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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.
Page 3
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
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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
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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
Page 66
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
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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.
Page 76
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.
Page 77
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
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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
Page 102
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
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