Abstract

The Census is the official count of population and dwellings in Tonga, providing a ‘snapshot’ of the society and its most precious resource, its people, at a point in time. The official reference period of the census was midnight, the 30th of November, 2006.

The census provides a unique source of detailed demographic, social and economic data relating the entire population at a single point in time. Census information is used for policy setting and implementation, research, planning and other decision-making. The census is often the primary source of information used for the allocation of public funding, especially in areas such as health, education and social policy. The main users of this information are the government, local authorities, education facilities (such as schools and tertiary organizations), businesses, community organizations and the public in general.

The 2006 Census was taken under the authority of Section 8 of Statistical Act Chap. 53 of 1978 which empowers the Minister of Finance to make regulations necessary to conduct the population Census. This regulation was approved by the Cabinet and cited as Census Regulation 2006. The Census regulations also indicate that the Government Statistician would be responsible for the administration and completion of the Census. In addition, the regulations enabled the Statistics Department to carry out the necessary activities required to plan, manage and implement all the necessary Census activities.

Census planning and management

From a planning and management perspective, the Census had two main objectives. Firstly, it was to ensure that the process of collecting, compiling, evaluating, analyzing and disseminating of demographic, economic and social data was conducted in a timely and accurate manner. The development of procedures and processes for the 2006 Census of Population and Housing made use of the lessons learned in previous censuses, and built upon recommendations for improvements.

Secondly, it was a valuable opportunity for building the capacities of employees of the Statistics Department (SD), thus resulting in enhancing the image, credibility and reputation of the Department and at the same time, strengthening its infrastructure. Emphasis was placed on having a senior staff with a wide perspective and leadership qualities. Through the use of vision, planning, coordination, delegation of responsibility and a strong team spirit, the census work was conducted in an effective and efficient manner. Staffs at all levels were encouraged to have an innovative mindset in addressing issues. Incentives for other parties to participate, both within Statistics Department Tonga Tonga 2006 Census of Population and Housing viii and outside the government, were encouraged. As a result, the wider community including donors such as AusAID, the Secretariat of the Pacific Community (SPC) in Noumea, that provided the technical assistance and the general public, were able to support the census project.

Extensive and detailed planning is needed to conduct a successful census. Areas that required planning include: enumeration procedures and fieldwork, public communication, data processing and output systems, mapping and the design of census block boundaries, dissemination procedures, content determination and questionnaire development and training. These aspects, and how they interacted with each other, played a crucial role in determining the quality of all of the census outputs. Each phase therefore required careful, methodical planning and testing. The details of such activities, and their implementation and responsibilities were assigned to 5 subcommittees composed of staff members of the SD.

Organizational structure of the Census

A census organizational structure is designed to implement a number of interrelated activities. Each of these activities was assigned to a specific sub-committee. The census manuals provided guidelines on processes, organizational structures, controls for quality assurance and problem solving. The challenge for managers was developing a work environment that enabled census personnel to perform all these tasks with a common goal in mind. Each sub-committee was responsible for its own outputs, and specific decisions for specific situations were delegated to the lowest level possible. Problem situations beyond the scope of the sub-committee were escalated to the next higher level.

The organizational structure of the census was as follows: a) The Steering Committee (consisting of the Head of both Government and nongovernment organizations), chaired by Secretary for Finance with the Government Statistician (GS) as secretary. b) The Census Committee (consisted of all sub-committee leaders plus the GS, and chaired by the Assistant Government Statistician (AGS) who was the officer in charge of all management and planning of the Census 2006 operations. c) There were five Sub-committees (each sub-committee consisted of about 5 members and were chaired by their Sub-committee leader). These committees included: Mapping, Publicity, Fieldwork, Training and Data Processing. In this way, every staff member of the SD was involved with the census operation through their participation on these committees.

The census steering committee was a high level committee that approved and endorsed the plans and activities of the census. Policy issues that needed to be addressed were submitted to the steering committee for approval prior to the census team and sub-committees designation of the activities necessary to address the tasks.

Part of the initial planning of the 2006 Census involved the establishment of a work-plan with specific time frames. This charted all activities that were to be undertaken and, their impact and dependencies on other activities. These time frames were an essential part of the overall exercise, as they provided specific guides to the progress of each area, and alerted subcommittees’ team leaders (TL) to areas where problems existed and needed to be addressed. These also provided the SD staff with a clear indication of where and how their roles impacted the overall Census process.

Monitoring of the timeframe was an essential part of the management of the Census program. Initially, weekly meetings were held which involved the GS, AGS and team leaders (TL) of the Census committee. As the Census projects progressed, the AGS and TL’s met regularly with their sub-committees to report on the progress of each area. Decisions were made on necessary actions in order to meet the designated dates. Potential risks that could negatively affect the deadlines and actions were also considered at these meetings.

For the 5 sub-committees, one of their first tasks was to verify and amend their terms of reference using the “Strengths, Weaknesses, Opportunities and Threats” (SWOT) analysis methodology, as it applied to past censuses. Each committee then prepared a work-plan and listed all activities for which that particular sub-committee was responsible. This listing included the assignment of a responsible person, together with the timeline indicating the start and end dates required to complete that particular activity. These work-plans, set up by all the 5 sub-committees, were then used by the AGS to develop a detailed operational plan for all phases of the census, the activities required to complete these phases, start and end dates, the person responsible and the dependencies, - all in a Ghant chart format. These combined work-plans were further discussed and amended in the Census team and reported to the Steering committee on regular basis as required.

Geographic coverage

National coverage, which includes the 5 Divisions and both Urban and Rural Areas of Tonga.

Analysis unit

Individual and Households.

Universe

All individuals in private and institutional households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The National Population Census was a complete enumeration census, hence no sampling procedure was employed. A Mapping Sub-committee was formed to ensure complete coverage of the country.

The Mapping Sub-committee

Led by Mr. Winston Fainga'anuku, this committee's mandate was to ensure that good quality maps were produced. The objective was to ensure that the maps provided complete coverage of the country, were designed to accommodate a reasonable workload of one census enumerator and, that geographic identifiers could be used for dissemination purposes by the PopGIS system. Collaborations with the Ministry of Land, Survey and Natural Resources (MLSNR) began in 2004 to ensure that digitized maps for Tonga could be used for 2006 Census. Mr. Fainga'anuku was attached to the MLSNR in April 2005 to assist 'Atelea Kautoke, Samuela Mailau, Lilika and others to complete the task of digitizing the maps for Tonga. In addition, frequent visits by Mr. Scott Pontifex from the Secretariat of the Pacific Community (SPC) in Noumea, assisted to ensure that quality digitized maps were prepared. SPC also assisted by lending its digitizer which was used in this mapping project. The staff of the Statistics Department (SD) visited household sites throughout Tongatapu and the main outer islands. This exercise was to redesign the Census Block boundaries by amalgamating or splitting existing census blocks to achieve an average of 50 households per census block. Various updates within the census block maps were made. These included the names of the head of household; roads and other landmarks to ensure that current and accurate information was provided to the enumerators. Reliable maps, both for enumerators and supervisors are necessary ingredients to assist in avoiding any under or over - counting during

Analysis unit

Persons and households

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

UNIT DESCRIPTIONS: - Dwellings: A census building is a free standing structure which is fixed on earth or on water permanently or temporarily (regardless the material used in building it) and it is used for residence or doing any activity in it (work, sport, pious work….etc.). - Households: Consist of one person or a group of persons (related or non related to each other) sharing their housing unit and food together. A household includes: a) servants and the like who are living with the household; b) visitors who spent the census night with the household (except military persons); c) household members who spent the census night apart from their household, like members of armed forces and persons who always or temporarily work at night shifts or otherwise would not be counted by the census elsewhere; d) workers on Egyptian or foreign means of transporation who were present within or out of the territorial boundaries but have no residing place outside the country. - Group quarters: Not applicable

Universe

All individuals (Egyptians and foreigners) who were present within the political boundaries of Egypt at census night.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Central Agency for Public Mobilisation and Statistics

SAMPLE SIZE (person records): 5902243.

SAMPLE DESIGN: Sample of private households drawn by Egyptian statistical office. Sample method unknown.

Mode of data collection

Face-to-face [f2f]

Research instrument

Special Households Questionnaires; Public Living Quarters Questionnaire; Household and Housing Condition Questionnaire

Abstract

The Beijing Platform for Action which emerged from the 1995 Fourth United Nations World Conference on Women called for the development of 'suitable statistical means to recognise and make visible the full extent of the work of women and all their contributions to the national economy, including their contribution in the unremunerated and domestic sectors'. During 2000, Statistics South Africa (Stats SA) conducted the fieldwork for the first national time use study in the country. The aim of the survey was to provide information on the way in which different individuals in South Africa spend their time. Such information contributes to greater understanding of policymakers on the economic and social well-being of different societal groups. In particular, the study was intended to provide new information on the division of both paid and unpaid labour between women and men, and greater insight into less well understood productive activities such as subsistence work,casual work and work in the informal sector.

The survey thus had dual objectives: (1) improvement of concepts, methodology and measurement of all types of work and work-related activity, and (2) the feeding of information into better policy-making, with a particular focus on gender equity.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis for the survey include households and individuals

Universe

The survey covered household members in South Africa, ten years old and above

Kind of data

Sample survey data [ssd]

Sampling procedure

The time use study sample frame was based on the frame prepared for the 1999 Survey of activities of young people (SAYP). This sample frame was based on the 1996 population census enumerator areas (EAs) and the number of households counted in the 1996 population census. The sampled population excluded all prisoners in prison, patients in hospital, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. The 16 EA types from the 1996 Population Census were condensed into four area types, or strata. The four strata were formal urban, informal urban, non-commercial farming rural, and commercial farming areas. Institution type EAs were excluded from the sample.

The EAs were explicitly stratified by province, and within a province by the four strata. The sample size (10 800 dwelling units, with 3 600 units in each of the three tranches) was disproportionately allocated to the explicit strata using the square root method. Within the strata, the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). Primary sampling units (PSUs) consisted of an EA of at least 100 dwelling units. Where an EA contained less than 100 dwelling units, EAs were pooled (using Kish's method of pooling) to meet this requirement. Most EAs had fewer than 100 dwelling units. The dwelling unit was taken as the ultimate sampling unit (USU).

Firstly, a two stage sampling procedure was applied. The allocated number of PSUs was systematically selected with probability proportional to size in each explicit stratum (with the measure of size being the number of dwelling units in a PSU). In each PSU, a systematic sample of 12 households was drawn.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the time use survey was comprised of three sections. Section one covered details of the household. Section two covered demographic details of the first person selected as a respondent in that household. Section three consisted of a Background and methodology diary in which to record the activities performed by the first person selected during the 24 hours between 4 am on the day preceding the interview and 4 am on the day of the interview. Sections four and five were for the second selected person in the household but were otherwise identical to sections two and three respectively.

The household and demographic sections of the questionnaire contained many of the standard questions of Stats SA household surveys. This was done so as to facilitate comparison across surveys. These sections also contained some additional questions on issues that would be likely to affect time use. For the household section, for example, there were questions on access to household aids such as washing machines and vacuum cleaners. In the demographic section there were questions about the presence of the respondent's young children in the household.

The diary, which forms the core instrument of a time use study, was divided into half-hour slots. Respondents were asked an open-ended question as to the activities performed during a given half-hour. These activities were then post-coded by the fieldworker according to the activity classification system (see below). The respondent could report up to three activities for each time slot. Where there was more than one activity reported for a half hour, the respondent was asked whether these activities were conducted simultaneously, or one after the other. For each recorded activity, the questionnaire also included two location codes. The first code provides for eight broadly defined locations plus the mobile activity of travel. Where the location of a particular activity could be classified as more than one of the given options, the option highest on the list took precedence. For example, a domestic worker was classified as working in someone else's dwelling rather than in a workplace. The second code distinguished between interior (inside) and exterior (outside) for the eight broadly-defined locations, and distinguished the mode of travel for all travel activity.

Cleaning operations

The data from the diary were captured in Sybase at Stats SA head office through a custom-designed data capture programme. The programme contained some in-built checks. Further checks were done manually prior to and after capture. The data were subsequently downloaded into SAS format, and the SAS programme was used for analysis.

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the magisterial district of Mitchell's Plain within the Cape Metropolitan area in South Africa. This includes the townships of Khayelitsha, Langa, Gugulethu and Nyanga and the informal settlements of Crossroads and Browns Farm.

Analysis unit

Households and individuals

Universe

The survey population of interest isl adults aged 18 years of age and older in households in every Enumeration Area in the Mitchell's Plain magisterial district. Before selecting Enumerator Areas, the survey excluded all non-residential and institutional Enumerator Areas (except for hostels) from the sample frame. Enumerator Areas were selected systematically to ensure that their probability of selection was proportionate to their population size. The Mitchell's Plain magisterial district consist of 1,486 populated Enumerator Areas (as defined in the 1996 Population Census). Dividing the target number of questionnaires (2,875) by the average number of adults per household (2.66), the survey determined to select 1,081 households.

A more detailed description of the universe for this survey can be found in the sampling method document available in the zipped folder under 'documentation'.

Kind of data

Sample survey data

Sampling procedure

The sample is based on the 1996 Population Census which recorded a total population of 728 916 people in the Mitchell's Plain Magisterial district. The survey sampled households in the designated geographic area using a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For the clusters of households, the survey relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. Enumerator Areas are neighbourhoods of roughly 50 to 200 households and drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell's Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS), where size is measured as the number of households in each Enumerator Area (as defined by the 1996 Population Census). This method provides the most efficient way to obtain equal sub-sample sizes across two stages of selection, i.e. to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available in the zipped folder under 'documentation'.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter, a separate set of questions covering education, health and work status was included.

The adult questionnaire was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.

Abstract

The Time Use Survey (TUS) is a household-based survey that measures and analyses the time spent by women and men, girls and boys, the rich and the poor, on different activities over a specified period. Statistics South Africa (Stats SA) conducts time use surveys using the 'yesterday' diary approach. A 'yesterday' diary is one in which the respondent is asked what they did for each period in the 24 hours of a day preceding the survey interview. Unlike data from other surveys, time use data reflects what activities are performed, how they are performed and how long it takes to perform such activities. Such activities include paid work, unpaid work, volunteer work, domestic work, leisure and personal activities.

Stats SA conducted the first TUS in 2000 and the second one in 2010. The TUS aims to provide information on the division of both paid and unpaid labour between women and men, shed light on the reproductive and leisure activities of household members, and provide information about less well-understood productive activities such as subsistence work, casual work and work in the informal sector. Therefore, TUS surveys can be used for gender policy analysis in relation to employment and unemployment, services for children, the elderly and people with disabilities, and provision of basic household services such as electricity and water that obviate the need for manual collection of fuel and water for household use.

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The TUS sample covered the non-institutional population aged 10 years and above excluding those living in worker hostels - thus representing an estimated 39,9 million people.

Kind of data

Sample survey data

Sampling procedure

The TUS 2000 sample frame was based on the frame prepared for the 1999 Survey of activities of young people (SAYP). This sample frame was based on the 1996 population census enumerator areas (EAs) and the number of households counted in the 1996 population census. The sampled population excluded all prisoners in prison, patients in hospital, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. The 16 EA types from the 1996 Population Census were condensed into four area types, or strata. The four strata were formal urban, informal urban, non-commercial farming rural, and commercial farming areas. Institution type EAs were excluded from the sample.

The sample is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The EAs were explicitly stratified by province, and within a province by the four strata. The sample size (10 800 dwelling units, with 3 600 units in each of the three tranches) was disproportionately allocated to the explicit strata using the square root method. Within the strata, the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). PSUs consisted of an EA of at least 100 dwelling units. Where an EA contained less than 100 dwelling units, EAs were pooled (using Kish's method of pooling) to meet this requirement. Most EAs had fewer than 100 dwelling units. The dwelling unit was taken as the ultimate sampling unit (USU).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the TUS is comprised of five sections:

Section 1 - details of all household members Section 2 - demographic details of the first person selected (respondent one) in each household Section 3 - recorded activities performed by respondent one in each household (diary) Section 4 - demographic details of the second person selected (respondent two) in each household Section 5 - recorded activities performed by respondent two in each household (diary)

The diary was divided into half-hour slots. Respondents were asked an open-ended question on the activities performed during each half-hour period. These activities were then post-coded by the fieldworker according to the activity classification system. The respondent could report up to three activities for each time slot. Where there was more than one activity reported for a half hour, the respondent was asked whether these activities were conducted simultaneously, or one after the other.

The sections of the questionnaire for household and demographic data collection also contained additional questions on issues likely to affect time use. For example questions on access to household appliances owned. The questionnaire includes two location codes for each recorded activity. The first code provides for eight broadly-defined locations plus the mobile activity of travel. Where the location of a particular activity could be classified as more than one of the given options, the option highest on the list took precedence. The second code distinguished whether the activity was done inside or outside for the eight broadly-defined locations, and distinguished the mode of travel for all travel activity.

Abstract

In the year 2000 a small team of social scientists from the Universities of Cape Town and Michigan collaborated on designing a survey with a special focus on labour market issues as a precursor to a Cape Area Panel Study with a special focus on youth planned for the year 2002. After much debate and taking due cognisance of time and budget constraints the team decided to target the magisterial district of Mitchell’s Plain within the Cape Metropole for the survey.

This decision was informed by data gleaned from the 1996 census which revealed that Mitchell’s Plain – demarcated a magisterial district in 1986 – contained almost thirty percent of the population in the Cape Metropolitan Council area. It straddled the two cities of Cape Town and Tygerberg and housed nearly 74% of the African and over 20% of the ‘coloured’ metropolitan population. It included the three established African townships of Langa, Gugulethu and Nyanga as well as informal settlements such as Crossroads and Browns Farm. It also included Khayelitsha an African township proclaimed in the early 1980s with the first houses being built in 1986. The 1996 census had recorded high unemployment rates of over 44%, for Africans and over 20% for Coloured people.

Geographic coverage

The survey covers the Khayelitsha and Mitchell's Plain areas of Cape Town, South Africa.

Analysis unit

The unit of analysis for this survey includes households and individuals.

Universe

The survey covers the African and Coloured populations of the Khayelitsha and Mitchell's Plain areas of Cape Town.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to represent all adults (18 years of age and older) in the Mitchell’s Plain Magisterial district. As discussed above, the most cost-efficient method of interviewing residents of such a large area is to use a two-stage cluster sample. The first stage of this sample entails selecting clusters of households and the second stage entails the selection of the households themselves. For our clusters of households, we relied on the Enumerator Areas as defined by Statistics South Africa for the 1996 Population Census. These Enumerator Areas are neighbourhoods of roughly 50 to 200 households. They are drawn up by the Chief Directorate of Demography at Statistics South Africa. This directorate is responsible for developing and maintaining a GIS system that provides the maps that are used for conducting the five-yearly national population census (Statistics South Africa, 2001:42-44). Although Enumerator Area boundaries do not cross municipal boundaries, they do not correspond to any other administrative demarcations such as voting wards. Enumerator Areas are designed to be homogeneous with respect to housing type and size. For example, Enumerator Area boundaries within the Mitchell’s Plain Magisterial District do not usually cut across different types of settlements such as squatter camps, site and service settlements, hostels, formal council estates or privately built estates. Instead, each Enumerator Area is homogeneous with respect to any one of these housing types.

The method of selection used was that of Probability Proportional to Size (PPS). The measure of size being the number of households in each Enumerator Area as measured by the 1996 Population Census. This method was chosen as it provides the most efficient way to obtain equal subsample sizes across two stages of selection, i.e. we are able to select the Enumerator Areas and then select from each Enumerator Area a constant number of households for all Enumerator Areas in the sample. The sample is implicitly stratified by location and by housing type.

A more detailed description of the sampling method and procedure for this survey can be found in the sampling method document available through this site under Other Study Materials.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire: Was aimed at establishing the household roster with the usual questions on age, gender and relationships. It was divided into two sections covering those aged 18 and older and those younger than 18. For the latter a separate set of questions covering education, health and work status was included.

The adult questionnaire: Was aimed to fit the international standard approach on the labour force by allocating the labour market status of ‘employee’ to all those ‘at work’ (for profit or family gain, in cash or in kind). One of the innovative aspects of the survey was that respondents were asked about all income-earning activities. In other words, they were not allocated into particular labour market categories during the process of the interview.

The adult questionnaire was divided into 13 sections:

• Section A on education and other characteristics covered age, racial classification, educational attainment, language, religion and health. • Section B on migration covered place of origin, relocation and destination. • Section C on intergenerational mobility aimed at capturing parental influence on the respondent. • Section D on employment history aimed at capturing the respondent’s work history. • Section E on wage employment attempted to capture respondents working for a wage or salary whether full-time, part-time, in the formal sector or the informal sector including those who had more than one job. • Section F on unemployment included questions on job search • Section G on self-employment included a question on more than one economic activity and the frequency of self-employment. • Section H on non-labour force participants was aimed at refining work status. • Section I on casual work aimed to capture not only those in irregular/short term employment but also people who might have more than one job. • Section J on helping other people with their business for gain was aimed at identifying respondents who assist others from time to time but who might not regard themselves as ‘working’. • Section K on reservation wages attempted to establish the lowest wage at which a respondent would accept work. • Section L on savings, borrowing and grants and investment income attempted to capture income derived from sources other than work • Section M on perceptions of distributive justice posed a number of attitudinal questions.

Abstract

The main objectives of LFS are to collect information, mainly on the supply side of the labour market, i.e., information on those who are working or who are actively looking for work. The LFS collects social and economic information for use in the following areas:

• Macro-economic monitoring: The change in the number of people employed is an indicator of changes in economic activity. It is necessary to track these changes, specifically the types of jobs and the industries in which people work.
• Human resource development policies: The economy is changing all the time. In order to meet the needs of the changing economy, people need to be vocationally trained. LFS enables the identification of areas of training.
• Employment policies: For an economy to work at its maximum potential, all those wanting to have work should have jobs. Some people may wish to have full-time jobs and can only find part-time work. Knowing how many of these people there are can enable the Government to design policies that encourage full-employment.
• Income support and social programmes: For the majority of people, employment income is their main means of support. People not only need jobs but also productive jobs in order to receive reasonable incomes. Government needs to know what levels of income are being earned by different groups of persons.

Geographic coverage

National coverage

Analysis unit

Household member age 15-64 years old.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified two-stage sample design was used for the 2014 Kosovo LFS. The sampling frame was based on the data and cartography from the 2011 Kosovo Census. For the purposes of the census enumeration, Kosovo was subdivided into enumeration areas (EAs), which are relatively small operational segments defined for the census enumeration. A total of 4,626 EAs were defined for Kosovo, and these were used as the primary sampling units (PSUs) selected at the first sampling stage for the LFS. The overall average number of households per EA in the sampling frame was 67; the average size of the urban EAs (103 households) was almost twice that for the rural EAs (53 households). One census enumerator was responsible for enumerating the households and population in each EA. KAS used the 2011 Census data to compile a sampling frame of EAs that was used for selecting the LFS sample.

Kosovo is divided geographically into seven regions. KAS uses these seven regions for stratifying the sampling frame and for reporting the results from their household surveys. Each region is divided into municipalities, which are further subdivided into towns or localities. The EAs were defined within the smallest administrative units. Each EA was classified as urban or rural, and this classification was used for defining sampling strata within each region.

Mode of data collection

Face-to-face [f2f]

Abstract

1. As well as in the implementation of the 1980 population census, has gathered data on potential village. So along with the implementation of the Agricultural Census (ST '83), will be collected also data 1983 Village Potential. Given the growth potential of the data in the field of Agriculture and Rural Agro Industries, the creative potential of the Village 1983 checklist in some ways changes and additions, especially in the matter relating to the Agricultural Census of 1983 so the results can be used as a correction factor results of the Census of Agriculture 1983. On the other hand, the data collected Village Potential will be very useful for development planning at the village level / village and for the national interest.

2. Potential structural information than the village that will be collected includes the status of Village / Village, Classification Village / Village, and Geographic Location of Rural / Village, General Remarks Village / Village, Education Facilities, Health Facilities, Recreation, Social Work, Tenure and Land Use, Resources in the field of Agriculture, Agriculture Business, Agriculture and Fisheries, Agriculture and Infrastructure Tool Marketing, Warehousing and Industrial Business Household / Crafts, Business Transportation and other business outside of the Agriculture and Industry, and Information and Communications Facilities Finance and Rural Development / wards.

3. 1983 Village Potential enumeration conducted in conjunction with the implementation of the Agricultural Census 1983 and for The next census will be done again every three years in accordance with the program of activities that have been carried out by the Central Bureau of Statistics.

4. Keteranga-value information generated from the Village Potential Enumeration will be highly dependent on the skills and determination of the officers Agricultural Census 1983 (including counter, inspector, supervisor and coordinator of field operators). This shows how important the role of the enumerator to the final value of the data. It is expected the census officers do their best to collect information in accordance with the actual situation.

5. To be doing a fine job, then every officer is required to pay attention, follow, and comply with the instructions given in the exercise and are listed in the guidebook that has been provided for this purpose.

Geographic coverage

Coverage of national, representative to the level of villages / wards.

Analysis unit

Village

Universe

Village

Kind of data

Census/enumeration data

Sampling procedure

Enumeration method is a method Village Potential Census / complete enumeration of all villages / urban, by visiting villages / urban or give penjelasa the village chief / headman or village staff / Sub that can represent the village chief / headman of data in order to provide data for the Village Potential can immediately fill the Village Potential Entry List and sesai well with the actual situation.

Mode of data collection

Face-to-face [f2f]