94 datasets found
  1. g

    Long-term unemployment rate of the population aged 16 to 74 in the Basque...

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    Long-term unemployment rate of the population aged 16 to 74 in the Basque Country and the countries of the European Union. | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_https-opendata-euskadi-eus-catalogo-tasa-paro-larga-duracion-16-74-anos-pais-
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    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Basque Country, Europe, European Union
    Description

    There are several objectives faced by the operation on Structural Indicators.The first and generic is to achieve the production, with the highest possible degree of quality, of a battery of basic or context indicators, which serve or can serve as a reference.The second objective, would be to achieve methodological homogeneity and precision in the calculation in relation to other internal systems of indicators, and especially those defined by Eurostat, to rework and elaborate series that add the temporal perspective and design and implement dynamic file formats that allow the organisation and access to all information. Finally, the specific objective of the operation would focus on the coordination, management, verification and archiving of the indicators system.

  2. e

    Employment and Unemployment Survey, EUS 2016 - Jordan

    • erfdataportal.com
    Updated Oct 22, 2017
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    Department of Statistics (2017). Employment and Unemployment Survey, EUS 2016 - Jordan [Dataset]. http://www.erfdataportal.com/index.php/catalog/133
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    Dataset updated
    Oct 22, 2017
    Dataset provided by
    Economic Research Forum
    Department of Statistics
    Time period covered
    2016
    Area covered
    Jordan
    Description

    Abstract

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

    The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

    It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

    The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

    The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

    Geographic coverage

    Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

    Analysis unit

    1- Household/family. 2- Individual/person.

    Universe

    The survey covered a national sample of households and all individuals permanently residing in surveyed households.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Cleaning operations

    ----> Raw Data

    A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

    ----> Harmonized Data

    • The SPSS package is used to clean and harmonize the datasets.
    • The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
    • All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
    • A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
    • A post-harmonization cleaning process is then conducted on the data.
    • Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.
  3. Household Labour Force Survey 2010 - Turkiye

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Jun 14, 2022
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    Turkish Statistical Institute (2022). Household Labour Force Survey 2010 - Turkiye [Dataset]. https://datacatalog.ihsn.org/catalog/4342
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    Dataset updated
    Jun 14, 2022
    Dataset authored and provided by
    Turkish Statistical Institutehttp://tuik.gov.tr/
    Time period covered
    2010
    Area covered
    Türkiye
    Description

    Abstract

    The main objective of the Household Labour Force Survey is to obtain information on the structure of the labour force in the country. This includes information on economic activity, occupation, status in employment and hours worked for employed persons; and information on the duration of unemployment and occupation sought by the unemployed.

    Geographic coverage

    Whole country

    Analysis unit

    • Households
    • Individuals

    Universe

    Population coverage: Whole population excluding the following groups: Non-settled population, persons living in institutions and conscripts (but army forces are included) The survey covers: The usual residents present and the usual residents temporarily absent Age coverage: The labour related questions of the survey relate to the population of age groups between 15 and 99 years old

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Household Labor Force Survey is designed to produce estimations on annually, quarterly (3 months) and monthly basis over 3 months moving average by carrying out the survey at each month in the country. Sample size of the survey is calculated in order to have annual estimations on Nuts1 x urban-rural and Nuts2 level. For the determination of the sample size, two studies were carried out:

    In the first study, the initial selection probabilities, f0, were calculated in parallel with the year of 2004. The number of households was allocated to the Nuts2xurban-rural groups (52) proportionally. Then, in order to achieve the sufficient sample size in each group, the number of households in the urban groups were weighted by 1.5*f0 and in the rural groups by f0. By this weighting, some groups had still under or over sample sizes. These groups were reweighted by f0 or 2*f0. Hence the final sample sizes from the first study were obtained.

    In the second study, the requirement of Eurostat 577/98 regulation was taken into account. The instructions in this regulation were applied on the 2007 data set and the sample sizes in each strata were calculated independently. Following the regulation, firstly, %5 of the working age population was calculated and the corresponding groups belonging to this %5 of the working age population were determined. The groups were chosen from age, gender and education level groups. Then the sample sizes for each strata (52) were calculated depending on both the %8 coefficient of variation criteria and the values of unemployment rate, design effect, overlapping factors between quarters and correlation coefficient values in each of the selected age, gender and education level groups.

    Number of interviewed persons 15 years of age and over: 384,846

    Number of interviewed households: 143,871

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Sampling error estimates

    Sampling errors related to proportion and total estimates of the survey are calculated based on Taylor Series approximation using SAS module.

  4. f

    Data from: Depression and unemployment incidence rate evolution in Portugal,...

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    Updated Jun 3, 2023
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    Ana Paula Rodrigues; Mafalda Sousa-Uva; Rita Fonseca; Sara Marques; Nuno Pina; Carlos Matias-Dias (2023). Depression and unemployment incidence rate evolution in Portugal, 1995–2013: General Practitioner Sentinel Network data [Dataset]. http://doi.org/10.6084/m9.figshare.5644444.v1
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    jpegAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    SciELO journals
    Authors
    Ana Paula Rodrigues; Mafalda Sousa-Uva; Rita Fonseca; Sara Marques; Nuno Pina; Carlos Matias-Dias
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Portugal
    Description

    ABSTRACT OBJECTIVE Quantify, for both genders, the correlation between the depression incidence rate and the unemployment rate in Portugal between 1995 and 2013. METHODS An ecological study was developed to correlate the evolution of the depression incidence rates estimated by the General Practitioner Sentinel Network and the annual unemployment rates provided by the National Statistical Institute in official publications. RESULTS There was a positive correlation between the depression incidence rate and the unemployment rate in Portugal, which was significant only for males (R2 = 0.83, p = 0.04). For this gender, an increase of 37 new cases of depression per 100,000 inhabitants was estimated for each 1% increase in the unemployment rate between 1995 and 2013. CONCLUSIONS Although the study design does not allow the establishment of a causal association between unemployment and depression, the results suggest that the evolution of unemployment in Portugal may have had a significant impact on the level of mental health of the Portuguese, especially among men.

  5. National Sample Survey 1987-1988 (43rd Round) - Schedule 10 - Employment and...

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    • dev.ihsn.org
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    Updated Mar 29, 2019
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    National Sample Survey Organisation (2019). National Sample Survey 1987-1988 (43rd Round) - Schedule 10 - Employment and Unemployment - India [Dataset]. https://catalog.ihsn.org/catalog/3245
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    National Sample Survey Organisation
    Time period covered
    1987 - 1988
    Area covered
    India
    Description

    Abstract

    The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 .

    The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

    Geographic coverage

    The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

    Analysis unit

    Randomly selected households based on sampling procedure and members of the household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

    SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.) URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

    Table (1.2) : Composition of urban strata

    Stratum population class of town

    number

    (1) (2)

    1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area) 7 " (other area) 8 another city with population 1 million and above

    9 " (other area)

    Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
    Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be collected, on the basis of which its means of livelihood will be identified as one of the following : "self-employed in non-agriculture " "rural labour" and "others" (see section Two for definition of these terms) . Also the area of land possessed as on date of survey will be ascertained from all households while listing. Now the households of sub-stratum 2 will be arranged in the order : (1)self-employed in non-agriculture, (2) rural labour, other households, with land possessed (acres) : (3) less than 1.00 (4) 1.00-2.49,(5)2.50-4.99, (6)

  6. p

    Labour Force Survey 2018 - Tonga

    • microdata.pacificdata.org
    Updated Jul 5, 2019
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    Tonga Statistics Department (TSD) (2019). Labour Force Survey 2018 - Tonga [Dataset]. https://microdata.pacificdata.org/index.php/catalog/256
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    Dataset updated
    Jul 5, 2019
    Dataset authored and provided by
    Tonga Statistics Department (TSD)
    Time period covered
    2018
    Area covered
    Tonga
    Description

    Abstract

    This is the fourth Labor Force Survey of Tonga. The first one was conducted in 1990. Earlier surveys were conducted in 1990, 1993/94, and 2003 and the results of those surveys were published by the Statistics Department.

    The objective of the LFS survey is providing information on not only well-known employment and unemployment as well as providing comprehensive information on other standard indicators characterizing the country labour market. It covers those age 10 and over in the whole Kingdom. Information includes age, sex, activity, current and usual employment status, hours worked and wages and in addition included a seperate Food Insecurity Experiences Survey (FIES) questionniare module at the Household Level.

    The conceptual framework used in this labour force survey in Tonga aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician.

    Geographic coverage

    National coverage.

    There are six statistical regions known as Division's in Tonga namely Tongatapu urban area, Tongatapu rural area, Vava'u, Ha'pai, Eua and the Niuas.Tongatapu Urban refers to the capital Nuku'alofa is the urban area while the other five divisions are rural areas. Each Division is subdivided into political districts, each district into villages and each village into census enumeration areas known as Census Blocks. The sample for the 2018 Labour Force Survey (LFS) was designed to cover at least 2500 employed population aged 10 years and over from all the regions. This was made mainly to have sufficient cases to provide information on the employed population.

    Analysis unit

    • Households (for food insecurity module questionnaire)
    • Individuals.

    Universe

    Population living in private households in Tonga. The labour force questionnaire is directed to the population aged 10 and above. Disability short set of questions is directed to all individuals age 2 and above and the food insecurity experience scale is directed to the head of household.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    2018 Tonga Labour force survey aimed at estimating all the main ILO indicators at the island group level (geographical stratas). The sampling strategy is based on a two stages stratified random survey.

    1. Computation of the survey parameters: Total sample size per strata, number of households to interview in each Primary Sampling Unit (PSU = census block) and number of PSUs to select The stratification of the survey is the geographical breakdown by island group (6 stratas Tongatapu urban, Tongatapu rural, Vava'u, Ha'apai, 'Eua, Niuas)
    2. The selection strategy is a 2 stages random survey where: Random selection of census blocks within each
    3. Census blocks are randomly selected in first place, using probability proportional to size
    4. 15 households per block are randomly selected using uniform probability

    5. The sampling frame used to select PSUs (census blocks) and household is the 2016 Tonga population census.

    The computation of sample size required the use of: - Tonga 2015 HIES dataset (labour force section) - Tonga 2016 population census (distribution of households across the stratas) The resource variable used to compute the sample size is the labour force participation rate from the 2015 HIES. The use of the 2015 labour force section of the Tonga HIES allows the computation of the design effect of the labour force participation rate within each strata. The design effect and sampling errors of the labour force participation rate estimated from the 2015 HIES in combination with the 2016 household population distribution allow to predict the minimum sample size required (per strata) to get a robust estimate from the 2018 LFS.

    Total sample size: 2685 households Geographical stratification: 6 island groups Selection process: 2 stages random survey where census blocks are selected using Probability Proportional to Size (Primary Sampling Unit) in the first place and households are randomly selected within each selected blocks (15 households per block) Non response: a 10% increase of the sample happened in all stratas to account for non-response Sampling frame: the household listing from the 2016 population census was used as a sampling frame and the 2015 labour force section of the HIES was used to compute the sample size (using labour force participation rate.

    Sampling deviation

    No major deviation from the original sample has taken place.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    The 2018 Tonga Labour Force Survey questionnaire included 15 sections:

    IDENTIFICATION SECTION B: INDIVIDUAL CHARACTERISTICS SECTION C: EDUCATION (AGE 3+) SECTIONS B & C: EMPLOYMENT IDENTIFICATION AND TEMPORARY ABSENCE (AGE 10+) SECTION D: AGRICULTURE WORK AND MARKET DESTINATION SECTION E1: MAIN EMPLOYMENT CHARACTERISTICS SECTION E2: SECOND PAID JOB/ BUSINESS ACTIVITY CHARACTERISTICS SECTION F: INCOME FROM EMPLOYMENT SECTION G: WORKING TIME SECTION H: JOB SEARCH SECTION I: PREVIOUS WORK EXPERIENCE SECTION J: MAIN ACTIVITY SECTION K: OWN USE PRODUCTION WORK FOOD INSECURITY EXPERIENCES GPS + PHOTO

    The questionniares were developed and administered in English and were translated into Tongan language. The questionnaire is provided as external resources.

    The draft questionnaire was pre-tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The pilot testing was undertaken on the 27th of May to the 1st of June 2018 in Tongatapu Urban and Rural areas. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.

    Cleaning operations

    The World Bank Survey Solutions software was used for Data Processing, STATA software was used for data cleaning, tabulation tabulation and analysis.

    Editing and tabulation of the data will be undertaken in February/March 2019 in collaboration with SPC and ILO.

    Response rate

    A total, 2,685 households were selected for the sample. Of these existing households, 2,584 were successfully interviewed, giving a household response rate of 96.2%.

    Response rates were higher in urban areas than in the rural area of Tongatapu.

    -1 Tongatapu urban: 97.30%
    -2 Tongatapu rural: 93.00%
    -3 Vava'u: 100.00% -4 Ha'pai: 100.00% -5 Eua: 95.20% -6 Niuas: 80.00% -Total: 96.20%.

    Sampling error estimates

    Sampling errors were computed and are presented in the final report.

    The sampling error were computed using the survey set package in Stata. The Finite Population Correction was included in the sample design (optional in svy set Stata command) as follow: - Fpc 1: total number of census blocks within the strata (variable toteas) - Fpc 2: Here is a list of some LF indicators presented with sampling error

    -RSE: Labour force population: 2.2% Employment - population in employment: 2.2% Labour force participation rate (%): 1.7% Unemployment rate (%): 13.5% Composite rate of labour underutilization (%): 7.3% Youth unemployment rate (%): 18.2% Informal employment rate (%): 2.7% Average monthly wages - employees (TOP): 12%.

    -95% Interval: Labour force population: 28,203 => 30,804 Employment - population in employment: 27,341 => 29,855 Labour force participation rate (%): 45.2% => 48.2% Unemployment rate (%): 2.2% => 3.9% Composite rate of labour underutilization (%): 16% => 21.4% Youth unemployment rate (%): 5.7% => 12.1% Informal employment rate (%): 44.3% => 49.4% Average monthly wages - employees (TOP): 1,174 => 1,904.

  7. e

    Unemployment rate of the male age 16-74 years by country of the Basque...

    • euskadi.eus
    csv, xlsx
    Updated Jun 30, 2023
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    (2023). Unemployment rate of the male age 16-74 years by country of the Basque Country and the countries of the European Union. [Dataset]. https://www.euskadi.eus/male-unemployment-rate-ages-16-to-74-by-country/web01-ejeduki/en/
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    xlsx(19.36), csv(2.22)Available download formats
    Dataset updated
    Jun 30, 2023
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Basque Country, European Union
    Description

    The operation on Structural Indicators takes on several objectives.The first and overall objective lies in achieving, with the highest possible quality, the production of a series of basic or context indicators that serve, or may serve, as a reference.The second objective is to achieve methodological homogeneity and precision in calculation in relation to other international systems of indicators ¿and especially those defined by Eurostat¿ to create and recreate series that add the time perspective.To design and implement dynamic file formats that allow for the organisation and access to all of the information.Ultimately, the specific objective of the operation focuses on the coordination, management, verification and archiving of the system of indicators.

  8. Household Labour Force Survey 2011 - Turkiye

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Jun 14, 2022
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    Turkish Statistical Institute (2022). Household Labour Force Survey 2011 - Turkiye [Dataset]. https://datacatalog.ihsn.org/catalog/4343
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    Dataset updated
    Jun 14, 2022
    Dataset authored and provided by
    Turkish Statistical Institutehttp://tuik.gov.tr/
    Time period covered
    2011
    Area covered
    Türkiye
    Description

    Abstract

    The main objective of the Household Labour Force Survey is to obtain information on the structure of the labour force in the country. This includes information on economic activity, occupation, status in employment and hours worked for employed persons; and information on the duration of unemployment and occupation sought by the unemployed.

    Geographic coverage

    Whole country

    Analysis unit

    • Households
    • Individuals

    Universe

    Population coverage: Whole population excluding the following groups: Non-settled population, persons living in institutions and conscripts (but army forces are included) The survey covers: The usual residents present and the usual residents temporarily absent Age coverage: The labour related questions of the survey relate to the population of age groups between 15 and 99 years old

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Household Labor Force Survey is designed to produce estimations on annually, quarterly (3 months) and monthly basis over 3 months moving average by carrying out the survey at each month in the country. Sample size of the survey is calculated in order to have annual estimations on Nuts1 x urban-rural and Nuts2 level. For the determination of the sample size, two studies were carried out:

    In the first study, the initial selection probabilities, f0, were calculated in parallel with the year of 2004. The number of households was allocated to the Nuts2xurban-rural groups (52) proportionally. Then, in order to achieve the sufficient sample size in each group, the number of households in the urban groups were weighted by 1.5*f0 and in the rural groups by f0. By this weighting, some groups had still under or over sample sizes. These groups were reweighted by f0 or 2*f0. Hence the final sample sizes from the first study were obtained.

    In the second study, the requirement of Eurostat 577/98 regulation was taken into account. The instructions in this regulation were applied on the 2007 data set and the sample sizes in each strata were calculated independently. Following the regulation, firstly, %5 of the working age population was calculated and the corresponding groups belonging to this %5 of the working age population were determined. The groups were chosen from age, gender and education level groups. Then the sample sizes for each strata (52) were calculated depending on both the %8 coefficient of variation criteria and the values of unemployment rate, design effect, overlapping factors between quarters and correlation coefficient values in each of the selected age, gender and education level groups.

    Number of interviewed persons 15 years of age and over: 385,231 Number of interviewed households: 144,361

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Sampling error estimates

    Sampling errors related to proportion and total estimates of the survey are calculated based on Taylor Series approximation using SAS module.

  9. e

    Employment and Unemployment Survey, EUS 2004 - Jordan

    • erfdataportal.com
    Updated Aug 29, 2019
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    Department of Statistics (2019). Employment and Unemployment Survey, EUS 2004 - Jordan [Dataset]. https://erfdataportal.com/index.php/catalog/155
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    Dataset updated
    Aug 29, 2019
    Dataset provided by
    Economic Research Forum
    Department of Statistics
    Time period covered
    2004 - 2005
    Area covered
    Jordan
    Description

    Abstract

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

    The Department of Statistics (DOS) carried out two rounds of the 2004 Employment and Unemployment Survey (EUS). The survey rounds covered a total sample of about fourteen households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design. It is noteworthy that the sample represents the national level (Kingdom), governorates, the three Regions (Central, North and South), and the urban/rural areas.

    The importance of this survey lies in that it provides a comprehensive data base on employment and unemployment that serves decision makers, researchers as well as other parties concerned with policies related to the organization of the Jordanian labor market.

    The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

    Geographic coverage

    Covering a sample representative on the national level (Kingdom), governorates, the three Regions (Central, North and South), and the urban/rural areas.

    Analysis unit

    1- Household/family. 2- Individual/person.

    Universe

    The survey covered a national sample of households and all individuals permanently residing in surveyed households.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire is divided into main topics, each containing a clear and consistent group of questions, and designed in a way that facilitates the electronic data entry and verification. The questionnaire includes the characteristics of household members in addition to the identification information, which reflects the administrative as well as the statistical divisions of the Kingdom.

    Cleaning operations

    Raw Data

    The plan of the tabulation of survey results was guided by former Employment and Unemployment Surveys which were previously prepared and tested. The final survey report was then prepared to include all detailed tabulations as well as the methodology of the survey.

    Harmonized Data

    • The SPSS package is used to clean and harmonize the datasets.
    • The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
    • All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
    • A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
    • A post-harmonization cleaning process is then conducted on the data.
    • Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.
  10. Labor Force Survey, LFS 2013-2014 - Yemen

    • erfdataportal.com
    Updated Oct 15, 2017
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    ILO Regional Office for Arab States (2017). Labor Force Survey, LFS 2013-2014 - Yemen [Dataset]. http://www.erfdataportal.com/index.php/catalog/132
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    Dataset updated
    Oct 15, 2017
    Dataset provided by
    International Labour Organizationhttp://www.ilo.org/
    Central Statistical Organization
    Economic Research Forum
    Time period covered
    2013 - 2014
    Area covered
    Yemen
    Description

    Abstract

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

    The primary objective of LFS 2013-2014 was to provide current data on the employment and unemployment situation at national and governorate level using the preliminary version of the new standards concerning statistics of work, employment and labour underutilization on adopted by the 19th International Conference of Labour Statisticians (Geneva, October 2013).

    ---> The survey was then designed to meet five main measurement objectives as follows: 1- To provide current data on the number of employed, unemployed, and underemployed, and their demographic and social characteristics, including the size of women's participation in economic activity with a view to future policies in expanding their participation in the labour market. 2- To collect data on qualifications of the labour force and participation in training programmes of the youth population and other data requirements for improving the performance of employers through knowledge on the levels of skill available to them. 3- To measure the volume and characteristics of labour migration of Yemenis outside the country. 4- To provide information on the amount of wages and employment-related income in different occupations, branches of economic activity and sectors of employment. 5- To collect appropriate data for evaluating the microfinance projects funded through the Social Fund for Development.

    Given the extent and diversity of data requirements, the survey was designed to spread over a one-year period, built around the five objectives of the survey. The core labour force survey was conducted throughout the four quarters of the survey period and incorporated the measurement of income from employment along the conventional items of data collection. Data on qualifications and participation in training was collected on the third quarter and on labour migration on the second quarter of the survey programme. Data collection on microfinance was undertaken as a separate survey over the four quarters.

    Geographic coverage

    Survey operations were carried out in all governorates except parts where recent events have disturbed the normal course of economic activity. In these circumstances, special procedures were used for compensation, either through the replacement of those areas with other areas having otherwise similar characteristics in the respective strata or through the adjustment of the sampling weights for missing values. There were 14 such cases, 5 each in quarters 1 and 4, and 2 each in quarters 2 and 3.

    Analysis unit

    1- Household/family. 2- Individual/person.

    Universe

    The labour force survey covered the civilian non-institutional settled population excluding certain areas with difficult access or low population densities, in particular, the nomad population, displaced populations who are homeless, population living in public housing (boarding, hotels, prisons, hospitals, etc.), individuals enlisted in the Armed Forces, who are residing permanently within camps and do not spend most days of the year with their families. Similarly, for marine crews and expatriates outside the country and other categories of persons in remote islands.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL STATISTICAL ORGANIZATION OF YEMEN (CSO)

    The sample design of the labour force survey of Yemen 2013-2014 is a two-stage stratified sample of enumeration areas in the first stage of sampling and a fixed number of sample households at the second stage of sampling. The resulting sample is spread evenly over the four quarters of the survey period.

    Accordingly, the Central Statistics Organization (CSO) has drawn a stratified sample of census enumeration areas recomposed as primary sampling units (PSUs). Sample selection has been made with probability proportional to the number of households as determined in the 2004 population on census. In the second stage of sampling, after relisting of the sample enumeration areas, a fixed number of households (16 sample households) are drawn as clusters with equal probability from each sample enumeration area. The strata consist of the urban and rural areas of the 21 governorates in Yemen.

    According to the sample design, urban areas are oversampled and rural areas under-sampled. This is because a relatively larger sample size is required in urban areas where heterogeneity is greater in comparison with rural areas. Also, because the cost of transportation and field operations is relatively greater in rural areas, it is more cost effective to under sample the rural areas relative to the less costly operations in urban areas. The differential sampling rates are then corrected through the sample weights so that the final results accurately reflect to the overall employment pattern.

    The sample selection of the cluster of 16 households in each sample enumeration area was drawn after fresh listing of the totality of the households living in the sample enumeration area at the time of listing. This procedure updates the census information that dates back to 2004. The listing operations are carried out in each quarter before survey interviewing. The updated lists are send to CSO in Sana'a for data entry and sample selection of households for transmission to the survey team in each area. Instructions were given so that sample households that could not be found in the field or were absent or refused to be interview should not be substituted with other households as this procedure may introduce bias in the results. Instructions were also given that in cases where the minimum number of households in the sample enumeration areas was to be found to be less than the required 16 in each quarter, all households in the enumeration area should be taken in the sample.

    The total sample size was determined on the basis of the requirement of producing national estimates of the unemployment rate with 1.5% margin of errors at the national level, assuming an overall non-response rate of 15%, and a design effect of 3. For the determination of the national sample size, the expected unemployment rate was set at 15% and the expected number of sample households to reach one person of working age, 15 years old and over, in the labour force was set at 0.6.

    A more detailed description of the allocation of sample across governorates is provided in the report document available among external resources in English.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire of the Yemen LFS 2013-2014 was designed on the basis of the ILO model LFS questionnaire (version A) and other national LFS questionnaires used in the region. The draft questionnaire was field tested with six households in Sana’a, each member of the field staff interviewing one sample household in his or her area. The experience gained in the field test was reviewed and led to some modifications of the draft questionnaire.

    Apart from the cover page and the back page, the core LFS questionnaire contains 52 questions. There are 11 questions on the social and demographic characteristics of the household members in the household roster. In the individual questionnaire addressed to the working age population 15 years of age or older, there are 3 questions to identify the employed persons and 19 questions on their employment characteristics including timerelated underemployment followed by 8 additional questions on income from employment. The individual questionnaire also includes 5 questions to identify the unemployment and the potential labour force and 5 follow-up questions on unemployment characteristics.

    Cleaning operations

    ----> Raw Data

    Data processing involved data entry, coding, editing and tabulation of the survey results. Data entry was carried out in parallel with the interviewing of sample households. It was conducted at the Central Statistical Organization headquarter in Sana'a where all data processing operations except tabulation were centralized.

    The supervisory staff of the data entry operations was responsible for editing the questionnaires before actual data entry. Editing at this stage involved review of the questionnaire regarding its filled-in contents including ensuring that there is no missing block of information for household members aged 15 years old and over and correct coding of occupation, branch of economic activity and other variables.

    The data files were further processed at ILO headquarters in Geneva. They were first converted into a single file with 86,778 records and augmented with several fields, in particular, the sampling weights (“weight”) and the key derived variables: employed (E), unemployed (U), time-related underemployment (TRU), potential labour force (PLF) as well as other derived variables such as informal sector employment (IS) and informal employment (IE).

    ----> Harmonized Data

    • The SPSS package is used to clean and harmonize the datasets.
    • The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
    • All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
    • A country-specific program is generated
  11. w

    Vocational Training Program for the Unemployed Impact Evaluation 2010-2012 -...

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jun 13, 2022
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    David McKenzie (2022). Vocational Training Program for the Unemployed Impact Evaluation 2010-2012 - Turkiye [Dataset]. https://microdata.worldbank.org/index.php/catalog/1973
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    Dataset updated
    Jun 13, 2022
    Dataset provided by
    Cristobal Ridao-Cano
    David McKenzie
    Rita Almeida
    Sarojini Hirschleifer
    Time period covered
    2010 - 2012
    Area covered
    Turkiye
    Description

    Abstract

    The Turkish National Employment Agency (ISKUR) provides services for individuals who register as unemployed through 109 offices in 81 provinces. The impact evaluation study was designed by researchers from the World Bank to evaluate the impact of the ISKUR vocational training programs. These programs average 336 hours over three months are available for a wide range of subjects, and are offered by both private and public providers. These training services were provided to over 250,000 registered unemployed in 2011.

    The Turkey Vocational Training Program for the Unemployed Impact Evaluation 2010-2012 was the first randomized experiment of a large-scale vocational training program for the general unemployed population (not just for disadvantaged youth) in a developing country. The program was able to trace longer-term impacts up to three years post-training, by complementing a follow-up survey with administrative data from the social security agency. A sample of 5,902 applicants was randomly allocated to treatment and control groups within 130 separate courses. Excess demand among the unemployed for many of the courses offered by ISKUR provided the possibility for an over-subscription design. The evaluation was carried out in collaboration with ISKUR and under the guidance of the Ministry of Labor.

    The baseline survey took place between 13 September, 2010, and 31 January, 2011. The follow-up survey was implemented approximately one year after the end of training, between December 27, 2011, and March 5, 2012. It collected data on employment outcomes, as well as individual and household well-being.

    Geographic coverage

    National

    Analysis unit

    • Vocational programs' students,
    • Vocational programs' staff

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The selection of provinces for evaluation began with a list of the 39 provinces which had at least two significantly oversubscribed training courses in 2009. These provinces were first stratified by whether they had an unemployment rate above or below the median of 10 percent in 2009. Ten provinces were then randomly selected from each strata with probability proportional to the percentage of individuals trained in 2009. Three additional provinces (Antalya, Gaziantep, and Diyarbakir) were included in the sample at the request of ISKUR because of their importance in representing varying labor market conditions across Turkey. As a result, 23 provinces were selected for inclusion in the evaluation.

    Power calculations gave a target sample size of 5,700 individuals. This target was divided among the 23 provinces in proportion to the number of trainees in these provinces in the previous year. Thus Istanbul accounts for 21.8 percent of the sample, Kocaeli, Ankara and Hatay collectively 28 percent, and the remaining half of the sample is split among the other 19 provinces.

    The evaluation team worked with regional ISKUR offices to determine the actual courses from within each province to be included in the evaluation. The key criteria used to decide which courses to include in the evaluation were i) the likelihood of the course being oversubscribed (which ensures the most popular types of training, for which there would be demand for further scale-up, are included); ii) inclusion of a diversity of types of training providers to enable comparison of private and public course provision; and iii) course starting and ending dates. The evaluation includes courses that started between October and December 2010 and finished by May 2011 (75 percent had finished by the end of February 2011). The timing of the evaluation was determined by the fact that it tends to be a time of year when people in Turkey are more likely to seek training through ISKUR.

    This resulted in a set of 130 evaluation courses spread throughout Turkey, of which 39 were offered by private providers and the remainder were mainly government-operated. Courses were advertised and potential trainees applied to them following standard procedures. Applications were then screened to ensure they met the eligibility criteria of ISKUR and the course provider. Training providers were then asked to select a list of potential trainees that was at least 2.2 times capacity.

    The ISKUR Management Information System (MIS) stratified applicants for each course by gender and whether or not they were less than 25 years old. Within these strata, the MIS randomly allocated trainees at the individual level into one of three groups: a treatment group who were selected for training, a control group who were not, and a waitlisted group who the training provider could select into the training if there were drop-outs. Since training providers are paid on the basis of number actually trained, if individuals assigned to treatment drop out of training, providers look to quickly fill in the empty spots.

    The final evaluation sample consisted of 5,902 applicants, of which 3,001 were allocated to treatment and 2,901 to control groups. There were 173 individuals who applied to more than one course.

    Mode of data collection

    Face-to-face [f2f]

    Response rate

    Baseline: 90% Follow-up: 94%

  12. o

    Replication data for: Assessing the Welfare Effects of Unemployment Benefits...

    • openicpsr.org
    Updated Oct 13, 2019
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    Camille Landais (2019). Replication data for: Assessing the Welfare Effects of Unemployment Benefits Using the Regression Kink Design [Dataset]. http://doi.org/10.3886/E114581V1
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    Dataset updated
    Oct 13, 2019
    Dataset provided by
    American Economic Association
    Authors
    Camille Landais
    Description

    I show how, in the tradition of the dynamic labor supply literature, one can identify the moral hazard effects and liquidity effects of unemployment insurance (UI) using variations along the time profile of unemployment benefits. I use this strategy to investigate the anatomy of labor supply responses to UI. I identify the effect of benefit level and potential duration in the regression kink design using kinks in the schedule of benefits in the US. My results suggest that the response of search effort to UI benefits is driven as much by liquidity effects as by moral hazard effects. (JEL D82, J22, J65)

  13. u

    Participation and Unemployment Rates and Student Population Counts by Age...

    • data.urbandatacentre.ca
    • beta.data.urbandatacentre.ca
    Updated Jun 24, 2025
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    (2025). Participation and Unemployment Rates and Student Population Counts by Age for Canada and Provinces (Annual Averages) (1996 - 2011) - Catalogue - Canadian Urban Data Catalogue (CUDC) [Dataset]. https://data.urbandatacentre.ca/dataset/ab-participation-and-unemployment-rates-and-student-population-counts-by-age-annual-averages-1996-20
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    Dataset updated
    Jun 24, 2025
    Area covered
    Canada
    Description

    (StatCan Product) Participation and unemployment rates and student population counts for Canada and provinces by selected age groups (annual averages in %). Customization details: This information product has been customized to present information onparticipation rates, unemployment rates and student population counts for Canada and provinces by selected age groups (annual averages in percentage). Participation and unemployment rates age groups presented are: - 18 to 24 years - 18 to 34 years - 15 years and over The variables presented for the student population are (18 to 34 years – 8 month averages): - Student and non-student - Number attending college - Number attending university - Others - Total attending PS - % Total attending PS Labour Force Survey The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. Target population The LFS covers the civilian, non-institutionalized population 15 years of age and over. It is conducted nationwide, in both the provinces and the territories. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; full-time members of the Canadian Armed Forces and the institutionalized population. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over. National Labour Force Survey estimates are derived using the results of the LFS in the provinces. Territorial LFS results are not included in the national estimates, but are published separately. Instrument design The current LFS questionnaire was introduced in 1997. At that time, significant changes were made to the questionnaire in order to address existing data gaps, improve data quality and make more use of the power of Computer Assisted Interviewing (CAI). The changes incorporated included the addition of many new questions. For example, questions were added to collect information about wage rates, union status, job permanency and workplace size for the main job of currently employed employees. Other additions included new questions to collect information about hirings and separations, and expanded response category lists that split existing codes into more detailed categories. Sampling This is a sample survey with a cross-sectional design. Data sources Responding to this survey is mandatory. Data are collected directly from survey respondents. Data collection for the LFS is carried out each month during the week following the LFS reference week. The reference week is normally the week containing the 15th day of the month. LFS interviews are conducted by telephone by interviewers working out of a regional office CATI (Computer Assisted Telephone Interviews) site or by personal visit from a field interviewer. Since 2004, dwellings new to the sample in urban areas are contacted by telephone if the telephone number is available from administrative files, otherwise the dwelling is contacted by a field interviewer. The interviewer first obtains socio-demographic information for each household member and then obtains labour force information for all members aged 15 and over who are not members of the regular armed forces. The majority of subsequent interviews are conducted by telephone. In subsequent monthly interviews the interviewer confirms the socio-demographic information collected in the first month and collects the labour force information for the current month. Persons aged 70 and over are not asked the labour force questions in subsequent interviews, but rather their labour force information is carried over from their first interview. In each dwelling, information about all household members is usually obtained from one knowledgeable household member. Such 'proxy' reporting, which accounts for approximately 65% of the information collected, is used to avoid the high cost and extended time requirements that would be involved in repeat visits or calls necessary to obtain information directly from each respondent. Error detection The LFS CAI questionnaire incorporates many features that serve to maximize the quality of the data collected. There are many edits built into the CAI questionnaire to compare the entered data against unusual values, as well as to check for logical inconsistencies. Whenever an edit fails, the interviewer is prompted to correct the information (with the help of the respondent when necessary). For most edit failures the interviewer has the ability to override the edit failure if they cannot resolve the apparent discrepancy. As well, for most questions the interviewer has the ability to enter a response of Don't Know or Refused if the respondent does not answer the question. Once the data is received back at head office an extensive series of processing steps is undertaken to thoroughly verify each record received. This includes the coding of industry and occupation information and the review of interviewer entered notes. The editing and imputation phases of processing involve the identification of logically inconsistent or missing information items, and the correction of such conditions. Since the true value of each entry on the questionnaire is not known, the identification of errors can be done only through recognition of obvious inconsistencies (for example, a 15 year-old respondent who is recorded as having last worked in 1940). Estimation The final step in the processing of LFS data is the assignment of a weight to each individual record. This process involves several steps. Each record has an initial weight that corresponds to the inverse of the probability of selection. Adjustments are made to this weight to account for non-response that cannot be handled through imputation. In the final weighting step all of the record weights are adjusted so that the aggregate totals will match with independently derived population estimates for various age-sex groups by province and major sub-provincial areas. One feature of the LFS weighting process is that all individuals within a dwelling are assigned the same weight. In January 2000, the LFS introduced a new estimation method called Regression Composite Estimation. This new method was used to re-base all historical LFS data. It is described in the research paper ""Improvements to the Labour Force Survey (LFS)"", Catalogue no. 71F0031X. Additional improvements are introduced over time; they are described in different issues of the same publication. Data accuracy Since the LFS is a sample survey, all LFS estimates are subject to both sampling error and non-sampling errors. Non-sampling errors can arise at any stage of the collection and processing of the survey data. These include coverage errors, non-response errors, response errors, interviewer errors, coding errors and other types of processing errors. Non-response to the LFS tends to average about 10% of eligible households. Interviews are instructed to make all reasonable attempts to obtain LFS interviews with members of eligible households. Each month, after all attempts to obtain interviews have been made, a small number of non-responding households remain. For households non-responding to the LFS, a weight adjustment is applied to account for non-responding households. Sampling errors associated with survey estimates are measured using coefficients of variation for LFS estimates as a function of the size of the estimate and the geographic area.

  14. Data from: Quarterly Census of Employment and Wages

    • icpsr.umich.edu
    Updated Oct 22, 2015
    + more versions
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    United States Department of Labor. Bureau of Labor Statistics (2015). Quarterly Census of Employment and Wages [Dataset]. https://www.icpsr.umich.edu/web/NADAC/studies/36312
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    Dataset updated
    Oct 22, 2015
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Labor. Bureau of Labor Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/36312/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36312/terms

    Area covered
    United States
    Description

    The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

  15. e

    Long term unemployment rate of the female aged 16-74 of the Basque Country...

    • euskadi.eus
    csv, xlsx
    Updated Jun 30, 2023
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    (2023). Long term unemployment rate of the female aged 16-74 of the Basque Country and the countries of the European Union. [Dataset]. https://www.euskadi.eus/long-term-female-unemployment-rate-ages-16-to-74-by-country/web01-tramite/es/
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    xlsx(32.02), csv(2.1)Available download formats
    Dataset updated
    Jun 30, 2023
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Basque Country, Europe, European Union
    Description

    The operation on Structural Indicators takes on several objectives.The first and overall objective lies in achieving, with the highest possible quality, the production of a series of basic or context indicators that serve, or may serve, as a reference.The second objective is to achieve methodological homogeneity and precision in calculation in relation to other international systems of indicators ¿and especially those defined by Eurostat¿ to create and recreate series that add the time perspective.To design and implement dynamic file formats that allow for the organisation and access to all of the information.Ultimately, the specific objective of the operation focuses on the coordination, management, verification and archiving of the system of indicators.

  16. Web Design Services in the US - Market Research Report (2015-2030)

    • ibisworld.com
    Updated Sep 15, 2024
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    IBISWorld (2024). Web Design Services in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/web-design-services-industry/
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    Dataset updated
    Sep 15, 2024
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2014 - 2029
    Area covered
    United States
    Description

    Web design service companies have experienced significant growth over the past few years, driven by the expanding use of the Internet. As online operations have become more widespread, businesses and consumers have increasingly recognized the importance of maintaining an online presence, leading to robust demand for web design services and boosting the industry’s profit. The rise in broadband connections and online business activities further spotlight this trend, making web design a vital component of modern commerce and communication. This solid foundation suggests the industry has been thriving despite facing some economic turbulence related to global events and shifting financial climates. Over the past few years, web design companies have navigated a dynamic landscape marked by both opportunities and challenges. Strong economic conditions have typically favored the industry, with rising disposable incomes and low unemployment rates encouraging both consumers and businesses to invest in professional web design. Despite this, the sector also faced hurdles such as high inflation, which made cost increases necessary and pushed some customers towards cheaper substitutes such as website templates and in-house production, causing a slump in revenue in 2022. Despite these obstacles, the industry has demonstrated resilience against rising interest rates and economic uncertainties by focusing on enhancing user experience and accessibility. Overall, revenue for web design service companies is anticipated to rise at a CAGR of 2.2% during the current period, reaching $43.5 billion in 2024. This includes a 2.2% jump in revenue in that year. Looking ahead, web design companies will continue to do well, as the strong performance of the US economy will likely support ongoing demand for web design services, bolstered by higher consumer spending and increased corporate profit. On top of this, government investment, especially at the state and local levels, will provide further revenue streams as public agencies seek to upgrade their web presence. Innovation remains key, with a particular emphasis on designing for mobile devices as more activities shift to on-the-go platforms. Companies that can effectively adapt to these trends and invest in new technologies will likely capture a significant market share, fostering an environment where entry remains feasible yet competitive. Overall, revenue for web design service providers is forecast to swell at a CAGR of 1.9% during the outlook period, reaching $47.7 billion in 2029.

  17. p

    Employment and Unemployment Survey 2004-2005 - Fiji

    • microdata.pacificdata.org
    • datacatalog.ihsn.org
    • +1more
    Updated May 16, 2019
    + more versions
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    Fiji Islands Bureau of Statistics (2019). Employment and Unemployment Survey 2004-2005 - Fiji [Dataset]. https://microdata.pacificdata.org/index.php/catalog/227
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    Dataset updated
    May 16, 2019
    Dataset authored and provided by
    Fiji Islands Bureau of Statistics
    Time period covered
    2004
    Area covered
    Fiji
    Description

    Abstract

    The 2004-05 Household Survey of Employment and Unemployment aimed to meet the data requirements of planners working towards improving the quality and productivity of Fiji's human resources.

    The principal objective of the survey was to obtain comprehensive statistical data on the economically active population, comprising employed and unemployed persons, as well as on the inactive population of working age. From the data, the size and structure of the country's workforce have been determined. When compared to figures of previous years, changes in the labour market and in the employment situation can be obtained.

    Geographic coverage

    National

    Analysis unit

    Household Individual

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Sampling Design The survey included all householders in conventional dwellings distributed in localities within the urban and urban sectors of the four administrative divisions namely Central, Eastern, Western and Northern.

    The target population was Fiji Citizens and permit holders in conventional dwellings excluding those found in households of non-Fiji citizens, hospitals, prisons, hotels, temporary construction sites, boarding schools and similar institutions.

    A sampling frame was constructed using the count of conventional households gather from the listing stage for HIES 2002-2003 and information gathered from updates to EAs identified to have had significant changes in household numbers. In previous surveys the sample was drawn from a sampling frame taken from the immediate past census. This would not have been suitable for this survey, as the last census was taken almost 10 years ago. Since then, there has been considerable rural: urban drift, while the urban boundaries have extended significantly in many areas, for example, along the Nadi and lautoka corridor.

    A sample of 3000 households was targeted using a two stage stratified systematic sampling. The first stage involved the selection of 300 EAs in proportion to the number of households in each stratum. In the second stage, a random sample of 10 households within each identified EA was selected. This sample, including a reserve pool, was drawn from a list of households in EA stratified by household size and ethnicity.

    Mode of data collection

    Face-to-face [f2f]

    Cleaning operations

    Coding and Data Entry Once the schedules were returned, coders tallied counts of population and households by ethnicity. Written responses were standardized. These tasks include coding the main occupation and industry of the employed and those involved in any economic activity including responses of those not in the labour force. Separate data entry screens were used for the Schedule 1- Listing, and Schedule 2- Main schedule using CSPro, a survey data processing software. The data entry screens had built in skip patterns derived from the questionnaire, simplifying data entry and editing.

    Editing Some editing were done in the field and verified at coding stages. However a more thorough check involved printing all entered information and the verifying against field records item by item. This ensured that data gathered from the field was not lost in transition during data entry through to output. Consistency and structural checks on the data were part of the tasks carried out at the compilation stages of the final database. The calculated weight was assigned to each record at this edit stage. Data frequencies on variables also provided an indication of the effectiveness of the data collection exercise, particularly in checking the required number of households to be visited per EA. Weighted frequencies further provided an indication of the accuracy of the data collection and monitoring survey processes as a whole.

    Verification Verification of information was done by enumerator on repeat household visits during the week allocated for completion of the main questionnaire. Checks on age and relationship of members of the household to the head were some of the initial tasks in making sure that respondents provided information with a highest acceptable degree of accuracy and consistency. For working employees. enumerators were able to access statements of emoluments and at times balance sheets for those involved in sale of goods and services.

    Response rate

    Response rate is 100%.

  18. FD Technologies (FDP): Future-Proofed Design or Dwindling Demand? (Forecast)...

    • kappasignal.com
    Updated Mar 21, 2024
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    KappaSignal (2024). FD Technologies (FDP): Future-Proofed Design or Dwindling Demand? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/fd-technologies-fdp-future-proofed.html
    Explore at:
    Dataset updated
    Mar 21, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

    FD Technologies (FDP): Future-Proofed Design or Dwindling Demand?

    Financial data:

    • Historical daily stock prices (open, high, low, close, volume)

    • Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

    • Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

    Machine learning features:

    • Feature engineering based on financial data and technical indicators

    • Sentiment analysis data from social media and news articles

    • Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

    Potential Applications:

    • Stock price prediction

    • Portfolio optimization

    • Algorithmic trading

    • Market sentiment analysis

    • Risk management

    Use Cases:

    • Researchers investigating the effectiveness of machine learning in stock market prediction

    • Analysts developing quantitative trading Buy/Sell strategies

    • Individuals interested in building their own stock market prediction models

    • Students learning about machine learning and financial applications

    Additional Notes:

    • The dataset may include different levels of granularity (e.g., daily, hourly)

    • Data cleaning and preprocessing are essential before model training

    • Regular updates are recommended to maintain the accuracy and relevance of the data

  19. C

    Unemployed labor; duration of unemployment, 2011-2022

    • ckan.mobidatalab.eu
    Updated Jul 13, 2023
    + more versions
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    OverheidNl (2023). Unemployed labor; duration of unemployment, 2011-2022 [Dataset]. https://ckan.mobidatalab.eu/dataset/57-werkloze-beroepsbevolking-werkloosheidsduur-persoonskenmerken
    Explore at:
    http://publications.europa.eu/resource/authority/file-type/atom, http://publications.europa.eu/resource/authority/file-type/jsonAvailable download formats
    Dataset updated
    Jul 13, 2023
    Dataset provided by
    OverheidNl
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table contains figures on the unemployment duration of the unemployed labor force aged 15 to 75 years. This makes it possible to determine the number of short-term and long-term unemployed. Short-term unemployed are unemployed for less than 12 months. The long-term unemployed are unemployed for 12 months or more. The figures in the table can be broken down by gender, age, migration background and level of education. The table contains both quarterly and annual figures. Due to changes in the research design and the EBB questionnaire, a revision of the figures for the 2021 reporting year was carried out in the first quarter of 2022. The figures for 2021 are not directly comparable with the previous reporting periods. Data available from 2011 to 2022 Status of the figures: The figures in this table are final. Changes as of August 17, 2022: None, this table has been discontinued. Changes as of February 15, 2022: The annual figures for 2021 and the quarterly figures for the third and fourth quarters of 2021 have been published. The figures for the first two quarters of 2021 have been revised. When will new numbers come out? Not applicable anymore. This table is followed by the table Unemployed labor force; duration of unemployment, personal characteristics. See section 3.

  20. n

    Data from: Essays in Unemployment Insurance and Housing

    • curate.nd.edu
    Updated Jun 15, 2025
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    Ethan Levi Jenkins (2025). Essays in Unemployment Insurance and Housing [Dataset]. http://doi.org/10.7274/28792496.v1
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    Dataset updated
    Jun 15, 2025
    Dataset provided by
    University of Notre Dame
    Authors
    Ethan Levi Jenkins
    License

    https://www.law.cornell.edu/uscode/text/17/106https://www.law.cornell.edu/uscode/text/17/106

    Description

    This dissertation seeks to better understand poverty in the United States. The first two chapters examine the impacts of receiving unemployment insurance (UI) on low-wage, recently displaced workers. The third chapter documents earnings growth and migration across neighborhoods, focusing on high-poverty neighborhoods. Housing is a common theme throughout the dissertation. The first chapter suggests that reducing housing insecurity may be a mechanism by which UI reduces crime, the second chapter explores how UI may prevent homelessness, and the third chapter examines individuals' neighborhood choices that vary with earnings and over the life cycle.

    The first chapter examines the impact of UI on subsequent criminal justice system involvement using linked UI and jail administrative data. I estimate this effect using a regression discontinuity design that exploits the minimum earnings requirements for UI. I provide evidence indicating that being barely eligible for UI decreases arrest probability. Most of this overall reduction is driven by reducing assault arrests. A back-of-the-envelope calculation suggests that this crime reduction generates large public benefits approximately equal to the fiscal cost of loosening monetary eligibility requirements.

    The second chapter, joint work with Robert Collinson, examines the impact of UI on extreme material distress, particularly stays in New York City homeless shelters. We estimate the impact of UI eligibility on homelessness using a regression discontinuity design (RDD) that exploits a cutoff based on workers' highest quarterly earnings in the past year. We find that UI eligibility reduces homelessness. Not accounting for how UI prevents extreme distress undervalues the benefits of UI.

    The third chapter, joint work with coauthors, uses administrative data to document a high degree of migration across neighborhoods and neighborhood types defined in terms of poverty rate and median income. Neighborhood quality increases over an individual's life cycle, and people also move to better neighborhoods in response to earnings improvements. Poor neighborhoods tend to remain poor because of a dynamic process in which initial residents experience high earnings growth but disproportionately out-migrate when earnings improve, contrasting with a pure ``poverty trap” understanding of persistent concentrated poverty.

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Click to copy link
Link copied
Close
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Long-term unemployment rate of the population aged 16 to 74 in the Basque Country and the countries of the European Union. | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_https-opendata-euskadi-eus-catalogo-tasa-paro-larga-duracion-16-74-anos-pais-

Long-term unemployment rate of the population aged 16 to 74 in the Basque Country and the countries of the European Union. | gimi9.com

Explore at:
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Basque Country, Europe, European Union
Description

There are several objectives faced by the operation on Structural Indicators.The first and generic is to achieve the production, with the highest possible degree of quality, of a battery of basic or context indicators, which serve or can serve as a reference.The second objective, would be to achieve methodological homogeneity and precision in the calculation in relation to other internal systems of indicators, and especially those defined by Eurostat, to rework and elaborate series that add the temporal perspective and design and implement dynamic file formats that allow the organisation and access to all information. Finally, the specific objective of the operation would focus on the coordination, management, verification and archiving of the indicators system.

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