United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over was 179.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over reached a record high of 514.00000 in January of 2000 and a record low of 179.00000 in January of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over - last updated from the United States Federal Reserve on October of 2025.

Graph and download economic data for Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men (LEU0254609800A) from 2000 to 2024 about occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women (LEU0254770000A) from 2000 to 2024 about second quartile, occupation, full-time, females, salaries, earnings, workers, 16 years +, wages, median, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women was 886.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women reached a record high of 886.00000 in January of 2024 and a record low of 429.00000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women - last updated from the United States Federal Reserve on October of 2025.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254556400A) from 2000 to 2024 about second quartile, occupation, full-time, salaries, earnings, workers, 16 years +, wages, median, employment, and USA.

AI Job Market & Salary Analysis 2025 Dataset

Dataset Overview

This comprehensive dataset contains detailed information about AI and machine learning job positions, salaries, and market trends across different countries, experience levels, and company sizes. Perfect for data science enthusiasts, career researchers, and market analysts for practice purposes.

Dataset Title

Global AI Job Market & Salary Trends 2025: Complete Analysis of 15,000+ Positions

Dataset Description

What's Inside

It includes detailed salary information, job requirements, company insights, and geographic trends.

Key Features: - 15,000+ job listings from 50+ countries - Salary data in multiple currencies (normalized to USD) - Experience level categorization (Entry, Mid, Senior, Executive) - Company size impact analysis - Remote work trends and patterns - Skills demand analysis - Geographic salary variations - Time-series data showing market evolution

Columns Description

Potential Use Cases

1. Salary Prediction Models

   • Build ML models to predict AI job salaries
   • Analyze factors affecting compensation
   • Compare salaries across different locations

2. Market Trend Analysis

   • Track the evolution of AI job market
   • Identify emerging job roles and skills
   • Analyze remote work adoption patterns

3. Career Planning

   • Understand skill requirements for different positions
   • Compare opportunities across countries
   • Plan career progression paths

4. Business Intelligence

   • Company hiring patterns analysis
   • Skills gap identification
   • Market competition insights

5. Geographic Studies

   • Cost of living vs. salary analysis
   • Regional market maturity assessment
   • Immigration pattern correlations

Data Collection Methodology

This is a synthetic dataset created for educational purposes to simulate AI job market patterns. All data is algorithmically generated based on industry research and market trends.

Sample Data Preview

job_id,job_title,salary_usd,experience_level,company_location,remote_ratio
AI001,Senior ML Engineer,145000,SE,United States,50
AI002,Data Scientist,89000,MI,Germany,100
AI003,AI Research Scientist,175000,EX,United Kingdom,0

Kaggle Dataset Tags

#artificial-intelligence #machine-learning #jobs #salary #career #data-science #employment #tech-industry #remote-work #compensation

Dataset Files Structure

ai-job-market-2025/
├── main_dataset.csv (15,247 rows)
├── skills_analysis.csv (skill frequency data)
├── company_profiles.csv (company information)
├── geographic_data.csv (country/city details)
├── time_series.csv (monthly trends)
└── data_dictionary.pdf (detailed documentation)

Acknowledgments

All personal information has been anonymized. This dataset is intended for educational and research purposes.

Discussion Topics for Community Engagement

1. What factors most influence AI job salaries in your analysis?
2. Interesting patterns you've discovered in remote work trends
3. Best visualization techniques for this type of employment data
4. Prediction model results - share your accuracy scores!
5. Geographic insights that surprised you

Suggested Notebook Titles for Analysis

• Predicting AI Salaries: A Complete ML Pipeline
• The Great Remote Work Shift in AI Jobs
• Skills That Pay: What Makes AI Engineers Valuable
• Global AI Talent Migration Patterns
• Company Size vs. Compensation: The AI Edition

*This dat...

Abstract

The main objective of the survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

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 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. These results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

• Electronic Processing: This stage began by defining the electronic processing team, which consisted of a system analyst, programmers and data entry staff. Work of the system analyst and programmers began in parallel with the work of the survey staff; starting by designing the questionnaire in a form that facilitates and ensures accuracy of data entry, preparing the required programs, then testing them by using hypothetical data and finalizing them before data entry. A liaision officer was appointed to provide the entry division with office-processed questionnaires which were returned in the form of batches to the archive upon completing data entry process. As for data entry, the data analyst of the survey trained a group of data entry staff on already prepared programs and systems. A set of data entry editing rules for all fields of the questionnaires were compiled. It included checking the permitted range of the value and quantity of each entered field and ensuring consistency between value and quantity of the field, and the related values and quantities of fields related to it in other questionnaires. The consistency rules were applied directly during the entry on various questionnaire items. That is, to ensure that entered data were consistent with each other and logical on the one hand, and conformed to given instructions related to the questionnaires’ data on the other hand. After completing the data entry process, special lists of data were printed. They were edited to reassure the correct entry and rectification of errors (if any).

• Tabulation and Dissemination of Results: Upon finalization of all office and electronic processing operations, the actual survey results were tabulated using the ORACLE package. The results were checked by extracting similar reports using the SPSS package to ensure that the results are correct and free of errors. This required checking the formality and phrasing of the used titles and concepts, in addition to editing of all data in each table according to its details and consistency within the same table and with other tables. The final report was then prepared, containing detailed tabulations, as well as, the methodology of the survey.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, sex, and age group, 1997 to 2018.

Israel Labour Input Index: 1990=100: Railway Service: Wages: Average per Employee per Month data was reported at 706.700 1990=100 in Jun 2011. This records an increase from the previous number of 420.300 1990=100 for May 2011. Israel Labour Input Index: 1990=100: Railway Service: Wages: Average per Employee per Month data is updated monthly, averaging 277.650 1990=100 from Jan 1989 (Median) to Jun 2011, with 270 observations. The data reached an all-time high of 814.600 1990=100 in Jan 2010 and a record low of 65.800 1990=100 in Jan 1989. Israel Labour Input Index: 1990=100: Railway Service: Wages: Average per Employee per Month data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.G041: Labour Input Index: Bus and Railway Services.

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 2011 (LFS). The survey rounds covered a total sample of about 31,190 households, and the number of completed questionaire is 28,083.

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: It consists of all Palestinian households who are staying normally in the Palestinian Territory (west bank and gaza strip) during the year of 2011.

---> Sampling Frame: The sampling frame consists of all enumeration areas which were enumerated in 2007, each numeration area consists of buildings and housing units with average of about 124 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

---> Sampling Size: The sample size was about 7,820 households in the 60th round and 7,802 households in the 61th round, and 7,784 households in the 62th round and 7,784 households in the 63th round, and there is 50% overlapping among households between each two consecutive rounds.

---> Sample Design The sample of the Labor Force Survey (LFS) which implemented periodically every quarter by PCBS since 1995, so this survey implement every quarter in the year 2011(distributed over 13 weeks). The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 502 enumeration areas for the whole round, and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 502 master sample enumeration 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 for 2 consecutive rounds, left for the next two rounds, then selected for the sample for another 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).

Mode of data collection

Face-to-face [f2f]

Research instrument

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 and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field. 2. Data entry: At this stage, data was entered into the computer using a data entry template designed in Access. The data entry program was prepared to satisfy a number of requirements such as: - Duplication of the questionnaires on the computer screen. - Logical and consistency check of data entered. - Possibility for internal editing of question answers. - Maintaining a minimum of digital data entry and fieldwork errors. - User friendly handling. Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

---> 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 survey sample consists of about 31,190 households in 2011, which 28,083 households completed the interview; whereas 18,650 households from the West Bank and 9,433 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 95% while in the Gaza Strip it reached 96%.

Sampling error estimates

---> Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and government level.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data

Abstract

A. Objective To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National Capital Region

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 50 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The sampling frame used for the survey was taken from the List of Establishments of the National Statistics Office. On a partial basis, this is regularly updated based on the responses to other surveys of the BLES, establishment reports on retrenchments and closures submitted to the Regional Offices of the Department of Labor and Employment and other establishment lists.

Sampling design: The OWS is a complete enumeration survey of non-agricultural establishments employing 50 persons or more in the National Capital Region.

Sample size: For OWS 2002, number of establishments covered was 5,954 of which, 3,974 were eligible units.

Note: Refer to Field Operations Manual

Sampling deviation

Not all of the fielded questionnaires are accomplished. During data collection, there are reports of permanent closures, non-location, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements (three consecutive survey rounds for "can not be located" establishments) of the frame and their count is not considered in the estimation. Non-respondents are made up of refusals, strikes or temporary closures, can not be located (less than three consecutive survey rounds) and those establishments whose questionnaires contain inconsistent item responses and have not replied to the verification queries by the time output table generation commences.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The 2002 OWS questionnaire is made up of the following sections:

Cover page (Page 1) This contains the address box for the establishment and other particulars.

Survey Information (Page 2) This section provides information on the purpose of the survey, coverage, reference period, collection authority, authorized field personnel, confidentiality clause, due date, availability of results and assistance available.

Part A: General Information (Page 3) This part inquires on the main economic activity, major product/s, goods or services, total employment, ownership (with foreign equity or wholly Filipino), spread of operations (whether establishment is a multinational), market orientation (for manufacturing only, engaged in export or domestic market only), presence of a union and existence of a collective bargaining agreement in the establishment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full Time Basis (Pages 4 - 5) It inquires data on the distribution of time-rate workers on full-time basis by time unit (hourly, daily, monthly) and basic pay and allowance intervals;

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6 - 11) For each occupation covered, the establishment is asked to report the time unit of work (hourly, daily, monthly), corresponding basic pay per worker and number of workers. Similar data are also asked for workers in the occupation that are given regular allowances. The total number of workers disaggregated by sex in each monitored occupation is likewise requested

Part D: Key and Representative Occupations in the Establishment (Page 12) This asks for the occupations and corresponding employment of those considered as unique to the industry/sector to which the establishment belongs, employs the most number of works, historically important in the wage structure or emerging/has a high growth potential.

Survey Results (Pages 13 - 14) Selected statistical tables from the previous two (2) survey rounds are provided for information of the respondents.

Part E: Certification of Respondent (Page 15) This box is provided for the respondent’s comments or suggestions (on the data it provided for the survey, results of previous survey rounds and improvements on the design/contents of the questionnaire) and for the name and signature, position, and telephone/fax numbers and e-mail address of the person responsible for filling out the form.

Part F: Survey Personnel (Page 15) This portion is allocated for the names of personnel involved in collection, editing and review of each questionnaire and dates when the activities were completed.

Part G: Industries with Selected Occupations (Page 16) This lists the selected 43 industries whose occupational wage rates and employment are being monitored.

Note: Refer to Questionnaire.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 78.7%.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Abstract

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.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates

Analysis unit

Household, individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame In the absence of a population census since 1967, the major task, with regard to constructing a master sample, was developing a frame of suitable units covering the whole country. Such units have been used as the PSUs (Primary Sampling Units) in the first stage of selection. For the second stage of selection, all PSUs have been listed in the field at the household level. This provided a sampling frame for selecting the households.

Sample Design The target population: consist of all Palestinian individuals aged 15 years and above living in West Bank and Gaza Strip, excluding nomads and persons living in institutions such as prisons, shelters.

Stratification Four levels of stratification have been made: Stratification by District. Stratification by type of (Locality) which comprises: (a) Municipalities (b)Villages (c)Refugee Camps
Stratification by locality size. Stratification by cell identification in that order.

Mode of data collection

Face-to-face [f2f]

Research instrument

TThe lfs questionnaire consists of four main sections: 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. 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. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. 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

Editing before data entry All questionnaires were edited again using the same instructions adopted for editing in the field.

Coding In this stage, the industry underwent coding according to WBGS Standard Commodities Classification, which is based on United Nations ISIC-3. The industry 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, 1988 at the third-digit-level (ISCO-88).

Data Entry In this stage data were entered to the computer using a data entry template written in BLAISE. The data entry program has satisfied many requirements such as: The duplication of the questionnaire on the computer screen. Logical and consistency checking of data entered. Possibility for internal editing of questions answers. Maintaining a minimum of digital data entry and field work errors. A User- Friendless Possibility of transferring data into another format to be used and analyzed using other statistical analytical systems such as SAS and SPSS.

Editing after data entry In this stage, all questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

" The overall response rate for the survey was 88.6%

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report

During the early days of professional baseball, the dominant major leagues imposed a “reserve clause” designed to limit player wages by restricting competition for labor. Entry into the market by rival leagues challenged the incumbent monopsony cartel’s ability to restrict compensation. Using a sample of player salaries from the first 40 years of the reserve clause (1880-1919), this study examines the impact of inter-league competition on player wages. This study finds a positive salary effect associated with rival league entry that is consistent with monopsony wage suppression, but the effect is stronger during the 20th century than the 19th century. Changes in levels of market saturation and minor-league competition may explain differences in the effects between the two eras.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals. NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data. NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

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 2011 (LFS). The survey rounds covered a total sample of about 31,190 households, and the number of completed questionaire is 28,083.

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: It consists of all Palestinian households who are staying normally in the Palestinian Territory (west bank and gaza strip) during the year of 2011.

---> Sampling Frame: The sampling frame consists of all enumeration areas which were enumerated in 2007, each numeration area consists of buildings and housing units with average of about 124 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

---> Sampling Size: The sample size was about 7,820 households in the 60th round and 7,802 households in the 61th round, and 7,784 households in the 62th round and 7,784 households in the 63th round, and there is 50% overlapping among households between each two consecutive rounds.

---> Sample Design The sample of the Labor Force Survey (LFS) which implemented periodically every quarter by PCBS since 1995, so this survey implement every quarter in the year 2011(distributed over 13 weeks). The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 502 enumeration areas for the whole round, and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 502 master sample enumeration 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 for 2 consecutive rounds, left for the next two rounds, then selected for the sample for another 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).

Mode of data collection

Face-to-face [f2f]

Research instrument

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 and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field. 2. Data entry: At this stage, data was entered into the computer using a data entry template designed in Access. The data entry program was prepared to satisfy a number of requirements such as: - Duplication of the questionnaires on the computer screen. - Logical and consistency check of data entered. - Possibility for internal editing of question answers. - Maintaining a minimum of digital data entry and fieldwork errors. - User friendly handling. Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

---> 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 survey sample consists of about 31,190 households in 2011, which 28,083 households completed the interview; whereas 18,650 households from the West Bank and 9,433 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 95% while in the Gaza Strip it reached 96%.

Sampling error estimates

---> Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and government level.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2010-2014 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Questions for "wage and salary" and "tips, bonuses and commissions" were asked separately for the first time during non-response follow-up via Computer Assisted Telephone Interview (CATI) and Computer Assisted Personal Interview (CAPI). Prior to 2013 these questions were asked in combination, "wages, salary, tips, bonuses and commissions."..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates

Abstract

The main objectives of the survey are as follows: To know the consumption expenditure patterns and the impact of social variables on them. To calculate the average monthly and annual expenditure of the individual and households on items of commodities and services and to know the factors affecting expenditure, such as educational, social and other levels.
To obtain data on household consumption and expenditure levels that can be used to determine poverty levels (monetary and multidimensional) and to analyze changes in living standards over time. It is also used by the Ministry of Social Development to calculate the aid eligibility equation. To provide data for national accounts for final consumption of the household sector. To provide weights data that reflect the relative importance of consumer spending items used in the preparation of consumer price index.
To access data on non-cash consumption such as consumption of own produced products and in-kind payments.
To know sources of income generation and household ownership of durable goods, tenure and agricultural property. To know characteristics of the dwelling, and the availability of services within the dwelling.

Geographic coverage

The Palestinian Expenditure and Consumption Survey (PECS) 2023 covers all Palestinian governorates in both the West Bank and Gaza Strip and all locality types; urban, rural and camps. The survey is designed to provide representative data at the national level and both regions; West Bank and Gaza Strip. However, due to the Israeli aggression on Gaza started in the last quarter of 2023, data collection in Gaza was forcibly stopped. While some indicators from Gaza are included in the published report, a note clarifies that the data is incomplete and does not reflect Gaza's situation.

Analysis unit

Palestinian Household

Universe

The target population of the Palestinian Expenditure and Consumption Survey (PECS) 2023 consists of all Palestinian households in the West Bank and Gaza Strip living in Palestine during 2023.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population The target population consists of all Palestinian households and individuals who were living normally with their households in the State of Palestine in 2023. Sampling Frame The sampling frame is based on a comprehensive sample selected from the Population, Housing, and Establishment Census, 2017. This comprehensive sample consists of geographically proximate areas (average of 150 households per area), known as enumeration areas (EAs) used in the census. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection. Sample Size The sample size for the Palestinian Expenditure and Consumption Survey, 2023 was 7,032 households for the entire year; 4,992 for the West Bank and 2,040 for Gaza Strip. The non-response rate was assumed based on data from 2016/2017 for each governorate. Sample Design The sample is a two-stage stratified cluster random sample: First stage: Selection of a stratified random sample of 586 enumeration areas. Second stage: Selection of a systematic random sample of 12 households from each enumeration area selected in the first stage. The enumeration areas were divided into four quarters, with each quarter's sample including all design strata as much as possible (governorate and locality type). Sample Strata The population was divided into strata as follows: 1.Governorate. 2.Locality type (urban, rural, refugee camps). Sample Allocation The sample was distributed using the Neyman allocation method, where the distribution relied on specific parameters such as the mean and standard deviation.

Mode of data collection

Computer Assisted Personal Interviewing - CAPI

Research instrument

The PECS, 2017 was the basis for designing the main survey questionnaire for the 2023 round. To ensure continuity and comparability between PECS surveys. The data will be collected during the registration month during the visit of the fieldworker to the household, and include the following sections: First part (Cover page): This section records the necessary household information, including the date of visit, data on field and office staff, and the number of household members by gender. Second part: Contains demographic and social questions about household members. Third part: Includes general questions about the characteristics of the labor force. Fourth part: Covers housing characteristics, including various topics such as type of housing, number of rooms, house ownership, rental value, access to services like electricity, water, and sanitation, main source of cooking fuel and heating, and distance to transportation, education, and health centers. Fifth part: Contains data on the consumer basket, which includes around 950 goods and services, described with their measurement units (kilogram, liter, and number), quantity, and value. Sixth part: Contains questions on social assistance and adaptation strategies, including the type and value of assistance received by the household or individuals, its source and frequency, and the circumstances and shocks experienced by the household or its members. Seventh part: Contains questions about income and means of income generation, as well as data on monthly and annual income, collected from different sources at the household level at the end of the registration period. Note: Additional questions have been added to some of the parts to cover indicators of poverty and multidimensional child poverty. The used language was Arabic.

Cleaning operations

Electronic Auditing: Tablets were used for data collection through an application reflecting the survey questionnaire, incorporating initial automatic audit rules for real-time data transfer to the central database. During this phase, initial audit rules enhanced data reliability by addressing potential errors during data collection through: -Validating responses in real-time to ensure they fall within expected ranges or formats. -Enforcing mandatory questions, preventing progress until all required fields are completed. -Automatically flagging inconsistent or abnormal responses with a note for the fieldworker to review and verify. Office Editing: For Jerusalem J1 forms, they were submitted weekly to the central office editor for review, ensuring data accuracy and consistency between sections, and addressing any inconsistent or abnormal values with fieldworkers. The reviewed forms were then handed over to the coding Division and subsequently to the data entry Division. Data Processing Tablets Application and Data Entry Platform The survey form was developed as a tablet application linked to the sample to facilitate data collection for fieldworkers. This application provided an easy-to-use interface, allowing fieldworkers to navigate the form easily, ensuring accurate and consistent data entry. Integrated with GPS and GIS technologies, the application provided real-time location tracking and interactive maps to guide fieldworkers in identifying household units in the sample. For paper forms in Jerusalem J1, the data entry program was designed to align with the survey form application with automatic audit rules. Data entry was done promptly after office editing and coding at PCBS main premises. Data entry program with the initial audit rules enhanced data reliability by addressing potential errors during data entry through: -Real-time validation of responses to ensure they fall within expected ranges or formats. -Enforcing mandatory questions, preventing progress until all required fields are completed. -Automatically flagging inconsistent or abnormal responses with a note for the fieldworker to review and verify. Office Data Cleaning Techniques The automatic and secure transfer of survey data to the central database was done in real-time, allowing data sets to be downloaded in the agreed format and design between project management and the data processing department. Data cleaning and quality assurance were performed using various statistical software, primarily R programming language via its RStudio interface, along with SPSS and Excel. This stage included detailed data cleaning and quality assurance processes to: -Verify variable types within the databases. -Detect outlier values for both numerical and categorical variables using different statistical methods and check relationships between variables to identify unexpected correlations or their absence. -Check data consistency and logical coherence across similar questions or sections. -Verify missing data due to technical or human errors. All values that failed cleaning stages were periodically sent to the field for verification and follow-up with households if necessary.

Response rate

Response rate was 70.3%

Sampling error estimates

Sampling Errors The data in this survey are subject to sampling errors, as they are derived from a sample rather than a full census of the study population. Consequently, there may be differences between the estimated values and the true population values that would be obtained from a complete census. To assess the reliability of the estimates, sampling variances were calculated for the survey's key indicators using SPSS, particularly focusing on the coefficient of variation (CV) as a measure of relative precision. The variance analysis indicated that there were no significant issues in data dissemination at the West Bank level. More information are seen in the attached report in page 39 in the English version after the Arabic.

Abstract

The Livestock Survey, 2013 aims to provide data on the structure of the livestock sector as the basis for formulating future policies and plans for development. It will also update existing data on agricultural holdings from the Agricultural Census of 2010 and build a database that will facilitate the collection of agricultural data in the future via administrative records

Geographic coverage

Palestine

Analysis unit

Agricultural holding

Universe

All animal and mixed holdings in Palestine during 2013.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The animal and mixed agricultural holdings frame was created from the agricultural census data of 2010 and extracted based on the following criteria: any number of cattle or camels, at least five sheep or goats, at least 50 poultry birds (layers and broilers), or 50 rabbits, or other poultry like turkeys, ducks, common quail, or a mixture of them, or at least three beehives controlled by the holder.

A master sample of 7,297 holdings from the animal and mixed holdings frame was updated prior to sample selection.

Sample Size The estimated sample size is 5,000 holdings.

Sample Design
The sample is a one-stage stratified systematic random sample.

Sample Strata The animal and mixed holdings are stratified into three levels, which are: 1. Governorates. 2. The main agricultural activities were identified by the highest holding size in the category: these activities are the raising cattle, raising sheep and goats, raising camels, poultry farming, beehives, mixed animals. The size of the holdings were classified into five categories

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Livestock Survey 2013 was designed based on the recommendations of the Food and Agriculture Organization of the United Nations (FAO) and the questionnaire used for the Agricultural Census of 2010. The special situation of Palestine was taken into account, in addition to the specific requirements of the technical phase of field work and of data processing and analysis The questionnaire consisted of the main items as follows: Identification data: Indicators about the holder, the holding and the respondent.

Data on holder: Included indicators on the sex, age, educational attainment, number in household, legal status of holder, and other indicators.

Holding data: Included indicators on the type of holding, tenure, main purpose of production, and other indicators.

Livestock data: Included indicators on the type, number, strain, age, sex, system of raising, main purpose of raising, number acquired or disposed of, quantity and value production, slaughtered in a holding, value of slaughtered, and other indicators.

Poultry data: Included indicators on the type, area of worked barns, average cycles per year, system of raising, quantity and value production, and other indicators.

Domestic poultry & equines data: Included indicators on type and number.

Beehive data: Included indicators such as the type, number, strain, quantity and value of production??.

Agricultural practices data: Included indicators on agricultural practices for livestock, poultry and bees.

Agricultural labor force data: Included indicators on the agricultural labor force in a holding such as the number, employment status, sex, age, average daily working hours, number of work days in an agricultural year and average daily wage.

Agricultural machinery and equipment: Included indicators on the number and source of machinery. Agricultural buildings data: Included indicators on the type and area of building.

Animal intermediate consumption: Included indicators on the type, quantity and value of animal intermediate consumption.

Cleaning operations

Preparation of Data Entry Program The data entry program was prepared using Oracle software and data entry screens were designed. Rules of data entry were established to guarantee successful entry of questionnaires and queries were used to check data after each entry. These queries examined variables on the questionnaire.

2.5.2 Data Entry Having designed the data entry program and tested it to verify readiness, and after training staff on data entry programs, data entry began on 4 November 2013 and finished on 8 January 2014 with 15 staff engaged in the data entry process.

2.5.3 Editing of Entered Data Special rules were formulated for editing the stored data to guarantee reliability and ensure accurate and clean data.

2.5.4 Results Extraction and Data Tabulation An SPSS program was used for extracting the results and empty tables were prepared in advance to facilitate the tabulation process. The report tables were formulated based on international recommendations, while taking the Palestinian situation into consideration in the data tabulation of the survey.

Response rate

Response rate was 94.3%

Sampling error estimates

Includes multiple aspects of data quality, beginning with the initial planning of the survey up to the final publication, plus how to understand and use the data. There are seven dimensions of statistical quality: relevance, accuracy, timeliness, accessibility, comparability, coherence, and completeness.

2.6.1 Data Accuracy
Includes checking the accuracy of data in multiple aspects, primarily statistical errors due to the use of a sample, as well as errors due to non-statistical staff and survey tools, in addition to response rates in the survey and the most important effects on estimates. This section includes the following:

Statistical Errors Survey data may be affected by sampling errors resulting from the use of a sample instead of a census. Variance estimation was carried out for the main estimates and the results were acceptable within the publishing domains as shown in the tables of variance estimation.

Data appraisal

Non-sampling Errors Non-statistical errors are probable in all stages of the project, during data collection and processing. These are referred to as non-response errors, interviewing errors, and data entry errors. To avoid and reduce the impact of these errors, efforts were exerted through intensive training on how to conduct interviews and factors to be followed and avoided during the interview, in addition to practical and theoretical exercises. Re-interview survey was conducted for 5% of the main survey and re-interview data proved that there is high level of consistency with the main indicators.

Abstract

A. Objectives

To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data

Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered

Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National coverage, 17 administrative egions

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 20 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2004 BLES Survey Sampling Frame (SSF2004) is a list frame of establishments that is a partial update of the 2003 BLES Sampling Frame based on the status of establishments reported in the 2003 BLES Integrated Survey (BITS) conducted nationwide.

Reports on closures and retrenchments of establishments submitted to the Regional Offices of the Department of Labor and Employment in December 2003 and January 2004 were also considered in updating the 2004 frame.

Sampling design: The OWS is a complete enumeration of non-agricultural establishments employing 50 persons or more. The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2004 OWS, number of establishments covered was 8,779 of which, 6,827 were eligible units.

Note: Refer to Field Operations Manual Chapter 1 Section 1.5.

Sampling deviation

While the OWS is a complete enumeration survey, not all of the fielded questionnaires are accomplished. Due to the inadequacy of the frame used, there are reports of permanent closures, nonlocation, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements of the frame and their count is not considered in the estimation. In addition to non-response of establishments because of refusals, strikes or temporary closures, there are establishments whose questionnaires contain inconsistent item responses that are not included in the processing as these have not replied to the verification queries by the time output table generation commences. Such establishments are also considered as non-respondents.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by BLES and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2006 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time Rate Workers on Full Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time Rate Workers on Full Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2006 OWS; results of the 2004 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the BLES and DOLE Regional Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in determining the correct occupational sheet that should be furnished to the respondent.

Results of the 2004 OWS (Page 12) The results of the 2004 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Note: Refer to questionnaire and List of Monitored Occupations.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 82.1%.

Sampling error estimates

Estimates of the sampling errors are not computed.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

Number of employees and average weekly earnings (including overtime) for all employees in the automotive industry, based on the North American Industry Classification System (NAICS), last 5 months.