The average annual self-employment income per household of those in the top decile group amounted to 26 thousand British pounds. This is nearly 25 times more than the average annual self-employment income per household of those in the bottom decile, which came to 1 thousand British pounds.

As of August 2023, self-employed persons between the ages of 25 to 54 years could expect a net monthly income of around **** million Indonesian rupiah. The average net monthly income for self-employed persons in that year was around two million Indonesian rupiah, with different degrees of variation among the different provinces.

As of August 2023, self-employed persons working in rural areas between the ages of 25 to 54 years could expect a net monthly income of around **** million Indonesian rupiah. The average net monthly income for self-employed persons in rural areas that year was around **** million Indonesian rupiah, with different degrees of variation among the different provinces.

This table contains statistics regarding income and capital of self-employed persons in the Netherlands. A distinction is made between, on the one hand, persons for whom self-employment provides for the main source of income, and on the other hand all persons with income from self-employed work. The figures in this table are broken down by type of self-employed person, sector, gender, age, migration background, position in the household, and by income and wealth decile groups.

All statistics in this table are at the individual level, this includes capital; (corporate) assets are summed per household and then assigned to all household members, thus serving as a measure of personal prosperity. The sample date for both population and capital is the first of January of the reporting year. For the older years 2007 up to and including 2010, capital is sampled on the first of January of the year following the reporting year.

The General Business Register (ABR) is used to determine the sector (SBI) of self-employed persons. The ABR has been subject to various trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed persons in the financial services (sector K) in 2010. Therefore caution is advised when consulting sector trends or comparing numbers across sectors.

Data available from: 2007.

Status of the figures: The figures for 2006 to 2022 are final. The figures for 2023 are preliminary.

Changes as of November 1 2024: Figures for 2022 have been finalized. Figures for 2023 have been added.

Changes as of March 2022: Figures on the wealth of the self-employed in 2010 were incorrect, and have been removed. For this year the wealth of 2011 applies, as 2011 marks a shift in sample date from December 31 to January 1. Missing wealth figures for 2013 have been supplemented.

Changes as of July 2021: Revised data for 2006 to 2019 have been added. Due to the availability of new sources and improvements in the methodology, wealth figures have changed. Additionally everyone with personnel is now classified as self-employed with employee (formerly this distinction was based solely on the enterprise constituting the main source of income).

When will new figures be published? New figures for 2024 will be published in December 2025.

Georgia Household Income: Income: Cash: Self Employment data was reported at 97.953 GEL in 2017. This records an increase from the previous number of 86.719 GEL for 2016. Georgia Household Income: Income: Cash: Self Employment data is updated yearly, averaging 57.021 GEL from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 97.953 GEL in 2017 and a record low of 24.294 GEL in 2001. Georgia Household Income: Income: Cash: Self Employment data remains active status in CEIC and is reported by National Statistics Office of Georgia. The data is categorized under Global Database’s Georgia – Table GE.H004: Household Income: Monthly Average.

Brazil Average Nominal Wages: Usual Earnings: Southeast: Self Employed data was reported at 2,042.000 BRL in Mar 2019. This records a decrease from the previous number of 2,060.000 BRL for Dec 2018. Brazil Average Nominal Wages: Usual Earnings: Southeast: Self Employed data is updated quarterly, averaging 1,857.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 2,060.000 BRL in Dec 2018 and a record low of 1,379.000 BRL in Jun 2012. Brazil Average Nominal Wages: Usual Earnings: Southeast: Self Employed data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD001: Continuous National Household Sample Survey: Average Nominal Wages: Usual Earnings.

This table replaces table 383-0009. Data in this table are not fully comparable with those previously published. Data by industry included in this table corresponds to S and M levels as well as some complementary details at L and W levels of aggregation. For concepts, methods, sources and details concerning the industry classification system, consult the following link http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Provincial and territorial data are available from 1997. Statistics are available from 1999, year of the creation of the Territory of Nunavut. The estimate of the total number of jobs covers two main categories: paid workers jobs and self-employed jobs. These are jobs held by workers whose base pay is calculated at an hourly rate, or on the basis of a fixed amount for a period of at least a week, or in the form of sales commission, piece rates, mileage allowances and so on. Includes workers drawing pay for services rendered or for paid absences and for whom the employer must complete a T-4 Supplementary form from Canada Revenue Agency. These are jobs held by unincorporated working owners, self-employed persons who do not have a business and persons working in a family business without pay. The number of hours worked in all jobs is the annual average for all jobs times the annual average hours worked in all jobs. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost in brief work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. The number of hours worked for paid workers jobs is the average number of paid workers during the year times the annual average number of hours worked in paid jobs. The number of hours worked for self-employed jobs is the average number of paid or unpaid self-employed workers during the year times the annual average number of hours worked in paid or unpaid self-employed jobs. Self-employed jobs are jobs held by unincorporated working owners, self-employed persons who do not have a business and persons working in a family business without pay. This is the annual average of hours worked for the respective job category mentioned in the variable title. The total compensation for all jobs consists of all payments in cash or in kind made by domestic producers to workers for services rendered. It includes labour income for paid workers and imputed labour income for self-employed workers. Often referred to as labour income, it includes two components— wages and salaries, and supplementary labour income. The wages and salaries include all types of regular earnings, special payments, stock options and bonus payments. Supplementary labour income comprises employers' contributions or payments to a variety of paid workers benefit plans for the health and financial well-being of paid workers and their families. Self-employed income consists of an imputed labour income for self-employed workers. The ratio between total compensation paid for all jobs, and the total number of jobs. The ratio between total compensation for all jobs, and the number of hours worked. The term 'hourly compensation' is often used to refer to the total compensation per hour worked. The ratio of labour income paid to paid workers to the number of hours worked. Total economic activities that have been realized within the country. This combines the North American Industry Classification System (NAICS) codes 11-91. This combines the North American Industry Classification System (NAICS) codes 111, 112. This combines the North American Industry Classification System (NAICS) code 111 excluding 1114. This combines the North American Industry Classification System (NAICS) codes 1151, 1152. This combines the North American Industry Classification System (NAICS) codes 212393, 212394, 212395, 212397, 212398. This combines the North American Industry Classification System (NAICS) codes 213111, 213118. This combines the North American Industry Classification System (NAICS) codes 213117, 213119. This combines the North American Industry Classification System (NAICS) codes 2212, 2213. Special hybrid: corresponds to sections of the North American Industry Classification System (NAICS) code 23. This combines the North American Industry Classification System (NAICS) codes 3112, 3118, 3119. This combines the North American Industry Classification System (NAICS) codes 31213, 31214. This combines the North American Industry Classification System (NAICS) codes 313, 314. This combines the North American Industry Classification System (NAICS) codes 315, 316. This combines the North American Industry Classification System (NAICS) code 324 excluding 32411. This combines the North American Industry Classification System (NAICS) codes 3255, 3256, 3259. This combines the North American Industry Classification System (NAICS) code 327 excluding 3273. This combines the North American Industry Classification System (NAICS) codes 3322, 3329. This combines the North American Industry Classification System (NAICS) codes 3332, 3333. This combines the North American Industry Classification System (NAICS) codes 3343, 3345, 3346. This combines the North American Industry Classification System (NAICS) codes 485, 487. This combines the North American Industry Classification System (NAICS) codes 4852, 4854, 4855, 4859, 487. This combines the North American Industry Classification System (NAICS) codes 4861, 4869. This combines the North American Industry Classification System (NAICS) codes 491, 492. This combines the North American Industry Classification System (NAICS) codes 51112, 51113, 51114, 51119. This combines the North American Industry Classification System (NAICS) codes 51211, 51212, 51219. This combines the North American Industry Classification System (NAICS) codes 521, 5221. This combines the North American Industry Classification System (NAICS) codes 52211, 52219. This combines the North American Industry Classification System (NAICS) codes 523, 526. Corresponds to code 53 of the North American Industry Classification System (NAICS). However, it differs from the Input-Output code BS53 since it excludes the industry of owner-occupied dwellings ( BS5311A). This combines the North American Industry Classification System (NAICS) codes 5312, 5313. This combines the North American Industry Classification System (NAICS) code 532 excluding 5321. This combines the North American Industry Classification System (NAICS) codes 5411, 5412. This combines the North American Industry Classification System (NAICS) codes 5414, 5416, 5417, 5419. This combines the North American Industry Classification System (NAICS) codes 5612, 5619. his combines the North American Industry Classification System (NAICS) code 61 excluding 6113. This combines the North American Industry Classification System (NAICS) codes 6114-6117. This combines the North American Industry Classification System (NAICS) code 62 excluding 624. This combines the North American Industry Classification System (NAICS) codes 6213, 6214, 6215, 6216, 6219. This combines the North American Industry Classification System (NAICS) codes 711, 712. This combines the North American Industry Classification System (NAICS) codes 7131, 7139. This combines the North American Industry Classification System (NAICS) codes 7212, 7213. This combines the North American Industry Classification System (NAICS) codes 8112, 8113, 8114. This combines the North American Industry Classification System (NAICS) codes 812, 814. This combines the North American Industry Classification System (NAICS) codes 8121, 8129. This combines the North American Industry Classification System (NAICS) code 813 excluding 8131. This combines the North American Industry Classification System (NAICS) code 911 excluding 9111. This combines the North American Industry Classification System (NAICS) codes 913, 914. Statistics are available until 1998 inclusively; starting in 1999, data for Northwest Territories and Nunavut are presented separately. This combines the North American Industry Classification System (NAICS) code 112 excluding 1125. Starting in 2014, the crop production industry incorporates the activities related to cannabis. Starting in 2014, the miscellaneous store retailers industry incorporates the activities related to cannabis. The ratio of wages and salaries paid to paid workers to their number of hours worked.

The statistic displays the results of a 2015 survey about the average monthly salary among self-employed Indians between April to December 2015. Approximately ** percent of self-employed Indians across the country earned an average monthly income of ***** to ***** Indian rupees during the surveyed time period.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Total number of workers and average annual number of self-employed workers (settlers and sharecroppers) in agricultural holdings divided by year, age groups and income brackets. Historical series, years 2011-2020.

This dataset presents the average gross earnings (in Rs.) during the last 30 days for individuals engaged in self-employment work under current weekly status (CWS). The data is sourced from the annual report of the Periodic Labour Force Survey (PLFS) conducted by the Ministry of Statistics and Programme Implementation. It provides insights into the income generated from self-employment activities across different sectors.

This table provides information on the income and wealth of self-employed persons by region. These are people for whom self-employment is the main source of income. A distinction is made between self-employed and economic activity. The data are broken down by various regional divisions, from municipality to country.

Net worth reference date is 1 January of the reporting year, for the years from 2011. For the older years 2007 to 2010 this is 1 January of the year following the research year.

The General Business Register (ABR) is used to determine the SBI of self-employed persons. The ABR suffered several trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed in financial services (K) in 2010. Therefore, caution should be taken when comparing the figures to SBI.

Data available from 2007 to 2015.

Status of the figures: The figures in this table are provisional. For 2015, regional data are not yet available at municipal level.

Changes as of 20 February 2018: None, this table has been discontinued

Changes as of 8 February 2017: The topics People with Business Capabilities and Median Business Capability have been added. In addition to average incomes, median incomes are now also available. Data for the years 2007 to 2010 and 2015 have been added. From the year 2011, an overall revision of income statistics has been carried out. Due to various changes in the methodology, this leads across the board to a trend break with previous years.

When are new figures coming? No longer applicable

Georgia Household Income: Urban: Income: Cash: Self Employment data was reported at 131.097 GEL in 2017. This records an increase from the previous number of 116.877 GEL for 2016. Georgia Household Income: Urban: Income: Cash: Self Employment data is updated yearly, averaging 90.183 GEL from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 131.097 GEL in 2017 and a record low of 54.597 GEL in 2006. Georgia Household Income: Urban: Income: Cash: Self Employment data remains active status in CEIC and is reported by National Statistics Office of Georgia. The data is categorized under Global Database’s Georgia – Table GE.H004: Household Income: Monthly Average.

Longitudinal income data are reported in 2018 constant dollars as two measures, including and excluding those with self-employment income. Data are available for selected trades, by sex and type of certification, for Canada, provinces, and the Atlantic region.

Brazil Average Nominal Wages: Usual Earnings: Mato Grosso: Self Employed data was reported at 2,035.000 BRL in Mar 2019. This records an increase from the previous number of 1,953.000 BRL for Dec 2018. Brazil Average Nominal Wages: Usual Earnings: Mato Grosso: Self Employed data is updated quarterly, averaging 1,707.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 2,036.000 BRL in Mar 2018 and a record low of 1,271.000 BRL in Mar 2012. Brazil Average Nominal Wages: Usual Earnings: Mato Grosso: Self Employed data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD001: Continuous National Household Sample Survey: Average Nominal Wages: Usual Earnings.

Brazil Average Nominal Wages: Actual Earnings: Amazonas: Self Employed data was reported at 949.000 BRL in Mar 2019. This records an increase from the previous number of 896.000 BRL for Dec 2018. Brazil Average Nominal Wages: Actual Earnings: Amazonas: Self Employed data is updated quarterly, averaging 919.000 BRL from Mar 2012 (Median) to Mar 2019, with 29 observations. The data reached an all-time high of 1,023.000 BRL in Mar 2015 and a record low of 795.000 BRL in Dec 2016. Brazil Average Nominal Wages: Actual Earnings: Amazonas: Self Employed data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBD002: Continuous National Household Sample Survey: Average Nominal Wages: Actual Earnings.

This table contains data on the income and wealth of self-employed persons per detailed industry. These are people for whom self-employment is the main source of income. A distinction is made by type of self-employed person and economic activity.

Net worth reference date is 1 January of the reporting year, for the years from 2011. For the older years 2007 to 2010 this is 1 January of the year following the research year.

The General Business Register (ABR) is used to determine the SBI of self-employed persons. The ABR suffered several trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed in financial services (K) in 2010. Therefore, caution should be taken when comparing the figures to SBI.

Data available from 2007 to 2015

Status of the figures: The figures in this table are provisional.

Changes as of 20 February 2018: This table has been discontinued.

Changes as of 8 February 2017: The topics People with Business Capabilities and Median Business Capability have been added. In addition to average incomes, median incomes are now also available. Data for the years 2007 to 2010 and 2015 have been added. From the year 2011, an overall revision of income statistics has been carried out. Due to various changes in the methodology, this leads across the board to a trend break with previous years.

When are new figures coming? No longer applicable.

United Kingdom Weekly Household Income: SI: Self Employed Income data was reported at 9.000 % in 2017. This records an increase from the previous number of 8.000 % for 2016. United Kingdom Weekly Household Income: SI: Self Employed Income data is updated yearly, averaging 9.000 % from Mar 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 11.000 % in 2006 and a record low of 7.000 % in 2014. United Kingdom Weekly Household Income: SI: Self Employed Income data remains active status in CEIC and is reported by Department for Work and Pensions. The data is categorized under Global Database’s United Kingdom – Table UK.H020: Weekly Household Income.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

On average, 50 percent of key workers have an employee status and 49 percent have a self-employed status. However, this divide differs greatly depending on the country income group. In low income and lower-middle income nations a majority of key workers belong to the self-employed category. In upper-middle income and high income nations, a majority of workers belong to the employee category. This divide can be explained by high rates of employment informality in low income and lower-middle income nations.