure1 shows that the annual GDP of all G7 
countries for the selected period has increasing trend 
except USA which has a non-linear trend. USA GDP 
stays on the uppermost part whereas France, Italy, 
UK, Germany and Japan are lying at the bottom. 
These countries have linear trend with low rate of 
increase.
Figure 2 shows that annual average Life expectancy 
of G7 countries linearly increases. Furthermore, at 
the beginning of the selected period, i.e. Japan has 
lowest life expectancy among all G7 countries but it 
gradually increases and gains the highest position in 
the graph. Although, life expectancy of USA 
gradually increases from 1960 to 2017 but occupies 
the lowest position among all. Comparing Figure 1 
and Figure 2 it is observed that GDP of USA 
increases exponentially from 1992 onward but the 
increase in Life expectancy does not follow this 
trend.
Figure 2: Graph of Life expectancy of G7 countries
0
20
40
60
80
100
120
140
160
180
200
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
2012
2016
CANADA GR
FRANCEGR
GERMANYGR
ITALYGR
JAPANGR
UKGR
USAGR
67
71
75
79
83
87
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
2014
2017
Canada LE
GermanyLE
FranceLE
UKLE
USALE
ItalyLE
Relationship between GDP, Life Expectancy and Growth Rate of G7 Countries
http://www.ijSciences.com Volume 8 – June 2019 (06)
77
Figure 3: Graph of Annual Population Growth of G7 Countries
Figure 3, represents Annual Population Growth of G7 
Countries. They do not come up with any clear trend,
all are moving randomly, indicating an over-all
decline in growth rate. Among all G7 countries, 
Germany has an unusual behaviour of positive and 
negative trend, specifically in the periods such as 
1974 to 1986 and 2003 to 2012. Besides the 
Germany; Italy, Japan and UK has also negative 
growth rate for some specific period. When graphs of 
life expectancy and growth rate (Figure 2&3) are 
compared it is found that growth rate of Canada has 
prominent place although it has high and low peaks.
Table1:Displays the descriptive statistics
Mean Median Maximum Minimum Std.Dev
UK
GDP 16.47 16.07 28.19 7.25 6.42
Life expectancy 75.71 75.48 81.30 70.83 3.34
Growth rate 0.40 0.35 0.85 -0.04 0.27
France
GDP 17.97 18.12 28.75 6.08 6.89
Life expectancy 76.25 76.22 82.67 69.87 4.03
Growth rate 0.64 0.57 1.41 0.08 0.29
Italy
GDP 15.52 16.87 22.34 5.46 5.31
Life expectancy 76.41 76.60 83.24 69.12 4.40
Growth rate 0.36 0.29 1.99 -0.17 0.38
Canada
GDP 10.20 9.91 18.84 3.16 4.64
Life expectancy 76.75 77.02 82.47 71.13 3.49
Growth rate 1.28 1.18 2.30 0.80 0.37
US
GDP 93.58 87.36 173.49 31.71 43.78
Life expectancy 74.67 74.89 78.84 69.77 2.96
Growth rate 1.04 0.98 1.70 0.64 0.23
Germany
GDP 22.30 23.95 38.84 0.00 12.05
Life expectancy 75.01 74.90 81.09 69.31 3.83
Growth rate 0.23 0.19 0.93 -1.85 0.48
Japan
GDP 39.09 43.80 61.58 7.96 17.21
Life expectancy 77.68 78.65 84.10 67.67 4.65
Growth rate 0.56 0.40 2.61 -0.19 0.58
Table1displays the descriptive statistics of the GDP, 
growth rate and life expectancy of G7 countries in the 
selected period. The average GDP of USA is high i.e. 
93.58 while Canada has the lowest GDP of all i.e. 
10.2 billion. Whereas, average Life Expectancy
(77.45) of Japan is high, on contrary USA has 
minimum average value (74.534). Moreover, Canada 
has high average growth rate while Germany has 
lowest value of average growth rate among G7 
countries. The high growth rate of Canada may be 
due to the fast and simple immigration policy as 
compared to other G7 countries. Additionally, the 
standard deviation of life expectancy is highest in 
Japan which shows high variability. In contrast, US 
has less variability in the life expectancy as the 
standard deviation is minimum among all. This fact 
can be observed from Figure 2. Population growth 
rate is highest in Canada(1.28) followed by US(1.04), 
the minimum value of population growth rate is 
observed for Germany(0.23). 
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2011
2014
2017
CanadaGR
FranceGR
ItalyGR
JapanGR
USAGR
GermanyGR
Relationship between GDP, Life Expectancy and Growth Rate of G7 Countries
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78
Table2:Regression output of G7 countries
Country b1 b2 R-sq
Canada 
10.25 
(0.32)*
(0)**
-0.169
(0.053)*
(.002)** 0.98
France 
7.53
(0.275)*
(0)**
-0.130
(.0310)*
(.0001)** 0.954
Germany
2.78
(0.155)*
(0.0068)**
-3.53
(1.25)*
(0.000)** 0.87
Italy
1.11
(0.03)*
(0.0002)**
-1.63
(0.402)*
(0.000)** 0.96
Japan
3.4
(0.143)*
(0)**
-2.37
(1.14)*
(.04)** 0.97
UK
1.87
(0.037)*
(0)**
0.823
(0.467)*
(0.08)** 0.98
USA
12.19
(0.368)*
(0)**
-0.094
(0.0688)*
(0.1778)** 0.97
Note: Authors calculations, *represents Standard 
error and ** indicates P-value 
Table 2 reports the regression coefficients when GDP 
is regressed on life expectancy and population growth 
rate. The coefficients of life expectancy are highly 
significant in each of seven countries with positive 
coefficients. The coefficients of population growth 
rate are significant in Canada, France, Germany, 
Italy and Japan and are insignificant for UK and 
USA. It is also found that all countries except UK
have negative coefficients (in significant). The high 
values of coefficient of determination R2
indicate 
fairly good fit to each of G7 countries. The findings 
in Table 2 are further confirmed by computing the 
correlations between the three variables of interest.
Table 3:Correlation Coefficient between GDP, population growth and growth rate of G7 countries 
Country Corr. Coefficient.
b/w GDP&Growth rate
Corr. Coefficient
b/w GDP& life 
expectancy
Corr. Coefficient b/w 
Growth rate & life 
expectancy
Canada -0.5734 0.9225 -0.7743
France -0.4753 0.97086 -0.5714
Germany -0.07322 0.96771 -0.07594
Italy -0.2223 0.95901 -0.3902
Japan -0.8219 0.91784 -0.8145
UK 0.52954 0.96935 0.38509
USA -0.54095 0.93985 -0.58296
Table 3 represents the correlation coefficients 
between GDP and Population Growth Rate, between 
GDP and Life Expectancy, and between Population 
Growth Rate and Life Expectancy. Correlation 
coefficients between GDP and Population Growth 
Rate are all negative except for UK, where it is 
positive, and the coefficient for Germany reports very 
weak negative correlation. The correlation 
coefficients between GDP and Life Expectancy are 
highly positive for all seven countries, showing a 
strong bonding between the two variables. Further 
the correlation coefficients between Population 
Growth Rate and Life Expectancy are negative for all 
countries excluding UK once again. 
Conclusion: 
This study aims to study the GDP (per capita 
income), population growth rate and life expectancy 
of G7 countries. Studies showed that high income 
group countries lead to increase in the life 
expectancy. Increase in life expectancy means a large 
number of elderly people which may cause 
overburden on economy of the country. Empirical 
analysis shows that Japan has highest average GDP 
and life expectancy. But the growth rate of Canada is 
high among all G7 countries, this may be due to a 
large proportion of immigrants. The findings of the 
study agree with the existing empirical studies, which 
say that countries in the higher income group have 
low population growth accompanied by higher life 
expectancy (see Table1). This phenomenon is a 
special feature of G7 countries which is quite un -
natural as the population structure in each of these 
seven countries is away from what is called the 
healthy structure of population. 
Future Studies:
The current study is carried out only for G7 countries 
having common trends regarding GDP and life 
expectancy. However the same study may yield 
Relationship between GDP, Life Expectancy and Growth Rate of G7 Countries
http://www.ijSciences.com Volume 8 – June 2019 (06)
79
different results if done for developing and under 
developing countries.
Reference:
1. Baker, D., Delong, J. B., &Krugman, P. R. (2005). Asset 
returns and economic growth. Brookings Papers on 
Economic Activity, 1, 289-330.
2. Becker, G.S., Philipson, T.J., and Soares, R.R. (2003).The 
quantity and quality of lifeand the evolution of world 
inequality.National Bureau of Economic 
Research.(NBERWorking Paper Series 9765).
3. Cervellati, M., &Sunde, U. (2011). Life expectancy and 
economic growth: the role of the demographic transition. 
Journal of economic growth, 16(2), 99-133.
4. Hasan, M. S. (2002). The long-run relationship between 
population growth and per capita income in Bangladesh. The 
Bangladesh Development Studies, 28(3), 65-84.
5. Hasan, M. S. (2010). The long-run relationship between 
population and per capita income growth in China. Journal of 
Policy Modeling, 32(3), 355-372.
6. Heady, D. D., & Hodge, A. (2009). The effect of population 
growth on economic growth: A meta-regression analysis of 
the macroeconomic literature. Population and Development 
Review, 35, 221-248.
7. Kelley, A. C., & Schmidt, R. M. (1995). Aggregate 
population and economic growth correlations: the role of the 
components of demographic change. Demography, 32(4), 
543-555.
8. Linden, M., & Ray, D. (2017). Aggregation bias-correcting 
approach to the health–income relationship: Life expectancy 
and GDP per capita in 148 countries, 1970–2010. Economic 
Modelling, 61, 126-136.
9. Maddison, A. (2003): The World Economy: Historical 
Statistics. OECD Development Centre, Paris.
10. Peterson, E. W. F. (2017). The role of population in 
economic growth. SAGE Open, 7(4), 2158244017736094.
11. Rodgers, G.B. (1979). Income and inequality as determinants 
of mortality: an international cross-section analysis. 
Population Studies 33(2): 343–351. doi:10.2307/2173539.
12. Schnabel, S. K., &Eilers, P. H. (2009).An analysis of life 
expectancy and economic production using expectile frontier 
zones.Demographic Research, 21, 109-134