This statistic shows the biggest cities in Zimbabwe in 2022. In 2022, approximately 1.49 million people lived in Harare, making it the biggest city in Zimbabwe.

Population in largest city in Zimbabwe was reported at 1603201 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Zimbabwe - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Zimbabwe ZW: Population in Largest City: as % of Urban Population data was reported at 28.651 % in 2017. This records a decrease from the previous number of 28.990 % for 2016. Zimbabwe ZW: Population in Largest City: as % of Urban Population data is updated yearly, averaging 35.422 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 52.533 % in 1961 and a record low of 28.651 % in 2017. Zimbabwe ZW: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

Population in the largest city (% of urban population) in Zimbabwe was reported at 29.5 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Zimbabwe - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Other Deposit Takers for Zimbabwe (ZWEFCBODDLNUM) from 2004 to 2015 about branches and Zimbabwe.

Zimbabwe ZW: Population in Largest City data was reported at 1,509,901.000 Person in 2017. This records an increase from the previous number of 1,504,803.000 Person for 2016. Zimbabwe ZW: Population in Largest City data is updated yearly, averaging 958,533.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,509,901.000 Person in 2017 and a record low of 247,978.000 Person in 1960. Zimbabwe ZW: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

These raster files show the land cover classification around Harare in 2006 and 2010. The classification results were based on Spot 5 imagery. Land cover classes in the attribute table are as follows: Class 1 Regular Residential (small planned buildings) Class 2- Regular Residential (small unplanned buildings) Class 3 Commercial/Industrial (large buildings) Class 4 Natural (Vegetation/Soil/non built-up This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost. When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394

The share of urban population in Zimbabwe saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 32.52 percent. However, 2023 marked the fourth consecutive increase of the share. A country's urbanization rate refers to the share of the total population living in an urban setting. International comparisons of urbanization rates may be inconsistent, due to discrepancies between definitions of what constitutes an urban center (based on population size, area, or space between dwellings, among others).Find more key insights for the share of urban population in countries like Seychelles and Uganda.

ZW：最大城市人口：占城镇人口百分比在12-01-2017达28.651%，相较于12-01-2016的28.990%有所下降。ZW：最大城市人口：占城镇人口百分比数据按年更新，12-01-1960至12-01-2017期间平均值为35.422%，共58份观测结果。该数据的历史最高值出现于12-01-1961，达52.533%，而历史最低值则出现于12-01-2017，为28.651%。CEIC提供的ZW：最大城市人口：占城镇人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的津巴布韦 – 表 ZW.世界银行：人口和城市化进程统计。

Abstract

The 2015 Zimbabwe Demographic and Health Survey (2015 ZDHS) is the sixth in a series of Demographic and Health Surveys conducted in Zimbabwe. As with prior surveys, the main objective of the 2015 ZDHS is to provide up-to-date information on fertility and child mortality levels; maternal mortality; fertility preferences and contraceptive use; utilization of maternal and child health services; women’s and children’s nutrition status; knowledge, attitudes and behaviours related to HIV/AIDS and other sexually transmitted diseases; and domestic violence. All women age 15-49 and all men age 15-54 who are usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed and for anaemia and HIV testing. All children age 6-59 months were eligible for anaemia testing, and children age 0-14 for HIV testing. In all households, height and weight measurements were recorded for children age 0-59 months, women age 15-49, and men age 15-54. The domestic violence module was administered to one selected woman selected in each of surveyed households.

The 2015 ZDHS sample is designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces (Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matebeleland South, Midlands, Masvingo, Harare, and Bulawayo).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members resident in the household, all women age 15-49 years, men age 15-54 years and their young children.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015 ZDHS sample was designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces: Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare, and Bulawayo. The 2012 Zimbabwe Population Census was used as the sampling frame for the 2015 ZDHS.

Administratively, each province in Zimbabwe is divided into districts, and each district is divided into smaller administrative units called wards. During the 2012 Zimbabwe Population Census, each ward was subdivided into convenient areas, which are called census enumeration areas (EAs). The 2015 ZDHS sample was selected with a stratified, two-stage cluster design, with EAs as the sampling units for the first stage. The 2015 ZDHS sample included 400 EAs-166 in urban areas and 234 in rural areas.

The second stage of sampling included the listing exercises for all households in the survey sample. A complete listing of households was conducted for each of the 400 selected EAs in March 2015. Maps were drawn for each of the clusters and all private households were listed. The listing excluded institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools. A representative sample of 11,196 households was selected for the 2015 ZDHS.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2015 ZDHS: - Household Questionnaire, - Woman’s Questionnaire, - Man’s Questionnaire, and - Biomarker Questionnaire.

These questionnaires were adapted from model survey instruments developed for The DHS Program to reflect the population and health issues relevant to Zimbabwe. Issues were identified at a series of meetings with various stakeholders from government ministries and agencies, research and training institutions, non-governmental organisations (NGOs), and development partners. In addition to English, the questionnaires were translated into two major languages, Shona and Ndebele. All four questionnaires were programmed into tablet computers to facilitate computer assisted personal interviewing (CAPI) for data collection, with the option to choose English, Shona, or Ndebele for each questionnaire.

Cleaning operations

CSPro was used for data editing, weighting, cleaning, and tabulation. In ZIMSTAT’s central office, data received from the supervisor’s tablets were registered and checked for inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure the completeness of work in the field. Any anomalies were communicated to the respective team through the technical team and the team supervisor. The corrected results were then re-sent to the central office.

Response rate

A total of 11,196 households were selected for inclusion in the 2015 ZDHS and of these, 10,657 were found to be occupied. A total of 10,534 households were successfully interviewed, yielding a response rate of 99 percent.

In the interviewed households, 10,351 women were identified as eligible for the individual interview, and 96 percent of them were successfully interviewed. For men, 9,132 were identified as eligible for interview, with 92 percent successfully interviewed.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Zimbabwe DHS (ZDHS) to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2015 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2015 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Completeness of information on siblings - Sibship size and sex ratio of siblings

Note: See detailed data quality tables in APPENDIX C of the report.

ZW：最大城市人口在12-01-2017达1,509,901.000人，相较于12-01-2016的1,504,803.000人有所增长。ZW：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为958,533.000人，共58份观测结果。该数据的历史最高值出现于12-01-2017，达1,509,901.000人，而历史最低值则出现于12-01-1960，为247,978.000人。CEIC提供的ZW：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的津巴布韦 – 表 ZW.世行.WDI：人口和城市化进程统计。

Addis Ababa, in Ethiopia, ranked as the most expensive city to live in Africa as of 2024, considering consumer goods prices. The Ethiopian capital obtained an index score of 46.7, followed by Harare, in Zimbabwe, with 37.4. Morocco and South Africa were the countries with the most representatives among the 15 cities with the highest cost of living in Africa.

Abstract

The Income, Consumption and Expenditure Survey is the main data source for the compilation of national accounts aggregates. The main objectives of the 2011/2012 PICES were to provide data on: Poverty; Income distribution of the population; Consumption level of the population; Private consumption; Consumer Price Index (CPI) weights; Living conditions of the population; Production account of agriculture (Communal Lands Small Scale Commercial Farms, Resettlement Areas, A1 and A2 farms and Large Scale Commercial Farms).

Geographic coverage

National

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2002 Zimbabwe Population Census Master Sample frame (ZMS202) provided an area sampling frame for the 2011/12 PICES. The survey was based on a sample of 31,248 households which is representative at province and district levels. The sample design entailed two stages: selection of Enumeration Areas (EAs) as the first stage and selection of households in these EAs as the second stage. In total 2,232 EAs were selected with Probability Proportional to Size (PPS), the measure of size being the number of households enumerated in the 2002 Population Census. Finally the number of each of the EAs which were successfully interviewed in the 12 months of the study was 2,220 giving a covering response rate of 99.5 percent. The sample is representative of all the population in Zimbabwe residing in private households. The population living in institutions such as military barracks, prisons and hospitals was excluded from the sampling frame.

Stratification In order to increase the efficiency of the sample design for PICES 2010/11, it was important to divide the sample design for PICES 2011/12 it was important to divide the sampling frame of EAs into strata which are as homogeneous as possible. At the first sampling stage the sample EAs are selected independently within each explicit stratum. The nature of the stratification depended on the most important characteristics measured in the surveym as well as the domains of analysis. The strata was made consistent with the geographic disaggregation used in the survey tables.

The first level of stratification corresponded to the 60 administrative districts of Zimbabwe, which are the geographic domains of analysis defined for the PICES. The rural and urban areas are domains at the national level. Some of the administrative districts are completely rural or urban, while most districts have a combination of rural and urban EAs. Since many districts have relatively few urban sample EAs, it would not be effective to use explicit urban and rural stratification within each district. Instead, the sampling frame of EAs for each district was sorted first by the rural/urban code in order to provide implicit stratification. Given that the sample EAs were selected systematically with Probabilty Proportional to Size (PPS), this provided a proportional allocation of the sample within each district by rural and urban areas. The sampling frame includes codes for land-use sectors, which can also be used for implicit stratification. The following land-use sextors have been identified:

1- Communal land 2- Small scale commercial farming area 3- Large scale commercial farming area 4- Resettlement area 5- Urban council area 6- Administrative centres (districts) 7- Growth Point 8- Other Urban Area, e.g. Service Centres and Mines 9- State Land, e.g. National Parks, Safari Areas

Sections 1.4 - 1.6 of the survey report (provided as external resources) provide more information on Sample size and allocation, Sample selection and Systematic selection of EAs.

Sampling deviation

Out of a total of 30,838 households interviewed 29,765 questionnaires were fully completed. Partly completed questionnaires were excluded from the analysis as they would distort average incomes and expenditures.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

PICES 2011/2012 data was captured by the ZIMSTAT data entry unit and CSPro was used to develop data entry programmes. About 80 people were involved in data processing each month from December 2011 to the end of July 2012. These members of staff worked overtime on average for 20 days in a month. Data was captured twice by different people for purposes of verification. Statistical Analysis System (SAS) was used for data processing programmes. Data cleaning was done at all stages i.e. data entry and data processing to check for the consistency of the data.

Quality Control Measures Used During Data Processing

Data processing involved coding and editing of the questionnaires and data entry. The main reason why data processing was started early was to ensure that data processing is started whilst data collection was in progress. This enabled field staff to be informed of the quality of data collection whilst they were still in the field. It was also found necessary that any queries on the data could be resolved whilst the field staff remembered what transpired. This was also deemed necessary because the number of questionnaires reveived could be checked promptly and discrepancies on the questionnaires received and those expected would be investigated immediately and resolved.

During data processing one member of staff was given 4 batches to be completed in six days. About 80 ZIMSTAT staff members were requested to work outside normal business hours on workdays and on Saturdays. The first two days were for initial entry while the other two days were for verification entry. Two persons exchanged questionnaires during the verification stage. The third stage was to check for differences between the two entries and any errors in initial entry were corrected at that stage. A clean file was then set aside to be copied by programmers at the end of each data processing exercise.

Control sheets were used for monitoring the movement of questionnaires from one person to another during the editing and data processing stage. Any errors made during the data entry were corrected and all data capture operators were informed of these errors to avoid the same errors being repeated. Furthermore, as part of quality control, the data entry programme had inbuilt quality control programmes such as the skip patterns of the questionnaire and the automatic refusal if an unknown identification code (Geocode) or inconsistent code was entered. Data Entry Supervisors also made spot checks to see work being entered while a Statistical Officer was placed in each of the data entry pools to correct any errors or inconsistencies in a process known as "online editing".

In order to check the quality of data processing ZIMSTAT staff began to generate tables to do validity checks using Population Census data for 2002 and other surveys such as Zimbabwe Demographic and Health Survey (ZDHS 2010-11). The Finscope Zimbabwe 2011 Survey Results were also used in validating the data. The validation exercise was done for both the 6 months data and the 12 months data and any deviations from the norm were investigated. An audit of the questionnaires received and processed was also done and any discrepancies were investigated and resolved. ZIMSTAT also compared the geocodes sampled and the geocodes with processed data and any differences were also corrected. As a quality control measure, a Sampling Consultant was engaged to work with ZIMSTAT PICES team to check and review the PICES weights for the 6 months data and 12 months data respectively.

Response rate

Based on a total of 29,765 households with fully completed questionnaires the response rate calculated using the original sample is 95.3 percent.

Before analysis was done it was essential to know the total number of questionnaires that were returned by the provinces. A total of 30,838 interviews were conducted and these included partially completed questionnaires. After removing the partually completed questionnaires the number of households which were successfully interviewed in the study were 29,756, giving a response rateof 95.3 percent based on the initial sample of 31,248 households. The households with partially completed questionnaires were left out from the analysis as they would distort averages for variables such as income and expenditures. The response rates were highest in Manicaland Province which had 97.9 percent, followed by Masvingo 97.1 percent. Harare province and Bulawayo province had the lowest response rates of 82.8 percent and 85.6 percent respectively. The main reason for these low response rates in Harare and Bulawayo is a large number of households who are not found at home, refusals and relocation of households to other areas within the month of the survey. This was prevalent particularly in dwelling units occupied by lodgers. The number of partly completed questionnaires was also high in urban areas. In terms of enumeration area coverage, a total of 2,220 EAs were enumerated out of a sample total of 2,232 EAs and this represented a coverage response rate of 99.5 percent of the total number of EAs sampled.

Abstract

The 1994 Zimbabwe Demographic and Health Survey (ZDHS) is a nationally representative survey of 6,128 women age 15-49 and 2,141 men age 15-54. The ZDHS was implemented by the Central Statistical Office (CSO), with significant technical guidance provided by the Ministry of Health and Child Welfare (MOH&CW) and the Zimbabwe National Family Planning Council (ZNFPC). Macro International Inc. (U.S.A.) provided technical assistance throughout the course of the project in the context of the Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S, Agency for International Development (USAID/Harare). Data collection for the ZDHS was conducted from July to November 1994.

As in the 1988 ZDHS, the 1994 ZDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and maternal and child health. How- ever, the 1994 ZDHS went further, collecting data on: compliance with contraceptive pill use, knowledge and behaviours related to AIDS and other sexually transmitted diseases, and mortality related to pregnancy and childbearing (i.e., maternal mortality). The ZDHS data are intended for use by programme managers and policymakers to evaluate and improve family planning and health programmes in Zimbabwe.

The primary objectives of the 1994 ZDHS were to provide up-to-date information on: fertility levels; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted diseases. The 1994 ZDHS is a follow-up of the 1988 ZDHS, also implemented by CSO. While significantly expanded in scope, the 1994 ZDHS provides updated estimates of basic demographic and health indicators covered in the earlier survey.

MAIN RESULTS

FERTILITY

Survey results show that Zimbabwe has experienced a fairly rapid decline in fertility over the past decade.

Despite the decline in fertility, childbearing still begins early for many women. One in five women age 15-19 has begun childbearing (i.e., has already given birth or is pregnant with her first child). More than half of women have had a child before age 20.

Births that occur too soon after a previous birth face higher risks of undemutrition, illness, and death. The 1994 ZDHS indicates that 12 percent of births in Zimbabwe take place less than two years after a prior birth.

Marriage. The age at which women and men marry has risen slowly over the past 20 years. Nineteen percent of currently married women are in a polygynous union (i.e., their husband has at least one other wife). This represents a small rise in polygyny since the 1988 ZDHS when 17 percent of married women were in polygynous unions.

Fertility Preferences. Around one-third of both women and men in Zimbabwe want no more children. The survey results show that, of births in the last three years, 1 in 10 was unwanted and in 1 in three was mistimed. If all unwanted births were avoided, the fertility rate in Zimbabwe would fall from 4.3 to 3.5 children per woman.

FAMILY PLANNING

Knowledge and use of family planning in Zimbabwe has continued to rise over the last several years. The 1994 ZDHS shows that virtually all married women (99 percent) and men (100 percent) were able to cite at least one modem method of contraception. Contraceptive use varies widely among geographic and socioeconomic subgroups. Fifty-eight per- cent of married women in Harare are using a modem method versus 28 percent in Manicaland. Government-sponsored providers remain the chief source of contraceptive methods in Zimbabwe. Survey results show that 15 percent of married women have an unmet need for family planning (either for spacing or limiting births).

CHILDHOOD MORTALITY

One of the main objectives of the ZDHS was to document the levels and trends in mortality among children under age five. The 1994 ZDHS results show that child survival prospects have not improved since the late 1980s. The ZDHS results show that childhood mortality is especially high when associated with two factors: short preceding birth interval and low level of maternal education.

MATERNAL AND CHILD HEALTH

Utilisation of antenatal services is high in Zimbabwe; in the three years before the survey, mothers received antenatal care for 93 percent of births. About 70 percent of births take place in health facilities; however, this figure varies from around 53 percent in Manicaland and Mashonaland Central to 94 percent in Bulawayo. It is important for the health of both the mother and child that trained medical personnel are available in cases of prolonged or obstructed delivery, which are major causes of maternal morbidity and mortality. Twenty-four percent of children under age three were reported to have had diarrhoea in the two weeks preceding the survey.

Nutrition. Almost all children (99 percent) are breastfed for some period of time; When food supplementation begins, wide disparity exists in the types of food received by children in different geographic and socioecoaomic groups. Generally, children living in urban areas (Harare and Bulawayo, in particular) and children of more educated women receive protein-rich foods (e.g., meat, eggs, etc.) on a more regular basis than other children.

AIDS

AIDS-related Knowledge and Behaviour. All but a fraction of Zimbabwean women and men have heard of AIDS, but the quality of that knowledge is sometimes poor. Condom use and limiting the number of sexual partners were cited most frequently by both women and men as ways to avoid the AIDS Virus. While general knowledge of condoms is nearly universal among both women and men, when asked where they could get a condom, 30 Percent of women and 20 percent of men could not cite a single source.

Geographic coverage

The 1994 Zimbabwe Demographic and Health Survey (ZDHS) is a nationally representative survey.

Analysis unit

• Household
• Women age 15-49
• Men age 15-54
• Children under five years

Universe

The population covered by the 1994 ZDHS is defined as the universe of all women age 15-49 in Zimbabwe and all men age 15-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

SAMPLING FRAME

The area sampling frame for the ZDHS was the 1992 Zimbabwe Master Sample (ZMS92), which was developed by the Central Statistical Office (CSO) following the 1992 Population Census for use in demographic and socio-economic surveys. The sample for ZMS92 was designed to be almost nationally representative: people residing on state land (national parks, safari areas, etc.) and in institutions, which account for less than one percent of the total population, were not included. The sample was stratified and selected in two stages. With the exception of Harare and Bulawayo, each of the other eight provinces in the country was stratified into four groups according to land use: communal land, large-scale farming, urban and semi-urban areas, and small scale fanning and resettlement areas. In Harare and Bulawayo, only an urban stratum was formed.

The primary sampling unit (PSU) was the enumeration area (EA), as defined in the 1992 Population Census. A total of 395 EAs were selected with probability proportional to size, the size being the number of households enumerated in the 1992 Population Census. The selection of the EAs was a systematic, one- stage operation, carried out independently for each of 34 strata. In each stratum, implicit stratification was introduced by ordering the EAs geographically within the hierarchy of administrative units (wards and districts within provinces).

An evaluation of the ZMS92 showed that it oversampled urban areas: in the ZMS92 the proportion of urban households is about 36 percent while, according to the preliminary results of the 1992 Population Census, this proportion is about 32 percent.

CHARACTERISTICS OF THE ZDHS SAMPLE

The sample for the ZDHS was selected from the ZMS92 master sample in two stages. In the first stage, 230 EAs were selected with equal probabilities. Since the EAs in the ZMS92 master sample were selected with probability proportional to size from the sampling frame, equal probability selection of a subsample of these EAs for the ZDHS was equivalent to selection with probability proportional to size from the entire sampling frame. A complete listing of the households in the selected EAs was carried out. The list of households obtained was used as the frame for the second-stage sampling, which was the selection of the households to be visited by the ZDHS interviewing teams during the main survey fieldwork. Women between the ages of 15 and 49 were identified in these households and interviewed. In 40 percent of the households selected for the main survey, men between the ages of 15 and 54 were interviewed with a male questionnaire.

SAMPLE ALLOCATION

Stratification in the ZDHS consisted of grouping the ZMS92 strata into two main strata only: urban and rural. Thus the ZDHS rural stratum consists of communal land, large scale farming, and small scale farming and resettlement areas, while the ZDHS urban stratum corresponds exactly to the urban/semi-urban stratum of the ZMS92.

The proportional allocation would result in a completely self-weighting sample but did not allow for reliable estimates for provinces. Results of other demographic and health surveys show that a minimum sample of 1,000 women i:; required in order to obtain estimates of fertility and childhood mortality rates at an acceptable level of sampling errors. Given that the total sample

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will