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 demand for evidence based decision making has reached unprecedented levels today more than ever before. The level of data usage has extended not only to cover basic administrative data but also to include more detailed household level information. Household surveys therefore, have become an invaluable source of information for monitoring outcome and impact indicators of national and international development frameworks.

As a key contributor to the monitoring framework, Uganda Bureau of Statistics (UBOS) has conducted large-scale surveys since 1989. The surveys have had a nationwide coverage with varying core modules and objectives. The 2005/06 round of household surveys was yet another in a series conducted by UBOS. The last household survey was conducted in 2002/03 with a focus on labourforce and informal sector in addition to the standard Socio-economic module. This time round, the survey carries an agriculture module in addition to the Socio-economic module. The surveys primarily collect socio-economic data required for measurement of human development and monitoring social goals with special reference to the measurement of poverty under the Poverty Eradication Action Plan (PEAP) and Millennium Development Goals (MDGs).

The main objective of the survey was to collect high quality and timely data on demographic, social and economic characteristics of the household population for national and international development frameworks.

Specifically, the objectives were to: 1. Provide information on the selected economic characteristics of the population including their economic activity status among others. 2. Design and conduct a country-wide agricultural survey through the household approach and to prepare and provide estimates of area and production of major crops and other characteristics at national and regional levels. 3. Meet special data needs of users for the Ministries of Finance, Planning and Economic Development, Agriculture, Animal Industry and Fisheries, Health, Education and Sports among others, and other collaborating Institutions like Economic Policy Research Centre, together with donors and the NGO community so as to monitor the progress of their activities and interventions. 4. Generate and build social and economic indicators and monitor the progress made towards social and economic development goals of the country; and 5. Consolidate efforts being made in building a permanent national household survey capability at UBOS.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered a sample of household members in each district.

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design A two stage sampling design was used to draw the sample. At the first stage, Enumeration Areas (EAs) were drawn with Probability Proportional to Size (PPS), and at the second stage, households which are the Ultimate Sampling Units, were drawn using Simple Random Sampling (SRS).

The sample of EAs for the UNHS 2005/06 was selected using the Uganda Population and Housing Census Frame for 2002. Initially, a total of 600 Enumeration Areas (EAs) was selected. These EAs were allocated to each region on the basis of the population size of the region. However, in the Northern region, the number of EAs drawn was doubled. The extra EAs were to be held in reserve to allow for EA attrition due to insecurity.

After this sample was drawn, it was realized that the sample size in 10 districts needed to be increased to about 30 EAs in each district to have an adequate sample size for separate analysis. These extra EAs were selected using an inter-penetrating sampling method which led to drawing an extra 153 EAs. Moreover, because a considerable proportion of the population in the North was in Internally Displaced People (IDPs) camps, this was treated as a separate selection stratum and an additional sample of 30 EAs was drawn from the IDPs. Thus, a total of 783 EAs representing both the general household population and displaced population was selected for the UNHS 2005/06.

Sample Size The size required for the sample was determined by taking into consideration several factors, the three most important being: the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design. The UNHS 2005/06 covered a sample size of about 7,400 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five types of questionnaires were administered, namely; socio-economic survey questionnaire, agriculture questionnaire, community questionnaire, price questionnaire and crop harvest cards. The Socio-economic questionnaire collected information on household characteristics including education and literacy, the overall health status, health seeking behavior of household members, malaria, fever and disability, activity status of household members, wage employment, enterprise activities, transfers and household incomes, housing conditions assets, loans, household expenditure, welfare indicators and household shocks. The Agricultural module covered the household crop farming enterprise particulars with emphasis on land, crop area, inputs, outputs and other allied characteristics. The Community Survey questionnaire collected information about the community (LC1). The information related to community access to facilities, community services and other amenities, economic infrastructure, agriculture and markets, education and health infrastructure and agricultural technologies. The Price questioonaire was administered to provide standard equivalents of non standard units through weighing items sold in markets. It was used to collect the different local prices and the non standard units which in many cases are used in selling various items. A crop card was administered to all sampled households with an agricultural activity. Respondents were requested to record all harvests from own produce.

Cleaning operations

Double entry was done to take care of data entry errors. Interactive data cleaning and secondary editing was done. All these processes were done using CSPro ( Census Survey Processing Data Entry application).

To ensure good quality of data, a system of double entry was used. A manual system of editing questionnaires was set-up in June 2005 and two office editors were recruited to further assess the consistency of the data collected. A computer program (hot-deck scrutiny) for verification and validation was developed and operated during data processing.

Range and consistency checks were included in the data-entry program that was developed in CSPro. More intensive and thorough checks were carried out using MS-ACCESS by the processing team.

Sampling error estimates

The estimates were derived from a scientifically selected sample and analysis of survey data was undertaken at national, regional and rural-urban levels. Sampling Errors (SE) and Coefficients of Variations (CVs) of some of the variables have been presented in Appendices of the Socio-Economic Report and Agricultural Module Reports to show the precision levels.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized allowing comparison between and within countries over time.

The Education SDIs include teacher effort, teacher knowledge and ability, and the availability of key inputs (for example, textbooks, basic teaching equipment, and infrastructure such as blackboards and toilets). The indicators provide a snapshot of the learning environment and the key resources necessary for students to learn.

Uganda Service Delivery Indicators Education Survey was implemented between June 20th and August 7th, 2013 by the Economic Policy Research Centre in close coordination with the World Bank SDI team. Overall, 400 primary schools were visited; 2,197 primary three, four, and five teachers were assessed on English, mathematics, and pedagogy; 3,806 teachers of all grades were followed for absence rate. Also, although learning outcomes were not part of the indicators, 3,966 primary four pupils were assessed on English, mathematics, and non-verbal reasoning. It was crucial that the indicators be correlated with pupil learning outcomes because the SDI was geared towards capturing the drivers of learning outcomes at the school level.

Geographic coverage

National

Analysis unit

• Schools
• Teachers
• Students.

Universe

All primary schools.

Kind of data

Sample survey data [ssd]

Sampling procedure

After the total sample size and its allocation across regions were decided, the next step was to sample the actual schools that would be included in the final sample and the pupils and teachers to be assessed within each school. This was done using a two-stage sampling method. First, in each stratum, schools were chosen within the selected counties, and then, teachers and pupils were selected in a second stage within each selected school.

The schools were chosen using probability proportional to size (PPS), where size was the number of primary four pupils as provided by the 2012 EMIS database. As for the selection of the cluster, the use of PPS implied that each primary four pupil within a stratum had an equal probability for his/her school to be selected.

Finally, within each school, up to 10 primary four pupils and 10 teachers were selected. Pupils were randomly selected among the primary four pupil body, whereas for teachers there were two different procedures for measuring absence rate and assessing knowledge. For absence rate, 10 teachers were randomly selected in the teachers' roster and the whereabouts of those teachers was ascertained in a return surprise visit. For the assessment, however, all teachers who were currently teaching in primary four or taught primary three the previous school year were included in the sample. Then a random number of teachers in upper grades were included to top up the sample. These procedures implied that pupils across strata, as well as teachers across strata and within school (for assessment), did not all have the same probability of selection. It was, therefore, warranted to compute weights for reporting the survey results.

Detailed information on the sampling procedure is available in the attached report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Education Survey Questionnaire consists of six modules:

Module 1: School Information - Administered to the head of the school to collect information about school type, facilities, school governance, pupil numbers, and school hours. Includes direct observations of school infrastructure by enumerators.

Module 2a: Teacher Absence and Information - Administered to head teacher and individual teachers to obtain a list of all school teachers, to measure teacher absence and to collect information about teacher characteristics.

Module 2b: Teacher Absence and Information - Unannounced visit to the school to assess absence rate.

Module 3: School Finances - Administered to the head teacher to collect information about school finances (this data is unharmonized).

Module 4: Classroom Observation - An observation module to assess teaching activities and classroom conditions.

Module 5: Pupil Assessment - A test of pupils to have a measure of pupil learning outcomes in mathematics and language in grade four.

Module 6: Teacher Assessment - A test of teachers covering mathematics and language subject knowledge and teaching skills.

Cleaning operations

Data entry was done using CSPro; quality control was performed in Stata.

Sampling error estimates

For more information about the sampling process, refer to “Annex A: Sampling Strategy” in the Country Report attached as documentation.

Abstract

Household surveys have provided valuable information for the Poverty Eradication Action Plan (PEAP) and other development frameworks in Uganda. Monitoring the performance of the plan and outcome of these interventions is critical to the whole evaluation of the progress made and challenges that require remedies. Over the years, household surveys have mainly aimed at addressing data gaps and demands that characterized the pre-UBOS era. To a large extent, these have been addressed through the provision of baseline information on a number of indicators. However, there are still data gaps in the agriculture sector which is a key component of the country's economy and a key determinant of the performance of our economy. Reliable data has eluded this important sector of the economy and current efforts to provide up-to-date baseline data have not yielded any success. Given the importance of agricultural sector in the national economy, an agricultural module alongside the socioeconomic module was proposed as a core module for the planned Uganda National Household Survey 2005/06. The last time an agricultural module was undertaken as a module was in the UNHS 1999/2000. From that survey, a number of lessons were learned which now require careful considerations. A strategic decision will have to be made to decide on how to record frequently harvested crops like Bananas (Matooke), cassava and Sweet potatoes. In addition, estimates of areas were based on both the respondent's and interviewers estimate. This time round, area estimate will be measured using the Geographical Positioning System (GPS). Hence additional GPS Units have been procured to cater for all the interviewers. In addition, the experiences gained from the Pilot Census of Agriculture (PCA) would be an important resource in the preparation and design phase of the survey instruments. It should also be noted that with the exception of a few indicators, previous household surveys have provided baseline information without regular monitoring of the same indicators over time. Important government programmes need to be monitored regularly to guide policy makers and other users of the information. This calls for an inclusion of some indicators that have baseline data so that trends could be built to guide/inform future decisions.

Geographic coverage

The Uganda National Household Survey 2005/2006 covered all the districts in Uganda.

Analysis unit

The following were the units of analysis; - Household - Community

Universe

The survey covered a sample of household members in each district.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two stage sampling design was used to draw the sample. At the first stage, Enumeration Areas (EAs) were drawn with Probability Proportional to Size (PPS), and at the second stage, households which are the Ultimate Sampling Units, were drawn using Simple Random Sampling (SRS). The sample of EAs for the UNHS 2005/06 was selected using the Uganda Population and Housing Census Frame for 2002. Initially, a total of 600 Enumeration Areas (EAs) was selected. These EAs were allocated to each region on the basis of the population size of the region. However, in the Northern region, the number of EAs drawn was doubled. The extra EAs were to be held in reserve to allow for EA attrition due to insecurity. After this sample was drawn, it was realized that the sample size in 10 districts needed to be increased to about 30 EAs in each district to have an adequate sample size for separate analysis. These extra EAs were selected using an inter-penetrating sampling method which led to drawing an extra 153 EAs. Moreover, because a considerable proportion of the population in the North was in Internally Displaced People (IDPs) camps, this was treated as a separate selection stratum and an additional sample of 30 EAs was drawn from the IDPs. Thus, a total of 783 EAs representing both the general household population and displaced population was selected for the UNHS 2005/06.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five types of questionnaires were administered, namely; socio-economic survey questionnaire, agriculture questionnaire, community questionnaire, price questionnaire and crop harvest cards. The Socio-economic questionnaire collected information on household characteristics including education and literacy, the overall health status, health seeking behavior of household members, malaria, fever and disability, activity status of household members, wage employment, enterprise activities, transfers and household incomes, housing conditions assets, loans, household expenditure, welfare indicators and household shocks. The Agricultural module covered the household crop farming enterprise particulars with emphasis on land, crop area, inputs, outputs and other allied characteristics. The Community Survey questionnaire collected information about the community (LC1). The information related to community access to facilities, community services and other amenities, economic infrastructure, agriculture and markets, education and health infrastructure and agricultural technologies. The Price questioonaire was administered to provide standard equivalents of non standard units through weighing items sold in markets. It was used to collect the different local prices and the non standard units which in many cases are used in selling various items. A crop card was administered to all sampled households with an agricultural activity. Respondents were requested to record all harvests from own produce.

Cleaning operations

Double entry was done to take care of data entry errors. Interactive data cleaning and secondary editing was done. All these processes were done using CSPro ( Census Survey Processing Data Entry application). To ensure good quality of data, a system of double entry was used. A manual system of editing questionnaires was set-up in June 2005 and two office editors were recruited to further assess the consistency of the data collected. A computer program (hot-deck scrutiny) for verification and validation was developed and operated during data processing. Range and consistency checks were included in the data-entry program that was developed in CSPro. More intensive and thorough checks were carried out using MS-ACCESS by the processing team.

Sampling error estimates

The estimates were derived from a scientifically selected sample and analysis of survey data was undertaken at national, regional and rural-urban levels. Sampling Errors (SE) and Coefficients of Variations (CVs) of some of the variables have been presented in Appendices of the Socio-Economic Report and Agricultural Module Reports to show the precision levels.

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.

Abstract

The 2019/20 Uganda National Household Survey (UNHS) is the seventh 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 2019/20 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.

Sampling procedure

Sampling Design The Uganda National Household Survey 2019/20 (UNHS VI1 will be seventh survey of its kind in Uganda following the one implemented in 2019/2017. The survey calls for a nationally representative sample of 14480 households from 1448 sample clusters. It is designed to collect high quality and timely data on demographic, social and economic characteristics of the household population to monitor international and national development frameworks. The survey is designed to produce representative estimates for the poverty indicators for the country as a whole, for the urban and rural areas separately, for each of the 15 geo-regions. The definition of the geo-regions and the study domains are given in section 2. In addition to the geo-regions, the survey indicators will be produced for the following areas: The Island, The Greater Kampala areas, PRDP.

Sampling Frame The sampling frame used for UNHS VII is the frame for the Uganda Population and Housing Census which conducted on August 2014 (UPHC 2014). The sampling frame is a complete list of census Enumeration Areas (EA) created for the census covering the whole country, consisting of 78,692EAs (excluding Refugees, forests and forest reserves and institutional population). Currently in Uganda there are 128 districts, each districts is sub-divided into Sub County, and each sub country into parish, and each parish into villages and then Enumeration areas. The frame file contains the administrative belongings for each EA and its number of households at the time of the census operation. Each EA has also a designated residence type, urban or rural. According to 2014 Population and Housing Census, an EA was either a village or part of the village. EAs with less than 50 households were linked to others EAs by GIS section so that the primary sampling units are not very small. The allocation of clusters (EA) per sub-region will be relatively equal across domains. The allocation per domain will be well balanced and small changes in the allocation will not affect the precision of estimates. The 2200 selected households should result in about 2000 households successfully interviewed. The sample will be selected independently from each stratum using probability proportional to size. The country currently has 134districts and 12 Cities, these are grouped into the following 15 sub-regions:

1. Kampala: Kampala
2. Buganda South: Butambala, Gomba, Mpigi, Bukomansimbi, Kalangala, Kalungu, Lwengo, Lyantonde, Masaka, Rakai, Sembabule, Wakiso, Kyotera, Masaka City
3. Buganda North: Buikwe, Buvuma, Kayunga, Kiboga, Kyankwanzi, Luwero, Mityana, Mubende, Mukono, Nakaseke, Nakasongola, Kassanda
4. Busoga: Bugiri, Namutumba, Buyende, Iganga, Jinja, Kaliro, Kamuli, Luuka, Mayuge, Namayingo, Bugweri, Jinja City
5. Bukedi: Budaka, Butaleja, Kibuku, Pallisa, Toororo, Busia, Butebo
6. Egon: Bulambuli, Kapchorwa, Kween, Bududa, Manafwa, Mbale, Sironko, Bukwo, Namisindwa, Mbale City
7. Teso: Amuria, Bukedea, Katakwi, Kumi, Ngora, Soroti, Kaberamaido, Serere, Kapelebyong
8. Lango: Alebtong, Amolatar, Dokolo, Lira, Otuke, Apac, Kole, Oyam, Kwania, Lira City
9. Acholi: Agago, Amuru, Gulu, , Lamwo, Pader,Kitgum ,Nwoya, Gulu City
10. Karamoja: Abim, Amudat, Kaabong, Kotido, Moroto, Nakapiripirit, Napak, Nabilatuk
11. West Nile: Adjumani, Arua, Koboko, Maracha, Moyo, Nebbi, Yumbe, Zombo, Pakwach, Obongi, Terego, Arua City
12. Tooro: Bundibugyo, Kabarole, Kasese, NTooroko, Kyenjojo, Kamwenge, Kyegegwa, Bunyangabu, Fort portal City
13. Bunyoro: Buliisa, Hoima, , Kibaale, Kiryandongo, Masindi, Kikuube, Hoima City, KItagwenda
14. Ankole: Buhweju, Bushenyi, Ibanda, Isingiro, Kiruhura, Mbarara, Mitooma, Ntungamo, Rubirizi, Sheema, Mbarara City, Rwampara, Kazo
15. Kigezi: Kabale, Kisoro, Kanungu, Rukungiri, Rukiga

Mode of data collection

Data collection The survey collected data on food, drinks and beverage consumption using a seven-day recall period on the four major food sources22. Information was collected both in terms of expenditures and quantities, except for food consumed away from home only having the expenditure recorded. To ensure the accuracy of the information provided by respondents, data on food quantities was collected in local units of measurement. Conversion factors were then used to transform local units of measurement into standard metric units of quantity derived from the market survey conducted during the survey. Macronutrients and micronutrient values were mainly derived from the recent "Food Composition Table for Central and Eastern Uganda" (Harvest-Plus 2012)23.

Abstract

The main objective of the Uganda National Household Survey 2002/03 was to collect high quality and timely data on demographic and socio-economic characteristics of household population for monitoring development performance of the country.

Specifically, the survey aimed at: (a) Providing information on the economic characteristics of the population and its economic activity status i.e. the employment, unemployment and underemployment. (b) Generating data for calculating gross output, value added, and other economic indicators required for National Accounts purposes. (c) Integrating household socio-economic and community level surveys in the overall survey programme so as to provide an integrated data set. This will provide an understanding of the mechanisms and effects of various government programmes and policy measures on a comparative basis over time; (d) Meeting special data needs of users for the Ministries of Health; Education; Gender, Labour and Social Development and other collaborating Institutions, together with donors and the NGO community so as to monitor the progress of their activities and interventions (e) Generating and building social and economic indicators for monitoring the progress made towards social and economic development goals of the country

The UNHS 2002/03 was conducted in all districts except Pader. Some parts of Kitgum and Gulu districts were also not covered due to insecurity.

The survey included the following modules: · Socio-economic module · Labour force module · Informal sector · Community survey

Geographic coverage

The Uganda National Household Survey 2002/03 was conducted in all districts except Pader. Some parts of Kitgum and Gulu districts were also not covered due to insecurity.

Analysis unit

• Individual
• Household
• Community

Universe

The survey covered all resident population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The UNHS sample was drawn through a stratified two-stage sampling design. The Enumeration Area (EA) was used as the first stage sampling unit and the household as the second stage-sampling unit. The sampling frame used for selection of first stage units (fsus) was the list of EAs with the number of households based on the cartographic work of the 2002 Population and Housing Census. A total of 972 EAs (565 in rural and 407 in urban areas) were covered. In order to select the second stage units, which are the households, a listing exercise using listing schedules was done in all selected EAs.

The sample size was determined by taking into consideration several factors, the three most important being: the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design. UNHS 2002/03 covered a sample of 9,711 households.

Note: Details of the sampling design are given in Appendix III of the socio-economic survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Nine types of questionnaires were used during the survey namely; Household Listing questionnaire, the Socio-Economic questionnaire, the Labourforce questionnaire, the Community questionnaire, Forestry Enterprise questionnaire, Trade and Services Enterprise questionnaire, Manufacturing, Mining and Quarrying Enterprise questionnaire, Livestock Enterprise questionnaire and Hotel Enterprise questionnaire. The last five questionnaires were administered to small-scale establishments and household enterprises. These were developed in consultation with various stakeholders. The household listing questionnaire was used to list all houses and households in the selected Enumeration Areas (EAs). Finally, the community questionnaire was administered at community level (Local Council level I).

Cleaning operations

A manual system of editing questionnaires was set up and a set of scrutiny notes to guide in manual checking was developed. In addition, range and consistency checks were included in the data-entry program. More intensive and thorough checks were carried out using MS-ACCESS by the processing team. Besides the editing done before data entry, the validation checks inbuilt in the program and double data entry, additional in-depth data cleaning on sections relevant for basic poverty analysis was done. For instance, individual level files were linked together to ensure that the same individual code reported in different sections of the questionnaire and in other modules corresponded to the same individual. Data cleaning on the other sections was also done. Any inconsistencies, data entry errors etc found were corrected after checking the original questionnaires.

Response rate

The response rate for the Uganda National Household Survey 2002/2003 was approximately 97%. A total of 9711 households were interviewed out of the 10,000 households initially targeted. A total of 289 households could not be interviewed mainly due to insecurity.

Sampling error estimates

There are two types of errors possible in any estimate based on a sample survey – sampling and non-sampling errors.

Non-sampling errors can be attributed to many sources which include: definitional difficulties, differences in the interpretation of questions by the interviewers, inability or unwillingness to provide correct responses on part of the respondents, mistakes in coding or recording the data, et cetera. Nonsampling errors would also occur in a complete census.

On the other hand, sampling errors occur because observations are made only on a sample, and not the entire population. Thus the accuracy of survey results is determined by the joint effects of the sampling and nonsampling errors.

For a given indicator, the sampling error is usually measured by the standard error. The standard error of a survey estimate is a measure of the variation among the estimates from all possible samples, and is a measure of the precision with which an estimate from a particular sample approximates the results from all possible samples. The accuracy of a survey result de pends on both the sampling and nonsampling error measured by the standard error and the bias; and other types of nonsampling errors not measured by the standard error.

The standard errors of the rates presented in this appendix were computed using the SASÓ PROC SURVEYMEANS procedure. This procedure does not assume that the data was taken from a simple random sample, but rather from a more complex design. The SurveyMeans Procedure takes into account the effect of clustering and stratifying in the calculation of the variances and standard errors, using the Taylor expansion method to estimate these sampling errors.

The sampling errors are computed for selected variables considered to be of interest, but can be computed for all variables in the dataset. The sampling errors are presented for the country as a whole, for women and men where relevant, and for rural and urban areas and for each of the four regions: Central, East, West and North. For each variable the type of statistic (mean, sum, rate) are given as well as the standard error, the 95% confidence limits, and the coefficient of variation.

Generally the standard errors of most national estimates are small and within acceptable limits, but there is wider variability for the estimates of the subpopulations. For example for the Net Attendance Ration (NER), the standard error for the whole country is 6.5 percent, while for urban and rural areas it is 7.6 and 7.3 percent respectively. For more details about the estimates of sampling error can be found in Appendix IV of "UNHS 2002/2003 Report on the Socio-Economic Survey"

Abstract

The Uganda Bureau of Statistics (UBOS) has been carrying out an integrated household survey, popularly known as Uganda National Household Survey (UNHS) every other year since the late 1980s. Through the UNHS, Uganda has very rich household time series data covering over 13 years. The data have been the main source of statistical information for monitoring poverty levels, trends and related welfare issues.

The UNHS 2009/10 was undertaken from May 2009 to April 2010 and covered about 6800 households scientifically selected countrywide. The survey was comprehensive and had six modules, namely; Socio-economic, Labor Force, Informal Sector, Community, Price and Qualitative modules.

The main objective of the survey was to collect high quality and timely data on demographic, social and economic characteristics of the household population to inform/monitor international and national development frameworks. The specific objectives of the survey were to: 1. Provide information on selected economic characteristics of the population including their economic activity status among others. 2. Meet data needs of key users such as Ministry of Finance, Planning and Economic Development; Health; Education and Sports, etc.., and other collaborating Institutions like Economic Policy Research Centre (EPRC); the Development Partners as well as the NGO community. 3. Generate and build social and economic indicators and monitor the progress made towards social and economic development goals of the country; and 4. Strengthen efforts being made in building a permanent national household survey capability at UBOS.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design The UNHS 2009/10 sample was designed to allow reliable estimation of key indicators for the Uganda, rural-urban, and separately for ten sub regions. A two-stage stratified sampling design was used. At the first stage, Enumeration Areas (EAs) were grouped by districts and rural-urban location; then drawn using Probability Proportional to Size (PPS). At the second stage, households which are the Ultimate Sampling Units were drawn using Systematic Sampling.

A total of 712 EAs representing the general household population were selected using the Uganda Population and Housing Census Frame for 2002. These EAs were allocated to the 10 sub-regions with consideration of the rural and urban areas which constituted the main domains of the sample.

Sample Size When determining the required sample size, the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design were taken into consideration. The UNHS 2009/10 covered a sample size of 6800 households.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are five questionnaires for the UNHS namely: (i) Listing questionnaire (ii) socio-economic Questionnaire (iii) Labour Force survey questionnaire (iv) Informal Sector Questionnaires (v) Community Questionnaire

Note that the informal sector survey questionnaires comprise 5 sets according to activity namely: (1) Livestock, poultry, bee-keeping, and fishing (2) Forestry (3) Mining, quarrying, and manufacturing (4) Hotels, lodges, bars, restaurants and eating places (5) Trade and services

Cleaning operations

A system of double data entry was utilized to ensure good quality data. Questionnaires were manually edited by five office based editors who were recruited to ensure consistency of the data collected. A computer program (hot-deck scrutiny) for verification and validation was developed and operated during data processing. Range and consistency checks were included in the data-entry program. More intensive and thorough checks were also carried out using MS-ACCESS by the data processing team.

Sampling error estimates

Household survey findings are usually estimates based on a sample of households selected using appropriate sample designs. Estimates are affected by two types of errors; sampling and non-sampling errors. Non-Sampling errors result from wrong interpretation of results; mistakes in recording of responses, definitional problems, improper recording of data, etc and are mainly committed during the implementation of the survey.

Sampling errors, on the other hand, arise because observations are based on only one of the many samples that could have been selected from the same population using the same design and expected size. They are a measure of the variability between all possible samples. Sampling errors are usually measured using Standard Errors (SE). SE is the square root of the variance and can be used to calculate confidence intervals for the various estimates. In addition, sometimes it is appropriate to measure the relative errors of some of the variables and the Coefficient of Variation (CV) is one such measure. It is the quotient of the SE divided by the mean of the variable of interest.

The SE and CVs were computed using STATA software and they each take into account the multi-stage nature of the survey design. The results below indicate the SE and CVs computed for the selected variables in the report. The SEs and CVs are presented for national, regional and rural-urban levels.

Note: Detailed sampling error tables are available in the 2009-2010 UNHS final report.

Abstract

The 2018-19 Uganda Malaria Indicator Survey (UMIS) used a nationally representative sample of 320 clusters and about 8,960 households.. The survey is designed to provide estimates of key malaria indicators for the country as a whole, urban and rural areas separately, each of the 15 regions, and refugee settlements.

The primary objective of the 2018-19 UMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2018-19 UMIS collected information on vector control interventions such as mosquito nets and indoor residual spraying of insecticides, on intermittent preventive treatment of malaria in pregnant women, on care seeking and treatment of fever in children, and malaria knowledge, behaviour, and practices. Children less than age 5 were tested for anaemia and malaria infection.

The information collected through the 2018-19 UMIS is intended to assist policy makers 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

• Household
• Individual
• Children age 0-5
• Woman age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018-19 UMIS followed a two-stage sample design and was intended to allow estimates of key indicators for the following domains: ▪ National ▪ Urban and rural areas ▪ 15 regions ▪ Although they were not included as separate sampling domains, the overall sample size permitted estimates to be produced for the 14 ongoing indoor residual spraying (IRS) intervention districts: Bugiri, Kaberamaido, Koboko, Lira, Otuke, Serere, Tororo, Alebtong, Amolatar, Budaka, Butaleja, Dokolo, Namutumba, and Paliisa and 11 former IRS intervention districts Oyam, Kole, Nwoya, Amuru, Agago, Gulu, Kitgum, Pader, Omoro, Apac, and Lamwo. ▪ Refugee settlements in Adjumani, Arua, Isingiro, Kamwenge, Kiryandongo, Kyegegwa, Lamwo, Moyo, and Yumbe districts were included as a separate sampling domain.

The first stage of sampling involved selecting sample points (clusters) from the sampling frames; the nonrefugee areas and the refugee settlements used separate sampling frames. Enumeration areas (EAs) delineated for the 2014 National Population and Housing Census (NPHC) were used as the sampling frame for the non-refugee areas.A sampling frame developed for the National Refugees’ Survey, conducted by UBOS in collaboration with the World Bank and Office of the Prime Minister in early 2018, was used as the frame for the refugee settlement domain. A total of 320 clusters were selected with probability proportional to size from the EAs covered in the 2014 NPHC. Of these clusters, 84 were in urban areas and 236 in rural areas. Urban areas were oversampled within regions in order to produce robust estimates for that domain. A total of 22 clusters were selected with probability proportional to size from the EAs covered in the refugee frame.

The second stage of sampling involved systematic selection of households. For the non-refugee areas, a household listing operation was undertaken in all of the selected EAs in November and December 2018, and households to be included in the survey were randomly selected from these lists. In the selected clusters for the refugee settlements domain, listing was undertaken immediately before fieldwork in those clusters. Twenty-eight households were selected from each EA, for a total sample size of 8,878 households. Because of the approximately equal sample sizes in each domain, the sample was not selfweighting at the national level.

Note: See Appendix A of the final survey report for additional details on the sampling procedures.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires—the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire—were used for fieldwork in the 2018-19 UMIS. Core questionnaires available from the Roll Back Malaria (RBM) Monitoring and Evaluation Reference Group (MERG) were adapted to reflect the population and health issues relevant to Uganda. The modifications were decided upon at a series of meetings with various stakeholders from the NMCD and other government ministries and agencies, nongovernmental organisations, and international donors. The questionnaires were in English; UBOS arranged for translation into Luganda, Luo, Lugbara, Ateso, Runyankole/Rukiga, and Runyoro/Rutoro. The Household and Woman’s Questionnaires were programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey. The Biomarker Questionnaire was filled out on hard copy and entered into the CAPI system when complete.

A fourth questionnaire, the Fieldworker Questionnaire, was adapted from The DHS Program’s standard questionnaire. It was completed by all fieldworkers in the 2018-19 UMIS; its purpose was to collect basic background information on the people who collect data in the field.

Cleaning operations

All electronic data files for the 2018-19 UMIS were transferred via ICF’s 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 UBOS staff who took part in the main fieldwork training and were supervised by senior staff from UBOS. The Census and Survey Processing (CSPro) System software package was used for data editing. Secondary editing and data processing were completed in February 2019.

Response rate

A total of 8,878 households selected for the sample in the main survey, 8,448 were occupied at the time of fieldwork. Among the occupied households, 8,351 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 8,389 women were eligible for individual interview, and 8,231 were successfully interviewed, yielding a response rate of 98%. In the refugee settlements, the household response rate was almost 100%, and the response rate among women was 99%.

Abstract

The 2018-19 Uganda Malaria Indicator Survey (UMIS) was implemented by the National Malaria Control Division (NMCD) and Uganda Bureau of Statistics (UBOS). Data collection took place from 11 December 2018 to 31 January 2019. ICF provided technical assistance. The United States Agency for International Development (USAID) through the President’s Malaria Initiative (PMI), the United Kingdom Department for International Development (DFID), and the Government of Uganda with Global Fund support coordinated the successful implementation of the survey through technical or financial support.

The primary objective of the 2018-19 UMIS is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2018-19 UMIS collected information on vector control interventions such as mosquito nets and indoor residual spraying of insecticides, on intermittent preventive treatment of malaria in pregnant women, on care seeking and treatment of fever in children, and malaria knowledge, behaviour, and practices. Children less than age 5 were tested for anaemia and malaria infection.

The information collected through the 2018-19 UMIS is intended to assist policy makers 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

Household Women 15-49 years Children 0-59 months

Universe

the survey covered all household members, all women 15-49 years and all children 0-59 months

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018-19 UMIS followed a two-stage sample design and was intended to allow estimates of key indicators for the following domains: - National - Urban and rural areas - 15 regions - Although they were not included as separate sampling domains, the overall sample size permitted estimates to be produced for the 14 ongoing indoor residual spraying (IRS) intervention districts: Bugiri, Kaberamaido, Koboko, Lira, Otuke, Serere, Tororo, Alebtong, Amolatar, Budaka, Butaleja, Dokolo, Namutumba, and Paliisa and 11 former IRS intervention districts Oyam, Kole, Nwoya, Amuru, Agago, Gulu, Kitgum, Pader, Omoro, Apac, and Lamwo. - Refugee settlements in Adjumani, Arua, Isingiro, Kamwenge, Kiryandongo, Kyegegwa, Lamwo, Moyo, and Yumbe districts were included as a separate sampling domain.

The first stage of sampling involved selecting sample points (clusters) from the sampling frames; the nonrefugee areas and the refugee settlements used separate sampling frames. Enumeration areas (EAs) delineated for the 2014 National Population and Housing Census (NPHC) were used as the sampling frame for the non-refugee areas. A sampling frame developed for the National Refugees' Survey, conducted by UBOS in collaboration with the World Bank and Office of the Prime Minister in early 2018, was used as the frame for the refugee settlement domain. A total of 320 clusters were selected with probability proportional to size from the EAs covered in the 2014 NPHC. Of these clusters, 84 were in urban areas and 236 in rural areas. Urban areas were oversampled within regions in order to produce robust estimates for that domain. A total of 22 clusters were selected with probability proportional to size from he EAs covered in the refugee frame.

The second stage of sampling involved systematic selection of households. For the non-refugee areas, a household listing operation was undertaken in all of the selected EAs in November and December 2018, and households to be included in the survey were randomly selected from these lists. In the selected clusters for the refugee settlements domain, listing was undertaken immediately before fieldwork in those clusters. Twenty-eight households were selected from each EA, for a total sample size of 8,878 households. Because of the approximately equal sample sizes in each domain, the sample was not elfweighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.

Because a separate sampling frame was used to identify the clusters containing refugee settlements, they are tabulated separately from the national total results throughout the report.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. After a parent's or guardian's consent was obtained, children age 0-59 months were tested for anaemia and malaria infection

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Three questionnaires—the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire—were used for fieldwork in the 2018-19 UMIS. Core questionnaires available from the Roll Back Malaria (RBM) Monitoring and Evaluation Reference Group (MERG) were adapted to reflect the population and health issues relevant to Uganda. The modifications were decided upon at a series of meetings with various stakeholders from the NMCD and other government ministries and agencies, nongovernmental organisations, and international donors. The questionnaires were in English; UBOS arranged for translation into Luganda, Luo, Lugbara, Ateso, Runyankole/Rukiga, and Runyoro/Rutoro. The Household and Woman’s Questionnaires were programmed onto tablet computers, enabling use of computer-assisted personal interviewing (CAPI) for the survey. The Biomarker Questionnaire was filled out on hard copy and entered into the CAPI system when complete.

A fourth questionnaire, the Fieldworker Questionnaire, was adapted from The DHS Program’s standard questionnaire. It was completed by all fieldworkers in the 2018-19 UMIS; its purpose was to collect basic background information on the people who collect data in the field.

Cleaning operations

All electronic data files for the 2018-19 UMIS were transferred via ICF’s 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 UBOS staff who took part in the main fieldwork training and were supervised by senior staff from UBOS. The Census and Survey Processing (CSPro) System software package was used for data editing. Secondary editing and data processing were completed in February 2019.

Response rate

of the 8,878 households selected for the sample in the main survey, 8,448 were occupied at the time of fieldwork. Among the occupied households, 8,351 were successfully interviewed, yielding a total household response rate of 99%. In the interviewed households, 8,389 women were eligible for individual interview, and 8,231 were successfully interviewed, yielding a response rate of 98%. In the refugee settlements, the household response rate was almost 100%, and the response rate among women was 99%.

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.

Abstract

Employment of young people is good for sustainable development. Young people globally suffer higher unemployment levels and their jobs are characterised by lower pay and high insecurity than that of other age groups. Therefore, identifying the nature of employment challenge of the young people at country level is necessary for formulating evidence-based integrated policy interventions. The global jobs crisis has, further exacerbated the vulnerability of young people in terms of: i) higher unemployment, ii) lower quality jobs for those who find work, iii) greater labour market inequalities among different groups of young people, iv) longer and more insecure school work transitions, and v) increased detachment from the labour market. At the global level, these challenges are envisaged to be addressed through the 2015 UN Sustainable Development Goals (SDGs), and at the national level through the Vision 2040 and the Second National Development Plan (NDP II).

To fulfil these policy strategies, countries can rely on the creativity and innivation of young people to deliver. It is, thus, important for government to provide a leadership role and commitment in providing a conducive environment for gainful employment. This can be achieved through collaboration with agencies such as trade unions, employers’ organisations, international community and the active participation of donors in supporting efforts by young people to make a good start in the world of work.

The “School-to-Work Transition Survey” (SWTS) was designed by the International Labour Organisation (ILO) and implemented for the first time in Uganda by the Uganda Bureau of Statistics (UBOS) in 2013 as one such collaboration. The second SWTS, undertaken by UBOS in 2015, was sponsored by a partnership between the ILO and The MasterCard Foundation through the Work4Youth (W4Y) Project. The W4Y Project entailed partnership with statistical agencies and policy makers of 34 low and middle income countries to undertake the SWTS and assist governments and the social partners in the use of the data for effective policy design and implementation.

All stakeholders including Policy makers, Academia, Civil Society Orgnaisations and the general public can use the results of SWTS to design and implement integrated policies in response to employment challenges faced by young people.

Geographic coverage

National coverage

Analysis unit

The units of analysis for the SWTS 2015 were: individuals, households.

Universe

The survey covered all de jure household members (usual residents), and all youth aged 15-30 years resident in the household

Kind of data

Sample survey data [ssd]

Sampling procedure

The SWTS sample was designed to allow reliable estimation of key indicators for Uganda and rural-urban. A two-stage stratified sampling design was used. At the first stage, Enumeration Areas (EAs) were grouped by rural-urban location, then drawn using Probability Proportional to Size (PPS). A total of 200 EAs (160 rural and 40 urban) were selected using the 2014 Uganda Population and Housing Census Mapping Frame. For the 200 PSUs (EAs) that were selected from the 2014 PHC sampling frame, a household listing process was carried out to update the number of households in these EAs. At the second stage, 15 households per EA, which were the Ultimate Sampling Units, were drawn using Systematic Sampling. This gave a total sample size of 3,000 households. When determining the required sample size, the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design were taken into consideration. Basic information was gathered from all persons within the sampled households and the youth aged 15-30 years were filtered out for administration of the detailed questions.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard ILO SWTS questionnaire developed in 2013 was adapted to the national context based on the consultative process between the ILO and UBOS. The questionnaire was detailed in nature and collected information on personal information, family and household information, formal education/ training, activity history, working criteria, activities, and non working youth. A pre-test exercise was carried out before the finalization of the questionnaire.

Response rate

The actual fully covered sample for the SWTS was 2,712 households, with a total response rate of 90 percent. The response rate was slightly higher in rural areas (91 percent compared to urban areas (89 percent).

The individual SWTS questionnaire targeted all persons aged 15-30 years. A total of 3,198 individuals aged 15-30 years were found from the responding households. Completion of the individual interviews was successful with 3,049 individuals yielding an individual response rate (complete interview) of 95 percent with no marked differences observed by residence.

Data appraisal

The estimates were derived from a scientifically selected sample and analysis of survey data was undertaken at national and rural-urban levels. In a few cases, regional estimates have been provided. The Coefficients of Variation (CVs) of all indicators presented were low (about 10 or less). During the analysis, variables with at least 30 valid responses were deemed reliable enough to be presented given that the CVs were good. Consequently, some variables with fewer observations were merged into related groups to ensure that reliability is maintained.

Abstract

Since 1995, the National Malaria Control Programme (NMCP) and its partners have been implementing and scaling up malaria interventions in all parts of the country. To determine the progress made in malaria control and prevention in Uganda, the Uganda Malaria Indicator Survey (UMIS) was implemented in 2009 and again in 2014-15 to provide data on key malaria indicators including mosquito net ownership and use, as well as prompt treatment using ACT.

The main objective of the UMIS is to obtain population-based estimates on malaria indicators including the prevalence of malaria and anaemia to inform strategic planning and programme evaluation. Specific objectives are: 1. To obtain estimates of the magnitude and distribution of anaemia and malaria parasitemia among children age 0-59 months 2. To estimate core malaria programme coverage indicators • Measure the extent of ownership and use of mosquito bed nets • Assess coverage of the intermittent preventive treatment programme for pregnant women • Identify practices used to treat malaria among children under age 5 and the use of specific antimalarial medications • Assess percentage of children under five with fever for whom advice or treatment was sought • Determine the species of plasmodium parasite most prevalent in children age 0-59 months 3. To measure indicators of knowledge, attitudes, and behaviour related to malaria control 4. To determine the factors associated with malaria parasitemia and anaemia

Geographic coverage

National

Analysis unit

• Household
• Children age 0-5
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) was designed to provide most of the key malaria indicators for the country as a whole, for urban and rural areas, and for 10 survey regions.

In addition, three study domains based on malaria endemicity were created to provide selected malaria indicators addressing NMCP/MOH programmatic needs: 1) to evaluate the effect of interventions such as indoor residual spraying (IRS) in the 10 districts in the north, 2) to provide baseline indicators for the 14 districts planned for future IRS programmes, and 3) provide estimates separately for high altitude areas with low malaria burden. The three study domains are arranged as follows: Domain 1: ten (10) districts in which IRS programmes are currently implemented; Domain 2: fourteen (14) districts planned for future IRS programmes (to provide baseline estimates); Domain 3: ten (10) high-altitude districts (low malaria burden areas).

Apart from the three study domains above, the region of Karamoja was over-sampled in order to be comparable to a DHS region, and the urban areas of Wakiso and Mukono districts, together with Kampala, were combined to form a special 'Greater Kampala' zone.

Each of the 10 regions and the 3 study domains comprise multiple administrative districts that share a similar malaria burden or have specific malaria prevention efforts. The capital city, Kampala, comprises its own district and is entirely urban.

The sampling frame used for the 2014-15 UMIS was the preparatory frame for the Uganda Population and Housing Census, which was conducted in August 2014. Provided by the Uganda Bureau of Statistics (UBOS), the sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks, and prisons.

The 2014-15 UMIS sample was selected using a stratified two-stage cluster design consisting of 210 clusters, with 44 in urban areas and 166 in rural areas. In the first stage, 20 sampling strata were created and clusters were selected independently from each stratum by a probability-proportional-to-size selection. In the selected clusters, a complete listing of households and a mapping exercise was conducted from 25 October to 20 November 2014, with the resulting list of households serving as the sampling frame for the selection of households in the second stage.

In the second stage of the selection process, 28 households were selected in each cluster by equal probability systematic sampling. Because of the nonproportional allocation of the sample to the different regions and study domains, the sample is not self-weighting. Weighting factors have been added to the data file so that the results will be representative at the national and regional level as well as the survey domain level.

All women age 15-49 who were either permanent residents of the households in the 2014-15 UMIS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In addition, all children age 0-59 months who were either permanent residents of the sampled households or visitors present in the households on the night before the survey were eligible to be tested for malaria and anaemia.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 UMIS used two questionnaires: a Household Questionnaire and a Woman’s Questionnaire for women age 15-49 in the selected households. Both of these instruments were based on the model Malaria Indicator Survey questionnaires developed by the Roll Back Malaria Monitoring and Evaluation Research Group, as well as other questionnaires from previous surveys conducted in Uganda, including the 2009 UMIS. The Technical Working Group organised stakeholders’ meetings in Kampala to review the draft questionnaires. Stakeholders comprised a range of potential users, including government institutions, nongovernmental organisations, and interested donor groups. The questionnaires were translated from English into six local languages (Ateso/Karamajong, Luganda, Lugbara, Luo, Runyankole/Rukiga, and Runyoro/Rutoro).

The Household Questionnaire captured data on all usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women who were eligible for the individual interview and children eligible for anaemia and malaria testing. The Household Questionnaire was also used to collect responses on indicators of ownership and use of mosquito bed nets. In addition, the Household Questionnaire collected data on housing conditions and assets to calculate the measures of household wealth.

The Woman’s Questionnaire was used to collect data from women age 15-49 years, including: background characteristics (age, education, etc.); reproductive history (number of births, survival of births, etc.); current pregnancy status, intermittent preventive treatment for malaria during recent pregnancies; and antimalarial treatment for children under five with recent fever). It also collected information on knowledge about malaria.

Cleaning operations

All questionnaires for the 2014-15 UMIS were returned to the data processing centre at the UBOS headquarters in Kampala. Activities performed included office editing, data entry, and editing of computeridentified inconsistencies. The data were processed by a team consisting of one data entry supervisor, one assistant supervisor, 24 data entry operators, and 7 staff who performed tasks related to questionnaire administration, office editing, and secondary editing. Data entry and editing were accomplished using CSPro software. The process of office editing and data processing was initiated in January 2015 and completed in mid- February 2015.

Response rate

A total of 5,802 households were selected for the sample, of which 5,494 were occupied. Of the occupied households, 5,345 were successfully interviewed, yielding a response rate of 97 percent. The response rate among households in rural areas was slightly higher (98 percent) than the response rate in urban areas (96 percent).

In the interviewed households, 5,494 women were identified as eligible for the individual interview; interviews were completed with 5,322 women, yielding a response rate of 97 percent. The eligible women’s response rate does not differ by urban or rural residence. The principal reason for non-response among eligible women was failure to find individuals at home despite repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) 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 selected 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 2014-15 Uganda Malaria Indicator Survey (2014-15 UMIS) 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 2014-15 UMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a

Uganda UG: GDP: % of GDP: Gross Value Added: Services data was reported at 47.117 % in 2017. This records a decrease from the previous number of 47.573 % for 2016. Uganda UG: GDP: % of GDP: Gross Value Added: Services data is updated yearly, averaging 33.398 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 52.032 % in 2004 and a record low of 18.814 % in 1977. Uganda UG: GDP: % of GDP: Gross Value Added: Services 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: Gross Domestic Product: Share of GDP. Services correspond to ISIC divisions 50-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average;

Abstract

The AAS is an integrated modular survey aiming to provide high quality and timely data on the performance of the Ugandan agricultural sector, as well as core indicators on crop and livestock for better agricultural policy making.

Data collection for the AAS is implemented in two waves, corresponding to the first (January-June) and second (July-December) seasons of the Ugandan agricultural year. For each season, households in the survey's sample are interviewed twice, during the Post-Planting (PP) period and the post-harvesting (PH) period. This results in a total of four visits during the agricultural year. For what concerns the AAS 2018, due to a change in the methodology and questionnaire in between seasons, data collected during the first and second season are not perfectly comparable and have been treated separately. Hence, this DDI only refers to microdata collected during the second season of 2018.

Among information collected with the AAS there is data on: - The use of agricultural land along with the health and quality of soils in Uganda; - The quantity and value of agricultural production; - The access to extension services, market information and agricultural facility; - Food security of agricultural households; - Livestock keeping and animal products production; - The socio-demographic characteristics of agricultural household members.

The collected data is used to produce a set of tables and indicators for tracking and evaluating the impacts of government and development programs on agriculture, and to compute SDG and CAADP indicators related to food and agriculture. For the main findings from the AAS 2018, see the Executive Summary of the AAS 2018 Report.

Geographic coverage

Regional Coverage

Analysis unit

Households

Universe

Agricultural households (i.e. agricultural holdings in the household sector)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the AAS 2018 was constituted by a complete list of census enumeration areas (EAs) covering the entire national territory of Uganda, for a total of 80183 EAs. An EA represents the smallest ground area portion, mapped with definite boundaries. EAs should not be intended as administrative area categories, but only as ground area portions defined to facilitate the selection of the sample and ease data collection activities. As of 2014, Uganda is divided into 112 administrative districts. In each district, the following hierarchical administrative division is in place: 1) County, 2) Sub county, 3) Parish, 4) Village, 5) Local council area. The frame file contains the administrative affiliation for each EA and number of households at the time of the census. Each EA has also a designated residence type: urban or rural.

The sampling design adopted is a two-stage sampling design. In order to increase the efficiency of the sampling design for the AAS, the sampling frame is divided into 10 Zonal Agricultural Research and Development Institutes (ZARDIs). At the first stage of selection, a sample of EAs (Primary Sampling Units) was drawn. At the second stage, a sample of agricultural households in the selected EAs was drawn (Secondary Sampling Units). The determination of the required number of EAs is based on the approach of Probabilities Proportional to Size (PPS), using the systematic sampling algorithm. The measure of size to be used in selecting the sample is the number of agricultural households resulting from the 2014 Population and Households Census (PHC). The employed sampling procedure led to the production of representative estimates at the region, sub region, and zardi level. Hence, the zardi is the maximum level of geographical disaggregation for which representative estimates can be computed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AAS 2018 was conducted using two main questionnaires i.e. the Post-Planting, and Post-harvesting questionnaires. For each season, agricultural households are interviewed twice: during the post-planting and the post-harvesting visit.

The questionnaire used during the post-planting season is called "Form 4 - Crop Area Module" and is organized as follows:

• Section 4.1: Cover Page;
• Section 4.2: Household Roster;
• Section 4.3: Enterprise Identification;
• Section 4.4: Parcel Roster;
• Section 4.5: Plot Roster;
• Section 4.6: Crop Roster.

The questionnaire used for the post-harvesting visit is called "Form 52- Crop Production, Household and Holding Characteristics Module" and is organized as follows: - Section 5.1: Cover Page; - Section 5.2: Household Roster; - Section 5.3: Production and Dispositions of Crops; - Section 5.4: Agricultural Inputs; - Section 5.5: Production Activities and their Costs; - Section 5.6: Labour Input on the Holding; - Section 5.7.1: Cattle and Pack Animals; - Section 5.7.2: Small Ruminants; - Section 5.7.3: Poultry; - Section 5.8.1: Cattle and Pack Animals: Input Costs; - Section 5.8.2: Small Ruminants: Input Costs; - Section 5.8.3: Poultry: Input Costs; - Section 5.9.1: Cattle Meat; - Section 5.9.2: Small Ruminants Meat; - Section 5.9.3: Poultry Meat; - Section 5.9.4: Cattle Milk; - Section 5.9.5: Small Ruminants Milk; - Section 5.9.6: Eggs Production; - Section 5.9.7: Other Animal Products; - Section 5.10: Sources of Agricultural Information; - Section 5.11: Access to facilities; - Section 5.12: Transport Means; - Section 5.13: Storage Facilities; - Section 5.14: Access to Credit; - Section 5.15: Fixed Costs; - Section 5.16: Shocks and Food Security; - Section 5.17: Extension Services; - Section 5.18: Land Disputes.

Cleaning operations

All data cleaning and editing operations were performed using the statistical software Stata.

Response rate

The response rate was about the 86% during the post-planting visit, and the 83% during the post-harvesting visit.

Sampling error estimates

The accuracy of a survey results depends on both sampling and non-sampling errors. The AAS 2018 had a large enough and representative sample hence limiting errors due to sampling. On the other hand, the non-sampling errors usually resulting from errors occurring during data collection, were controlled thorough training of the data collectors, field supervision by the headquarter team, and a well-developed CAPI program. The standard errors and Coefficients of Variations (CVs) for selected indicators at national, ZARDI & sub-regional levels are presented in an Appendix of the final Survey Report.

Abstract

The main objective of the survey is to collect high quality and timely data on demographic and socio-economic characteristics of the household population for monitoring economic performance of the country. Specifically, the survey aims; (a) To provide information on the economic characteristics of the population, including their economic activity status among others,

(b) To plan, design and conduct a country-wide Informal Sector Survey to feed into the development of the National Employment Policy,

(c) To meet special data needs of key data users namely Ministries, Departments and Agencies, other collaborating Institutions like Economic Policy Research Centre, together with donors and the NGO community so as to monitor the progress of their activities and interventions,

(d) To generate and build social and economic indicators and to monitor the progress made towards socio-economic development goals of the country; and

(e) To consolidate efforts being made in building a permanent national household survey capability at UBOS.

Geographic coverage

National Coverage

Analysis unit

The units of analysis of the survey are; individuals, households, and communities.

Universe

The survey covered all sampled households.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified two stage sampling design was used to draw the sample. At the first stage, Enumeration Areas (EAs) were selected using the 2002 Uganda Population and Housing Census Frame, based on Probability Proportional to Size (PPS). Then at the second stage, an exhaustive list of households was generated in each selected EA from which 10 households were drawn using systematic sampling. A total of 712 EAs were selected for UNHS 2009/10 yielding a sample of approximately 7000 households.

Sampling deviation

In the karamoja sub-region, 20 households were selected per EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the UNHS 2009/2010 were structured questionnaires with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes education, health, household characteristics, Household assets, Outstanding loans, Household consumption expenditure, household income, Welfare indicators and Cultural particpation.

In addition to a household questionnaire, Labour questionnaires were administered in half of the households in selected EAs for all household members age 5 years and above. For households with establishments, informal sector questionnaires were administered to the operator of the establishment.

The questionnaires were developed in English, however, interviews were conducted in the local languages. All questionnaires and modules are provided as external resources.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

The response rate was 90 percent.

Sampling error estimates

Sampling errors have been calculated for a select set of statistics for the national sample, urban and rural areas, and for each of the ten strata. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Details of the sampling errors are presented in the sampling errors appendix to the report and in the sampling errors table presented in the external resources.

Abstract

The demand for and use of statistical information for evidence-based policy and decision making has transcended the margins of administrative boundaries to cover household activities and behavior. Monitoring changes at household level through household surveys has, therefore, become more important now than ever before. The Uganda Bureau of Statistics (UBOS) has been conducting an integrated household survey, popularly known as Uganda National Household Survey (UNHS) every other year since the late 1980s. Through the UNHS, Uganda has a very rich household time series data covering almost one and half decades. The data have been the main source of statistical information for monitoring poverty levels, trends and related welfare issues. The UNHS 2012/13 covered all the 112 districts in Uganda. Field data collection was spread over a 12-month period from June 2012 to June 2013 to take care of seasonality factors and also enable comparability with previous surveys. A total of 7500 households scientifically selected countrywide were covered. The Survey was comprehensive and had four modules, namely; Socio-economic, Labour Force, Community and Price modules.

Geographic coverage

National Coverage

Analysis unit

The UNHS 2012/13 had the following units of analysis: individuals, housheholds, and communities.

Universe

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

Sampling procedure

The 2012/13 UNHS sample was designed to allow for reliable estimation of key indicators at the national, rural-urban, regions levels and separately for 10 sub-regions. A two-stage stratified sampling design was used. At the first stage, Enumeration Areas (EAs) were grouped by districts and rural-urban location, 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 750 EAs were selected using the 2012 Uganda Population and Housing Census Mapping Frame. These EAs were distributed to the 10 sub-regions in equal proportions with consideration of the rural-urban domains. The 10 sub-regions stratified on the basis of common socio-demographic characteristics were as follows: 1. Kampala (comprising of the entire Kampala City Council Authority (KCCA)); 2. Central I (Kalangala, Masaka, Mpigi, Rakai, Sembabule, Wakiso, Lyantonde, Bukomansimbi, Butambala, Gomba, Kalungu and Lwengo); 3. Central II (Kiboga, Luwero, Mubende, Mukono, Nakasongola, Kayunga, Mityana, Nakaseke, Buikwe, Buvuma and Kyankwanzi); 4. East Central (Bugiri, Iganga, Jinja, Kamuli, Mayuge, Kaliro, Namutumba, Buyende and Luuka); 5. Eastern (Busia, Kapchorwa, Katakwi, Kumi, Mbale, Pallisa, Soroti, Tororo, Kaberamaido, Sironko, Amuria, Budaka, Buduuda, Bukedea, Bukwo, Butaleja, Manafwa, Bulambuli, Kibuku, Kween, Namayingo, Ngora and Serere); 6. Mid-Northern (Apac, Gulu, Kitgum, Lira, Pader, Amolatar, Amuru, Dokolo, Oyam, Agago, Alebtong, Kole, Lamwo, Nwoya and Otuke); 7. North-East (comprising the districts of Kotido, Moroto, Nakapiripirit, Abim, Kaabong, Amudat and Napak); 8. West-Nile (comprising the districts of Adjumani, Arua, Moyo, Nebbi, Yumbe, Koboko, Maracha and Zombo); 9. Mid-Western (comprising the districts of Bundibugyo, Hoima, Kabarole, Kasese, Kibaale, Masindi, Kamwenge, Kyenjojo, Buliisa, Kiryandongo, Kyegegwa and Ntoroko); 10. South Western (comprising the districts of Bushenyi, Kabale, Kisoro, Mbarara, Ntungamo, Rukungiri, Kanungu, Ibanda, Isingiro, Kiruhura, Buhweju, Mitooma, Rubirizi and Sheema).

Mode of data collection

A centralized approach was employed during data collection whereby 12 mobile field teams hired at the headquarters were dispatched to different sampled areas. Each team comprised of one Supervisor, 3 to 4 Enumerators and a Driver. The field interviewers were recruited based on fluency of local language spoken in the respective regions of deployment. At the headquarters, a team of Regional Supervisors, Editing Officers, Data Entry Staff and Computer Programmers were assigned to undertake other survey activities respectively. The field data collection commenced in the month of June 2012 and was completed in June 2013 Data Processing and Management Scanning technology was used to capture and process images from the questionnaires. This process involved a number of stages: i) System Development and Testing

This process involved building various alternative scenarios and templates for data capture. System efficiency, stability and scalability were taken into account. ii) Hardware & Software

The major components of the scanning process included: ? Bar-code Scanning Suite ? Guillotine Machine ? Document Scanners & Software ? Computers ? Recognition Stations (High Capacity Computers) ? Server and Server Software ? Local Area Network Installation ? Backup Software ? Recognition Engines Software Licenses

iii) Scanning Technique

Scanning is a method whereby images and/or text are transformed into digital form that is recognized by a computer. Digitized images of questionnaire forms were processed to extract the data to be stored in file formats e.g. American Standard Code for Information Interchange (ASCII) usable in analysis.

Uganda UG: GDP: % of GDP: Gross Value Added: Agriculture data was reported at 24.897 % in 2017. This records an increase from the previous number of 23.708 % for 2016. Uganda UG: GDP: % of GDP: Gross Value Added: Agriculture data is updated yearly, averaging 47.773 % from Jun 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 73.655 % in 1978 and a record low of 21.385 % in 2008. Uganda UG: GDP: % of GDP: Gross Value Added: Agriculture 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: Gross Domestic Product: Share of GDP. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

Time series data for the statistic Net barter terms of trade index (2000 = 100) and country Uganda. Indicator Definition:Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.The indicator "Net barter terms of trade index (2000 = 100)" stands at 104.40 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.2882 percent compared to the value the year prior.The 1 year change in percent is 0.2882.The 3 year change in percent is -1.79.The 5 year change in percent is 11.18.The 10 year change in percent is 16.00.The Serie's long term average value is 120.36. It's latest available value, on 12/31/2023, is 13.26 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2001, to it's latest available value, on 12/31/2023, is +32.26%.The Serie's change in percent from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is -61.32%.

Abstract

The Non-Profit Institutions Survey (NPI) was carried out in 2009 by the Uganda Bureau of Statistics (UBOS). The objective of the NPI was to collect data relating to activities of Non-Profit Institutions in Uganda. The survey covered five regions in Uganda namely: Central, South Central, East, West. UBOS volunteered to collect the NPI data and also provided financial and technical support.

In this case, the Non-Profit Institutions (NPI) refers to all legal or social entities created for the purpose of producing goods and services whose status does not permit them to be a source of income, profit, or other financial gain for the units that establish, control or finance them. In practice their productive activities are bound to generate either surpluses or deficits but any surpluses they happen to make cannot be appropriated by any other institutional units. The basis for defining NPIs focuses on the common characteristic that they do not distribute their profits.

Geographic coverage

National

Analysis unit

Institutions

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for NPI survey was designed to provided estimates hence making available data relating to activities of Non-Profit Institutions (NPIs) in Uganda. The data provided was used to compile statistics on the contributions made by NPIs to the economic growth and development of the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was comprised of the Non-Profit Institutions Survey Questionnaire

Cleaning operations

The data were entered in 5 computers using the specially prepared software in Access. The data were entered in the regional offices of the UBOS, with 10 staff trained prior to data processing. In order to ensure quality control, the software was programmed to check the internal consistency of data entered. Procedures and standard programs developed under NPI. The Stata 10 statistical package was used for data tabulation and analysis.

Sampling error estimates

To estimate the standard errors for NPI indicators we used the estimation of variance for the proportion given in the formula: Vp'= Def*p (1-p)/(n-1), where: p - proportion for the variance estimate, n - sample size, and Def - effect of sample planning for the observed group of indicators. The standard error is the square root of Var xd'.