This statistic shows the biggest cities in Ethiopia in 2022. In 2022, approximately 3.86 million people lived in Adis Abeba, making it the biggest city in Ethiopia.

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

Abstract

The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is a nationwide survey with a nationally representative sample of 9,150 selected households. All women age 15-49 who were usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In the selected households, all children under age 5 were eligible for height and weight measurements. The survey was designed to produce reliable estimates of key indicators at the national level as well as for urban and rural areas and each of the 11 regions in Ethiopia.

The primary objective of the 2019 EMDHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the main objectives of the survey are: ▪ To collect high-quality data on contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; child nutrition; and other health issues relevant to achievement of the Sustainable Development Goals (SDGs) ▪ To collect information on health-related matters such as breastfeeding, maternal and child care (antenatal, delivery, and postnatal), children’s immunizations, and childhood diseases ▪ To assess the nutritional status of children under age 5 by measuring weight and height

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Health facility

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC) and conducted by the Central Statistical Agency (CSA). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The sampling frame contains information about EA location, type of residence (urban or rural), and estimated number of residential households.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2019 EMDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation wherein 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).

In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) and with independent selection in each sampling stratum. A household listing operation was carried out in all selected EAs from January through April 2019. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs for the 2019 EMDHS were large, with more than 300 households. To minimise the task of household listing, each large EA selected for the 2019 EMDHS was segmented. Only one segment was selected for the survey, with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2019 EMDHS cluster is either an EA or a segment of an EA.

In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women age 15-49 who were either permanent residents of the selected households or visitors who slept in the household the night before the survey were eligible to be interviewed. In all selected households, height and weight measurements were collected from children age 0-59 months, and women age 15-49 were interviewed using the Woman’s Questionnaire.

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019 EMDHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Anthropometry Questionnaire, (4) the Health Facility Questionnaire, and (5) the Fieldworker’s Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. They were shortened substantially to collect data on indicators of particular relevance to Ethiopia and donors to child health programmes.

Cleaning operations

All electronic data files were transferred via the secure internet file streaming system (IFSS) to the EPHI central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by EPHI staff members and an ICF consultant who took part in the main fieldwork training. They were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro System software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing, double data entry from both the anthropometry and health facility questionnaires, and data processing were initiated in April 2019 and completed in July 2019.

Response rate

A total of 9,150 households were selected for the sample, of which 8,794 were occupied. Of the occupied households, 8,663 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 9,012 eligible women were identified for individual interviews; interviews were completed with 8,885 women, yielding a response rate of 99%. Overall, there was little variation in response rates according to residence; however, rates were slightly higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling 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 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) to minimize this type of error, nonsampling 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 2019 EMDHS 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 among 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% 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 2019 EMDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances 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.

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

Abstract

The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is the second Mini Demographic and Health Survey conducted in Ethiopia. The Ethiopian Public Health Institute (EPHI) implemented the survey at the request of the Federal Ministry of Health (FMoH). Data collection took place from March 21, 2019, to June 28, 2019.

Financial support for the 2019 EMDHS was provided by the government of Ethiopia, the World Bank via the Ministry of Finance and Economic Development’s Enhancing Shared Prosperity through Equitable Services (ESPES) and Promoting Basic Services (PBS) projects, the United Nations Children’s Fund (UNICEF), and the United States Agency for International Development (USAID). ICF provided technical assistance through The DHS Program, which is funded by USAID and offers support and technical assistance for the implementation of population and health surveys in countries worldwide.

SURVEY OBJECTIVES The primary objective of the 2019 EMDHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the main objectives of the survey are: - To collect high-quality data on contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; child nutrition; and other health issues relevant to achievement of the Sustainable Development Goals (SDGs) - To collect information on health-related matters such as breastfeeding, maternal and child care (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - To assess the nutritional status of children under age 5 by measuring weight and height

Four full-scale DHS surveys were conducted in 2000, 2005, 2011, and 2016. The first Ethiopia Mini-DHS, or EMDHS, was conducted in 2014. The 2019 EMDHS provides valuable information on trends in key demographic and health indicators over time. The information collected through the 2019 EMDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country's population.

Geographic coverage

National coverage

Analysis unit

Households Women age 15-49 Children age 0-59 months

Universe

Household members Woman aged 15-49 years Children aged 0-59 months

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC) and conducted by the Central Statistical Agency (CSA). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The sampling frame contains information about EA location, type of residence (urban or rural), and estimated number of residential households.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2019 EMDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation wherein 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).

In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) and with independent selection in each sampling stratum. A household listing operation was carried out in all selected EAs from January through April 2019. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs for the 2019 EMDHS were large, with more than 300 households. To minimise the task of household listing, each large EA selected for the 2019 EMDHS was segmented. Only one segment was selected for the survey, with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2019 EMDHS cluster is either an EA or a segment of an EA.

In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women age 15-49 who were either permanent residents of the selected households or visitors who slept in the household the night before the survey were eligible to be interviewed. In all selected households, height and weight measurements were collected from children age 0-59 months, and women age 15-49 were interviewed using the Woman’s Questionnaire.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019 EMDHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Anthropometry Questionnaire, (4) the Health Facility Questionnaire, and (5) the Fieldworker’s Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. They were shortened substantially to collect data on indicators of particular relevance to Ethiopia and donors to child health programmes.

Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After the questionnaires were finalised in English, they were translated into Amarigna, Tigrigna, and Afaan Oromo.

The Household Questionnaire was used to list all of the usual members of and visitors to selected households. Basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, education, and relationship to the head of the household. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women who were eligible for individual interviews. The Household Questionnaire was also used to collect information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the dwelling unit, and ownership of various durable goods.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49. These women were asked questions on the following main topics: background characteristics, reproduction, contraception, pregnancy and postnatal care, child nutrition, childhood immunisations, and health facility information.

In the Anthropometry Questionnaire, height and weight measurements were recorded for eligible children age 0-59 months in all interviewed households.

The Health Facility Questionnaire was used to record vaccination information for all children without a vaccination card seen during the mother’s interview.

The Fieldworker’s Questionnaire collected background information about interviewers and other fieldworkers who participated in the 2019 EMDHS data collection.

Cleaning operations

All electronic data files were transferred via the secure internet file streaming system (IFSS) to the EPHI central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by EPHI staff members and an ICF consultant who took part in the main fieldwork training. They were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro System software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing, double data entry from both the anthropometry and health facility questionnaires, and data processing were initiated in April 2019 and completed in July 2019.

Response rate

A total of 9,150 households were selected for the sample, of which 8,794 were occupied. Of the occupied households, 8,663 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 9,012 eligible women were identified for individual interviews; interviews were completed with 8,885 women, yielding a response rate of 99%. Overall, there was little variation in response rates according to residence; however, rates were slightly higher in rural than in urban areas.

In 2023, 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 being particularly rapid in Burundi, Uganda, Niger, and Tanzania. In these countries, the urban population grew by over 4.2 percent in 2020 compared to the previous year. 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.

Abstract

The Water and Sanitation Sector of the World Bank is facing increasing demands for access and performance data. This is driven in part by a corporate focus on the Millennium Development Goals and the World Bank’s contribution to their achievement, and in part by a general need to better monitor the impact of sector interventions. The aim of this report is to propose a range of dedicated project level WSS indicators at household level and a survey methodology on how to obtain these indicators.

The goal of the household survey presented in this report is to obtain a better understanding of households' access to water, sanitation, and hygiene across a project area, and the safety and quality of the services provided from a household perspective. The methodology has been developed and tested simultaneously on urban households in Ethiopia and rural households in Peru.

The survey instrument has been developed taking the work of the Joint Monitoring Programme (JMP) as a starting point. Hence the questions on water and sanitation in the JMP-questionnaire form part of survey instrument presented in this report. However, whereas the JMP approach aims at few comparable indicators which can be measured as part of a general national household survey, the aim of the methodology described here is to obtain more detailed information on water, sanitation and hygiene in a geographically limited project area.

For the sample selection, a multiple stage cluster sampling methodology was proposed. When this procedure is followed carefully, a statistically representative sample is obtained which is crucial for the applicability of the results. The approach enables data collection with a balance between the precision of the estimates and practical implementation costs.

A subset of 73 towns in the Oromia region was selected as the target of the survey. These towns were selected for projects to improve the water and sanitation services and are generally characterised by a low level of services. A two-stage cluster sampling procedure was applied to select the sample. From a total of 73 towns, 24 towns were selected randomly with a probability proportionate to size (PPS). In the second stage of the sample selection, enumeration maps were used in order to obtain a sample of households that is spread across each town. A total of 1,013 households were interviewed.

The practical steps of survey administration included preparatory activities, testing, and data collection. Here we have placed emphasis on the training of the enumerators to ensure that they understand the task they are undertaking, and on quality control to ensure that the interviews are conducted in a professional manner which yields more accurate results.

A set of 28 indicators is proposed from which the most locally relevant may be selected as the basis on which the monitoring should be carried out. The results of the survey are reported by presenting the 28 selected indicators, their mean values, and the confidence interval for the mean value. The survey indicates that 46% (±7%) of the households have access to improved water services and 38% (±5%) of the households have access to an improved sanitation facility, according to the definitions adopted in this report. More than four out of every five households have soap, while few households (5%, ±3%) are observed to have adopted appropriate hand washing practices, (as gauged by the absence of one or more of the necessary preconditions for hand washing: soap, water and sink, bucket or equivalent.) At the same time 30% (±9%) of the respondents claim to wash their hands at critical times. This means that there is a discrepancy in the results depending on the method used to obtain the data; this may be due to biases in both indicators; the enumerators may not be probing sufficiently to see the hand washing practice of household members, and the respondents may respond with “expected” or “desirable” behaviour rather than actual behaviour when asked to state when they wash their hands. It is proposed to use both observation and questions, although most emphasis should be put on the results from the questions when interpreting the results.

Ten percent of the sample was back-checked after the end of the main data collection phase by having a separate group of enumerators visiting the interviewed households. The back-checking results include information about the two central indicators; access to improved drinking water and access to improved sanitation; and household size. The back-checking reveals a discrepancy between the results from the main data collection phase and the back-checking. This underlines that the backchecking should be used actively during the data collection in order to ensure early correction of quality flaws.

Geographic coverage

The survey was carried out among households in urban areas in the Oromia region. There are approximately 550 towns in this region, and 73 towns had been chosen as candidates for possible water and sanitation projects. A number of selection criteria provided the background for this selection and, generally, the selected towns have a low level of water and sanitation services while there is some potential for cost recovery. The towns vary in size between 2,000 and 45,000 inhabitants. Thus the population from which the sample was taken is the 73 towns and not the entire population of towns in Oromia. A sample of households from 24 towns was selected to represent this universe of 73 towns.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

1. Sample size

Through discussion with the regional planning office in Oromia and the World Bank it was decided to carry out the survey among households in urban areas in the Oromia region. There are approximately 550 towns in this region, and 73 towns had been chosen as candidates for possible water and sanitation projects. A number of selection criteria provided the background for this selection and, generally, the selected towns have a low level of water and sanitation services while there is some potential for cost recovery. The towns vary in size between 2,000 and 45,000 inhabitants.

A subset of 73 towns in the Oromia region was selected as the target of the survey. Households were selected in 23 of these 73 towns. A total of 1,013 households were interviewed. The population from which the sample was taken is thus the 73 towns and not the entire population of towns in Oromia.

1. Sample design

The two-stage cluster sampling procedure is proposed for household surveys on water and sanitation. The term cluster refers to a natural grouping within the population, such as a neighbourhood, town, district or other community, from which a sub-sample may be selected. This procedure is based on the selection of a certain number of these clusters, i.e. primary sampling units (PSUs) with a probability proportional to size. 73 towns were selected in the first stage as PSUs. The selection of towns with a probability proportional to size (PPS) is carried out by creating a cumulative list of town populations and selecting a systematic sample from a random start. Within each of these PSUs a fixed number of households or basic sampling units (BSUs) is selected. Each BSU in the population had an equal probability of being in the sample. Such a sampling procedure is said to be self-weighting and leads to simplified formulas for analysis.

1. Sample frame

A sample frame was defined for PSUs as well as BSUs. - For PSUs, the 73 towns in Oromia that were selected as candidates for water and sanitation improvement constituted the population from which the PSUs is selected. A simple sample frame with information about the population in these 73 towns in 2004 was available and applied as the basis for a PPS sample selection. - For BSUs (i.e. the selection of households), the ideal frame would be a list containing all households in each town that allows the analysts to choose a random selection from the list. But such a list did not exist. Enumeration maps from population censuses were thus used as the sample frame (enumeration maps are demarcated geographical areas, each of them consisting of approximately 200 households). The maps were from 1994 and it was therefore expected that, to some degree, they were inaccurate. In order to determine how inaccurate they were, a linear regression analysis was carried out with the number of inhabitants in the towns in 2004 as the response variable and the number of enumeration maps per town as the explanatory variable. As each enumeration map includes around 200 households, the total number of maps per town would indicate the total number of inhabitants. The regression analysis showed that the number of enumeration maps in 1994 is highly correlated with the number of inhabitants in 2004 (R2=0.98) and, more importantly, that there were no extreme observations (no standadized residual above 2). This means that the population size increased consistently in the towns over the period. The advantage of using enumeration maps is that they clearly limit a small geographical area from which it is easy to take a random selection of households. In addition, it is straightforward to select the maps randomly from each town in order to get a representative sample of geographical areas in each town.

1. Sample selection

• First stage - selection of towns (PSUs) Information about the population in the 73 towns in 2004 was applied as the basis for a sample selection based on probability proportionate to size (PPS). A total of 25 clusters were selected resulting in 24 towns, because two clusters were chosen from one large town (Waliso). The number of primary sampling units was decided on the basis of the available