About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in L''Anse, MI, 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 13,933, while the mean income for the highest quintile (20% of households with the highest income) is 171,871. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 298,210, which is 173.51% higher compared to the highest quintile, and 2140.31% 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 L'Anse median household income. You can refer the same here

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

The estimated median household income and estimated median family income are two separate measures: every family is a household, but not every household is a family. According to the U.S. Census Bureau definitions of the terms, a family “includes a householder and one or more people living in the same household who are related to the householder by birth, marriage, or adoption,”[1] while a household “includes all the people who occupy a housing unit,” including households of just one person[2]. When evaluated together, the estimated median household income and estimated median family income provide a thorough picture of household-level economics in Champaign County.

Both estimated median household income and estimated median family income were higher in 2023 than in 2005. The changes in estimated median household income and estimated median family income between 2022 and 2023 were not statistically significant. Estimated median family income is consistently higher than estimated median household income, largely due to the definitions of each term, and the types of household that are measured and are not measured in each category.

Median income data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Median Household Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) and Median Family Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars).

[1] U.S. Census Bureau. (Date unknown). Glossary. “Family Household.” (Accessed 19 April 2016).

[2] U.S. Census Bureau. (Date unknown). Glossary. “Household.” (Accessed 19 April 2016).

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (18 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (3 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).

About this dataset

Context

The dataset presents median household incomes for various household sizes in L''Anse, MI, 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, L'Anse did not include 6, or 7-person households. Across the different household sizes in L'Anse the mean income is $70,077, and the standard deviation is $30,177. The coefficient of variation (CV) is 43.06%. 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 $28,375. It then further increased to $56,875 for 5-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 L'Anse median household income. You can refer the same here

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

Historical ownership data of EATON VANCE HIGH INCOME by L & S Advisors Inc

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in L''Anse Township, Michigan, 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 13,883, while the mean income for the highest quintile (20% of households with the highest income) is 169,454. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 293,368, which is 173.13% higher compared to the highest quintile, and 2113.15% 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 L'Anse township median household income. You can refer the same here

We present here a new gridded dataset of per capita disposable income for 42 European countries at a 1km resolution in 2015. This dataset was created by downscaling a vectorial dataset of disposable income. Income is expressed in constant 2015 EU27 PPP€. Income was only estimated for grid-points with a population equal or higher to one. We provide a European file (Europe_disp_inc_2015.tif) and individual country files.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for EATON VANCE HIGH INCOME held by L & S Advisors Inc from Q1 2017 to Q2 2025

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]

The 21st century has been a period of rising inequality in both income and health. In this paper, we find that geographic inequality in mortality for midlife Americans increased by about 70 percent between 1992 and 2016. This was not simply because states like New York or California benefited from having a high fraction of college-educated residents who enjoyed the largest health gains during the last several decades. Nor was higher dispersion in mortality caused entirely by the increasing importance of "deaths of despair,'' or by rising spatial income inequality during the same period. Instead, over time, state-level mortality has become increasingly correlated with state-level income; in 1992 income explained only 3 percent of mortality inequality, but by 2016 state-level income explained 58 percent. These mortality patterns are consistent with the view that high-income states in 1992 were better able to enact public health strategies and adopt behaviors that, over the next quarter-century, resulted in pronounced relative declines in mortality. The substantial longevity gains in high-income states led to greater cross-state inequality in mortality.

We present here a new gridded dataset of per capita disposable income consistent with the Shared Socioeconomic Pathways (SSP) over the 21st century in Europe. Decadal income maps were created using a top-down iterative approach that ensures quantitative consistency with other SSP socio-economic datasets (GDP, demographics projections, inequality metrics, etc.). This dataset is structured into 39 sub-folders (one for each European country + the whole studied area). Files were created using the following nomenclature: "ISO3_disp_inc_year_SSP.tif" (where countries are represented by their ISO3 codes).

United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Better: Income is Higher data was reported at 42.000 % in May 2018. This records an increase from the previous number of 40.000 % for Apr 2018. United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Better: Income is Higher data is updated monthly, averaging 32.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 46.000 % in Mar 2018 and a record low of 9.000 % in Nov 2009. United States CSI: Personal: HH Fin'l Situation: 1Yr Ago: Better: Income is Higher data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: We are interested in how people are getting along financially these days. Would you say that you (and your family living there) are better off or worse off financially than you were a year ago? Responses to the query 'Why do you say so?'

ObjectivesTo identify and evaluate the difference between voluntary and mandatory food marketing policies and regulations targeting childhood obesity and to study the role of media, the food industry, and private associations in implementing such policies.MethodsA review of policies and legislation about unhealthy food marketing was conducted by searching and extracting relevant grey literature from the websites of international health agencies, food marketing pledge databases, GINA, and NOURISHING policy databases. Statutory laws and self-regulations of high-income countries were compared with each other and with the WHO recommendations.ResultsRegulations differ regarding target audience, nutrient profiling, communication media, and marketing techniques. To date, no country has implemented comprehensive regulations restricting all forms of unhealthy food marketing. Statutory laws are more meticulous and rigorous than self-regulatory policies.ConclusionThe goal of reducing childhood obesity through restrictions on unhealthy food advertising has not been met. While not welcomed by all actors, mandatory regulations may be more effective than voluntary measures in reaching this goal. A system for monitoring adherence to regulations and providing both incentives and penalties for violations is warranted.

Existing research analyzes the effects of cross national and temporal variation in income inequality on public opinion; however, research has failed to explore the impact of variation in inequality across citizens' local residential context. This article analyzes the impact of local inequality on citizens' belief in a core facet of the American ethos--meritocracy. We advance conditional effects hypotheses which collectively argue that the effect of residing in a high inequality context will be moderated by individual income. Utilizing national survey data, we demonstrate that residing in more unequal counties heightens rejection of meritocracy among low income residents and bolsters adherence among high income residents. In relatively equal counties, we find no significant differences between high and low income citizens. We conclude by discussing the implications of class-based polarization found in response to local inequality with respect to current debates over the consequences of income inequality for American democracy.

Russia Purchasing Capacity: Avg Household Income per Capita: Year to Date: Milk data was reported at 501.800 l in Nov 2018. This records an increase from the previous number of 501.700 l for Oct 2018. Russia Purchasing Capacity: Avg Household Income per Capita: Year to Date: Milk data is updated monthly, averaging 488.300 l from Jan 1999 (Median) to Nov 2018, with 239 observations. The data reached an all-time high of 669.400 l in Dec 2007 and a record low of 199.800 l in Jan 1999. Russia Purchasing Capacity: Avg Household Income per Capita: Year to Date: Milk data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HD001: Purchasing Capacity: Average Household Income per Capita: ytd.

Low socioeconomic status (SES) is associated with adverse pregnancy outcomes and infertility. Low-dose aspirin (LDA) was shown to improve livebirth rates in certain subsets of women, and therefore, may impact pregnancy rates differentially by SES status. Therefore, the aim of the current study was to examine whether daily preconception-initiated LDA affects rates of pregnancy, livebirth, and pregnancy loss differently across strata of socioeconomic status (SES). This is a secondary analysis of The Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial, a multisite, block- randomized, placebo-controlled trial conducted at four U.S. medical centers (n = 1,228, 2007–2012). Women attempting spontaneous conception with a history of pregnancy loss were randomly allocated preconception to 81mg of aspirin + 400mcg of folic acid (n = 615) or placebo + 400mcg of folic acid (n = 613). Study medication was administered for six menstrual cycles or until 36 weeks’ gestation if pregnancy was achieved. For this analysis, women were stratified by SES, which included income (low, mid, high) and a combined grouping of education and income (low-low, low-high, high-low, high-high). Log binomial models with robust variance estimated risks of pregnancy, livebirth, and pregnancy loss for LDA versus placebo. LDA increased pregnancy and livebirth rates (RR 1.23, 95% CI: 1.03, 1.45) in the high-income, but not mid- or low-income groups. LDA increased pregnancy rates in both the low education-low income group (RR 1.22, 95% CI: 1.02, 1.46) and the high education-high income group (RR 1.23, 95%CI: 1.06, 1.42), with no effect observed in mid-SES groupings. LDA, a low-cost and widely available treatment, may be particularly beneficial to women at the highest and lowest ends of the socioeconomic spectrum, though underlying mechanisms of this disparity are unclear. Confirming these findings and identifying factors which may modulate the effectiveness of LDA will ultimately facilitate personalized clinical care and improvements in population-level reproductive health.Trial registration number: ClinicalTrials.gov, NCT00467363.

This dataset provides values for HOUSEHOLDS DEBT TO INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.