Economy Watchers: DI: CEC: sa: HT: Housing Related data was reported at 41.700 NA in Jun 2019. This records an increase from the previous number of 41.300 NA for May 2019. Economy Watchers: DI: CEC: sa: HT: Housing Related data is updated monthly, averaging 46.800 NA from Jan 2002 (Median) to Jun 2019, with 210 observations. The data reached an all-time high of 62.800 NA in Sep 2013 and a record low of 21.700 NA in Jan 2009. Economy Watchers: DI: CEC: sa: HT: Housing Related data remains active status in CEIC and is reported by Cabinet Office. The data is categorized under Global Database’s Japan – Table JP.S070: Economy Watchers Survey: Seasonally Adjusted.

The project will produce a valuation function that depends on factors related to Steller sea lion (SSL) protection measures, and may include some combination of the expected aggregate size of the population and improvements to the ESA listing status resulting from protection measures, cost of the protection measures, and effects of protection measures on local economies, fishery participants, and consumer fish prices. This function can be used to identify non-consumptive use values for SSLs and how these values are affected by protection measures, thereby providing valuable information to policy makers.

Release Date: 2024-09-26.Key Table Information:.The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series...Data Items and Other Identifying Records: .This file contains data classified by Firm age and Employment size of firms.Number of firms.Number of establishments.Number of employees.(DHS) denominator.Number of establishments born during the last 12 months.Rate of establishments born during the last 12 months.Number of establishments exited during the last 12 months.Rate of establishments exited during the last 12 months.Number of jobs created from expanding and opening establishments during the last 12 months.Number of jobs created from opening establishments during the last 12 months.Number of jobs created from expanding establishments during the last 12 months.Rate of jobs created from opening establishments during the last 12 months.Rate of jobs created from expanding and opening establishments during the last 12 months.Number of jobs lost from contracting and closing establishments during the last 12 months.Number of jobs lost from closing establishments during the last 12 months.Number of jobs lost from contracting establishments during the last 12 months.Rate of jobs lost from closing establishments during the last 12 months.Rate of jobs lost from contracting and closing establishments during the last 12 months.Number of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of reallocation during the last 12 months.Number of firms that exited during the last 12 months.Number of establishments associated with firm deaths during the last 12 months.Number of employees associated with firm deaths during the last 12 months...Geography Coverage:.The data are shown at the U.S. level...Industry Coverage:.The data are shown at the 2-digit NAICS level...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/bds/data/BDSFAGEFSIZE.zip..API Information:.Business Dynamics Statistics (BDS) data are housed in the Business Dynamics Statistics (BDS) API. For more information, see Business Dynamics Statistics (BDS) Data (census.gov)...Methodology:.In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see the CBP methodology webpage..In addition to noise infusion, cells with fewer than three firms are suppressed with a publication flag 'D'. In addition, cells with identified data quality concerns are suppressed with a publication flag 'S'. Cells that are "structurally missing" or "structurally zero" are indicated with a publication flag of 'X'. Finally, rate cells that cannot be calculated are indicated with a publication flag of 'N'..For more information about BDS methodology, see the BDS methodology pages...Source:.U.S. Census Bureau, 2022 Business Dynamics Statistics..Contact Information:.U.S. Census Bureau.Economy-Wide Statistics Division.Business Dynamics Statistics.Tel: (301) 763 - 6090 .Email: ewd.bds@census.gov

Employment: ES: Non Saudi: ow Health and Social Services (HS) data was reported at 127,076.000 Person in 2016. This records an increase from the previous number of 118,989.000 Person for 2015. Employment: ES: Non Saudi: ow Health and Social Services (HS) data is updated yearly, averaging 76,078.500 Person from Dec 1995 (Median) to 2016, with 18 observations. The data reached an all-time high of 127,076.000 Person in 2016 and a record low of 43,826.000 Person in 1995. Employment: ES: Non Saudi: ow Health and Social Services (HS) data remains active status in CEIC and is reported by General Authority for Statistics. The data is categorized under Global Database’s Saudi Arabia – Table SA.G015: Employment: Economic Survey of Establishments: by Industry and Size: Non Saudi.

general authority for statistics, annual economic survey of establishments - Economic indicators for Wholesale & Retail Trade Activity-2010 | gimi9.com

Background: Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

Methods: A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pretested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles' households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Results: Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x2 = 20.392, df=3), educational status(x2 = 19.358, df=4), source of income (x2 = 21.777, df=3), monthly income (x2 = 13.322, df=3), availability of additional facilities (x2 = 98.144, df=7), cleanness status (x2 =42.979, df=4), scarcity of water (x2 = 5.1388, df=1) and family size (x2 = 9.934, df=2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

Conclusion: The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore, ; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

Saudi Arabia Economic Survey of Establishments: ER: ow Oil and Gas Extraction data was reported at 665,325,998.000 SAR th in 2016. This records a decrease from the previous number of 747,668,545.000 SAR th for 2015. Saudi Arabia Economic Survey of Establishments: ER: ow Oil and Gas Extraction data is updated yearly, averaging 824,639,180.500 SAR th from Dec 2005 (Median) to 2016, with 12 observations. The data reached an all-time high of 1,355,124,781.000 SAR th in 2013 and a record low of 654,937,072.000 SAR th in 2009. Saudi Arabia Economic Survey of Establishments: ER: ow Oil and Gas Extraction data remains active status in CEIC and is reported by General Authority for Statistics. The data is categorized under Global Database’s Saudi Arabia – Table SA.S001: Economic Survey of Establishments: Enterprise Revenues and Expenditures.

Abstract

The primary objective of the survey is collecting information on household income and household expenditures, household consumptions, changes in assets and liabilities, the durable goods ownerships, and housing characteristics including other living conditions of households.

Geographic coverage

National Regional Area (Municipal, Non-municipal)

Analysis unit

Household, Individual

Universe

The survey covered all private, non-institutional households residing permanently in municipal areas and non-municipal areas of all regions. However, it excluded that part of the population living in transient hotels and rooming houses, hostels, boarding schools, temples, military barracks, prisons, welfare institutes, hospitals and other such institutions. It also excluded households of foreign diplomats and other temporary residents.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used: - Questionnaire of Household Members and Expenditures - Including the Village/Community Fund (SES 2) - Questionnaire of Household Income (SES 3)

About this dataset

Context

The dataset tabulates the population of Economy by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Economy. The dataset can be utilized to understand the population distribution of Economy by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Economy. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Economy.

Key observations

Largest age group (population): Male # 65-69 years (412) | Female # 60-64 years (490). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Economy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Economy is shown in the following column.
• Population (Female): The female population in the Economy is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Economy for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Economy Population by Gender. You can refer the same here

Odisha Budget 2020-21: Economic Survey

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Ethnicity for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01B.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Ethnicity Hispanic Equally Hispanic/non-Hispanic Non-Hispanic Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the...

🇸🇦 사우디아라비아

Abstract

Statistical information on all aspects of socio-economic activities is essential for the designing, monitoring, evaluation of development plans and policies. Labour force surveys are one of the important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Furthermore, the information obtained from such surveys is useful for the purpose of macro-economic monitoring and evaluation of human resource development planning. The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

CSA has been providing labour force and related data at different levels and with varying content details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. A comprehensive national labour force result representing both urban and rural areas was also provided based on the 1999 Labour Force Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Moreover, some information can be derived from small, large and medium scale establishment surveys.

As the sector is dynamic and sensitive to economic and social changes, it is important to have up to date data that will show current levels and that will be used for trend and comparative analysis. Earlier data in this regard were not regular and up to date. Thus, to fill-in the data gap in this area, a series of current and continuous labour force surveys need to be undertaken. Recognizing this fact and in response to request from different data users, the CSA had launched a Bi-annual Employment and Unemployment Survey program starting October, 2003 G.C.

This survey is the second in the series. Like the first round, it covered only urban areas of all regions with the exception of Gambella.

Objectives of the survey The Bi-annual Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of this survey were to: - Up date data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; - Generate data to trace changes over time.

Geographic coverage

The 2004 Urban Bi-annual Employment and Unemployment Survey (UBEUS) covered only urban parts of the country. Except three zones of Afar, six zones of Somali regions, where the residents are pastoralists, and every part of Gambella region, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 1994 Population and Housing Census was utilized to develop the sampling frame for the 2004 Urban Bi-annual Employment and Unemployment Survey. It was by taking in to account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. From each selected urban centers enumeration areas (EA's) were selected as a second-stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage.

The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2004 Urban Bi-annual Employment Unemployment Survey Round 2 report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Same questionnaire used for the first round survey was administered in this round (round 2).

The questionnaire was organized in to five sections; Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, states & types of training and marital status.

Section - 3: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of Employment /Unemployment and reasons for not usually working.

Section - 4: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work occupation, industry, employment status, and Earnings from employment.

Section - 5: Unemployment and characteristics of unemployed persons: the section focused on the size and characteristics of the unemployed population.

Note: The questionnaire is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs used in data entry, machine editing and tabulation were prepared using the Integrated Microcomputer Processing System (IMPS).

Response rate

As regards the response rate of the survey, a total of 99 urban centers were selected and incorporated in to the survey. To be covered by the survey, 527 enumeration areas was initially selected, and the survey could successfully be carried out in 507 (96.20%) out of all the 527 of the EA's. The total number of expected households that were to be interviewed was 15810; however, due to different reasons 740 sample households were not interviewed, including households from 20 EAs of Gambella Region. As a result only 15070 households were actually covered by the survey, which made the ultimate response rate of the survey 95.32 %.

Sampling error

Provide an overview of the satisfaction with the annual training plan of this center.

The size of the five original BRICS economies in 2023 - Brazil, Russia, China, India, South Africa - is comparable to the United States and the EU-27 put together. On a PPP (purchasing power parity) basis, China ranks as the world's largest economy. India takes up the economic parity of about **** the EU-27. The rise of these developing economies gave rise to questions on the role the United States plays in international trade and cross-border finance. FX reserve managers around the world expect to shift their holdings towards the Chinese yuan in the long term, as of 2023.

L'enquête auprès des observateurs de l'économie au Japon est passée à 45 points en juin contre 44,40 points en mai 2025. Cette dataset fournit - Enquête auprès des observateurs de l'économie japonaise - valeurs réelles, données historiques, prévisions, graphique, statistiques, calendrier économique et actualités.

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 PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2003(LFS).

The importance of this survey lies in that it focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level , occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

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 representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

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

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

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 PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

Target Population:

All Palestinians aged 10 years or older living in the Palestinian Territory, excluding those living in institutions such as prisons or shelters.

Sampling Frame:

The sampling frame consisted of a master sample of Enumeration Areas (EAs) selected from the population housing and establishment census 1997. The master sample consists of area units of relatively equal size (number of households), these units have been used as Primary Sampling Units (PSUs).

Sample Design:

The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made:

1. Stratification by Governorates.
2. Stratification by type of locality which comprises: (a) Urban, (b) Rural, and (c) Refugee Camps
3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
4. Stratification by size of locality (number of households).

Sample Size:

The sample size in the first round consisted of 7,559 households, which amounts to a sample of around 29,149 persons aged 10 years and over (including 22,742 aged 15 years and over). In the second round the sample consisted of 7,563 households, which amounts to a sample of around 29,486 persons aged 10 years and over (including 22,916 aged 15 years and over), in the third round the sample consisted of 7,563 households, which amounts to a sample of around 29,268 persons aged 10 years and over (including 22,653 aged 15 years and over). In the fourth round the sample consisted of 7,563 households; which amounts to a sample of around 28,250 persons aged 10 years and over (including 21,926 aged 15 years and over).

The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Sample Rotation:

Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

One of the main survey tools is the questionnaire, the survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

1. Identification Data:

The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

2. Quality Control:

This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

3. Household Roster:

This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

4. Employment Part:

This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

Raw Data

The data processing stage consisted of the following operations:

1. Editing Before Data Entry All questionnaires were then edited in the main office using the same instructions adopted for editing in the field.

2. Coding At this stage, the Economic Activity variable underwent coding according to West Bank and Gaza Strip Standard Commodities Classification, based on the United Nations ISIC-3. The Economic Activity for all employed and ever employed individuals was classified at the fourth-digit-level. The occupations were coded on the basis of the International Standard Occupational Classification of 1988 at the third-digit-level (ISCO-88).

3. Data Entry In this stage data were entered into the computer, using a data entry template BLAISE.

The data entry program was prepared in order to satisfy the following requirements:

-Duplication of the questionnaire on the computer screen. -Logical and consistency checks of data entered. -Possibility for internal editing of questionnaire answers. -Maintaining a minimum of errors in digital data entry and fieldwork. -User- friendly handling.

Accordingly, data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data filesData editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

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.

Response rate

The overall response rate for the survey was 84.3%

More information on the distribution of response rates by different survey rounds is available in Page 11 of the data user guide provided among the disseminated survey materials under a file named "Palestine 2003- Data User Guide (English).pdf".

Sampling error estimates

Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are,

SHE: All Japan: Exp: MC: Hospital Charges Excl. Delivery data was reported at 2,195.000 JPY in Oct 2018. This records a decrease from the previous number of 2,208.000 JPY for Sep 2018. SHE: All Japan: Exp: MC: Hospital Charges Excl. Delivery data is updated monthly, averaging 2,433.500 JPY from Jan 2002 (Median) to Oct 2018, with 202 observations. The data reached an all-time high of 3,999.000 JPY in Dec 2005 and a record low of 1,716.000 JPY in Jan 2018. SHE: All Japan: Exp: MC: Hospital Charges Excl. Delivery data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.H069: Survey of Household Economy: All Japan.

Graph and download economic data for Consumer Opinion Surveys: Composite Consumer Confidence for United Kingdom (CSCICP02GBM460S) from Jan 1974 to May 2025 about consumer sentiment, composite, United Kingdom, and consumer.

Table NameAdministrative and Support and Waste Management and Remediation Services: Subject Series: Estab & Firm Size: Summary Statistics by Employment Size of Firms for the U.S.: 2012ReleaseScheduleThe data in this file are scheduled for release in March 2016.Key TableInformationEC1256SSSZ1 through EC1256SSSZ4, and EC1256SSSZ6 through EC1256SSSZ7 present data by employment and receipts size for establishments and firms, single unit and multiunit firms, concentration by largest firms, and legal form of organization for the United States. See Methodology. for additional information on data limitations.UniverseThe universe of this file is all establishments of firms with payroll in business at any time during 2012 and classified in Administrative and Support and Waste Management and Remediation Services (Sector 56).GeographyCoverageThe data are shown at the United States level only.IndustryCoverageThe data are shown for 2- through 7-digit 2012 NAICS codes.Data ItemsandOtherIdentifyingRecordsThis file contains data on:.Firms.Establishments.Receipts.Annual payroll.First-quarter payroll.Paid employees.Each record includes an EMPSZFF code which represents a specific employment size category of firms.FTP DownloadDownload the entire table athttps://www2.census.gov/econ2012/EC/sector56/EC1256SSSZ5.zip. ContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff. Washington, DC 20233-6900. Tel: (800) 242-2184. Tel: (301) 763-5154. ewd.outreach@census.gov. . .For information on economic census geographies, including changes for 2012, see the economic census Help Center..Includes only firms and establishments of firms with payroll. Excludes data for corporate, subsidiary, and regional managing offices and establishments of these firms that are classified in other categories than those specified in this file. See Table Notes for more information. Data based on the 2012 Economic Census. For method of assignment to categories shown and for information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.