In 2024, Gabon had the highest urbanization rate in Africa, with over 90 percent of the population living in urban areas. Libya and Djibouti followed at around 82 percent and 79 percent, respectively. On the other hand, many countries on the continent had the majority of the population residing in rural areas. As of 2023, urbanization in Malawi, Rwanda, Niger, and Burundi was below 20 percent. A growing urban population On average, the African urbanization rate stood at approximately 45 percent in 2023. The number of people living in urban areas has been growing steadily since 2000 and is forecast to increase further in the coming years. The urbanization process is particularly rapid in Burundi, Uganda, and Tanzania. In these countries, the urban population grew by over five percent in 2023 compared to the previous year. However, in 39 countries on the continent, the urban population growth was over three percent. The most populous cities in Africa Africa’s largest city is Lagos in Nigeria, counting around nine million people. It is followed by Kinshasa in the Democratic Republic of the Congo and Cairo in Egypt, each with over seven million inhabitants. Moreover, other cities on the continent are growing rapidly. The population of Bujumbura in Burundi will increase by 123 percent between 2020 and 2035, registering the highest growth rate on the continent. Other fast-growing cities are Zinder in Niger, Kampala in Uganda, and Kabinda in the Democratic Republic of the Congo.

The urbanization rate in Africa was estimated at nearly **** percent in 2024. Urbanization on the continent has increased steadily since 2000, when close to ** percent of the total population lived in urban areas. This share is expected to increase further in the coming years. However, the proportion of the rural and urban population varies significantly on the continent. In 2024, Gabon and Libya were the most urbanized countries in Africa, each exceeding ** percent. In contrast, Burundi and Niger registered the lowest urbanization rates, which recorded only ** and ** percent of their populations living in urban areas, respectively. Overall, the degree of urbanization on the continent was lower than the world average, which was set at ** percent as of 2025. In that year, Africa and Asia were the continents with the lowest urbanization rate.

In 2018, roughly 23 percent of the population in East Africa were living in urban areas. The most urbanized country was Djibouti (78 percent), while Burundi was the least urbanized (13 percent). In absolute numbers, Ethiopia had the largest number of urban residents, approximately 23 million.

The average for 2024 based on 47 countries was 46.62 percent. The highest value was in Gabon: 91.31 percent and the lowest value was in Burundi: 15.16 percent. The indicator is available from 1960 to 2024. Below is a chart for all countries where data are available.

In 2025, the degree of urbanization worldwide was at 58 percent. North America, Latin America, and the Caribbean were the regions with the highest level of urbanization, with over four-fifths of the population residing in urban areas. The degree of urbanization defines the share of the population living in areas defined as "cities". On the other hand, less than half of Africa's population lives in urban settlements. Globally, China accounts for over one-quarter of the built-up areas of more than 500,000 inhabitants. The definition of a city differs across various world regions - some countries count settlements with 100 houses or more as urban, while others only include the capital of a country or provincial capitals in their count. Largest agglomerations worldwideThough North America is the most urbanized continent, no U.S. city was among the top ten urban agglomerations worldwide in 2023. Tokyo-Yokohama in Japan was the largest urban area in the world that year, with 37.7 million inhabitants. New York ranked 13th, with 21.4 million inhabitants. Eight of the 10 most populous cities are located in Asia. ConnectivityIt may be hard to imagine how the reality will look in 2050, with 70 percent of the global population living in cities, but some statistics illustrate the ways urban living differs from suburban and rural living. American urbanites may lead more “connected” (i.e., internet-connected) lives than their rural and/or suburban counterparts. As of 2021, around 89 percent of people living in urban areas owned a smartphone. Internet usage was also higher in cities than in rural areas. On the other hand, rural areas always have, and always will, attract those who want to escape the rush of the city.

In 2023, Burundi and Niger had the highest share of their populations living in rural areas in Africa, with approximately 85 percent and 83 percent, respectively. Rwanda and Malawi followed, each with around 82 percent. In contrast, Gabon and Libya were the countries with lowest share of rural inhabitants on the continent.

South Africa ZA: People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data was reported at 96.686 % in 2015. This records an increase from the previous number of 96.657 % for 2014. South Africa ZA: People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data is updated yearly, averaging 96.466 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 96.686 % in 2015 and a record low of 96.246 % in 2000. South Africa ZA: People Using At Least Basic Drinking Water Services: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Health Statistics. The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted average;

South Africa ZA: People Using At Least Basic Sanitation Services: Urban: % of Urban Population data was reported at 75.550 % in 2015. This records an increase from the previous number of 75.220 % for 2014. South Africa ZA: People Using At Least Basic Sanitation Services: Urban: % of Urban Population data is updated yearly, averaging 73.080 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 75.550 % in 2015 and a record low of 70.611 % in 2000. South Africa ZA: People Using At Least Basic Sanitation Services: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Health Statistics. The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

This statistic depicts the share of urban population living in slums in the Southern Tier Middle East and North Africa region in 2014, by country. During the surveyed time period, ** percent of the Sudanese urban population dwelled in slums.

This statistic shows the degree of urbanization in the MENA countries in 2023. MENA stands for Middle East and North Africa. Urbanization is defined as the share of urban population in the total population. Kuwait was the most urbanized country in the region. In 2023, *** percent of the total population of the country lived in urban areas. On the other hand, Yemen had the lowest urban population in the region as of the same period.

Rapid urbanization in most African countries is increasing impervious surfaces, including building roofs, glass, concrete, asphalt, and paved roads. However, regionally consistent urban data are lacking to support large-scale research and assessment of impervious surface expansion and its impacts on urban heat islands, hydrology and flood risk, infectious disease risk, cropland and biodiversity loss, habitat fragmentation, and carbon sequestration. The West Africa Dataset of Impervious Surface Change (WADISC) uses all available Landsat data to map urban change in Ghana, Togo, Benin, and Nigeria. The approach combines machine learning algorithm with LandTrendr time series analysis to generate annual maps of urban impervious surface cover from 2001 - 2020. The overall mean absolute error was less than 6% cover and the root mean squared error was less than 10% cover, giving us confidence that the predictions can effectively distinguish areas with high versus low impervious cover. We further classified the impervious cover into developed (pixel value is greater than 20%) and undeveloped (pixel value is less than or equal to 20%) with 93% overall accuracy and approximately similar producer (79%) and user (80%) accuracies in developed areas. WADISC is available in two forms: 1) continuous impervious cover with values ranging from 0% – 100% and 2) Developed area classification with pixel values of 1 and 0, representing the presence and absence of developed area. These data can support consistent city, national and regional assessments and research on urbanization and its impacts.WADISC_Impervious.zip contains GeoTIFF files with continuous impervious cover data from 2001-2020.WADISC_Developed.zip contains GeoTIFF files with classified developed area data from 2001-2020.

South Africa ZA: Rural Population Growth data was reported at -0.235 % in 2017. This records a decrease from the previous number of -0.168 % for 2016. South Africa ZA: Rural Population Growth data is updated yearly, averaging 1.217 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.679 % in 1972 and a record low of -0.329 % in 2008. South Africa ZA: Rural Population Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

South Africa: Percent urban population: The latest value from 2024 is 69.3 percent, an increase from 68.82 percent in 2023. In comparison, the world average is 61.70 percent, based on data from 196 countries. Historically, the average for South Africa from 1960 to 2024 is 54.94 percent. The minimum value, 46.62 percent, was reached in 1960 while the maximum of 69.3 percent was recorded in 2024.

In 2023, over 68.82 percent of South Africa's total population lived in urban areas and cities. Urbanization defines the share of urban population from the total population of a country. Just like urbanization, the population density within the nation has risen, reaching 46 inhabitants per square kilometer, meaning more people are sharing less space. Many opportunities for work and leisure can be found in the urban locations of South Africa, and as such the five largest municipalities each now have over three million residents. Facing its economic strengths and drawbacks South Africa is a leading services destination, as it is one of the most industrialized countries in the continent of Africa. The majority of the country’s gross domestic product comes from the services sector, where more than 70 percent of the employed population works. Unemployment is seen as a critical indicator of the state of an economy, and for South Africa, a high rate of over 25 percent could indicate a need for a shift in economic policy. As of 2017, South Africa was one of the twenty countries with the highest rate of unemployment in the world.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

South Africa ZA: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 0.109 % in 2010. This records an increase from the previous number of 0.108 % for 2000. South Africa ZA: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 0.108 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.109 % in 2010 and a record low of 0.106 % in 1990. South Africa ZA: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics. The surveys have been undertaken at periodic intervals since 1999. The Afrobarometer's coverage has increased over time. Round 1 (1999-2001) initially covered 7 countires and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, and Round 4 (2008) 20 countries.The survey covered 34 countries in Round 5 (2011-2013), 36 countries in Round 6 (2014-2015), and 34 countries in Round 7 (2016-2018). Round 8 covered 34 African countries. The 34 countries covered in Round 8 (2019-2021) are:

Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Geographic coverage

The survey has national coverage in the following 34 African countries: Angola, Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d'Ivoire, eSwatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia and Zimbabwe.

Analysis unit

Households and individuals

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.

Kind of data

Sample survey data

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is 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 being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalised settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewers alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found in Section 5 of the Afrobarometer Round 5 Survey Manual

Mode of data collection

Face-to-face

Research instrument

The questionnaire for Round 3 addressed country-specific issues, but many of the same questions were asked across surveys. The survey instruments were not standardized across all countries and the following features should be noted:

• In the seven countries that originally formed the Southern Africa Barometer (SAB) - Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe - a standardized questionnaire was used, so question wording and response categories are the generally the same for all of these countries. The questionnaires in Mali and Tanzania were also essentially identical (in the original English version). Ghana, Uganda and Nigeria each had distinct questionnaires.

• This merged dataset combines, into a single variable, responses from across these different countries where either identical or very similar questions were used, or where conceptually equivalent questions can be found in at least nine of the different countries. For each variable, the exact question text from each of the countries or groups of countries ("SAB" refers to the Southern Africa Barometer countries) is listed.

• Response options also varied on some questions, and where applicable, these differences are also noted.

The average for 2022 based on 24 countries was 46 percent. The highest value was in South Africa: 80.5 percent and the lowest value was in the Central African Republic: 11.3 percent. The indicator is available from 2000 to 2022. Below is a chart for all countries where data are available.

The Consuming Urban Poverty (CUP) project - based at the University of Cape Town’s African Centre for Cities - sought to generate an understanding of the connections between poverty, governance, urban space, and food. CUP research focused on secondary cities in three countries: Kisumu, Kenya; Kitwe, Zambia; and Epworth, Zimbabwe.The research included three quantitative surveys: A retail mapping exercise, a food vendor and retailer survey, and a household survey. Over 2,200 households and 1,200 food retailers were interviewed (between April 2016 and February 2017) in the three secondary cities. In addition, nearly 4,500 traders were mapped as part of a retailer census in these cities. The surveys examined the nature of the urban food system and the experience of food poverty. Qualitative in-depth interviews were also carried out in households across the three cities. A qualitative reverse value chain assessment was also undertaken, which traced five key food items (aligned to the food groups of protein, staple, vegetable, traditional food item and snack food) from the point of consumption to origin (or a point where no further information was available) in each city.Urban areas in sub-Saharan Africa are growing rapidly. While there has been considerable attention paid to the challenges of African mega-cities, the experiences of smaller urban areas have been relatively neglected. Secondary cities, with populations of less than half a million, are absorbing two-thirds of all urban population growth in Africa. This project focuses on three such cities to build a clearer picture of the dynamics of poverty in these kinds of urban spaces and to provide information and insights which can address poverty reduction. Poverty cannot be understood or addressed by focusing on poor individuals or households alone. Rather it needs to be understood as having many intersecting drivers operating at a range of scales, from the individual, to the neighbourhood, to the city and beyond. Nor can it be understood or addressed by focusing on governance, infrastructure or economic growth, alone. The challenge of this project is to understand the dynamic connections between poverty, governance and urban spaces. We argue that the study of food is a powerful lens to understand these connections. As Carolyn Steel writes, "In order to understand cities properly, we need to look at them through food". The project therefore asks the central question: What does the urban food system in three secondary cities in Africa reveal about the dynamics of urban poverty and its governance, and what are the lessons for generic poverty reduction? There are significant gaps in knowledge about African urban growth and urban poverty. This project therefore consolidates existing survey and census data to understand patterns and trends of urbanization and poverty in the three case study countries and cities. Because there are data gaps, we will also use remote sensing to generate new data on the spread of urban areas. This information provides the basis for general statements to be made about urban poverty, and for poverty reduction strategies generated in the project to be assessed against a broader representation of poverty. The project turns its focus to food as a way to understand the connections between poverty, governance and urban space. It will conduct a survey in each of three cities to assess how many households, and what kinds of households and individuals, are unable to get enough safe and nutritious food. Poor nutrition is an important indicator and driver of poverty. Most work on food poverty has focused on the household scale alone. This project argues that if food poverty, and poverty more generally, is to be addressed, it will be necessary to take a broader view and look at the food system. The food system in these cities is shifting rapidly as the supermarket sector increases and the flows of food become more global. This project assesses these changes by mapping the food retail environment, interviewing key people involved in the food system and analyses policy in order to test the impact of a changing food system on food poverty, and what appropriate governance responses might be. The project therefore scans the globe for useful precedents in addressing urban poverty through strategic planning of, and interventions in the urban food system. Throughout the project the focus will be on working with local governments, NGOs and civil society organisations to generate local solutions that are adaptable to multiple contexts. The outputs from this project are designed to have both practical and academic impacts. Policy impact will be generated by policy briefs and city reports that support the workshops to be held with municipal officials and policy makers. These will be translated into popular media resources to raise public awareness. Reports addressing urbanization, poverty and governance at a wider scale will be produced. These will be disseminated at major urban events and included in university curricula. Peer-reviewed academic publications will be produced in order to influence academic debates. Two questionnaires were used in the survey, one for retailers and one for households. A retailer mapping questionnaire was used in the mapping of a census of retailers in the survey cities.

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