Paper prepared for African Economic Conference 2013 on “Regional Integration in Africa”
The Impact of Demographic Changes on Economic Growth in Egypt
Mahmoud Mohamed ElSarawy
Central Agency for Public Mobilization and Statistics (CAPMAS), Economic department
3 Salah Salem Street, Nasr city, Cairo, Egypt
m_sarawy@yahoo.com
ABSTRACT:
Demographic changes have a strong impact on economy, especially in GDP and there are
many of interconnections between them. The demographic variables had many changes in recent
years. This study will describe some of demographic variables such as life expectancy at birth,
Education rate of women to men, Relative distribution of the population by age group, Fertility rate,
Labor force in Egypt during the period 1980 to 2010. This study will analyze and test if there is a
significant relation between the demographic variables and GDP, it will also measure the impact on
economic during this period. It will give an interpretation of these relationship and link between
them whether positive or negative relation and calculate the regression equation to predict GDP
depending on demographic variables using the regression in simple and multiple forms, linear and
non-linear methods. This study will use statistical measures to test the efficiency of the estimates.
So it should take the most benefits of these changes and opportunities that arise from it, do
more studies that illustrate these overlaps, prepare development and strategy policies which lead
ultimately to achieve economic growth rates on difference run.
Key Words: Demographic changes, economic growth, age structure, life expectancy.
INTRODUCTION:
Changes in age distribution of the population can have important economic effects. These
effects reflect the influence of changes in the number of working-age individuals per capita and of
shifts in behavior, such as, decreased Total dependency ratio lead to increase in GDP. However,
these effects are not automatic consequences of fertility decline. They depend on many policies,
institutions, and conditions that determine an economy’s capacity to equip its people with human
and physical capital and to absorb them into productive employment.
These recent findings indicate that when it comes to economic growth and development,
population matters. This conclusion gains significance in light of existing knowledge regarding
policy interventions aimed at accelerating the pace of fertility decline. In basic and secondary
education, especially for girls as this paper will insure that; in family planning and reproductive
health; and in improved labor market opportunities for women this also will increase GDP due to
the increase in labor force then it also improve not just income per capita, but also social equity.
IMPORTANCE OF STUDY:
The Importance of this study is to measure current demographic changes in Egypt, which
change with clear rates such as population age structure, labor force, fertility rates, life expectancy
at birth, women's education level and their participation in the labor force and change in
dependency ratio which affects on the Gross domestic product (GDP) and thus influence the
economic growth rate and use the better regression model to predict GDP depending on
Demographic variables.
OBJECTIVES:
The main objective of this study is to investigate the relationship between changes in the
characteristics of population and economic growth through a theoretical framework that defines that
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relationship. And describe and analyze the demographic variables affecting population growth and
significant interpretation of the relations between these variables which related to the economy.
REVIEW OF LITERATURE:
- Many previous studies dealt with measuring the impact of the change in demographics and
economic growth, including a study published in more than 40 years, entitled:(Population
Growth and Economic Development in Low-Income Countries (1958) , Coale and Hoover’s
seminal book).
- This study concluded that the decline in population growth does not necessarily lead to
lower growth in the labor force. It also stressed that people with high incomes appreciate the
value of time and believe that raising children need more time and therefore are willing to
reduce the number of children, so that income growth reduce fertility rate.
- Previous studies indicated that there is strong evidence on the mutual influence between the
demographic changes, especially reproductive and income growth. It also stressed that
population growth and changes in the age structure of the population have a direct impact on
economic growth in general.
THE CONCEPTUAL FRAMEWORK OF THE STUDY:
Life expectancy
at birth
Annual growth rate of the
population
Total fertility
rate
Labor
force
Economic
growth
Demographic
dependency ratio
The relative distribution of
the population by age groups
Women's
education
The contribution of women
in the labor market
METHODOLOGY:
This study is based on the use of quantitative analytical and descriptive methods to study the
demographic variables which affecting economic growth through uses the statistical analysis
techniques in simple forms and multiple linear and non-linear regression and use statistical
measures to test the efficiency of the estimates. This study based on time-series data for the
variables that have been obtained from various sources for the period (1980-2010).
1. Description of some important explanatory variables:
1.1 Annual growth rate of the population:
Figure (1)
Figure (2)
Population ages 15-64 (% of total)
Population growth (annual %)
2.50
65.00
60.00
2.00
55.00
1.50
50.00
Source: Figure 1,2 The United Nations Population Division’s World Population Prospects The 2010 Revision.
There are decreasing in annual population growth from 2.33 in 1980 to 1.73 in 2010 as
we can see from figure (1) but The ratio of working age (from 15-65) to total population
keeps increasing from 54% in 1980 to reach 63% in 2010 as we can see from figure (2).
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1.2 The relative distribution of the population by age groups:
Figure (3) reveals three main features of the Egypt demographic transition:
a. The ratio of working age (from 15-65) to total population keeps increasing until it
will reach 65% in 2030. This obviously brings about potential for economic
growth in one hand, and pressure on employment creation on the other hand.
b. The ratio of old people is also on an increase from 5% in 1981 to around 10% in
2030. This sharp increase requires a well-built plan for health care system as well
as social security.
c. The ratio of young children (0-14) is decreasing and this decline is about enough
to off-set the increase in the rate of population, leaving the number of young
children remains unchanged or decreases a bit.
Figure (3)
Percintage of population categories, by five-year age group %
70%
60%
50%
40%
30%
20%
10%
0%
0-4
5-14
15-64
64+
Source: The United Nations Population Division’s World Population Prospects The 2010 Revision.
Millions Person
1.3 Labor force:
As a result of increased population category (15-64) years over the past 30 years as we
can see from figure (4) it led to an increase in labor force from 16.8 to 27.1 million, mean
increase of 58% and this leads to an increase in GDP per capita as increase in output of the
labor force.
Figure (4)
30
Labor force
25
20
15
Source: International Labour Organization, Key Indicators of the Labour Market database.
1.4 Total fertility (children per woman):
Egypt had a high fertility rate ,it reached 5.7 in 1970-1975 and it was 4.5 in the world ,
while today it enjoys a rate that equal the world average level 2.7. The decline in fertility rate
is most speedy since 1980-1985.
The total fertility rate in the world decline from 4.5 in 1970 to 2.5 in 2010 it mean
decrease of 44% and in Africa it decline from 6.5 in 1970 to 4.5 in 2010 it mean decrease of
33% Also Egypt it decline from 5.7 in 1970 to 2.7 in 2010 it mean decrease of 52% as we can
see from figure (5).
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Figure (5)
Figure (6)
Total fertility in Egypt
Total fertility ( world ,Africa and Egypt)
6
children per woman
children per woman
As a medium expected, the total fertility rate in Egypt will reach to 2.2 in 2030 as we
can see from figure (6).
5
4
3
7
6
5
4
2
3
1
2
High
MEDIUM
World
Low
Africa
Egypt
Source: Figure 5,6 Population Division of the Department of Economic and Social Affairs of the United Nations
Secretariat. World Population Prospects: The 2010 Revision.
1.5 Female Labor participation rate and their education :
In the last years the Female Labor participation rate increase from 19.8 % in 2000 to
23.5 % in 2010 as we can see from figure (7) as a result of increase in female education and
their skills as it is shown in figure 8 ,that lead to increase in number of labor force and its
qualification.
Figure (7)
Figure (8)
Ratio of girls to boys in primary and
secondary education (%)
Labor participation rate, female
(% of female population ages 15+)
100
90
80
70
60
24
22
20
18
Source: Figure 7. International Labour Organization, Key Indicators of the Labour Market database.
Figure 8. UNESCO Institute for Statistics.
1.6 Life expectancy at birth:
Figure (9)
years
Life expectancy at birth for both sexes combined (years)
85
75
65
WORLD
55
AFRICA
45
Egypt
Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.
World Population Prospects: The 2010 Revision. http://esa.un.org/unpp
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In Egypt Life expectancy at birth as we can see from figure (9) increased from 57 to 73
years from 1980 to 2010, and Expected to reach 77 years in 2030 . It means that Life
expectancy at birth in Egypt will increase 20 years through 50 years ,this result of
improvement of the health care system of Egypt as well as the innovations in medicine. But
Life expectancy at birth in the world increase 10 years and Africa increase 15 years only from
the period (1980-2010).
1.7 Total dependency ratio
Total dependency ratio in 1970 in Egypt 90 % as equal to Africa Ratio but the world
total dependency ratio 75 % in the same year , and in 2030 expected that ratio in Egypt to be
less than world and Africa ratio and decrease 40 % to reach 50 % also the world ratio
expected to be 52% and Africa 67% in the same year as we can see from figure (10) . This
decrease in total dependency ratio in Egypt as a result of increase in labor force in the same
period as we explained before.
Figure (10)
Ratio of population aged 0-14 and 65+ per 100 population 15-64
100.0
90.0
80.0
70.0
60.0
50.0
40.0
WORLD
AFRICA
Egypt
Source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat.
World Population Prospects: The 2010 Revision. http://esa.un.org/unpp
2. Analyzing and testing the relationship between some important explanatory variables:
Analyze and test relations between some demographic variables (Life expectancy at
birth for both sexes combined, Ratio of girls to boys in primary and secondary education (%) ,
Population ages 15-64 (% of total) , Total fertility (children per woman), Labor force, Total
dependency ratio) and measure the impact on GDP.
2.1 Multiple regression model of GDP on all demographic variables in the study:
Table )1( Coefficients 1
Model
1
Unstandardized Coefficients
t
Sig.
Dependent Variable: GDP
Correlations
Collinearity Statistics
B
Std. Error
Zero-order Partial Part Tolerance
VIF
(Constant)
-2892.792
2713.130
-1.066 .304
PA
8.433
27.900
.302 .767
.968
.081
.010
.002
511.361
LF
3.306E-5
.000
4.168 .001
.991
.744
.134
.015
64.618
TF
-108.853
211.445
-.515 .615
-.959
-.136 -.017
.002
566.806
GE
.903
2.100
.430 .674
.885
.114
.014
.087
11.439
DR
17.156
19.174
.895 .386
-.955
.233
.029
.001
1.748E3
LE
16.836
12.602
1.336 .203
.913
.336
.043
.008
120.500
Source: by the author
1
Gross domestic product in constant prices ( GDP) , Life expectancy at birth for both sexes combined ( LE) , Ratio of
girls to boys in pre-university education (%) (GE) , Population ages 15-64 (% of total) (PA) , Total fertility (children
per woman) (TF) , Labor force (LF) ,Total dependency ratio (DR).
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It is clear that these demographic variables included in the model explain by 98.3% change
that occurs in the GDP. And significant of F test emphasizes significant regression model as we
can see from equation number (1), but in Table (1) we find multicollinearity2 problem.
Multiple linear regression equation (1)
GDP = -2892.792+ 8.433 PA + 3.306E-5 LF - 108.853 TF + .903 GE + 17.156 DR + 16.836 LE
To ensure the multicollinearity problem between the independent variables it should be
done a scatter plot (Matrix) to measure the correlation between the demographic variables to
each other in the presence of GDP as the dependent variable so as not to affect those correlations
for the next analysis.
It is clear that from Table (2) there are significant correlation relationships (strong,
moderate) between demographic variables, whether positive correlation relationships such as
fertility rate and dependency ratio (97%) or negative such proportion of the population in the age
group of (15-64) and the dependency ratio (96%).
And also the Figure (12) Scatter plot matrix for independent variables of each other in the
presence of the dependent variable GDP it show strong relationships between these variables,
which in some variables reach to be linear.
Table (2)
Scatter plot (Matrix) for all Variables
GDP PA
LF
TF
GE
DR
Figure (12)
Scatter plot for all Variables on GDP
LE
GDP 1.000 .968 .991 -.959 .885 -.955 .913
PA
.968 1.000 .973 -.993 .939 -.996 .975
LF
.991 .973 1.000 -.963 .880 -.958 .910
Pearson Correlation TF -.959 -.993 -.963 1.000 -.929 .998 -.982
GE .885 .939 .880 -.929 1.000 -.940 .944
DR -.955 -.996 -.958 .998 -.940 1.000 -.987
LE
Sig.
(1-tailed)
.913 .975 .910 -.982 .944 -.987 1.000
GDP
.
PA
.000
.000 .000 .000 .000 .000 .000
LF
.000 .000
TF
.000 .000 .000
.
.000 .000 .000 .000 .000
.
.000 .000 .000 .000
.
GE .000 .000 .000 .000
.000 .000 .000
.
DR .000 .000 .000 .000 .000
LE
Source: by the author
.000 .000
.
.000 .000 .000 .000 .000 .000
.000
.
Source: by the author
It’s clear that the multiple linear regression model is non significant and to avoid the
problem of Multicollinearity we will use simple linear and quadratic regression models
between each of the independent demographic variables on GDP as the dependent variable.
2.2 Simple linear and quadratic regression of GDP on all demographic variables in the study:
As we can see from the estimates of curves in figures (13, 14, 15, 16, 17 and 18) below, the
data points are not clustered about a straight line but instead follow a curve. This means we
should not determine a regression line but instead should try to fit a curve to the data. We handle
this quadratic relationship in our model and analysis by including a term for the square
(quadratic) of the independent variable as an additional regression covariate:
Yi = a + b1 Xi + b2 Xi2 + ei
(2)
Where: Yi= individual values for the dependent variable (GDP)
Xi= individual values for each independent variable (PA, LF, TF, GE, LE and DR)
2
Multicollinearity is a problem in multiple regression that develops when one or more of the independent variables is
highly correlated with one or more of the other independent variables.
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The equation (2) will give us linear and quadratic regressions of Y on X in the study.
Coefficient of Determination R2 =
([S(yi-yAVE)2 ] -{[(n-1) / (n-p)] * [S(yi-Yi)2]}) / (S(yi-yAVE)2) (3)
Where: yi= individual values for the dependent variable (GDP)
xi= individual values for each independent variable (PA, LF, TF, GE, LE and DR)
yAVE= average of the y values
n = number of pairs of data
p = number of parameters in the polynomial equation
Yi= {[(2a*(Cx/Cis)2]-b2+b+(4ac)}/(4a)
S = the sum of all the individual values
2.2.1 Regression models of GDP on change in the age structure of the population (PA):
Using Person correlation coefficient shows that there is a strong positive relationship
between GDP and Change in the age structure of the population it is about 96.8%. Table (3)
shows that there is a significant relationship between the two variables. According to the
determination coefficient value in linear regression model the change in GDP by 93.8% is a
result of change in population ages (15-64) and this percentage is 97% in quadratic model. So
that the equation (5) of quadratic regression model could be more dependable than linear
model in forecasting the value of GDP and that confirmed by figures (13).
Figure (13) Curve Estimation
Table (3) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
Linear
0.938
0.97
Quadratic
F
df1
df2
Sig.
287.14
1
19
.000
295.43
2
18
.000
The independent variable is PA.
Regression Equations
y= -1843.492 + 37.029x + ei
y= 9902.674 - 358.812x + 3.328x2+ ei
(4)
(5)
Source: by the author
2.2.2 Regression models of GDP on labor force (LF):
Figure (14) Curve Estimation
Table (4) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
Linear
.983
Quadratic
.983
F
df1
df2
Sig.
1083.091
1
19
.000
524.926
2
18
.000
The independent variable is LF.
Regression Equations
y= -265.939 + (3.032E-005) x+ ei
(6)
y= -168.464 + (2.098E-005)x + (2.175E-013) x2+ ei (7)
Source: by the author
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Using Person correlation coefficient shows that there is a strong positive relationship
between GDP and Labor force it is about ..99%. Table (4) shows that there is a significant
relationship between the two variables. According to the determination coefficient value the
change in GDP by ..99% is a result of change in Labor force according to both models that
confirmed by figures (14) .
2.2.3 Regression models of GDP on total fertility (children per woman) (TF):
Using Person correlation coefficient shows that there is a strong negative relationship
between GDP and Fertility rate it was about ..9.%. Table (5) shows that there is a significant
relationship between the two variables. According to the determination coefficient value in
linear regression model the change in GDP by .99.% is a result of change in Fertility rate and
this percentage is ..99% in quadratic model. So that the equation (9) of quadratic regression
model could be more dependable than linear model in forecasting the value of GDP that
confirmed by figures (15).
Figure (15) Curve Estimation
Table (5) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
Linear
.919
Quadratic
.974
F
df1
df2
Sig.
215.810
1
19
.000
343.363
2
18
.000
The independent variable is TF.
Regression Equations.
y= 1202.423 - 263.862 x+ ei
y= 3103.325 - 1460.969x + 185.792 x2+ ei
(8)
(9)
Source: by the author
2.2.4 Regression models of GDP on Ratio of girls to boys in pre-university education (GE)
Using Person correlation coefficient shows that there is a strong positive relationship
between GDP and women's participation in education it was about ..9.%. Table (6) shows
that there is a significant relationship between the two variables. According to the
determination coefficient value in linear regression model the change in GDP by ..99% is a
result of change in women's participation in education and this percentage is ..9.% in
quadratic model. So that the equation (11) of quadratic regression model could be more
dependable than linear model in forecasting the value of GDP that confirmed by figures (16).
Figure (16) Curve Estimation
Table (6) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
F
df1
df2
Sig.
Linear
.783
68.623
1
19
.000
.928
116.540
2
18
Quadratic
.000
The independent variable is GE.
Regression Equations.
y= -1168.985 + 17.030 x+ ei
y= 13474.393 - 315.655x + 1.882 x2+ ei
(10)
(11)
Source: by the author
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2.2.5 Regression models of GDP on life expectancy at birth (LE):
Using Person correlation coefficient shows that there is a strong positive relationship
between GDP and Life expectancy at birth it was about .999%. Table (7) shows that there is a
significant relationship between the two variables. According to the determination coefficient
value in linear regression model the change in GDP by .999% is a result of change in Life
expectancy at birth and this percentage is .999% in quadratic model. So that the equation (13)
of quadratic regression model could be more dependable than linear model in forecasting the
value of GDP and that confirmed by figures (17).
Figure (17) Curve Estimation
Table (7) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
F
df1
df2
Sig.
Linear
.833
94.570
1
19
.000
Quadratic
.946
157.810
2
18
.000
The independent variable is LE.
Regression Equations.
y= -1899.856 + 32.464 x+ ei
y= 21050.169 -635.758x + 4.854 x2+ ei
(12)
(13)
Source: by the author
2.2.6 Regression models of GDP on total dependency ratio (DR):
Using Person correlation coefficient shows that there is a strong negative relationship
between GDP and Total dependency ratio it was about ..9.%. Table (8) shows that there is a
significant relationship between the two variables. According to the determination coefficient
value in linear regression model the change in GDP by .999% is a result of change in Total
dependency ratio and this percentage is 96.8 % in quadratic model. So that the equation (15)
of quadratic regression model could be more dependable than linear model in forecasting the
value of GDP and that confirmed by figures (18).
Figure (18) Curve Estimation
Table (8) Model Summary and Parameter Estimates
Dependent Variable: GDP
Equation
Model Summary
Model
R Square
F
df1
df2
Sig.
Linear
.911
195.386
1
19
.000
Quadratic
.968
271.225
2
18
.000
The independent variable is DR.
Regression Equations.
y= 1279.526 - 13.567x+ ei
y= 3798.290 - 88.031x + .544 x2+ ei
(14)
(15)
Source: by the author
CONCLUSION:
- Egypt faced during the past 30 years to clear changes in the demographic characteristics
where the annual population growth rate dropped from 2.33 in 1980 to 1.73 in 2010, The
proportion of the population (15-64) to the total population increased from 54% in 1980 to
63% in 2010, Labor force increased from 16.8 million in 1990 to 27.1 million in 2010, an
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-
-
increase average of 58%, The fertility rate decreased from 5.7 on average during the period
from 1970 to 1975 to 2.7 during the period from 2005 to 2010 and will reach almost 2.2 on
average as expected in 2030, Participation rate of women in the labor market increased
from 19.8% in 2000 to 23.5% in 2010, Life expectancy at birth increased from 57 years to
73 years from 1980 to 2010 and expected to continue this increase to up to 77 years in 2030
and finally Dependency ratio fell from 90% in 1970 to 57% in 2010 and expected to
decline this rate to reach 50% in 2030.
Quadratic regression is a process by which the equation of a parabola of "best fit" is found
for a set of data rather than linear regression so it can be used to predict GDP depending on
Demographic variable.
Demographic changes have strong impact on the economy and especially GDP and
gathered many of the interconnections and must make the most of these changes and
opportunities that arise from it and do more studies that illustrate these entanglements and
prepare development and strategy policies which leading ultimately to achieve economic
growth rates on Short-term timeframe, medium and long term.
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