Actual value and historical data chart for Uganda Poverty Headcount Ratio At National Poverty Line Percent Of Population

Historical dataset showing Uganda poverty rate by year from 1989 to 2019.

Uganda Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 48.000 % in 2019. This records a decrease from the previous number of 48.600 % for 2016. Uganda Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 58.000 % from Dec 1989 (Median) to 2019, with 10 observations. The data reached an all-time high of 67.900 % in 1999 and a record low of 46.200 % in 2012. Uganda Poverty Headcount Ratio at Societal Poverty Lines: % of Population 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: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Uganda UG: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 21.400 % in 2016. This records an increase from the previous number of 19.700 % for 2012. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 32.450 % from Dec 1992 (Median) to 2016, with 8 observations. The data reached an all-time high of 56.400 % in 1992 and a record low of 19.700 % in 2012. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: % of Population 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: Poverty. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Uganda made enormous strides in improving household welfare between 1992/93 and 2002/03. Domestic policy brought about consistent, strong, broad-based growth reduced poverty by nearly 50 percent and increase household welfare, despite a decline in world prices for Uganda's key exports. Earnings rose and job growth was strong in the private sector. Mobility was high, allowing Ugandans at all levels of income to take advantage of opportunities, economic growth provided. Services to the poor also improved. Uganda's success early on in achieving virtually the highest net primary school enrollment ratios among low income countries in Africa for poor and vulnerable children, has reduced poverty in the early years of the decade and promises to pay solid dividends in the future. Challenges remain, however. Increases in income inequality and high fertility rates muted the effect of growth on poverty, and the negative impact of these trends on the poverty rate has increased. Poverty reduction was lowest among agricultural households, where the poor were concentrated. Uganda's health indicators remained unacceptably high and showed little improvement over the decade. Meeting these needs requires a continuation of successful policies combined with new approaches in areas such as smallholder agriculture, expansion of infrastructure to improve the environment for private sector growth, and ensuring that high quality health services are accessible to the rural poor, especially for women with unmet needs in family planning and maternal and child health services.

In 2025, over **** million people in Madagascar lived in extreme poverty (less than **** U.S. dollars a day), the highest number within East Africa. However, this accounts for ** percent of the overall population living below the poverty line in the country. Uganda and Malawi followed, with almost **** million and more than **** million people living in destitution, respectively.

Uganda UG: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data was reported at 41.600 % in 2016. This records an increase from the previous number of 35.900 % for 2012. Uganda UG: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population data is updated yearly, averaging 56.800 % from Dec 1989 (Median) to 2016, with 9 observations. The data reached an all-time high of 66.000 % in 1999 and a record low of 35.900 % in 2012. Uganda UG: Poverty Headcount Ratio at $1.90 a Day: 2011 PPP: % of Population 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: Poverty. Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The share of Uganda’s population that lives below the poverty line has fluctuated over the last seven years, greatly influenced by shocks that have tested the resilience of the people. The COVID-19 pandemic pushed both urban and rural residents into poverty. Inequality, which reflects the extent to which different population groups benefit from Gross Domestic Product (GDP) growth, and affects the transmission of growth into poverty reduction, remained largely unchanged over this period and may even have worsened in urban areas. The findings of this report show that previously identified patterns and drivers of Uganda’s poverty changes persisted well into 2020 – shaped by low productivity and high vulnerability. Identified inequality of economic opportunities and unequal accumulation of the human capital could hold back structural change in employment. Accelerating poverty reduction in such a setting requires a two-pronged strategy. While at the macroeconomic level, policies addressing growth fundamentals are important for reducing poverty, from a microeconomic perspective, the report’s analysis shows that two strategies will be crucial. The first strategy is to lift the productivity and incomes of poor households in both rural and urban areas. While tackling agricultural productivity and job creation are at the top of the agenda here, making mobile phone services more widely accessible and affordable is a potential opportunity. The second strategy is to strengthen people’s resilience to shocks, particularly in rural areas. To have an impact, policies in both these areas will have to address the inequality in opportunities analyzed in the report. This document provides an overview of key report findings and identifies priority actions.

Uganda UG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 69.800 % in 2016. This records an increase from the previous number of 67.400 % for 2012. Uganda UG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 80.000 % from Dec 1989 (Median) to 2016, with 9 observations. The data reached an all-time high of 86.600 % in 1999 and a record low of 67.400 % in 2012. Uganda UG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population 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: Poverty. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

In 2022, the international poverty (based on 2017 purchasing power parity (PPP)) and the lower-income poverty rate (3.65 U.S. dollars in 2017 PPP), was highest in Burundi within the East African region, with 83 percent and 96.6 percent, respectively. However, the upper middle-income poverty rate was highest in Somalia, at 98.8 percent.

Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data was reported at 22.400 % in 2012. This records a decrease from the previous number of 27.200 % for 2009. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data is updated yearly, averaging 37.400 % from Dec 1992 (Median) to 2012, with 7 observations. The data reached an all-time high of 60.300 % in 1992 and a record low of 22.400 % in 2012. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population 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: Poverty. Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data was reported at 9.600 % in 2012. This records an increase from the previous number of 9.100 % for 2009. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data is updated yearly, averaging 13.700 % from Dec 1992 (Median) to 2012, with 7 observations. The data reached an all-time high of 28.800 % in 1992 and a record low of 9.100 % in 2009. Uganda UG: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population 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: Poverty. Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

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.

Geographic coverage

National coverage

Analysis unit

Households, Individuals, Plots of land, Communities

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 (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.

Sampling deviation

Of the 17,495 individuals from wave 4 that were to be interviewed in the UNPS 2015/16, 16,748 (96%) were found and interviewed while 747 (4%) had attrited (dropped out). In addition, 2,498 individuals joined or re-joined the panel during the UNPS 2015/16. In total 3300 households were covered in the UNPS 2015/16.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The 2015/16 round of UNPS used a computerized system of data collection whereby field staff directly captured information using Ultra Mobile Personal Computers (UMPCs) during data collection. The UMPCs were loaded with a data entry application with in-built range and consistency checks to ensure good quality data. Field Team Leaders run checks on the data while still in the field thereafter electronically transmitting it to UBOS Headquarters for verification. Every team was facilitated with an internet modem, a generator and extra UMPC batteries to ensure uninterrupted power supply and internet connectivity while in the field.

Response rate

96 percent

Abstract

The overall objective of the UNPS Program is to collect high quality data on key outcome indicators such as poverty, service delivery, governance and employment among others; to monitor Government's development programmes like the NDP and the JAF among others on an annual basis. The specific objectives of the survey are: - To provide information required for monitoring the NDP and other development objectives like the JAF, MDGs as well as specific programs such as the National Agricultural Advisory Services (NAADS) among others. - To provide high quality nationally representative information on income dynamics at the household level as well as annual consumption expenditure estimates to monitor poverty in years between the Uganda National Household Surveys (UNHS) - To supply regular data on agriculture in order to characterize and monitor the performance of the agricultural sector.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual / Person
• Woman aged 15-49
• Community

Sampling procedure

The 2011/12 UNPS survey maintained the 2010/11 UNPS sample design whereby all households that were sampled for Wave II (2010/11) were tracked and re-interviewed in Wave III (2011/12). Out of the 7,400 households interviewed during the UNHS 2005/06, 3,123 households were selected for the panel surveys. As a result, the same sample was maintained in 2009/10, 2010/11 and 2011/12 round of surveys. During data collection, households or individuals that had permanently left the original households to known locations were tracked and interviewed. The new households formed are known as split-off households whereas the individuals are termed as movers.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2011/12 UNPS questionnaires comprised of the following survey instruments: 1) Household Questionnaire: Core and rotating modules 2) Agriculture Questionnaire (for the subset of UNPS households engaged in agricultural activities) 3) Price Questionnaire 4) Community / Facility Questionnaires for schools, health facilities and other facilities (potentially conducted on a rotating basis and not in every year of the UNPS)

Cleaning operations

The 2011/12 round of UNPS used a computerized system of data collection whereby field staff directly captured information using Ultra Mobile Personal Computers (UMPCs) during data collection. The UMPCs were loaded with a data entry application with in-built range and consistency checks to ensure good quality data. Field Team Leaders run checks on the data while still in the field thereafter electronically transmitting it to UBOS Headquarters for verification. Every team was facilitated with an internet modem, a generator and extra UMPC batteries to ensure uninterrupted power supply and internet connectivity while in the field.

Uganda Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 52.200 % in 2019. This records a decrease from the previous number of 58.000 % for 2016. Uganda Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 58.000 % from Dec 2012 (Median) to 2019, with 3 observations. The data reached an all-time high of 65.700 % in 2012 and a record low of 52.200 % in 2019. Uganda Multidimensional Poverty Headcount Ratio: World Bank: % of total population 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: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Human Poverty Index 2004

Abstract

The 2016/17 Uganda National Household Survey (UNHS) is the sixth in a series of national household surveys that Uganda Bureau of Statistics (UBOS) has undertaken. The survey collected information on socio-economic characteristics at both household and community levels. The main objective of the survey was to collect high quality data on demographic and socio-economic characteristics of households for monitoring Uganda’s development performance of key indicators in the various sectors. The 2016/17 UNHS comprises four (4) modules. Those are the Socio-Economic, Labour Force, Community, and Market price modules. The main findings are based on the four modules and include trends of several indicators on Education, Health, Household Expenditure and Poverty, Food security, Income and loans, Information and Communication Technology, Vulnerable Groups, Community Characteristics and Non-crop household enterprises, presented at national, rural-urban, regional and sub-regional levels. The survey collected much more information besides what has been included in the main findings. Therefore, UBOS calls upon all stakeholders to utilize the wealth of data collected and availed over the years to undertake in-depth empirical analysis so as to better inform future policy debate.

Geographic coverage

National coverage

Analysis unit

The UNHS 2016/17 had the following units of analysis: individuals, housheholds, and communities.

Universe

The survey covered all de jure household members (usual residents), all currently employed and unemployed persons aged 5 years and above, resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2016/17 UNHS sample was designed to allow for generation of separate estimates at the national level, for urban and rural areas and for the 15 sub-regions of Uganda. At the time of the survey there were only 112 districts. This number later increased to 122 districts. A two-stage stratified sampling design was used. At the first stage, Enumeration Areas (EAs) were grouped by districts of similar socio-economic characteristics and by rural-urban location. The EAs were then drawn using Probability Proportional to Size (PPS). At the second stage, households which are the ultimate sampling units were drawn using Systematic Random Sampling. A total of 1,750 EAs were selected from the 2014 National Population and Housing Census (NPHC) list of EAs which constituted the Sampling Frame. The EAs were then grouped into 15 sub-regions, taking into consideration the standard errors required for estimation of poverty indicators at sub-regions and the rural-urban domains. In addition to the sub-regions, the other sub-groups that were considered during the analysis of the 2016/17 UNHS include the Peace and Recovery Development Plan (PRDP) districts and Hard-to-reach areas such as the mountainous areas. The survey targeted to interview 10 households per EA, implying a total sample of 17,540 households. Prior to the main survey data collection, all the sampled EAs were updated by listing all the households within their boundaries.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The UNHS 2016/17 adminstered four questionnaires including: Socio-Economic, Labour Force, Market Prices, and Community. All questionnaires and modules are provided as external resources in this documentation.

Response rate

Out of the total 17,320 households selected for the 2016/17 UNHS sample, 15,672 households were successfully interviewed, giving a response rate of 91 percent. The response rate was higher in rural areas (93%) compared to urban areas (88%).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors usually result from mistakes made during data collection and capture and those include misunderstanding of the questions, either by the respondent or by the interviewer and by capture of wrong entries. Such errors were controlled through rigorous training of the data collectors and through field spot-checks undertaken by the supervisors at the different levels. On the other hand, sampling errors (SE) are evaluated statistically. The 2016/17 UNHS sample is one of the many possible samples that could have been selected from the same population using the same sampling design. Sampling errors are a measure of the variability between all possible samples that would yield different results from the selected sample. Sampling errors are usually measured in terms of the standard error for a particular statistic such as the mean, percentages, etc. The Tables in Appendix III present standard errors and Coefficients of Variations (CVs) for selected indicators at national, rural-urban and sub-regional levels.

Abstract

The Uganda National Panel Survey (UNPS) was carried out to collect high quality data on key outcome indicators such as poverty, service delivery, employment and to monitor government's development programs like the National Development Plan (NDP) on an annual basis. The 2010/11 survey collected information on socio-economic characteristics at household, individual and community levels.The UNPS 2010/11 was comprised of six modules: the Socio-Economic, Woman, Agriculture, Fisheries, Community and Market Price modules. The survey covered 3,200 households that were scientifically selected and followed for re-interview in 2009/10 and 2010/11.

The objectives of the survey were: 1) To provide information required for monitoring the NDP and other development objectives like the JAF, MDGs as well as specific programs such as the National Agricultural Advisory Services (NAADS) among others. 2) To provide high quality nationally representative information on income dynamics at the household level as well as annual consumption expenditure estimates to monitor poverty in years between Uganda National Household Surveys (UNHS). 3) To supply regular data on agriculture in order to characterize and monitor the performance of the agricultural sector.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2010/11 UNPS survey maintained the 2009/10 UNPS sample design where all the households that were sampled for Wave I (2009/10) were tracked and re-interviewed in Wave II (2010/11).

Out of the 7,400 households interviewed during the UNHS 2005/06, 3,200 households were selected for the UNPS and the same sample was maintained in both 2009/10 and 2010/11 Panel surveys. During data collection, the population of persons interviewed in Wave II was slightly higher than that of Wave I due to the following reasons:

1. About 20 households that had initially been missed in Wave I were found and successfully interviewed in Wave II.
2. Changes in household composition contributed to the increase in the number of persons that were added to the panel. Most importantly, if a household member split-off from his/her original household (e.g. children leaving home to set up their own household,or a couple separates), all the new households were included/ joined the panel. Inclusion of split-offs was the main way in which panel surveys, maintain sample representativeness over the years. The new households formed are known as Split-off households while the individuals are termed as Movers.

Mode of data collection

Face-to-face [f2f]

Research instrument

To suit its multiple objectives, the UNPS was comprised of a set of survey instruments. These were the following questionnaires: 1. Agriculture Questionnaire (administered to the subset of UNPS households engaged in agricultural activities) 2. Household Questionnaire 3. Community Questionnaire

Response rate

84%

This paper presents data collected in July 2016 to assess the consumption patterns and dietary quality among vulnerable urban consumers at the Base of Pyramid (BoP). The data was collected within the project ‘Making Value Chains Work for Food and Nutrition Security of Vulnerable Populations in East Africa’ which was funded by the German Federal Ministry for Economic Cooperation and Development (BMZ). The project was led by the Bioversity International and the International Center for Tropical Agriculture and implemented in partnership with KALRO, NARO, Goettingen University and UHOH. The project was under the CGIAR flagship program “Food Systems for Healthier Diets” under the Research Program on Agriculture for Nutrition and Health (A4NH) A cross-sectional survey was conducted to collect data with the goal of assessing critical and sensible ways in which market systems work to improve the consumption of more diverse, safe and nutrient-dense foods. The questionnaire had five sections. Section A captured the geographical location of the households and interview day details. Section B captured household demographic details. Section C focused on household nutritious porridge consumption and preferences. In Section D, household access to nutrition information was captured while Section E details household assets and their nominal values. The anonymized data is arranged into six files; 01Identifier16 file contains all the data from section A. Similarly, household demographic information is in file 02Demography16. 03Consumption16, 04Flourattributes16, 05Assets16 and 06Text16 contain household nutritious porridge consumption and sources of the flour, important porridge flour quality attributes, household assets and their values, and crosscutting general household level data respectively. Metodology:Data collection site The data was collected in Nairobi, Kenya and Kampala, Uganda. Nairobi is Kenya’s capital city. Projections by the Kenya Bureau of Statistics (KNBS) indicate that the county’s population will rise from 3.14 million recorded in the 2009 census to 5.96 million by 2022 with an inter-censual growth rate of 3.8 per cent (County Government of Nairobi, 2018). The city has the largest slum in East and Central Africa; Kibera slum, and others such as Kawangware, Mathare, Kangemi, Korogocho, Majengo, Kitui village and Kiambiu. Poverty levels are high in the city with the most affected groups being the unemployed youth, women, persons with disabilities, female and child-headed households, slum dwellers and the aged (County Government of Nairobi, 2018). Poor access to basic infrastructure is also a common characteristic of the many slums in Nairobi. On the other hand, Kampala is Uganda's administrative and commercial capital city with a population of approximately 1.2 million inhabitants (Robinah et al., 2013). Kampala is also a rapidly growing city and is home to Slums such as Bwaise, Katwe, Kisenyi, Kibuli, Katanga, Nabulabye, Naguru2 and Nsambya (Association of Physicians of Uganda, 2018). In Nairobi, Kibera, Embakasi, Mathare and Dagoreti slums were selected as the study site while Bwaise, Kawempe, Kamwokya and Kasubi parishes were the study areas in KampalaA multi-stage sampling strategy was used to select respondents. First, we used the national statistics (Emwanu et al., 2004; KNBS, 2015) and information from the administrative offices to identify four urban BoP locations with the highest poverty levels in each of the two cities. In Nairobi, the selected locations were Kibera, Embakasi, Mathare and Dagoreti while in Kampala data collection was done in Bwaise, Kawempe, Kamwokya and Kasubi parishes. Second, households from these locations were randomly selected, using a systematic random sampling technique. We interviewed a total of 600 households, 300 from Kenya and 300 from Uganda. Survey preparation involved several activities. First, survey tool development, design and programming into SurveyCTO. Second, enumerator recruitment and training. We selected enumerators from a pool of recent graduate applicants with sufficient experience in carrying out household surveys and a good knowledge of the two cities (Nairobi and Kampala). The selected enumerators were then intensively trained for 3 days (11th – 13th July 2016). The training covered each question in the questionnaire, the purpose of each question and a suitable means of handling each question. Enumerators were additionally trained on Computer Aided Personal Interview (CAPI) tools and using tablets in data collection. Prior to the actual fieldwork, the teams held a pretest of the survey in non-sampled villages in Nairobi and Kampala. Actual data collection took 15 days (16th – 30th July 2016) under the guidance of team leaders in collaboration with local authorities and village elders. During the survey, a research associate from the Bioversity International and the International Center for Tropical Agriculture checked for inconsistencies, patterns and outliers in the data on a daily basis and provided detailed feedback to data collection teams as a strategy for quality assurance. After the survey, we downloaded the raw data as Comma Separated Values (CSV) files. We later imported the CSV files into STATA for cleaning.