The total population in Uganda was estimated at 50.0 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Uganda Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through integrating census, survey, satellite and GIS data sets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Uganda data available from WorldPop here.

United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. License : CC BY-4.0

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Uganda UG: Population: Growth data was reported at 3.260 % in 2017. This records a decrease from the previous number of 3.291 % for 2016. Uganda UG: Population: Growth data is updated yearly, averaging 3.287 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3.550 % in 1988 and a record low of 2.648 % in 1973. Uganda UG: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: The accommodation occupied by one household is the dwelling unit. - Households: A household is a group of persons who normally live and eat together, regardless of whether they are related. - Group quarters: Sometimes groups of people live together but cannot be said to belong to a household. Persons in hospitals, colleges, barracks and prisons are examples.

Universe

All persons who are in Uganda the night of the census, regardless of their nationality. Floating population refers to those who will not spend census night in households, institutions or hotels. They include persons who are travelling on census night, those in transit at airports or on ships or in railway stations. They include also beggars, vagrants and other homeless people who spend the night at bus parks, on the streets or similar places.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Uganda Bureau of Statistics

SAMPLE SIZE (person records): 1548460.

SAMPLE DESIGN: A sample of approximately 10% of the rural enumeration areas where a long questionnaire was administered to the households, while all urban areas were enumerated with a long questionnaire. Thus the data set consists of these two sets (LONG RURAL and URBAN). Use the weights that are record specific to be representative of the household population. Floating population refers to those who will not spend census night in households, institutions or hotels. They include persons who are travelling on census night, those in transit at airports or on ships or in railway stations. They include also beggars, vagrants and other homeless people who spend the night at bus parks, on the streets or similar places.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule A: short form and Schedule B: long form

The dataset results from WRI's calculation based on: 1. NATIONAL FOREST AUTHORITY (NFA). 1996. Land Cover GIS Database. Kampala, Uganda: Government of Uganda, NFA. 2. UGANDA BUREAU OF STATISTICS (UBOS). 2002b. 2002 Uganda Population and Housing Census GIS Database. Kampala, Uganda: Government of Uganda, UBOS. Data used in map 3 of ""Mapping a Better Future: How Spatial Analysis Can Benefit Wetlands and Reduce Poverty in Uganda."" from Wetlands Management Department, Ministry of Water and Environment, Uganda; Uganda Bureau of Statistics; International Livestock Research Institute; and World Resources Institute. 2009. Cautions Dataset is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, WRI cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. WRI makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. Citation Please see description.

The dataset provides information on the distribution of population by disagregatd down to Sub-County level as per the National Census conducted by the Uganda Bureau of Statistics (UBOS) in August 2014. By then Uganda had a total population of 34,856,813 people and was growing at a rate of 3.3% per annum.

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 countries and was later extended to 12 countries. Round 2 (2002-2004) surveyed citizens in 16 countries. Round 3 (2005-2006) 18 countries, Round 4 (2008) 20 countries, Round 5 (2011-2013) 34 countries, Round 6 (2014-2015) 36 countries, Round 7 (2016-2018) 34 countries, Round 8 (2019-2021), and Round 9 (2021-2023). The survey covers about 40 countries in Round 10.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

Citizens of Uganda who are 18 years and older

Kind of data

Sample survey data [ssd]

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 institutionalized 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 interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

Uganda - Sample size: 2,400 - Sample design: Nationally representative, random, clustered, stratified, multi-stage area probability sample - Stratification: Region and urban-rural location - Stages: PSUs (from strata), start points, households, respondents - PSU selection: Probability Proportionate to Population Size (PPPS) - Cluster size: 8 households per PSU - Household selection: Randomly selected start points, followed by walk pattern using 5/10 interval - Respondent selection: Gender quota filled by alternating interviews between men and women; respondents of appropriate gender listed, after which computer randomly selects individual - Weighting: Weighted to account for individual selection probabilities - Sampling frame: 2014 Uganda National Population and Housing Census; Uganda Bureau of Statistics (UBOS)

Mode of data collection

Face-to-face [f2f]

Research instrument

The Round 10 questionnaire has been developed by the Questionnaire Committee after reviewing the findings and feedback obtained in previous Rounds, and securing input on preferred new topics from a host of donors, analysts, and users of the data.

Response rate

Response rate was 80%.

Sampling error estimates

The sample size yields country-level results with a margin of error of +/-3 percentage points at a 95% confidence level.

Abstract

The UNPS aims at producing annual estimates in key policy areas; and providing a platform for experimenting with and assessing national policies and programs. Explicitly, the objectives of the UNPS include: 1. To provide information required for monitoring the National Development Strategy, of major programs such as National Agricultural Advisory Services (NAADS) and General Budget Support, and also to provide information for the compilation of the National Accounts (e.g. agricultural production); 2. To provide high quality nationally representative information on income dynamics at the household level as well as information on service delivery and consumption expenditure estimates annually; to monitor poverty and service outcomes in interim years of other national survey efforts, such as the Uganda National Household Survey (UNHS), Uganda Demographic and Health Survey (UDHS) and National Service Delivery Surveys (NSDS); 3. To provide a framework for low-cost experimentation with different policy interventions to e.g. reduce teacher absenteeism, improve ante-natal and post-natal care, and assess the effect of subsidies on agricultural inputs among others; 4. To provide a framework for policy oriented analysis and capacity building substantiated with the UGDR and support to other research which feed into the Annual Policy Implementation Review; and 5. To facilitate randomized impact evaluations of interventions whose effects cannot currently be readily assessed through the existing system of national household surveys.

Analysis unit

The study describes (including but not limited to): - Household - Individual - Parcel - Plot - Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The UNPS is carried out over a twelve-month period (a “wave”) on a nationally representative sample of households, for the purpose of accommodating the seasonality associated with the composition of and expenditures on consumption. The survey is conducted in two visits in order to better capture agricultural outcomes associated with the two cropping seasons of the country. The UNPS therefore interviews each household twice in a year, in visits approximately six months apart. In 2009/10, the UNPS set out to track and interview 3,123 households that were distributed over 322 Enumeration Areas (EAs), selected out of 783 EAs that had been visited during the Uganda National Household Survey (UNHS) in 2005/06. The distribution of the EAs covered by the 2009/10 UNPS was such that it included all 34 EAs in Kampala District, and 72 EAs (58 rural and 14 urban) in each of the other regions i.e. Central excluding Kampala, Eastern, Western and Northern which make up the strata. Within each stratum, the EAs were selected with equal probability with implicit stratification by urban/rural and district (in this order). However, the probabilities of selection for the rural portions of ten districts that had been oversampled by the UNHS 2005/06 were adjusted accordingly. Since most IDP (Internally Displaced People) camps in the Northern region are currently unoccupied, the EAs that constituted IDP camps were not part of the UNPS sample. This allocation allows for reliable estimates at the national, rural-urban and regional levels i.e. at level of strata representativeness which includes: (i) Kampala City, (ii) Other Urban Areas, (iii) Central Rural, (iv) Eastern Rural, (v) Western Rural, and (vi) Northern Rural. In the UNPS 2010/11, the concept of Clusters instead of EAs was introduced. A cluster represents a group of households that are within a particular geographical area up to parish level. This was done due to split-off households that fell outside the selected EAs but could still be reached and interviewed if they still resided within the same parish as the selected EA. Consequently, in each subsequent survey wave, a subset of individuals was selected for tracking. The UNPS is part of the long term Census and Household Survey Program hence questionnaires and the timing of data collection are coordinated with the current surveys and census implemented by UBOS. SAMPLE REFRESH Starting with the UNPS 2013/14 (Wave 4) fieldwork, one third of the initial UNPS sample was refreshed with the intention to balance the advantages and shortcomings of panel surveys. Each new household will be visited for three consecutive waves, while baseline households will have a longer history of five or six years, given the start time of the sample refresh. This same sample was used for the UNPS 2015/16 (Wave 5) Once a steady state is reached, each household will be visited for three consecutive years, and at any given time one third of the households will be new, one third will be visited for the second time, and one third for the third (and last) time. The total sample will never be too different from a representative cross-section of the country, yet two-thirds of it will be a panel with a background of a year or two. New households were identified using the updated sample frames developed by the UBOS in 2013 as part of the preparations for the 2014 Uganda Population and Housing Census.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The UNPS had six questionnaires namely: Household Questionnaire; Woman Questionnaire; Agriculture & Livestock Questionnaire; Fisheries Questionnaire; Community Questionnaire and Market Questionnaire. A module on Biological data collection was also administered in 2019/20. Each of these questionnaires is divided into a number of sections and the number of questions in each section varies accordingly. It should be noted that in 2013/14, 2015/16, 2018/19, and 2019/20, all questionnaires were administered using the CAPI software .

UG: Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: Total: % Cumulative data was reported at 8.145 % in 2012. This records an increase from the previous number of 2.957 % for 2010. UG: Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: Total: % Cumulative data is updated yearly, averaging 3.882 % from Dec 2002 (Median) to 2012, with 4 observations. The data reached an all-time high of 8.145 % in 2012 and a record low of 1.842 % in 2008. UG: Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: Total: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank: Education Statistics. The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.; ; UNESCO Institute for Statistics; ;

Abstract

The 2016 Uganda Demographic and Health Survey (2016 UDHS) was implemented by the Uganda Bureau of Statistics. The survey sample was designed to provide estimates of population and health indicators including fertility and child mortality rates for the country as a whole, for the urban and rural areas separately, and for each of the 15 regions in Uganda (South Central, North Central, Busoga, Kampala, Lango, Acholi, Tooro, Bunyoro, Bukedi, Bugisu, Karamoja, Teso, Kigezi, Ankole, and West Nile).

The primary objective of the 2016 UDHS project is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2016 UDHS collected information on: • Key demographic indicators, particularly fertility and under-5, adult, and maternal mortality rates • Direct and indirect factors that determine levels of and trends in fertility and child mortality • Contraceptive knowledge and practice • Key aspects of maternal and child health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery • Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of women, men, and children • Knowledge and attitudes of women and men about sexually transmitted infections (STIs) and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviours and condom use), and coverage of HIV testing and counselling (HTC) and other key HIV/AIDS programmes • Anaemia in women, men, and children • Malaria prevalence in children as a follow-up to the 2014-15 Uganda Malaria Indicator Survey • Vitamin A deficiency (VAD) in children • Key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • The extent of disability • Early childhood development • The extent of gender-based violence

The information collected through the 2016 UDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2016 UDHS is the frame of the Uganda National Population and Housing Census (NPHC), conducted in 2014; the sampling frame was provided by the Uganda Bureau of Statistics. The census frame is a complete list of all census enumeration areas (EAs) created for the 2014 NPHC. In Uganda, an EA is a geographic area that covers an average of 130 households. The sampling frame contains information about EA location, type of residence (urban or rural), and the estimated number of residential households.

The 2016 UDHS sample was stratified and selected in two stages. In the first stage, 697 EAs were selected from the 2014 Uganda NPHC: 162 EAs in urban areas and 535 in rural areas. One cluster from Acholi subregion was eliminated because of land disputes. Households constituted the second stage of sampling.

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

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All electronic data files for the 2016 UDHS were transferred via IFSS to the UBOS central office in Kampala, where they were stored on a password-protected computer. The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by four staff (two programmers and two data editors) who took part in the main fieldwork training. They were supervised by three senior staff from UBOS. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in August 2016 and completed in January 2017.

Response rate

A total of 20,791 households were selected for the sample, of which 19,938 were occupied. Of the occupied households, 19,588 were successfully interviewed, which yielded a response rate of 98%.

In the interviewed households, 19,088 eligible women were identified for individual interviews. Interviews were completed with 18,506 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 5,676 eligible men were identified and 5,336 were successfully interviewed, yielding a response rate of 94%. Response rates were higher in rural than in urban areas, with the ruralurban difference being more pronounced among men (95% and 90%, respectively) than among women (98% and 95%, respectively).

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 2016 Uganda Demographic and Health Survey (UDHS) to minimise 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 2016 UDHS 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 2016 UDHS 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 linearisation 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.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final 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 - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

Uganda UG: Population: Total data was reported at 42,862,958.000 Person in 2017. This records an increase from the previous number of 41,487,965.000 Person for 2016. Uganda UG: Population: Total data is updated yearly, averaging 16,554,311.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 42,862,958.000 Person in 2017 and a record low of 6,788,214.000 Person in 1960. Uganda UG: Population: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

Abstract

The objectives of the Smallholder Household Survey in Uganda were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Uganda according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

The CGAP smallholder household survey in Uganda is a nationally-representative survey with a target sample size of 3,000 smallholder households. The sample was designed to provide reliable survey estimates at the national level and for the following administrative four regions: Central, Eastern, Northern, and Western regions. The Central region includes central metro (i.e., four municipalities surrounding Kampala), the parishes in Kampala with poultry activity but it excludes Kampala city which is entirely urban.

Sampling Frame

The sampling frame for the smallholder household survey is the list of enumeration areas (EAs) created for the 2014 Uganda National Population and Housing Census. Uganda is divided into 112 districts with each district comprised of counties/municipalities. Each county/municipality consists of sub-counties/town councils with each of them being further divided into parishes/wards and villages/cells.

For the 2014 population census, each village and cell was further divided into EAs. Information on the number of agricultural households at the EA level will be available in December 2015, and thus not on time for the smallholder survey. As a result, the sample allocation of the survey was based on the distribution of households per region and urban and rural according to the 2014 Census.

Sample Allocation and Selection

In order to take non-response into account, the target sample size was increased to 3,158 households assuming a household non-response rate of 5% observed in similar national households. The total sample size was first allocated to the four regions proportionally to their number of households. Within each region, the resulting sample was then distributed to urban and rural areas proportionally to their size.

The sample for the smallholder survey is a stratified multistage sample. Stratification was achieved by separating each region into urban and rural areas. The urban/rural classification is based on the 2014 population census. Therefore, eight strata were created and the sample was selected independently in each stratum. Prior to the sample selection, the sampling frame was sorted by the nine agricultural zones called Zardi (Zonal Agriculture Research Development Institute).

In the first stage, 216 EAs were selected as primary sampling units with probability proportional to size, the size being the number of households in the EAs. A household listing operation was carried out in all selected EAs to identify smallholder households according to the definition used in the survey, and to provide a frame for the selection of smallholder households to be included in the sample.

In the second stage, 15 smallholder households were selected in each EA with equal probability. Due to rounding, this yielded a total of 3,240 smallholder households. In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviors and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Building on secondary research on the smallholder sector and discussions with stakeholders, the design process for the survey instrument began. This process involved defining the end goal of the research by: • Drawing from existing survey instruments; • Considering the objectives and needs of the project; • Accounting for stakeholder interests and feedback; • Learning from the ongoing financial diaries in country; and, • Building from a series of focus groups conducted early on in the study.

Using this foundation, a framework for the survey instrument was developed to share with stakeholders and capture all the necessary elements of a smallholder household. The framework consisted of five main subject areas: (i) demographics, (ii) household economics, (iii) agricultural practices, (iv) mobile phones, and (v) financial services.

In order to capture the complexity inside smallholder households, the smallholder household survey was divided into three questionnaires: the household questionnaire, the multiple respondent questionnaire and the single respondent questionnaire. In addition to English, the questionnaires were translated into nine local languages: Lugishu, Luganda, Ateso, Lugbara, Runyakole, Lutooro, Ngakaaramojong, Langi, and Acholi.

The household questionnaire collected information on: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head) • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires.
• Household assets and dwelling characteristics

Both the Multiple and Single Respondent questionnaires collected different information on: • Agricultural practices: farm information such as size, crop types, livestock, decision-making, farming associations and markets • Household economics: employment, income, expenses, shocks, borrowing and saving habits, and investments

In addition, the Single respondent questionnaire collected information on: • Mobile phones: attitudes toward phones, usage, access, ownership, desire and importance • Financial services: attitudes towards financial products and services such as banking and mobile money, including ownership, usage, access and importance.

Following the finalization of questionnaires, a script was developed to support data collection on mobile phones. The script was tested and validated before its use in the field.

Cleaning operations

During data collection, InterMedia received a weekly partial SPSS data file from the field which was analyzed for quality control and used to provide timely feedback to field staff while they were still on the ground. The partial data files were also used to check and validate the structure of the data file.

The full data file was also checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The user guide includes household and individual response rates for the CGAP smallholder household survey in Uganda.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Uganda UG: Birth Rate: Crude: per 1000 People data was reported at 42.144 Ratio in 2016. This records a decrease from the previous number of 42.631 Ratio for 2015. Uganda UG: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 48.974 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 50.082 Ratio in 1992 and a record low of 42.144 Ratio in 2016. Uganda UG: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This file contains data on Ugandan households from six nationwide surveys conducted between 1992 and 2013. Data is available on aggregate household consumption, earnings activities, location of the household, and characteristics of the household head. The file also contains cohort level data where household have been aggregated into cohorts for pseudo-panel analysis. National household surveys have become the standard source of data for analysis of poverty in developing countries. A major limitation of these surveys for Africa, in terms of the potential to analyse poverty dynamics, is that they are not a panel - different households are surveyed in each wave so they constitute repeated cross sections. It is therefore not possible to track the same households over time to investigate the drivers of poverty reduction. This creates challenges for analysis with endogenous variables, such as interactions between household size and poverty or migration, remittances and household income. The absence of a panel also limits analysis of determinants of household welfare over long periods. The strategy we propose to address this data restriction is to identify representative household types to construct pseudo panels making use of the repeated cross section household surveys (see the Case for Support). Analysis of the pseudo panel allows one to track similar households and complements household-level analysis for each survey. The project will develop methods for constructing pseudo-panels that can be applied, with suitable modifications for specific features of the surveys, in any country with three or more national household surveys. In principle, the methods are also applicable to census and Demographic and Health Survey data. Although the project focuses on Uganda (1992-2012 using eight existing surveys), the methods for constructing and analysing pseudo-panels can be applied to other African countries. Utilising established links with local research partners, hence largely 'off-budget', the pseudo-panel method will be applied to Ghana (1991-2013 using 6 surveys) and Tanzania (1991-2012 using 4 surveys).These three countries all have managed to roughly halve headcount poverty since the early 1990s. We use the repeated cross-section survey data to form a pseudo panel of 'representative' households by grouping individual households (the observational units) into cohorts on the basis of time invariant characteristics (location, gender and birth cohort of household head). The cohorts are then traced over time as they appear in successive surveys, forming a pseudo panel with 'lagged values'. As the cohort fixed effect is correlated with cohort (household) characteristics that are unobserved and not constant over time due to the changing membership of the cohorts in each survey, an errors-in-variables estimator is used to correct the cohort means as estimates of the unobservable population means. The lagged dependent variable is constructed from an auxiliary regression with an augmented instrumental variables estimator using time-invariant instruments. The pseudo panel therefore permits a long (20 years or more) analysis of determinants of household welfare and poverty reduction, with the potential to generate internal instruments for endogenous variables and to identify effects of policy changes (such as Universal Primary Education in Uganda). Data is taken from nationwide household surveys conducted by the Ugandan Bureau of Statistics.

Uganda Population Statistics by Sub-County (Admin 5) - Projected based on Uganda Bureau of Statistics (UBOS) 2002 population census.