Correlation of changing infant and child mortality and fertility in relation to development programmes in selected ECA member states

ECA/POP/TP/93/4/[2(b)(V)

ECONOMIC COMMISSION FOR AFRICA Population Division

Correlation of Changing Enfant and Child Mortality and Fertility in Relation to Development Programmes

In Selected ECA member States

Table of Contents

Page

I. INTRODUCTION 1

A. Population and development debate B. Population and development views

of governments

C. Conceptual Framework of Analysis D. Objectives and coverage of study

II. INFANT AND CHILD MORTALITY DETERMINANTS 11

A. Levels, trends and differentials B. Demographic determinants

C. Social and economic determinants

III. FERTILITY DETERMINANTS 24

A. Levels, trends and differentials B. Demographic determinants

C. Social and economic determinants

IV. CORRELATION ANALYSIS OF POPULATION

AND DEVELOPMENT 3 2

A. Correlation of Determinants of fertility and mortality

1. Fertility analysis

2. Infant and child mortality analysis

B. Socio-economic development

programmes

V. RECOMMENDATIONS AND CONCLUSION 48

I.INTRODUCTION

Africa has been associated with rapid population growth because the estimates of population growth rates are more than three percent in a majority of countries in the region. These high rates of population growth are largely caused by persistently high fertility rates which hover around 45 births per 1000 population in

most African countries. While mortality has experienced deceleration in many countries, it still remains quite high when compared with other developing countries of South Asia or Latin America. There are therefore very few countries which have infant

mortality rates of below 100 in the region. Similarly, estimates of

life expectancy at birth are around 50 years for most African

countries as compared with more than 60 years for developing countries of Latin America or well over 70 years for Western Europe or the United States.1/

This state of demographic situation in the region appear to be associated with the level of socio-economic development. At many

fora, the African Governments have associated their poor conditions

of health, high rates of population growth because of high rates of fertility etc, to low levels of socio-economic development.

Although the continent is endowed with abundant natural resources

of any kind, including human resources, poverty is widespread in

the region. Technological capacity is one of the major obstacles to

the development of the continent. Directly linked to this is the

inadequacy or complete lack of knowledge and information relevant

for policy formulation and programme implementation for socio- economic development. Research studies designed to identify interrelationships between population and development have been considered to be most appropriate in achieving the goal of improved

the socio-economic status of the people in Africa. This study will

therefore attempt to identify linkages of changing infant and child mortality and fertility in relation to development among countries

in the region.

A. Population and development debate

The relationship between population and socio-economic development was recognized at the 1974 World Population Conference held at Bucharest. The World Population Plan of Action (WPPA) adopted at the conference clearly recognized that economic and social development is a central factor in the solution of population problems. The Plan indicates further that national efforts should aim at accelerating economic growth in order to improve the levels of living and as a consequence to being conducive to a reduction in population growth rates particularly where such rates are high 2/.

The Bucharest Plan of Action hence forthwith recommended specific socio-economic policies to reflect an awareness of crucial role played by development in influencing population dynamics.

However, it was observed that economic developmen per se was less important in influencing fertility and mortality, than other aspects of development. For example, levels of fertility and mortality experienced substantial reductions in most developing countries despite low rates of economic growth. Therefore, qualitative aspects of development appear to be more important for

lowering rates of fertility and mortality. The WPPA further, states explicitly that "the principal aim of social, economic and cultural development ... is to improve levels of living and the quality of life of the people." The development goals that tended to moderate

2/UN, nnited Natj ~r° w™-™ Population Conference Afca_tfc

Bucharest,PP 25,New York 1974. —Review and .Appraisal of the

Population Plan of Action,PP3-18, New York 1986.

levels of fertility and mortality include promotion of social justice, social mobility and social development as well as a more equitable distribution of income, land, social services and amenities^,/ .

Almost a decade later, the International Conference on Population held in Mexico,1984 endorsed and reaffirmed the WPPA adopted at Bucharest conference of 1974. The reaffirmation was particular to the understanding of the importance of interrelationship between population and development. In preparation of the Mexico conference, the Second African Population Conference adopted the Kilimanjaro Programme of Action (KPA).

The KPA stressed the importance of population and development in order to achieve development objectives, like intensifying national programmes to reduce high levels of fertility and mortality, creating awareness of interrelationships between population and development on the dynamics of population change and the impact of such change on current and future development, and that governments should seek to attain targets set out in the WPPA4./- The report of the third African Population Conference held in Senegal,1992 clearly spelt out, in its Dakar/Ngor Declaration, that the importance of integrating population and development in order to achieve set targets of reduced levels of fertility and mortality, as well as combating the spread of AIDS and other diseases and improving the role and status of women including youths5/.

2/ UN.ibid, PP 3, New York 1986

4./ECA, Kilimanjaro Programme of Action for African Population and Self-Reliant Development,Addis Ababa 1984

f>/ECA, Report of the Third African Population Conference. Addis

B. Population and development views of governments

The African Governments, like in the whole international community, have recognized the importance of knowledge about interrelationships between population and socio-economic development. The world and regional population conferences and meetings have therefore provided major sources of knowledge and

information which is conducive to creating and sustaining greater

awareness of complex linkages and their influence between

population and development. Changes in governments perception on population and development relations have been given cognisance in

the recent response to this matter.

Results of the Sixth Population Inquiry among Governments bear testimony on the importance of population and development. The

views of governments compiled from responses concerning the

prevailing population growth rates clearly demonstrated the

concerns that population growth rates for many countries were considered to be unsatisfactory because they were too high. For example, 42.6 percent of all countries had this view. In addition, views of governments were different concerning the role played by the rate of growth in contributing to socio-economic development.

However, about a third of countries that responded believed that the rate of population growth had a major negative contribution to socio-economic development in their countries. It was only in one out of ten that considered the rate of population growth as having a positive contribution to socio-economic development in their countries. Similarly, about half of the number of countries viewed

the current levels of mortality to be unacceptable because

estimates of life expectancy at birth were too low. Using the

infant mortality rates as indicators of mortality conditions, the

majority of countries, about 64.8 percent, did not accept the

prevailing conditions of mortality. About half of countries viewed

existing levels of fertility as not satisfactory.

Therefore, the results of the sixth inquiry demonstrated that awareness was strong among governments about population and development linkages. For example, some results from the inquiry show that about 10.2 percent of governments have policies to raise levels of fertility as compared to 41.7 percent having policies to lower levels of fertility. Similarly, the majority of governments (67.6 percent) had policies giving priority to health and mortality programmes for development. As a direct result of the recognition of the importance of population and development, about 68.5 percent of governments that responded to the sixth population inquiry indicated the existence of policy to integrate population variables into development planning6/.

In the same inquiry, governments identified a specified set of obstacles to the integration of population in development planning process. Nearly one third of governments listed the major obstacles to development as (1) lack of personnel trained in integrating population and development. Such personnel lacking in most countries in Africa include development economists, demographers and statisticians, planners and other researchers. High frequency of expert labour turnover, especially for this gi-oup of trained personnel, aggravates the difficulty; (2) lack of - dequate data and research findings on population development interrelationships.

Although knowledge is inadequate about linkages between demographic and development processes, some research studies have indicated that there exists a complex relationship between population and development (see studies recently completed by the ECA secretariat and others which have been completed under the WFS programme)7/.

For example, there is a relationship between mortality or fertility and development which has been difficult to explain and identify

6/UN, Results of the Sixth Population Inquiry among GovernmentsfNew York,1990

which specific variables of economic development that have

influence to reduce fertility and/or mortality without achieving

higher levels of living or development; (3) lack of adequate methodologies for integrating population and development. For example, it has been difficult to assess health status of the people because relevant methods and techniques have not yet been developed. This fact of difficulty was established in another study about assessing the goals of Health For All by the year 2000 in ECA Member statesS./.

These difficulties and other obstacles encountered by

researchers and other experts interested in establishing the linkages between population and development have caused to raise general statements of theories. For example, information is inadequate to establish hypothesis that improved socio-economic

conditions can result in fertility or mortality decline. Another group of people believe that high growth rates hold back advances

in levels of living or that improved conditions of living and development could bring down levels of mortality, hence fertility.

In addition, fertility or mortality differentials have not been the result of economic development influences alone These gaps in knowledge hinder ability to trace the sequence of interrelationships between demographic and socio-economic changes.

C. Conceptual Framework of Analysis

Beginning from the 1974 World Population Conference, the WPPA

adopted at that conference and subsequently revised at the Mexico Conference, the WPPA continues to recognize the importance of interrelationships between population trends and socio-economic

8/ ECA, Assessment of Mortality Levels. Trends and Differentials in relation to the goal of "Health For All" by the year 2000 in .some

F.CA Member states, Addis Ababa 1992.

development world wide. Therefore, governments have been urged to undertake research studies in the area of population and development in order to bridge the gap in knowledge and take necessary measures to harmonize population and development.

In 1977 the United Nations and UNFPA organized an expert group

meeting at which conceptual frameworks about population and socio-

economic development were discussed. It was agreed that research studies that involve interrelationships require a defined methodology of analysis^/. Other researchers like Mosley have also proposed other types of conceptual framework in order to facilitate analysis!^/. For this study, we have defined relevant variables and categorized them into three groups. The first group

consists of demographic variables of fertility and mortality

processes. The second principle group of variables are those

associated with economic development processes linked to increasing levels of living. The third group is the intermediate variables which are largely catalyst for interactions between demographic and

economic processes, like government policies of development and population, cultural factors and traditions. The diagrammatic illustration is presented in Fig 1.

9/UN, Demographic transition and socio-economic development,

proceedings of the United Nations/UNFPA Expert Group Meeting.

Istanbul. 27 April - 4 May 1977. pp 1-37, New York 1979.

,10/W.H.Mosley, Biological and socioeconomic determinants of child

survival-Approximate determinants framework integrating fertility

and mortality variables, pp 189-202, IUSSP International Population

Fig. 1: The Conceptual Framework of Analysis.

Demographic processes fertility

TFR CBR

mortality IMR

CDR

e0 1

Intermediate Variables

policies of

environment culture and tradition

Economic Development living std

income literacy nutrition

urban

pop/doctor pop/bed |

The conceptual framework illustrated in Fig 1 is based on assumptions that demographic processes are strongly associated with social and economic development of countries and their communities.

For example, countries with lower levels of living are likely to

have higher levels of fertility and mortality. Furthermore, there

are intermediate variable which interact between population and socio-economic development, like government policies on population and development. The conceptual framework of analysis presented in the introduction forms the vital basis of analysis for this study.

This framework forms the backbone because for difficulties of available methodologies of data analysis and collection, that relations defined in the framework have been considered in this

study.

D. Objectives and coverage of study

In the previous programme of work of the Economic Commission

for Africa, the secretariat in its endeavour to understand the

complex relationships between demographic processes and their

interactions with socio-economic and other variables, several studies were conducted. Among the studies which have been completed

included a working paper on " Interrelationships among infant and child mortality, socio-economic factors and fertility in Africa."

The main focus of the paper was to identify determinants of fertility and mortality which were strongly influenced by socio- economic and demographic factors. In addition to these determinants, the paper proposed that a relationship existed between fertility and mortality processes and that each influenced the levels of either processes. The second study was on "

Comparative study on Trends in Infant and Childhood Mortality and

their Implications for Population Growth in Africa-" Among the

results of analysis, the study indicated that infant mortality

strongly influenced higher fertility among women who experienced

several child losses. Since data were not adequate to deduce any

other reasons for linkages, but it was believed that two theories

existed which influenced that behaviour. Similar studies have

proposed that women with child loss experience tend to replace dead

child and another is that couples tend to have more children born

as insurance against expected child deaths. The paper suggested

policy action to reduce child mortality in oruer to influence

population growth. The third study was on " A Comparative study of

Infant and Childhood Mortality and its relationship with fertility,

Cultural factors and Socio-economic Development in selected African

countries." The results of the analysis suggested that infant and

child mortality was strongly determined by socio-economic factors

of development like maternal education, place of residence and

marital status as well as cultural attitudes which were associated

with length of birth intervals. Among the recommendations, were

that policies aimed at reducing childhood mortality and fertility

child spacing practice and so on. These studies have been revised for publication in a study series 11/.

The secretariat has since added more literature to the knowledge about relationships existing within demographic processes and in a complex linkage among other variables. This study has been undertaken in an attempt to identify correlates between demographic dynamics and development processes that would be useful for policy formulation and implementation for development. The major objectives for this study therefore, are (1) Analyze levels, trends and differentials of fertility and mortality; (2) Identify socio- economic determinants of fertility and mortality and (3) Synthesize

linkages between population and development processes.

In order to manage the study, some countries were selected to be included in the study. Criteria was arbitrary, but selection included countries in at least all subregions of ECA. Therefore the countries included in this study are (1) Egypt and (2) Sudan in North Africa, (3) Ghana and (4) Senegal in West Africa, (5) Cameroon and (6) Equatorial Guinea in Central Africa and (7) Ethiopia, (8) Kenya,(9) Malawi (10) Tanzania, (11) Zambia and (12) Zimbabwe in Eastern and Southern Africa.

The organization of the this study and analysis has been arranged that following the introductory chapter; Chapter II, focuses on analysis of infant and child mortality determinants in all the selected countries. Correlation analysis has been used to identify the determinants with greater influence on infant and child mortality for policy action to reduce levels of mortality. In the Chapter III, presentation is on analysis of fertility determinants in order to identify factors of fertility that could be applicable for policies designed to reduce family sizes. Chapter IV presents analyses interrelationships between demographic H/ECA, African Population Studies Series Number 11, forthcoming.

10

processes and development in the selected countries. The last chapter summarizes the results of analysis and provides recommendations for action to reduce levels of fertility and mortality with implementation of specific development programmes.

II. INFANT AND CHILD MORTALITY ANALYSIS.

It is important to understand the factors that influence infant and child mortality in order to design health policies and implement programmes to improve health status of the people. It is known that different levels of mortality are associated by different kinds of factors of mortality. For example, where levels of mortality are high the major causes of death are likely to be infectious and parasitic diseases as it is the case in most developing countries in Africa. In countries of Western Europe and other developed regions, the factors of mortality are dominated by different types of causes of deaths like cancer etc. The social, cultural and economic factors influence health conditions differently among various countries. The lower levtxs of status of these factors have been associated with poorer health conditions and therefore higher levels of infant and child mortality. Before discussion of factors and determinants of mortality, it is essential to present an analysis of mortality levels,trends and differentials among countries included in this study.

B. Mortality Levels. Trends and Differentials

In order to use comparable data for analysis, recent estimates and projections prepared by the United Nations have been selected

for this purpose.12./ • Table 1 has estimates of mortality for each

country, beginning from 1950-55 to 1990-95.

12/UN, Estimates and Projections of Population, forthcoming

Table1:Estimatesofindicesofmortalitybycountry. COUNTRY Egypt Sudan

1950-55 IMR 200 185

e0 42.5 37.2

1960-65 IMR 175 165

e0 47.4 39.2

1970-75 IMR 150 145

e0 T2.1 42.6

1980-85 IMR 115 118

e0 56.6 47.8

1990-95 IMR 57 99

eo 61.6 51.8 Ghana Senegal

149 184

42.0 36.5

127 168

46.0 37.5

107 122

50.0 40.3

98 97

52.0 45.3

81 80

56.0 49.3 Cameroon Eq.Guinea

190 204

36.0 34.5

154 183

40.5 37.5

119 157

45.8 40.5

88 137

51.0 44.0

63 117

56.0 48.0 Ethiopia Kenya Malawi Tanzania Zambia Zimbabwe

190 150 212 160 150 120

33.0 40.9 36.2 37.0 37.5 41.5

170 118 204 143 130 106

37.0 45.9 38.4 41.7 42.8 46.5

155 98 191 130 100 93

41.0 51.0 41.0 46.5 47.3 51.5

159 81 163 116 88 76

40.0 55.8 44.8 51.0 51.0 55.9

122 66 142 102 84 59

47.0 58.9 44.2 50.9 44.1 55.8

The data presented in the table illustrate the

conditions of mortality among different countries over time. It can be observed that conditions of health improved in all countries because infant mortality rates dropped gradually and estimates of life expectancy at birth increased progressively since 1950 in all the countries. Infant mortality rate, at about 1950 ranged approximately from 150 to over 200 infant deaths per 1000 live births among the selected countries. Infant mortality was highest in Malawi, Equatorial Guinea and Egypt where rates were 200 and over. Zimbabwe, followed by Ghana, Kenya and Zambia, had infant mortality rates of 150 or less. The other countries had intermediate estimates of mortality at the time.

The conditions of mortality changed some years later among these countries. By 1970-75, countries with highest estimates of infant mortality, were Malawi, Equatorial Guinea and Ethiopia all having estimates which lay between 150 and 200. Zimbabwe, Kenya and Zambia had lowest rates of 100 or less. The other six countries were in the intermediate levels of conditions of infant mortality.

The positions among countries changed significantly during the 1990-95 projected estimates. Egypt had the largest drop of 143 points in infant mortality, followed by Cameroon and Senegal with 127 and 104 points respectively, during the forty year period since 1950. The other countries which had significant declines in mortality were Equatorial Guinea, Sudan and Kenya, followed by Malawi, Ghana, Ethiopia and Zambia. Zimbabwe which was experiencing better conditions of mortality registered gradual and consistent declines in infant mortality. The gradual decline in infant mortality appeared to have been reduced in Zambia, Tanzania, Ethiopia and Kenya, for example. Data available were not adequate to provide explanation for these apparent shifts in the trend of infant mortality.

The estimates of life expectancy at birth for these countries also concur with the evidence suggested by estimates of infant mortality rates that conditions of mortality improved steadily among the countries. During the period 1950-55, only four countries, Egypt, Ghana, Zimbabwe and Kenya had, in that order, estimates of life expectancy at birth of 40 years or more. All other countries at that time had life expectancies lying between 33.0 years for Ethiopia and 37.5 years for Zambia. Around 1970s four countries achieved e0 of more than 50 years, such that it was estimated to be 52.1 years for Egypt, 51.5 years for Zimbabwe, 51.0 years for Kenya and 50.0 years in the case of Ghana. All the other countries had estimates of their life expectancy ranging from 40.3 years for Senegal to 47.3 years for Zambia.

The conditions of mortality appeared to have continued to improve throughout the 1980s and that only five countries had life expectancies of less than 50 years. These countries were notably Ethiopia, Equatorial Guinea, Malawi, Senegal and Sudan. The progressive improvement in conditions of mortality suggest changes in some countries where estimates of life expectancy dropped or

barely changed from levels of 1980s. Countries which experienced

the apparent reversal are Zambia, Tanzania, Zimbabwe, Malawi and Kenya. Worsening of conditions of health should have been associated with poor economic conditions and resurgence of killer diseases which most countries experienced, among many other f actors!!/ •

B. Demographic Determinants of Mortality.

Many studies have shown that there are different kinds of

factors which influence levels of morbidity and mortality, in

H/ECA, Assessment of Mortality levels, trends and differentials in

relation to the goal of "Health For All" bv year 2000 in some EGA

member states. ECA/POP/TP/92/3(B(IV)), Addis Ababa, 1992

14

particular as well as the whole question of survival status of the people. The life styles of people, their social and economic status, place of residence being either rural or urban, geographic region where they live within the country or elsewhere and the demographic characteristics, all these factors have influence on mortality.

It is known that demographic factors like age of mother, sex of child, birth spacing and birth order of child are some of the determinants of mortality in Africa. The importance of knowledge about the influence these factors exert on infant and child mortality is essential for formulation and implementation of development policies designed to improve conditions of health in Africa. In order to present quantifiable analysis, only four demographic determinants of mortality have been used to demonstrate the influence which they have on infant and child survival. The four determinants are (1) Sex of child, (2) Age of mother (3) Birth order of child and (4) Birth interval length for sibling. These determinants do not have independent influence on mortality and there are other forces which influence demographic processes like cultural and traditional factors of societies such as beliefs about births and child deaths, taboos on food types, traditional attitudes concerning birth spacing etc.

Table 2 below present information about different estimates of infant and child mortality categorized by the four different demographic factors for some countries in Africa.

able2:InfantmortalityDifferencesofDemographicfactors,latestdataavailable COUNTRY EGYPT SUDAN

SEXOFCHILD MALE 95.1 83.4

FEMALE 93.4 70.6

AGEOFMOTHER -20YRS 130.8 87.5

20-29 88.4 75.8

30-39 87.3 73.3

4049 95.6 79.7

BIRTHORDER 1 90.8 97.2

2-3 82.2 65.7

4-6 93.4 71.3

7+ 125.6 83.5

BIRTHINTERVAL -2YRS 153.1 94.5

2-3YRS 57.8 58.0

4+YR S 39.1 37.4 3HANA SENEGAL

88.8 98.0

73.5 83.6

97.0 119.1

73.1 83.2

82.8 84.4

118.6 85.8

86.3 112.3

67.9 86.2

82.6 82.2

101.8 91.8

114.6 114.9

67.7 72.4

51.5 57.9 CAMEROON86.474.6105.167.882.7-89.774.468.7103.0138.153.162.0 CENYA rANZANIA GAMBIA UMBABWE

63.0 103.7 106.2 64.9

54.3 95.1 90.3 49.7

67.5 126.4 123.2 78.4

54.8 89.0 92.4 47.8

60.2 105.3 87.1 62.0

58.3 69.9 101.5 67.1

65.3 113.3 121.5 63.5

54.8 93.3 96.2 53.2

49.7 92.6 86.0 48.7

71.9 105.1 93.9 75.4

75.6 160.1 155.8 78.9

47.7 79.7 76.5 47.9

35.9 65.1 56.1 42.8 aurces:VariousDHScountryreports

1. Sex of child

The differentials of mortality according to sex exist and that

knowledge available indicates males having higher mortality than

females. The extent of differentials varies inversely to level of

mortality in general. For example, in countries of high mortality, the difference is small between male and female mortality.

Similarly, for infant and child mortality, male children tend to have higher than female mortality. Factors are complex and varied at play in influencing mortality differentials by sex of child.

These factors range from purely biological, cultural, traditional

to socio-economic and demographic reasons.

Data analyzed according to estimates of infant and child mortality categorized by sex of child are presented in Table 2 above. From the estimates of infant and child mortality it is

apparent that mortality is higher for males (boys) than female

(girls) in all countries with relevant data. However,, the sizes and magnitude of the differences varies among the countries. Reasons that possibly cause variations include differences of levels of mortality among different countries, differences in the quality of demographic data because of deliberate tendency to omit reporting

of sex of child for cultural and other taboos.

The analysis and data presented in Table 2, further suggest

that smallest difference in mortality between boys and girls was

noticed in Egypt followed perhaps by Kenya. In both countries the

difference was of the magnitude of about two and nine points,

respectively. The largest difference was observed in Zambia,

followed closely by Ghana and Zimbabwe. The sex mortality

differential was about 16 points for Zambia and about 15 points for

both Ghana and Zimbabwe. Sudan, Senegal and Cameroon were in the

intermediate position.

2. Age of the Mother

The demographic process of childbearing among women is

dependent upon and confined to defined age group. This age group

normally extends from 15-19 to 40-44 or 45-49. The other factor of importance of the age groups in which childbearing occurs is that age of mother has been identified as a strong function of infant and child mortality. The association is that child mortality is higher among younger and older women than for women in central

childbearing age groups. The factors that influence the type of

pattern of infant and child mortality categorized by age are many and complex. Younger mothers aged less than 2 0 years and older women, are regarded as biologically not very safe to have children, hence they have higher risk of child survival.

Estimates of infant mortality classified by four age groups of women presented in Table 2 above, suggest there exist a link between mortality and age of the mother. The pattern appears to be generally a U-shape, with its minima occurring in either age group 20-29 or 30-39. Five countries had the minima of ' urves of infant mortality in age group 20-29 and these are Ghana, Senegal, Cameroon, Kenya and Zimbabwe. The association that countries do not have the same age group where infant mortality is minimal, perhaps cannot be explained with the type of data available.

On the type of differentials of infant mortality among countries, it can be observed that for mothers under 2 0 years,

highest risks of child survival were among women in Egypt, followed by those in Zambia, Senegal and Cameroon. Estimates of infant

mortality ranged from 130.a for Egypt to 105.1 for Cameroon.

Estimates of infant mortality among younger women were lowest; for Kenya followed by Zimbabwe, Sudan and Ghana. For older women continuing childbearing after age of 40 years, infant mortality risks were highest for women in Ghana and Zambia. The risk of infant mortality was very low in Kenya and Zimbabwe. This

18

observation suggest probably that childbearing is less among older women in Kenya or Zimbabwe than in Ghana or Zambia.

3. Order of births

Data from demographic surveys conducted under the auspices of the World Fertility Survey (WFS) have shown that risks of infant mortality were very high for first order births and those birth orders of seven and more. The results of analysis which show that such a relationship existed, are important for policy formulation

concerning completed family size and health programmes for children.

Estimates of infant mortality presented in Table 2 show that

risks of infant mortality were very high for first order births in all the countries. The second and third order births had

comparatively lower estimates of infant mortality rates than those of first order or higher. Birth orders of seven and more were

associated with highest estimates of infant mortality of all birth

orders, in all the countries.

Differentials of infant mortality also exist among the countries. Zambia, followed by Senegal, had highest estimates of

infant mortality for first order births. Zimbabwe and Kenya appear

to show that they had lowest risks of infant mortality for first

order births. On the other hand infant mortality for second and

third birth orders were about of same magnitude in all countries.

The estimates of infant mortality were higher for fourth to sixth than second and third birth orders.

4. Birth Intervals

Birth spacing is practised in all human societies world wide

and communities in Africa too, also do practice spacing of births

of the children. However, the lengths of these birth intervals vary

significantly from one country or community to the other. For

example, birth intervals are as long as 30 months in Senegal to as short as 12 months in Sudani!/. Studies have established that there exist a relationship between length of birth interval and child survival. Short and too long birth intervals are associated with increased risks of childhood mortality. Therefore, information of child spacing and survival becomes essential for policy formulation and implementation targeting family planning for mother

and child health.

Data in Table 2 indicate that birth intervals of less than two

years are associated with highest estimates of infant mortality rates of all categories of birth intervals. The risk of infant mortality among women in this category of birth interval was highest for Zambia followed by Egypt, Cameroon, Senegal and Ghana.

Infant mortality risks for children of mothers with short birth intervals of less than two years were comparatively better in Kenya, Zimbabwe and Sudan. Better family planning programme in Kenya and Zimbabwe might have improved birth spcjing and child survival in both countries. Secondly, infant mortality is expected to be lower for intermediate birth intervals of two to three years than short and longer intervals of less than two years and four years and over, respectively. The apparent declining trend observed for the three categories of birth intervals might have been due to quality of data for the longest category of birth interval in all the countries. In spite of the flaw in the data, it is apparent that infant mortality is closely linked to length of birth intervals and therefore, child spacing practice would contribute positively to reduction of child mortality.

14/ ECA, African Population Studies Series Number 11, (forthcoming)

20

C. Socio-Economic determinants of Mortality

It is important to study socio-economic factors which influence mortality in order to identify programmes designed to improve health status of the people. Different social and economic

groups of people have different conditions of living, life-styles,

exposure to disease, income, etc. and there are also differences in

levels and conditions of morbidity and mortality. In this study, the socio-economic determinants of mortality chosen are the type of

residence, either rural or urban and maternal education. The limitation to this group was due to inadequate data available for

other types of socio-economic factors like paternal education, occupation, marital status, religion and other cultural and traditional factors.

1. Urban-Rural Type of Residence

In nearly all developing countries of Africa, type of place of residence, being either urban or rural area has a very strong influence on demographic processes, notably mortality. Relationship between mortality and living in an urban area is so strong that it doubles chances of survival compared to being in a rural area. Data in Table 3 show that in all countries infant mortality rates were lower in urban than rural type of residence. In some countries, the difference was almost twice as great like in Zimbabwe and Egypt. In countries like Kenya, the gap was negligible between urban and rural mortality, perhaps suggesting a fair distribution of health services, more improved transportation facilities between rural and urban areas or sampling errors in the data.

Improved knowledge about mortality differentials would necessitate changing focus of health programmes to improve survival chances among different population groups. A policy of expanding primary health care in disadvantaged rural areas would enhance reduction in mortality differentials in countries like Egypt,

Zambia or Senegal. Tanzania on the other hand, having quite high infant mortality levels, the difference was very small between urban and rural estimates of mortality.

2. Maternal Education

In many instances, acquiring formal education provide a basic social ladder to higher levels of status of all the people. On the other hand education of women has been associated with enhancing and improving the overall status of women. Maternal education has been found to enable women participate fully in various societal roles. In childbearing and health care, educated women more than others, have increasingly been taking independent decisions without opinion of elders as it has been suggested by other researchers like John Caldwel. For example, women who have attended formal education are associated with lower levels of childbearing and child mortality than women without formal training. Of course, there are other factors which interact with the effect of education in determining these demographic processes and linkages.

Data presented in Table 3 show estimates of infant mortality rates classified by educational status of women in the various countries. It can be observed that infant mortality rates were highest for women without formal training in all countries. Next to them were women with a couple of years of schooling and that they too did not complete the primary level of education. The data further suggest that higher levels of education such as completing

secondary school and higher training are associated with very low

levels of infant mortality rates. Since education is an avenue for higher social status, it is very likely that these categories of women have better conditions of living, have greater access and

better utilization of health services etc, for them to provide

better child care.

22

The estimates in the table also indicate that infant mortality rates for uneducated women were highest in Tanzania followed by Zambia, Egypt and Cameroon. Although the proportion of population without formal education was not known among respective countries, the information available suggest that mere acquiring of formal education in the three countries made all the greatest difference upon child survival. For Kenya, it might appear that other factors apart from acquiring education have greater influence on determining levels of infant mortality among women without

education. In addition, it appears that completing primary

education would entail reduction by almost half of the estimates of mortality rates for uneducated women in Tanzania, Egypt or Cameroon, for example. Further more attainment of secondary and higher education might be linked strongly to achieving lower levels of infant mortality, as data suggested for Egypt, Zimbabwe, Kenya and Cameroon, where the rates were 50 or less.

Table 3: Infant Mortality Differences flMR) Socio-economic factors, latest data and country

COUNTRY

EGYPT SUDAN

GHANA SENEGAL

CAMEROON

KENYA TANZANIA ZAMBIA ZIMBABWE

RESIDENCE URBAN

65.6 74.1

66.9 69.8

71.7

56.8 108.3 78.0 37.8

RURAL 114.8 78.6

86.8 102.3

86.1

58.9 97.2 115.8 64.5

EDUCATION NONE

113.3 82.1

87.7 96.4

113.1

71.7 103.3 114.9 77.0

PRINC 88.8 69.7

84.8

—

—

59.1 95.0

—

—

PRICOMP 64.4 74.5

69.7 65.9

51.6

49.3 99.4 98.7 55.0

SEC.+

39.0 64.7

79.1 49.8

50.6

41.8 71.8 79.4 39.9

Sources: Various DHS country reports for 1980s

III. FERTILITY DETERMINANTS

Among the three components of population growth, fertility rather than mortality or migration, has the most important role.

Reproductive behaviour of any society is determined by tradition, cultural factors, social and economic groupings of the people, but above all, childbearing is demographically confined to defined age

group 15 through to 49, for both males and females. In addition, childbearing is increasingly being influenced by both traditional and modern contraceptive practice of reproductive behaviour designed to space child births and better manage population growth rates. Before proceeding on analysis of determinants of fertility

24

among selected countries, it is necessary to understand the levels, trends and differentials of fertility in these countries.

A. Fertility Levels, Trends and Differentials.

United Nations estimates and projections prepared recently have been used to analyze fertility levels, trends and differential among the selected countries ljj/. For purpose of comparative analysis, the period covered is between 1950 and 1990, which is long enough to be able to observe changing fertility pattern, if any among the countries. Two commonly used parameters of fertility selected for analysis are the crude birth rate (CBR) and the average total fertility rate (TFR). These data are presented in Table 4, below.

.15/UN. Population estimates and projections. 1992 Assessment. New York, 1993.

Table4:FertilitylevelsandTrendsfortheperiodbetween1950and1995forselected countries COUNTRY EGYPT SUDAN

1950-55 CBR 48.6 47.0

TFR 6.56 6.67

1960-65 CBR 45.4 47.0

TFR 7.07 6.67

1970-75 CBR 38.4 47.0

TFR 5.53 6.67

1980-85 CBR 39.1 45.9

TFR 5.06 6.58

1990-95 CBR 31.3 42.0

TFR 4.12 6.05 GHANA SENEGAL

48.3 49.0

6.90 6.70

47.6 49.6

6.90 7.00

45.8 49.2

6.64 7.00

45.2 47.2

6.50 6.70

41.7 43.0

5.96 6.06 CAMEROON EQ,GUINEA

43,4 42=3

5.68 5.50

44.4 41.3

5.90 5,53

45.3 42.4

6.30 5.68

43.9 43.3

6.34 5.79

40.7 43,5

5.70 5.89 ETHIOPIA KENYA MALAWI TANZANIA f7t\mrr>ttv ZIMBABWE

52.3 52.8 52.3 51.1 ent*/*j.j. 51.8

6.70 7.51 6.78 6.74 CCO 7.20

49.9 52.8 54.7 51.1 49.4 51.6

6.70 8.12 7.00 6.80 cen 7.50

48.0 52.9 56.6 49.6 49.1 48.6

6.80 8.12 7.40 6.80 c.nnD■^v 7.20

44.5 48.8 56.6 47.5 ano■*j•j 42.7

6.50 7.50 7.60 6.80 nnr» 6.19

49.1 43.7 54.5 48.1 46.4 40.6

7.00 6.28 7.60 6.80 5.33 Source:UN,Estimatesandprojections,1992Assessment

Data in Table 4 indicate that levels of fertility have remained high in all countries since the 1950s. Estimates of CBR

were around fifty births per 1000 population for the period 1950-

55. The highest estimates were observed for Kenya were CBR was as

high as 52.8, followed by Ethiopia and Malawi with CBR of 52.3 for both countries. The lower fertility levels were noticed in Equatorial Guinea and Cameroon, with Sudan at a distant second.

Similarly, TFR during same period remained at about six or more children in majority of the countries. Kenya, again had highest estimate of total fertility rate of more than seven children, followed by Zimbabwe. Only Cameroon and Equatorial Guinea, had TFR of less than six children. These apparent high levels of fertility remained generally constant throughout the 1960s and 1970s in most

countries.

In some countries, levels of fertility dropped during 1960s as in the case of Egypt and Ghana™ Declining trends in levels of fertility were observed during 1970s like in case of Ethiopia, Tanzania, and Zimbabwe. On the other hand, through the 1960s and

1970s fertility levels were rising in some countries like Malawi,

Cameroon, Equatorial Guinea, and Senegal. By the 1980s levels of

fertility were lower than the decade before, in all the countries, except Malawi, Equatorial Guinea and to a certain extent, for

Zambia.

Estimates for the period 1990-95 appear to strengthen the

downward trend of fertility in nearly all the countries. Fertility

levels dropped by large margins, especially in Egypt where the drop

was 35.6 percentage points since 1950. The other countries which

experienced significant declines of more than ten percentage points

were Zimbabwe, Kenya, Senegal and Ghana. It may also be deduced

that these countries also had strong population development

programmes of family planning designed to reduce fertility,

encourage child spacing and reduce population rate of growth.

Surprisingly, fertility in some countries like Malawi and Equatorial Guinea kept rising during same period.

The levels of fertility have undergone different changes among

these countries. Throughout the periods, levels of fertility remained highest in Malawi and Ethiopia where total fertility rates were above seven, followed by Tanzania, Zambia, Kenya, Senegal and Sudan where TFR ranged from six to less than seven. Egypt, on the other hand had lowest fertility followed by Zimbabwe.

B. Socio-economic Determinants of Fertility

There are many factors thcit interact together in a complex manner to influence extent of childbearing in human societies. Even though both the magnitude and direction of respective factors cannot be known, the levels, trends and differentials of fertility are largely determined collectively by various factors,. Studies that endeavour to identify specific determinants r; fertility are therefore important for policy formulation and implementation of strategies to influence family planning programmes, etc. In this study, socio-economic determinants of fertility have been analyzed.

Many demographic studies have focused mostly on fertility and knowledge available point to fact that there are both direct and proximate determinants of fertility. Data available for study was not sufficient to analyze proximate determinants like, cultural factors, age at first marriage, type of union and marital status.

The analysis was therefore possible only for socio-economic factors like type of residence and maternal education. Estimates of fertility categorized by these socio-economic factors are presented

in Table 5, below.

28

Table5:FertilityDifferencemeasuredbvTFRandcountry COUNTRY EGYPT SUDAN

RESIDENCE URBAN 3.48 3.9

RURAL 5.35 5.2

EDUCATION NONE 5.38 5.5

PRIMARYINC. 4.76 4.7

PRIMARYCOM. 3.61 4.7

SEC.+ 3.15 3.3 GHANA SENEGAL

5.13 5.4

6.63 7.1

6.74 6.8

6.10 5.2

5.93 5.2

3.61 3.7 CAMEROON5.176.296.26.446.444.54 ETHIOPIA KENYA MALAWI TANZANIA ZAMBIA ZIMBABWE

4.5 5.14 5.8 5.2

7.1 6.59 7.1 7.2

7.5 6.5 7.1 6.7

7.5 6.4 — 6.9

6.4 6.0 6.8 6.9

4.8 4.2 4.9 4.5 Sources:VariousDHScountryreports

1. Type of place of Residence

In most developing of Africa, for example, the type of place of residence is very critical depending upon whether one lives in urban or rural area. In comparatively more urbanized countries such as Zambia, large concentration of social services and amenities, is undoubtedly located in towns and cities. It is in these urban areas which have become centres of development at the expense of rural

areas, where in some countries, like Kenya with its small

proportion of urban population, lag far behind. Regarding demographic distinction, urban areas are associated with lower levels of either mortality or fertility than rural areas.

Data in Table 5 may have shown that levels of fertility are lower for urban than rural places of residence, in all the countries included in this study. However, it is not known clearly, as what would be the extent on these differentials could be influenced by the degree of urbanization in the countries concerned. However, the differences observed between urban and rural places of residence, vary significantly among different countries. The data indicate that the gap was largest for Kenya and followed closely by Egypt, where fertility levels in urban areas

stood at about 63.4 and 65.0 percent of rural areas, respectively.

On the other hand the gap between fertility levels was smaller in Cameroon and surprisingly in Zambia with its very high urban

proportion of population. These data further suggest that

programmes designed to reduce fertility should critically

reconsider relocating development programmes in rural areas as a measure to influence urban life styles among people in country

side.

30

2. Maternal Education

Knowledge available suggests that maternal education is the single most important determinant of major components of demographic processes. For example, women who have received education were associated with lower levels of fertility or child

mortality than women without any level of education. Literature

available to explain these factors only speculates that education

for women enables greater freedom of choice in matters concerning

childbearing and childrearing Xj6 /. In addition, there is an almost inverse relationship between level of education completed

and the total fertility rate, for instance. This relationship that

might be observed is that women with highest completed level of education would have the smallest number of total fertility rate of all educated women.

Data presented in Table 5 confirms these ideas of education being a strong factor closely associated with fertility levels

among different countries included in this stud, . Estimates of total fertility rates are highest for women without education and lowest for highly educated women, in all the countries.

Furthermore, the ability to complete primary school level of education appears to remove these women completely from the

uneducated to those highly educated, in spite of their few years of

having been at school. For example, compared to level of fertility of the uneducated women, the drop in TFR for those women who

completed primary education is much greater than it is for women

who had not completed primary education, in all countries. The

implications of education policy for development is for women to

complete at least the primary level of education to effect any demographic processes of reduction in levels of fertility.

16/John Caldwell "Mass Education as a determinant of mortality

decline" in Selected Readings in the cultural, social and

behavioural determinants of health, pp 101-109 Ed. Caldwell and

Sautow, Australia, 1989.

The data also suggested that the impact of education, considering other factors constant, varied significantly among the different countries. The estimates of total fertility rates for women without education as compared to highly educated women, suggest that the impact of education on fertility was felt greatest among women in Ghana followed by Senegal. The TFR for uneducated women were almost twice those for women who completed secondary and higher education in both countries. On the other hand, in Zambia women with highest level of education have about three quarters as much fertility level of uneducated women. This observation implies that development policy to reduce fertility should consider contributing factors other than education alone, in the complex analysis of determinants of fertility.

Other proximate determinants of fertility like age at marriage, level of contraceptive use, level of income, traditional and cultural factors, as well as other variables about the status of women, would have provided more knowledge about behaviour of fertility. However, data were not available to be aole to analyze

the influence of these variables and other factors on fertility in

the countries studied in this report. The part of analysis which follows considered the analysis of correlation of determinants of fertility in order to identify the strengths of the relationships between fertility levels and different factors.

IV. CORRELATION ANALYSIS OF POPULATION AND DEVELOPMENTS

In Figure 1 presented earlier, an attempt was made to define the framework of analysis of relationships which exist between different demographic processes, particularly of variables of fertility and mortality, and processes of social and economic development such as raising levels of standard of living, improvement of literacy and levels of education, income per capita and improved conditions of health. The relationships that exist between demographic factors and socio-economic development are very

32

complex. For example, there are intermediate variables which influence these relationships. Many times there are government policies on population and development that may invariably have the impact on the relationship between population and development. For example, policy on industrialization may locate development projects in a sparsely populated hinterland which subsequently becomes a significant population growth pole with different levels of fertility or mortality from entire country. The cultural and traditional attitudes and aspirations of the people are some of the intermediate variables that exert influence on relationships between population and development. The framework in Figure 1 has therefore been used in this part of the study in order to analyze some of the relationships.

The procedure adopted for analysis is descriptive interpretation of coefficients of correlation, despite the fact that the relationships may not also be collinear. In some cases ranking of variables was considered in order to enhance interpretation of correlation analysis. This approach was chosen for lack of adequate data and models of methodologies of analysis.

In the first part, analysis of zero order correlation coefficients has been done in order to analyze social and economic determinants of fertility and mortality in the countries covered by this study.

The second part is about socio-economic aspects of development programmes and population variables in different countries.

A. Correlation analysis of Determinants of Fertility and Mortality.

There exist complex relationships between different determinants and the demographic processes of fertility or mortality. These relationships may not necessarily be monotonic as the influence may be exerted in either direction. Because of inadequacy of data and methodology of analysis, multiple

correlation analysis of determinants of fertility and mortality was adopted in order to understand the extent to which each determinant influences the demographic processes. For order of presentation, determinants of fertility have been analyzed first, followed by

analysis of mortality.

1. correlation Analysis of Fertility

in this analysis of fertility, the major socio-economic determinants covered in this study are the type of residence being either urban or rural and education. The latter was classified into four categories, namely the NO EDUCATION, PRIMARY EDUCATION NOT COMPLETED, PRIMARY EDUCATION COMPLETED AND SECONDARY AND HIGHER EDUCATION, of the mother. Of course, there are other determinants of fertility like income, culture and tradition, family plannxng programmes and demographic factors such as age at marriage, lengths of birth intervals etc., but there were no relevant data for these to be included in the analysis. Table 6 has coefficients of correlation of socio-economic determinants of fertility in the selected countries. It is important to interpret the coefficients cautiously because the relations are complex and are not monotonic.

34

6:Zeroordercorrelationcoefficientsoflowertriangleofsocio-economic determinantsoffertility EDUCATION INC COM +

URBAN 1.0000 0.8213 0.7026 -0.2646 0.7901 0.6331

RURAL 1.0000 0.9321 0.0348 0.7896 0.7514

NOEDUCATION 1.0000 0.0293 0.7670 0.7171

PRIMARYINC 1.0000 0,0260 -0.0078

PRIMARYCOM 1.0000 0.9147

SECONDARY+ 1.0000

The results presented in Table 6 suggest the following deduction that place of residence and education of mothers have significant influence on fertility in the various countries. Living in an urban area is associated with better facilities of any kind.

Normally urbanization is understood to imply industrialization and economic development. In terms of cost of different services, it is more expensive in an urban than a rural place of residence and that people in towns and cities tend to have fewer children and younger dependents. In addition most services in urban areas promote lower fertility since family planning services are easily more available than in rural areas. Some people have argued that the kind of new life style in urban areas is linked to attitudinal change and willingness to depart from traditional behavioral pattern that

fertility is depressed in urbanl2/» As the coefficients

demonstrate, urban place of residence is positively correlated with all levels of education except for incomplete primary level of education. The implication for those that did not complete primary education tended to live in rural than urban where they would be unable to cope with expensive urban life styles.

Education is understood to be the vehicle of social and economic progress in most countries in Africa. The educated group is associated with wealth, hygiene, better methods of child care, lower levels of fertility and better chances of child survival. In that type of ideal environment, those without having been educated

or were unable to complete primary school level of education are

most expected to be in the peripheral and marginal levels of existence. The fact that education would postpone marriage and the onset of fertility, increase economic mobility, increase employment possibilities and provide alternatives to early and frequent motherhood, education would tend to depress fertility. However, the

N. Hess, Population Growth and Socio-economic Progress in

Less Developed Countries, pp 17-26, Praeger, New York and London,1988.

36

relationship is complex. Women without education and those having low education like failure to complete primary school level would tend to have an increased fertility!^/. The coefficient in Table 6 also indicate that women without education and who live in rural area were very strongly correlated with high fertility. Furthermore living in rural areas has the strongest determinant of fertility for all women with and without education. The analysis which follows is on determinants of infant and child mortality

2. Correlation Analysis of Infant and Child Mortality

Most often, the same determinants of fertility also influence infant and child mortality. Since there are difficulties of adequate data only few determinants of infant and child mortality have been analyzed. The determinants whose coefficients are presented in Table 7, are sex of child (MALE or FEK\LE), maternal age in ten year age groups(<20YRS, 20-29, 30-39, 4-/-49) , order of birth of child(B01, BO2-3, BO4-6, BO7+) and birth intervals (BK2YRS, BI2-3YRS, BI4+YRS) .

The relationship of each of these demographic determinants of infant and child mortality is complex and not monotonic, therefore it is necessary to interpret cautiously the coefficients presented in Table 8. The importance of sex of child as a determinant of infant and child mortality is linked strongly to the cultural tradition and attitudes of the people. In societies where the belief is in the male child to continue the linage and therefore, inheritance of family property, more care is accordingly accorded to such a child. The data suggest that for women in age group 20-29 and having birth order of 2 to 6 with median birth interval of 2 to 3 years have greater influence on male than female child survival.

JJL/Hess, Ibidem, 1988

The association of age of mother with infant and child

mortality has the relation that younger mothers of less than 2 0

years and older women of 35 or 40 years and over have likelihood of higher probabilities of their children dying in infancy and

childhood than other women in central childbearing age group 2 0-29

or 20-39. The data in the Table 7 suggest that there exist a strong relationship for women aged less than 3 0 years between infant and child mortality and Birth Order 1 and that the relationship is

weaker for shorter birth intervals of less than two years. The

association with very long birth intervals of four or more years is negative. On the other hand, it appears that older women tend to have longer birth intervals, suggesting the fact that such women are approaching the end of their childbearing experiences. The relationship between socio-economic determinants of infant and child mortality is considered in the data presented in the next

table.

38

7:Zeroordercorrelationcoefficientsoflowertriangleofdemographicdeterminantsofinfant andchildmortality. 20-29 30-39 4049 BO1 BO2-3 BO4-6 BO7+

MALE 1.000 -0.0150 0.9199 0.9551 0.9533 0.7575 0.8925 0.7747 0.9254 0.6555 0.5281 0.6732 -0.5114

FEMALE 10000 -0.1966 0.1227 -0.1069 -0.1127 0.1986 -0.1907 0.0128 -0.1339 -O.0046 -0.1580 -0.2276

<20YRS 1.0000 0.8874 0.9309 0.6426 0.7257 0.777O 0.9006 0.7864 0.5072 0.5747 -0.3827

20-29 1.0000 0.8902 0.6731 0.8962 0.6566 0.9455 0.7241 0.6457 0.4898 -0.6417

30-39 1.0000 0.8252 0.7233 0.8160 09384 0.7499 0.3872 0.7344 -0.5084

4049 1.0000 0.5366 0.4709 0.8220 0.6969 0.4383 0.5387 -0.3183

BOt 1.0000 0.5380 0.7703 0.4179 0.6437 0.4454 -0.4840

BO2-3 1.0000 0.5975 0.3462 -0.0541 0.9186 -0.4416

BO4-6 1.0000 0.8718 0.6378 0.4703 -0.5531

BO7+ 1.0000 0.6405 0.1503 •0.3508

<2YRS 1.0000 -0.2435 -0.1918

2-3YRS 1.0000 ■0.3740

4+YRS 1.0000