This map layer portrays 1979, 1980, and 1981 estimates for total personal income, per capita personal income, annual number of full-time and part- time jobs, average wage per job in dollars, population, and per capita number of jobs, for counties in the United States. Total personal income is all the income that is received by, or on behalf of, the residents of a particular area. It is calculated as the sum of wage and salary disbursements, other labor income, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and transfer payments to persons, minus personal contributions for social insurance. Per capita personal income is calculated as the total personal income of the residents of a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. The average annual number of full-time and part-time jobs includes all jobs for which wages and salaries are paid, except jury and witness service and paid employment of prisoners. The jobs are counted at equal weight, and employees, sole proprietors, and active partners are all included. Unpaid family workers and volunteers are not included. Average wage per job is the total wage and salary disbursements divided by the number of wage and salary jobs in the county. Wage and salary disbursements consist of the monetary remuneration of employees, including the compensation of corporate officers; commissions, tips, and bonuses; and receipts in kind, or pay-in-kind, such as the meals furnished to the employees of restaurants. It reflects the amount of payments disbursed, but not necessarily earned during the year. Per capita number of jobs is calculated as the average annual number of full-time and part-time jobs in a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. All dollar estimates are in current dollars, not adjusted for inflation. The information in this map layer comes from the Regional Economic Information System (REIS) that is distributed by the Bureau of Economic Analysis, http://www.bea.gov/. This is an updated version of the November 2004 map layer.

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.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Holmes County. The dataset can be utilized to gain insights into gender-based income distribution within the Holmes County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Holmes County, among individuals aged 15 years and older with income, there were 4,594 men and 6,013 women in the workforce. Among them, 1,626 men were engaged in full-time, year-round employment, while 1,981 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 18.45% fell within the income range of under $24,999, while 28.17% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 13.71% of men in full-time roles earned incomes exceeding $100,000, while 4.04% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Holmes County median household income by race. You can refer the same here

The data set recorded the statistical data of the average salary and index of employed non-private sector employees in Qinghai Province in major years from 1978 to 2020, divided by year and regions such as Xining city, Haidong Prefecture, Haibei Prefecture, Huangnan Prefecture, Hainan Prefecture, Goluo Prefecture, Yushu Prefecture and Haixi Prefecture. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 17 data tables, which are: Average wages and indices of major years 1978-2004 XLS, 1978-2005 XLS, 1978-2006 XLS, 1978-2007 Average Wages and Indices of Employed Staff in non-private Sector of major years 1978-2011 XLS, main years 1978-2009 XLS, main years 1978-2010 XLS, Main years 1978-2011 Main years Average Wage and Index of Employed Staff and Workers in Non-private Sector, 1981-2012, main years Average Wage and Index of Employed Staff and workers in Non-private Sector, 1981-2013. XLS, main years Average wage and index of Employed Staff and workers in Non-private Sector, 1981-2014. Average Wage and index of Employed Staff and Workers in non-private Sector in major years 1981-2015 XLS, Average Wage and Index of Employed Staff and workers in Non-private Sector in major years 1981-2016 XLS, average Wage and Index of Employed Staff and workers in Non-private Sector in major years 1981-2017 XLS, Average Wage and Index of Employed Staff and Workers in Non-private Units in Main years 1981-2018. XLS, Average wage of Employed Staff in Non-private Units by Industry and Region in Qinghai Province (2019). XLS, Average wage of Employed Staff in Non-private Units by Industry and Region in Qinghai Province (2020). For example, the 2018 table has 4 fields: Field 1: Year Field 2: region Field 3: Average salary Field 4: Index

This survey consists of 6 parts: - migration: data on migration, i.e. immigration and emigration of households or individual; this information is important for population forecasts and the evaluation of the development of the individual geographic area - handicaps: reasons for including questions on this topic are the “year of handicapped persons” (1981), proclaimed by the UN and paying attentions to these questions from a statistical point of view; the questions had already been posed in September 1987 (Mikrozensus MZ7803) - additional occupation: these questions should give information on additional occupation of employed and unemployed persons; of course the Mikrozensus can only document legal additional occupation, not illegal employment - social stratification: questions on occupational stratum and the receiving of benefit payments serve the in-depth analysis of the other questions - income: the currently available income data in Austria do not allow the representation of the population according to the total income of one person and according to the income of the household; a detailed income survey is not possible in the Mikrozensus: the question program on income is limited to a single question and self-employed, as well as persons helping in the family business are not interviewed. - birth-biography and desire to have children: these questions were for the most part already posed in June 1967 (Mikrozensus MZ7602).

The 1981 Census Collection District Summary File (CDSF) presents summary characteristics of persons and their dwellings for every Collection District (CD) in Australia for 1981. The census information is made up of 34 tables giving data for both persons and dwellings. This table contains data on income. Census counts were based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by Census Collection District 1981 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2103.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1981 geographic boundaries are available from data.gov.au. For more information please refer to "Making Sense of Census 1981".

Abstract copyright UK Data Service and data collection copyright owner.The Family Expenditure Survey (FES), which closed in 2001, was a continuous survey with an annual sample of around 10,000 households. They provided information on household and personal incomes, certain payments that recurred regularly (e.g. rent, gas and electricity bills, telephone accounts, insurances, season tickets and hire purchase payments), and maintained a detailed expenditure record for 14 consecutive days. The original purpose of the FES was to provide information on spending patterns for the United Kingdom Retail Price Index (RPI). The survey was a cost-efficient way of collecting a variety of related data that the government departments required to correlate with income and expenditure at the household, tax unit and person levels. The annual FES began in 1957 (with an earlier large scale survey conducted in 1953/54) and was one of the first Department of Employment (DE) systems to be computerised in the early 1960s. The UKDA holds FES data from 1961-2001. The Northern Ireland Family Expenditure Survey (NIFES), which ran from 1967-1998, was identical to the UK FES and therefore used the same questionnaires and documentation. However, starting in 1988, a voluntary question on religious denomination was asked of those aged 16 and over in Northern Ireland. The UKDA holds NIFES data from 1968-1998, under GN 33240. Significant FES developments over time include: 1968: the survey was extended to include a sample drawn from the Northern Ireland FES and a new computer system was introduced which was used until 1985 1986: DE and the Office of Population Censuses and Surveys (OPCS) converted the FES into a new database system using the SIR package 1989: the Central Statistical Office (CSO) took over responsibility for the survey 1994: in April, computerised personal interviewing was introduced using lap-top computers, the database system changed to INGRES and the survey changed from a calendar year to financial year basis 1996: in April, OPCS and CSO were amalgamated into the Office for National Statistics (ONS), who assumed responsibility for the FES 1998: from April onwards information from expenditure diaries kept by children aged 7 to 15 was included in data, and grossing factors were made available on the database From 2001, the both the FES and the National Food Survey (NFS) (held at the UKDA under GN 33071) were completely replaced by a new survey, the Expenditure and Food Survey (EFS). Prior to the advent of the EFS, there had previously been considerable overlap between the FES and NFS, with both surveys asking respondents to keep a diary of expenditure. Thus, the 2000-2001 FES was the final one in the series. The design of the new EFS was based on the previous FES; further background to its development may be found in the 1999-2000 and 2000-2001 Family Spending reports. From 2008, the EFS became the Living Costs and Food Survey (LCF) (see under GN 33334). Main Topics:Household Schedule: This schedule was taken at the main interview. Information for most of the questions was obtained from the head of household or housewife, but certain questions of a more individual character were put to every spender aged 15 or over (or 16 or over from 1973 onwards). Until the introduction of the community charge, information on rateable value and rate poundage was obtained from the appropriate local authority, as was information on whether the address was within a smokeless zone. Information was collected about the household, the sex and age of each member, and also details about the type and size of the household accommodation. The main part of the questionnaire related to expenditure both of a household and individual nature, but the questions were mainly confined to expenses of a recurring nature, e.g.:Household: housing costs, payment to Gas and Electricity Boards or companies, telephone charges, licences and television rentalIndividual: motor vehicles, season tickets for transport, life and accident insurances, payments through a bank, instalments, refund of expenses by employer, expenditure claimed by self-employed persons as business expenses for tax purposes, welfare foods, education grants and feesIncome Schedule: Data were collected for each household spender. The schedule was concerned with income, national insurance contributions and income tax. Income of a child not classed as a spender was obtained from one or other of his parents and entered on the parent's questionnaire. Information collected included: employment status and recent absences from work, earnings of an employee, self-employed earnings, National Insurance contributions, pensions and other regular allowances, occasional benefits - social security benefits and other types, investment income, miscellaneous earnings of a 'once-only' character, tax paid directly to Inland Revenue or refunded, income of a child. Diary Records: The diary covered fourteen days. Each household member aged 15 or over (or 16 or over from 1973 onwards) was asked to record all expenditure made during the 14 days. Children aged between 7 and 15 were also asked to complete simplified diaries of their daily expenditure. Data from the children's diaries was included in the survey results for the first time in 1998-99. Multi-stage stratified random sample For specific details of the sampling procedures for individual years, please refer to the annual report. Face-to-face interview Diaries

The 1981 Census Local Government Area Summary File (LGASF) presents summary characteristics of persons and their dwellings for every Local Government Area (LGA) in Australia for 1981. The census information is made up of 34 tables giving data for both persons and dwellings. This table contains data on income. Census counts were based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by Local Government Area 1981 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2103.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1981 geographic boundaries are available from data.gov.au. For more information please refer to "Making Sense of Census 1981". Please note: Some LGAs were broken down into parts in the original LGA Summary File (e.g. CABONNE (S) (PART A), CABONNE (S) (PART B), CABONNE (S) (PART C)). AURIN has aggregated the data values for the LGA parts where this has occurred.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

Time series data for the statistic Net primary income (Net income from abroad) (current US$) and country Tonga. Indicator Definition:Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). Data are in current U.S. dollars.The indicator "Net primary income (Net income from abroad) (current US$)" stands at 45.53 Million usd as of 12/31/2023, the highest value at least since 12/31/1982, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 19.91 Million compared to the value the year prior.The Serie's long term average value is 8.98 Million usd. It's latest available value, on 12/31/2023, is 36.56 Million higher, compared to it's long term average value.The Serie's change from it's minimum value, on 12/31/2005, to it's latest available value, on 12/31/2023, is +46.17 Million.The Serie's change from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0 Million.

This table contains 201 series, with data for years 1981 - 2010 (not all combinations necessarily have data for all years), and was last released on 2012-11-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (16 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Income-based estimates (13 items: Gross domestic product (GDP) at market prices; Net domestic product (NDP) at basic prices; Corporation profits before taxes; Wages; salaries and supplementary labour income ...).

This table contains 248 series, with data for years 1981 - 2010 (not all combinations necessarily have data for all years), and was last released on 2012-11-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (16 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Sources and disposition of personal income (16 items: Personal savings; Personal disposable income; Personal income; Wages; salaries and supplementary labour income (national basis) ...).

This table contains 182 series, with data for years 1981 - 2009 (not all combinations necessarily have data for all years), and was last released on 2012-11-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...), Government investment income (13 items: Total government investment income; Total federal; Interest and other investment income ...).

This dataset tracks the average applied tariff rates in both industrial and developing countries. Data is averaged for the years 1981-2005. Figures for 2005 have been estimated. Notes: All tariff rates are based on unweighted averages for all goods in ad valorem rates, or applied rates, or MFN rates whichever data is available in a longer period. Tariff data is primarily based on UNCTAD TRAINS database and then used WTO IDB data for gap filling if possible. Data in 1980s is taken from other source.** Tariff data in these countries came from IMF Global Monitoring Tariff file in 2004 which might include other duties or charges. Country codes are based on the classifications by income in WDI 2006, where 1 = low income, 2 = middle income, 3 = high incone non-OECDs, and 4 = high income OECD countries. Sources: UNCTAD TRAINS database (through WITS); WTO IDB database (through WITS); WTO IDB CD ROMs, various years and Trade Policy Review -- Country Reports in various issues, 1990-2005; UNCTAD Handbook of Trade Control Measures of Developing Countries -- Supplement 1987 and Directory of Import Regimes 1994; World Bank Trade Policy Reform in Developing Countries since 1985, WB Discussion Paper #267, 1994 and World Development Indicators, 1998-2006; The Uruguay Round: Statistics on Tariffs Concessions Given and Received, 1996; OECD Indicators of Tariff and Non-Tariff Trade Barriers, 1996 and 2000; and IMF Global Monitoring Tariff data file 2004. Data source: http://go.worldbank.org/LGOXFTV550 Access Date: October 17, 2007

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Dallam County. The dataset can be utilized to gain insights into gender-based income distribution within the Dallam County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Dallam County, among individuals aged 15 years and older with income, there were 2,635 men and 1,981 women in the workforce. Among them, 1,646 men were engaged in full-time, year-round employment, while 915 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.47% fell within the income range of under $24,999, while 37.27% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 5.10% of men in full-time roles earned incomes exceeding $100,000, while 7.32% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/dallam-county-tx-income-distribution-by-gender-and-employment-type.jpeg" alt="Dallam County, TX gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dallam County median household income by gender. You can refer the same here

The Social Security Rate For Companies in Poland stands at 22.14 percent. This dataset provides - Poland Social Security Rate For Companies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains 182 series, with data for years 1981 - 2009 (not all combinations necessarily have data for all years), and was last released on 2012-11-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...), Government investment income (13 items: Total government investment income; Total federal; Interest and other investment income ...).

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Barton County. The dataset can be utilized to gain insights into gender-based income distribution within the Barton County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Barton County, among individuals aged 15 years and older with income, there were 4,411 men and 4,065 women in the workforce. Among them, 1,981 men were engaged in full-time, year-round employment, while 1,369 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 16.56% fell within the income range of under $24,999, while 22.79% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 9.74% of men in full-time roles earned incomes exceeding $100,000, while 10.59% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/barton-county-mo-income-distribution-by-gender-and-employment-type.jpeg" alt="Barton County, MO gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Barton County median household income by gender. You can refer the same here

The Swedish income panel was originally set up in the beginning of the 90s to make studies of how immigrants assimilate in the Swedish labour market possible. It consists of large samples of foreign-born and Swedish-born persons. Income information from registers is added for nearly 40 years. In addition income information relating to spouses is also available as well as for a subset of mothers and fathers. This makes it possible to construct measures of household income based on a relatively narrow definition. However, starting in 1998 there is also more information making it possible to include children over 18 and their incomes in the family. By matching with some different additional registers information has been added for people who have been unemployed or involved in labour market programmes during the 90s, on causes of deaths for people who have deceased since 1978 and on recent arrived immigrants from various origins. It has turned out that the data-base is quite useful for analysing research-questions other than originally motivating construction of the panel. The panel has been used for cross country comparisons of immigrants in the labour market and to analyse income mobility for different breakdowns of the population, and analyses the development in cohort income. There have been analyses of social assistance receipt among immigrants as well as studies of intergeneration mobility of income, the labour market situation of young immigrants and the second generation of immigrants. On-going work includes evaluation of labour market training programmes and studies of early retirement among immigrants. Planned work includes studies of the economic transition from child to adulthood during the 80s and 90s as well as studies of how frequent immigrant children are subject to measures under the Social Service Act and the Care of Youth Persons Act. The potentials of the Swedish Income Panel can be understood if one compares it with better known income-panels in other countries. For example SWIP covers more years and has a larger sample than the German Socio-Economic Panel (GSOEP). On the other hand, the fact that information is obtained from registers only makes this Swedish panel less rich in variables. There are striking parallels between the Gothenburg Income Panel and the labour market panel at the Centre for Labour Market and Social Research in Aarhus for the Danish population.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Tipton. The dataset can be utilized to gain insights into gender-based income distribution within the Tipton population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Tipton, among individuals aged 15 years and older with income, there were 2,024 men and 1,981 women in the workforce. Among them, 1,249 men were engaged in full-time, year-round employment, while 737 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.08% fell within the income range of under $24,999, while 19.81% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 5.68% of men in full-time roles earned incomes exceeding $100,000, while 4.88% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/tipton-in-income-distribution-by-gender-and-employment-type.jpeg" alt="Tipton, IN gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tipton median household income by gender. You can refer the same here