Dataset

This dataset was created by lololoaaa

Contents

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Deptford Township, New Jersey, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Deptford Township, New Jersey reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Deptford township households based on income levels.

Key observations

• For Family Households: In Deptford township, the majority of family households, representing 20.48%, earn $200,000 or more, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.29%, have incomes falling $150,000 to $199,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Deptford township, the majority of non-family households, accounting for 13.25%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 2.19%, earn $150,000 to $199,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Deptford Township, New Jersey (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Deptford Township, New Jersey
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Deptford Township, New Jersey
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Deptford Township, New Jersey

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 Deptford township median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Amherst, New York, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 18,852, while the mean income for the highest quintile (20% of households with the highest income) is 296,153. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 495,426, which is 167.29% higher compared to the highest quintile, and 2627.98% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Amherst town median household income. You can refer the same here

This table contains figures on the income of private households. The data can be broken down by income concept (primary income, gross income, disposable income, standardised income), income classes and various household background characteristics.

Data available from: 2011.

Status of the figures: The figures for 2011 to 2019 are final. The figures for 2020 are provisional.

Amendments as of 19 December 2021: Figures for 2019 are final and preliminary figures for 2020 have been added.

When are new figures coming? Final figures for 2020 and preliminary figures for 2021 will be available in autumn 2022.

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Additional Information

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

This data was extracted from the census bureau database found at http://www.census.gov/ftp/pub/DES/www/welcome.html Donor: Ronny Kohavi and Barry Becker, Data Mining and Visualization Silicon Graphics. e-mail: ronnyk@sgi.com for questions. Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random). 48842 instances, mix of continuous and discrete (train=32561, test=16281) 45222 if instances with unknown values are removed (train=30162, test=15060) Duplicate or conflicting instances : 6 Class probabilities for adult.all file Probability for the label '>50K' : 23.93% / 24.78% (without unknowns) Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

Graph and download economic data for Median Household Income in California (MEHOINUSCAA646N) from 1984 to 2024 about CA, households, median, income, and USA.

Abstract

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44.

The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Kind of data

Aggregate data [agg]

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Winchester, VA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,125, while the mean income for the highest quintile (20% of households with the highest income) is 215,015. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 344,621, which is 160.28% higher compared to the highest quintile, and 2439.79% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Winchester median household income. You can refer the same here

This table describes the income distribution of the sector households in the national accounts over different household groups. Households are identified by main source of income, living situation, household composition, age classes of the head of the household, income class by 20% groups, and net worth class by 20% groups.

Data available from: 2015.

Status of the figures: All data are provisional.

Changes as of October 19th 2023: The figures of 2015-2020 are revised, because national accounts figures are changed due to the revision policy of Statistics Netherlands. Results for 2021 are added to the table.

When will new figures be published? New figures will be released in October 2024.

Context

For my final project for my Data Science Principle course at University of Texas at Austin, our team decided to do income prediction using census data. However, we had a hard time finding any data set that was not from the 1994 "Adult Data Set". We wanted a more updated version of the data set that we can use alongside the 1994 data set to do various comparisons and data analysis. So, we found data from the ASEC survey from 2020 on the US Census Bureau's website and preprocessed the data to look like the 1994 data set. This data set is derived from: https://www.census.gov/data/datasets/time-series/demo/cps/cps-asec.html.

Content

Data was extracted using the same conditions as the 1994 data set: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)).

Columns are almost exactly the same as the 1994 data set with a few changes: - We are missing a "capital-loss" column in the newer 2020 data set because we could not find such feature in the original ASEC data. - "income-90k-threshold" added, because 50k (which is the threshold used in the "Adult Data Set") in 1994 adjusted for inflation is 90k. - "coded-income" added. This contains more specific income brackets. -"native-country" and "occupation" may have countries and occupations not existing in the 1994 data set, and some countries and occupations existing in 1994 data set may not be in the 2020 data set.

https://www2.census.gov/programs-surveys/cps/datasets/2020/march/ASEC2020ddl_pub_full.pdf A_AGE = age A_FNLWGT = fnlwgt A_SEX = gender A_MARITL = marital-status A_PFREL = relationship A_HGA = education PRDTRACE = race PTOT_R (income recode)= income A_CLSWKR = class of worker A_MJOCC = occupation HRSWK = hours per week PENATVTY(coded) = native-country CAP_VAL = capital gain

"coded-income" means: 0 = NO INCOME 1 = UNDER $2,500 OR LOSS 2 = $2,500 TO $4,999 3 = $5,000 TO $7,499 4 = $7,500 TO $9,999 5 = $10,000 TO $12,499 6 = $12,500 TO $14,999 7 = $15,000 TO $17,499 8 = $17,500 TO $19,999 9 = $20,000 TO $22,499 10 = $22,500 to $24,999 11 = $25,000 to $27,499 12 = $27,500 to $29,999 13 = $30,000 to $32,499 14 = $32,500 to $34,999 15 = $35,000 to $37,499 16 = $37,500 to $39,999 17 = $40,000 to $42,499 18 = $42,500 to $44,999 19 = $45,000 to $47,499 20 = $47,500 to $49,999 21 = $50,000 to $52,499 22 = $52,500 to $54,999 23 = $55,000 to $57,499 24 = $57,500 to $59,999 25 = $60,000 to $62,499 26 = $62,500 to $64,999 27 = $65,000 to $67,499 28 = $67,500 to $69,999 29 = $70,000 to $72,499 30 = $72,500 to $74,999 31 = $75,000 to $77,499 32 = $77,500 to $79,999 33 = $80,000 to $82,499 34 = $82,500 to $84,999 35 = $85,000 to $87,499 36 = $87,500 to $89,999 37 = $90,000 to $92,499 38 = $92,500 to $94,999 39 = $95,000 to $97,499 40 = $97,500 to $99,999 41 = $100,000 and over

Acknowledgements

We wouldn't be here without the help of others. Special thanks to my team members: Julian Fritz, Chris Karouta, and Samuel Rizzo.

About this dataset

Context

The dataset presents median household incomes for various household sizes in New Bern, NC, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, New Bern did not include 7-person households. Across the different household sizes in New Bern the mean income is $86,062, and the standard deviation is $31,120. The coefficient of variation (CV) is 36.16%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $34,733. It then further increased to $125,602 for 6-person households, the largest household size for which the bureau reported a median household income.

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 New Bern median household income. You can refer the same here

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. I encountered it during my course, and I wish to share it here because it is a good starter example for data pre-processing and machine learning practices.

Fields

The dataset contains 16 columns Target filed: Income -- The income is divide into two classes: 50K Number of attributes: 14 -- These are the demographics and other features to describe a person

We can explore the possibility in predicting income level based on the individual’s personal information.

Acknowledgements

This dataset named “adult” is found in the UCI machine learning repository

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This table includes figures on the average increase of rent broken down by income class. A distinction is made here between rental of regulated dwellings by social and other landlords, mid-tier rental and liberalised rental.

Data available from: 2015.

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

Changes as of 10 October 2025: The figure for the income class ‘Income unknown’ in the category ‘Total; regulated’ has been corrected for the reporting year 2025. In the earlier calculation, not all homes were correctly classified. This has no impact on the other figures in this table.

Changes as of 5 September 2025: The 'Mid-tier rental' category has been added to the dimension 'Type of rental'. The figures of 2025 have been published.

Changes as of 20 May 2025: The figures broken down by income class have been removed from this table for the categories of 'Liberalised rental' and 'Total'. These figures are not applicable and were previously published in error. Landlords can only request income data for regulated rents, which form the basis for this table.

Changes as of 8 September 2023: The category 'Middle income' has been added to the dimension 'Income classes'.

When will new figures be published? New figures of 2026 will become available in September 2026.

Net-Income Time Series for Fox Corp Class B. Fox Corporation operates as a news, sports, and entertainment company in the United States. It operates in four segments: Cable Network Programming, Television, Credible, and The FOX Studio Lot. The Cable Network Programming segment produces and licenses news and sports content for distribution through traditional cable television systems, direct broadcast satellite operators, telecommunication companies, virtual multi-channel video programming distributors, and other digital platforms. Its Television segment produces, acquires, markets, and distributes programming through the FOX broadcast network, advertising-supported video-on-demand service Tubi, and operates full power broadcast television stations, including duopolies and other digital platforms. This segment also produces content for third parties. The Credible segment engages in the consumer finance marketplace. Its FOX Studio Lot segment provides television and film production services along with office space, studio operation services, and all operations of the facility. Fox Corporation was incorporated in 2018 and is headquartered in New York, New York.

Net-Income Time Series for Yusys Technologies Co Ltd Class A. Yusys Technologies Co., Ltd. provides financial technology solutions in China. The company offers credit processing; post-loan management and credit risk management; intelligent omnichannel banking; data intelligence, data mart, data operation, data middle platform, data assets; unified payment platform; operation management and data application; regulatory compliance; and comprehensive risk management, capital management, and asset and liability management. It also provides consumer finance services; treasury management, including internal liquidity, investment and financing, risk management, and industry upstream and downstream; auto finance; securities and trust; finance leasing; integrated management solution; and universal solutions, such as technology platform, technical tools, intelligent operation and maintenance tools, automated test, and enterprise resource management. In addition, the company offers operational services comprising computing resources, cloud compliance, internet resources, storage, database service, cloud security, and cloud operation management; and online loan smart bank branch, risk control, and scenario-based financial operations. Further, it provides IT services, including IT planning, business and management, and commercial software implementation consulting services; user experience and design; application development; testing; operation and maintenance; and IT systems value added system services which includes financial data center consulting planning, construction implementation, and security maintenance. The company was founded in 1999 is headquartered in Beijing, China.

Dataset

This dataset was created by Dhafer

Released under Other (specified in description)

Contents