According to a survey from 2023, physicians in the United States worked an average of ** hours per week. However, physicians in three specialties, critical care, general surgery, and cardiology worked on average over ** hours a week. In comparison, the average working week of all U.S. workers amounted to **** hours in 2023.

United States SBP: MG: Total Number of Hours Worked by Employees: Number Effect data was reported at 84.300 % in 11 Apr 2022. This records a decrease from the previous number of 100.000 % for 04 Apr 2022. United States SBP: MG: Total Number of Hours Worked by Employees: Number Effect data is updated weekly, averaging 93.350 % from Nov 2020 (Median) to 11 Apr 2022, with 54 observations. The data reached an all-time high of 100.000 % in 04 Apr 2022 and a record low of 75.500 % in 10 Jan 2022. United States SBP: MG: Total Number of Hours Worked by Employees: Number Effect data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

United States SBP: HC: Total Number of Hours Worked by Employees: Increased data was reported at 6.200 % in 11 Apr 2022. This records a decrease from the previous number of 7.400 % for 04 Apr 2022. United States SBP: HC: Total Number of Hours Worked by Employees: Increased data is updated weekly, averaging 6.550 % from Nov 2020 (Median) to 11 Apr 2022, with 54 observations. The data reached an all-time high of 8.700 % in 31 May 2021 and a record low of 3.400 % in 30 Nov 2020. United States SBP: HC: Total Number of Hours Worked by Employees: Increased data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

The federally mandated minimum wage in the United States is 7.25 U.S. dollars per hour, although the minimum wage varies from state to state. As of January 1, 2025, the District of Columbia had the highest minimum wage in the U.S., at 17.5 U.S. dollars per hour. This was followed by Washington, which had 16.66 U.S. dollars per hour as the state minimum wage. Minimum wage workers Minimum wage jobs are traditionally seen as “starter jobs” in the U.S., or first jobs for teenagers and young adults, and the number of people working minimum wage jobs has decreased from almost four million in 1979 to about 247,000 in 2020. However, the number of workers earning less than minimum wage in 2020 was significantly higher, at about 865,000. Minimum wage jobs Minimum wage jobs are primarily found in food preparation and serving occupations, as well as sales jobs (primarily in retail). Because the minimum wage has not kept up with inflation, nor has it been increased since 2009, it is becoming harder and harder live off of a minimum wage wage job, and for those workers to afford essential things like rent.

This data set shows the number of employed persons by occupation for all states in Malaysia for year 1982 until 2021. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach from 1982-2020. Employed persons are those between the working age of 15-64 years old who at any time during the reference week of LFS had worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker). Occupation is classified according to the following classification: (i) Year 1982-2000, Dictionary of Occupational Classification, Malaysia 1980 based on the International Standard Classification of Occupations (ISCO-68). (ii) Year 2001-2010, Malaysia Standard Classification of Occupations (MASCO) 1998 based on the International Standard Classification of Occupations (ISCO-88). (iii) Starting 2011, Malaysia Standard Classification of Occupations (MASCO) 2008 based on the International Standard Classification of Occupations (ISCO-08). For a person having more than one job, only the job at which he worked for the longest number of hours during the reference week is treated as his principal occupation. Should the number of hours worked for each job is the same, then the job with the highest income is the principal occupation. In cases where the number of hours worked and the income earned from each job are the same, the job at which he was working for the longest period of time is considered as the principal occupation. W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards. LFS was not conducted during the years 1991 and 1994. Value for year 2011-2014 were updated based on the population estimates of the respective years.

This statistic represents the number of weekly hours in the construction sector in the United States between 1965 and 2016. In 2016, employees in the U.S. construction sector worked 40 hours per week.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in State Line City. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In State Line City, the median income for all workers aged 15 years and older, regardless of work hours, was $39,521 for males and $32,090 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 19% between the median incomes of males and females in State Line City. With women, regardless of work hours, earning 81 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of State Line City.

- Full-time workers, aged 15 years and older: In State Line City, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,708, while females earned $32,427, leading to a 40% gender pay gap among full-time workers. This illustrates that women earn 60 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that State Line City offers better opportunities for women in non-full-time positions.

https://i.neilsberg.com/ch/state-line-city-in-income-by-gender.jpeg" alt="State Line City, IN gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

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.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 State Line City median household income by gender. You can refer the same here

In 2023, law firms in Nebraska had the highest professional utilization rate in the United States. In that year, non-lawyers in Colorado had the highest utilization rate amongst legal support staff and administrative staff. The utilization rate shows the difference between the number of billable hours worked and the number of hours in an eight-hour work day.

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 State Line City. The dataset can be utilized to gain insights into gender-based income distribution within the State Line City population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within State Line City, among individuals aged 15 years and older with income, there were 41 men and 38 women in the workforce. Among them, 30 men were engaged in full-time, year-round employment, while 14 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 20% fell within the income range of under $24,999, while 28.57% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 6.67% of men in full-time roles earned incomes exceeding $100,000, while none 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 State Line City median household income by race. You can refer the same here

The share of working hours lost in the United States due to heat stress is expected to double between 1995 and 2030, from **** percent to **** percent of total working hours. The most affected economic sectors are agriculture and construction (carried out in shade), with an expected **** percent of working hours lost due to heat stress in 2030 for both sectors.

According to a survey conducted in 2022, working husbands spent an average of ** hours per week on paid work in the United States, compared to ** hours per week on paid work spent by working wives. However, working wives were found more likely to spend extra time on caregiving and housework and have less time for leisure activities per week than working husbands.

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1.Employment by education, working hours, industrial classification and employment status, 2.Unemployment and underemployment by different characteristics and efforts on looking for work, 3.Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data for annual SAKERNAS was gathered in August 2002 covered all provinces in Indonesia with 68.608 households, scattered both in rural and urban areas and representative until provincial level. The main household data is taken from core questionnaire of SAK2002-AK.

Geographic coverage

National coverage*, including urban and rural area, representative until provincial level.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10 years and over that resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Annual SAKERNAS 2002 was implemented in the whole territory of the Republic of Indonesia with a total sample of about 68.608 households, both in rural and urban areas and representative until provincial level. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

The sampling method* for annual SAKERNAS 2002 is two-stages cluster sampling design with census block as the primary sampling unit (PSU) and households as the ultimate sampling unit. PSUs were selected with probability proportional to size. A number of households were taken randomly from selected PSUs. However, there is documentation explained about how the sample size was determined at the domain level, or stratification measures that were implemented and also, the sample size allocation across strata, and also detail information about sample frame**.

The sampling for the urban areas and rural areas is done separately, and by following this procedure: 1. In the first stage, from the sample frame of census block, selected some census block number with probability proportional to size (pps) to the number of household size. 2. At the second stage, from each selected census blocks selected some households in linear systematic household sampling.The first stage sample selection is done by the BPS, while the second level is done by the supervisor/examiner of SAKERNAS.

*) Sampling method used is varied in different years. For example, in SAKERNAS period of 1986-1989 sampling method used is the method of rotation, where most of the households selected at one period was re-elected in the following period. This often happens on quarterly SAKERNAS on that period. At other periods often use multi-stages sampling method (two or three stages depend on whether sub block census included or not), or a combination of multi stages sampling also with rotation method (e.g. SAKERNAS 2006).

**) Commonly, annual SAKERNAS sample frame comes from the last population census result undertaken before SAKERNAS. For example, for annual SAKERNAS 2003 used sample frame derived from "listing process" of household results of Population Census 2000. Also can refer to sampling frame of some periodic household based census like Economic Census, e.g. block census sample frame of SAKERNAS 2007 formed using Economic Census 2006 result. In the other hand sample frame used for quarterly SAKERNAS is from the list of households obtained from National Socio-Economic Survey (SUSENAS) Core activities held before Sakernas, e.g. for quarterly SAKERNAS 2002/2003 activities, used sample frame which derived from households of the selected districts of SUSENAS 2002.

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison.

A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulation

Sampling error estimates

Sampling error results are presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows hours worked, and those unemployed and not in labor force. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of unemployed population within the civilian labor force. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B23020, B23025 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1.Employment by education, working hours, industrial classification and employment status, 2.Unemployment and underemployment by different characteristics and efforts on looking for work, 3.Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data for annual SAKERNAS was gathered in August 1997 covered all provinces in Indonesia with 65,664 households, scattered both in rural and urban areas and representative until provincial level. The main household data is taken from core questionnaire of SAK1997.AK.

Geographic coverage

National coverage* including urban and rural area, representative until provincial level.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10 years and over that resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Annual SAKERNAS 1997 was implemented in the whole territory of the Republic of Indonesia, with a total sample of about 65,664 households, both in rural and urban areas and representative until provincial level. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

The sampling method* for annual SAKERNAS 1997 is three-stages cluster sampling design (with separate processes for rural areas and urban areas) with segment group ( kelseg) as the primary sampling unit (PSU) and household as the ultimate sampling unit. PSUs were selected with probability proportional to size from chosen enumeration area (wilcah). A number of households were taken randomly from selected PSUs. However, there is documentation explained about how the sample size was determined at the domain level, or stratification measures that were implemented and also, the sample size allocation across strata, and also detail information about sample frame formation**.

In SAKERNAS 1990 - 2000 the terms "census block and sub-block census" is not used, instead terms "enumeration area" (wilcah) and "segment group" (kelseg) used. Sample design of SAKERNAS 1997 through differentiated sample selection between urban areas and rural areas by these procedure. The design of urban sample: 1. In the first stage, selected a number of areas of systematic census urban areas list the results of the 1996 Economic Census. 2. In the second stage, from any number of selected areas, selected a group of segments in PPS (Probability Proportional to Size) based on the number of households in the group segment, SE96 listing results. 3. In the third stage, selected 14 households from each group selected segments systematically. The design of rural sample: 1. In the first stage: selected a number of enumeration areas systematically from KCI (Framework of Example Sources). 2. In the second stage, from each selected enumeration areas, selected segment groups by PPS regarding the number of households in the group segment, result of Population Census 1990 listing process. 3. In the third stage, systematically selected 14 households from each segment group previously chosen.

Establishment of the sample frames is in three stages, namely: 1. Establishment of the sample frame used in the selection of enumeration area, 2. Establishment of the sample frame for the selection of segment group (kelseg), 3. Establishment of the sample frame for the selection of households.

*) Sampling method used is varied in different years. For example, in SAKERNAS period of 1986-1989 sampling method used is the method of rotation, where most of the households selected at one period was re-elected in the following period. This often happens on quarterly SAKERNAS on that period. At other periods often use multi-stages sampling method (two or three stages depend on whether sub block census / segment group included or not), or a combination of multi stages sampling also with rotation method (e.g. SAKERNAS 2006-2010).

**) Commonly, annual SAKERNAS sample frame comes from the last population census result undertaken before SAKERNAS. For example, for annual SAKERNAS 2003 used sample frame derived from "listing process" of household results of Population Census 2000. Also can refer to sampling frame of some periodic household based census like Economic Census, e.g. block census sample frame of SAKERNAS 2007 formed by using Economic Census 2006 result. In the other hand sample frame used for quarterly SAKERNAS is from the list of households obtained from National Socio-Economic Survey (SUSENAS) Core activities held before Sakernas, e.g. for quarterly SAKERNAS 2002/2003 activities, used sample frame which derived from households of the selected districts of SUSENAS 2002.

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison. A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulation

Sampling error estimates

Sampling error results of Sakernas presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Nevada. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Nevada, the median income for all workers aged 15 years and older, regardless of work hours, was $45,090 for males and $32,920 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 27% between the median incomes of males and females in Nevada. With women, regardless of work hours, earning 73 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thestate of Nevada.

- Full-time workers, aged 15 years and older: In Nevada, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,010, while females earned $50,977, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the state of Nevada.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Nevada.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

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.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Nevada median household income by race. You can refer the same here

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 State Center. The dataset can be utilized to gain insights into gender-based income distribution within the State Center population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within State Center, among individuals aged 15 years and older with income, there were 538 men and 525 women in the workforce. Among them, 312 men were engaged in full-time, year-round employment, while 281 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 2.56% fell within the income range of under $24,999, while 14.59% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10.58% of men in full-time roles earned incomes exceeding $100,000, while none 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/state-center-ia-income-distribution-by-gender-and-employment-type.jpeg" alt="State Center, IA 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 State Center median household income by gender. You can refer the same here

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, 2018-2022 American Community Survey 5-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..The 2018-2022 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 delineation lists 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.

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1. Employment by education, working hours, industrial classification and employment status, 2. Unemployment and underemployment by different characteristics and efforts on looking for work, 3. Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data for annual SAKERNAS was gathered in August 1995 covered all provinces in Indonesia, scattered both in rural and urban areas. The main household data is taken from supplemental module of Intercensal Population Survey (SUPAS) 1995.

Geographic coverage

National coverage*, including urban and rural area.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10 years and over that resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Annual SAKERNAS 1995 was implemented in the whole territory of the Republic of Indonesia both in rural and urban areas. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

The sampling method for annual SAKERNAS 1995 which is integrated with Intercensal Population Survey (SUPAS) 1995 remain unknown since we do not have the supporting documentations.

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison.

A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulation

Sampling error estimates

Sampling error results of Sakernas presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.

Release Date: 2016-09-23..Table Name. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014. ..Release Schedule. . This file was released in September 2016.. ..Key Table Information. . These data are related to all other 2014 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2014 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2014 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. For Characteristics of Business Owners (CBO) data, all estimates are of owners of firms responding to the ASE. That is, estimates are based only on firms providing gender, ethnicity, race, or veteran status; or firms not classifiable by gender, ethnicity, race, and veteran status that returned an ASE online questionnaire with at least one question answered. The ASE online questionnaire provided space for up to four owners to report their characteristics.. CBO data are not representative of all owners of all firms operating in the United States. The data do not represent all business owners in the United States.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The top fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for Owners of Respondent Employer Firms by Owner's Average Number of Hours Per Week Spent Managing or Working in the Business by Sector, Gender, Ethnicity, Race, Veteran Status, and Years in Business for the U.S., States, and Top 50 MSAs: 2014 contains data on:. . Number of owners of respondent firms with paid employees. Percent of number of owners of respondent firms with paid employees. . The data are shown for:. . Gender, ethnicity, race and veteran status of owners of respondent firms. . All owners of respondent firms. Female. Male. Hispanic. Non-Hispanic. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Nonminority. Veteran. Nonveteran. . . Years in business. . All firms. Firms less than 2 years in business. Firms with 2 to 3 years in business. Firms with 4 to 5 years in business. Firms with 6 to 10 years in business. Firms with 11 to 15 years in business. Firms with 16 or more years in business. . . Owner's average number of hours per week spent managing or working in the business in 2014. . None. Less than 20 hours. 20 to 39 hours. 40 hours. 41 to 59 hours. 60 or more hours. Total reporting. Item not reported. . . . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (ASECBO). Years in business (YIBSZFI). Owner's average number of hours per week spent managing or working in the business in 2014 (HRSWRKD). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1400CSCBO04 table at: https://www2.census.gov/programs-surveys/ase/data/2014/SE1400CSCBO04.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at https://www.census.gov/programs-surveys/ase.html.. Email general, nonsecure, and unencrypted messages to ewd.annual.survey.of.entrepreneurs@census.gov.. Call 301.763.1546 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Annual Survey of Entrepreneurs. 4600 Silver Hill Road. Washington, DC 20233. . . ...Data User Notice posted on June 9, 2017: Census Bureau staff identified a pr...