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 Southeast Fairbanks Census Area. 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 Southeast Fairbanks Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $37,992 for males and $22,978 for females.

These income figures highlight a substantial gender-based income gap in Southeast Fairbanks Census Area. Women, regardless of work hours, earn 60 cents for each dollar earned by men. This significant gender pay gap, approximately 40%, underscores concerning gender-based income inequality in the county of Southeast Fairbanks Census Area.

- Full-time workers, aged 15 years and older: In Southeast Fairbanks Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $68,125, while females earned $65,825, resulting in a 3% gender pay gap among full-time workers. This illustrates that women earn 97 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 county of Southeast Fairbanks Census Area.

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 Southeast Fairbanks Census Area.

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 Southeast Fairbanks Census Area median household income by race. You can refer the same here

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 Dillingham Census Area. 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 Dillingham Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $28,333 for males and $28,891 for females.

Contrary to expectations, women in Dillingham Census Area, women, regardless of work hours, earn a higher income than men, earning 1.02 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Dillingham Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,563, while females earned $68,636, resulting in a 4% gender pay gap among full-time workers. This illustrates that women earn 96 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 county of Dillingham Census Area.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Dillingham Census Area. This might indicate a more favorable income scenario for female workers across different employment patterns within the county of Dillingham Census Area, especially in non-full-time positions.

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 Dillingham Census Area median household income by race. You can refer the same here

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California, USA, as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during 7 annual surveys consisting of two censuses each over a 34-year period. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. For this model we incorporated 3 size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary) and 3 survival states (not-yet-entered, entered/alive, and dead/removed). We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states. The model was incorporated into the manuscript that included several other databases for publication in Wildlife Monographs in 2020 by Berry et al.

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 Nome Census Area. 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 Nome Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $24,271 for males and $27,364 for females.

Contrary to expectations, women in Nome Census Area, women, regardless of work hours, earn a higher income than men, earning 1.13 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Nome Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $78,594, while females earned $75,938, resulting in a 3% gender pay gap among full-time workers. This illustrates that women earn 97 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 county of Nome Census Area.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Nome Census Area. This might indicate a more favorable income scenario for female workers across different employment patterns within the county of Nome Census Area, especially in non-full-time positions.

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 Nome Census Area median household income by race. You can refer the same here

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.

This dataset contains information about the sales of shoes in a particular region. The data includes information on the brand, model, type of shoe, gender, size, color, material, and price.

Column Details

Brand: The brand of the shoe, such as Nike, Adidas, or Reebok.

Model: The specific model name or number of the shoe, such as Air Jordan 1, Ultra Boost 21, or Classic Leather.

Type: The type of shoe, such as running, casual, or skate. This column describes the intended use or function of the shoe.

Gender: The gender the shoe is designed for, such as men, women, or unisex. This column specifies the target demographic for the shoe.

Size: The size of the shoe, using US sizing. This column indicates the length of the shoe in inches or centimeters.

Color: The color of the shoe's exterior. This column describes the predominant color or color combination of the shoe.

Material: The primary material of the shoe, such as leather, mesh, or suede. This column indicates the material that comprises the majority of the shoe's construction.

Price: The price of the shoe, in US dollars. This column specifies the cost of purchasing the shoe.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

cover image: https://pin.it/6Eb04Gf

This dataset contains data from a survey of new-car buying households in 13 US states conducted December 2014 to January 2015. The original study is described in these technical reports:

Kurani, K S., N. Caperello, J. TyreeHageman New Car Buyers' Valuation of Zero-Emission Vehicles: California, Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-16-05 (2016). https://escholarship.org/uc/item/28v320rq

Kurani, K.S., N. Caperello, J. TyreeHageman NCST Research Report: Are We Hardwiring Gender Differences into the Market for Plug-in Electric Vehicles? Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-18-05 (2018). https://itspubs.ucdavis.edu/publication_detail.php?id=2888

This dataset is associated specifically with a subsequent technical report:

Kurani, K.S. and K. Buch Across Early Policy and Market Contexts Women and Men Show Similar Interest in Electric Vehicles, National Center for Sustainable Transportation, University of California, Davis, Research Report. 2019. https://escholarship.org/uc/item/9zz8n5x5

Data are from households who had a acquired at least one household vehicle as new (rather than used) since January 2008. The questionnaire was administered on-line to households in the following US states: California, Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Oregon, Rhode, Island, Vermont, and Washington. Most of these states are so called "ZEV states," i.e., they had adopted California's Zero Emission Vehicle (ZEV) Mandate. Those states that were not ZEV states were included to facilitate regional analysis or because they were otherwise important to the initial launch of retail ZEV sales in 2011. The primary regional analysis was for the Northeast States for Coordinated Air Use Management (NESCAUM). The NESCAUM member states are Connecticut, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont. The total sample size is 5,654 for all states; individual state samples sizes are available in the above referenced, Kurani et al (2016).

Analyses were conducted at the state and regional, i.e., NESCAUM, levels. Thus, there are individual data sets for each state for which there is a state-level analysis (California, Delaware, Maryland, Massachusetts, New Jersey, New York, Oregon, and Washington) and NESCAUM. Data for California are included in this release despite the fact its analysis was previously conducted under a separate study. California serves as the reference case because it has the most supportive policy and market context for ZEVs and its analysis is specifically referenced in the report associated with these data sets.

Since the goal was to produce the best possible analysis for each state or region, there are differences in their data sets. While variable names and codes follow consistent rules across all the data sets, which variables are in the data does vary across states and the NESCAUM region. The data released here are those required to replicate the analyses in the associated report.

For each state and region, data are available in two formats indicated by their file extensions: .jmp and .csv. Files with the .jmp extension are proprietary to the JMP© statistics program from SAS Institute. These files contain the data and as well as information about variable coding, variable values, value ordering, and other information in column notes. In effect, the .jmp files contain the data and the code book. The .csv files are generally accessible for import into a wide variety of analytical software but contain no explanatory notes.

Finally, an annotated version of the on-line questionnaire is available as Appendix F of the original report from California (Kurani et al 2016) cited above. The on-line instrument is customized to each respondent as they complete it. More than simple skip patterns, as respondents answer questions content of subsequent questions is populated with information participants provide. Some of this requires calls to data external to the survey instrument; some of these data are proprietary and some are no longer available. Therefore, no "live" version of the on-line questionnaire from 2014 is maintained. The annotated version and the description of the survey provided in the linked report are provided to assist data users.

While household ownership and purchase of all light-duty passenger cars and trucks approach gender parity, to date zero emission vehicles (ZEVs) are being purchased by far more men than women. Prior analysis of data from California finds no reason based in the prospective interest in ZEVs of female and male respondents why this difference should persist. The present report extends the California analysis to 12 other US states with varying ZEV policy and market contexts.

Among many other contextual, socio-economic, demographic, and attitudinal measures, the survey solicited participants' prospective interest in acquiring an ZEV, that is, their interest in their next new car. Participants then indicated why they were motivated to select a ZEV or what motivated them to not select one. Factor analysis was used to reduce the dimensionality of participants' prior awareness, experience, knowledge, and assessments of ZEVs. Via nominal logistic regression modeling, differences in prospective interest in ZEVs between female and male respondents are examined. Given their prospective interest, the motivations of female and male respondents are compared.

Overall, no difference between female and male participants in prospective interest in a ZEV rises to the level of the observed differences in real markets. Further, the multivariate modeling indicates no statistically significant effect of a sex indicator on prospective interest in ZEVS almost anywhere in these states. Where there is a difference, female participants are estimated to be more likely to choose a ZEV than their male counterparts.

While participants from both sexes tend to give high scores to the same ZEV (de)motivations, differences in their rank orders repeat generalizations from other research. On average, female respondents score environmental motivations higher than do male respondents. On average, interest in "new technology" is more motivating to male than female participants. Conversely, on average female respondents who do not select a ZEV score "unfamiliar technology" more highly than their male counterparts.

Within the variation in policy and market contexts represented by the states in this study, no finding here explains why similar prospective interest among female and male participants in ZEVs from the beginning of 2015 has yet to be turned toward equal participation in ZEV markets. Explanations may lie in factors not modeled here.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter.

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This layer shows age and sex demographics in Tempe. Data is from US Census American Community Survey (ACS) 5-year estimates and joined with Tempe census tracts.

This layer is symbolized to the percent of the population ages 18 to 24 years old. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online).

Layer includes:

Key demographics

• Total population
• Male total population
• Female total population
• Percent male total population (calculated)
• Percent female total population (calculated)

Age and other indicators

• Total population by AGE (various ranges)
• Total population by SELECTED AGE CATEGORIES (various ranges)
• Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)
• Percent total population by AGE (various ranges)
• Percent total population by SELECTED AGE CATEGORIES (various ranges)

Male by age

• Male total population by AGE (various ranges)
• Male total population by SELECTED AGE CATEGORIES (various ranges)
• Male total population Median age (years)
• Percent male total population by AGE (various ranges)
• Percent male total population by SELECTED AGE CATEGORIES (various ranges)

Female by age

• Female total population by AGE (various ranges)
• Female total population by SELECTED AGE CATEGORIES (various ranges)
• Female total population Median age (years)
• Percent female total population by AGE (various ranges)
• Percent female total population by SELECTED AGE CATEGORIES (various ranges)

Data is from US Census American Community Survey (ACS) 5-year estimates.

Current Vintage: 2015-2019

ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)

Data downloaded from: Census Bureau's API for American Community Survey

Date of Census update: December 10, 2020

National Figures: data.census.gov

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger. Teens and social media As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious. Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

This layer shows age and sex demographics. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to the percent of the population ages 18 to 24 years old. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the filter settings. Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges)Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Current Vintage: 2018-2022ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyDate of Census update: Dec 15, 2023Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

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 Prince of Wales-Hyder Census Area. 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 Prince of Wales-Hyder Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $35,086 for males and $25,296 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Prince of Wales-Hyder Census Area. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecounty of Prince of Wales-Hyder Census Area.

- Full-time workers, aged 15 years and older: In Prince of Wales-Hyder Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $64,635, while females earned $56,792, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 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 county of Prince of Wales-Hyder Census Area.

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 Prince of Wales-Hyder Census Area.

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 Prince of Wales-Hyder Census Area median household income by race. You can refer the same here

Gender pay gap reporting is due to be introduced nationally for all employers from 2017. This shows a snapshot of the Council as at March 2016. All staff are included in the calculation for the mean and median hourly earnings. The quartile salary information shows the amount of men and women in each quartile. This is the range from the lowest paid employee to the highest paid employee split into 4 equal parts.

The 1998 Philippines National Demographic and Health Survey (NDHS). is a nationally-representative survey of 13,983 women age 15-49. The NDHS was designed to provide information on levels and trends of fertility, family planning knowledge and use, infant and child mortality, and maternal and child health. It was implemented by the National Statistics Office in collaboration with the Department of Health (DOH). Macro International Inc. of Calverton, Maryland provided technical assistance to the project, while financial assistance was provided by the U.S. Agency for International Development (USAID) and the DOH. Fieldwork for the NDHS took place from early March to early May 1998. The primary objective of the NDHS is to Provide up-to-date information on fertility levels; determinants of fertility; fertility preferences; infant and childhood mortality levels; awareness, approval, and use of family planning methods; breastfeeding practices; and maternal and child health. This information is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving health and family planning services in the country. MAIN RESULTS Survey data generally confirm patterns observed in the 1993 National Demographic Survey (NDS), showing increasing contraceptive use and declining fertility. FERTILITY Fertility Decline. The NDHS data indicate that fertility continues to decline gradually but steadily. At current levels, women will give birth an average of 3.7 children per woman during their reproductive years, a decline from the level of 4.1 recorded in the 1993 NDS. A total fertility rate of 3.7, however, is still considerably higher than the rates prevailing in neighboring Southeast Asian countries. Fertility Differentials. Survey data show that the large differential between urban and rural fertility levels is widening even further. While the total fertility rate in urban areas declined by about 15 percent over the last five years (from 3.5 to 3.0), the rate among rural women barely declined at all (from 4.8 to 4.7). Consequently, rural women give birth to almost two children more than urban women. Significant differences in fertility levels by region still exist. For example, fertility is more than twice as high in Eastern Visayas and Bicol Regions (with total fertility rates well over 5 births per woman) than in Metro Manila (with a rate of 2.5 births per woman). Fertility levels are closely related to women's education. Women with no formal education give birth to an average of 5.0 children in their lifetime, compared to 2.9 for women with at least some college education. Women with either elementary or high school education have intermediate fertility rates. Family Size Norms. One reason that fertility has not fallen more rapidly is that women in the Philippines still want moderately large families. Only one-third of women say they would ideally like to have one or two children, while another third state a desire for three children. The remaining third say they would choose four or more children. Overall, the mean ideal family size among all women is 3.2 children, identical to the mean found in 1993. Unplanned Fertility. Another reason for the relatively high fertility level is that unplanned pregnancies are still common in the Philippines. Overall, 45 percent of births in the five years prior to the survey were reported to be unplanned; 27 percent were mistimed (wanted later) and 18 percent were unwanted. If unwanted births could be eliminated altogether, the total fertility rate in the Philippines would be 2.7 births per woman instead of the actual level of 3.7. Age at First Birth. Fertility rates would be even higher if Filipino women did not have a pattem of late childbearing. The median age at first birth is 23 years in the Philippines, considerably higher than in most other countries. Another factor that holds down the overall level of fertility is the fact that about 9 or 10 percent of women never give birth, higher than the level of 3-4 percent found in most developing countries. FAMILY PLANNING Increasing Use of Contraception. A major cause of declining fertility in the Philippines has been the gradual but fairly steady increase in contraceptive use over the last three decades. The contraceptive prevalence rate has tripled since 1968, from 15 to 47 percent of married women. Although contraceptive use has increased since the 1993 NDS (from 40 to 47 percent of married women), comparison with the series of nationally representative Family Planning Surveys indicates that there has been a levelling-off in family planning use in recent years. Method Mix. Use of traditional methods of family planning has always accounted for a relatively high proportion of overall use in the Philippines, and data from the 1998 NDHS show the proportion holding steady at about 40 percent. The dominant changes in the "method mix" since 1993 have been an increase in use of injectables and traditional methods such as calendar rhythm and withdrawal and a decline in the proportions using female sterilization. Despite the decline in the latter, female sterilization still is the most widely used method, followed by the pill. Differentials in Family Planning Use. Differentials in current use of family planning in the 16 administrative regions of the country are large, ranging from 16 percent of married women in ARMM to 55 percent of those in Southern Mindanao and Central Luzon. Contraceptive use varies considerably by education of women. Only 15 percent of married women with no formal education are using a method, compared to half of those with some secondary school. The urban-rural gap in contraceptive use is moderate (51 vs. 42 percent, respectively). Knowledge of Contraception. Knowledge of contraceptive methods and supply sources has been almost universal in the Philippines for some time and the NDHS results indicate that 99 percent of currently married women age 15-49 have heard of at least one method of family planning. More than 9 in 10 married women know the pill, IUD, condom, and female sterilization, while about 8 in 10 have heard of injectables, male sterilization, rhythm, and withdrawal. Knowledge of injectables has increased far more than any other method, from 54 percent of married women in 1993 to 89 percent in 1998. Unmet Need for Family Planning. Unmet need for family planning services has declined since I993. Data from the 1993 NDS show that 26 percent of currently married women were in need of services, compared with 20 percent in the 1998 NDHS. A little under half of the unmet need is comprised of women who want to space their next birth, while just over half is for women who do not want any more children (limiters). If all women who say they want to space or limit their children were to use methods, the contraceptive prevalence rate could be increased from 47 percent to 70 percent of married women. Currently, about three-quarters of this "total demand" for family planning is being met. Discontinuation Rates. One challenge for the family planning program is to reduce the high levels of contraceptive discontinuation. NDHS data indicate that about 40 percent of contraceptive users in the Philippines stop using within 12 months of starting, almost one-third of whom stop because of an unwanted pregnancy (i.e., contraceptive failure). Discontinuation rates vary by method. Not surprisingly, the rates for the condom (60 percent), withdrawal (46 percent), and the pill (44 percent) are considerably higher than for the 1UD (14 percent). However, discontinuation rates for injectables are relatively high, considering that one dose is usually effective for three months. Fifty-two percent of injection users discontinue within one year of starting, a rate that is higher than for the pill. MATERNAL AND CHILD HEALTH Childhood Mortality. Survey results show that although the infant mortality rate remains unchanged, overall mortality of children under five has declined somewhat in recent years. Under-five mortality declined from 54 deaths per 1,000 births in 1988-92 to 48 for the period 1993-97. The infant mortality rate remained stable at about 35 per 1,000 births. Childhood Vaccination Coverage. The 1998 NDHS results show that 73 percent of children 12- 23 months are fully vaccinated by the date of the interview, almost identical to the level of 72 percent recorded in the 1993 NDS. When the data are restricted to vaccines received before the child's first birthday, however, only 65 percent of children age 12-23 months can be considered to be fully vaccinated. Childhood Health. The NDHS provides some data on childhood illness and treatment. Approximately one in four children under age five had a fever and 13 percent had respiratory illness in the two weeks before the survey. Of these, 58 percent were taken to a health facility for treatment. Seven percent of children under five were reported to have had diarrhea in the two weeks preceeding the survey. The fact that four-fifths of children with diarrhea received some type of oral rehydration therapy (fluid made from an ORS packet, recommended homemade fluid, or increased fluids) is encouraging. Breastfeeding Practices. Almost all Filipino babies (88 percent) are breastfed for some time, with a median duration of breastfeeding of 13 months. Although breastfeeding has beneficial effects on both the child and the mother, NDHS data indicate that supplementation of breastfeeding with other liquids and foods occurs too early in the Philippines. For example, among newborns less than two months of age, 19 percent were already receiving supplemental foods or liquids other than water. Maternal Health Care. NDHS data point to several areas regarding maternal health care in which improvements could be made. Although most Filipino mothers (86 percent) receive prenatal care from a doctor, nurse, or midwife, tetanus toxoid coverage is far from universal and

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 Bethel Census Area. 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 Bethel Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $16,066 for males and $16,660 for females.

Contrary to expectations, women in Bethel Census Area, women, regardless of work hours, earn a higher income than men, earning 1.04 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Bethel Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $68,022, while females earned $65,746, resulting in a 3% gender pay gap among full-time workers. This illustrates that women earn 97 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 county of Bethel Census Area.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Bethel Census Area. This might indicate a more favorable income scenario for female workers across different employment patterns within the county of Bethel Census Area, especially in non-full-time positions.

https://i.neilsberg.com/ch/bethel-census-area-ak-income-by-gender.jpeg" alt="Bethel Census Area, AK 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 Bethel Census Area median household income by gender. You can refer the same here

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

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 Yukon-Koyukuk Census Area. 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 Yukon-Koyukuk Census Area, the median income for all workers aged 15 years and older, regardless of work hours, was $28,111 for males and $33,299 for females.

Contrary to expectations, women in Yukon-Koyukuk Census Area, women, regardless of work hours, earn a higher income than men, earning 1.18 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Yukon-Koyukuk Census Area, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,113, while females earned $57,065, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 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 county of Yukon-Koyukuk Census Area.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Yukon-Koyukuk Census Area. This might indicate a more favorable income scenario for female workers across different employment patterns within the county of Yukon-Koyukuk Census Area, especially in non-full-time positions.

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 Yukon-Koyukuk Census Area median household income by race. You can refer the same here