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 ****** 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: 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 ****** 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 ****** U.S. dollars.

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

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, males, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Explore the progression of average salaries for graduates in Business Information Technologydata Entry from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Business Information Technologydata Entry relative to other fields. This data is essential for students assessing the return on investment of their education in Business Information Technologydata Entry, providing a clear picture of financial prospects post-graduation.

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: Men (LEU0254663200A) from 2000 to 2023 about second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Abstract

The main objective of the HEIS 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. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

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

• Household/families
• 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. This 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

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

Harmonized Data - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets - The harmonization process started with cleaning all raw data files received from the Statistical Office - Cleaned data files were then all merged to produce one data file on the individual level containing all variables subject to harmonization - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables - A post-harmonization cleaning process was run on the data - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format

Since 1946, the Central Bureau of Statistics has held regularly research on regional income distribution. These studies are mainly based on registers from the Ministry of Finance (the tax registers) and the Dutch municipalities (de population registers = GBA). The final results from the Regional Income research (RIO) is based on a sample of more than 1.9 million households.

Income distributions of persons or households, by country, province, COROP area, metropolitan agglomeration, urban region and municipality.

Data available from: 2005 These renewed figures from the 2005 RIO relate to 'provisional figures. For RIO 2005, a new production run of the income production system took place with improved input data from the tax registers of 2005. With this improved input data, the number of data to be imputed on micro level from previous research years (2004 and 2003) substantially less this improves the quality of output. It is now apparent from the plausibility contoles that in the numbers and amounts small differences are found in relation to the previous production run from the beginning of this year, we are forced to have the existing RIO 2005 output to be revised. The reference date is 1 January 2006; the data relate to the research year 2005.

Frequency: one-off Because the municipal division changes annually, the results are published from the RIO for each individual research year; merging or division of municipalities will result in all information related to income in a newly formed or split municipality can change so that comparability over time is not possible.

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.

Context

This dataset provides information on the average wage in various countries. Understanding the average wage in different countries is essential for economic analysis, benchmarking, and comparisons. Researchers, analysts, and policymakers can use this dataset to gain insights into global income disparities, labor market conditions, and economic trends.

Country_Gross Average Monthly Wages in 2020

The dataset comprises two primary columns: "Country" and "Gross Average Monthly Wages in 2020 (US$, at current Exchange Rates)." Each entry in the "Country" column represents a distinct country or region, while the corresponding entry in the "Gross Average Monthly Wages" column denotes the average earnings in US dollars for the specified location in the year 2020.

Development of Average Annual Wages

The "Development of Average Annual Wages" dataset, available on Kaggle, offers a comprehensive collection of average annual wage data spanning from the year 2000 to 2022. This dataset is a valuable resource for researchers, analysts, economists, and data enthusiasts interested in understanding the economic trends and wage dynamics across various countries over the past two decades.

Geographic coverage

West Bank and Gaza

Analysis unit

Household

Universe

It consists of all Palestinian households and individuals who are staying normally in the state of Palestine during 2013

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is two stage stratified cluster (pps) sample: First stage: selection a stratified sample of 300 EA with (pps) method. Second stage: selection a random area sample of 25 responded households from each enumeration area selected in the first stage, the selection starts from a random point in the enumeration area ( building number).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire Represents the main tool for the data collection, and so it must achieve the technical specifications for all phases of the survey, and the questionnaire consists of several sections: · Cover Page: Contains the identification data for the family, the date of the visit, data on the team work of the field, office and data entry. · The Roaster: Which contains demographic, social and economic data for the family members selected. · Housing Characteristics: It includes data on the type of dwelling, tenure, number of rooms, housing unit connection to public networks (water, electricity), the method of waste disposal, the main source of energy used in the housing unit, durable goods available to the family as well as data on the confiscation / Isolation Lands of the family by the Israeli occupation and land area. · Agriculture: The family ownership of agricultural land and land area, and sources of irrigation of agricultural crops, livestock and their numbers and data on the number of workers in agriculture from family members. · Assistances and Coping Strategy: Contains data about the family receiving of all kinds of assistances (food, cash, employment, school feeding), and source of assistance, and satisfaction for assistance and the reason for the dissatisfaction for assistance. And It contains data on the length of time in which the family can survive financially in the future, and the difficulties faced by the family and the actions carried out by the family to cope with difficulties. · Consumption/Expenditures: This section contains data on household expenditure in terms of increase or decrease, as well as the average household expenditure during the past six months, the rate of household expenditure on food and water during the past six months ... etc.. · Dietary Diversity and Facing Food Shortages: Includes data about how many days the family consume some food during the past week and the origin and source of such food. · Income: This section contains data on the sources of family income and the value of the family's monthly income over the past month and the value of annual income, and the percentage of annual income from agriculture. Freedom of Movement: The data includes all restrictions on the movement of the family during the past six months, and the problems prevent any family member from access to work, land, school or university and health facilities

Cleaning operations

Both data entry and tabulation were performed using the Access and SPSS software programs. Data entry was organized corresponding to the main parts of the questionnaire.
A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consisting checks and cross-validation. Complete manual inspection of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Response rate

Response rate was 83.6%

Sampling error estimates

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 regional level (west bank , gaza strip).

Data appraisal

Non-sampling 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 in how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey and practical and theoretical training during the training course.

Also data entry staff was trained on the entry program that was examined before starting the data entry process. Continuous contacts with the fieldwork team were maintained through regular visits to the field and regular meetings during the different field visits. Problems faced by fieldworkers were discussed to clarify issues and provide relevant instructions.

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.

Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg per Employee per Month data was reported at 265.200 1990=100 in Jun 2011. This records a decrease from the previous number of 271.300 1990=100 for May 2011. Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg per Employee per Month data is updated monthly, averaging 236.350 1990=100 from Jan 1990 (Median) to Jun 2011, with 258 observations. The data reached an all-time high of 406.100 1990=100 in Sep 2000 and a record low of 83.800 1990=100 in Aug 1990. Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg 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.

Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg per Actual Man Days Worked data was reported at 309.300 1990=100 in Jun 2011. This records an increase from the previous number of 294.300 1990=100 for May 2011. Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg per Actual Man Days Worked data is updated monthly, averaging 264.800 1990=100 from Jan 1990 (Median) to Jun 2011, with 258 observations. The data reached an all-time high of 521.900 1990=100 in Sep 2001 and a record low of 80.700 1990=100 in Aug 1990. Israel Labour Input Index: 1990=100: Bus Service: Wage: Avg per Actual Man Days Worked 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.

Since 1946, the Central Bureau of Statistics has held regularly research on regional income distribution. These studies are

mainly based on registers from the Ministry of

Finance (the tax registers) and the Dutch municipalities (de

population registers = GBA). The final results from the Regional

Income research (RIO) is based on a sample of more than 1.9

million households.

Income distributions of persons or households, by country, province,

COROP area, metropolitan agglomeration, urban region and municipality.

Data available from: 2005

These renewed figures from the 2005 RIO relate to 'provisional

figures.

For RIO 2005, a new production run of the

income production system took place with improved input data from the

tax registers of 2005.

With this improved input data, the number of data to be imputed on micro

level from previous research years (2004 and 2003) substantially less

this improves the quality of output. It is now apparent from the

plausibility contoles that in the numbers and amounts small

differences are found in relation to the previous production run

from the beginning of this year, we are forced to have the existing RIO 2005

output to be revised.

The reference date is 1 January 2006; the data relate to the

research year 2005.

Frequency: one-off

Because the municipal division changes annually, the results are

published from the RIO for each individual research year; merging

or division of municipalities will result in all information related

to income in a newly formed or split municipality

can change so that comparability over time is not possible.

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.

In 2023, it was estimated that the CEO-to-worker compensation ratio was 290.3 in the United States. This indicates that, on average, CEOs received more than 290 times the annual average salary of production and non-supervisory workers in the key industry of their firm.

German law graduates holding a doctorate degree can currently expect the ******* 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 ****** of the starting salary food chain. Law courses among most attended Law, economics and social sciences were the subject groups seeing the ******* student numbers in German universities, totaling over *** 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 ***** 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.