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 April 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.

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.

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: Men (LEU0254609800A) from 2000 to 2024 about occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

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.

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work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

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.

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 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

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.

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

The Household Budget Survey 2010 provides a source of information for qualitative and quantitative indicators characterising living standards in Latvia. The main purpose of the data collection is to estimate the level and structure of consumption expenditure in the country as a whole as well as by type of locality (NUTS 2 level). The HBS data are used for the calculation of weights for a consumer price index and estimates of the private final consumption expenditure of the household sector in the National Accounts.

Geographic coverage

The survey sample covers whole territory of Latvia.

Analysis unit

• Households,
• Individuals.

Household is defined as a person or group of persons tied by relationship or other personal relations, having common subsistence expenditures and inhabiting the same living unit (house, flat, etc.), maintenance of which is covered by such persons jointly.

Universe

The sample represents private households in Latvia as well as their most typical groups. Collective households are not included in this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified two-stage random sample was used for the HBS in Latvia. Stratified systematic sampling with inclusion probabilities proportional to unit size was carried out at the first stage and simple random sampling was carried out at the second stage. The annual address sample is evenly distributed over time (the same number of addresses is sampled within each of the 52 weeks of the year) and space.

Two sampling frames are built for each sampling stage. At the first stage the counting areas of the Population and Housing Census are used as sampling frame. The list contains information on the number of addresses in each counting area. At the second stage the sampling frame is built from the statistical register of dwellings. The sampling frame provides information about resident population of the Republic of Latvia legally registered at the dwelling as well as its gender and age. Sampling frame is made on a quarterly basis.

Mode of data collection

Face-to-face [f2f]

Research instrument

Following types of principal survey forms and diaries have been developed for the collection of data: - Household Questionnaire - Household Diary - Pocket Individual Diary

Cleaning operations

Data processing is started with entering the Questionnaire and Diary data in computer. It is performed with the help of two data entry applications in ACCESS environment. Simultaneously with data entry the consumption expenditure data are coded according to COICOP/HBS at 8-digit level (for several groups at 9-digit level). This software allows systematic update of the consumption expenditure code dictionary with new types of goods and services or even their synonyms. Entry of Household questionnaire and Diary data is performed by the personnel specialising in entry of the particular information. Data verification methods at source data level are following: 1. Arithmetical correlations; 2. Logical correlations; 3. Verification of coherence between various sections of the questionnaire.

Along with the data entry simple data checking procedures are performed. During the data entry following verifications were performed: coherence among household member demographic characterisation variables, coherence among household member socio-economic characterisation variables (education, employment etc.), and coherence among elements characterising housing conditions. In the income part the verification of the income component minimum and maximum values was made. During the Diary data entry price intervals were controlled (minimum and maximum thresholds). This control is made with the help of data on sum paid for the goods purchased. In the ACCESS data entry programme code dictionary each good or service approximate minimum and maximum possible values in LVL are specified. Data entry operator verifies whether Diary record indicating the purchase value and amount meets the specified average price range.

When data are entered, verification of the sub-sections is carried out. Correlations in the mutually related sections of the Household Questionnaire and Household Diary are verified: 1. between utilities payments and housing characterisation; 2. among purchase of durable goods recorded in the Diary and analogous records on purchase of these goods during the last 12 months in tables 8 and 9 of the Questionnaire;

All discrepancies discovered are recorded in the error protocol.

Verification of errors When entry of the data is completed, further data verification procedures are continued in the ACCESS software: - Compliance of entered Questionnaire data and households included in the sample list. - Compliance between income, education and labour status. - Compliance between respondent age, socio-economic status and income. - Correlations between household demographic composition and State social transfers received. - Repeated verification of the price intervals.

In the following stage initial ACCESS file is converted into SPSS format file.

Response rate

Overall response rate for HBS 2010 comprised 43.1%

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 regions

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

Sample survey data [ssd]

Sampling procedure

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

Survey universe/Sampling frame: The 2006 BLES Survey Sampling Frame (SSF 2006) is an integrated list of establishments culled from the 2004 List of Establishments of the National Statistics Office, updated 2004 BLES Sampling Frame based on the status of establishments reported in the 2003/2004 BLES Integrated Survey (BITS). Reports on closures and retrenchments of establishments submitted to the Regional Offices of the Department of Labor and Employment were also considered in preparing the 2006 frame.

Sampling design: The OWS is a sample survey of non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for more industry coverage.

Sample size: For 2006 OWS, number of establishments covered was 7,630 of which, 6,432 were eligible units.

Note: Refer to Field Operations Manual Chapter 2 Section 2.5.

Sampling deviation

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

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

Mode of data collection

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

Research instrument

The 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 were manually and electronically processed. Upon collection of accomplished questionnaires, enumerators performed field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the Field Operations Manual. The forms were again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertaook 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 were 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 87.56%.

Sampling error estimates

Estimates of the sampling errors computed.

Note: Refer to Coefficients of Variation.

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.

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.

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country.

The main objectives of this survey - update the weight of each expenditure item (from COICOP) and obtain weights for the revision of the Consumer Price Index (CPI) for Funafuti - provide data on the household sectors contribution to the National Accounts - design the structure of consumption for food secutiry - To provide information on the nature and distribution of household income, expenditure and food consumption patterns household living standard useful for planning purposes - To provide information on economic activity of men and women to study gender issues - To generate the income distribution for poverty analysis

The 2010 Household Income and Expenditure Survey (HIES) is the third HIES that was conducted by the Central Statistics Division since Tuvalu gained political independence in 1978.

This survey deals mostly with expenditure and income on the cash side and non cash side (gift, home production). Moreover, a lot of information are collected:

at a household level: - goods possession - description of the dwelling - water tank capacity - fruits and vegetables in the garden - livestock

at an individual level: - education level - employment - health

Geographic coverage

National Coverage: Funafuti and /Outer islands.

Analysis unit

• Household level
• Individual level

Universe

The scope of the 2010 Household Income and Expenditure Survey (HIES) was all occupied households in Tuvalu. Households are the sampling unit, defined as a group of people (related or not) who pool their money, and cook and eat together. It is not the physical structure (dwelling) in which people live. HIES covered all persons who were considered to be usual residents of private dwellings (must have been living in Tuvalu for a period of 12-months, or have intention to live in Tuvalu for a period of 12-months in order to be included in the survey). Usual residents who are temporary away are included as well (e.g., for work or a holiday).

All the private household are included in the sampling frame. In each household selected, the current resident are surveyed, and people who are usual resident but are currently away (work, health, holydays reasons, or border student for example. If the household had been residing in Tuvalu for less than one year: - but intend to reside more than 12 months => he is included - do not intend to reside more than 12 months => out of scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tuvalu 2010 Household Income and Expenditure Survey (HIES) outputs breakdowns at the domain level which is Funafuti and Outer Islands. To achieve this, and to match the budget constraint, a third of the households were selected in both domains. It was decided that 33% (one third) sample was sufficient to achieve suitable levels of accuracy for key estimates in the survey. So the sample selection was spread proportionally across all the islands except Niulakita as it was considered too small. The selection method used is the simple random survey, meaning that within each domain households were directly selected from the population frame (which was the updated 2009 household listing). All islands were included in the selection except Niulakita that was excluded due to its remoteness, and size.

For selection purposes, in the outer island domain, each island was treated as a separate strata and independent samples were selected from each (one third). The strategy used was to list each dwelling on the island by their geographical position and run a systematic skip through the list to achieve the 33% sample. This approach assured that the sample would be spread out across each island as much as possible and thus more representative.

Population and sample counts of dwellings by islands for 2010 HIES Islands: -Nanumea: Population: 123; sample: 41 -Nanumaga: Population: 117; sample: 39 -Niutao: Population: 138; sample: 46 -Nui: Population: 141; sample: 47 -Vaitupu: Population: 298; sample: 100 -Nukufetau: Population: 141; sample: 47 -Nukulaelae: Population: 78; sample: 26 -Funafuti: Population: 791; sample: 254 -TOTAL: Population: 1827; sample: 600.

Mode of data collection

Face-to-face [f2f]

Research instrument

3 forms were used. Each question is writen in English and translated in Tuvaluan on the same version of the questionnaire. The questionnaire was highly based on the previous one (2004 survey).

Household Schedule This questionnaire, to be completed by interviewers, is used to collect information about the household composition, living conditions and is also the main form for collecting expenditure on goods and services purchased infrequently.

• composition of the household and demographic profile of each members
• dwelling information
• dwelling expenditure
• transport expenditure
• education expenditure
• health expenditure
• land and property expenditure
• household furnishing
• home appliances
• cultural and social payments
• holydays/travel costs
• Loans and saving
• clothing
• other major expenditure items

Individual Schedule There will be two individual schedules: - health and education - labor force (individual aged 15 and above) - employment activity and income (individual aged 15 and above): wages and salaries working own business agriculture and livestock fishing income from handicraft income from gambling small scale activies jobs in the last 12 months other income childreen income tobacco and alcohol use other activities seafarer

Diary (one diary per week, on a 2 weeks period, 2 diaries per household were required) The diaries are used to record all household expenditure and consumption over the two week diary keeping period. The diaries are to be filled in by the household members, with the assistance from interviewers when necessary. - All kind of expenses - Home production - food and drink (eaten by the household, given away, sold) - Goods taken from own business (consumed, given away) - Monetary gift (given away, received, winning from gambling) - Non monetary gift (given away, received, winning from gambling).

Cleaning operations

Consistency of the data: - each questionnaire was checked by the supervisor during and after the collection - before data entry, all the questionnaire were coded - the CSPRo data entry system included inconsistency checks which allow the National Statistics Office staff to point some errors and to correct them with imputation estimation from their own knowledge (no time for double entry), 4 data entry operators. 1. presence of all the form for each household 2. consistency of data within the questionnaire

at this stage, all the errors were corrected on the questionnaire and on the data entry system in the meantime.

• after data entry, the extreme amount of each questionnaire where selected in order to check their consistency. at this stage, all the inconsistency were corrected by imputation on CSPRO editing.

Response rate

The final response rates for the survey was very pleasing with an average rate of 97 per cent across all islands selected. The response rates were derived by dividing the number of fully responding households by the number of selected households in scope of the survey which weren't vacant.

Response rates for Tuvalu 2010 Household Income and Expenditure Survey (HIES): - Nanumea 100% - Nanumaga 100% - Niutao 98% - Nui 100% - Vaitupu 99% - Nukufetau 89% - Nukulaelae 100% - Funafuti 96%

As can be seen in the table, four of the islands managed a 100 per cent response, whereas only Nukufetau had a response rate of less than 90 per cent.

Further explanation of response rates can be located in the external resource entitled Tuvalu 2010 HIES Report Table 1.2.

Sampling error estimates

The quality of the results can be found in the report provided in this documentation.

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