Republic of Namibia POPULATION PROJECTIONS

Transcription

1 Republic of Namibia Population Projections POPULATION PROJECTIONS Omusati Oshana Ohangwena Oshikoto Kavango Caprivi Kunene Otjozondjupa Erongo Omaheke Khomas Hardap Karas Central Bureau of Statistics Telephone: National Planning Commission Fax: / Private bag Website: Windhoek

2 TABLE OF CONTENTS Population Projections Preface... i 1. Background... ii 2. Assumptions... iv 2.1 Fertility... iv 2.2 Mortality... vi 3.1 Namibia, viii 3.2 Migration... x 3.3 Method of Projection... x PART 1: Summary of the Projections... 1 Table 1.1 Projected Population by year and sex, Low, Medium, High Variants, Figure 1.1 Projected Population by year, Low, Medium, High Variants, Table 1.2 Vital rates - Crude Birth Rate (CBR), Crude Death Rate (CDR) and... 3 Annual growth rate, Table 1.3 Projected population by region, PART 2 NATIONAL PROJECTIONS BY AGE AND SEX: LOW VARIANT... 7 PART 3 NATIONAL PROJECTIONS BY AGE AND SEX: MEDIUM VARIANT PART 4 NATIONAL PROJECTIONS BY AGE AND SEX: HIGH VARIANT PART 5 REGIONAL POPULATION PROJECTIONS Table 5.1 Regional projected population by year and sex, Medium variant, Table 5.2 Regional population projections by age, sex and percentages Medium variant, APPENDICES APPENDIX 1: TECHNICAL WORKING GROUP APPENDIX 2: Total Fertility Rates (TFR), 1991, 1996, and APPENDIX 3: Mortality: Life Expectancy at Birth APPENDIX 4: INTERNAL MIGRATION IN AND OUT BY AGE AND SEX, i

3 Preface The need for data for planning and policy making cannot be over-emphasized. Information about the future population is an important prerequisite for any objective and realistic development plan. It is equally important that, for consistency purposes, all those involved in any planning exercise should utilize the same set of population figures. Population projections provide information on the age and sex structure of the future population of a country. Such information is needed for many purposes. For example, the size of the future population is useful for the estimation of the future demand for food, housing and social services. The future demand for education can only be estimated if the number of children in the coming years is known. Local authorities may want to know the number of children in the coming years in order to assess the future demand for education, particularly at primary level. Information on future population is also required to determine the proportions of public funds to be allocated to different sectors, including social services. Health authorities are also interested in knowing the future population of women in childbearing ages for the delivery of primary health care services. Private companies and investors are interested in the potential future labour force not only at national level but also at regional level. Businesses are interested in future age structures as an indicator of consumption patterns. However, nobody can predict with reality the future population of a country. Instead, population projections are made on the basis of assumptions relating to the future behaviour of the levels of fertility, mortality and migration, which make up the components of population change. This is the second time that population projections have been constructed at both national and regional levels. The projected period is thirty years, that is, from 2001 (as the base year) to For both national and regional levels, projections are based on three variants low, medium and high. The medium variant is the one that is recommended for use by planners and decision makers. In the case of the regional projections, the three variants were constructed corresponding to the three national variants. However, for the purpose of this report, only (one) the medium variant at regional level is presented. The other regional variants are available in electronic forms. On behalf of the Government of The Republic of Namibia, I would like to extend my gratitude to our development partners, in particular the UNFPA and SIDA for their assistance in the production of these projections. I would also like to express my appreciation for the efforts and inputs from the Inter-agency Technical Committee which assisted the Central Bureau of Statistics throughout the process of producing the projections. The staff of the Central Bureau of Statistics also need to be commended for their efforts in producing the second version of the population projections of Namibia. I hope policy makers, planners, researchers, learners and the general public will find these projections very useful in their day to day activities. Helmut Angula, Director General National Planning Commission Windhoek, January 2006 ii

4 1. Background One of the main objectives of the 2001 Population and Housing Census is to provide the basis for estimating future population trends through population projections. Usually, population projections are undertaken soon after the release of population census results. This is because the census figures provide a baseline period for the projections. The Central Bureau of Statistics (CBS), under the National Planning Commission Secretariat, has been involved in undertaking population projections since 1994, when the first set of such projections was published, with 1991 as the base year. However, due to lack of data at regional level, set of projections were done at national level only, and made use of the 1991 Namibia Population and Housing Census. Another set of projections were released in These were for a period of 30 years with 1991 as the base period. These projections were undertaken at both national and regional levels and utilized information from the 1991 Population and Housing Census as well as the 1996 Inter-censal Demographic Survey. There is now an urgent need to undertake new projections utilizing the results from the 2001 Population and Housing Census. The demand for these projections is obvious especially in the evaluation of NDP2 and formulation of NDP3, as well as monitoring the implementation of all development plans and programs at national and regional levels.. The previous set of projections are now out of date. New information on the levels of fertility, mortality and migration is also available from the 2001 census as well as from other data sources. Population projections are made on the basis of assumptions relating to the future behaviour of the levels of fertility, mortality and migration, which make up the components of population change. Efforts were made to consult all stakeholders to assist in arriving to realistic assumptions on the future behaviour of the population. Technical Working Group The CBS is responsible for the undertaking of the population projections. However, the whole exercise requires inputs from all the relevant stakeholders. The involvement of the stakeholders is important in the initial stages of preparing the input data for the projections. A Technical Working Group consisting of the relevant stakeholders was therefore formed to advise and assist the CBS to come up with objective assumptions on the future behaviour of the levels of fertility and mortality. Two meetings were held with the Technical Working Group. The list of institutions represented in the Technical Working Group is given in the Appendix. Certain targets with regard to fertility and mortality levels were already documented in NDP II as well as in Vision However, there was still a need to critically examine them to assess whether they are consistent with other population characteristics, which are likely to influence the direction of the levels of fertility and mortality. iii

5 Assumptions 2. Assumptions Three sets of projections were undertaken at both national and regional levels. These were the low, medium and high variants. Assumptions on the future behaviour of the levels of fertility and mortality were made for each variant. It is not possible to make assumptions for each one of the years. Where this was not possible, it was left for the program to interpolate. Since it was not possible to make assumptions on future trends of migration patterns, therefore the information provided is for the period 1996 to 2001 which was derived from the 2001 Population and Housing Census. 2.1 Fertility Fertility is the main component in population change. There are various factors which influence the level of fertility. These include, among others, age of entry into child bearing, family planning, marital status, level of education and employment status. An early age of entry into child bearing is likely to result in higher levels of fertility, while a late age of entry may lead to the opposite situation. In Namibia, child bearing starts relatively early. At the time of the 2001 census, teen-age mothers contributed 9 percent of the total fertility in the country. The 2001 Demographic and Health Survey (DHS) indicated that 18 percent of women aged had already began the child-bearing process. This proportion is down from 22 percent which was observed in a similar survey in Family planning can be used to regulate fertility, either to prolong the birth interval or to prevent further pregnancies. This will, in turn, result in lower levels of fertility. The 2000 DHS showed that 38 percent, as against 23 percent in 1992, of all the women aged years were practicing a method of contraception. The same survey indicated that contraceptive use is higher (44%) among currently married women, and is even higher (52%) among sexually active women. The effect of marital status on the level of fertility is generally quite clear. Legally married women and those in consensual unions have relatively higher levels of fertility than those who have never married, widowed or divorced. The 2001 census showed that the women in the former group had about 6 children while the rest had less than 4 children. The reason behind this is that women in unions, whether legally or consensually, are more consistently exposed to the risk of pregnancy. With regard to employment status, the 2001 census showed that homemakers and the unemployed women have higher levels of fertility than those who are employed. The census also showed that women who are employed in the private and public sectors have relatively lower levels of fertility than own account workers or homemakers. This may be explained by the fact that the former group of women put their professional careers ahead of child-bearing. The effect of the level of formal education on fertility is also quite clear. It has universally been observed that levels of education and fertility are inversely related. The higher the level of education, the lower the level of fertility, and vice versa. Results from the 1991 and 2001 confirmed this relationship for Namibia. In the 2001 census, women with no education had over 5 children while those with secondary or higher education had less than 3 children. The level of education of mothers influences all the other factors. For example, the more years a woman spends in school, the longer she will delay her age of entry into child bearing. Most of the women employed in the public and private sector are likely to have had some form of formal education. Women with education are more likely to practice some form of family planning in order to regulate their fertility. The 2000 DHS showed that the proportion of sexually active women who were currently using a contraceptive method almost doubled from slightly more than one-third of those with no education to two-thirds of those who had completed secondary education. iv

6 v Assumptions It is therefore clear that the level of education has a relatively big impact on the level of fertility. The assumptions on the future trend on the level of fertility are based on this fact. It has also been observed that the level of fertility has been declining since It is quite likely that as the level of education of mothers increases, this declining trend will continue in the future. The measure of fertility which is used is the total fertility rate. This defined as the average number of children a woman will have if she survives throughout her reproductive period. The table below provides the future trend of fertility from 2001 to 2031, the end of the projection period for the total country and for each of the 13 regions. This is based on the observed levels of education of mother from either 2001 census or the 2000 DHS. Table 1. The trend of the total fertility rates by variant and by area, Namibia, Namibia Year Medium Low High (DHS primary) (Govt employees TFR) (Secondary) Caprivi Year Medium Low High (Secondary) (Tertiary) Erongo Year Medium Low High (regional urban) (Urban tertiary) (Walvis Bay Urban) Hardap Year Medium Low High (Urban primary) (Rural Sec & Tertiary) (Urban Ter.& Sec) 2.4(National level) 2.8(Rural tertiary) Karas Year Medium Low High (Sec+) (Sec+) 2.1(Sec+) 2.7(Urban tertiary) Kavango Year Medium Low High (Primary) 4.6(Primary) (Urban Sec) 2.9(Secondary) 4.6(Primary) Khomas Year Medium Low High (Secondary) 2.3(Tertiary) 2.4

7 Kunene Year Medium Low High (Primary) 3.3(Rural tertiary) 4.0(Rural primary) (Tertiary) 2.3(Urban tertiary) 3.3(Rural tertiary) Ohangwena Year Medium Low High (Rural primary) 4.0(Rural tertiary) 4.9(Urban primary) (Rural tertiary) 3.5(Tertiary) 4.0 Omaheke Year Medium Low High (Urban primary) 3.1(Rural Sec) 4.5(Rural primary) (Tertiary) 2.5(Tertiary) 3.1(Rural Sec) Omusati Year Medium Low High (Rural Sec) 3.0(Urban tertiary) 3.7(Primary) (Secondary) (Rural tertiary) Assumptions Oshana Year Medium Low High (Secondary) 2.5(Tertiary) (Urban tertiary) 2.4(urban tertiary) 2.6 Oshikoto Year Medium Low High (Urban primary) 3.3(Urban tertiary) 4.5(Primary) (Tertiary) 2.4(Secondary) 3.3(Urban tertiary) Otjozondjupa Year Medium Low High (Urban tertiary) 2.8(Urban secondary) 4.0(Urban sec) (Rural tertiary) 2.8(Urban secondary) 3.0(Urban tertiary) 2.2 Mortality The assumptions on the future behaviour of mortality are more difficult to make. Before the onset of the HIV/AIDS pandemic, there was a strong relationship between child and adult mortality. This implied that high child mortality resulted in low life expectancy at birth, and vice versa. That relationship does not hold any more. This is because the HIV/AIDS pandemic is prevalent in the sexually active age groups. The age specific death rates are relatively high in these age groups. Figure 1 below shows the shape of the age-specific rates for Namibia, while Figure 2 shows the standard pattern in the absence of HIV/AIDS. vi

8 Figure 1. Age-Specific Mortality Pattern, Namibia 2001 Census Assumptions Death rates Death rates Age group Figure 2. Sketch of a presumed standard pattern in the absence of HIV/AIDS 0.25 Sketch of presumed standard pattern 0.2 ASDR-presumed standard ASDR Age group Namibia is one of the few countries with relatively high prevalence of the HIV virus. This has resulted in relatively high levels of mortality. The gains from improved primary health care have been eroded by the pandemic. It is not an easy task to predict the future direction of this pandemic. However, the bi-annual sentinel surveys on pregnant women, which are undertaken by the Ministry of Health and Social Services have indicated that the prevalence which had been steadily increasing since the 1990s has shown signs of stabilizing and may even be declining. Assuming that this is the case, objective trends of the level of mortality can be made. vii

9 Assumptions The main indicator of mortality is the life expectancy at birth. This is defined as the average number of years a new-born will be expected to live if it is exposed to the mortality pattern for year under consideration. The process of coming up with assumptions on the future trend of life expectancy at birth involved the examination of the past trend as well as the 2001 census results relating to mortality. The expected trend of the life expectancy at birth for the projection period by sex and area is shown in Table 2 below. 3. Table 2. Assumed life expectancy at birth by variant and by area 3.1 Namibia, Namibia Medium Low High Year Females Males Females Males Females Males Caprivi Medium Low High Year Females Males Females Males Females Males Erongo Medium Low High Year Females Males Females Males Females Males Hardap Medium Low High Year Females Males Females Males Females Males Karas Medium Low High Year Females Males Females Males Females Males Kavango Medium Low High Year Females Males Females Males Females Males viii

10 Assumptions Khomas Medium Low High Year Females Males Females Males Females Males Kunene Medium Low High Year Females Males Females Males Females Males Ohangwena Medium Low High Year Females Males Females Males Females Males Omaheke Medium Low High Year Females Males Females Males Females Males Omusati Medium Low High Year Females Males Females Males Females Males Oshana Medium Low High Year Females Males Females Males Females Males Oshikoto Medium Low High Year Females Males Females Males Females Males Otjozondjupa Medium Low High Year Females Males Females Males Females Males ix

11 3.2 Migration Assumptions There are no statistics on international migration. It was therefore assumed that, at national level migration is not significant. This implies that in and out migrants will cancel each other out. However, there is significant internal migration between the regions. This was clearly observed in the 2001 census. The northern regions lose out people to the urban areas of the regions of Khomas, Erongo and Karas regions. The calculated migration pattern for each region is given in the appendix. 3.3 Method of Projection Two projection methods were used in this undertaking. The United Nations software package, MORTPAK, was first utilized. Input data are required for the base and final years of the projection period, in this case 2001 and 2031 respectively. These are: 1. Base year population by age and sex, i.e. the 2001 population from the census 2. Total fertility rates for Total fertility rates for Fertility patterns, i.e. age-specific fertility rates, for the 2001and The sex ratio at birth for Life expectancy at birth by sex for Life expectancy at birth by sex for Number of in-migrants by sex for Number of in-migrants by sex for Number of out-migrants by sex for Number of out-migrants by sex for The migration pattern, i.e. the age distribution of both in and out migrants, by sex Option indicators can be inserted in between the base and final years. The input data as given using the assumptions provided above were then used to generate the projections for the three variants for the country and for each of the 13 regions. It should be noted that the aggregate total for all the regions would not tally with the projections generated at country level. This is because the assumptions at national and regional levels are not the same. There is therefore a need to adjust the regional figures in order to make them consistent with the national one. The adjustment factor is the ratio of the national figure to the regional aggregate total. This ratio is multiplied by the regional figures. However, on examination of the aggregate total population based on the regions, it was discovered that it was too high and therefore unrealistic. This figure was almost double that generated nationally in the final year of the projection period. Several alternatives were done by revising the regional migration patterns. The results were still not convincing. Finally, it was decided to make use of the growth rates and apply the exponential growth rate to generate projections of the total population for all the years in the projection period. An inspection of the aggregate total population for all the regions indicated that this figure was fairly close to that generated by MORTPAK for the medium variant. These figures generated through the exponential growth rate approach were adopted and applied to the national total figures for each variant for each year in order to get the adjustment factors as explained above. The adjustment factors so obtained were then multiplied by the MORTPAK figures generated for each region to arrive at the final projection figures for each region and for each year. A similar adjustment had to be applied for each variant and each to the figures disaggregated by age and sex in order to make them consistent with the national figures. x

12 Part 1: Projections Summary-National PART 1: Summary of the Projections Table 1.1 Projected Population by year and sex, Low, Medium, High Variants, Namibia Year Low Variant Medium Variant High Variant Males Females Total Males Females Total Males Females Total

13 Part 1 :Projections Summary-National Figure 1.1 Projected Population by year, Low, Medium, High Variants, Low Population Medium High Year 2

14 Part 1: Vital Statistics Summary-National Table 1.2 Vital rates - Crude Birth Rate (CBR), Crude Death Rate (CDR) and Annual growth rate, Period Low variant Medium variant High variant Growth Growth Growth CBR CDR rate (%) CBR CDR rate (%) CBR CDR rate (%)

15 Part 1: Projections Summary Regional Medium Variant Table 1.3 Projected population by region, Male Population by region Year Caprivi Erongo Hardap Karas Kavango Khomas Kunene Ohangwena Omaheke Omusati Oshana Oshikoto Otjozondjupa Total

16 Part 1: Projections Summary Regional Medium Variant Female Population by region Year Caprivi Erongo Hardap Karas Kavango Khomas Kunene Ohangwena Omaheke Omusati Oshana Oshikoto Otjozondjupa Total

17 Part 1: Projections Summary Regional Medium Variant Total Population by region Year Caprivi Erongo Hardap Karas Kavango Khomas Kunene Ohangwena Omaheke Omusati Oshana Oshikoto Otjozondjupa Total

18 Part 2 Projections National-Low Variant PART 2 NATIONAL PROJECTIONS BY AGE AND SEX: LOW VARIANT POPULATION BY SINGLE YEARS OF AGE AND SEX, 2001 AGE MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL

19 Part 2 Projections National-Low Variant POPULATION IN HUNDREDS BY FIVE YEAR AGE GROUPS AND SEX, 2001 NUMBERS PERCENT AGE GROUP MALES FEMALES TOTAL MALES FEMALES TOTAL TOTAL

20 Part 2 Projections National-Low Variant POPULATION BY SINGLE YEARS OF AGE AND SEX 2006 AGE GROUP MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL

21 Part 2 Projections National-Low Variant POPULATION BY FIVE YEAR AGE GROUPS AND SEX, 2006 AGE GROUP NUMBERS PERCENT MALES FEMALES TOTAL MALES FEMALES TOTAL ' ' TOTAL VITAL STATISTICS SUMMARY: ABSOLUTE NUMBERS ANNUAL VITAL RATE MALES FEMALES TOTAL MALES FEMALES TOTAL BIRTHS DEATHS GROWTH

22 Part 2 Projections National-Low Variant POPULATION BY SINGLE YEARS OF AGE AND SEX, 2011 AGE MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL

23 Part 2 Projections National-Low Variant POPULATION BY FIVE YEAR AGE GROUPS AND SEX, 2011 NUMBERS PERCENT AGE GROUP MALES FEMALES TOTAL MALES FEMALES TOTAL TOTAL VITAL STATISTICS SUMMARY: ABSOLUTE NUMBERS ANNUAL VITAL RATE MALES FEMALES TOTAL MALES FEMALES TOTAL BIRTHS DEATHS GROWTH

24 Part 2 Projections National-Low Variant POPULATION BY SINGLE YEARS OF AGE AND SEX, 2016 AGE MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL

25 Part 2 Projections National-Low Variant POPULATION BY FIVE YEAR AGE GROUPS AND SEX, 2016 NUMBERS PERCENT AGE GROUP MALES FEMALES TOTAL MALES FEMALES TOTAL TOTAL VITAL STATISTICS SUMMARY: ABSOLUTE NUMBERS ANNUAL VITAL RATES MALES FEMALES TOTAL MALES FEMALES TOTAL BIRTHS DEATHS GROWT

26 Part 2 Projections National-Low Variant POPULATION BY SINGLE YEARS OF AGE AND SEX, 2021 AGE MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL

27 Part 2 Projections National-Low Variant POPULATION BY FIVE YEAR AGE GROUPS AND SEX, 2021 ABSOLUTE NUMBERS ANNUAL VITAL RATES AGE GROUP MALES FEMALES TOTAL MALES FEMALES TOTAL TOTAL VITAL STATISTICS SUMMARY: ABSOLUTE NUMBERS ANNUAL VITAL RATE MALES FEMALES TOTAL MALES FEMALES TOTAL BIRTHS DEATHS GROWTH

28 Part 2 Projections National-Low Variant POPULATION BY SINGLE YEARS OF AGE AND SEX, 2026 AGE MALES FEMALES TOTAL AGE MALES FEMALES TOTAL TOTAL