DECLARATION

EXCEPT WHERE OTHERWISE INDICATED, THIS THESIS IS MY OWN WORK

omar '̂yussuf mzee

ACKNOWLEDGEMENTS

In completing this thesis I have received many forms of assistance, I would take this opportunity to express my profound thanks to Dr. D.W.Lucas, my supervisor, who was so sincere and

encouraging in providing guidance and assistance. I also wish to express my sincere gratitude to Dr. G.Santow for her invaluable

advice and comments. I also wish to thank all staff and students of the Department of Demography, who spared some of their valuable time in discussing some of the problems and making comments on specific

points. I sincerely appreciate the assistance of Mrs. C.McMurray and A.Mutiah for advice on the style and presentation of this thesis.

In Tanzania, I would like to thank Dr. C.L.Kamuzora who allowed

me to use his data. Also my deepest gratitude are due to my parents who gave me morale support and encouraged me to work hard. I also owe many thanks to my Director Mr. Ali Athumani who allowed me to take this scholarship and to process my trip to Australia without any problem.

Finally, I gratefully acknowledge my daughter Fatma Omar forbeing patient during my absence.

ABSTRACT

This study is based on the 1980 Mwanza Pregnancy History Survey

data which was collected by Dr. C.L.Kamuzora of Department of

Statistics, University of Dar-es-Salaam. The study had three

objectives. The first, to examine whether the fertility of the

Wa-Sukuma differs according to their demographic and socio-economic

backgrounds. The second, to examine the differentials in abstinence

and breastfeeding. The third, to identify the relative importance of

variables related to fertility and breastfeeding. The analysis is

confined to 1505 and 1123 ever married women aged 15+ in urban and

rural areas respectively. The index of fertility used here is the

mean number of children ever born to ever married women. Fertility

differentials were examined in terms of selected demographic and

socio-economic characteristics of ever married women in both rural and

urban areas. The study also has built up fertility and breastfeeding

"Multiple Classification Analysis (MCA)" models separately for rural

and urban areas.

The descriptive analysis revealed that the fertility

differentials among the Wa-Sukuma in various socio-economic and

demographic variables existed in both rural and urban areas; also the

mean number of children ever born (fertility) of rural women is higher

than their urban counterparts but this difference disappeared when

women's age was controlled. The MCA showed that the marriage duration

is the most important factor affecting fertility in both rural and

urban areas. Within data limitations, this study shows that the

practice of abstinence and breastfeeding are also important factors

for some socio-economic groups.

ACKNOWLEDGEMENTSABSTRACT

LIST OF TABLESLIST OF FIGURESCHAPTER 1: INTRODUCTION Page

1.1 Objectives and Importance of the Study 1

1.2 Hypotheses 21.3 Data Source And Description 3

1.4 Data Limitations 5

1.5 The Geographical Setting 61.6 Population growth and density 81.7 Fertility Levels and Patterns 91.8 Mortality Levels and Patterns 111.9 Migration Levels and Patterns 121.10 Literacy and Education 141.11 Organization of the Study 15

CHAPTER 2: DEMOGRAPHIC AND SOCIO-ECONOMIC CHARACTERISTICSOF THE EVER MARRIED WOMEN

2.1 Introduction 172.2 Age Distribution and Place of Residence 172.3 Marital Status 192.4 Educational levels 22

2.5 Occupational levels 24

CHAPTER 3: FERTILITY DIFFERENTIALS

3.1 Introduction 27

3.2 Rural-Urban Place of Residence and

fertility 283.3 Education and fertility 323.4 Occupation and fertility 383.5 Age at first marriage and fertility 403.6 Marital Status and fertility 45

3.7 Duration of marriage and fertility 48

CHAPTER 4: DIFFERENTIALS IN ABSTINENCE AND BREASTFEEDING

4.1 Introduction 514.2 Postpartum abstinence 524.3 Differentials in abstinence 534.4 Breastfeeding 57

4.5 Attitude of Wa-Sukuma towards breastfeeding584.6 Differentials in breastfeeding 61

CHAPTER 5: MULTIPLE CLASSIFICATION ANALYSIS5.1 Introduction 705.2 Results of the analysis 725.3 Analysis of breastfeeding 78

CHAPTER 6: SUMMARY AND CONLUSION 81REFERENCES

APPENDICES

List of Tables PageTable 1.1 Age Specific Fertility Rates For Tanzania

Mainland 10

1.2 Percentage of population 10 years and overby completed educational attainment 14

2.1 Percentage Distribution of Ever MarriedWomen Aged 15+ By Age Group and Place of Residence 18

2.2 Percentage Distribution of Ever Married Women Aged 15+ By Marital Status, Age Groupand Place of Residence 20

2.3 Percentage Distribution of Ever Married Women Aged 15+ Whose Marriage Duration is not known By their characteristics andPlace of Residence 21

2.4 Percentage Distribution of Ever Married Women Aged 15+ By Educational Levels, AgeGroup and Place of Residence 23

2.5 Percentage Distribution of Ever Married Women Aged 15+ By Occupational Levels, AgeGroup and Place of Residence 25

3.1 Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group andPlace of Residence 30

3.2 Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Duration of

Marriage and Place of Residence 31

3.3 Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group, Women's

Education and Place of Residence 363.4 Mean Number of Children Ever Born to Ever

Married Women Aged 15+, By Duration of

Marriage, Women's Education and Place of Residence 37

3.5 Mean Number of Children Ever Born to Ever

Married Women Aged 15+, By Age Group, Occupationand Place of Residence 39

3.6 Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group, Age atFirst Marriage and Place of Residence 43

3.7 Mean Number of Children Ever Born to EverMarried women Aged 15+, By Age at First Marriage, Women's Education and Place of Residence 44

3.8 Mean Number of Children Ever Born to EverMarried Women Aged 15+, By Age Group, Marital Status and Place of Residence 47

3.9 Mean Number of Children Ever Born to EverMarried women Aged 15+, By Age at First Marriage, Total Duration of Marriage and Place of Place of Residence 49

4.1 Mean Duration of Last Completed Period of

Postpartum Abstinence(in months) By Age and

Place of Residence 534.2 Mean Duration of Last Completed Period of

Postpartum Abstinence(in months) By Women'sAge, Education and Place of Residence 55

4.3 Mean Duration of Breastfeeding(in months) in

the Last Closed Birth Interval By Women's

Age and Place of Residence 62

4.4 Mean Duration of Breastfeeding(in months) in

the Last Closed Birth Interval By Women's

Age, Education and Place of Residence 65

4.5 Mean Duration of Breastfeeding(in months) in

the Last Closed Birth Interval By Women's

Age, Occupation and Place of Residence 66

4.6 Mean Number of Children Ever Born to Ever

Married Women Aged 15+, By Duration of

Breastfeeding in the Last Closed Birth Interval

and Place of Residence 68

5.1 Effects of Predictors other than marriage

duration on Total Number of Children Ever Born

to Ever Married Women 73

5.2 Effects of Predictors other than women's age on Total Number of Children Ever Born to

Ever Married Women 77

5.3 Effects of women's age, education and

occupation on the duration of breastfeeding

for the Closed Birth Interval 78

6.1 Summary of urban and rural differences

between average number of children ever born,

according to selected socioeconomic and

demographic variables and standardized according

age and duration of marriage. Mwanza region 1980 83

6.2 Summary of urban and rural differences

between average duration of breastfeeding (in months)

according to selected socioeconomic variables and

standardized according to age. Mwanza region 1980 86

Figure 1

List of figures

1 Area of study, 1980 Mwanza Pregnancy History

Survey Tanzania 7

CHAPTER 1

INTRODUCTION

1.1 Objectives and Importance of The Study

This study of fertility differentials has three major

objectives. The first is to examine whether the fertility of Sukuma women differs according to their demographic and socio-economic backgrounds. The second is to examine the differentials in abstinence and breastfeeding. The third one is to identify the relative importance of variables related to fertility and breastfeeding.

A study of fertility differentials is useful in analyzing the general trend in population growth. More importantly, assessing the extent of differences among various groups in a population is often the first step in identifying important determinants of fertility behaviour. Information on fertility differentials also provides a basis for projecting the changes in the over all level of fertility which may be expected with shifting demographic, social and economic conditions. However, the theory of the demographic transition shows

that fertility often starts to decline first in certain sections of the society, such as the urban, the educated and those belonging to high socio-economic classes. Lastly, information about fertility differentials helps to explain,at least in part,the variation in birthrates observed among societies and countries.

Page 2

1.2 Hypotheses

'A hypothesis is an untested or unproved relationship among two

or more variables'(Forces and Richer, 1973:40). Based on the three objectives of the study, six hypotheses will be formulated as follows:

Hypothesis

Hypothesis

Hypothesis

Hypothesis

Hypothesis

Hypothesis

1 Marriage Pattern And Fertility

There will be a negative relationship between fertility and age at first marriage.

2 There will be a positive relationship between fertility and total marriage duration.

3 Education And FertilityFertility is inversely related to levels of education.

4 Occupation And FertilityThe fertility of farmers will be higher

than non-farmers.5 Rural-Urban Place of Residence And Fertility

The fertility of rural women will be higher than their urban counterparts.

6 Abstinence and Breastfeeding.

The period of abstinence and breastfeeding will be shorter for urban residents, young, educated and non-farmers.

Note: Fertility means the average number of children ever born.

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1.3 Data Source And Description

This study is predominantly based on a subset of the April-June

1980 Mwanza Pregnancy History Survey data which was collected by Chris

Lwechungura Kamuzora of the Department of Statistics, University of

Dar-es-Salaam. The survey was carried out in collaboration with Mwanza

residents, and especially nurses who were the field enumerators, to

give baseline information on the population of the Sukuma ethnic

group. The survey collected data on individual characteristics,

socio-economic and demographic characteristics such as education,

occupation, age, marital status, and in addition, for women aged 15

and over, a complete record of their pregnancies. (See Appendix 1 for

the questionnaire).

According to the 1978 census, the Mwanza Region consisted of 23

wards, of which 13 were urban, 2 mixed (that is, composed of rural and

urban parts), and 8 rural. The result of this census were used as the

frame for this survey. Also, the pilot survey of the 1980 Mwanza

Pregnancy History Survey showed that two of the urban wards were mixed

as they had significant numbers of agricultural workers. Hence 11

urban wards were left as a sample frame. The target population for the

1980 Mwanza Pregnancy History Survey was adult Sukuma women.

The sample areas chosen were based on the first stage of

stratification in which Mwanza region was divided into 23 wards. Of

these 14 were known to have a concentration of Sukuma people (6 from

urban and 8 from rural areas). Three wards therefore, were randomly

selected from the 6 urban wards, three from 8 in rural areas and 2

Page 4

suburban wards out of 4 mixed wards were also selected randomly.

Within each ward, households were also selected randomly. The number

of households selected for interview was 3052, out of which 1328

households came from rural areas and 1724 from urban and suburban

areas. Due to the fact that Sukuma people in Mwanza region are not

evenly spread across these areas, urban and rural samples had to be

drawn separately. However, as the target population were women of the

Sukuma tribe, the operation was a house-to-house listing of those

households selected by the sample. Eventually a total of 2932 eligible

women (all women aged 15-69) were covered by the actual survey, 1672

from urban and suburban areas and the others from the rural areas.

Because of the similarity between urban and suburban Wa-Sukuma, the

suburban Wa-Sukuma were coded as urban. The survey found that 124

women were single, 2628 were ever married and 180 not stated. In

rural areas 66 were found to be single and 71 not stated while in

urban areas 58 were single and 109 not stated. The majority of single

women were concentrated in the young ages (15-24) in both areas. The

small number of single women in this survey is unexpected. Since the

questions were asked to the respondents and not to the head of the

household, it is most likely that the survey was taken during the

morning time (10.00am to 12.00 noon) where it is possible to miss

single women at home in Tanzania. The "Not stated" also were found to

be concentrated in the same younger age groups. Since the sample size

for the single women is too small, this study will concentrate on ever

married women only.

Page 5

1.4 Data Limitations

The data from the survey, like most demographic data from developing countries, is fraught with errors and biases. These errors

and biases can be classified, according to their sources, as errors having their origin in sampling, coverage or content. Attempts to

produce marginals from the data have revealed a number of biases and

inconsistencies. The design of the interview schedule could be one of the plausible reason for the biases and inconsistencies. For example,

in the interview schedule no question was asked about whether or not the women were breastfeeding at the time of the interview. This means that the length of breastfeeding of the last child for all women is not known. Also, we do not know the age of the woman when she completed her last abstinence period or when she was breastfeeding the next-to-last child; also there was no question in the survey to enable us to determine this age of the women. There was also the unavailability of materials such as tax records which would have given the employment history of women, especially urban women, or Registration records, which would have given the number of live births

and still births by age and residence of mother. Bush clinics and urban child-health clinics could have provided information on diet,

breastfeeding and mortality associated with weaning and unhygienic

bottles. Households surveys could have given the required data on the household structure, this study will not examine variables such as diet, household structure, religion, local customs and taboos,

sterility rates, abortion and infanticide which would have presented auseful and interesting deviation from the norm. Joseph (1975:328)

Page 6

pointed out that, generally, in many surveys respondents have been

found to give incorrect answers either through ignorance,

misunderstanding the question or deliberately lying to avoid ill-luck,

for example to a pregnant woman, or a bad omen for the family. Age

mis-statement is also a problem in illiterate societies where the

concept of age or date of birth is of no particular significance. As a

result the ages of mothers should be treated with caution.

1.5 The Geographical Setting of Tanzania and the Study Area

Historically, Tanzania is a union of two countries, Tanganyika

and Zanzibar. Tanganyika achieved independence from British colonial

rule in 1967, while Zanzibar had to fight in 1964 in order to be free

from the domination of the Arabian Sultan of Muscut. On the 26th April

1967, the Republic of Tanzania was born and now is one of the

countries in East Africa. It is one of the developing nations in the

world. From 1967, the country was divided into two areas, the

Tanzanian Mainland (Tanganyika) and the island (Zanzibar). The

Mainland borders with Kenya in the north-east, Uganda in the

north-west, Ruwanda, Burundi and Zaire in the west, Zambia and Malawi

in the south-west and Mozambique (Msumbiji) in the south. The Island

is off the east coast of the Mainland and is surrounded by the Indian

Ocean (see Figure 1.1). The country lies wholly to the south of the

equator and extends over some 939,700 square kilometres. The

rainfall is high and falls in the range of 50-80 centimeters in April.

Page 7

Figure 1.1

Area of Study, 1980 Mwan?a Pregnancy History Survey,Tanzania

MAP APPROXIMATE LOCATION AND RANK OF URBAN LOCALITIES, MAINLAND: .0

K e n y a

| m ÖU \\U 1

R E F E R E N C E\V J 1 V

i n t e r n a t i o n a l b o u n d a r y

R E G I O N B O U N D A R YR E G I O N.... nrR A N K.......

D O D O M A 01 P W A N I 0 6 I R I N G A 11 K I G O M A 16A R U S H A 02 D A R E S S A L A A M 0 7 M B E Y A 12 S H I N Y A N G A 17K IL IM A N J A R O 03 L I N D I 0 8 S I N G I D A 13 K A G E R A 18T A N G A 0 4 M T W A R A

0 9 T A B O R A 14 M W ^ Z A 1 9«M O R O G O R O 0 5 R U V U M A 10 R U K W A 15 M A R A 2 0

■r

Note: Rank means the size of the population living in the urban areas,starting with Rank No. 1 which is the largest population.

Page 8

The mean maximum temperature is 38 degrees Celsius between

Nov-Feb. The country shares the largest lake in Africa, Lake

Victoria, with Kenya and Uganda. The country has 25 regions and all

have the same climate except those regions with mountains and those

bordering Lake Victoria. For example, during the wet season, the study

areas, Mwanza region experiences temperature of 10-15 degrees

Celsius. This is because the northern side of the region borders on

Lake Victoria.

1.6 Population growth and density

Tanzania had an enumerated population of 12.3 million according

to the 1967 census and 17.5 million in the 1978 census. The total

population thus increased by 5.2 million, or 42 per cent, during the

eleven year intercensal period. This represented an annual growth rate

of 3.2 per cent. Population density increased during this period from

13.5 to 19.8 persons per square kilometres in an area of 885,987

square kilometres (Maro, 1981:91-109).

The population of the Mwanza region increased from 1.1 million to

1.4 million during the same period, with an annual growth rate of 2.8

per cent. The 1978 census showed that the Mwanza region contained 8

per cent of the country's population which occupied 19,683 square

kilometres with a density of 73.3 persons per square kilometre. It is

interesting to note that the 1978 census shows that the Mwanza region

recorded the largest population of the Tanzanian regions, but ranked

only 21st in land area.

Page 9

Some ethnic groups experienced faster population growth than others. For example, the Sukuma ethnic group, the dominant group in Tanzania, whose population was 0.9 million and 1.1 million

respectively, according to the 1948 and 1957 censuses. Ten years later, in 1967 the Sukuma population was 1.5 million, about 12 per

cent of the whole country's population, and was estimated to average six persons per household. In the 1978 census the population of Wa-Sukuma was found to be 2.2 million, which is also 12 per cent of the 1978 population of Tanzania. Also, it was estimated that the population of Wa-Sukuma will grow at a rate of 3.4 percent, which is faster than the country's population and the Mwanza region population.

However the Mwanza region had a higher percentage of Wa-Sukuma than any other region in the country.

1.7 Fertility Levels and Patterns

The 1980 official estimates show that the Crude Birth Rates (CBR)

for Tanzania fell slightly from 47.0 per 1000 total population in 1960-65 to 46.3 in 1975-80, and was predicted to fall to 40.9 in 1995-2000 and 32.1 in 2010-15. However the total fertility rate for Tanzania increased during 1960-65 to 1970-75 and is expected to remain constant till 1980-85 and then to decline from 6.3 to 4.0 in 1985-90 to 2010-15. In an attempt to estimate the levels of fertility for Tanzania and its regions, the 1967 and 1978 census data on fertility

were used to arrive at an adjusted fertility using Brass's °P/F'

method. The Crude Birth Rate was 47 per 1000 total population in the

1967 census and was 46 in the 1978 census. The CBR for Mwanza region

Page 10

was 49 during 1967, and this declined to 48 by the 1978 census. The

Total Fertility Rate for the region was high in 1967, at 6.9, while

the Total Fertility Rate for the country was 6.6. The 1978 census

showed an increment in the Total Fertility Rate, which was 7.1 and 6.4

for the region and country respectively. The Gross Reproduction Rate

for the whole country was estimated at 3.2 in the interval 1975-80 and

was projected to fall to 1.98 in 2010-15.

The Age Specific Fertility Rates can help to explain the

fertility pattern of the country. Table 1.1, based on the 1967 and

1978 census data, shows the pattern of Age Specific Fertility Rates of

the Tanzanian Mainland. Age Specific Fertility Rates were not prepared

and thus are unavailable by region, but the Age Specific Fertility

Rates for the Mainland are considered to be close enough to those of

the study area (Mwanza region). Estimates of Age Specific Fertility

Rates by five year age groups showed that in the Tanzanian Mainland,

the fertility curves were essentially unimodal, rising steeply from

the age of 15 and reaching the peak in 20-24 age group.

Table 1.1

Age Specific Fertility Rates For Tanzania Mainland

Age group:Recorded :Adjusted:Recorded : 1967(a) : 1967(a): 1978(b)

15-19 0. 169 0.200 0. 13520-24 0.334 0.334 0.30525-29 0.316 0.296 0.29530-34 0.260 0.226 0.23935-39 0.201 0.162 0 . 18340-44 0.115 0.078 0.09345-49 0.060 0.024 0.038

Sources: (a) Egero and Hennin (1973) (b) Ngallaba (1983)

Page 11

An analysis of the trend of the age specific fertility rates

shows that the peak remained at the 20-24 age group in the 1978

census. However, the age specific fertility rates declined at younger

ages (15-29) and started to rise from age group 30-34. The United

Nation categorized fertility into three peak groups, the early peak,

the broad peak and the late peak. Mwanza region was observed to have

an early peak with the mean age of fertility schedule at 29.7 and a

broad peak with the mean age of fertility schedule of 28.4 in the 1967

and 1978 censuses, respectively.

1.8 Mortality levels and Patterns

The levels of mortality in Tanzania are still high, similar to

the levels prevailing in some of the other least developed countries

of the world (World Bank,1980). Yet this does not mean that

conditions of health have not improved in Tanzania over the last two decades.

According to the censuses, the estimated Crude Death Rates were

23.0, 20.8 and 23.0 per 1000 total population in 1967 and 19.1, 17.0

and 19.2 in 1978 for the Tanzanian Mainland, Zanzibar and Mwanza

region respectively. These crude measures suggest that although

mortality rates are still high in Tanzania, mortality is on the

decline. This decline is attributed to the impact of social and

economic development and perhaps due to the introduction and use of

modern medicine such as antibiotics and improved medical technology

(Sembajwe, 1983:281-324).

Page 12

Indeed, there have been significant declines in the mortality rates in Tanzania during the past twenty years, particularly in infancy and early childhood. For example, infant mortality rate appears to have declined in Zanzibar from 160 per 1000 live births in 1957 to 140 in 1967. For the Tanzanian Mainland the infant mortalityrate declined from 190 to 160 per 1000 in the same period.

Nevertheless, infant and child mortality is still high in

Tanzania. The 1978 census showed an Infant Mortality Rate of 137 for Tanzanian Mainland, 125 for Zanzibar and 139 for Mwanza region. The

child mortality rates were 231, 209 and 233 per 1000 total population aged 1-4 year for Tanzanian Mainland, Zanzibar and Mwanza respectively. This suggests that infections and communicable diseases were still very prevalent in Tanzania. Average life expectation rose from about 37 years in 1957 to around 41 in 1967 and 44 years in 1978 for the Tanzanian Mainland. The comparable values for Zanzibar were 42 years in 1957, 43 in 1967 and 47 in 1978, while for the Mwanza region it was 44 in 1978 . In addition, the census shows that life expectancy is generally higher for females than for males, 48.3 and 39.1 years for females and males respectively for the whole country.

1.9 Migration Levels and Patterns

Apart from fertility and mortality, migration is clearly one of

the most important changes affecting the characteristics of the population and hence the rate of growth. But the refinement in the

study of the contribution of migration is hampered by lack of adequate

Page 13

data in most African countries (Ominde, 1975:40). Since 1948 the

population censuses are the main source of data for the comprehensive

study of human migrations across administrative boundaries in

Tanzania. Migration studies in Tanzania are therefore based on

estimates derived from indirect methods of which the birth-place and

age and sex ratios are the most commonly used.

A classification of the population by citizenship indicated that

in 1967 there were 463,600 non-Tanzanian citizens in Tanzania, the

majority of whom were from neighbouring countries. The data also

showed that during the ten year period (1957-67), the non-African

migrants declined from 115,134 to 105,384. Taking the net gains

between regions, the 1967 census analysis of the net migration

percentage of those born in the Mwanza region shows a net outflow of

population. The analysis also concluded that about 39 per cent of

Tanzanian residents were intra-regional and approximately 9 per cent

were inter-regional migrants. In this exchange the men dominated the

movement and covered larger distances whilst the women were more

predominant in short range migration within Tanzania (Cleason, 1973

cited in Ominde, 1975). An analysis of urban born in Tanzania shows

that approximately a third of the urban population was born in the

urban areas in which they live. About the same proportion were born in

other regions and about 10 per cent recorded birth places in

neighbouring and other countries. The 1978 census shows a similar

pattern and magnitude of migration as can be discerned from the 1967

population census.

Page 14

1.10 Literacy and Education

From the beginning Tanzania's educational programme was, and is

still, meant to serve the interests of the socio-economic development

of the nation. Therefore deliberate attempts have been made throughout

to link education to socio-economic development by making it relevant

to the demands of the various sectors of the economy. Literacy is

functional in the sense that knowledge and skills relevant to the

development needs of the recipients form a major part of the literacy

programmes. Reading and writing are just two of the goals. Literacy

and education data from the 1978 census provide vital information

about the level of achievement and distribution of education within

the country's population.

Table 1.2

Percentage of Population 10 years and Over by completed educational attainment

Area Completed:CompletediCompleted :Completed Primary :Secondary:University:Various Class 1-8:Class : :Other

: 9-14 : iCourses

Completed:Total Other :Educ/ Education ration

MwanzaZanzibarMainland

26.7 : 0.7 : 0.1 : 0.814.6 : 6.0 : 0.1 : 0.727.7 : 1.0 : 0.1 : 0.9

0.2 : 28.5 0.1 :21.5 0.4 :30.1

Source:Noah and Lasway (1983).

The 1967 and 1978 censuses data show that Tanzania had a literacy

rate of 31 and 52 per cent respectively for the population aged 10

years and over. The higher proportion literate in 1978 could be

Page 15

attributed to the literacy campaign in Tanzania that started in

1970. Looking at the regional level, in Mwanza region there was a 76

per cent increase in the literacy between 1967 and 1978 as the census

showed literacy rates of 25 and 44 per cent respectively. Literacy

rates for males and females were 59 and 30 per cent respectively. In

rural areas the percentages for males and females were 55 and 27 while

in urban areas the percentages were 89 and 57. The higher literacy of

males is evidence of greater male participation in literacy programmes

and perhaps of their greater achievement in formal education. The

proportion of population aged 10 years and over which has attended and

completed formal education is shown on Table 1.2 by level of

attainment. Zanzibar has the lowest proportion of educated people

compared with the Mwanza region. Overall, the data show that in all

regions, few people completed University education, and that

considerably more people stopped with primary education (Noah et al.,

1983:211-236) .

1.11 Organization of the study

This study consists of six chapters. The first Chapter is the

introduction and comprises the objectives, hypotheses, data sources,

data limitations and general background of the country and the study

area. The second Chapter covers the demographic and socio-economic

characteristics of the respondents, while the third Chapter presents

the differentials in fertility by demographic and socio-economic

status. The completed period of abstinence and the length of

Page 16

breastfeeding in the closed birth interval and their socio-economic

differentials are discussed in the fourth Chapter. Multiple

Classification Analysis is used in the fifth Chapter to determine the

effect of each socio-economic and demographic variable on the total

number of children ever born and the length of breastfeeding. The

concluding Chapter is the summary of the findings.

Page 17

CHAPTER 2

DEMOGRAPHIC AND SOCIO-ECONOMIC CHARACTERISTICS

OF EVER MARRIED WOMEN

2.1 Introduction

The purpose of this Chapter is to describe the demographic and

socio-economic characteristics of the 1980 Mwanza Pregnancy History

Survey female respondents prior to the analysis of fertility

differentials in Chapter Three. This study considers place of

residence, education, occupation, age at first marriage and marriage

duration as independent variables. The number of children ever born

has been treated as the dependent variable. In this study only ever

married women are considered.

2.2 Age distribution and Place of Residence

In the Tanzanian national censuses, an urban area is defined as

an administrative area or an area that has more than 50 per cent of

the population who are engaged in non agricultural activities. A

rural area is defined as an area which has more than 50 per cent of

the population in the agricultural sector. Although the country

definition of the urban area is more like "agrourban" or "agrotown" in

this study the category "urban" will be used. These definitions were

adopted in selecting the areas for the 1980 Mwanza Pregnancy History

Survey.

Page 18

Table 2.1

Percentage Distribution of Ever Married Women Aged 15+ By Age Group and Place of Residence

AgeGroup

Place of Rural

ResidenceUrban

15-19 6.9 13.820-24 18.9 29.525-29 23.5 23. 130-34 16.6 11.835-39 11.8 10.040-44 8.5 3.945+ 16.6 6.6

Not Stated 1.2 1.3

Total 100.0 100.0

Base for %

1123 1505

Source:1980 Mwanza Pregnancy History Survey data tape

The distribution of the 1980 Mwanza Pregnancy History Survey

respondents is shown in Table 2.1. The data shows that 1123 ever

married women were currently residing in rural areas and 1505 in urban

areas. About 1 per cent of the total respondents did not state their

age in both areas. The table shows that the urban sample of ever

married women has a younger age structure than the rural sample. This

could be explained by the attraction of the urban areas and the

greater likelihood of employment in the government, industrial and

services sectors. The rural areas have little to offer in terms of

salaried employment which leads to the need to migrate to urban areas

for better opportunities and prospects. Nevertheless, 17 per cent of

the rural population were in age group 45 and above during the survey

compared with 7 per cent in urban areas which suggests that this

rural-urban migration of young Sukuma women is a recent phenomenon.

Page 19

2.3 Marital Status

In the 1980 Mwanza Pregnancy History Survey, marital status was

classified under four categories: single, married, widowed and

divorced. Those who had never married were classed as single; those

currently married (either by religious ceremony, civil marriage or

traditional marriage) were classed as married; those enumerated as

widowed included all those whose spouses had died and who had not

remarried at the time of the survey; the divorced/separated category

included all those who reported being permanently separated either by

divorce or by informal separation. In this study divorced/separated

and widowed were combined because of the small size of these groups.

The distribution of marital status of Sukuma women by five year

age groups and place of residence is presented in Table 2.2. The Table

shows that the proportion of currently married women is higher than

divorced/separated and widowed combined group in both residential

areas. The Table also shows that the proportion of divorced/separated

and widowed women is lower in rural areas than urban areas in all age

groups except age group 20-24. For both rural and urban respondents,

the proportion of divorced, separated and widowed was higher in the

20-24 age group, this is presumably due to a rapid increase in the

proportion of women becoming divorced in this age group. It should be

noted that Sukuma society has a high rate of remarriage compared with

other societies in Tanzania (Nag, 1968).

Page 20

Table 2.2

Percentage Distribution of Ever Married Women Aged 15+ By Marital Status, Age Group and Place of Residence

Marital Status and Place of ResidenceGroup : Mwanza Rural : Mwanza Urban

:Currently Divorced/ Base:Currently Divorced/ Basermarried Separated for :married Separated for

and % : and %Widowed Total : Widowed Total

15-19 : 80.5 19.5 100 77: 75.5 24.5 100 20820-24 : 67.9 32.1 100 212: 69.8 30.2 100 44425-29 : 85.6 14.4 100 264: 80.7 19.3 100 34830-34 : 96.8 3.2 100 185: 88.6 12.4 100 17835-39 : 91.7 8.3 100 133: 90.7 9.3 100 15140-44 : 90.6 9.4 100 96: 82.5 17.5 100 5745+ : 85.9 14.1 100 142: 74.0 26.0 100 100

Not Stated 78.6 21.4 100 14: 52.6 47.4 100 19All Ages: 84.9 15.1 100 1123: 77.9 22.1 100 1505Source:1980 Mwanza Pregnancy History Survey data tape

Marriage duration is not known for 19 per cent (217) of women in

rural areas and 29 per cent (430) in urban areas (Table 2.3). Because

there was no direct question in the survey about marriage duration the

following calculations were made:-

First marriage duration .... The period between year at first

marriage to the divorce year or widowhood year (or to the survey year

if the respondent was still with the first husband).

Second marriage duration .... The period between year of

remarriage to the divorce year or widowhood year for the second

husband (or to the survey year if the respondent was still with the

Page 21

second husband). These calculations were continued until the last

marriage duration.

Total marriage duration ..... The sum of all marriage durations.

The high proportion for whom the duration is not known could be that

the interviewers did not write down the answers.

Table 2.3

Percentage Distribution of Ever Married Women Aged 15+ Whose Marriage Duration is not known

By their Characteristics and Place of Residence

Characteristics of: Place of Residencethe respondents : Rural Urban

Total : 217 430

Marital Status

Currently Married 9.2( 87) 13.9(163)Widowed/Divorcedand Separated 76.5(130) 80.2(267)

Age Group15-19 24.7(19) 27.9( 58 )20-24 37.7(80) 38.7(172)25-29 19.3(51) 29.9(104)30-34 10.3(19) 15.7( 28)35-39 12.0(16) 19.9( 30)40-44 8.3 ( 8) 17.5( 10)45+ 10.6(15) 16.0( 16)

Not Stated 64.3( 9) 63.2( 12)

Educational Level(in years)

0 21.3( 33) 29.6( 82)1-4 13.9( 23) 28.4( 87)5-8 24.9(102) 27.8(180)9+ 11.9( 5) 34.8( 23)

Not Stated 15.8( 54) 27.8( 58)

Source:1980 Mwanza Pregnancy History Survey data tape

Page 22

The Table shows that for about 77 and 80 per cent of thedivorced/separated and widowed group in rural and urban areasrespectively, marriage duration is not known. In addition the

majority of women whose marriage duration is not known were found tobe concentrated at the young age groups. It can also be seen that the

high proportion of these women have a high level of education in both rural and urban areas. However, in every case the proportion is higher in urban areas, this is an unexpected result but the design of

the interview schedule and insufficient training of the interviewers

could be the explanation.

2.4 Educational levels

Tanzania is one of the developing countries which has a high rate of literacy. The principle of universal free education was adopted by the country in the 1967, after independence. Since then, the education campaign, which was mounted in 1970, has been expanded rapidly because the Tanzanian government has increased facilities for the programme. In the National censuses, Tanzania has measured education by the highest level of schooling achieved.

The 1980 Mwanza Pregnancy History Survey adopted the same measure

of education and recorded the level of education of the respondents according to the highest standard achieved. For the purpose of this study, educational levels have been classified into four groups; the

first group includes those respondents who had no schooling; the

second, the third and the fourth groups includes those who completed 1-4, 5-8 and 9 years or more respectively.

Page 23

Table 2.4

Percentage Distribution of Ever Married Women Aged 15+ By Educational Levels, Age Group and Place of Residence

Age Group and

Place of Residence

Educational Levels(in years)

0 1-4 5-8 9+NotStated Total

Base for %

15-19 9. 1 24.7Mwanza59.7

Rural6.5 100 77

20-24 10.4 11.3 67.5 1.4 9.4 100 21225-29 12.1 14.4 53.0 4.6 15.9 100 26430-34 11.4 22.7 30.3 7.0 28.6 100 18535-39 21.1 9.8 15.0 5.3 48.8 100 13340-44 27.1 10.4 3.1 3.1 56.3 100 9645 + 11.3 6.3 0.7 2.1 79.6 100 142

Not Stated 21.4 78.6 - - - 100 14

All Ages 13.8 14.8 36.4 3.7 31.3 100 1123

15-19 5.3 29.8Mwanza52.4

Urban1.9 10.6 100 208

20-24 10.8 19.8 55.6 5.6 8.2 100 44425-29 13.2 19.0 46.8 6.8 14.1 100 34830-34 27.0 18.0 34.8 3.4 16.8 100 17835-39 19.9 24.5 32.5 3.3 19.8 100 15140-44 54.4 12.3 10.5 1.8 21 .0 100 5745+ 53.0 12.0 4.0 - 31.0 100 100

Not Stated 52.6 10.6 36.8 - 36.8 100 19

All Ages : 18.4 20.3 43.0 4.4 13.9 100 1505

Source:1980 Mwanza Pregnancy History Survey data tape

The proportion of female respondents aged 15 years and over who

have completed formal education is shown in Table 2.4. The majority

of Wa-Sukuma completed 5-8 years of schooling in both areas. However,

the data show that the younger population are most educated. In

addition, the proportion with no schooling are highest among the older

women. The higher proportion with no schooling in urban areas than

rural areas is unexpected, but this could be because the rural sample

had a high percentage of respondents who did not state their

Page 24

educational level. The Table also indicates that the proportion who did not state their educational level increased with age in rural and urban areas and was much higher in rural areas. The low levels of

education in rural areas and reluctance to admit the low level of education could be the explanation for this high proportion in rural

areas.

2.5 Occupational levels

"One fundamental basis for social differentiation in human

society is the position a person occupies in the economic

structure. Perhaps the most succinct index of economic position is occupation" (Knodel and Prachuabmoh, 1973:42). Countless studies have documented the relevance of occupation in the study of human behavior. Hot only do the specific activities and social interactions associated with carrying out a job condition people's attitudes,

values and perceptions of environment, but also their occupation can serve as a major determinant of social class, thereby exerting substantial influence far beyond the work situation.

The distribution of manpower among occupational categories shifts with industrialization, and as agriculture becomes more productive workers are released for other economic activities. In developed countries like Japan and France less than 20 per cent of the total economically active population is engaged in the agricultural

sector. In Tanzania, the 1978 census found that 79 and 85 per cent of adult males and females respectively were engaged in the agricultural

Page 25

sector. This proportion had increased by 10 per cent compared with

the 1967 census (Amani,1983). The increase is probably due to the

world food shortage in the 1970s to which the Tanzanian Government

responded by encouraging a dependency on agriculture. This forced the

Tanzanian population into the agricultural sector, especially urban

residents to grow food in the rural areas.

Table 2.5

Percentage Distribution of Ever Married Women Aged 15+ By Occupational Levels,

Age Group and Place of Residence

Age Group Occupational Level

Not BaseFarmers Non-Farmers Stated Total for %

Mwanza Rural15-19 81.8 18.2 - 100 7720-24 74.1 25.9 - 100 21225-29 87.1 12. 1 0.8 100 26430-34 96.8 1.6 1.6 100 18535-39 94.7 5.3 - 100 13340-44 95.8 3.2 1.0 100 9645+ 93.7 4.9 1.4 100 142

Not Stated 78.6 14.3 7.1 100 14

All Ages : 88.2 11.0 0.8 100 1123

Mwanza Urban15-19 45.7 32.2 22.1 100 20820-24 34.9 45.5 19.6 100 44425-29 32.8 43.1 24.1 100 34830-34 46.6 32.0 21.4 100 17835-39 53.0 31.8 15.2 100 15140-44 49.1 28. 1 22.8 100 5745+ 63.0 15.0 22.0 100 100

Not Stated 52.6 47.4 - 100 19

All Ages 41.7 37.5 20.8 100 1505

Source:1980 Mwanza Pregnancy History Survey data tape

Page 26

The 1980 Mwanza Pregnancy History Survey collected information on the occupation of the Sukuma women. As shown in Table 2.5 the major

occupation of all Sukuma women is farming. Nevertheless, the percentage of women in rural areas who were farmers at the time of the survey is higher than in urban areas. This is because most of the

women in rural areas in Tanzania depend more on agriculture for their

livelihood than on any other work. The figures also show that there were higher proportions of farmers in the older age groups.

The surprisingly high proportion of farmers in the urban areas is to be expected. This reflects the real situation in Tanzania that the majority of the population are engaged in the agricultural sector and it could be because of a circularity in definition of urban area. The

urban farmers treat their farms(shamba) as their office. It is easy for them to take a bus early in the morning to go to the farm and to catch the late bus to return home to the urban area.

The non-farmer group consists of professional, technical, administrative, executive, managerial, clerical, sales and service workers. These occupational groups were combined because of the small number of cases. In addition the data show that 21 per cent of the

respondents in the urban areas did not mention an occupation compared

with only 1 per cent in the rural areas, presumably these werehousewives in urban areas.

Page 27

CHAPTER 3

FERTILITY DIFFERENTIALS

3.1 Introduction

This chapter aims to describe the fertility differentials among the Wa-Sukuma in relation to the demographic and socio-economic characteristics of the respondents, using children ever born (CEB) as a measure of fertility based on the 1980 Mwanza Pregnancy History Survey data.

In the present study the cumulative fertility is analysed according to the women's age, age at first marriage, marriage duration and her marital status as the demographic variables. The second set of variables which affect fertility are the socio-economic variables. Two variables are identified for this study: women's education and her occupation.

The current residence of the respondents at the time of the survey: rural or urban, will also be used as a variable considered to be affecting fertility. The research presented in this chapter will be descriptive. Throughout the analysis, rural and urban will be

presented separately and whenever there is a need for standardization, the urban population will be used as the standard population.

Page 28

3.2 Rural-Urban Place of residence and fertility

The existence of rural-urban fertility differentials is well

documented in many parts of the world. Describing the factors

responsible for differences between rural and urban fertility,

Landis( 1943:101 ) says:

°The urban family has become rather highly individualized,

unstable, and pleasure-motivated rather than progeny-

motivated, whereas in more isolated rural cultures, where

primary group restraints are characteristic, where individual

behavior is under the close surveillance of neighbours and

friends, and where even the countership and mate selection of

youth has some guidance by the elders, the family still

maintains many of its important functions as a social

institution, being primarily concerned with family and community

welfare than with pleasure considerations of the individual'.

In European countries rural fertility was general higher than

urban in the 1960's. In some, such as Poland and Yugoslavia, it was

over 30 per cent higher(United Nations, 1976:48). In Asia the

situation is different. It has been observed that in most Asian

countries there is a lack of any real pattern. Davis(1951), using the

child-women ratio as an index of fertility, found significantly higher

rural than urban fertility in the Indian sub-continent in the 1940's.

His finding also suggested that fertility differed not only between

rural and urban areas but also between larger and smaller cities.

Page 29

Robinson's (1961) analysis, however, suggests that the rural-urban

fertility differentials observed by comparing fertility ratios are

spurious and that 40 to 50 per cent of the differences in India can be

explained by differing infant and childhood mortality. Driver's

(1963:84) study of India in 1960's, supports the finding of Landis

(1943), on the point that high fertility tends to be associated with

joint families, which are more likely in rural than urban areas. In

contrast, the World Fertility Survey(1978) observed in Indonesia that

the rural-urban difference was too small to be significant.

The estimation of differentials in fertility of rural and urban

residents is particularly difficult in Africa where the quality of the

data required for the analysis is poor. In several countries,

including Congo, Gabon and Zaire, urban fertility may be higher. One

possibility is that the better health facilities in urban areas may

lead to reductions in sterility(Page, 1975:52-53). Morgan(1975:234)

supports the view of Page(1975) that Nigerian fertility is higher in

Lagos, the capital, than in rural areas. In contrast, Henin(1973:111)

from the 1967 census data observed that in the United Republic of

Tanzania the average parities for different age groups were apparently

lower in the capital city, Dar-es-Salaam, than in the rural areas.

Using the Tanzanian 1978 census data, Ngallaba (1983:372-74) found

that the average number of children ever born to women aged 20-34

years in the urban areas is 9 per cent lower than in rural areas. He

concluded that in most regions the fertility in urban areas was lower

than in rural, including the study region (Mwanza).

Page 30

Table 3.1

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group and Place of Residence

Age Place of residenceGroup Rural Urban

15-19 1.1 (77) 1.0(208 )20-24 2.3(212) 2.4(444)25-29 3.6(264) 3.5(348 )30-34 5.0(185) 5.0(178)35-39 6.0(133) 5.6(151)40-44 6.7 (96) 5.7 (57)45+ 6.4(142) 5.6(100)

Not Stated 5.4 (14) 4.7 (19)

Observedmean (INS) 4.3(1123) 3.5(1505)

Observedmean (ENS) :4.3(1109) 3.5(1486)

Standardizedmean (ENS) 3.6 3.4

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population is used as the standard population

:2)Standardized by age:3)INS=Includes Not Stated and ENS=Excludes Not Stated

The Mwanza Pregnancy History Survey data also show that the

observed mean parity of Wa-Sukuma in rural areas is 23 per cent higher

than in urban areas. The reason for this large difference could be the

older age structure in rural areas. This study has used urban

population as the standard population. After standardization by age

the rural average parity becomes 3.6, only 6 per cent higher than the

urban average parity 3.4 (Table 3.1). It can be seen that for each

five year age group the rural-urban difference is negligible below age

35. In both areas, the mean number of children ever born to ever

Page 31

married women aged 45 and above is slightly lower than the age group 40-44 in both areas. This could be explained by the omission of

children who died when these women were young.

When the total duration of marriage is controlled, the rural-urban difference in fertility is reduced to 11 per cent (Table 3.2). Furthermore, the lower fertility of urban women is evident in

all marriage duration categories.

Table 3.2Mean Number of Children Ever Born to Ever

Married Women Aged 15+, By Duration of Marriage and Place of Residence

Duration of Place of residencemarriage Rural Urban

0-4 1.3(115) 0.9(255)5-9 3. 1 (204 ) 3.0(230)10-14 4.4(201) 4.2(273)15-19 5.9(131) 5.4(105)20 + 6.7(255) 5.9(212)

Not Stated 3.4(217) 3. 1 (430 )Observedmean (INS) 4.3(1123) 3.5(1505)

Observedmean (ENS) :4.6( 906) 3.6(1075)Standardizedmean (ENS) : 4.0 3.6

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population used as the standard population

:2 Standardized by duration of marriage:3)INS=Includes Not Stated and ENS=Excludes Not Stated :4)Duration of marriage refer to the total marriage

duration

Page 32

3.3 Education and fertility

Education, it is hypothesised, decreases the demand for children,

and the evidence seems to support this inverse relationship. In most

of the countries where the data are available the evidence seems to

indicate that the decrease is greater with the education of women than

men. As women get educated, they may find alternatives to

childbearing and generally prefer to have fewer but better cared for

children. Furthermore, the higher the educational level of the women

the greater their knowledge and practice of family planning is likely

to be. Holsinger and Kasarda(1976:156-196) explained that education

influences fertility in three fundamental ways:

1) By exerting a direct influence on fertility;

2) By affecting other variables that have a direct influence

on fertility; and

3) By an interaction effect of education and other independent

variables .They identified as most important the factors listed below.

1 )Direct effects of education

°Education affects fertility directly by changing individuals

attitude,values, and beliefs towards small family size. It

affects a broad spectrum of psychological attributes,

including freedom from tradition, heightened aspirations,

views concerning ideal family size, contraception and other

modern values which are germane to the motivation for limiting

family size'.

Page 33

2)Indirect effects of education

a) “Formal schooling delays age at marriage and thereby reduces

the total possible number of childbearing years of the wife'

b) “Education provides directly or facilitates the acquisition of

information on modern contraceptive devices and use'.

c) “Education increase exposure to mass media and printed

materials concerning family planning'.

d) “Education increases aspirations for upward mobility and the

accumulation of wealth which reduce the desirability of large

families1 (Holsinger and Kasarda, 1976:156).

This hypothesis has been tested empirically across a wide range

of societies, but not all of the data indicate that education and

fertility are inversely related (Heer,1971). Mason et

al.( 1971:48-52 ) , for example, uncovered a consistent and in most cases

strong inverse relationship between education and fertility in only 24

of the 32 empirical studies reviewed. Cochrane(1979), after an

intensive review of the relationship between these two variables,

summarized as follows: Several recent reviews on the determinants of

fertility have concluded that the inverse relation between education

and fertility is one of the most consistent and best documented in the

literature. The fairly extensive review of the evidence in her study,

however, shows that education is not always inversely related to

fertility.

The experience of the developed countries points to a marked

convergence in recent years(Kiser, 1971). For most developing

countries for which data are available, the evidence indicates more

Page 34

variation. The United Nations(1979) concluded there is convincing evidence that in African countries fertility is negatively correlated with the level of women's education. While the influence of formal

education on fertility is related more to the duration of schooling itself than to any significant education linked differentials in the

practice of birth control, better educated women, however, tend to be

more receptive to innovative factors that contribute to fertility decline.

In Ghana, for example, fertility falls with length of education.

Completed fertility for those whose highest level was elementary,

secondary, or tertiary education was 6, 66 and 94 per cent less respectively of the mean number of children ever born of those with no education (Caldwell, 1971:752). Similarly, Ngallaba(1983) showed from the Tanzanian 1978 census data that the fertility of women aged 20-34 with primary education lies 17 per cent below that of women with no education, while for women with secondary education,the mean number of live births lies about 50 per cent below that of women with no education. Data from the 1978 Kenya fertility survey also observed negative correlation between fertility and women's education (Republic of Kenya, 1979:35). Caldwell(1980) argued that mass compulsory education will cause fertility decline. He added that if education affects only a small section of the society, fertility differentials by levels of wages are likely to prevail.

Pullum(1975:168) using data from the 1968 National Demographic Survey in the Philippines, concludes that 'better educated women tendto have higher fertility than less educated women, controlling for

Page 35

marital exposure, up to about age 35'. This conclusion acts against

the general hypothesis.

Fertility differentials among Wa-Sukuma of different educational

levels and age groups as recorded from the 1980 Mwanza Pregnancy

History Survey are shown in Table 3.3. The mean number of children

ever born to ever married women aged 15 and above is used to determine

the magnitude of differences in fertility. In rural areas women with

nine or more years of education show 18 per cent higher parity than

those with five to eight years of schooling. After standardization by

age a strong inverse relationship is apparent in both rural and urban

areas between years of schooling and number of children ever born.

Rural women with five to eight years of education bore, on the

average, 21 per cent less children than those with no schooling.

Women with one to four years of schooling bore 5 per cent less

children than those with no schooling. After standardized by age,

women in rural areas who did not state their educational level have a

mean number of children ever born similar to those whose educational

level is 5-8 years. A similar pattern was also observed in urban

areas. Rural-urban differentials exist in all educational groups.

Before and after standardization by age rural women show higher

fertility than their urban counterparts in all educational groups

except educational group 5-8 years. The highest difference between

rural and urban was found among the women with 1-4 years of education.

Page 36

Table 3.3

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group,

Women's education and Place of Residence

AgeGroup

Women's education

0 1-4 5-8 9+ Not Stated

Mwanza Rural15-19 1 • 6 ( 7) 1.5(19) 0.9 (46) - *( 5)20-24 2.9(22) 3.0(24) 2.0(143) ( 3) 1.2( 20)25-29 4.6(32 ) 4.1 (38 ) 3. 1(140) 3.0(12) 4.5( 42)30-34 5.8(21 ) 5.4(42) 4.5 (56) 3.8(13) 5.0( 53)35-39 6.8(28) 6.6(13) 6.9 (20) ( 7) 5.4( 65)40-44 7.6(26 ) 7.0(10 ) * ( 3) ( 3) 6.3( 54)45+ 6.4(16) *( 9) * ( 1 ) ( 3) 6.4(113)

Observed mean5.5(152) 4.4(155) 2.8(409) 3.4(41) 5.5(352)Standardized:mean 4.3 4. 1 3.4 ** 3.5

Mwanza Urban15-19 1.9(11) 1.0(62) 1.0(109) ( 4) 1.0(22)20-24 3.1(48) 2.4(88) 2.3(247 ) 2.2(25) 2.3(36 )25-29 3.9(46) 3.8(66) 3.8(163) 2.4(24 ) 3.4(49)30-34 5.4(48) 5.2(32) 4.9 (62) ( 6) 4.7(30)35-39 6.3(30) 5.8(37) 5.3 (49) ( 5) 5.2(30 )40-44 6.4(31) *( 7) * ( 6) ( 1 ) 4.8(12)45+ 5.3(53 ) 6.3(12) * ( 4) - 5.5(31)

Observed mean4.8(267) 3.3(304 ) 3.0(640 ) 2.3(65 ) 3.8(210)Standardizedmean : 4.0 3.6 3.4 ★ ★ 3.4

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population used as the standard population

:2)Standardized by age:3)* The mean value for no education group was assumed

in calculating standardized value :4)** Because of too many empty cells standardization was

not perfomed:5)Excluding age not stated, 14 in rural and 19 in urban

The observed mean parity by duration of marriage reveals that the

higher the education the lower the mean parity in all educational

groups except 9 years and above in rural areas(Table 3.4). After

standardizing by duration of marriage, women in both rural and urban

Page 37

areas show virtually no difference in mean parity between each of the

educational groups. The rural-urban difference is 0.4 children ever

born in all educational groups.

Table 3.4

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Duration of Marriage, Women's education and Place of Residence

Marriage duration and

Place of residence

Women1s education

0 1-4 5-8 9+ Not Stated

Mwanza Rural0-4 2.1(10) 2.0(20) 1.0( 74) - 1.4( 11)5-9 3.7(23) 2.9(25) 3.0(115) 2.8(13) 2.9( 28)10-14 4.7(23) 4.7(36) 4.3( 82) 4.3(11) 4.3( 49)15-19 5.9(14) 6.1(30) 5.5( 24) 5.3(12) 6.0( 51 )20 + 7.7(52) 7.3(21 ) 7.5( 12) - 6.3(170 )

Not Stated 4.7(33) 3.6(23) 2.0(102) 2.2 ( 5) 5.2( 54)

Observed mean(INS) 5.5(155) 4.5(155) 2.9(409) 3.9(41) 5.4(363)

Observed mean(ENS) 5.8(122) 4.7(132) 3.2(307) 4.1(36 ) 5.5(309)

Standardized mean 4. 1 4.1 4.0 ★ ★ 3.9

Mwanza Urban0-4 0.9(15 ) 0.7(61 ) 0.9(141) 1.0(11) 1.1(27)5-9 3.3(28) 2.9(41) 3.2(116) 2.2(13) 2.8(32)10-14 4. 1 (47 ) 4.2(55) 4.4(128) 4.8(18) 3.8(25)15-19 5.5(20 ) 5.9(24) 4.7( 38) - 5.7(23)20 + 5.9(89) 6.5(39) 5.7( 32) - 5.7(52)

Not Stated 5.0(77) 2.4(87) 2.6(185 ) 2.6(23) 3.3(58)

Observed mean(INS) :5.2(276) 3.2(307 ) 3.1 (640 ) 2.9(65) 3.9(217)

Observed mean(ENS) 5.3(194) 3.6(220) 3.3(455) 3.0(42) 4.1(159 )

Standardized mean : 3.7 3.7 3.6 ★ ★ 3.5

Source:1980 Mwanza Pregnancy History Survey data tapeNote :1)The Urban Population used as the standard population

:2)Standardized by duration of marriage Excluded Not Stated :3)INS=Includes Not Stated and ENS=Excludes Not Stated :4)** Since many empty cells standardization was not perfomed

Page 38

3.4 Occupation and fertility

The historical decline in fertility in many parts of the world has generally been attributed to factors related to the process of modernization, economic development or industrialization. In industrialized societies, women's labour force participation is

considered as one of the major factors that rival family formation

(Schultz,1969:153-180). Olusanya(1968) pointed out that labour force participation and childbearing are in most instances compatible in

developing countries and especially in Africa. He elaborated that a working mother in Africa takes her children with her or can rely on relatives or cheap hired nursemaids to take care of her children. Under such circumstances it is difficult to envisage a strong inverse relationship between fertility and women's labour force participation outside the home.

In Tanzania for example, Egero and Henin(1973) found from the 1967 census data that the fertility of professional, technical administrative and executive group was slightly higher than other groups. Similarly the 1964 Demographic Survey data of Western Cameroon showed that both general fertility and average parity for

women in professional, technical and clerical jobs were higher than for women in all other occupational groups (United Nations, 1979:251).

However, this pattern of relationship is not maintained all the time in all countries. In some countries the higher occupational group haslower fertility.

Page 39

In Nigeria, Igun et al.(1972) for example, in a study of family

planning clients by occupational groups showed that women in

professional and clerical jobs plan to have fewer children than

housewives, service and craft workers. The results of the 1978 census

of the United Republic of Tanzania confirm that women in the

agricultural sector have the highest mean number of live births

(Ngallaba, 1983:371-388), farmers were found to have higher fertility

than non-farmers in Nigeria and Tanzania. In contrast, Hanna et

al. ( 1971: 127-219 ) , analyzing the 1960 Egyptian data for urban areas

(Cairo and Alexandria) and rural areas (Upper and Lower Egypt) found

no significant differences in fertility occurs among the occupational

groups studied.

Table 3.5

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group, Occupation and Place of Residence

AgeGroup

Women 's Occupation and Place of residence

Mwanza Rural Farmers Non-

Farmers

: Mwanza UrbanNot : Farmers NonStated : Farmers

NotStated

15-19 1.1 (63) 1.2(14) - : 0.9( 95) 1.2 (67) 0.8(46 )20-24 2.5(157) 1.6(55) - :2.7(155) 2. 1 (202 ) 2.8(87 )25-29 3.8(230) 2.9(32) ( 2 ) : 3.8(114) 3.2(150) 3.8(84)30-34 5.0(179) *( 3) ( 3 ): 5.5 (83) 4.7 (57) 4.7(38 )35-39 6.0(126 ) *( 7) - : 5.7 (80) 5.6 (48) 5.5(23 )40-44 6.8 (92) *( 3) ( 1 ) :5.8 (28) 5.3 (16) 6.1(13)45+ 6.2(133) *( 7) ( 2 ) : 5.8 (63) 5.6 (15) 5.6(22 )

Observed m:4.5(980) Standardized

1.7(121 ) ( 8):3.8(618) 3.0(555) 3.5(313)mean : 3.7 3.2 - : 3.7 3.3 3.6

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population used as standard population

:2)Standardized by age:3)* The mean values for farmers were used in calculating the

the standardized value for none farmers in rural areas :4)Excluding age not stated, 14 in rural and 19 in urban areas

Page 40

The mean number of children ever born to ever married Wa-Sukuma by occupational group is shown in Table 3.5. As mentioned in chapter 2 in Tanzania about 85 per cent of the female population are engaged in

the agricultural sector, so the existence of urban farmers in this survey is not unexpected. In both rural and urban areas before and after standardizing for age the farmers appear as the most fertile

group. The difference between the age standardized mean children ever born of these two occupational groups is 0.4 and 0.5 in urban and

rural area respectively. In addition, after standardized by age the data show that there is no difference in mean number of children ever born between farmers and also virtually no difference between

non-farmers in rural areas and their urban counterparts. The 'Not Stated' in urban areas appear to have a value similar to the farmers.

3.5 Age at first marriage and fertility

Female age at first marriage has been one of the most frequently included variables in the the study of the determinants of

fertility. In countries where the frequency of premarital pregnancies is not significant, age at marriage can be considered as an important factor influencing the differential exposure to pregnancies. Raising

age at marriage has been put forward as an anti-natalist measure. It

is one of the 'intermediate variables', through which any social or cultural factor affecting fertility must operate(Davis and

Blake,1956). One of the ways through which education is believed to have a depressing effect on fertility is by increasing age at marriage. For a women in a specific age group, age at first marriage

Page 41

tends to be a direct indicator of the duration of marital relations

and exposure to pregnancy. Hence, it is hypothesized that age at

marriage will have a negative indirect effect on the number of own

children through the number of pregnancies. On this basis, a

differential in the age at first marriage is an important indication

of likely social and demographic differences between two groups of

women.

The importance of age at first marriage in relation to fertility

stems from the fact that in any society where fertility is rarely

controlled, it marks the beginning of exposure to childbearing. If

rural women marry earlier, they are, on average, exposed longer than

urban women. The extent of the effect of age at marriage on fertility

was explained by Henry(1961) in the context of °natural fertility'. He

defined “natural fertility' as that fertility °which exists or has

existed in the absence of deliberate birth control'. He suggested that

the mean number of children is an approximate linear function of age

at first marriage and that it declines to zero when the age at

marriage is about 40 years(Henry,1961:81-88). McDonald et al.(1980)

found from World Fertility Survey data that in many societies the

difference in age at marriage does not bring much variance in the

level of completed fertility for women of the same birth cohorts,

except where age at marriage reaches relatively high levels. Speare et

al. ( 1973:333 ) suggested that delays in childbearing tended to result

in fewer children on average, since those women might encounter

subfecundity due to increased age and would not be able to achieve the

number of children they desire.

Page 42

Many scholars who have studied determinants of fertility

differentials have found that there is a strong inverse relation

between age at first marriage and fertility. Using the 1979 World

Fertility Survey data, an inverse relation between the age at first

marriage and fertility of married women was found in Sudan. Using the

1978 Kenya fertility Survey, a strong inverse relationship was also

noted in Kenya. Agarwala(1967) estimated in 1965 that the birth rate

would decline by as much as 29 per cent in 1991-92 if Indian women

married at a mean age slightly higher than 19, instead of the present

average of 16 years.

The information concerning the mean number of children ever born

according to age at first marriage and women®s age among the Wa-Sukuma

shows that in general the higher the age at first marriage the lower

the fertility in most age groups in rural and urban areas (Table

3.6). However, the observed mean tends to show same pattern between

rural and urban areas. Women who marry at ages above 20 years are

shown to have relatively fewer children than those married at ages

14-20 years in rural areas. When the differences in age distribution

of women are controlled, the mean parity decreases as age at first

marriage increases in rural areas but in urban areas women married

14-20 years are shown to have the lowest mean parity. The mean number

of children ever born is higher in rural areas than the urban areas

among the women who married less than 14 or at 14-20 years of age.

Page 43

Table 3.6

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group, Age at First Marriage and Place of Residence

AgeGroup

Age at first marriage and place of residence

<14 14-20 >20 Not Stated

Mwanza Rural15-19 1.6(32) 0.4(37) - ( 8)20-24 3.0(71 ) 1.9(52) 1.9(81) ( 8)25-29 4.7(83) 3.4(98) 2.8(70) 4.5(13 )30-34 5.9(80 ) 4.4(55) 4. 1 (40 ) 5.4(10)35-39 6.6(59) 5.5(28 ) 5.3(36 ) 6.2(10 )40-44 7.3(50 ) 6.1(16) 5.7(24) ( 6)45+ 6.7(51 ) 5.8(24) 6.2(54 ) 6.5(13 )

Observed:mean 5.2(426) 3.6(310) 3.9(305) 5.6(68 )

Standardizedmean 4.3 3.4 2.9 **

Mwanza Urban15-19 0.9(108) 0.9(100) - -20-24 2.6(173) 2.1 ( 80 ) 2.4(189) ( 2)25-29 4.1(125) 3.3( 89) 3.2(119) 4.1(15)30-34 5.3( 74) 4.7( 54) 4.9( 39) 5.2(11)35-39 5.9( 55) 5.0( 48) 5.1 ( 40) ( 8)40-44 6.0( 30) 5.3( 15) 4.8( 12) -45+ 5.8( 55) 5.4( 10) 6.0( 30 ) ( 5)Observed:mean 3.7(620) 3.2(396) 3.4(429) 4.6(41)

Standardizedmean 3.7 3.1 3.2 ★ ★

Source:1980 Mwanza Pregnancy History Survey data tape Note :1 )The Urban Population used as standard population

:2 )Standardized by age:3)Excluding age not stated, 14 in rural and 19 in urban :4)**Since many empty cells standardization was not

performed

Page 44

Table 3.7

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age at First Marriage, Women's

Education and Place of Residence

:Women's : :Education: : (in years)

Age at First Marriage and Place of residence

<14Rural14-20 >20 : <14

Urban14-20 >20

: 0 :6.3(92 ) 4.1 ( 16) 4.9( 44) : 4.8(130 ) 4.2( 42) 5.0 ( 95 ): 1-4 :4.7(88) 4.0 ( 21 ) 3.8( 33) : 3.5(139) 3.4( 69) 2.7 ( 94 ): 5-8 :3.9(101 ) 3.9(175) 3.1(122) : 3.1(233) 3. 1 (200 ) 2.7(202): 9+ : - 3.0( 18) 3.0( 18) : 3. 1 ( 24 ) 2.1 { 26 )Not Stated 5.7(146) 5.0( 70) 5.1(111 ) : 4.3(115) 2.8( 32) 3.3 ( 63 )

Source:1980 Mwanza Pregnancy History Survey data tape note :1)Excluding age at first marriage not stated

In the Mainland Tanzania including Mwanza region children start

obligatory schooling at age eight and education is continuous to

standard eight which is year eight where children are supposed to sit

for the examinations. In this respect it was thought useful to

analyse the data on the mean number of children ever born by age at

first marriage and educational attainment of the women. In Table 3.7

an inverse correlation between the age at marriage and educational

levels of the women is evident. Hence it is quite likely that women

with higher educational attainment have a higher age at first marriage

and therefore lower mean family size. By looking at the data by

columns, it can be seen from the Table that for any age at marriage

the differentials in mean fertility due to differential educational

attainment persisted. The mean parity generally decreased with the

increase in education for any age at marriage group. Another effect

can be seen by looking at the data in rows, that is by looking at a

given educational group with regard to each age at first marriage. The

Page 45

data show that within the educational level the fertility of Wa-Sukuma

declines with the increase of age at first marriage in all educational

levels except for those who have no schooling.

3.6 Marital status and fertility

The impact of marital disruption, both by death and marital

discord, has been studied in several countries. The findings have

indicated that marital instability does affect childbearing behaviour,

and eventually reduces the amount of time women spend in fertile

unions. The studies reviewed elsewhere consistently found that women

experiencing dissolution had fewer children at the time of their

divorce or widowhood than those in stable marriages. These

differences could arise from differences in average length of time

spent in a state of non-exposure during the time preceding the census

or survey. The tendency to bear illegitimate children is almost

negligible in traditional societies in Tanzania. The termination of

such pregnancies by abortion seems to be common to some of the African

societies and including the Sukuma (Nag,1968).

Onaka and Yaukey(1973) have estimated the reproductive time lost

due to marital disruption in San Jose, Costa Rica. They found from

women who experienced marital dissolution that approximately 10 per

cent of the their reproductive time after first marriage was spent

outside a marital union. They did not, however, provide estimates of

the extent to which this reduced the fertility of women. Pool(1968)

has estimated the relationship between type of marital status and

fertility in Ghana in 1960's. He pointed out that the instability of

Page 46

unions decreases fertility because of the amount of time spent outside

unions.

Udry(1971) has indicated that the conventionality of a couple may

bring both stability and children. From a study of Thai women,

Goldstein et al.(1973) noted that currently married women reported

more live births than women who had at some time experienced marital

dissolution. Using the data from Post Enumeration Survey of the 1960

population census of Ghana, it was found that both current and

cumulative fertility were also related to the current marital status

of women. It was also pointed out that currently married women have

more children than any other marital status group. Explaining the

reason for differentials, Baker(1953) suggests that the demand for

fewer children and individualism produces both marital instability and

small families. The studies described suggest that larger families tend to hold marriages together.

A country statement from Kenya noted that rising fertility and

declining mortality has resulted in unprecedented population increase

in the past decade. It was noted that this rise in fertility was

associated with numerous and stable marriages(United ions,1979:16).

The review fertility differentials in Senegal using the 1970

Demographic Survey showed a higher fertility in rural than in urban

areas. The fertility results demonstrated that higher divorce rates

for urban than for rural areas led to lower fertility in urban areas

(United ions,1979:17). In a case study of the Rungwe District in

Tanzania, Sterkenburg and Luning(1980:189) explain that spectacular

improvements in medical conditions could have led to a downward trend

Page 47

in the mortality rate and a concomitant increase in fertility. The

increase in fertility was due particularly to a decrease in sterility

and lower mortality of women in reproductive period. It was also

pointed out that a higher fertility rate could also have been

influenced by an increase in monogamous marriges and higher marriage

stability.

Table 3.8

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age Group, Marital Status and Place of Residence

Age Mwanza Rural : Mwanza UrbanGroup Currently Divorced/:Currently Divorced/

married Separatedimarried Separatedand : and

Widowed : Widowed15-19 1.0 (62) 1.4 (15 ): 0.9 (157) 1.3 (51 )20-24 2.6 (144 ) 1.6 (68 ):2.6 (310 ) 2.0 (134)25-29 3.9 (226 ) 2.4 (38 ):3.8 (281 ) 2.2 (67)30-34 5.0 (179) * ( 6 ): 5.1 (156) 4.7 (22)35-39 6.0 (122) 7.0 (1 1 ) :5.8 (137) 5.3 (14)40-44 6.7 (87) ★ ( 9 ): 5.9 (47 ) 4.9 (10 )45+ 6.3 (122 ) 6.0 (20 ) :5.9 (74) 5. 1 (26)

Observed : :mean : 4.6 (942 ) 2.8 (167 ):3.7(1 162) 2.6 (324 )

Standardized ;mean :3.7 3.2 : 3.6 2.9

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population used as the standard population

:2)Standardized by age:3)*The mean values for currently married women in rural areas were assumed in calculating the standardized values for divorced/separated and widowed group :4)Excluding age not stated 14 in rural and 19 in urban

Page 48

Table 3.8 presents the mean number of children ever born by

marital status for all ever married Sukuma women. When the mean

number of children ever born to women of the same age and marital

status are compared, predictably, in both rural and the urban areas,

currently married women have higher cumulative fertility than

divorced/separated and widowed women of the same age. This suggests

that time spent between marital unions perceptibly lowers

childbearing. This differential also persisted after standardization

for age. Currently married women were found to have 14 and 19 per

cent more children respectively than divorced/separated and widowed

women in rural and urban areas. Nevertheless, after standardizing by

age the data show a fertility difference of only 0.1 children between

currently married women in rural and urban areas. However, the

standardized figure for divorced/separated and widowed women combined

differ by 0.3 between the urban and rural areas.

3.7 Duration of marriage and fertility

Duration of marriage is one of the most important variables which

determine fertility. The theoretical basis of this relationship

originated from the assumption that by frequently moving in and out of

marriage, women lose some of their reproductive years by not being

exposed to the risk of childbearing. Since the achievement of desired

family size is closely related to the duration of marriage, one might

expect greater relationship between fertility and marriage duration

than between fertility and age(Pressat, 1972:198-199). In a study of

Page 49

women in Lagos, the capital of Nigeria, it was noted that an early age

at marriage and a longer marriage duration both contribute to higher

fertility (Ohadike, 1963:379-392).

Table 3.9

Mean Number of Children Ever Born to Ever Married Women Aged 15+, By Age at First Marriage, Total

Duration of Marriage and Place of Residence

Age at First Total Marriage duration (years)Place of : 0-4Residence :

5-9 10-14 15-19 20 + Not Stated

<1414-20>20

1.6(40) 0.8(40) 2.3(35)

3.7( 69) 2.8( 86) 3.2( 49)

Mwanza Rural 4.4( 80 ) 6.2(75 )4.4( 86) 5.1(29)4.9( 35) 5.4(27)

6.9(160 ) 6.0( 45) 6.2( 50)

( 3)3.6( 14) 3.3(132 )

Observedmean 1.5(115) 3.2(204) 4.5(201 ) 5.8(131) 6.6(255) 3.3(149)Standardized mean :1.6 3.3 4.5 5.7 6.4 2.0

<1414-20>20

0.6(105) 1.0( 82 ) 2.0( 68)

2.9(113) 2.9( 78) 3.7( 39)

Mwanza Urban 4.2(150) 5.3(64)4.2(101) 5.4(29)5.1 ( 22) 6.0(12)

5.9(148 ) 5.7( 45) 6.8( 19)

4.0( 37 ) 3.5( 32) 3. 1(320 )

Observedmean 1.1(255) 3.0(230 ) 4.3(273) 5.4(105 ) 5.9(212) 3.2(389)

Standardized mean :1.2 3.2 4.5 5.8 6.1 2.7

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban Population used as standard population

:2)Standardized by age at first marriage:3)Excluding age at first marriage not stated, 68 in rural and 41 in urban areas

Page 50

The results of the present analysis show the expected strong

positive relationship between duration of marriage and fertility of

Wa-Sukuma in both areas. Table 3.2 indicated that the mean number of

live births increases from about 1.5 to 6.6 in rural and 1.1 to 5.9 in

urban areas as the duration of marriage increases from under five

years to twenty years and over. When age at first marriage is

controlled, the standardized means show that rural women married for

more than 20 years have 4.8 children more than those married less than

four years. Urban women have even more 4.9 additional children after

20 years marriage compared with those married less than four

years. This is because the childbearing in urban areas is concentrated

in the late marriage duration(Table 3.9). At the observed level the

rural-urban fertility differentials exist in all marriage duration

groups. The highest difference was found among the women who have

been married for the duration of 20 years and over, which is 0.7 mean

children ever born and the lowest among those married for a duration

of 5-9 and 10-14 years which is 0.2 mean children ever born. After

standardized by age at first marriage the rural-urban difference

between those married for a period of 20 years and above reduced to

0.3 mean children ever born and those married for 15-19 and 5-9 years

duration shows 0.1 mean children ever born. Women married for 10-14

shows no difference in mean number of children ever born between rural

and urban, while those who married for a duration of less than five

years were found to have a difference of 0.4 mean children ever born

between rural and urban women.

Page 51

CHAPTER 4

DIFFERENTIALS IN ABSTINENCE AND BREASTFEEDING

4.1 Introduction

Having identified the differentials in fertility by the

socio-economic and demographic characteristics of the respondents in

the previous chapter, we now turn to examine the differentials in postpartum abstinence and breastfeeding. The main objective of this

chapter is to answer the following questions.

1. How does the duration of postpartum abstinence and

breastfeeding vary among different subgroups classified by age, women's education, occupation and place of residence?.

2. What is the effect of breastfeeding on fertility?.

In some studies, it has been argued that contraception is the most important proximate variable that accounts for the differences between populations in their marital fertility levels(Bongaarts, 1978). But, due to the small proportion of the respondents reporting ever use of contraception (less than 5 per cent), the differentials for contraceptive use will not be examined. Although it has been reported in several studies that coitus interruptus is very common among the Wa-Sukuma(Varkerisser, 1973:235-237, cited in Schoenmaekers

et al.1981) this study can not analyse the differentials in coitus interruptus, as Mwanza Pregnancy History Survey did not collect anyinformation on this.

Page 52

4.2 Postpartum abstinence

Abstinence from coitus on the part of a mother for a few weeks immediately after parturition is common in almost all societies. It has been observed in many developing countries that the child-spacing has been traditionally achieved, by prolonged breastfeeding which extends the period of postpartum amenorrhoea and by prolonged

postpartum abstinence.

In generally the abstinence period is shorter in East Africa than

West Africa between three to six months. In Kenya for example, the abstinence period was found to be only three months (Mosley et al., 1982). Schoenmaeckers et al.(1981:38-39) pointed out that 'there is convincing evidence that the West-African populations are clearly distinct from the East-African ones, where the period of abstinence is not only shorter, but where coitus interruptus quite commonly acts as a competing or alternative method of child-spacing'.

In some societies coitus is not prohibited after a few weeks after giving birth, but lactating mothers are expected to avoid

pregnancy by practicing coitus interruptus. The phenomenon of shortening the abstinence period and its partial replacement by coitus interruptus seems also to operate among Wa-Sukuma. It was also found

that most of the Wa-Sukuma returned to normal conjugal routine between

the second and the fifth month following birth and then coitus interruptus is practised (Varkevisser, 1973:235-237 cited in

Schoenmaeckers et al. 1981).

Page 53

Also, it was observed in many societies that the period of abstinence is shorter than the period of breastfeeding. Locoh and

Adaba(1981:256-260) for example, found in Southeast Togo that the median duration of abstinence was 7.6 months compared with 23.4 months for breastfeeding. Among Ghanaians, a World Fertility Survey (1975)

pilot study found that the mean duration of abstinence was 2.7 months shorter than that of breastfeeding, but this pattern is not the same in all societies. Conversely among the Yoruba in Nigeria, the

abstinence period was found to be longer than the period of breastfeeding (Caldwell and Caldwell, 1981).

4.3 Differentials in abstinence

a) Association of age and residence with abstinence

Table 4.1Mean Duration of last completed period of Postpartum Abstinence(in months)

By Age and Place of ResidenceAge Place of residenceGroup :Rural Urban15-19 4.6( 64) 5.4(154)20-24 4.5(149) 5.4(311 )25-29 4.8(235 ) 5.5(252)30-34 5.0(181 ) 5.6(136)35-39 4.8(128) 5.7(132)40-44 4.9( 94) 5.4( 51 )45+ 5.0(140 ) 5.5( 94)

Not Stated 4.6( 11) 5.8( 13)Overall :mean :4.8(1002) 5.5(1143)

Source:1980 Mwanza Pregnancy History Survey data tape

Page 54

The mean duration of the last completed period of postpartum abstinence in months for the Wa-Sukuma by age group and place of

residence is given in Table 4.1. Since the differences between age

groups are small the unstandardized means are quite a good indication of the overall differences between rural and urban women. The longer

abstinence period in all age groups in urban areas is unexpected, but the reason for this may be the following: In Tanzania, after giving birth, a wife usually stays with her mother or stepmother for a few months in order to get some help in the caring of the new baby. In rural areas where the residentially extended family is the norm and

where there is not much shortage of accommodation, the young mother tends to return to her husband's house after a few months, as she could get help from the members of the husband's family in looking after the infant. This help largely frees her from caring for the child thus facilitating more time and sexual activity with her husband. Conversely in urban areas, where the young mother usually lives in a nuclear family and where there is acute shortage of accommodation, she may tend to stay longer at her mother's place and be away from her husband for a longer time than in rural areas.

Page 55

b) Association of education and residence with abstinence

Table 4.2

Mean Duration of the last completed period of Postpartum Abstinence(in months) By Women's

Age, Education and Place of residence

Women's : Age Group :OverallEducation 15-24 25-34 35+ :mean

Mwanza Rural ;0 4.0( 25) 4.3( 52) 4.8 ( 67):4.5(144)1-4 4.0( 35) 4.8( 77) 4.4 ( 32):4.5(144)5-8 3.8(133 ) 4.9(175) 4.8 ( 23 ) :4.5(331 )9+ - 4.0( 21) 4.0 ( 1 3 ) :4.0( 34)

Not Stated 4.1( 20 ) 4.7( 91 ) 5.0(227 ) :4.9(338)

Overall : ;mean(INS) :3.9(213) 4.7(416 ) 4.9(362) -.4.6(991 )

Overall .mean(ENS) :3.9(193) 4.7(325 ) 4.6(135 ) :4.4(653)

Mwanza Urban .0 5.5( 42) 5.5( 75) 5.6(108):5.6(225)1-4 5.1(111) 5.4( 77) 5.4( 51 ):5.3(239)5-8 4.4(248) 5.5(164) 5.1 ( 51 ):4.9(463)9+ 3.8( 10) - :3.8( 10)

Not Stated 5.6( 54) 5.7( 72) 5.8( 67):5.7(193)

Overall ;mean(INS) :4.8(465) 5.5(388) 5.5(277 ):5.2(1 130 )

Overall :mean(ENS) :4.7(411) 5.5(316) 5.4(219) :5. 1 (937 )

Source:1980 Mwanza Pregnancy History Survey data tapeNote :1)INS=Includes Not Stated and ENS=Excludes Not Stated

:2)Excluding age not stated, 11 in rural and 13 in urban

Education is hypothesized to be negatively associated with

duration of postpartum abstinence. Explaining the association of

education with abstinence period, Santow and Bracher(1981:205), using

CAFN1 survey data in Ibadan city, Nigeria contended that 0within the

Page 56

age groups the better-educated systematically abstain for much shorter periods'; they continued that age is the secondary and education the primary factor in this association. Also, Caldwell and Caldwell (1981:181), pointed out that the declines in abstinence periods among the Yoruba in Nigeria are associated with the higher education of

either of the spouses.

The mean period of abstinence by the levels of education, age

groups and place of residence among Wa-Sukuma is given in Table 4.2. The data shows that the younger and more educated women reported

slightly shorter abstinence periods in both areas. Another point is the difference between age groups within the educational categories and particularly among women who have no schooling and living in rural

areas, whose average duration of abstinence drops from 4.8 months in the 35 and above age group to about 4 months in the 15-24 age group. Santow and Bracher(1981) pointed out the reason: they explained that young women with no schooling abstaining for shorter periods than their older counterparts suggests that new ideas have filtered through to them quite independently of formal education. The overall mean shows that there is no difference in abstinence period between those who have no education and those who have 1-4 and 5-8 years of schooling in rural areas, while in urban areas the difference in

abstinence by educational levels persists. Since the abstinence period for this society is only between three to six months and since lactational amenorrhoea on average is much longer than abstinence,

abstinence would not contribute much to the depression of fertility among these women. In general the differentials in abstinence among the various groups is quite small. This suggests that the abstinence

Page 57

period among Sukuma ethnic group does not depend much on the levels of

socio-economic characteristics and especially among the rural women.

4.4 Breastfeeding

There is considerable evidence demonstrating that breastfeeding

is of importance in the maintenance of child health and development.

Several studies show that breast-fed infants have a better pattern of

growth than artificially fed infants during at least the first six

months of life and often for longer periods of time(Jelliffe and

Jelliffe, 1978). In countries like Western Samoa and Kiribati,

maternal and child health services are encouraging longer

breastfeeding (Kiribati National Development Plan, 1979-82, cited in

Lucas and Ware, 1981). The duration of breastfeeding varies from one

country to another and within the country varies from one society to

another. In Ghana for example, using the World Fertility pilot study

data in 1975, it was observed that the mean duration of breastfeeding

was 21.4 months among Dagomba and 13.2 months among Asante (Gaisie,

1981 ) .

Prolonged breastfeeding is identified as an important factor in

achieving longer birth intervals in countries where little or no

contraception is practiced. Studies conducted elsewhere have shown

that breastfeeding prolongs postpartum amenorrhoea. For instance,

Jelliffe and Jelliffe( 1972 ) from their review of literature on

breastfeeding conclude that ovulation and menstruation are delayed

among the lactating mothers for atvleast ten weeks and up to twenty

Page 58

six months but only if breastfeeding is complete, successful and

unsupplemented. Potter et al.(1965) found that in some Punjab

villages the median length of postpartum amenorrhoea among lactating

women was eleven months while that for non-lactating women was two

months. They also noted that the average interval between successive

live births was thirty and fifteen months respectively.

It has been demonstrated in several studies that after the

delivery of a child, the fertility of breastfeeding women is

substantially lower than the fertility of non-breastfeeding women

(Buchanan, 1975; Van Ginneken, 1977) # This could be that the

contraceptive effect of breastfeeding is attributable to the

suppression of ovulation and menstruation that is associated with

lactation. The positive association between the duration of

breastfeeding and the length of the birth interval has been observed

in many societies, but such contraceptive effect of breastfeeding may

often be incidental rather than intentional.

4.5 Attitude of Wa-Sukuma towards breastfeeding

Breastfeeding is considered very important by Sukuma people for

the baby to grow up strong. Sukuma women usually do not have any

problems in breastfeeding. However, if the baby is a weak sucker,

then the mother keeps trying until the baby does suck and all babies

eventually learn. Mother can squeeze milk into baby's mouth to give

him/her the idea. Baby is fed when she/he is hungry and crying and as

often as she/he likes. Schedule feeding is rarely practiced. Baby

Page 59

usually sleeps with mother until weaned. Weaning takes place when the

child loses interest and this appears to occur when the child is

running around, usually during 1-2 years of age. Sukuma men feel very

happy about mothers breastfeeding the child. Although breastfeeding

is considered completely natural, it is also considered embarrassing

to breastfeed in public without covering the breasts. The breasts are

considered particularly important for sexual excitement in many

societies in Tanzania, and therefore men feel uncomfortable when women

expose breasts in public even for feeding the child. Although

Wa-Sukuma belive that the mother's milk is good for the health of the

child, better economic conditions can make women to take up bottle

feeding after short periods of breastfeeding. Since the economic

situation varies between place to place, there is a great deal of

variation between rural and urban areas with respect to the duration

of breastfeeding. The Mwanza Pregnancy History Survey recorded 997

and 1216 respondents having two or more live births in rural and urban

areas respectively. In rural areas 97 per cent of these women

reported that they breastfed their next-to-last child compared with 94

per cent in urban areas. The average duration of breastfeeding among

rural women was found to be 16.4 months which is 2.4 more than for the

urban women, after excluding women who never breastfed.

Though in other societies the tendency to report in multiples of

six months may be considered as a recall problem, among Wa-Sukuma to

breastfeed a child for twelve or twenty four months is a cultural

norm. In rural areas about 52.2 and 30.7 per cent reported the

duration of breastfeeding as 12 and 24 months respectively, while in

urban areas the percentages were 59.3 and 11.9. It would be possible

Page 60

to analyse these one-year and two-year breastfeeders as two separate

groups but the numbers would be too small especially in the urban

areas for those breastfeed for a period of 24 months.

The breastfeeding information for the last closed birth interval

is thought to be relatively more reliable since this live birth

constitutes the most recent event and does not involve a long recall

period. Also, it should be noted that in the Mwanza Pregnancy History

Survey no distinction was made between full and partial breastfeeding.

Nevertheless, from the reported data the mean duration of

breastfeeding(in months) in the closed birth interval has been

calculated for all socio-economic groups. In this survey, there was

no question asked about amenorrhoea, but Kamuzora(1983) reported the

following: 61 women who gave live births during the period of

December-January(1980-81) were followed by nurses for twelve months by

visiting once in a month. They were found to be breastfeeding through

out this period, and it was also observed that the length of

amenorrhoea was a median of 7.4 months, ranging from one to ten

months. In addition, it should be noted that the current age of the

respondents is used in this analysis. As mentioned in Chapter 1, one

of the data limitations is that we do not know the age of the mother

when she breastfed the child and no question was asked about the age

of the next-to-last birth in the survey to enable us to determine the

age of mother at time of the index birth.

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4.6 Differentials in breastfeeding

a) Association of age and residence with breastfeeding

In earlier studies mother's age has been found to have a positive

association with the duration of breastfeeding (Jain et al., 1970;

Chen et al. 1974). For example the World Fertility Survey(1977)

found in Pakistan that women of age 15-24 years breastfeed on the

average for 20.4 months compared to women age 35-44 years who

breastfeed for 25.2 months. But this is not true for every country.

Another factor that influences the extent of breastfeeding is whether

she lives in rural or urban area. In Ruwanda for example, rural

mothers carry their babies and breastfeed frequently on demand while

urban mothers rarely carry their babies and breastfeed by a more rigid

time schedule (Bonte et al. 1974). It has been argued that the

decline in breastfeeding is associated with the process of

modernization (Rosa, 1976; Berg, 1973). To explain this , Berg

(1973:99) contended that in urban areas “breastfeeding is often viewed

as an old fashioned or backward custom and by some as a vulgar peasant

practice'. It was also found in Pakistan that the increase in

urbanization is likely to curtail the current levels of prolonged

breastfeeding since urban women as well as those coming from rural

areas and settling in urban areas tend to breastfeed less than their

counterparts in rural areas(WFS, 1977). It was suggested in several

studies that the poor nutritional conditions could be the possible

explanation of why rural women tend to nurse their children for longer

periods.

Page 62

Table 4.3Mean Duration of Breastfeeding(in months)

in the Last Closed Birth Interval By Women's Age and Place of Residence

Age :Place of residenceGroup : Rural Urban

15-19 14.4( 50) 12.7( 81)20-24 15.8(166) 13.7(330 )25-29 17.8(224) 14.2(296)30-34 16.8(173) 13.7(150)35-39 15.9(121) 13.8(130)40-44 16.6( 86) 14.8( 53)45+ 15.4(133) 15.6( 87 )

Not Stated 15.4( 14) 15.8( 16)

Overallmeant INS) 16.4(967) 14.0(1143)Overallmean(ENS) 16.4(953) 14.0(1127)Standardizedmean(ENS) 16.4 14.0

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)The Urban population used as the standard population

2 Standardized by age3)INS=Includes Not Stated and ENS=Excludes Not Stated

A comparison of the mean duration of breastfeeding between the Mwanza rural and urban areas based on the reported duration of breastfeeding in the last closed birth interval is provided in Table 4.3. The results presented indicated large differences in

breastfeeding patterns between rural and urban areas, rural women

breastfeed for 2.4 months longer on average than urban women before and after standardized by age. This differential between rural and

urban areas persists in all ages and the results also indicate that there is a difference in the extent of breastfeeding between youngerand older women. Although there is no consistent result, the data

Page 63

show that the younger women breastfeed less than the older women. It has been argued by many of the researchers that bottle feeding is usually adopted first by elite younger and urban women. Because breasts are considered important for sexual excitement among Wa-Sukuma, the young and urban Sukuma women fear that breastfeeding

will change the shape of their breasts and thus make them less attractive to their men.

b)Association of education with breastfeeding

Improvement in socio-economic conditions and the spread of the

new ideas are creating pressures that encourage reductions in the duration of breastfeeding. Education is among the socio-economic variables that have been put forward to have a negative association with breastfeeding. Educated women with access to modern amenities may breastfeed for a comparatively shorter period without adversely affecting the survival chances of her child. Lesthaeghe et al. (1981), for example, found among Lagos women in Nigeria in 1976 that the median duration of breastfeeding in the last closed birth interval of the women with no education was 16.3 months and those with secondary education was 5.6 months. Among the Yoruba in Nigeria it was observed that women with no education breastfeed their babies for twice the duration of those with secondary education (Lucas, 1976).

In Lesotho using the 1977 World Fertility Survey data, it was found that women with secondary education breastfeed for about 15 months

which is 5 months less than those with no education, between no

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education, lower primary and upper primary women little difference was

found (World Fertility Survey, 1981). A similar pattern was also

observed in Sudan by the World Fertility Survey using 1979 data (World

Fertility Survey, 1982 ) .

The results from Mwanza Pregnancy History Survey confirm the

differentials in breastfeeding among Wa-Sukuma with no education and

those with education. The examination of the trend in breastfeeding

by years of schooling showed generally less breastfeeding among women

with at least five years of schooling(Table 4.4). This pattern can be

seen even after standardization by age in both rural and urban areas.

Although a general decline in breastfeeding is noticed for younger

cohorts of women, the decline also occured among the groups that

represent traditional characteristics. For example, in the group of

women who have no schooling, women aged 15-24 years breastfed on the

average 15.0 months compared to 17.1 for those aged 35 and over in

rural areas, while in urban areas the durations were found to be 13.1

and 15.1 months respectively. Other differentials suggest that the

overall average breastfeeding period (excluding those who did not

state their education) was one month more among the rural women than

their urban counterparts. Findings from several studies consistently

indicate a much shorter duration of breastfeeding for younger women

who are more educated and live in urban areas(Jain et al. 1979;

Lesthaeghe et al. 1981). Taking age, education and place of

residence together it can be seen that the younger, educated (9+) and

urban women have the shortest duration of breastfeeding while the

longest duration was found among Wa-Sukuma who have no education, are

older and living in the rural areas.

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Table 4.4

Mean Duration of Breastfeeding(in months) in the Last Closed Birth Interval By Women's Age,

Education and Place of residence

Women's Age Group :Overall StandardizedEducation 15-24 25-34 35 + :mean :mean(in years): :

Mwanza Rural «0 15.0( 24) 15.6( 47) 17.1 ( 62): 16.2(133 ) 15.71-4 15.6( 40 ) 16.2( 72) 15.7( 30 ): 15.9(142 ) 15.95-8 13.4(130) 15.1(177) 15.5( 23 ) : 14.5(330 ) 14.69+ 13.2( 19) 15.3( 1 1 ) : 13.9( 30) 13.8

Not Stated 15.8(22) 15.8( 82) 15.7(214):15.7(318) 15.8

Overall :mean(INS) 14.2(216) 15.4(397) 16.0(340):15.3(953) 15.1

Overallmean(ENS) -.14.1(194) 15.3(315) 16.3(126): 15.1 (635 ) 15.1

Mwanza Urban =0 13.2( 46) 14.3( 86) 15.1(105): 14.4(237 ) 14.11-4 13.7( 90 ) 13.2( 89) 14.7 ( 48): 13.7(227) 13.75-8 13.5(222) 13.7(183) 13.7( 53 ) : 13.5(458 ) 13.69+ 13.0( 21) 14.1 ( 22) - : 13.6( 43) 13.6

Not Stated 15.3(32) 14.3( 66) 14.4( 64): 14.5(162) 14.7

Overall . .meant INS) 13.6(411) 13.8(446) 14.6(270):13.9(1127) : 13.9

Overall : :mean(ENS) :13.5(379) 13.7(380) 14.6(206):13.8(965) : 13.8

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)INS=Includes Not Stated and ENS=Excludes Not Stated

: 2 Standardized by age:3)The Urban population used as the standard population :4)Excluding age not stated, 14 in rural and 16 in urban

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c) Association of Occupation with breastfeeding

One of the socio-economic factors included in this analysis is

the women's occupation. It is believed that the practice of early

bottle feeding is least prevalent among farmers and poor families and

as a result they breastfed their babies for longer periods than

non-farmers and rich families. But this is not always the case, in

the country like Sri Lanka, for example, Gaminiratne(1978:75) showed

that women who were working in non-agricultural sectors have a

relatively higher duration of breastfeeding than their counterparts.

Table 4.5

Mean Duration of Breastfeeding(in months) in the Last Closed Birth Interval By Women's Age,

Occupation and Place of Residence

Women's ; Age Group :Overall StandardizedOccupation : 15-24 25-34 35+ :mean :mean

: Mwanza Rural :Farmers : 15.0(167) 17.4(368) 16.0(325):16.4(860) 16.2Non-farmers : 14. 1 ( 49) 16.8( 24) 17.0( 12): 15.3( 85) 15.9

Overall mean 14.8(216) 17.4(392) 16.0(337 ): 16.3(945) 16.1

: Mwanza Urban .Farmers : 13.3(133) 13.3(175) 14.9(157): 13.8(465) 13.7Non-farmers:12.7(200) 14.5(170) 14.7( 66) : 13.7(436) 13.9Not Stated : 13. 1 (78) 14.3(101) 13.6( 47 ): 13.8(226 ) 13.7

Overall : : :mean(INS) : 13.0(411 ) 14.0(446) 14.6(270):13.7(1127) 13.8

Overall ; :meant ENS) : 12.9(333 ) 13.9(345) 14.8(223): 13.8(901 ) 13.8

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)INS=Includes Not Stated and ENS=Excludes Not Stated

: 2 Standardized by age:3)The Urban population used as the standard population :4)Excluding Occupation not stated in rural areas (8) :5)Excluding age not stated, 14 in rural and 16 in urban

Page 67

The mean duration of breastfeeding by age group, occupation and place of residence were calculated in order to determine the association of occupation and breastfeeding. In urban areas the

overall means show that the occupation does have little influence on breastfeeding, while in rural areas non-farmers have 1.1 months less breastfeeding than farmers(Table 4.5). After standardized by age

non-farmers in urban areas showed slightly longer breastfeeding duration than farmers, while in rural areas non-farmers were found to breastfeed for a period of 0.3 months less than farmers. The

differences are present between the age groups in both areas but are inconsistent. The rural areas show that in all age groups except age group 35 years and over, the farmers have longer duration of breastfeeding than non-farmers. In urban areas the situation is different. The age group 25-34 non-farmers have longer duration of breastfeeding than farmers but in other age groups farmers have longer

duration than non-farmers. Nevertheless, in all age groups and occupational categories rural women exhibit longer duration of breastfeeding than their urban counterparts. The overall means indicate that urban women breastfeed 2.6 months less than rural women.

d) Association of breastfeeding with fertility

The effect of breastfeeding on fertility is suggested by a number

of studies in which it is shown that in the absence of contraception the period of survival of a child is positively associated with the birth or pregnancy interval (Henry, 1961; Jain et al. 1979). It was also estimated that as the duration of breastfeeding increases, so

Page 68

does the amenorrhoea interval, approximately one additional month of

amenorrhoea for a two month increment in breastfeeding duration

(Corsini, 1979). There is also some empirical evidence that the

continuation of breastfeeding beyond the resumption of menstruation

supresses the probability of conception (Jain et al., 1979). One

reason that nursing in developing countries is common and prolonged is

the widespread belief that it is effective in postponing the next

conception and improves the health of the child (Yaukey, 1961).

Rosa(1975) has estimated in developing countries that approximately

one third more protection is provided by lactational amenorrhoea than

by all family planning programme contraceptive methods.

Table 4.6

Mean Number of Children Ever Born to Ever Married Women Aged 15 + , By Age, Duration of Breastfeeding in the Last

Closed Birth Interval and Place of residence

AgeGroup

Duration of Breastfeeding(in months) and Place of Residence

Mwanza Rural Less 13

than 13 and more

Mwanza Urban Less 13

than 13 and more

15-19 2.8( 34) 2.3 ( 16) 2.4( 60) 1 . 1 ( 21 )20-24 3.0(102) 2.9( 64) 3.3(250 ) 3.0 ( 80 )25-29 4.3(105) 3.7(119) 3.9(216 ) 3.7 ( 80 )30-34 5. 1(106) 5.0( 67) 5. 1(102) 5.1 ( 48 )35-39 5.9( 69) 6.2( 52) 5.9( 86) 6.0 ( 44 )40-44 6.5( 43) 7.3( 46) 6. 0( 37) 6.0 ( 16)45+ 7. 1 ( 87 ) 6.7( 43) 6.4( 52) 5.9 ( 35)

Observed mean:4.9(546) 4.8(407) 4.3(803) 4.2(324)

Standardizedmean : 4.2 4.0 : 4.0 3.8

Source:1980 Mwanza Pregnancy History Survey data tape Note :1)Urban population used as the standard population

:2)Excluding age not stated, 14 in rural and 16 in urban

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One way to measure the impact of lactation on fertility in non

contracepting populations is to compare birth (or pregnancy) intervals

of women who initiated nursing after childbirth with those of women

who did not nurse or those who did nurse for a shorter period. Since

the Mwanza Pregnancy History Survey data is not good enough to permit

the analysis of birth intervals by the levels of breastfeeding, the

total number of children ever born to ever married women was used to

measure the relationship between breastfeeding and fertility (Table

4.6). In this analysis the recorded duration of breastfeeding for the

next-to-last child is assumed to be the regular duration of

breastfeeding for that mother since the variation of breastfeeding

duration by parity for the majority of women in the developing

countries is almost negligible (Jain, et al.,1979). The Table 4.6

shows that in rural and urban areas women who breastfeed less than 13

months have slightly higher mean number of children ever born compared

to those who breastfeed for 13 months and more. The observed mean

number of children ever born shows 0.1 more children for those who

breastfeed for less than 13 months in rural areas. When women's age

is controlled, the difference in mean number of children ever born

between these two breastfeeding durations was found to be 0.2. A

similar pattern was observed in urban areas. This is to say Sukuma

women who breastfeed for less than 13 months have 0.2 more mean

children ever born than those who breastfeed for 13 months and more in

both areas. The rural-urban differentials persists. At the observed

level the difference was found to be 0.6 mean children ever born in

both breastfeeding levels, after standardized by age this difference

is reduced to 0.2 mean parity.

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CHAPTER 5

MULTIPLE CLASSIFICATION ANALYSIS (MCA)

5.1 Introduction

The previous two chapters discussed the differentials in fertility, abstinence and breastfeeding among Wa-Sukuma in Mwanza region. Certain differentials in these parameters among various socio-economic and demographic groups within each area have been observed. Rural-urban differences in fertility, abstinence and

breastfeeding between various groups continued to be present even while controlling for variables other than women's age. However, the rural-urban differences were reduced when age of women was controlled.

Describing differences by cross-classification of the data may leave us with a large gap between dependent and independent variables. In other words cross-classification of the data does not determine the relative contribution of independent variables to the dependent

variable (Goldberg, 1959:214). However, the purpose of this chapter is to appraise the relative importance of each independent variable and the net effect of all independent variables on the dependent

variable, using multivariate approach.

This study has used Multiple Classification Analysis to determine the importance of each predictor to the dependent variable (for details see Andrew et al., 1973). The 'MCA is a technique for

examining the interrelationship between several predictors and a

Page 71

dependent variable within the context of an additive model'(Andrew et

al. , 1973:1 ). It is very useful for social research in which many of

the data used as predictors are likely to be categorical rather than

continuous variables with normal distributions. MCA can also better

handle multicollinearity and non-linear relation compared to simple

multiple regression (Andrew et al. 1973). The appropriate data for a

MCA consist of one dependent and several predictor variables. For the

purpose of this study the total number of children ever born (CEB) is

used as the dependent variable in the first part of this analysis.

The CEB is used as an indicator of fertility as it may be less

influenced by temporary changes in the conditions of the society in

question (Park, 1978:118). Women's age, education, occupation, age at

first marriage and marriage duration are used as the predictors in

this part of the analysis. These variables were included in order to

test the hypothesis that were formulated in Chapter 1. In the second

part the length of breastfeeding in the last closed birth interval is

used as the dependent variable and women's age, education and her

occupation as the predictors in both rural and urban areas.

The statistics presented by MCA show how each predictor is

related to the dependent variable, both before and after adjusting for

the effects of other variables. Unadjusted deviations are simple

deviations of category mean from the grand mean. Adjusted deviations

are the deviations which adjust these for the effects of other

independent variables in the model. Eta is a measure of association

conceptually similar to a simple correlation coefficient. Beta is a

measure of the independent contribution of the variable to the

collective relationship established by a MCA model after controlling

Page 72

for other variables in the model. In other words, Beta is an indicator of the relative importance of the independent variables in their joint explanation of the dependent variable (Phillips, 1978).

R-Squared, which is the ratio of the regression sum of square to the total sum of squares, represents the proportion of the variance of the

dependent variable explained by all independent variables.

Because of the high correlation between marriage duration and women’s age (See Appendix II), this study produced two separate MCA models, other independent variables are not highly correlated between

them. The first model contained women's age, age at first marriage, education and her occupation as the predictors. In the second model women's age was replaced by marriage duration. Since many of the respondents did not state their marriage duration and educational levels, the second MCA model where education and marriage duration were put together the total number of the respondents in Table 5.2 would be less compared with Table 5.1. Though, women who have completed their fertility experience could be a better choice in the study of correlates of fertility, because of the smaller number of these women in the study group, this study will consider all women in

the survey (aged 15+).

5.2 Results of the analysis

The effects of women's age, age at first marriage, education and

women's occupation on total number of children ever born are shown in

Table 5.1. The grand mean of the total number of children ever bornis 3.8 in rural areas and 3.4 in urban areas.

Page 73

Table 5.1

Effects of Predictors other than marriage duration on Total Number of Children Ever Born to Ever Married Women

Variables: Rural Urban

:Number Deviation from : of Grand Mean:cases Unadj. Adj(a)

Number Deviation fromof Grand Mean

cases Unadj. Adj(a)

Age : (Eta=0.69; Beta=0.62) (Eta=0.66; Beta=0.63)

15-19 : 72 -2.65 -2.65 136 -2.37 -2.3420-24 :185 -1.50 -1 .32 310 -1.08 -1.0225-29 :209 -0.25 -0.11 210 -0.10 -0 . 1830-34 :125 1 . 17 1.09 112 1.79 1.7135-39 : 62 2.68 2.42 97 2.30 2.2540-44 : 38 3.48 2.72 35 2.60 2.3145+ : 28 2.79 2.41 55 2.21 1.93

Age at firstmarriage : (Eta=0.36; Beta=0.23) (Eta=0. 14 ; Beta=0.06 )

<14 :276 1.17 0.73 363 0.39 0.2014-20 :228 -0.62 -0.30 230 0.03 -0 . 1620 > :215 -0.85 -0.63 362 -0.40 -0.09

Education: (Eta=0.42; Beta=0.34 ) (Eta=0.32; Beta=0.29)

0 :147 1.77 0.54 224 1.36 0.261-4 :141 0.64 0.24 234 -0.29 -0 . 165-8 :394 -0.89 -0.23 475 -0.42 -0 . 179+ : 37 -0.05 -0.62 22 -1.61 -1 . 12

Occupation (Eta=0.25; Beta=0.11) (Eta=0.17; Beta=0.06 )

Farmers :621 0.26 0.01 493 0.40 0. 15Non/ :farmers : 98 -1.63 -0.08 462 -0.43 -0. 16

R Square Adjusted (%) 56.2 46.0Total Number andGrand Mean 719 3.79 955 3.43

Source:1980 Mwanza Pregnancy History Survey data tape Note :(a) Adjusted for other predictors

Consideration of the close relationship between a mother's age

and fertility is essential to understanding the concept and

measurement of fertility in terms of the number of children ever born.

Page 7 4

The age of mother has always been found to have a strong positive

impact on the number of children ever born. This relationship is also

supported by the 1980 Mwanza Pregnancy History Survey data. The

positive effects of increasing women's age at the unadjusted and

adjusted levels are prominent in both rural and urban areas (Table

5.1). At the unadjusted level women's age contributes 48 per cent to

CEB in rural areas and 44 per cent in the urban areas. However, after

adjusting age at first marriage, education and occupation, the net

deviation of current age is reduced in both areas which suggests the

exaggeration of the effect of current age on cumulative fertility by

its low correlation with other predictors.

Another variable which is included in this model is age at first

marriage. The previous review studied found that women's age at first

marriage is inversely related to cumulative fertility. At the

cross-classification stage this study also confirmed that the negative

effect of rising age at first marriage on fertility was observed. In

urban areas those who have been married at the ages 14-20 years were

found to have slightly lower fertility compared to those married at

ages below 14 years and above 20 years (Chapter 3). A similar pattern

was observed when the MCA table was produced at the unadjusted and

adjusted levels in rural areas and only at the adjusted level in urban

areas. The table also shows that the contribution of age at first

marriage was 13 per cent and 5 per cent at the unadjusted and adjusted

levels respectively in rural areas. In urban areas it was 2 per cent

and less than 1 per cent at these levels. Only in rural areas age at

first marriage was found to be statistical significantly related to

the CEB.

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The statistics also show that education is highly related to cumulative fertility in both areas. The significant effect of

education on fertility was observed in both rural and urban areas. The MCA table shows that education is inversely related to CEB in such a way that the higher the educational level of women the fewer the

total number of children. At the unadjusted level, the difference is

1.8 and 2.9 children between the highest and the lowest categories in rural and urban areas respectively. However, when women's age, age at first marriage and occupation were controlled its deviations are reduced to 1.2 and 1.4 children between these two extreme groups in rural and urban areas respectively. The contribution of education to

CEB reduced from 18 per cent to 12 in rural areas and from 10 to 8 per

cent in urban areas while controlling for other predictors in the model.

The relationship of women's occupation with cumulative fertility is relatively strong in rural areas, while less in urban areas. In rural areas, before adjustments the occupation contributes 6 per cent to the cumulative fertility, after adjusted by women's age, age at first marriage and education, the deviation was reduced to 1 per cent. In urban areas after adjustments the contribution was found to be less than 1 per cent, while before adjustment it was 2 per cent. The Table

also shows the distinctively higher level of fertility among farmers in both areas. The higher fertility among farmers is probably due to the contribution of children to agriculture and this productive

contribution of farmer's children would reduce their cost to the

parents.

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In terms of R-Squared, which indicates the proportion of the variance of cumulative fertility explained by the independent variables, women's age, age at first marriage, education and her

occupation together explain 56 per cent of the total variation in the CEB in rural areas and 46 per cent in urban areas.

The marriage duration was included in the place of age in the

second MCA model. The positive effects of increasing marriage duration are pronounced in both rural and urban areas. The difference between those who have been married for a duration of less than 5

years and those 20 years and more at the unadjusted level was found to be 6.3 and 5.2 children in rural and urban areas respectively. Nevertheless, when age at first marriage, education and women's occupation were controlled, the deviations by marriage duration are reduced only in rural areas to 5.8 children. In addition, the marriage duration contributes 56 per cent (0.75 squared) before adjustments and 49 per cent after adjustments to the CEB in rural areas. In urban areas the contribution was found to be 55 and 53 per cent before and after adjustments respectively.

The proportion of variance of cumulative fertility explained by

marriage duration, age at first marriage, education and occupation

together was found to be 59 per cent of the total variance in the CEB in rural areas and 56 per cent in urban areas. In both MCA models fertility of rural women was found to be higher than their urban

counterparts.

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Table 5.2

Effects of Predictors other than Women's age on Total Number of Children Ever Born to Ever Married Women

Variables: Rural Urban

:Number : of:cases

Deviation from Grand Mean

Unadj. Adj(a)

Number Deviation fromof Grand Mean

cases Unadj. Adj(a)

Marriage : (Eta=0. 75; Beta=0.70) (Eta=0.74; Beta=0.73)Duration :

0-4 : 99 -2.88 -2.76 144 -2.89 -2.925-9 :165 -1.01 -0.92 127 -0.69 -0.6910-14 : 140 0.37 0.44 165 0.62 0.6215-19 : 72 1.59 1.49 60 1.46 1.4420+ : 83 3.43 3.08 135 2.32 2.36

Age at firstmarriage : (Eta=0. 34; Beta=0. 19 ) (Eta=0.11; Beta=0.08)

<14 :277 0.88 0.18 364 0.07 -0.0814-20 :228 -0.94 -0.28 231 -0.26 0.00>20 : 54 -0.52 -0.33 36 0.94 0.77

Education: (Eta=0. 40; Beta=0.30 ) (Eta=0.27; Beta=0.26)

0 : 117 1.75 0.41 161 1.08 0 . 131-4 : 11 8 0.48 0.11 154 -0.10 0.075-8 :292 -0.89 -0.16 304 -0.45 0.079+ : 32 -0.92 -0.42 12 -1.81 -0.83

Occupation (Eta=0. 02; Beta=0.02) (Eta=0.03; Beta=0.04)

Farmers :541 0.01 0.02 386 0.05 0.09Non-/ :farmers : 18 -0.28 -0.70 245 -0.09 -0 . 14

R Square Adjusted (%) 58.8 56.0

Total Number andGrand Mean 559 4.11 631 3.73

Source:1980 Mwanza Pregnancy History Survey data tape Note :(a) Adjusted for other predictor and women's age

Page 78

5.3 Analysis of Breastfeeding

This section will examine the effects of women's age, education

and occupation on the duration of breastfeeding for the closed birth

interval through multiple classification analysis.

Table 5.3

Effects of Women's Age, Education and Occupation on the duration of breastfeeding for the Closed Birth Interval

VariablesRural Urban

Numberof

cases

Deviation from Grand mean

Unadj. Adj(a)

Numberof

cases

Deviation from Grand mean

Unadj. Adj(a)

Age (Eta=0.17; Beta=0 • 19) (Eta=0 .08; Beta=0 .07)

15-24 194 -1 . 30 -1 .73 299 -0.41 -0.3625-34 310 -1 . 02 1.00 288 -0.01 0.0135+ 124 0.51 1.22 175 0.72 0.59

Education (Eta=0.21; Beta=0 .21 ) (Eta=0 .12; Beta=0 . 12)

0 130 0.48 0.54 202 0.85 0.901-4 141 -1.05 -1 . 17 181 0.46 0.415-8 326 -1 . 15 -1.24 360 -0.34 -0.389+ 31 -1.69 -2.21 19 -1.89 -1.63

Occupation (Eta=0.02; Beta=0 .05) (Eta=0 .06; Beta=0 . 09 )

Farmers 555 0.05 0 . 10 385 -0.29 -0.50Non/Farmers 73 -0.38 -0.78 377 -0.30 -0.51

R-Squared Adjusted (%) 7.7 2.4

Total Number andGrand mean 628 16.76 762 13.90

Source:1980 Mwanza Pregnancy History Survey data tape Note :(a)Adjusted for other predictors

In the Table 5.3 we see that among women who have no education

breastfeeding duration was found to be 0.5 of a month more than the

overall sample average (16.8 months) in rural areas, while in the

Page 79

urban areas it is 0.9 of a month longer than the overall sample (13.9

months). The nine years of schooling group has a duration of 1.7

months shorter than the average sample in rural and 1.9 months in

urban areas. The difference at unadjusted level between the two

education extremes is 2.8 months in urban areas and 2.2 months in

rural areas. When adjusted for the effects of age and occupation (see

Adjusted column), the difference is reduced to 2.5 months in urban

areas and increased to 2.8 months in rural areas. The education

factor explains 4 per cent of the variance in duration of

breastfeeding before and after adjustments in rural areas and 1 per

cent in urban areas.

Another variable introduced in Table 5.3 is the women's

occupation. It is hypothesized that farmers are more likely to

breastfeed for a longer period than non-farmers. The MCA results

shows that only in rural areas farmers breastfeed their babies for

relatively longer periods than non-farmers. In urban areas farmers

and non-farmers show no difference in the breastfeeding duration. In

both areas the women's occupation shows no statistical significant

relation to the length of breastfeeding. The contribution of women's

occupation to the duration of breastfeeding is almost zero before and

after adjusting for education and age in both rural and urban areas.

An analysis of the data of women's age has indicated that there

is a positive relationship between age and duration of breastfeeding.

In both areas Table 5.3 shows that the older women breastfeed for

longer periods than the younger women before and even after adjusting

for education and occupation. In rural areas the contribution of

Page 80

women's age to the breastfeeding duration was found to be nearly 3 per cent and 4 per cent before and after adjustments respectively. In urban areas the contribution was found to be less than 1 per cent at the unadjusted and adjusted levels.

When all the variables are introduced together (R-Squared adjusted), nearly 8 per cent of the variance in breastfeeding duration

is explained in rural areas and only 2 per cent in urban areas. Since the contribution of these variables to the breastfeeding duration is very small, there are obviously other variables besides these that

influence the duration of breastfeeding.

Among the most important difference in breastfeeding duration within the Sukuma population is the one found between the rural and urban areas. The rural women breastfeed for a period of 16.4 months,

which is 2.4 months longer than their urban counterparts. When the separate F-Test was produced, the analysis confirmed that this difference is statistically significant.

Page 81

CHAPTER 6

SUMMARY AND CONCLUSION

This study is based on the 1980 Mwanza Pregnancy History Survey

data which was collected by Chris Lwechungura Kamuzora; of the

Department of Statistics, University of Dar-es-Salaam. The present

study had three major objectives. The first objective was to examine

whether the fertility of Wa-Sukuma differs according to their

demographic and socio-economic characteristics. Second was to examine

the differentials in abstinence and breastfeeding. Third was to

identify the relative importance of the demographic and socio-economic

variables in their relation to fertility and breastfeeding. For the

third objective, this study has used the MCA technique.

Fertility differentials among the Wa-Sukuma according to their

characteristics were presented in Chapter 3. As hypothesized, rural

women had significantly higher fertility than their urban

counterparts. The rural-urban differences in the mean number of

children ever born persisted in all socio-economic and demographic

groups, but reduced when women's age was controlled. In other words

rural-urban difference in fertility was mostly due to the difference

in age composition of the married women in these two places.

In both, rural and urban areas, an inverse association of

fertility and women's education was evident after standardization by

age (Table 6.1). In rural areas after standardization, women who had

5-8 years of schooling had a mean number of children ever born of 0.9

Page 82

less than those with no schooling. In urban areas those with 5-8

years of schooling had a mean of 0.5 less than those with no

schooling. However, when standardized by marriage duration, these

educational groups seem to have 0.1 difference of the mean number of

children ever born in both rural and urban areas.

Fanners, whether in rural or urban areas, have higher fertility

than non-farmers. When standardized by women's age farmers still have

higher fertility, but the difference between the two groups is reduced

(Table 6.1). In rural areas farmers shows to have 0.5 mean children

ever born more than non-farmers, while in urban areas the difference

was found to 0.4 mean number of children ever born.

The pattern of the relation between age at first marriage and

fertility appears to be that as age at first marriage increases the

mean parity of rural ever married women decreases up to age 20, then

increases slightly. In urban areas the mean parity remained the same

for those married at ages 14-20 years and those married above 20

years. After controlling for age, an inverse relationship is

pronounced only in rural areas (Table 6.1). In urban areas women

married at ages 14-20 years have lower fertility than those married

above 20 years of age. The mean parity of women married below 14

years of age is considerably higher than that of those married at ages

14 and above in both rural and urban areas. When classified by

women's education, the effect of education on fertility was present in

all age at first marriage groups. The data show that the higher the

level of education the lower the fertility in all marriage cohorts in

both areas.

Page 83

Table 6.1

Summary of urban and rural differences between average number of children ever born, according to the selected socioeconomic and demographic

variables and standardized according to age and duration of marriage. Mwanza region 1980.

Standardized according to:

Characteristics Age of women : Duration of: : marriage

Rural : Urban : Rural Urban

Total 3.6 3.4 4.0 3.6

Years of educationNone 4.3 4.0 4. 1 3.71-4 4.1 3.6 4.1 3.75-8 3.4 3.5 4.0 3.69 or more * * * *Not Stated 3.4 3.3 3.9 3.5

Occupation of womenFarmers 3.7 3.7 NOTNon-farmers 3.2 3.3 CALCULATEDNot stated * 3.6

Age at first marriage years)Less than 14 4.3 3.7 NOT14-20 3.4 3. 1 CALCULATED20 or more 2.9 3.2

Marital statusCurrently married 3.7 3.6 NOTPreviously married 3.1 2.9 CALCULATED

Note :1)* Numbers too small for calculation

Page 84

Currently married Sukuma women tend to have borne more children

than the divorced/separated and widowed combined group in both rural

and urban areas. When controlling for women's age the mean parity of

currently married women remains higher than that of the other group

(Table 6.1). However, the difference between these two groups is

reduced after controlling for age in both areas.

Among the most important differences in fertility levels within

the Sukuma population is the one by duration of marriage. The

positive effect of marriage duration was revealed in both rural and

urban areas. The data show that the lower fertility was associated

with a shorter marriage duration among the Wa-Sukuma. A similar

pattern was observed when age at first marriage was controlled in both

rural and urban areas.

Differentials in postpartum abstinence and breastfeeding by

characteristics of the respondents were examined in chapter 4.

Women's age, education and her occupation were the characteristics

used in this analysis. Rural-urban differentials in postpartum

abstinence were evident. The higher abstinence period among the urban

Wa-Sukuma was unexpected; as explained in Chapter 4 the shortage of

accommodation and little existence of residentially extended families

in urban areas could be the reason.

An inverse relationship between women's education and postpartum

abstinence was found in urban areas only. The analysis shows that the

higher the level of education the shorter the period of postpartum

abstinence. In rural areas the situation is different, the overall

data show that women who have no schooling, 1-4 years and 5-8 years of

Page 85

schooling were found to have no difference in their abstinence

periods. This suggests that education below 9 years of schooling does

not have any influence on postpartum abstinence among the rural Sukuma

women.

Women's age was also hypothesized to have an effect on the

abstinence period. Younger women were hypothesized to abstain for a

shorter period than their older counterparts. This pattern was also

observed among the Wa-Sukuma in both rural and urban areas. However,

the differentials in abstinence among various groups are quite small.

The literature review studied pointed out that the length of

breastfeeding could be used to increase the interval between

successive births. As Rosa(1976) pointed out prolonged breastfeeding

is equally important in giving fertility protection as is use of

contraception. However, the 1980 Mwanza Pregnancy History Survey

analysis of Wa-Sukuma found that the less educated, farmers and older

groups of women in the rural and urban samples have a relatively

longer duration of breastfeeding (Table 6.2). The fertility of many

of these groups was high because the length of breastfeeding appeared

not to be sufficient to offset the high fertility pressures of the

variables, mainly longer marriage duration. This pattern holds for

both rural and urban women. It has been urgued by many researchers

that bottle feeding is usually adopted first by urban women because of

their higher levels of education and their life style. As expected,

the results of this study showed that urban Sukuma women breastfeed

for a relatively shorter period than their rural counterparts.

Page 86

Table 6.2Summary of urban and rural differences between average

duration of breastfeeding (in months) according to selected socioeconomic variables and standardized

according to age. Mwanza region 1980.

Characteristics

Standardizedwomen

according to s age

Rural UrbanTotal 16.4 14.0

Years of educationof women

None 15.7 14.11-4 15.9 13.75-8 14.6 13.69 or more 13.8 13.6Not stated 15.8 14.7

Occupation of womenFarmers 16.2 13.7Non-farmers 15.9 13.9Not stated * 13.7

Note :1)* Number too small for calculation

The MCA analysis in chapter 5 enabled the incorporation of a

number of variables into the model at one time. Variables whose effects were statistically significant were scrutinized while controlling for others. The individual as well as the combined

effects of the variables on the total number of children ever born and

on the duration of breastfeeding were observed. Of the four variables hypothesized to affect of total number of children ever born in urban

areas, a statistically significant effect was found to be exerted by each of three variables: marriage duration, education and occupation.The variable which does not show a statistically significant effect on

Page 87

fertility in urban areas was age at first marriage.

The above four variables were also hypothesized to affect fertility in the rural areas. All showed a statistically significant

effect on fertility. One striking aspect was that in both rural and urban areas marriage duration was found to be the most influential variable on the total number of children ever born. The MCA analysis

showed that the rural women have higher fertility than their urban counterparts. In both rural and urban areas, the MCA result confirmed the findings from chapters 3 and 4. In addition, it should be noted

that the MCA was slightly different because it excludes all those with "not stated" components or variables.

The educated, non-farmers and younger Sukuma women were hypothesized to breastfeed for a shorter period than their counterparts (Chapter 1). The MCA result of the 1980 Mwanza Pregnancy History Survey data (Chapter 5) showed that the educated and younger women breastfeed for a shorter period than their uneducated and older counterparts, the effects of education and age were statistically significant in both rural and urban areas. This is to say that women's age and her educational level among the Wa-Sukuma have influence on the duration of breastfeeding. The only variable which

did not have a significant effect on the duration of breastfeeding in both rural and urban areas is occupation. Also, the analysis showed

that the rural ever married women breastfeed for a longer period than

their urban counterparts. The rural-urban difference in breastfeeding duration was also found to be significant.

Page 88

REFERENCES

AGARWALA, S . 1967.

"Effect of a rise in female marriage age on birth rate in India",WORLD POPULATION CONFERENCE, 1965. VOL.2:172,New York: United Nations.

1968.------- "Widowhood, age and length of fertile

union in India" in WORLD VIEWS OF POPULATION PROBLEMS, ed. E. Szabady pp. 11-15. Budapest: Akademiai Kiado.

AMANI, H. 1983

"Economic Characteristics" in POPULATION OF TANZANIA. VOL 8:237-280 ed. M.RafikDar-es-Salaam:Bureau of Statistics

ANDREWS, F et al. 1975

MULTIPLE CLASSIFICATION ANALYSIS,A REPORT ON A COMPUTER PROGRAM FOR MULTIPLE REGRESSION USING CATEGORICAL PREDICTORS.Ann Arbor: The Institute for Sociol Research, The University of Michigan.

BAKER, R. 1953.

MARRIAGE AND THE FAMILY. New York: McGraw-Hill.

BERG, A . 1973

THE NUTRITIONAL FACTOR.Washington:The Brookings Institution.

BONGAARTS, J. 1978

"A framework for analyzing the proximate determinants of fertility", POPULATION AND DEVELOPMENT REVIEW, VOL.4, NO.1:105-132.

BONTE, M. et al. 1974

"Influence of Socio-economic level on the conception rate during lactation" INTERNATIONAL JOURNAL OF FERTILITY. VOL. 19:97-102

BUCHANAN, R. 1975

"Breastfeeding. Aid to infant health and fertility control" POPULATIONREPORTS, VOL.4:49-67,Washington D.C: The George Washington Medical Center.

Page 89

CALDWELL. J. 1971

1980.

CALDWELL, J. and 1981

CHEN, L. AHMAD S, and MOSLEY H.1974

COCHRANE, S. 1979

CORSINI, C. 1979

DAVIS, K. 1951 .

DAVIS, K. and J.BLAKE.1956.

DRIVER, E. 1963.

"Some factors affecting fertility in Ghana", INTERNATIONAL POPULATION CONFERENCE,London 1969, Vol.1:Liege:IUSSP

"Mass education as a determinant of the timing of fertility decline", in POPULATION AND DEVELOPMENT REVIEW VOL.6, NO.2:225-256.

P. CALDWELL. "Cause and sequence in the reduction of postnatal abstinence in Ibadan City, Nigeria", in CHILD SPACING IN TROPICAL AFRICA, ed. H.J.Page and R.Lesthaeghe. pp. 181-199.London: Academic Press.

GESCHE M "A prospective study of birth intervaldynamic in rural Bangladesh", POPULATION STUDIES, VOL.28,NO.2:277-297

FERTILITY AND EDUCATION. Baltimore: The Johns Hopkins University Press.

"Is the fertility reducing effect lactation really substantial?" in PATTERNS AND DETERMINANTS OF NATURAL FERTILITY, ed. J.Menken Liege:Ordina

THE POPULATION OF INDIA AND PAKISTAN. New Jersey: Princeton University Press.

"Social Structure and fertility: An analytical frame work", ECONOMIC DEVELOPMENT AND CULTURAL CHANGE VOL.4:211-235

DIFFERENTIALS FERTILITY IN CENTRAL INDIA. Princeton: Princeton University Press.

Page 90

EGERO, B. and R. HEN IN.1973

FORCES, D. and RICHER S.1973

GAISIE, S. 1981

GAMINIRATNE, 1978

GOLDSTEIN, S. 1973.

HANNA, B., A. M. MAMISH. 1971 .

HEER, D. 1971 .

HENIN, R. 1973.

HENRY, L. 1961 .

"Fertility" in THE POPULATION OF TANZANIA, ed. B Egero and R. Henin Dar-es-Salaam:BRALUP and Bureau of Statistics.

SOCIOAL RESEARCH METHODS Englewood Cliff,New Jersey:Prentice Hall

"Child Spacing pattern and fertility differentials in Ghana" in CHILD SPACING IN TROPICAL AFRICA ed. H.J.Page and R.Lesthaeghe pp 237-252London:Academic Press.

FERTILITY DIFFERENTIALS IN SRI LANKA M.A. thesis.Canberra:Australia National University

et al. "The effect of broken marriage onfertility levels in Thailand", SOCIOL SCIENCE, VOL.10:47-87

ABOUD and "Socio-economic fertilitydifferentials in Arab countries", in FERTILITY TRENDS AND DIFFERENTIALS IN ARABS COUNTRIES, Research Monograph Series, No 2, Cairo: Egypt.

"Education advance and fertility change". INTERNATIONAL POPULATION CONFERENCE,London 1969, VOL.6:1903-1914 Liege:IUSSP.

THE POPULATION OF TANZANIA VOL.6:140-144 Dar-es-Salaam:BRALUP and Bureau of Statistics.

"Some data on natural fertility" in EUGANICS QUARTERLY, VOL.8:81-89

Page 91

HOLSINGER, D. and J .KASANDA.1976.

"Education and Human Fertility: Sociological Perspectives", in POPULATION AND DEVELOPMENT, ed. G.R.Ronald pp 154-181.Baltimore: The Johns Hopkins University Press.VOL.31, NO.1:43-54.

IGUN, A., Z. JOHNSON and T .ACSADI.1972.

SURVEY OF FERTILITY AND FAMILY PLANNING IN NIGERIA, Nigeria: University of Ife.

JAIN, A.,T.HSU.,R.FREEDMAN and M.CHANG.1970

"Demographic aspects of lactation and postpartum amenorrhoea". DEMOGRAPHY VOL.7: 255-27 1

JAIN, A.,A.HERMALIN and T.SUN.1979

"Lactation and natural fertility", in PATTERNS AND DETERMINANTS OF NATURAL FERTILITY, ed. J.Menken, Liege:Ordina.

JELLIFFE, D. and JELLIFFE E. 1972

"Lactation, Conception, and the nutrition of the nursing mother and child". TROPICAL PEDIATRICS VOL.81, NO.4:45-49

1978"Feeding of infants and children" in DISEASES OF CHILDREN IN THE SUB-TROPICS AND TROPICS ed. D.B.Jelliffe and J .P.Stanfield p.168, London: E.Arnord.

JOSEPH, W. 1975

"Liberia" in POPULATION GROWTH AND SOCIO-ECONOMIC CHANGE IN WEST AFRICA, ed. J.C.Caldwell et al. pp 527-542 New York:Columbia University Press

KAMUZORA, C. 1983

The process of fertility determination in high fertility countries. A study of Pregnancy History of Rural and Urban Wa-Sukuma of Mwanza Region, Tanzania Canberra Australian National University

Page 92

KISER, C. 1971 .

KNÖDEL, J andV.PRACHUABMOH.1973

LANDIS, P. 1943.

LESTHAEGHE, R. et 1981

LOEBNER, H. and E.DRIVER.1973.

LOCOH, T. and G.ADABA.1981

LUCAS, D. 1976

LUCAS, D and H.WARE.1981

MARO, P 1983

"Education differentials in fertility in relation to demographic transition". INTERNATIONAL POPULATION CONFERENCE 1969, VOL.3, IUSSP.

THE FERTILITY OF THAI WOMEN.Research Report NO.10 Chulalongkorn University:Institute of Population Studies

POPULATION PROBLEMS: A CULTURAL INTERPRETATION.New York: American Book Company.

al. "Child Spacing and fertility inLagos" in CHILD SPACING IN TROPICAL AFRICA. ed. H.J.Page and R.Lesthaeghe pp 148-173 London:Academic Press

"Differentials fertility in Central India: A path analysis", DEMOGRAPHY VOL.10, NO.3:329-350.

"Child-Spacing in Togo:the Southeast Togo Survey".in CHILD SPACING IN TROPICAL AFRICA, ed. H.J.Page and R.Lesthaeghe. pp.256-260.London:Academic Press

"Urban and Rural fertility in Southern Nigeria" in THE ECONOMIC AND SOCIAL SUPPORTS FOR HIGH FERTILITY, ed. L.T.Ruzicka, pp. 409-419 Canberra Australian National University

"Fertility and family planning in the South Pacific". STUDIES IN FAMILY PLANNING. VOL.12, NO.8:303-315

"Population Distribution and Density" in POPULATION OF TANZANIA.VOL. 8:91-109. ed. M.Rafik.Dar-es-Salaam:Bureau of Statistics.

Page 93

MASON, K. et al. 1971 .

MOSLEY, H. et al 1982

MCDONALD, P., L. J.CALDWELL.1980

MORGAN, R. 1975.

NAG, M. 1968.

NGALLABA, S. 1983.

NOAH, G. andR.LASWAY1983.

OHADIKE. P. 1963.

SOCIAL AND ECONOMIC CORRELATES OF FAMILY FERTILITY: A SURVEY OF THE EVIDENCE. Research Triangle Park: Research Triangle Institute.

"The dynamics of birth spacing and marital fertility in Kenya"World Fertility Survey:Scientific Reports. NO.30(August)

RUZICKA and "Interrelationship betweennuptility and fertility: the evidence from the World Fertility Survey". A paper presented at World Fertility Survey Conference, London.

"Fertility levels and fertility change" in POPULATION GROWTH AND SOCIO-ECONOMIC CHANGE IN WEST AFRICA, ed. J.C.Caldwell pp 187-235. New York: Columbia University Press.

FACTORS AFFECTING HUMAN FERTILITY IN NON INDUSTRIAL SOCIETIES:A CROSS-CULTURAL STUDY Yale:Yale University Press.

"Fertility differentials" in POPULATION OF TANZANIA. VOL.8:371-387 ed. M.RafikDar-es-Salaam: Bureau of Statistics.

"Literacy and Education" in POPULATION OF TANZANIA. VOL.8:211-236 ed. M.RafikDar-es-Salaam: Bureau of Statistics.

"Marriage, fertility and family growth in Lagos, Nigeria", in THE POPULATION OF TROPICAL AFRICA, ed. J.C.Caldwell and Okonjo. pp. 379-392.London:Longmans.

Page 94

OLUSANYA, P. 1968.

OMINDE, S. 1975

ONAKA, A. and D.YANKEY.1973.

PAGE, H. 1975.

PARK, S. 1978

PHILLIPS, J. 1978

POOL, D. 1968.

POTTER, R.,M. and J.GORDON. 1965

PRESSAT, R. 1972.

"Urban fertility patterns in Western Nigeria: A study of reproductive behavior and some factors affecting it in selected Yoruba towns". PhD thesis University of London.

THE POPULATION OF KENYA-UGANDA-TANZANLA Nairobi:Kenya Litho Ltd.

"Reproductive time lost due to sexual union dissolution in San Jose,Costa Rica", POPULATION STUDIES VOL.2, No. 11 :457-465.

"Fertility Levels" in POPULATION GROWTH AND SOCIO-ECONOMIC CHANGE IN WEST AFRICA, ed.J.C.Caldwell, pp 29-57.New York: Columbia University Press.

URBANIZATION AND FERTILITY IN KOREA 1960-70, Population and development study center, Seoul: Seoul National University.

"Continued Use of Contraception among Philippines Family Planning Acceptors: A Multivariate Analysis" in Studies in Family Planning. VOL. 9, NO. 7.

"Conjugal patterns in Ghana". THE CANADIAN REVIEW OF SOCIOLOGY AND ANTHROPOLOGY. VOL.5:241-253.

NEW,J.WYON "Applications of field studies toresearch on the physiology of human reproduction:Lactation and its effect upon birth intervals in eleven Punjab villages" in PUBLIC HEALTH AND POPULATION CHANGE:CURRENT RESEARCH ISSUE, ed.M.C.Sheps and J.C.Ridley pp.377-399. Pittsburgh Pittsburgh University Press.

DEMOGRAPHIC ANALYSIS.Chicago and New York: Aldine Atherton.

Page 95

PULLUM, T. 1975.

"Differentials in marital fertility" in A DEMOGRAPHIC PATH TO MODERNITY. ed.W.Flinger and P.Smith Quenzon City: University of the Philippines Press.

REPUBLIC OF KENYA. 1979.

KENYA FERTILITY SURVEY, MAJOR HIGHLIGHTS. Central Bureau of Nairobi: Central Bureau of Statistics.

ROBINSON, W.1961 .

"Urban-Rural differences in Indian fertility", in POPULATION STUDIES. VOL.14, NO.3:218-234.

ROSA, R. 1976

"Breastfeeding: a motive for family planning", in PEOPLE VOL.3, NO.1:10-13

SANTOW, G. and M.BRACHER 1981

"Patterns of postpartum sexual abstinence and their implications for fertility in Ibadan, Nigeria", in CHILD SPACING IN TROPICAL AFRICA, ed. H.J.Page and R.Lesthaeghe. pp. 201-217.London: Academic Press.

SCHOENMAECKERS, R. et al. 1981

"The child spacing tradition and the postpartum taboo in Africa: anthropological evidence" in CHILD SPACING IN TROPICAL AFRICA, ed. H.J.Page and R.Lesthaeghe. pp. 25-71.London: Academic Press.

SCHULTZ, T . 1969.

"An economic model of family planning and fertility", JOURNAL OF POLITICAL ECONOMY VOL.77:153-180.

SEMBAJWE, I. 1983.

"Mortality levels" in POPULATION OF TANZANIA. VOL.8:281-324 ed. M.RafikDar-es-Salaam: Bureau of Statistics.

SPEARE, A., M .SPEARE and H.LIN.1973.

"Urbanization, non-familial work, education and fertility in Taiwan". POPULATION STUDIES VOL.27:328-334.

Page 96

STERKENBURG, J. and H.LUNING.1980.

"Population growth and economic growth in Tanzania ...A case study of Rungwe District". in POVERTY AND POPULATION CONTROL, ed. L.Bondestam and S.Bergstrom pp 179-194.London: Academic Press.

UDRY, J. 1971 .

THE SOCIAL CONTEXT OF MARRIAGE. (Second edition)Philadelphia: J .B .Lippincott Company.

UNITED NATIONS. FERTILITY AND FAMILY PLANNING IN1976. EUROPE AROUND 1970.

New York: United Nations.

1979POPULATION DYNAMICSFERTILITY AND MORTALITY IN AFRICA. Monrovia:United Nations Economic Commission for Africa.

VAN GINNEKEN 1977

"The chance of conception during lactation". BIOSOCIAL SCIENCE, supplement VOL.4:41-56.

WORLD BANK 1980

WORLD DEVELOPMENT REPORT. Washington D.C.

WOLRD FERTILITY SURVEY. 1977.

"The Pakistan Survey 1975" SUMMARY OF FINDINGS,NO.3 (Nov.).

1977"The Fiji Fertility Survey 1974" SUMMARY OF FINDINGS,

1981 .

1981

"The Indonesia Fertility Survey, 1976" SUMMARY OF FINDINGS,NO.11 (Oct.).

"Lesotho Fertility Survey 1977"FIRST REPORT. VOL. 1 Maseru:Central Bureau of Statistics

1981

1982"The Sudan Fertility Survey 1979 VOL.2.London:Huddersfied.

Page 97

YAUKEY, D. 1961 .

FERTILITY DIFFERENTIALS IN MODERNIZING COUNTRIES: A Survey of Lebanese couples. Princeton: Princeton University Press.

APPENDIX 1

MWANZA PREGNANCY HISTORY SURVEY FOR SUKUMA

WOMEN AGED 15+...... APRIL-JUNE 1980

IDENTIFICATION:

1. Name of the respondent ...........................

2. Location ..... 1 Rural 2 urban

3. Date of interview ........ 1980

4. Age of the respondent ........

5. Have you ever been to school? .... Y/N

6. If yes

What was the highest level and

year of schooling completed? ......................

7. What is your occupation? ..........................

MARITAL STATUS:

8. Have you ever been married? .... Y/N

9. If yes

Are you now married(M), widowed(W),

divorced(D) or separated(S)? ......................

10. If she is married(M), widowed(W),

divorced(D) or separated(S) fill the table below

Page 99

:Husband : Month and year: Month and year of :

:in order : of marriage : marriage dissolution:

• ’:1st Husband: : :

:2nd Husband: * «:3rd Husband: « :

: . : « «: . : = =:nth Husband: :

11. How old were you on your first marriage?

NUMBER OF LIVE BIRTHS:

12. Do you have any children of your own

living with you? ..... Y/N

13. If yes

How many? .....

14. Do you have any children of your own

who do not live with you? ..... Y/N

15. If yes

How many? .....

16. Did you give birth to a child who

later died? ..... Y/N

17. If yes

How many? .....

18. Just to make sure I have this right you had .....(SUM) births. Is that correct?

IF NO: CORRECT RESPONSES.19. Do you still want additional

children? ..... Y/N

20. If yes

How many more children do you want and why?

BIRTH CONTROL:21. Did you know that it was very easy for

a person like you to fallen pregnantafter given birth? ..... Y/N

22. If yesDid you use any method of family

planning? ..... Y/N23. If yes

Which method did you use? .....

1. Local method (Rhythm etc.)2. Modern method (Pill, IUD etc.)

3. Both (Local and Modern)

24. After given birth usually women abstained

for few months. Did you abstain? ..... Y/N

25 . If yes

What was the recent completed abstinence

period? ..... months

BREASTFEEDING:

26. Have you ever breastfeed your

children? ...... Y/N

27. If yes

What was the period of breastfeeding for

your second to last child? ...... months

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APPENDIX II CORRELATION MATRIX

RURAL AREASCEB EDU AGE AFM MDR woe

CEB 1 . 0 0 0

EDU -0 . 0 7 6 1 . 0 0 0

AGE 0 . 1 4 0 -0 . 1 2 3 1 . 0 0 0

AFM -0 . 1 7 3 0 . 1 0 6 -0 . 0 1 6 1 . 0 0 0

MDR 0 . 3 3 3 -0 . 1 3 5 0 . 7 7 2 0 . 2 0 9 1 . 0 0 0

woe - 0 . 1 4 3 0 . 0 1 8 0 . 0 2 3 0 . 2 5 5 0 . 2 9 4 1 . 0 0 0

URBAN AREASCEB EDU AGE AFM MDR woe

CEB 1 . 0 0 0

EDU -0 . 1 1 8 1 . 0 0 0

AGE 0 . 1 4 6 -0 . 0 3 2 1 . 0 0 0

AFM -0 . 0 9 9 0 . 0 1 9 0 . 0 1 8 1 . 0 0 0

MDR 0 . 3 1 4 -0 . 0 7 1 0 . 7 5 6 0 . 4 1 2 1 . 0 0 0

woe -0 . 0 7 3 0 . 1 3 4 -0 . 0 6 0 0 . 0 5 4 -0 . 0 8 3 1 . 0 0 0

Source: 1980 Mwanza Pregnancy History Survey data tape Note:CEB=Children Ever Born

EDU=Education

AGE=Women's Current Age AFM=Age at First Marriage MDR=Marriage Duration

WOC=Women's Occupation