The School-to-work Transition Survey (SWTS) report for Uganda was launched on 25th November 2014 at Sheraton Kampala Hotel. The survey was conducted in 2013 by the Uganda Bureau of Statistics and International Labour Organization (ILO). The SWTS is part of a global initiative by ILO to understand the labour market conditions faced by youth. A major finding from the Uganda survey is that the transition from school to work by Ugandan youth takes a very long time.
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.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The scope of this study includes:
- Labour market
Other Work Activities
Children & Youth
A purposive sample refers to selection of units based on personal judgement rather than randomization.
Producers and sponsors
Uganda Bureau of Statistics
Ministry of Finance, Planning and Economic Development
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.
Deviations from the Sample Design
- 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.
By weighting data to compensate for imbalances between the proportions of targeted participants among subgroups in the population and the proportions in those subgroups who choose to respond, we ensure that the estimates are adjusted to provide a better fit to what we believe to be the true characteristics of the target group. Generally speaking, weighting provides us with more accurate population estimates. When the sample data are to be weighted, it is highly recommended to attach to each individual case or record its weight as a variable in the data file. Most of the required population estimates, such as proportions, means, ratios and rates, can then be produced easily, without the need to refer to the structure of the sample.
Dates of Data Collection
Data Collection Mode
The informal enterprise sample was chosen from lists of enterprises prepared by the data collection teams. Each team was assigned to list all the enterprises doing business in the same sample unit visited by the team, and then an enterprise was chosen randomly from the list. A total of 184 were selected from the informal sector. The number of employer interviews completed was 347.
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 7
Total questions 35
The arrangement for publishing of the final report is process as follows: If deemed to be of sufficient quality, the final analytical report should be published. This task could entail arranging for additional editing and formatting of the report. The outlet for publishing depends on the organizing unit.
Estimates of Sampling Error
The estimates from a sample survey are affected by two types of errors: 1) non-sampling errors, and 2) sampling errors. Therefore, we can define them as follows:
- Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors.
- Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the SWTS survey is one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
Uganda Bureau of Statistics
Ministry of Finance, Planning and Economic Development