GAFSP funds in Uganda support the Agriculture Sector Development Strategy and Investment Plan (DSIP). Specifically, the funds will target vulnerable smallholder households to help increase and diversify production of nutritious foods, improve nutrition knowledge and practices (especially in the "critical window" of conception through 23 months), and strengthen coordination mechanisms between agriculture, health, and education sectors to address cross-cutting nutrition issues at national and local government levels. This interactive map of Uganda highlights the districts targeted by the project. This map overlays sub-national poverty data and demographic indicators relevant to the project. The project will target the poorest sub-counties in 15 districts, based on high stunting and low dietary diversity. For the first phase of implementation five districts were selected: Bushenyi, Nebbi, Ntugamo, Maracha and Namutumba. For the second phase of implementation 10 more districts will be selected from the following list of priority districts: Isingiro, Yumbe, Arua, Bigiri, Iganga, Kygegwa, Kiryandongo, Kamwenge, Masindi, Kyenjojo, Kabarole, Kabale, Hoima, Kibale and Kaese. Synergies between the project and the Agriculture Cluster Development Project will ensure greater coverage of beneficiaries and will provide a scalable model integrating the two projects. Data Sources:Uganda Multisectoral Food Security and Nutrition Project priority districts.Source: World Bank and GAFSP Documents. Poverty (Proportion of population below the poverty line) (2012-2013): Proportion of the population living on less than UGX 1,387 per adult per day. Source: Uganda Bureau of Statistics UBOS. "Uganda National Household Survey 2012/13" Malnutrition (Proportion of underweight children under 5 years) (2011): Prevalence of severely underweight children is the percentage of children ages 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Uganda Bureau of Statistics UBOS. "Uganda Demographic and Health Survey 2011". MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS. Population: (Total population) (2014): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Uganda Bureau of Statistics UBOS. “National Population and Housing Census 2014 - Provisional Results Nov 2014”. Population Density (Persons per square kilometer) (2014): Total population divided by land area in square kilometers.Source: Uganda Bureau of Statistics UBOS. “National Population and Housing Census 2014 - Provisional Results Nov 2014”. Market Centers: Key market centers for retail, assembly and/ or wholesale of agricultural products. FEWS NET Reference markets.Source: FEWS Net. The Famine Early Warning Systems Network (FEWS NET) is a USAID-funded activity that collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. Geographic boundaries.

The maps displayed on the GAFSP web site are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

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

The ERT 1 Monitoring Survey will involve randomly selecting and interviewing households in the selected sites. These respondents will be asked questions about their household characteristics, access to and utilization of grid electricity, alternatives to grid electricity, issues to do with reliability, market availability as well as the perceived cost of the different types of fuel, household appliances, household expenditure on energy and communication among other topics which will be helpful in monitoring the impact of ERT 1.

Household surveys provide valuable information for monitoring government programmes. Monitoring the performance and the outcomes of the ERT I interventions is critical to the evaluation of the progress made and challenges that can be remedied in ERT II.

Geographic coverage

Like the Baseline Survey, the ERT 1 Monitoring Survey 2010 aimed at covering three (3) sites that were covered namely Kisiizi, Paidha and Sironko. Both Rural and Urban areas were covered for the survey.

Analysis unit

Household, Enterprise , Service Provider, Community

Universe

The Education Institutions, Enterprises, Households, Communities and Health Institutions

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was a sample survey. A sample of the population was selected and data collected from them but used to draw conclusions for the entire population. The main reason for using sample surveys instead of a complete enumeration are to reduce the time and cost of collecting information. The accuracy of a sample survey depends among other things, upon the size of the sample. Sample size was predetermined by the survey organizers according to the level of accuracy needed for the results.

The accuracy of a sample survey was also dependent upon another major factor, the absence of bias which would affect the proportions found through the sample. To control or prevent bias from creeping into the results, the selection of households included in the sample was completely random. This means that every person in the total population to be studied has an opportunity to be selected in the sample. This is why it was so important to make callbacks to reach those people who are not at home, since they may be different from people who are at home.

A two stage sampling design was used. At the first stage, a sample of about 30 Enumeration Areas (EAs)/villages has been selected from a sampling frame generated during the pre-visits to the sites. At the second stage, 10 households and 10 establishments was selected using simple random sampling from the selected EAs.

Sampling deviation

There was no deviation from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

(i) Household Questionnaire: it contains information about the HOusehold characteristics, individual (ii) Enterprise Questionnaire (iii) Service Provider Questionnaires Education Questionnaire Health Questionnaire (v) Community Questionnaire

Cleaning operations

The data were entered in 10 microcomputers using the specially prepared software in CsPro. The data were entered at statistical offices, 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. The Stata v.10 statistical package was used for data tabulation and analysis.

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 Uganda Government is faced with the challenge of elimination of child Labour in the Country. Child Labour contributes to a violation of the rights of Children to education and protection and it is putting at risk the country's progress by limiting the potential of its workforce. The Child Labour Baseline Survey exercise was carried out in three districts of Rakai, Mbale, and Wakiso districts. Lessons learnt will help to re-design Child Labour intervention programmes for the rest of the districts. In Uganda, a child is defined as someone below the age of 18 years. Generally speaking the term child Labour refers to involvement of children in the kind of work that is not allowed for them. When measuring Statistics on Child Labour two issues are considered, i.e;

(i) Age of the child;

(ii) The productive activities in which the child is involved, the nature and conditions in which activities are performed including the time spent in the activity.

The main objective of the 2009 child labour baseline Survey was to facilitate the measurement of the levels and nature of child labour in the focus districts. More than half of the population of surveyed districts is below 15 years of age. The proportion of child headship is low in all the districts. The proportion of paid employees and self employed is highest in Wakiso and lowest in Rakai district. Agriculture is the most dominant sector in which people are engaged followed by the trade sector. The purpose of the 2009 child labour Baseline Survey was to facilitate the measurement of the levels and nature of child labour in the focus districts of Rakai, Mbale and Wakiso. The specific objectives were:

(i) To collect information on the main characteristics of working children and those of the households they live in ( i.e. their demographic composition and details by age/ sex/ ethnicity/ marital status/disability status/orphan hood/ literacy and educational status/ classification by industry occupation and status in employment/ earnings and weekly hours of work/ location of work place/ reasons for not attending school/ reasons for working/ types of unpaid household services done and weekly hours performed/ etc);

(ii) To obtain information to support the analysis of the causes and consequences of children engaged in work, including household earnings and debt, perceptions of parents/ guardians/ children, and the hazards and abuses faced by children at their work;

(iii) To obtain (through FGDs and KIIs) information on

(a) the various forms of child labour prevailing in the districts, particularly on WFCL such as CSEC, street children, children engaged for illicit activities, and forced work by children (b) the underlying forces leading to the persistence of child labour especially the impact of HIV/AIDS, poverty, adult unemployment, OVC issue, and lack of proper schooling facilities; (c) Child trafficking (v) To provide policy makers, researchers and other stakeholders with a comprehensive information and a set of indicators on child labour to guide interventions;

(vi) To act as a basis for the creation of a long -term database on child labour in Uganda.

Geographic coverage

The Child Labour Baseline Survey (2009) was carried out in the districts of Rakai, Wakiso and Mbale.

Analysis unit

The Child Labour Baseline Survey 2009 had the following units of analysis: individuals, and households.

Universe

The survey covered all de jure household members aged 5 years and above resident in the household, and all children aged 5 - 17 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to achieve the objectives of the Child Labour Baseline Survey, the study targeted all households with children and communities in the focus districts. The Enumeration Areas (EAs) from the 2002 Population and Housing Census household counts were used as the sampling frame for each of the districts. Each EA was accurately and uniquely identified together with the number of households in it. Independent representative samples were selected from each of the districts using Population proportional to Size (PPS) with the number of households in the EA with children taken as a measure of size. A representative sample was selected from each of these focus districts. In order to ensure that reliable estimates are got for each district, EAs were distributed among the districts according to the measures of size. Allocation of EAs and households per district was as indicated below:

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Due to the need to have the child labour baseline survey records processed fast enough, this exercise started shortly after the commencement of fieldwork. The office editing/coding and data capture process for the survey took approximately 2 weeks. It involved double data entry which ensured that the accuracy of the captured data was checked in the second data capture routine hence increasing on its accuracy. After the data capture machine editing involving structural and consistency edits was carried out before data analysis. The data capture screen was developed using the CSPro (Census and Survey Processing) software.

Response rate

A total of 1,617 households were selected for the Child Labour Baseline Survey (CLBS) Sample. Out of these, 1,585 households were successfully interviewed, yielding a household response rate of 98 percent. A total of 4,431 children aged 5-17 years were listed from the selected households in the household schedule, of which 4,306 children successfully responded to questions about activity status. This gave a children response rate of 97.2 percent

Sampling error estimates

The CLBS 2009 was a sample survey and hence likely to be affected by sampling and non-sampling errors. The following was carrying out to minimize on errors at different stages of implementation: Using a standard child labour questionnaire adjusted to national requirements; Ensuring effective supervision during data collection and use of experienced interviewers; Supervising experienced staff used in the data capture process in addition to carrying out double data entry; Drawing the sample from complete frame of EAs with their corresponding number of households ( as distributed by district); Carrying on edits on the captured data before data analysis.

Annex 3 of the final report presents the standard errors, CVs and confidence intervals for selected indicators.

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 objective of the National Livestock Census was to establish Livestock and poultry numbers at national and district levels. Specific Objectives • To obtain data on basic characteristics of livestock such as age, sex, breed, use and livestock system. • To obtain information on farm infrastructure, equipment and implements. • To establish ownership and tenure regime for land used for livestock rearing. • To establish labour use of households that engage in livestock rearing.

Geographic coverage

The National Livestock Census was carried out in all the 80 districts of Uganda.

Analysis unit

The census had the following units of analysis: livestock/poultry, households and farms.

Universe

This census covered both household-based farms as well as institutional farms. While a complete enumeration of all institutional farms was conducted in all districts; a representative sample of household-based farms was enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A two-stage stratified cluster sampling design in which districts formed strata at the first stage was used in this census. At the second stage, Enumeration Areas (EAs)/villages were systematically selected from each selected sub-county. All households in the selected EAs were supposed to be completely enumerated. The sample of EAs for the National Livestock Census was selected using the 2002 PHC sampling frame. Countrywide, a total of 8,870 EAs was selected. These EAs were allocated to each district on the basis of the number of households with cattle. The use of households with cattle gave a representative spread of EAs by district. This sampling design resulted into a huge sample of 964,047 households representing 15.1% of the total number of households in Uganda as of 2008. Compared to other livestock censuses conducted in the past in this country and other developing countries; which usually consider sample sizes of 1%-5% of the total number of households; this census stands out as one of the most comprehensive livestock censuses.

Mode of data collection

Face-to-face [f2f]

Sampling error estimates

On the basis of the huge sample and the high precision of estimates as evidenced by the minimal coefficients of variation of almost all estimates (<20%); the results to be presented in the next section provide among other things; the most precise estimate of 6 the total number of livestock of their kind in this country as of 2008 and should be used as a benchmark for any future livestock surveys and censuses in Uganda.

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

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone include districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS:

1) Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; 2) Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; 3) Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; 4) Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; 5) Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; 6) Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu; 7) Nabuin: districts included are Moroto, Nakapiripirit, Kotido, Napak, Amudat, Kaabong and Abim; 8) Serere: districts included are Serere, Kumi, Bukedea Amuria, Ngora, Katakwi, Soroti and Kaberamaido; 9) Mbarara: districts included are Mbarara, Ntungamo, Bushenyi, Kiruhura, Lyantonde, Sheema, Rubirizi, Mitoma, Isingiro,Ibanda, Buhweju, Sembabule, and Rakai; 10) Rwebitaba: districts included are Bundubugyo, Kabarole, Kamwenge, Kasese, Kyegegwa, Kyenjojo and Ntoroko.

Being an urban area, Kampala has been excluded from the survey. Also Ntoroko district was not included in the sample.

Analysis unit

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

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 implemented 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 collects information on:

1) Household member socio-demographic characteristics; 2) Agricultural enterprises undertaken by the household in the current agricultural season; 3) Land use (Parcel and plots used by the agricultural households) i.e. Access to land, land use rights, decision making, land area, seed/seedlings utilization, etc. The main objective of this questionnaire is to estimate land areas for crops planted. This is done combining objective measurement (i.e., GPS) on plots and parcels and then collecting the share of land area covered by each crop on each plot (based on farmer's assessment). In addition, the questionnaire collects information on land tenure and use of agricultural inputs. This questionnaire contains a roster of household members, a roster of parcels, a roster of plots for each parcel and a list of crops by plot.

The questionnaire used for the post-harvesting visit is called "Form 52- Crop Production, Household and Holding Characteristics Module" and collects information on: 1) Household member socio-demographic characteristics (only for new household members) 2) Crop production and disposals 3) Use of agricultural inputs for crop production 4) Cost of labour used for crop production 5) Labour input used on the agricultural household 6) Animal raised on the holding 7) Inputs used for livestock production 8) Livestock production and dispositions 9) Access to agricultural information 10) Access to means of transportation 11) Access to storage facilities 12) Access to agricultural credit 13) Fixed costs of the agricultural household 14) Shocks and food security of the agricultural household 15) Access to extension services 16) Land disputes

The main objective of this questionnaire is to collect data on crops harvested by agricultural households, based on farm declarations. In addition, the questionnaire collects information concerning the disposition of crops, labour input and use of inputs such as seed/seedlings. Furthermore, it aims to collect livestock capital, animal production and inputs over a 12-month reference period, thus covering the entire agricultural year.

The post-harvesting questionnaire also collects information concerning household and holding characteristics, such as the access to market and agricultural information, household food security, shocks and their impact on food security etc.

Cleaning operations

All data cleaning and editing operations were performed using the statistical software Stata. The anonymization process has been carried out with the aid of the statistical software R and the package sdcMicro with functions for risk measurement and the application of SDC methods.

Response rate

The response rate was about the 86% during the PP visit, and the 83% during the PH 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 Annual Agricultural Survey (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 visit, households in the survey's sample are interviewed twice, during the visit1 period and visit2. This results in a total of two visits during the agricultural year. The data collection activities were delayed by the pandemic. Among information collected with the AAS there is data on: The quantity and value of agricultural production; The access to extension services, market information and agricultural facility; 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 2020, see the Executive Summary of the AAS 2020 Report (see external resources/downloads section).

Geographic coverage

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone includes districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS: Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu;

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling design was adopted for the AAS 2020. To increase the efficiency of the sample design, the sampling frame was stratified into 10 ZARDIs. In each stratum, the first stage was the selection of the Primary Sampling Unit (PSU), which is the EA (enumerator area) and the second stage was the selection of the Secondary Sampling Unit (SSU), which are the Ag HHs. The survey covered households cultivating crops and/or raising livestock, including households that were cultivating a few crops or raising a limited number of animals. No minimum threshold on the amount of land cultivated or animals raised was set nor did the survey aim to generate estimates concerning aquaculture, forestry and fisheries. Sample size The survey generated national, regional and sub-regional level estimates. A sample of 593 EAs and an average of 12 Ag HHs were selected from each EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Annual Agricultural Survey (AAS 2020) adopted three main questionnaires: the post-planting (PP), the post-harvest (PH) and the livestock and holding questionnaires. Normally, the PP and PH questionnaires are administered each season, while the livestock and holding questionnaire is administered at the end of the second season and covers the entire agricultural year. Nonetheless, in the AAS 2020, a different survey calendar was adopted due to movement limitations imposed as a result of the COVID-19 pandemic.

Cleaning operations

All the data captured from the field were stored in the cloud with a local backup. Editing and validation was done electronically using STATA software.

Response rate

The response rate was about the 94.5 %.

Sampling error estimates

The accuracy of the survey results depends on the sampling and the non-sampling errors. The AAS 2020 had a large enough and representative sample to limit sampling errors. On the other hand, the non-sampling errors, usually errors that arise during data collection, were controlled through thorough training of the data collectors, field supervision by the headquarters team, and a well-developed CAPI programme. The Coefficients of Variations (CVs) and Confidence Intervals (CIs) for selected indicators at national, ZARDI and sub-regional levels are presented in the Annex tables.

Abstract

The Uganda Bureau of Statistics (UBOS), on behalf of the Northern Uganda Social Action Fund (NUSAF) under the Office of the Prime Minister conducted the Northern Uganda Survey between August and December 2004.

The survey covered all the 18 districts within the NUSAF region namely, West Nile (covering; Adjumani, Arua, Moyo, Nebbi and Yumbe); Acholi (comprising Gulu, Kitgum and Pader); Lango (consisting of Apac and Lira); Teso (comprising Kaberamaido, Katakwi, Kumi, Soroti and Pallisa); Karamoja (consisting of Kotido,Moroto, Nakapiripirit).

The main objective of the Northern Uganda Survey (NUS) was to collect high quality and timely data on demographic and socio-economic characteristics of household population for monitoring development performance as well as providing baseline indicators for the different socio- economic and vulnerable groups.

The total estimated population in the NUSAF region was 7.1 million persons.Overall, about 53 percent of the population was aged below 15 years. An average household size of 5.2 persons was revealed, similar to that revealed by the 2002 Population and Housing Census for the Northern region. Findings show that the literacy rate for males (68 percent) was higher than that of females (41 percent). Of all persons aged 6-25 years, about 14 percent had no formal schooling. About one in every ten children who had left school was an orphan. About 26 percent of the study population reported at least one illness or symptom in the thirty days preceding the survey. This finding is consistent with the NSDS 2004 where incidence of sickness was reported at 26 percent in the northern region.

The Labour-force participation rate was 67 percent. The monthly household consumption expenditure in the NUSAF region (Shs.72,800) was lower than the national monthly consumption expenditure (Shs.139,300) recorded in UNHS 2002/03. In the NUSAF region, most houses were grass thatched and had walls made of either un-burnt bricks and mud, or poles and mud. The majority of households in the NUSAF region have access to safe drinking water.Households that had experienced shocks were asked to state a maximum of three shocks in descending order of severity. Rebel attacks emerged as the most serious household shock (36 percent) followed by drought or famine (32 percent). Communities had poor access to Agricultural input markets as well as other financial services.

Geographic coverage

The survey covered all the 18 districts within the NUSAF region namely, West Nile (covering; Adjumani, Arua, Moyo, Nebbi and Yumbe); Acholi (comprising Gulu, Kitgum and Pader); Lango (consisting of Apac and Lira); Teso (comprising Kaberamaido, Katakwi, Kumi, Soroti and Pallisa); Karamoja (consisting of Kotido, Moroto, Nakapiripirit)

Analysis unit

• Communities
• Households
• Individuals

Universe

The survey covered all usual residents.

Kind of data

Sample survey data [ssd]

Sampling procedure

The NUS sample was drawn through a stratified two-stage sampling design. The Enumeration Area (EA) was the first-stage sampling unit and the household was 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 2002 Population and Housing Census. In order to select the second stage units,which are the households, a listing of households was done in all selected EAs.In the case of the camps, the first stage consisted of selecting IDP camps based on the population in each IDP camp. Each IDP camp is divided into blocks/zones and a sample of blocks was selected using simple random sampling. Within each block, households were selected and interviewed. The details of the sampling design are given in Appendix I of the NUS Report in External Resources.

The size required for the sample was determined by taking into consideration the degree of precision (reliability) desired for the survey estimates, the cost and operational limitations, and the efficiency of the design. NUS covered a sample size of 4787 households in 479 communities (EAs). Of these, about 900 households were in IDP camps. In addition, about 262 households in 100 Enumeration Areas were panel households (interviewed in the 1999, and where possible, 1992 household surveys).

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were administered, namely a community questionnaire and a socio-economic questionnaire.

The socio-economic questionnaire included a section on vulnerability and addressed matters relating to internally displaced persons (IDPs), children who have been abducted (ex-abductees), youth who have given up arms for peaceful livelihood alternatives (gun drop outs), youth whose lives have been disrupted by long civil strife, the aged, members of female headed households, and orphans. This module also covered the following areas: health of household members, disability, education, migration, housing conditions, household and enterprise assets, household shocks,and consumption expenditure.

The community questionnaire addressed community facilities including access to schools, health centers, roads, extension services and markets. It also addressed major community events, land tenure, community history, social capital, community projects undertaken and characteristics of the education and health infrastructure used by the community.

Cleaning operations

All questionnaires for NUS 2004 were returned to UBOS for processing. The questionnaires were manually edited using a set of scrutiny notes to guide the manual checking. In addition, range and consistency checks were included in the data-entry computer program. More intensive and thorough checks were carried out using MS-ACCESS.

Response rate

The response rate for the NUS 2004 was about 98 percent. A total of 4787 households were interviewed out of the 4888 households initially targeted. Non-response mainly resulted from insecurity, out migration and resettlement into IDP camps.

Abstract

The agricultural sector is the most important sector of the Ugandan economy. Empirical evidence attests to this; for example the share of the agricultural sector to Gross Domestic Product (GDP) is about 21 percent (at the then current prices). According to the Agricultural Module of the 2002 Population and Housing Census, the agricultural sector accounted for 73 percent of the total employment for the persons aged 10 years and above. In addition, 74 percent of the households had an agricultural holding. The long term vision of the Government of Uganda is to eradicate poverty and the strategies for this vision are defined in the then Poverty Eradication Action Plan (PEAP) which has been transformed into the National Development Plan (NDP).

The vision of PMA was to eradicate poverty through transforming subsistence agriculture to commercial agriculture. The whole process of transformation requires accurate and reliable agricultural data to monitor the progress made and inform policy and planning processes

Further, countries are focusing on the need to monitor progress towards the Millennium Development Goals (MDGs) through their National Statistical systems. The World Census of Agriculture (WCA), 2010 was formulated with this in mind and specifically to monitor eradication of extreme poverty and hunger, achievement of Universal Primary Education, Promotion of gender equality and empowerment of women and ensuring environmental sustainability.

Within the framework of the FAO/World Bank Agricultural Statistics Assistance to Uganda, a Data Needs Assessment Study was undertaken in August 1999. One of the major findings was that the Agricultural Statistics System was fragile, vulnerable, un-sustainable and above all, unable to meet the data needs of users. A Census of Agriculture (CA) is major source to meet these demands.

Census taking in Uganda Prior to the conducting of the Uganda Census of Agriculture (UCA), 2008/09 two (2) other censuses had been conducted. The first CA was conducted during 1963/65. The Government of Uganda was assisted by FAO and the then Department for Technical Cooperation of the United Kingdom both of which provided international and census equipment to a varying degree.

The second CA called the National Census of Agriculture and Livestock (NCAL) was conducted during 1990/91. It was funded by United Nations Development Programme (UNDP) and executed by FAO. Therefore the UCA 2008/09 formed the third CA in the history of census taking in Uganda.

Preparatory activities An Agricultural Module was included in the Population and Housing Census 2002, to collect the data that would form a basis for constructing an up-to-date and appropriate sampling frame for a Uganda Census of Agriculture (UCA), 2004/05. A Pre-Test was conducted in 2002 followed by a pilot Census of Agriculture (PCA) which was conducted in 2003.

Lack of financial resources militated against conducting the UCA, 2004/05. During the Financial Year (FY) 2007/08 Government made a budgetary provision for conducting a census of agriculture.

The FY 2007/08 was mainly a preparatory year. As mentioned earlier, the plan had been to conduct a UCA during 2004/05, which did not take place. By 2008/09 (the census reference year), many changes had taken place and needed to be addressed. To this end, another Pre -Test was conducted in May 2008. Based on the findings from the Pre-Test, the UCA instruments had to be revised. Another very important factor for the instruments' revision was an input from the International Consultants (like FAO Statisticians). Other preparatory activities included arrangements to procure census equipment and transport as well as recruiting and training of Field Staff.

Objectives of the UCA.2008/09 While the long-term objective of the UCA, 2008/09 was to have a system of Food and Agriculture Statistics (FAS) in place, the immediate objective was to collect and generate benchmark data needed for monitoring and evaluation of the agricultural sector at all levels, through a nation-wide CA.

Geographic coverage

The Uganda Census of Agriculture 2008/09 covered all the 80 districts in the country as of July 2007.

Analysis unit

Agricultural households, Agricultural holdings

Universe

The Uganda Census of Agriculture 2008/09 was therefore planned to cover all the 80 districts at the time and collect data on various structural characteristics of agricultural holdings. Limited data on livestock variables was planned to be collected because comprehensive livestock data was to be collected in a Livestock Census, 2008.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A stratified two-stage sample design was used for the small and medium-scale household-based agricultural holdings. At the first stage Enumeration Areas (EAs) were selected with Probability Proportional to Size (PPS), and at the second stage, households which were the ultimate sampling units were selected using systematic sampling.

For each of the sampled EAs, listing took place in the field and a number of filter questions (using Listing Module) were administered to determine eligibility (i.e., only the Households with Agricultural Activity would be eligible). Further, the eligible households were stratified into two strata namely, the small/medium holdings stratum and the Private Large-Scale holdings stratum.

On the other hand, district supervisors compiled separate lists of Institutional Farms and Private Large Scale Farms. These were to be covered on a complete enumeration basis.

During sampling, two (2) lists namely for EAs and PLS&IFs were used to identify possibilities of duplication and address them. If a PLS&IF was in both lists, it was deleted from the EA frame. However, if it was found only in the EA frame, it was left as part of the frame from which to sample. In other words, the List was not updated based on the information collected from the EAs sampled from the Area Frame.

The UCA2008/09 estimates were planned to be generated at national, regional and district levels. To achieve this, a sampling scheme of 3,606 EAs and 10 agricultural households in each selected EA, leading to 36,060 households was adopted.

In this design, an optimum number of households to be sampled per EA was determined on the basis of a suitable cost ratio (ratio of the cost per PSU to cost per SSU) and intra-class correlation, calculated from the Agricultural Module data from PHC 2002. For a cost ratio of 40 and intra-class correlation as 0.29, optimum number of households to be selected was obtained as 10.

The required sample size of EAs was selected from each district with probabilities proportional to size (PPS), using the systematic sampling algorithm described in Hansen, Hurwitz, and Madow (1953) while Agricultural Households were selected with equal probability systematic sampling procedure. The measure of Size (MOS) which was used for sample selection was the number of Agricultural Households determined from the 2002 PHC.

Sampling deviation

EAs where there was no enumerations due to insecurity: There were EAs which could not be listed or even enumerated due to insecurity , resistance by residents or nonexistent etc. These were in Moroto, Nakapiririt, Mubende, Kampala etc. Since there were no replicate EAs, the number of sampled EAs in those districts was lowered reducing the estimated number of EAs expected to give good results in those respective districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The principles of validity, optimization and efficiency which refer to ability for the questionnaires to yield more reliable information per unit cost; measured as a reciprocal of the variance of the estimate and enables objective interpretation of the results was followed. While costs involved man hours and money expended for data collection from sampled units, the design of questionnaires had to collect a minimum set of internationally comparable core data(indices) for Uganda, as enshrined in the pillars of FAO.

Cleaning operations

Data Processing monitored the data quality parameters and data quality team could continuously report to the field operations team who could make feed back to the DSs for improvement. Returned questionnaires were subjected to the following steps Coding, Data capture, Editing, Secondary Editing and Quality control.

Coding This involved making sure that all forms/questionnaires had correct geographical identification information and correct crop codes. The coding team reviewed the sampling of holdings within an enumeration area to see that only eligible/sampled holdings were actually enumerated.

Editing This involved the process of identifying inconsistencies within the data and removing them. At the beginning of UCA data processing, a set of editing rules and guidelines where developed by the data processing team with technical guidance from the subject matter specialists. Many of these were incorporated into the data entry application and others were left for the secondary editing stage.

Secondary Editing Errors that passed the data entry stage were subjected to the editing stage. This stage was meant to find inconsistencies within the data. It brought out problems that required subject matter specialists to resolve. To resolve most of such errors, consultations were made with the national supervisors, district supervisors, UBOS and MAAIF technical teams.

Response rate

The UCA2008/9 had several forms namely; Agricultural Households and holding Characteristics Module; Crop Area Module; Crop Production Module

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.

Among information collected with the AAS there is data on: - The quantity and value of agricultural production; - The access to extension services, market information and agricultural facility; - 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 2019, see the Executive Summary of the AAS 2019 Report (see external resources/downloads section).

Geographic coverage

The AAS is a national survey representative at the regional, sub-regional and zardi level. The National territory has been divided in 10 ZARDIs which are aligned to 10 Agro-ecological zones in Uganda. Each agro-ecological zone includes districts with similar climate, land use and cropping patterns. The following are the 10 Zardis considered for the AAS:

1) Abi: districts included are Arua, Nebbi, Moyo, Adjumani, Koboko, Yumbe, Maracha-Terego and Zombo; 2) Buginyanya: districts included are Sironko, Mbale, Iganga, Jinja, Tororo, Mayuge, Namutumba, Namayingo, Luuka,Kamuli, Kaliro, Buyende, Bugiri, Pallisa, Kibuku, Butaleja, Busia, Budaka, Manafwa, Kween, Kapchorwa, Bulambuli, Bukwo and Bududa; 3) Bulindi: districts included are Hoima, Masindi, Kiryandongo, Kibaale, and Buliisa; 4) Kachwekano: districts included are Kabale, Rukungiri, Kanungu and Kisoro; 5) Mukono: districts included are Mukono, Mpigi, Kayunga, Kalangala, Kampala, Luwero, Masaka, Nakasongola, Mubende, Wakiso, Nakaseke, Buikwe, Buvuma, Mityana, Kiboga, Kyankwanzi, Gombe, Kalungu, Bukomansimbi, Butambala and Lwengo; 6) Ngetta: districts included are Lira, Apac, Dokolo, Lamwo, Nwoya, Agago, Albetong, Amolatar, Kole, Otuke, Oyam, Pader,Kitgum, Amuru and Gulu; 7) Nabuin: districts included are Moroto, Nakapiripirit, Kotido, Napak, Amudat, Kaabong and Abim; 8) Serere: districts included are Serere, Kumi, Bukedea Amuria, Ngora, Katakwi, Soroti and Kaberamaido; 9) Mbarara: districts included are Mbarara, Ntungamo, Bushenyi, Kiruhura, Lyantonde, Sheema, Rubirizi, Mitoma, Isingiro,Ibanda, Buhweju, Sembabule, and Rakai; 10) Rwebitaba: districts included are Bundubugyo, Kabarole, Kamwenge, Kasese, Kyegegwa, Kyenjojo and Ntoroko. Being an urban area, Kampala has been excluded from the survey. Also Ntoroko district was not included in the sample.

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage sampling design was adopted for the AAS 2019. To increase the efficiency of the sample design, the sampling frame was stratified into 10 ZARDIs. In each stratum, the first stage was the selection of the Primary Sampling Unit (PSU), which is the EA (enumerator area) and the second stage was the selection of the Secondary Sampling Unit (SSU), which are the Ag HHs. The survey covered households cultivating crops and/or raising livestock, including households that were cultivating a few crops or raising a limited number of animals. No minimum threshold on the amount of land cultivated or animals raised was set nor did the survey aim to generate estimates concerning aquaculture, forestry and fisheries.

Sample size The survey generated national, regional and sub-regional level estimates. A sample of 593 EAs and an average of 12 Ag HHs were selected from each EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The AAS 2019 implemented 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 collects information on:

1) Household member socio-demographic characteristics; 2) Agricultural enterprises undertaken by the household in the current agricultural season; 3) Land use (Parcel and plots used by the agricultural households) i.e. Access to land, land use rights, decision making, land area, seed/seedlings utilization, etc. The main objective of this questionnaire is to estimate land areas for crops planted. This is done combining objective measurement (i.e., GPS) on plots and parcels and then collecting the share of land area covered by each crop on each plot (based on farmer's assessment). In addition, the questionnaire collects information on land tenure and use of agricultural inputs. This questionnaire contains a roster of household members, a roster of parcels, a roster of plots for each parcel and a list of crops by plot.

The questionnaire used for the post-harvesting visit is called "Form 52- Crop Production, Household and Holding Characteristics Module" and collects information on:

1) Household member socio-demographic characteristics (only for new household members) 2) Crop production and disposals 3) Use of agricultural inputs for crop production 4) Cost of labour used for crop production 5) Labour input used on the agricultural household 6) Animal raised on the holding 7) Inputs used for livestock production 8) Livestock production and dispositions 9) Access to agricultural information 10) Access to means of transportation 11) Access to storage facilities 12) Access to agricultural credit 13) Fixed costs of the agricultural household 14) Shocks and food security of the agricultural household 15) Access to extension services 16) Land disputes

Information 1-5 are collected in both first and second season while 6-16 is asked during the second season only. The main objective of this questionnaire is to collect data on crops harvested by agricultural households, based on farm declarations. In addition, the questionnaire collects information concerning the disposition of crops, labour input and use of inputs such as chemicals. Furthermore, it aims to collect livestock capital, animal production and inputs over a 12- month reference period, thus covering the entire agricultural year. The post-harvesting questionnaire also collects information concerning household and holding characteristics, such as access to market and agricultural information, household food security, shocks and their impact on food security etc.

Cleaning operations

Supervision

Data collection for the AAS 2019 was performed by 15 teams constituted by, on average, three enumerators and 1 supervisor. After recruitment, both supervisors and enumerators received two trainings, one on the post-planting (PP) and one on the post-harvesting (PH) questionnaires. During these trainings, the CAPI PP and PH applications to be used for data collection were tested and refined. During the data collection stage, after completing a CAPI interview, enumerators submitted the electronic interview to their supervisors through Survey Solutions. Then, Supervisor checked the quality of data collected and decided on whether accepting or rejecting the completed case. When a supervisor rejected an interview, the interview was sent back to the interviewer tablet in order to be corrected as requested. On the other hand, when the supervisor accepted an interview, this was sent to the headquarter for final validation. This process continued until the quality of collected data was considered as satisfactory.

Response rate

The response rate was about the 84%.

Sampling error estimates

The accuracy of the survey results depends on the sampling and the non-sampling errors. The AAS 2019 had a large enough and representative sample to limit sampling errors. On the other hand, the non-sampling errors, usually errors that arise during data collection, were controlled through thorough training of the data collectors, field supervision by the headquarters team, and a well-developed CAPI programme. The Coefficients of Variations (CVs) and Confidence Intervals (CIs) for selected indicators at national, ZARDI and sub-regional levels are presented in the Annex tables.

Abstract

The 2003 Consolidated Expenditure and Motivation Survey provides baseline statistics on general characteristics of tourist visitors to Uganda.

The data from both surveys was combined to produce a single combined report. The strategy of combining the survey results was intended to provide more reliable statistics for reference in the five-year medium-term period.

One of the key findings of the survey is that in 2003 the country received a total of at least US dollars 265.35 million as tourist expenditures in the country, excluding all expenditures made abroad. Further analysis indicated that tourism contributed about 4.0 percent to the country's Gross Domestic product. The contribution to foreign exchange earnings of the country was 29.1 percent, excluding all donor financing and remittances of externalized manpower.

To guide planning and policy, five year (2004 -2008) forecasts of tourist arrivals and corresponding receipts have been made and included in this report.

Geographic coverage

The survey covered all international passengers departing from Entebbe International Airport, Malaba and Busia during the survey period.

Analysis unit

• Tourist/ Visitor

Kind of data

Sample survey data [ssd]

Sampling procedure

The systematic sampling survey technique was used; and involved interviewing every 8th, 10th and 9th departing tourist at Entebbe Airport, Malaba and Busia, respectively. Error terms (desired precision) were fixed at 2 percent and 3 percent, for air and road transport, respectively. The lower error term of 2% was set for Entebbe because of the higher tourist traffic experienced there compared to the road traffic. A 95 percent confidence level was adopted during the survey design.

Sampling deviation

Systematic sample; no deviation from sample design

Mode of data collection

Face-to-face [f2f]

Response rate

The combined total of 7735 respondents was realized. The three entry/exit points realized 4327, 1850 and 1558 respondents for Entebbe International Airport, Malaba and Busia, respectively.

Abstract

The survey was conducted in Uganda between August and October 2006. Data from 563 establishments were analyzed.

The Enterprise Surveys are applied to a representative sample of firms in the non-agricultural economy. The sample is consistently defined in all countries and includes the entire manufacturing sector, the services sector, and the transportation and construction sectors. Public utilities, government services, health care, and financial services sectors are not included in the sample. Enterprise Surveys collect a wide array of qualitative and quantitative information through face-to-face interviews with firm managers and owners regarding the business environment in their countries and the productivity of their firms. The topics covered in Enterprise Surveys include the obstacles to doing business, infrastructure, finance, labor, corruption and regulation, law and order, innovation and technology, trade, and firm productivity.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for countries participating in the Enterprise Surveys is stratified by industry, firm size, and geographic region.

For stratification by industry, the main manufacturing sectors in each country in terms of value added, number of firms, and contribution to employment are selected. The retail trade sector is also included in all countries as a representative of the services sector, and depending on the size of the economy, the information technology (IT) sector is included. The rest of the universe is included in a residual stratum. In Uganda, Manufacturing sector included 307 firms, Retail sector - 129 companies and Other sectors (Residual) - 127 businesses.

Size stratification is defined the following way: small establishments (5 to 19 employees), medium establishments (20 to 99 employees), and large establishments (more than 99 employees).

Regional stratification includes the main economic regions in each country. In Uganda, Jinja, Kampala, Lira, Mbale, and Mbarara were surveyed.

Through this methodology estimates for the different stratification levels can be calculated on a separate basis while at the same time inferences can be made for the economy as a whole, weighting individual observations by corresponding sample weights. Sample sizes for each stratification level are defined ensuring a minimum precision level of 7.5% with 95% confidence intervals for estimates with population proportions.

For more technical details on the sampling strategy, please review "Sampling Methodology" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module; - Core Questionnaire + Retail Module; - Core Questionnaire.

Most of the questions in all three questionnaires are the same.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, informality, business-government relations, conflict resolution and legal environment, innovation and technology, and performance measures. The questionnaires also assess respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Abstract

The School-to-Work Transition Survey (SWTS) was implemented by the Uganda Bureau of Statistics (UBOS) with funding from the Work4Youth partnership between the International Labour Organisation (ILO) Youth Employment Programme and the MasterCard Foundation. In Uganda the first round of the survey was conducted in 2013 and the second round took place between January and April 2015. This report presents the highlights of the second round of SWTS and compares the results to those of the first round. The analysis is updated and expanded to supplement the portrait of the youth labour market situation in Uganda presented in the first survey report. The report also outlines the institutional framework and relevant employment policies in the country. The SWTS is a unique survey instrument that generates relevant labour market information on young people aged 15 to 29 years, including longitudinal information on transitions within the labour market. The SWTS thus serves as a unique tool for demonstrating the increasingly tentative and indirect paths to decent and productive employment that today’s young men and women are facing. The SWTS serves a number of purposes:

• First, it detects the individual characteristics of young people that determine labour market disadvantage. This, in turn, is instrumental to the development of policy response to prevent the emergence of risk factors, as well as measures to remedy those factors that negatively affect the transition to decent work.
• Second, it identifies the features of youth labour demand, which help determine mismatches that can be addressed by policy interventions.
• Third, in countries where the labour market information system is not developed, it serves as an instrument to generate reliable data for policy-making and for monitoring progress towards the achievement of MDG1. In countries with a reasonably developed labour market information system, the survey helps to shed light on areas usually not captured by household-based surveys, such as youth conditions of work, wages and earnings, engagement in the informal economy, access to financial products and difficulties experienced by young people in running their business.

Geographic coverage

Whole country.

Analysis unit

• Individuals
• Households

Universe

A purposive sample refers to selection of units based on personal judgement rather than randomization.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the SWTS can be one of two types. The first type is a list of all members of the target population, while the second type is a method of selecting any member of this population. Sampling frames for the general population can be electoral rolls, street directories, telephone directories and customer lists from utilities which are used by almost all households, such as water, electricity, sewerage, and so on. It is preferable to use a list that is the most accurate, complete and up to date. The nature of this list is expected to differ from country to country. Some countries use a list of households, while other countries use a list of people.

Sampling deviation

• First stage: In the first stage, the whole country may be divided into administrative regions, such as governorates or provinces. Then a sample of these regions is selected, preferably using a purposive sampling technique to guarantee representativeness. A maximum variation technique, which is described earlier, can be used in the sample selection. Financial, accessibility and time constraints should be taken into consideration in the selection of the first-stage sample.

• Second stage: In this stage, each administrative region selected in the first stage may be divided into localities or census enumeration areas (EAs), and a sample of these areas is selected using a stratified technique. The units selected at this stage are usually called primary sampling units (PSUs). At this stage, a frame of PSUs is needed which a) lists the units covering the entire population in each selected administrative region exhaustively and without overlaps, and b) provides information for the selection of units efficiently, such as maps and good household listings. This frame is usually called the primary sampling frame (PSF). A self-weighted stratified systematic sampling technique is recommended in the selection of the PSUs. Self-weighted means that the number of PSUs selected from each administrative region should be proportionate to the population size in this region. In this stage, good maps and descriptions for identification and demarcation for each PSU are needed, together with up-to-date information on their size and characteristics.

• Third stage: The third stage may consist of dividing each of the PSUs selected in the second stage into smaller areas such as blocks, and then selecting one or more of these third-stage units (TSUs) from each selected PSU. This process may continue until a sample of sufficiently small ultimate area units (UAUs) is obtained. Again, self-weighted stratified systematic sampling techniques are recommended in the selection of the UAUs. The choice of the type of area units to be used in the survey, and the number of such units to be selected for the sample, are very important issues since the type of units chosen to serve as the PSUs and other higher-stage units can greatly affect survey quality, cost and operation. Here we present some general advice in the choice of such units. Firstly, it is not necessary to use units of the same type or size as PSUs in all governorates. Secondly, the survey team should not confuse the formal administrative label with the actual type of units involved.

• Fourth stage: At this stage, which is the last stage, in each selected sample area (or UAU) individual households may be listed and a sample selected with households as the ultimate sampling units (USUs). In the survey, information are collected and analysed for the USUs themselves including youth in the target age group, or just individual youth within sample households. A systematic sampling technique is recommended in the selection of the households in this stage if a list of all households in the UAU is available.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is designed to gather general information – personal, family and household information and education, activity history and aspirations from the respondent and then information relevant to the respondent’s current economic activity (whether still in school, unemployed, employed or outside of the labour force and not in school). The structure and flow of the questionnaires are as follows:

                                                       Structure and length of the questionnaire for youth sample

Section Number of questions in section Maximum number of questions asked of the individual

A Reference details (filled in by surveyors N.A. N.A. and used for control purposes)

B Personal, family and household information 20 20

C Education, activity history and aspirations 20 20 Based on response at end of section C, respondent jumps to section D, E, F or G

D Youth in education 7 47 E Unemployed youth 22 62 F Young employees, employers 48 (employees), 88 (employees), and own account workers 52 (self-employed) 92 (self-employed) G Youth not in the labour force 5 45

                                       Structure and length of the questionnaire for employer

Section Number of questions in section

A Reference details (filled in by surveyors and used for control purposes) N.A.

B Characteristics of the enterprise 15 C Recruitment and employment of young people 13 D Education and training of workers

Abstract

The Uganda National Household Survey (UNHS) 1999/2000 aims to provide estimates of area and production of major crops and other characteristics of the agricultural enterprises at national, regional and to a limited extent, some district level estimates. In addition, the survey findings will provide insights to the effects of various Government policy measures and programmes at household and community levels. Moreover, the results would assist in addressing specific needs of different users and also to fill in gaps in the socioeconomic indicators for monitoring development performance.

The UNHS 1999/2000 covered all districts except Kitgum, Gulu, Kasese and Bundibugyo. The results therefore do not portray the situation prevailing in these districts. That notwithstanding, the estimates are generally representative of the prevailing situation in the country.

The specific objectives of the survey are as follows: - To plan, design and conduct a country-wide crop farming survey through the household approach. This will provide estimates of area and production of major crops and other characteristics of the agricultural enterprise at national and regional levels including district level estimates for some districts; - Integrate household socio-economic and LC 1 level community surveys in the total survey programme to provide an integrated data-set so as to understand the mechanisms and effects of structural adjustment programmes and other government policy measures on a comparative basis over time; - Meet special data needs of users such as the Ministry of Health, Nutrition and Early Childhood Development Project (NECDP), National Council for Children (NCC), and others, in order to monitor the progress and/or act as a base-line for their project activities and interventions aimed at improvement of child health and mother care; - Fill in gaps in the socio-economic data to serve needs of planning and building social and economic indicators to monitor the progress towards development goals of the country, and to consolidate efforts being made in building a permanent national survey capability in UBOS.

Geographic coverage

The UNHS 1999/00 covered all districts in the country, except the districts of Kitgum, Gulu, Kasese and Bundibugyo.

Analysis unit

• Individuals
• Households
• Communities
• Consumption expenditure commodities/ items

Universe

The survey covered the following populations: - All the resident population with the exception of the nomads, homeless, and refugees - Women aged 12 years and above - Children under 5 years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design The sampling design adopted for the survey is similar to what was used for the Integrated Household Survey (IHS) 1992/93 and the subsequent monitoring surveys. It is typically a stratified two-stage sampling design except in some districts where the sample was selected in three stages due to lack of an Enumeration Area (EA) frame.

The first stage sampling unit was the EA of the 1991 Population Census in districts with two-stage sampling design, and households as the second stage sampling units. For districts with a three stage design, the first stage sampling units was the parish, while the second stage sampling unit was the LC 1 (village) and the third stage sampling unit is the household.

The survey included panel EA’s and panel households from the 1992/93 Integrated Household Survey as well as new EA’s and new households. In implementing this rather complicated design, services of a Survey Design Consultant were utilised.

Stratification The sampling frame is divided into fairly homogeneous strata in order to improve the efficiency of the sampling design. The first level of stratification is also designed to provide separate and reliable estimates of several parameters for the different domains of interest. In addition to national level estimates, separate estimates are desired for the urban and rural sectors of the statistical regions and 16 selected districts. All districts were sub-stratified into urban, other urban and rural areas (with the exception of Kampala, which is wholly urban). The district headquarters are designated as urban and other urban areas are the town boards, trading centres, etc. as defined during the 1991 Population Census.

To increase the efficiency of the domain estimates, a second level of stratification is created by dividing the domains into homogeneous strata and selecting samples from each stratification. Within the selected rural EA’s, households are classified as small scale farmers, large scale farmers and non-farming households (details stated under Listing below). It should be noted, however, that this stratification is not intended for the purpose of producing reliable estimates for each stratification separately, but only to increase the precision of the rural estimates.

Sampling Frame The sampling frame is made up of EA’s from the 1991 Population Census which were provided at district level with their corresponding number of households. Additionally, the IHS, 1992/93 provided the sampling frame for the panel EA’s and subsequently, the panel households.

Sample Size The size required for a sample is 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.

In the case of UNHS 1999/00, cost and operational limitations allowed a maximum sample size of approximately 10,700 households.

The precision of survey estimates in a domain is a function of the sample size in the domain and the amount of variability among the population units in the domain. Since there are no available estimates of the variance of the different characteristics of interest within the domains for which similar levels of precision for the domains are desirable, a more or less equal allocation was used. Initially a total sample of 1,400 first stage sampling units were selected based on cost and efficiency. These comprised of a common panel from IHS of 637 first stage units selected by simple random sampling and a new independent sample of 773 first stage sampling units selected by probability proportional to the number of households from the Census frame.

Due to some constraints including late procurement of field vehicles, the sample size was reduced proportionately to about 1,100 first stage sampling units. The adjusted sample comprises of about 518 panel EA’s and 563 new EA’s.

Detailed information on the sampling procedure is available in 'Socio-Economic Report'

Sampling deviation

The UNHS 1999/2000 covered all districts in the country, except the districts of Kitgum, Gulu, Kasese and Bundibugyo. The report therefore has quantitative analysis exclusive of these four districts.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the UNHS were based on the previous Household Survey Questionnaires with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including duration of stay in household, relationship to household head, sex, age, literacy, marital status, activity status and orphanhood status. The household questionnaire also includes education, and health, characteristics of dwelling, past experiences of dwelling, and household consumption and non - consumption expenditure modules. Questions were also asked in each household for women aged 15-49 and children under age five. For children, the questions were answered by the mother or caretaker of the child.

Cleaning operations

A manual system of editing questionnaires was set-up in September 1999 (a month after commencement of fieldwork). A set of scrutiny notes to guide in manual checking was developed to assess the consistency of the data collected. This is referred to as cold-deck scrutiny. A computer program (hot-deck scrutiny) for verification and validation was developed and operated during data processing. In addition, a set of matching-rules for the panel households was developed in September 1999. These were straightforward by using four variables namely; name, sex, age and education of the head of household. The matching exercise as well as manual scrutiny was a continuous process, which was finally accomplished in September 2000.