Dr. Ka-fu Wong
ECON1003
Analysis of Economic Data
Ka-fu Wong © 2003
Project A - 1
A test of the relation between fertility rate
and mortality rate?
Ka-fu WONG (Presenter)
&
Alice LEE (Writer)
A trimmed down version
Ka-fu Wong © 2003
Project A - 2
Are mortality and fertility related?
 Various studies by demographers and economists have
suggested a relationship between fertility, infant mortality
and income.
 A partial list of studies by economists include
 Barro and Becker (1989)
 Kalemli-Ozcan (2002)
 Doepke (2002)
 Burdsall (1988)
• Barro, Robert and Gary S. Becker (1989): “Fertility Choice in a Model of Economic
Growth,” Econometrica 57(2): 481-501.
• Kalemli-Ozcan, Sebnem (2002) “A Stochastic Model of Mortality, Fertility, and Human
Capital Investment.” Forthcoming, Journal of Development Economics.
• Doepke, Matthias (2002): “Child Mortality and Fertility Decline: Does the Barro-Becker
Model Fit the Facts?” Manuscript, UCLA.
• Birdsall, N. (1988): “Economic Approaches to Population Growth”, in Handbook of
Development Economics, by H. Chenery and T.N. Srinivasan, Eds, Vol. 1, Elsevier:
Amsterdam.
Ka-fu Wong © 2003
Project A - 3
Theme of this project
 We use fertility data across countries to estimate
the relationship between fertility and mortality
and per capita income.
Ka-fu Wong © 2003
Project A - 4
Data sources and description
 World Development Indicator (WDI) 2002, available from
the HKU main library.
 Time: year 2000 only.
 172 countries (out of 207) with relevant variables
 GDP per capita (in 1995 US) – a proxy for income per
capita.
 Infant mortality rate (per 1,000 live births)
 Fertility rate (births per woman)
 Drop 35 countries:
 32 countries do not report GDP per capita.
 Additional 3 countries do not report fertility rate.
 Also consider adult illiteracy rate but substantial number of
developed countries (such as UK and US) do not report this
variable.
 Not considered in our final analysis.
Ka-fu Wong © 2003
Project A - 5
Descriptive statistics: Fertility rate
count
172
mean
3.15
Standard deviation
1.60
1st quartile
1.77
minimum
1.02
median
2.63
maximum
7.22
3rd quaritle
4.42
range
6.20
interquartile range
2.64
34.3% countries below replacement fertility rate: (=2.1).
0
1
2
3
4
5
6
7
8
Hong Kong
Ka-fu Wong © 2003
Project A - 6
Descriptive statistics: Mortality rate
count
172
mean
38.76
Standard deviation
35.99
1st quartile
10.01
minimum
2.90
median
23.60
maximum
153.60
3rd quaritle
60.00
range
150.70
interquartile range
50.00
0
50
100
150
200
250
Hong Kong
Ka-fu Wong © 2003
Project A - 7
Descriptive statistics: GDP per capita
count
172
mean
6,617.45
Standard deviation
10,809.61
1st quartile
528.212
minimum
115.88
median
1,611.19
maximum
56371.99
3rd quaritle
5,372.00
range
56256.12
interquartile range
4,843.79
0
10000
20000
30000
Hong Kong
Ka-fu Wong © 2003
40000
50000
60000
Luxembourg
Project A - 8
Scatter plot: fertility vs. GDP per capita
y = -7E-05x + 3.6178
R2 = 0.2245
8
7
fertility rate
6
5
4
3
2
1
0
-1 0
10000
20000
30000
40000
50000
60000
GDP per capita
Ka-fu Wong © 2003
Project A - 9
Scatter plot: fertility vs. mortality
8
7
fertility rate
6
5
4
3
2
y = 0.0382x + 1.6748
R2 = 0.739
1
0
0
50
100
150
200
mortality rate, infant
Ka-fu Wong © 2003
Project A - 10
Regression model:
Fertility
=
1.7950
-
0.00973
GDP
+
0.0367
Stderror
(0.1230)
(0.00664)
(0.0020)
P-value
[9.44E-32]
[0.1446]
[2.83E-42]
Not statistically different
from zero even at 10%
level of significance.
Economically, holding
mortality rate constant,
we expect fertility rate
to lower by 0.00973 per
woman when the per
Ka-fu Wong © 2003
capita income increases
by US$1000.
mortality
Statistically different
from zero at 1% level
of significance.
Economically, holding per
capita income constant,
we expect the fertility rate
to rise by 0.0367 per
woman when mortality
increases by 1 infant
death per thousand births.
Project A - 11
Regression model:
ANOVA
Source
SS
df
MS
F
p-value
Regression
324.125
2
162.0623
243.34
1.76E-50
Residual
112.555
169
0.666
Total
436.677
171
R-square
0.742
Rejects the hypothesis that all
coefficients are jointly zero.
The explanatory variables
together explain 74.2% of
the variation in fertility
rate.
Ka-fu Wong © 2003
Project A - 12
Conclusion
 Fertility rate is strongly directly related to mortality rate.
 When mortality rate is included, the explanatory power of
income per capita on fertility rate seems small.
 Cautions:
 Although the model setup seems to suggest a low
mortality rate will cause a low fertility rate. The reverse
could be true. Countries with a low fertility rate may
spend more on infant survival and hence a low mortality
rate.
 The true relationship may not be linear, e.g., Strulik and
Sikandar (2002).
Strulik, Holger and Siddiqui Sikandar (2002): “Tracing the income-fertility
nexus: Nonparametric Estimates for a Panel of Countries,” Economics
Bulletin, 15 (5), pp. 1-9.
Ka-fu Wong © 2003
Project A - 13
A test of the relation between fertility
rate and mortality rate?
- End -
Ka-fu Wong © 2003
Project A - 14