Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

In statistical analysis and inference statistics is playing an important role nearly in all phases of human life. Formally dealing only with affair of the state and this account for its name statistics. The influence of statistics is now spread to manufacturing companies, agricultural sectors, bank, business communication, economic, education, political science, psychology, sociology and other numerous held of life. It’s therefore, a body of scientific method and theory of collecting, organizing, presenting and analyzing of data as well as drawing valid conclusion and making a reasonable decision. The application is found in all discipline of numerical form. Furthermore, this project is to explain and to analyze more on fertility and mortality rate of people in “Osogbo Local Government”. This project cover four fiscal year from 2006 – 2009 and data is being collected at LAUTECH Teaching Hospital OSOGBO. Also computation would be determined from the total number of fertility and mortality rate and this will be use for information required for the actualization of facts.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay



LAUTECH Teaching Hospital is owned by both Osun State Government and Oyo State Government. The hospital is situated at Idi-seke area in Osogbo Local Government. LAUTECH Teaching Hospital is equiped to standard that no other hospital in the state can complete with it. LAUTECH Teaching Hospital has many professional doctors and well trained and very efficient nurses.


i. To know whether the number of life birth (Fertility) of Osogbo Local Government is more than number of death (Mortality).

ii. To be able to study the future occurrence in the rate of fertility and mortality by using least square method.

iii. To measure the trend value of fertility and mortality rate by using moving average method.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay


There are two types of statistics namely: Descriptive statistics and Inference statistics. In descriptive statistics, the research has information on every member of the population and also describes the characteristics of the population such as: Total, average, Means or more (Population Parameters). But in Inferential Statistics, the researcher has information on part of population (sample). Mean, mode and standard deviation are collected for the sample; each of them is called statistics.


Statistics is useful in all field of human endeavour. We can use the knowledge of statistics to find the number of employed people in a country, we can use it to find market trend, to make any possible request in any business ventures. Infact, there is no aspect of our daily life that statistics is not applicable.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay


Data can be defined as pieces of information collected for specific purpose. In any survey, what determines the types of data to be collected is the topic studying upon, which enquiries are to be made.


Primary data can be define as the data collected or drawn from the original source for specific reason, from which it will be useful. These types of data are always accurate and reliable upon it can be collected through these methods:

Mail questionnaire

Telephone interview

Personal interview

Direct observation



This is the data collected for a purpose different for which is originally meant for. It is a secondhand data collected for a purpose direct from statistical enquiries in which they used. It has the advantage of reducing the time and cost to be used.


The data collected is a secondary data and it’s liable to some kind of difficulties which limits the extent to which it can be used dealing with secondary data. Therefore, any suggestion or recommendation made as the outcome of analysis is not only meant for LAUTECH Teaching Hospital.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

In this article we describe a new method to estimate the total fertility rate (TFR) over
time from multiple sources of imperfect data. The procedure is automated and therefore
reproducible, and it takes account of data quality by characterizing bias and measurement
error separately. We illustrate the method using fertility data from seven countries in West
Africa whose fertility data are of widely varying quality and coverage. We assess it using
cross-validation and show that it is reasonably well calibrated.
Estimating demographic indicators is challenging for many developing countries because of limited data and varying data quality. This is illustrated in Figure 1 for Burkina
Faso in West Africa. The black and red dots are nationally representative observations of
the total fertility rate in Burkina Faso constructed using age-specific fertility rates. For
the period from 1950 until the mid 1970s, there are very few observations for the TFR.
After 1970 the number of observations increases, but they vary a great deal because of
issues with data quality, e.g. observations are biased because of the collection process, or
measured with large errors.
The United Nations (UN) Population Division produces estimates of the TFR from
1950 up to the most recent five-year period, for all countries in the world. Here we
use the 2006 estimates (United Nations, Department of Economic and Social Affairs,
Population Division 2007). UN analysts estimate the fertility rates in a labor-intensive,
iterative fashion: initially, age-specific fertility rates are estimated based on all available
nationally representative data combined with expert knowledge of the reliability of the
different subsets of observations (e.g. known issues with a particular survey or census,
or general knowledge of undercounts or overcounts of particular retrospective estimates
Demographic Research: Volume 26, Article 15
of fertility rates). The initial fertility estimates are combined with estimates of mortality
and migration to derive estimates of population counts. The population count estimates
are then compared to bias-adjusted census counts. If estimated and observed population
counts differ substantially, the estimates of the three input components of the population
counts are reconsidered. Uncertainty in these input components allows for adjustments of
the initial input values until population estimates and observations are in agreement. The
UN estimates for Burkina Faso are shown in Figure 1.
Figure 1: Observations of the TFR in Burkina Faso, and UN estimates  Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

DHS observation
Non−DHS observation
UN estimates
The UN estimates of the TFR are generally considered to be of good quality and are
widely used. The observations from Demographic and Health Surveys (DHS), shown in
red in Figure 1, are also considered to be of good quality and widely used. Figure 1 shows
that when examining the TFR in Burkina Faso, different conclusions about its level and
trend can be drawn depending on whether the UN estimates or the DHS estimates are
being used. The DHS observations are estimates from nationally representative surveys
and as such are subject to sampling and other errors. The UN estimates use a wide variety
of available information including what is known about other demographic indicators.
However, the UN estimates have the drawback of being hard to reproduce because they are
not produced in an automated way, and of having no associated statements of uncertainty.
There are no standardized, reproducible methods for estimating trends in fertility rates
in developing countries based on different data sources that assess the uncertainty of the
estimates. Much of the literature on fertility estimation methods focuses on the devel 333
Alkema et al.: Estimating trends in the total fertility rate with uncertainty using imperfect data
opment of indirect estimation techniques (Brass 1964; Brass et al. 1968; Trussell 1975;
United Nations 1983; Brass 1996; Feeney 1998). These techniques deal with bias caused
by recall lapse errors, omissions of births (especially soon after birth), and misinterpretation of the reference period in retrospective estimates of fertility rates (Som 1973; Potter
1977; Becker and Mahmud 1984; Pullum and Stokes 1997) by reconciling information
from recent fertility (in the last year or years) with lifetime fertility. Recent fertility is
adjusted rather than the full retrospective birth histories stretching back 25 years.
These methods typically rely on just one source of data, and the assumptions they
require can affect their accuracy (Moultrie and Dorrington 2008). The indirect methods
address a particular type of bias but do not confront differences in the variance of the
measurement error. More recently, Schoumaker (2010, 2011) attempted to improve reconstructed fertility trends by analyzing birth histories for multiple DHS simultaneously,
and showed the challenges in reconciling differences in levels and trends between surveys
within the same country, especially for the earliest and latest observation periods from
one survey. The drawbacks of this kind of approach are that they can only be applied
to countries with multiple birth history surveys, and that other data sources, including
adjusted fertility from indirect methods, cannot easily be taken into account.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay
Here we introduce a new, automated, reproducible method for estimating trends in the
TFR, with measures of uncertainty for countries with limited data from multiple sources
of varying quality. We assess the quality of our method using cross-validation by excluding subsets of the data, and evaluating how well we can predict both the excluded data
and the errors in the predictions, using the remaining data. We apply our method to data
from seven countries in West Africa which have experienced among the highest fertility
rates in the world in recent years. We compare our findings to the results from a method
that is similar except that it does not weight the observations, and so does not take into
account data quality.
2. Data
The data we use to illustrate and test our method come from seven countries in West
Africa that represent the type of situation for which this method has been developed.
Data on fertility in these countries come from multiple sources of uneven quality; the
problems include limited coverage through time, bias, and measurement error. Moreover
countries like these, with similar data problems, are likely to be those with high fertility,
and therefore important for understanding the population dynamics of their regions.
The data set consists of nationally representative observations of the TFR for Burkina
Faso, Gambia, Guinea, Mali, Mauritania, Niger and Senegal. All the observations were
collected retrospectively by either asking women about their births in a restricted period
Demographic Research: Volume 26, Article 15
(e.g. the number of births in the last year before the survey/census) or for their complete
birth histories (birth of their first child, second child, etc.). Figure 2 shows the observations in each of the seven countries. For each observation the recall period is displayed by
a horizontal line that joins the midpoint of the observation period to the year of data collection. The UN estimates are shown in grey (United Nations, Department of Economic
and Social Affairs, Population Division 2007).
The observations are from sources that fall into three groups: censuses, Demographic
and Health Surveys (DHS) and non-DHS surveys – the World Fertility Surveys and other
surveys. Censuses and non-DHS surveys generally collect only lifetime fertility and/or
recent fertility (in the past year). The retrospective estimates of the TFR produced by the
DHS are based on complete birth histories of all women who participated in the survey,
typically aged 15-49. The birth history data are tabulated by period. For example, based
on the DHS in Burkina Faso in 2003, age-specific fertility rates were obtained for periods
0-4 years, 5-9 years, 10-14 years, 15-19 years and 20-24 years before the survey, and the
TFR was calculated for each period from the age-specific fertility rates. For the periods in
which no estimates of the age-specific fertility in older age groups were available (because
women aged 50 and over were not interviewed), the outcomes for the older age groups
were extrapolated from the outcomes in the younger age groups in that period, and from
the observed age pattern of fertility in the most recent period.
Table 1 summarizes the observations based on their data quality covariates. For each
observation four data quality covariates are available: source, period before survey (PBS),
direct/indirect estimation method, and time span. Source is either Census, DHS or other
survey. Period before survey is the midpoint of the period before the survey to which the
retrospective estimate refers. Time span is the length of the observation period in years.
The data quality covariate “Direct” in Table 1 divides the data set into direct and indirect
estimates. Direct estimates are observations based on the reported number of births in a
given period, as described above. Indirect estimates are for the most recent period before
a survey or census, and they are constructed using indirect estimation methods that correct
for recall lapse biases in retrospective observations of fertility (Som 1973; Potter 1977;
Becker and Mahmud 1984; Pullum and Stokes 1997). 335
Alkema et al.: Estimating trends in the total fertility rate with uncertainty using imperfect data
Figure 2: Direct observations (dots) and indirect observations (crosses) for
different data sources. Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Fertility rates are higher in sub-Saharan Africa (Africa) than in any other major region of the world, and considerable controversy surrounds the likelihood of these rates declining in the near future. Although mortality and fertility rates fell substantially in Latin America and Asia between 1965 and 1985, only mortality declined in Africa; fertility remained relatively stable, well above a level required to replace the population. Consequently, the region experienced extremely rapid population growth, with rates for some populations considerably above 3 percent per year (United Nations, 1991; Freedman and Blanc, 1992). A few countries, most notably Kenya, Botswana, and Zimbabwe, have begun the transition toward lower fertility, but smaller declines in fertility have been observed recently in many other countries. Nevertheless, fertility rates generally remain above six children per woman, and the question of whether Africa is more resistant to fertility change than other regions of the world is a topic of considerable debate

Barney Cohen is a research associate for the Committee on Population, National Research Council. He thanks Anouch Chahnazarian, James Gribble, Carole Jolly, and the editors for helpful comments on an earlier draft. The author is also grateful to George Bicego, Bill Chu, Timothy Fowler, Ronald Freedman, Bill House, Vasantha Kandiah, Tim Miller, Sidney Moore, and Pat Rowe for their help in providing some of the data used in this report. Anne Scott assisted with the computer programming of the B60s.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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(Boserup, 1985; World Bank, 1986; Caldwell and Caldwell, 1987, 1988, 1990; Lesthaeghe, 1989; van de Walle and Foster, 1990; Caldwell et al., 1992).


The level of fertility in sub-Saharan Africa, as measured by the total fertility rate (TFR),1 is approximately 6.0–6.5 births per woman. This figure masks considerable variation between regions and between individual countries. For example, the most recent estimate of the total fertility rate in Rwanda (8.5 births per woman in 1983) is almost double the most recent estimate for the population of black South Africa (4.6 births per woman in 1987–1989). More generally, fertility rates in East and West Africa are greater than those in Central Africa, in part because of the historically high prevalence of sexually transmitted diseases (STDs) in certain areas of Central Africa (Frank, 1983; Tambashe, 1992). The prevalence of STDs is associated with unusually high rates of infecundability in the region especially prior to the 1970s. Fertility was probably higher in East Africa than in West Africa during the 1970s and 1980s, although the difference appears to have lessened in the more recent past. Reported fertility rates rose in certain parts of Africa in the late 1960s and 1970s; however, it is not clear what proportion of the increase was the result of improvements in data accuracy.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

In addition to the regional and national variation in fertility rates, there is often considerable variation in fertility within countries. Repeatedly, fertility surveys have recorded substantial differences in rates among ethnic, geographic, and socioeconomic groups. For example, fertility rates are consistently lower among women who live in urban areas, women who have more than primary school education, and women who work in the formal labor market. In Africa, the number of women in each of these socioeconomic groups has, at least until recently, been small, and the groups overlap considerably. Consequently, lower fertility among these women has a minimal effect on national-level TFRs.

The objective of this chapter is to summarize existing knowledge on levels, trends, and differentials in achieved fertility in sub-Saharan Africa. Although there have been several comprehensive reviews of fertility levels in Africa in the past (see, for example, Brass et al., 1968; Page and Coale, 1972; United Nations, 1987), new sources of data make it possible to update Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay


There is no single, readily agreed upon best measure of fertility. The total fertility rate is a synthetic measure that expresses the total number of children a hypothetical woman would have if she survived to the end of her reproductive years (taken to be 49) and if she experienced the same level and pattern of fertility throughout her reproductive life as women at the time the data are collected. An advantage of using the TFR over other measures of fertility, such as the crude birth rate, is that it is independent of the age structure of the population.

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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the analysis to the early 1990s. By employing a wide variety of data sources, including some that have not been readily accessible in the past, estimates of fertility rates are presented for virtually all countries in sub-Saharan Africa.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

True understanding of fertility trends in Africa is clouded by the extremely variable quality of demographic data in the region. Close examination of much of the data reveals gross inconsistencies that are the result of misreporting of ages and omitting or systematically displacing vital events. In an attempt to correct for obvious data errors, a mixture of direct and indirect estimation techniques is used to determine fertility rates. The indirect techniques are based on the examination of inconsistences within the reported data or on comparisons of observed data to values expected from various demographic models.

The chapter is organized as follows: Issues of data availability and quality are discussed in the following section. In the third section, four methods for estimating TFRs are presented. Characteristics of African fertility are presented in the fourth section. Next, recent data from the Demographic and Health Surveys are used to examine the possible evidence for declining fertility levels in Africa. The penultimate section compares recent fertility trends in Africa to those in other developing areas of the world. Finally, there is a summary and some concluding observations.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

The state of demographic data collection in Africa has recently been reviewed by de Graft-Johnson (1988). Despite dramatic improvements since the 1960s, our knowledge and understanding of fertility levels and trends in Africa are still surprisingly weak. Until 1960, virtually no sub-Saharan African country had conducted a complete census. Consequently, little was known about the size or structure of the region’s population. In the few countries where censuses were undertaken, they were often unreliable and of very limited content. A fundamental problem facing researchers in Africa was that a large percentage of the adult population was unable to report its age accurately. Further, many early censuses did not include questions related to the number of children ever born and childhood mortality. In addition, vital registration data were virtually nonexistent throughout the region and, when available, were of questionable quality.

Fortunately, demographic data collection in Africa has improved considerably over the last 30 years. Although vital registration is still rare, most countries have conducted one and in many cases several censuses, though quality has been uneven. In addition, many countries have supplemented efforts to collect reliable demographic data with various ad hoc  Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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national and subnational household demographic surveys. Some of the most accurate information comes from these large-scale demographic surveys. In particular, the World Fertility Surveys (WFS), an international data collection effort undertaken from the mid-1970s to the early 1980s, and the ongoing Demographic and Health Surveys (DHS) begun in the mid-1980s, have generated a reasonably accurate data base for calculating fertility levels and differentials for countries in sub-Saharan Africa. The WFS carried out surveys in nine sub-Saharan African countries: To date, the DHS has published demographic reports for 13 sub-Saharan African countries and issued preliminary results for 4 others. Reports for 4 additional African countries are scheduled for release by the end of 1993. Special attention is given in this chapter to the DHS because it is the source of most of the recent demographic data on Africa.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

The quality of DHS data was recently analyzed by DHS staff and found to be generally acceptable. But, in cases where data problems were identified, they were determined to be most severe in sub-Saharan Africa in comparison to other regions of the developing world (Institute for Resource Development, 1990:2). For example, Arnold (1990) identified errors in the coverage and timing of births, including (1) systematic displacement of children’s birth dates, (2) disproportionate numbers of women’s ages heaped on digits ending in 0 and 5, and (3) missing or incomplete information in some birth histories. These problems were determined to be most severe in Botswana, Burundi, Liberia, Mali, and Togo. Problems in the first category arose, in part, because some interviewers appear to have deliberately altered the ages of children under 5 to avoid asking an extensive series of questions on the health and well-being of young children. A second assessment of the quality of DHS data focused on women’s age at first birth and judged that response problems were most severe in African countries, especially Mali and Liberia (Blanc and Rutenberg, 1990). The African data suggest that some women omit information about early births or displace the dates of low-parity births forward in time, making children appear younger than they really are. Finally, Rutstein and Bicego (1990) report that less than 80 percent of women interviewed in Africa provide accurate birth dates for their children.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Fortunately, the effect of displacement problems on fertility levels is relatively minor. For example, Arnold and Blanc (1990) calculate that without any displacement, the total fertility rate in Liberia, the country with the most displacement, would have been 6.5 instead of 6.3 births per woman between 1983 and 1988. Nevertheless, it is important to acknowledge that there is always the danger of drawing incorrect conclusions from data collected in areas where vital events go unrecorded. Consequently, a single point estimate of fertility from Africa should be interpreted with some caution.

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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In this chapter, DHS and WFS data are supplemented by data collected in censuses and other national demographic surveys. Where no other information was available these data are augmented with findings from large-scale subnational studies. Data from small-scale studies conducted at the district or provincial level have not been used due to concerns regarding their generalizability.

Naturally, censuses and surveys are carried out in different countries at different times. But, unlike the estimates presented by several organizations (including the United Nations and the U.S. Bureau of the Census), the estimates presented here are not standardized on a specific year. Rather, the current goal is to present the reader with the original data from which standardized estimates are derived.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Four distinct strategies are used here to obtain independent estimates of fertility. The first strategy is to calculate fertility directly, without adjusting for any apparent inconsistencies in the data. This method requires information on the number of women of childbearing age, their ages, and the number of births to these women during a given time period, typically five years. Direct estimates of fertility are reported only when the quality of the data was thought to be adequate, for example, as in all the WFS and the DHS. In these cases, fertility estimates are derived by using retrospective birth histories.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Experience has shown, however, that response errors in census and survey data can often lead to biased or inaccurate estimates of the fertility rate. Response errors in birth history data arise mainly from age misreporting and the omission or systematic displacement of vital events. For example, many women incorrectly report their own age or the ages of their children. Similarly, in the absence of written records, women often forget births that occurred in the distant past and make systematic errors when estimating the timing and spacing of events (Potter, 1977). Older women, women with little education, women who were not in sanctioned unions at the time of their first birth, and women whose children have moved away or died are particularly likely to make these types of errors. Obvious errors, such as birth intervals of less than 6 months or first births to women under 10 years of age, can often be detected by the interviewer or the researcher and perhaps corrected by cross-referencing birth dates with well-known historical events. Errors resulting from omitted births are much harder to correct.

Demographers have developed alternative methods designed to improve their ability to make “indirect” inferences about fertility from poor or incomplete data (Brass et al., 1968; United Nations, 1983). Most of these methods involve the identification of internal inconsistencies in the reported

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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data or the comparison of observed data to model fertility schedules. In cases where the direct and indirect estimates of fertility are substantially different, the indirect estimates are usually preferred. Three of the four strategies used in this chapter employ indirect techniques.

One strategy is based on the principle of comparing reported births in a given time period with women’s responses to questions regarding the number of children ever born. A full description of this method (commonly called the method of P/F ratios) can be found in United Nations (1983). The data requirements for this method are identical to those for direct estimation except that they include information about the number of children ever born. Where the data allowed, this technique was used to check and, if necessary, to adjust the survey or census estimates for apparent misreporting. Unfortunately, because this method relies on equating current and past experiences, it has the potential for producing biased estimates of the total fertility rate when fertility has recently declined (United Nations, 1983:32). Nonetheless, at least for the earlier time periods, this method arguably produces the most accurate estimates possible.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Because early censuses often did not include specific questions on fertility, the age structure of the population may be the only information available to estimate the total fertility rate. In these cases, fertility estimates are inferred by using stable population theory, which is based on assumptions of constant fertility and mortality. The only data requirements for these estimates are the age structure of the population, the growth rate, and an estimate of the level and pattern of mortality. Because results from this estimation method are not particularly robust and are quite sensitive to different mortality assumptions, it is used only in the absence of other alternatives. In an attempt to check the robustness of these approximations, similar estimates are also derived by using a method developed by Coale (1981) and later extended by Venkatacharya (1990). This method, labeled the Coale method, also relies on stable population theory and requires an estimate of the population growth rate, the proportion of both sexes under the age of 15, and an estimate of mortality for children up to age 5. Assuming constant fertility rates for the population under consideration, Coale (1981) suggested that his method would yield reasonable estimates of the total fertility rate for 7.5 years prior to the census date, even if the census or survey was characterized by severe age misreporting.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay


Another indirect method, the “variable-r” technique suggested by Preston (1983), was dropped after it was ascertained that age structure data in the earlier African censuses were not of sufficiently high quality to provide accurate independent estimates of the fertility rate.

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Suggested Citation:”2 Fertility Levels, Differentials, and Trends.” National Research Council. 1993. Demographic Change in Sub-Saharan Africa. Washington, DC: The National Academies Press. doi: 10.17226/2207.×
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Estimates of Total Fertility Rates
Table 2–1 provides estimates of the TFR for 38 African countries for which data were available at some point during 1960–1992. For ease of comparison, all estimates were converted into point estimates of the total fertility rate. In reality, a particular figure may be the midpoint of a range of plausible estimates. Table 2–1 highlights the paucity of demographic data in many African countries. Despite considerable improvements in the availability of data during the past 10 years, 12 countries still have fewer than four data points since 1960. In other cases, although data exist, they are of extremely variable quality. Consequently, fertility trends over time may appear more erratic than they truly are. For example, in Ethiopia, the data imply a substantial increase in fertility during the 1970s followed by a rapid decline in the 1980s. Both trends are almost certainly exaggerated.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

Although the data are often sketchy, several important conclusions may still be drawn about fertility in Africa. Few countries in Africa have TFRs less than 6.0, and nowhere is fertility currently less than 4.0 births per women, a rate well above that required for replacement. Africans have a strong preference for large families. Children are prized not only as the means of preserving family lines, but as positive economic assets that provide labor, wealth, risk insurance, and old-age security to their parents.Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay

In the past, high fertility in Africa resulted from early and near universal marriage,3 and extremely low rates of efficient contraception. Fertility has been controlled (outside geographic areas of pathological sterility) by social pressures against premarital sex, the practice of postpartum sexual abstinence, and long breastfeeding periods that lead to lengthy lactational amenorrhea (see Chapter 3; also Caldwell and Caldwell, 1977, 1987; Page and Lesthaeghe, 1981). Bongaarts et al. (1990) recently estimated that fertility in Africa would increase by 72 percent if the fertility-inhibiting effects of breastfeeding and postpartum abstinence were removed. These fertility-reducing practices have probably evolved principally to ensure exceptionally long birth intervals in an effort to combat high rates of infant mortality. Recently there are signs that some of these cornerstones of African fertility may be weakening (see Chapter 3; also Schoenmaeckers et al., 1981; Caldwell et al., 1992; Westoff, 1992). Statistical Analysis On Fertility And Mortality Rate In Osogbo Essay