Statistique, Développement et Droits de l‘Homme
Session C-Pa 5c
Factors Affecting Total Fertility Rates in
Developing Countries
Abu Jafar SUFIAN
Montreux, 4. – 8. 9. 2000
Statistique, Développement et Droits de l‘Homme
Factors Affecting Total Fertility Rates in Developing
Countries
Abu Jafar SUFIAN
Professor, Department of Urban and regional Planning, King Faisal University
PO Box 2397
Dammam 31451, Saudi Arabia
T. + 966 3 8951748 (private) F. + 966-3 8578739 (office)
drsufian@yahoo.com
ABSTRACT
Factors Affecting Total Fertility Rates in Developing Countries
The effects of socioeconomic factors and family planning program effort on total fertility rate
have been examined in this paper with national data for 50 developing countries. The explanatory
variables chosen are: life expectancy at birth, infant mortality rate,per capita gross national
product, energy consumption per capita, male literacy rate, percent of population with access to
sanitation service, population density, per capita daily calories, female literacy rate, percent of
urban population, percent of population with access to safe water supply, population per hospital
bed, population per physician, number of oral dehydratation solution packets used per 100
diarrhea episodes, and family planning program effort score. The data came from the "Family
Planning and Child Survival: 100 Developing Countries" compiled by the Center for Population
and Family Health, Columbia University, and from the 1987 World Population Data Sheet.
The multiple regression technique has been employed to identify variables that play important
roles in determining the total fertility rate. The analysis shows that the family planning program
effort has the largest contribution in lowering the total fertility rate, followed by percent of urban
population, female literacy rate in that order. Policy implications are discussed
RESUME
Facteurs d’influence du taux total de fécondité dans les pays en voie de développement
Ce document examine, à l’aide de données nationales de 50 pays en voie de développement,
les effets des facteurs socio-économiques et des efforts effectués en matière de plannings familiaux
sur le taux de fécondité. Les explications retenues sont les suivantes : l’espérance de vie à la
naissance, le taux de mortalité infantile, le produit national brut par habitant, la consommation
d’énergie par habitant, le taux d’alphabétisation chez les hommes, le pourcentage de la population
ayant accès aux services sanitaires, la densité de la population, le nombre de calories par jour et
par habitant, le taux d’alphabétisation chez les femmes, le pourcentage de la population vivant
dans les villes, le pourcentage de la population ayant accès à l’eau potable, le nombre de lits
d’hôpitaux pour la population, le nombre de médecins pour la population, le nombre de paquets de
solution d’hydratation pour 100 cas de diarrhée et les efforts du programme de planning familial.
Les données sont issues de l’ouvrage « Planning familial et survie chez l’enfant : 100 pays en voie
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Statistique, Développement et Droits de l‘Homme
de développement », édité par le « Centre pour la santé de la population et des familles »,
Université de Columbia, et du rapport sur les données démographiques mondiales de 1987.
La technique de régression a été employée pour identifier les variables qui jouent un rôle
majeur dans la détermination du taux général de fécondité. Ces analyses montrent que les efforts
du programme de planning familial ont très largement contribué à faire baisser le taux de
fécondité, ainsi que la population vivant dans les villes et le taux d’alphabétisation chez les femmes.
Les conséquences au niveau des mesures politiques sont également discutées.
1. Introduction
It is evident that in recent years substantial fertility declines occurred in many of the
developing countries – the average total fertility rate declined by half from 6.01 births per woman in
1965-70 to 3.00 births per woman in 1995-2000 (United Nations, 1999). A question of concern to
demographers and other social scientists is whether this decline in fertility has been fostered mainly
by the family planning programs. Indeed, this reduction in fertility has in some cases led to the
belief that the gap between the fertility levels of the developing and developed countries can be
substantially reduced by the socialization of family planning services.
Available evidence, however, suggests that developing countries have considerable fertility as
well as contraceptive use differentials among themselves (Population Reports, 1985). These
differentials can well be attributed to the fact that socio-economic factors are often differentially
distributed across social groups that exist in a society or between societies. Moreover, given that
developing countries themselves differ considerably in terms of socio-economic development, it
may be that the greatest reductions in fertility occurred in those countries that experienced
significant socio-economic development.
The effects of socio-economic factors on fertility have been examined in a number of studies.
Education depresses fertility by increasing the age at marriage, and by increasing the likelihood of
contraceptive use (Casterline, et al., 1984; Entwisle and Mason, 1985; Jiang, 1986; Kim, 1987;
Krishnan, 1988; Prada and Ojeda, 1986; Rubin-Kurtzman, 1987). Total fertility rates are higher
among rural women than among urban women (Alam and Casterline, 1984; Rubin-Kurtzman, 1987;
Prada and Ojeda, 1986). Income has been found to be negatively related to fertility (RubinKurtzman, 1987; Jiang, 1986). Family planning programs exert very strong direct negative effects
on fertility (Poston and Baochang, 1987; Cutright and Kelly, 1981; Mauldin and Berelson, 1978;
Tsui and Bogue, 1978).
The pivotal question in this paper is whether, and if so, to what extent, socio-economic and
other developmental factors do induce changes in the national fertility levels, and how do these
effects compare to that induced by the family planning program effort. It is believed that the socioeconomic and other developmental factors do exert significantly independent as well as joint
influence on fertility after eliminating the effect of the family planning program effort. An attempt
has been made in this paper to identify these factors and their relative contributions towards the
variations in national fertility levels. The importance of the study derives from the fact that it is
necessary to identify those population groups whose fertility is high but reducible through changes
in government policy and a redistribution of available resources.
2. Methods and Findings
The total fertility rate (defined as the number of live births a hypothetical woman would have
if she survived tFactors Affecting Total Fertility Rates in Developing Countrieso the end of her
reproductive period and experienced the given set of age-specific fertility rates) of 50 developing
countries from Asia, Africa, aFactors Affecting Total Fertility Rates in Developing Countriesnd
Latin America have been analyzed in this paper on the basis of data obtained from Family Planning
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Statistique, Développement et Droits de l‘Homme
and Child Survival: 100 Developing Countries (Ross et. al., 1988) compiled by the Centre for
Population and Family Health, Columbia University, New York, as well as from the 1987 World
Population Data Sheet (Population Reference Bureau, 1987).
The explanatory variables considered in this study are those that appeared influential in earlier
studies in accounting for fertility variation. These variables are infant mortality rate, i.e., infant
deaths per thousand live births, life expectancy at birth, population per square kilometer, per capita
daily calories, male and female literacy rates i.e., percent of population 15 years old and over who
can read and write, per capita gross national product, per capita energy use, percent of urban
population, percent of population with access to safe water supply, population per hospital bed,
population per physician, number of oral rehydration solution packets used per hundred diarrhea
episodes, percent of population with access to sanitation services, and family planning program
effort score based on four components: policy and stage setting, service, record keeping and
evaluation, availability and accessibility. Ross et al., (1988) and Population Reference Bureau
(1987) discussed these variables in more details. The final analysis was based on 50 observations
for which values were available for all sixteen variables.
Table 1. Means and Standard Deviations of the Dependent and the Explanatory Variables: 50
Developing Countries
Variable
Mean
Total Fertility Rate
5.548
Life Expectancy at Birth
55.980
Infant Mortality Rate
92.366
Population per Square Kilometer
147.930
Per Capita Daily Calories
2302.980
Male Literacy Rate
68.040
Female Literacy Rate
51.440
Per Capita Gross National Product
796.304
Per Capita Energy Use
12.149
Percent of Urban Population
34.280
Percent of Population With Access
to Safe Water Supply
46.240
Population per Hospital Bed
922.180
Population per Physician
11390.600
Number of Oral Rehydration Solution Packets
Used per Hundred Diarrhea Episodes
31.900
Percent of Population With Access to
Sanitary Services
41.833
Family Planning Program Effort
Score
35.142
Standard deviation
1.460
8.639
37.665
376.970
392.486
20.480
26.815
895.908
21.989
17.838
23.723
1041.986
11718.343
32.782
27.791
25.473
The means and standard deviations for the dependent as well as for the explanatory variables
are presented in Table 1. The total fertility rate has an average value of 5.6 children per woman
varying from lows of 2.3 children in Maritius, and 2.4 children in Chile to highs of 8.5 children in
Rwanda, and 8.0 children in Kenya. We hypothesize that life expectancy at birth, per capita gross
national product, per capita energy use, percent of urban population, female and male literacy rates,
percent of population with access to safe water supply, percent of population with access to
sanitation services, per capita daily calories, number of oral rehydration solution packets used per
hundred diarrhea episodes, and family planning program effort score will be nagatively related to
total fertility rate while positive relationships are expected between total fertility rate and each of
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Statistique, Développement et Droits de l‘Homme
infant mortality rate, population per square kilometer, population per hospital bed, and population
per physician.
Among the explanatory variables, female literacy rate and male literacy rate are highly
correlated (0.93), and so are percent of population with access to safe water supply and percent of
population with access to sanitation services (0.80) (correlation matrix not shown here). To avoid
the problem of multicollinearity which is associated with unstable estimated regression coefficients,
male literacy rate and percent of population with access to sanitation services have not been
included in the regression model.
The bivariate correlations do not take into account the relationship of an independent variable
with all other independent variables (Lewis-Beck, 1980), and as such each independent variable
(excluding those deleted before) has been regressed on all the other independent variables in order
to assess whether there can be any further problem of multicollinearity. Four of the R 2 s from these
equations corresponding to the regressions of infant mortality rate, life expectancy at birth, per
capita energy use, and per capita gross national product are near 1.0 (0.93, 0.95, 0.94, 0.95
respectively), indicating that multicollinearity is still a problem. To get rid of this problem, these
four variables have been dropped out from further analysis. The final analysis has, therefore, been
based on the variables total fertility rate (response Y), percent of urban population (X1), percent of
population with access to safe water supply (X2), population per square kilometer (X3), per capita
daily calories (X4), female literacy rate (X5), family planning program effort score (X6), population
per hospital bed (X7), population per physician (X8), and number of oral rehydration solution
packets used per hundred diarrhea episodes (X9).
The results of fitting the multiple regression model:
(1)
Y = 0 + 1X1 + 2X2 + 3X3 + 4X4 + 5X5 + 6X6 + 7X7 + 8X8 + 9X9
are presented in table 2. The value 0.72 of R2 can be considered as quite large. This does not,
however, imply a good fit (Anscombe, 1973), nor that the model assumptions have not been
violated (Chatterjee, and Price, 1977). Plots of the standardized residuals against the fitted values as
well as against the explanatory variables did not show any systematic pattern of variation and all the
standardized residuals fell between +2 and -2. Neither did they detect the presence of any outliers.
Consequently, there is no evidence for model misspecification or for serious violations of model
assumptions.
The high value of R2 indicates that 72 percent of the variation in the total fertility rate is due to
fluctuations in the nine explanatory variables. This reflects the adequacy of the explanatory
variables as well as the accuracy of the equation. The F test of the model has also shown a very high
significance of the equation. The explanatory variables - percent of urban population, female
literacy rate, and the family planning program effort score - affect the total fertility rate
significantly, and their effects are negative. The slope estimates show that a one percent increase in
the urban population is associated with a decrease of 0.028 children per woman, a one percent
increase in the female literacy rate is associated with a decrease of 0.016 children per woman, and a
unit increase in the family planning program effort score decreases the number of children per
woman by 0.036, in each case holding all other variables constant.
Table 2. Unstandardized and Standardized Coefficients of Regression of Total Fertility Rate on
the Nine Explanatory Variables.
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Statistique, Développement et Droits de l‘Homme
Variable
Unstandardized
coefficients
t-Value
pr > T
Standardized
coefficients
Intercept
8.75181651
7.54
0.0001
Percent of urban population (X1) -0.02806448
-2.75
0.0088
-0.3429972
Percent of population with access
to safe water supply (X2)
-0.00175078
-0.21
0.8312
-0.0284555
Population per square
kilometer (X3)
-0.00046436
-1.28
0.2085
-0.1199180
Per capita daily calories (X4)
0.00003280
0.07
0.9415
0.0088201
Female literacy rate (X5)
-0.01557470
-2.48
0.0173
-0.2861435
Family planning program
effort score (X6)
-0.03606516
-5.82
0.0001
-0.6294263
Population per hospital bed (X7) 0.00027246
1.85
0.0716
0.1944681
Population per physician (X8) -0.0000334
-1.99
0.0535
-0.2681588
Number of oral rehydration
solution packets used per
hundred diarrhea episodes (X9) 0.00095609
0.24
0.8149
0.0214720
---------------------------------------------------------------------------------------------------------------N=50
R2= 0.716068  0.72
S=0.86078647  0.86
---------------------------------------------------------------------------------------------------------------In order to evaluate the relative importance of the explanatory variables in determining the
total fertility rate, the standardized coefficients are examined (table 2). These coefficients show that
a one standard deviation increase in the percent of urban population is associated with a 0.34
standard deviation decrease, on the average, in the total fertility rate, a one standard deviation
increase in the female literacy rate is expected to induce, on an average, a 0.29 standard deviation
decrease in the total fertility rate, while a one standard deviation increase in the family planning
program effort score is associated with a 0.63 standard deviation decrease, on the average, in the
total fertility rate, in each case, with all other explanatory variables held constant. We conclude that
the impact of family planning program effort, as measured in standard deviation units, is the largest
in lowering total fertility rate, followed by percent of urban population, and female literacy rate.
3. Summary and Conclusions
The cross-national variation in total fertility rate has been analyzed in this paper using
multiple regression technique with national data for 50 developing countries. The analysis shows
that percent of urban population, female literacy rate, and family planning program effort score are
significantly related to total fertility rate. The standardized coefficients have been examined to
assess the relative importance of the explanatory variables in determining the total fertility rate. The
family planning program effort has the highest impact on the total fertility rate followed by percent
of urban population, and female literacy rate. Indeed, the effect of the family planning program
effort is almost two times greater than that of the percent of urban population, and more than two
times greater than that of the female literacy rate.
This study has a number of policy implications. The family planning program effort is the
most important contributor to the reduction of total fertility rate. This lends support to the
contention that the determinative factor that has fostered the recent decline in fertility in the
developing countries has been mainly the government's family planning programs.
The other variable significantly related to the total fertility rate is the percent of urban
population. The higher this percentage the lower the total fertility rate. Urban areas are usually the
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Statistique, Développement et Droits de l‘Homme
centres of political and economic power and a great part of resources and social services are
concentrated in them. As such, people living in urban areas enjoy relatively more opportunities of
modern life which is conducive to having smaller family size. In developing countries larger
segments of population live in rural areas. Benefits of socio-economic development are distributed
unequally among the social groups: rural population who need more care, receive less care indeed.
The third variable significantly related to the total fertility rate is the female literacy rate. The
higher the literacy rate, the lower the total fertility rate. Better educated women enjoy better access
to opportunities of life, and hence lower fertility is felt more advantageous to them than higher
fertility since with lower fertility it is easier to reap the benefits of those opportunities. Among
women with no education, even significant difference in the number of children fails to make any
observable difference in the level of living, and as a result lower fertility does not appear to them as
a favourable life condition. As such, societies with lower levels of literacy have greater likelihoods
of having larger fertility rates.
Thus, although the family planning programs have played the most important role in
developing countries in the recent declines of fertility levels, these declines should not be viewed as
due solely to successful family planning programs. The results of this analysis indicate that an
egalitarian distribution of the benefits of socio-economic development over the rural and urban
areas might produce better results in terms of fertility reduction in developing countries. Also,
raising the level of female literacy may be one of the important strategies for reducing the fertility
rate. These results also parallel those of Freedman et al., (1988) who observed that in China the
"family planning program has been able to transcend the barriers of illiteracy and low educational
levels, but that education was nevertheless related to reproductive levels, both before and after the
major program effects".
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