Abstract copyright UK Data Service and data collection copyright owner.

The New Earnings Survey is almost certainly the most detailed and comprehensive earnings series anywhere in the world. It is a one in a hundred sample survey of employees in Britain, giving information on aspects of earnings and employment based on a week in April each year. The NES enquiry is conducted by the Department of Employment under the provisions of the Statistics of Trade Act (1947). Under the terms of this Act, data so obtained and relating solely to any individual may not be released into the public domain. All the data described here are in a form that ensures that there is no disclosure of individual information. They have been processed into a minimally aggregated form approved by the Department of Employment: any data record released relates to an aggregate of not less than three individuals.
Main Topics:
The dataset consists of fourteen separate extract data files from the original New Earnings Survey files held by the Department of Employment. Each extract file had been constructed to allow investigation of a particular aspect of the data contained in the Survey, as follows:
AGG01 National Collective Agreements
AGG02 Manual Skill Differentials
AGG03 Regional Implications
AGG04 Age implications
AGG05 Dispersion of Pay within Occupations
AGG06 Shiftwork
AGG07 Pay in relation to hours worked
AGG08 Public/Private Sector Pay Movements
AGG09 White Collar Pay Movements
AGG10 Sex Differentials
AGG11 Incentive Pay and Payment Schemes
AGG12 Incentive Payment Schemes and Age
AGG14 Pay in Relation to Size of Company and Plant
AGG15 Pay in Relation to Company Size and Region
Eight of the aggregate files (numbers 2,3,4,5,7,8,9 and 10) relate to dimensions recorded in the Survey in each year and comprise 13 annual files, one for each year 1970-1982. A further two aggregate files (numbers 1 and 6) contain 10 annual files for the years 1973-1982 inclusive, omitting the years 1970-1972, AGG01, due to the introduction of new occupations codes in 1973, and AGG06 due to the lack of shift pay premium prior to 1973. The remaining four files (numbers 11,12,14 and 15) relate to a single year only and are based on the special question included in that year.

Abstract copyright UK Data Service and data collection copyright owner.The New Earnings Survey is almost certainly the most detailed and comprehensive earnings series anywhere in the world. It is a one in a hundred sample survey of employees in Britain, giving information on aspects of earnings and employment based on a week in April each year. The NES enquiry is conducted by the Department of Employment under the provisions of the Statistics of Trade Act (1947). Under the terms of this Act, data so obtained and relating solely to any individual may not be released into the public domain. All the data described here are in a form that ensures that there is no disclosure of individual information. They have been processed into a minimally aggregated form approved by the Department of Employment: any data record released relates to an aggregate of not less than three individuals. Main Topics: The dataset consists of fourteen separate extract data files from the original New Earnings Survey files held by the Department of Employment. Each extract file had been constructed to allow investigation of a particular aspect of the data contained in the Survey, as follows: AGG01 National Collective Agreements AGG02 Manual Skill Differentials AGG03 Regional Implications AGG04 Age implications AGG05 Dispersion of Pay within Occupations AGG06 Shiftwork AGG07 Pay in relation to hours worked AGG08 Public/Private Sector Pay Movements AGG09 White Collar Pay Movements AGG10 Sex Differentials AGG11 Incentive Pay and Payment Schemes AGG12 Incentive Payment Schemes and Age AGG14 Pay in Relation to Size of Company and Plant AGG15 Pay in Relation to Company Size and Region Eight of the aggregate files (numbers 2,3,4,5,7,8,9 and 10) relate to dimensions recorded in the Survey in each year and comprise 13 annual files, one for each year 1970-1982. A further two aggregate files (numbers 1 and 6) contain 10 annual files for the years 1973-1982 inclusive, omitting the years 1970-1972, AGG01, due to the introduction of new occupations codes in 1973, and AGG06 due to the lack of shift pay premium prior to 1973. The remaining four files (numbers 11,12,14 and 15) relate to a single year only and are based on the special question included in that year.

Nearly 1.6 million passengers fished aboard for-hire recreational fishing vessels during 2011 in the Northeast United States (ME - NC). While the National Marine Fisheries Service (NMFS) regularly collects detailed catch, effort, and expenditure information from anglers fishing aboard for-hire vessels, no data are collected about the business structure and costs of the marine for-hire fishing industry operating in the Northeast. This study is intended to fill that gap. Survey results show that the overall financial condition of marine recreational for-hire fishing businesses in the Northeast is mixed. Assets exceed liabilities by over four times for the average charter and head boat, and over 90% of charter and head boat owners carry insurance coverage. This implies that a rather strong financial for-hire fishing fleet exists in the Northeast. The results also reveal that the average charter boat produced only a little over $5.1 thousand in net income in 2010 and that over half of the charter boats in the Northeast actually incurred higher expenses than revenues in 2010. In contrast, the average head boat generated over $95.1 thousand in net income in 2010 although median net income per head boat was lower at $50.1 thousand. In addition to providing a detailed overview of the operating structure of the "average" Northeast for-hire head boat and charter boat, we constructed an input-output model to estimate the economic activity that for-hire businesses contribute to the Northeast's economy as measured by total employment, labor income, and sales. Model results show that in 2010 the for-hire industry earned $140.3 million in revenue, generated $50.4 million in income to owners, hired captains, crew/mates, and office staff, and employed over 6,200 individuals. The multiplier effects of this activity were substantial. An additional $193.7 million in sales, $66.5 million in income, and 1,290 jobs in other businesses in the Northeast were supported by the for-hire industry through indirect and induced transactions. Service businesses (real estate, food services, marinas, repair shops, etc.), wholesale and retail trade businesses (sporting goods stores, bait shops, gas stations, etc.), and manufacturing businesses (fishing gear manufactures, fuel refineries, commercial fishermen [bait], etc.) were the enterprises most reliant on the for-hire fleet. Over 700 service sector jobs, 360 wholesale and retail trade jobs, and 63 manufacturing jobs were dependent upon the for-hire fleet in the Northeast in 2010. In total, an estimated 7,530 jobs, in the overall Northeast regional economy, were supported by the active for-hire fleet in 2010.

Structure of earnings survey: monthly earnings

Structure of earnings survey: hourly earnings

The metadata set does not comprise any description or summary. The information has not been provided.

These data represent a cost-earnings study of the Main Hawaiian Islands bottomfish fishery for the 2010 operating year. Data collected include fisher classification, vessel characteristics, levels of investment, trip-level expenditures, fishing behavior, market participation, social aspects of the fishery, and demographics. Additionally, attitudes and perceptions towards fisheries management ag...

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

Structure of earnings by sex, nationality, federal state and employment relationship

Abstract

The EESs are conducted by the National Bureau of Statistics (NBS), as mandated by Statistics Act 2015 and its 2018 and 2019 Amendments, which empowers NBS to collect, compile and disseminate official statistics in the country. The summary is presented for the six main topical areas namely: -Employment Profile; Wage Rates Profile; Cash Earnings Profile; Annual Wage Bill Profile; Newly Recruited Workers; and Job Vacancies

Geographic coverage

Tanzania Mainland Regional level

Analysis unit

Formal establishment

Universe

The survey covers all formal establishment with employees in both public and private sectors. establishment are divided in three main which are all public sector establishment, all registrated private establishmnent employ at least 50 persons and a sample of registrated of private establishments whose number of employees are from 5-49 persons.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017Employment and Earnings Survey is an establishment- based survey which covered a total of 10,896establishments from a frame of 52,429establishments. The frame consisted ofall public establishments and formal private establishments employing 5 persons or above

The survey covered all public -sector establishments and private sector establishments with at least 50 employees. Furthermore, the survey covered a sample of private establishments employing 5 to 49 persons. The sampling for this group involved stratifying establishments into those with 5 to 9 employees and those with 10 to 49 employees. Establishments in these strata were further stratified on the basis of their economic activities and ultimately a single stage sampling technique was used to derive representative establishments from each activity using the probability proportion to size (PPS).

Mode of data collection

Face-to-face [f2f]

Research instrument

Establishment based questionairre was development in english and was translated in swahili language

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structural checking of SPSS data files

Response rate

81.4

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the EES 2017 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Abstract

The main objective of the 2013 Employment and Earnings Survey was to obtain comprehensive data on the annual status of employment and earnings as well as data on the socio-economic characteristics of the labour market.

Geographic coverage

Tanzania Mainland Regions

Analysis unit

Establishment

Universe

Formal establishments of both public and private sectors

Kind of data

Sample survey data [ssd]

Sampling procedure

Employment and Earnings Survey 2013 sample was based on a sampling frame obtained from the Central Register of Establishments (CRE) maintained by the NBS. The existing sampling frame was developed on the basis of International Standard Industrial Classification Revision 4 (ISIC Rev.4). 10

Employment and Earnings Survey 2013 covered all establishments of public and all private sector establishments employing at least 50 employees. For all private sector establishments employing 5 - 49 employees, multistage sampling technique was used. The first stage within a region included stratification of all private establishments employing 5 - 49 employees into two strata namely 5 - 9 employees and 10 - 49 employees. Then, the sample size for each stratum was developed in each region. Finally, probability proportional to size (PPS) was used to draw the sample within each industry.

A similar approach was used in all the 25 regions to draw the sample size across all industrial major divisions in the two strata separately to enhance representation of all economic activities to the economy.

Sampling deviation

No deviation from the sample

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Annual Employment and Earnings Survey uses an English Questionnaire which devided into several sections namely, Identification, Regular Employees. Employment and Earnings, Casual Workers, Number of Workers Recruited during the last 12 Months and Job Vacancies.

Cleaning operations

After questionnires received to Head Quarters, Labour and Price Statistics Department recruits temporary editors for editing and coding the filled questionnaires before data entered to the computer to continue with further data processing steps. Completion of data entry followed by computer data editing for consistent and data entry error checks.

Response rate

The accuracy of the statistical data provided in the tables is dependent on the rate of response, especially where a few establishments are dominant in the industry. On average, the response rate was about 89.2% for Employment and Earnings Survey 2013.

Sampling error estimates

No sampling errorestimates

Data appraisal

No Forms of other Data Appraisal

Graph and download economic data for Average Hourly Earnings of All Employees, Other Services (CES8000000003) from Mar 2006 to Feb 2025 about earnings, establishment survey, hours, wages, services, employment, and USA.

New Earnings Survey (NES) and Annual Survey of Hours and Earnings (ASHE) percentile and median time series by full-time employees, full-time males and full-time females.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Surveys related to the family budget are considered one of the most important surveys types carried out by the Department Of Statistics, since it provides data on household expenditure and income and their relationship with different indicators. Therefore, most of the countries undertake periodic surveys on household income and expenditures. The Department Of Statistics, since established, conducted a series of Expenditure and Income Surveys during the years 1966, 1980, 1986/1987, 1992, 1997, 2002/2003, 2006/2007, and 2008/2009 and because of continuous changes in spending patterns, income levels and prices, as well as in the population internal and external migration, it was necessary to update data for household income and expenditure over time. Hence, the need to implement the Household Expenditure and Income Survey for the year 2010 arises. The survey was then conducted to achieve the following objectives: 1. Provide data on income and expenditure to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. 2. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 3. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 4. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty. 5. Identify consumer spending patterns prevailing in the society, and the impact of demographic, social and economic variables on those patterns. 6. Calculate the average annual income of the household and the individual, and identify the relationship between income and different socio-economic factors, such as profession and educational level of the head of the household and other indicators. 7. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it.

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 household surveys in several Arab countries.

Geographic coverage

The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the Kingdom. Where the Kingdom is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

Analysis unit

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

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 CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Household Expenditure and Income survey sample, for the year 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map. It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

To reach the survey objectives, 3 forms have been developed. Those forms were finalized after being tested and reviewed by specialists taking into account making the data entry, and validation, process on the computer as simple as possible.

(1) General Form/Questionnaire This form includes: - Housing characteristics such as geographic location variables, household area, building material predominant for external walls, type of tenure, monthly rent or lease, main source of water, lighting, heating and fuel cooking, sanitation type and water cycle, the number of rooms in the dwelling, in addition to providing ownership status of some home appliances and car. - Characteristics of household members: This form focused on the social characteristics of the family members such as relation to the head of the family, gender, age and educational status and marital status. It also included economic characteristics such as economic activity, and the main occupation, employment status, and the labor sector. to the additions of questions about individual continued to stay with the family, in order to update the information at the beginning of the second, third and fourth rounds. - Income section which included three parts · Family ownership of assets · Productive activities for the family · Current income sources

(2) Expenditure on food commodities form/Questionnaire This form indicates expenditure data on 17 consumption groups. Each group includes a number of food commodities, with the exception of the latter group, which was confined to some of the non-food goods and services because of their frequent spending pattern on daily basis like food commodities. For the purposes of the efficient use of results, expenditure data of the latter group was moved with the non-food commodities expenditure. The form also includes estimated amounts of own-produced food items and those received as gifts or in an in-kind form, as well as servants living with the family spending on themselves from their own wages to buy food.

(3) Expenditure on non-food commodities form/Questionnaire This form indicates expenditure data on 11 groups of non-food items, and 5 sets of spending on services, in addition to a group of consumption expenditure. It also includes an estimate of self-consumption, and non-food gifts or other items in an in-kind form received or sent by the household, as well as servants living with the family spending on themselves from their own wages to buy non-food items.

Cleaning operations

Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Organizing forms/questionnaires A compatible archive system, with the nature of the subsequent operations, was used to classify the forms according to different round throughout the year. This is to effectively enable extracting the forms when required for processing. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms are back to the archive system. 2- Data office checking This phase is achieved concurrently with the data collection phase in the field, where questionnaires completed in the fieldwork are immediately sent to data office checking phase. 3- Data coding A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were use, while for the rest of the questions, all coding were predefined during

Abstract

The employment and earnings survey was designed to collect data on employment, earnings by occupations, hours of work, details on job vacancies and occupational accidents and injuries. The objectives were; i) To generate the current and potential size and composition of the Uganda’s workforce; ii) To asses the characteristics of the existing manpower levels in the country in the selected sectors; iii) To form a baseline monitor salary and wage rate changes over a specific period of time; iv) To monitor sector remuneration movements i.e. changes in salary and wage rates paid in the private and public sectors; v) To form a baseline for monitoring future industry and occupation movements i.e. industry group changes and major occupational groups.

Geographic coverage

The survey covered 16 districts.

Analysis unit

Firm/establishment

Universe

The survey was covered all establishments employing 15 persons or more in the sampled districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was intended to cover all establishments employing 15 persons or more. These were selected from the Uganda Business Register of 2006. The study covered only seven types of industries namely Manufacturing, Construction, Hotels, Financial intermediation, Education (private schools), Health (private health institutions) and Horticulture. Due to resource constraints, only establishments in 16 districts were covered. The selection of districts was based on the total number of employment. The selected districts collectively had a total employment of about 92,000 persons out of the 111,000 persons in all the districts according to the Uganda Business Register and this represented 83 percent of the total number of persons employed. Out of the 1,554 establishments employing 15 and more employees, the selected districts had a total of 1,204 establishments (77 percent).

Mode of data collection

Face-to-face [f2f]

Research instrument

The data collected by the questionnaire included; Identification particulars of the establishment, Industry of the establishment, Period covered (reference period, which was March 2007 in this survey), Occupational hierarchy of the establishment, Usual number of working days per week, Normal Hours of Work per Week, Number of employees by sex, Mode of payment, Wages and Salaries Regular Allowances, Employment Status, Job vacancies that existed in a given quarter, Occupational accidents and diseases and their causes.

Cleaning operations

To ensure good quality of data, a system of double entry was used. A manual system of editing questionnaires was set-up and Statistical Assistants further assessed the consistency of the data collected. A machine editing computer program for verification and validation was developed and operated during data processing.

Response rate

Out of the 1,204 establishments that were selected, only 664 establishments responded to the questionnaires giving a 55 percent response rate. Four establishments were covered in Masindi and Hoima districts.

South Korea's Occupational Wage Survey (OWS) is an annual business establishment survey conducted since 1970 by South Korea's Ministry of Labor. The dataset contains detailed information on individual workers' earnings, hours worked, educational attainment, actual labor market experience, occupation, industry, and region. The surveyed establishments must employ at least ten workers and were selected by a stratified random sampling method. Because they exclude workers in small enterprises, the self-employed, family workers, temporary workers, and public sector workers, the surveys represent approximately one-half of South Korea's total nonagricultural labor force. The samples for each year are randomly drawn from the original surveys. The surveys cover all industries up through 1986. After 1986, agriculture, forestry, hunting, and fishing are excluded. This change in sampling procedure does not appear to cause a significant change in the types of nonfarm enterprises covered by the survey.

The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

This layer shows median earnings by occupational group broken down by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24022 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

During May-August 2004, face-to-face interviews with owners and/or captains of all NWHI bottomfish vessels were conducted to collect primary information on costs of fishing operations in 2003. Information on physical characteristics of the vessels, motivation of fishermen, and other topics was also collected. Follow-up interviews were conducted in September 2004 and March-April 2005 to collect data missed during the first interviews. M. Pan and A. Griesemer conducted the project.