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 Happy. 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 Happy, the median income for all workers aged 15 years and older, regardless of work hours, was $39,041 for males and $23,667 for females.

These income figures highlight a substantial gender-based income gap in Happy. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the town of Happy.

- Full-time workers, aged 15 years and older: In Happy, among full-time, year-round workers aged 15 years and older, males earned a median income of $46,500, while females earned $45,391, resulting in a 2% gender pay gap among full-time workers. This illustrates that women earn 98 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 town of Happy.

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 Happy.

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

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Happy Valley. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Happy Valley, the median household income for the households where the householder is White increased by $12,600(9.94%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $126,711 in 2011 and $139,311 in 2021.
• Black or African American: In Happy Valley, the median household income for the households where the householder is Black or African American increased by $21,203(14.80%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $143,250 in 2011 and $164,453 in 2021.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/happy-valley-or-median-household-income-by-race-trends.jpeg" alt="Happy Valley, OR median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Happy Valley.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

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

Data collected in a series of laboratory experiments examining context effects on judgment and decision-making. Experiments tested the idea that people's answers to everyday questions such as whether they are exercising enough, whether they are satisfied with their educational experience, whether they are drinking too much, or what constitutes an appropriate prison sentence, are influenced by their (often inaccurate) beliefs about the context of comparison. How satisfied are we with our wages? Do British citizens pay more or less tax than they should? Does the UK take in more than its fair share of asylum seekers? People's answers to such questions are typically, highly relative. Wages are judged with reference to those of similar others in the same workplace or neighbourhood; many UK citizens believe that the UK takes too many asylum seekers in comparison to other countries. Thus judgements and decisions may be strongly determined by inaccurate prior beliefs or by a context of available information. For example, opinions about levels of immigration or taxation may be strongly Influenced by people's (often inaccurate) beliefs about levels in other European countries. The research is developing and testing a rank-based model of everyday judgement and decision-making. Attitudes are hypothesised to be influenced by the rank-ordered position of an option in a distiibution. For example, most people overestimate the number of very wealthy people in the UK. We find that individuals' life satisfaction is influenced by where they believe themselves to rank in this assumed wage distribution, rather than by their actual wage. The project applies rank-based models to judgements about various socially and politically important quantities. We have conducted a large number of laboratory-based experiments to test the predictions of the contextual model of judgement that formed the theoretical backbone of the project. Many of these laboratory experiments have been quite labour-intensive, involving individual subject testing. For a number of the experiments involving economic behaviour (such as the effects of “anchors” on people’s estimates of how much they are willing to pay for products) we have gone beyond what is normal within experimental psychology and made use of incentive compatible methodologies of the type developed within behavioural economics. Detailed methods information is available in the cited publications (Related resources).

This study investigates the level and predictors of life satisfaction in people living in slums in Kolkata, India. Participants of six slum settlements (n = 164; 91% female) were interviewed and data on age, gender, poverty indicators and life satisfaction were collected. The results showed that the level of global life satisfaction in this sample did not significantly differ from the level of global life satisfaction measured in a representative sample from the general population of another large Indian city. In addition, the slum residents were most satisfied with their social relationships and least satisfied with their finances. Global life satisfaction was predicted by age, income and non-monetary poverty indicators (deprivation of health, education and living standards) (R2 15.4%). In conclusion, the current study replicates previous research which showed that people living in slums tend to report higher levels of life satisfaction than might be expected, given the objective circumstances of their lives. The results further suggest that factors other than objective poverty make life more, or less, satisfying. The findings are discussed in terms of theory about psychological adaptation to poverty.

Using micro-data on six surveys–the Gallup World Poll 2005–2023, the U.S. Behavioral Risk Factor Surveillance System, 1993–2022, Eurobarometer 1991–2022, the UK Covid Social Survey Panel, 2020–2022, the European Social Survey 2002–2020 and the IPSOS Happiness Survey 2018–2023 –we show individuals’ reports of subjective wellbeing in Europe declined in the Great Recession of 2008/9 and during the Covid pandemic of 2020–2021 on most measures. They also declined in four countries bordering Ukraine after the Russian invasion in 2022. However, the movements are not large and are not apparent everywhere. We also used data from the European Commission’s Business and Consumer Surveys on people’s expectations of life in general, their financial situation and the economic and employment situation in the country. All of these dropped markedly in the Great Recession and during Covid, but bounced back quickly, as did firms’ expectations of the economy and the labor market. Neither the annual data from the United Nation’s Human Development Index (HDI) nor data used in the World Happiness Report from the Gallup World Poll shifted much in response to negative shocks. The HDI has been rising in the last decade reflecting overall improvements in economic and social wellbeing, captured in part by real earnings growth, although it fell slightly after 2020 as life expectancy dipped. This secular improvement is mirrored in life satisfaction which has been rising in the last decade. However, so too have negative affect in Europe and despair in the United States.