United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over was 923.00000 $ 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 reached a record high of 923.00000 in January of 2024 and a record low of 437.00000 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 - last updated from the United States Federal Reserve on March 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: Women (LEU0254770000A) from 2000 to 2024 about second quartile, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

In 2022, the top paying state for date entry keyers in the United States was the District of Columbia, where this workforce earned an annual mean wage of approximately 52,000 U.S. dollars. The state with the second highest annual mean wage for data entry keyers was Massachusetts, where those employed within this industry earned 46,450 U.S. dollars.

Graph and download economic data for Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254503000A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men was 49.00000 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: Men reached a record high of 95.00000 in January of 2000 and a record low of 46.00000 in January of 2013. 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: Men - last updated from the United States Federal Reserve on March of 2025.

In 2023, the best paying industry in the United States for data entry keyers was in the federal postal service. The second best paying industry was in natural gas distribution, where data entry keyers earned an annual wage of approximately 58,000 U.S. dollars in 2023.

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, workers, earnings, 16 years +, wages, median, employment, and USA.

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.

In 2022, it was estimated that the CEO-to-worker compensation ratio was 344.3 in the United States. This indicates that, on average, CEOs received more than 344 times the annual average salary of production and nonsupervisory workers in the key industry of their firm.

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 10 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 87.1%

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

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

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 2000 (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 quarter consisted of 7,559 households, which amounts to a sample of around 29,650 persons aged 15 years and over (including 23,677 aged 15 years and over). In the second round the sample consisted of 7,559 households, which amounts to a sample of around 29,894 persons aged 10 years and over (including 23,890 aged 15 years and over), in the third round the sample consisted of 7,559 households, which amounts to a sample of around 29,709 persons aged 10 years and over (including 23,670 aged 15 years and over). In the fourth round the sample consisted of 7,559 households; of these only 7349 households have been interviewed due to the Israeli comprehensive closure and aggression against the Palestinian people, which amounts to 28380 persons aged 10 years and over (including 22495 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.

1. 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).

2. 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 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 89.5%

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 2000- 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, therefore, in principle measurable by the statistical concept of standard error.

A

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.

German law graduates holding a doctorate degree can currently expect the highest average gross starting salary in the country when they enter the job market. Other degrees with good earning prospects include medicine, computer science (also with a doctorate degree), and industrial engineering. In comparison, those who studied graphics/design, humanities and social sciences are at the bottom of the starting salary food chain. Law courses among most attended Law, economics and social sciences were the subject groups seeing the highest student numbers in German universities, totaling over one million in 2023/2024. Engineering and mathematics rounded up the top three. German universities offer a variety of internationally recognized degrees, the Bachelor being the most frequently taken type of final exam. Slow yearly salary increase Among selected countries in the European Union, Germany ranks ninth in terms of average annual wages. All the same, when studying the change in average annual pay specifically in Germany during the last decade, a slow, but steady increase is visible year after year, until the coronavirus (COVID-19) pandemic hit in 2020. Since then, the average wage has been decreasing and in 2023 was around the same level as in 2017.

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 2015 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..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, 2015 American Community Survey 1-Year Estimates

As of the 2023/24 academic year, graduates from the Massachusetts Institute of Technology (MIT) had a starting salary of 110,200 U.S. dollars, and a mid-career salary of 196,900 U.S. dollars. Top universities in the United States One of the top universities in the United States, Harvey Mudd College, is located in Claremont, California. Not only do graduates earn a high salaries after graduation, they also pay the most. In the academic year of 2020-2021, Harvey Mudd College was one of the most expensive school by total annual cost. The best university in the United States in 2021 belonged to the University of California, Berkeley. The Ivy League The Ivy League is a group of eight private universities in the Northeastern United States. It is not only a collegiate athletic conference, but also a group of highly respected academic institutions. They are usually regarded as the best eight universities in the United States and the world. They are extremely selective with their admissions process. However, these universities are extremely expensive to attend. Despite the high price tag, students who graduate from Princeton University have the highest early career salary out of all Ivy League attendees in 2021. This is compared to the overall expected starting salaries of recent college graduates across the United States, which was less than 35,000 U.S. dollars.

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.

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. 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.

Abstract

The Ghana Living Standards Survey (GLSS), with its focus on the household as a key social and economic unit, provides valuable insights into living conditions in Ghana. The survey was carried out by the Ghana Statistical Service (GSS) over a 12-month period (April 1998 to March 1999). A representative nationwide sample of more than 5,998 households, containing over 25,000 persons, was covered in GLSS IV.

The fourth round of the GLSS has the following objectives: · To provide information on patterns of household consumption and expenditure disaggregated at greater levels. · In combination with the data from the earlier rounds to serve as a database for national and regional planning. · To provide in-depth information on the structure and composition of the wages and conditions of work of the labor force in the country. · To provide benchmark data for compilation of current statistics on average earnings, hours of work and time rates of wages and salaries that will indicate wage/salary differentials between industries, occupations, geographic locations and gender.

Additionally, the survey will enable policy-makers to · Identify vulnerable groups for government assistance; · Analyze the impact of decisions that have already been implemented and of the economic situation on living conditions of households; · Monitor and evaluate the employment policies and programs, income generating and maintenance schemes, vocational training and similar programs. The joint measure of employment, income and expenditure provides the basis for analyzing the adequacy of employment of different categories of workers and income-generating capacity of employment-related economic development.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Community
• Commodity

Kind of data

Sample survey data [ssd]

Sampling procedure

A nationally representative sample of households was selected in order to achieve the survey objectives. For the purposes of this survey the list of the 1984 population census Enumeration Areas (EAs) with population and household information was used as the sampling frame. The primary sampling units were the 1984 EAs with the secondary units being the households in the EAs. This frame, though quite old, was considered the best available at the time. Indeed, this frame was used for the earlier rounds of the GLSS.

In order to increase precision and reliability of the estimates, the technique of stratification was employed in the sample design, using geographical factors, ecological zones and location of residence as the main controls. Specifically, the EAs were first stratified according to the three ecological zones namely; Coastal, Forest and Savannah, and then within each zone further stratification was done based on the size of the locality into rural or urban.

A two-stage sample was selected for the survey. At the first stage, 300 EAs were selected using systematic sampling with probability proportional to size method (PPS) where the size measure is the 1984 number of households in the EA. This was achieved by ordering the list of EAs with their sizes according to the strata. The size column was then cumulated, and with a random start and a fixed interval the sample EAs were selected. It was observed that some of the selected EAs had grown in size over time and therefore needed segmentation. In this connection, such EAs were divided into approximately equal parts, each segment constituting about 200 households. Only one segment was then randomly selected for listing of the households. At the second stage, a fixed number of 20 households was systematically selected from each selected EA to give a total of 6,000 households. Additional 5 households were selected as reserve to replace missing households. Equal number of households was selected from each EA in order to reflect the labor force focus of the survey.

NOTE: The above sample selection procedure deviated slightly from that used for the earlier rounds of the GLSS, as such the sample is not self-weighting. This is because: - given the long period between 1984 and the GLSS 4 fieldwork the number of households in the various EAs are likely to have grown at different rates. - The listing exercise was not properly done as some of the selected EAs were not listed completely. Moreover, it was noted that the segmentation done for larger EAs during the listing was a bit arbitrary.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main questionnaire used in the survey was the household questionnaire. In addition to this, there were community and Price questionnaires.

• Household Questionnaire: The household questionnaire was used to collect information on various topics some of which pertain to eligible individual household members. The questionnaire is in two parts, A and B.
• Community Questionnaire: The main aim of the community questionnaire was is to identify the economic infrastructure, education and health facilities existing in the villages, as well as any related problems that affects their welfare. The questionnaire was administered in the rural EAs only.
• Price Questionnaire: As part of the survey a price questionnaire was designed to collect prices of most essential commodities in the local markets.

Cleaning operations

Training: The project had 3 experienced computer programmers responsible for the data processing. Data processing started with a 2-weeks training of 15 data entry operators out of which the best 10 were chosen and 2 identified as standby. The training took place one week after the commencement of the fieldwork.

Data entry: Each data entry operator was assigned to one field team and stationed in the regional office of the GSS. The main data entry software used to capture the data was IMPS (Integrated Microcomputer Processing System). The data capture run concurrently as the data collection and lasted for 12 months.

Tabulation/Analysis: The IMPS data was read into SAS (Statistical Analysis System), after which the analysis and generation of the statistical tables were done using SAS.

Response rate

Out of the selected 6000 households 5999 were successfully interviewed. One household was further dropped during the data cleaning exercise because it had very few records for many of the sections in the questionnaire. This gave 5998 household representing 99.7% coverage. Overall, 25,694 eligible household members (unweighted) were covered in the survey.