This dataset is about books. It has 2 rows and is filtered where the book is The politics of inequality : a political history of the idea of economic inequality in America. It features 7 columns including author, publication date, language, and book publisher.

Historical dataset showing Central America income inequality - gini coefficient by year from N/A to N/A.

Historical dataset showing Latin America & Caribbean income inequality - gini coefficient by year from N/A to N/A.

Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations. A custom missing-data algorithm was used to standardize the United Nations University's World Income Inequality Database and data from other sources; data collected by the Luxembourg Income Study served as the standard. The SWIID provides comparable Gini indices of gross and net income inequality for 192 countries for as many years as possible from 1960 to the present along with estimates of uncertainty in these statistics. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broadly cross-national research on income inequality than previously available sources: it offers coverage double that of the next largest income inequality dataset, and its record of comparability is three to eight times better than those of alternate datasets. In any papers or publications that use the SWIID, authors are asked to cite the article of record for the data set and give the version number as follows: Solt, Frederick. 2016. "The Standardized World Income Inequality Database." Social Science Quarterly 97(5):1267-1281. SWIID Version 7.1, August 2018.

Wealth inequality has been sharply rising in the United States and across many other high-income countries. Due to a lack of data, we know little about how this trend has unfolded across locations within countries. Investigating this subnational geography of wealth is crucial, as from one generation to the next, wealth powerfully shapes opportunity and disadvantage across individuals and communities. Using machine-learning-based imputation to link newly assembled national historical surveys conducted by the U.S. Federal Reserve to population survey microdata, the data presented in this paper addresses this gap. The Geographic Wealth Inequality Database ("GEOWEALTH-US") provides the first estimates of the level and distribution of wealth at various geographical scales within the United States from 1960 to 2020. The GEOWEALTH-US database enables new lines investigation into the contribution of inter-regional wealth patterns to major societal challenges including wealth concentration, spatial income inequality, equality of opportunity, housing unaffordability, and political polarization.

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 Story County. 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 Story County, the median income for all workers aged 15 years and older, regardless of work hours, was $35,727 for males and $25,502 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in Story County. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecounty of Story County.

- Full-time workers, aged 15 years and older: In Story County, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,158, while females earned $53,495, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Story County, showcasing a consistent income pattern irrespective of employment status.

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

Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2022. This records a decrease from the previous number of 47.000 % for 2020. Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 49.600 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 57.200 % in 1990 and a record low of 43.000 % in 2022. Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;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).

Context

The Gender Inequality Index (GII) is a comprehensive measure devised to evaluate gender disparities and inequities within a society by taking into account various critical dimensions. This index provides insights into the differences and imbalances experienced by individuals based on their gender. The GII is an extension of the Human Development Index (HDI) and concentrates on three principal dimensions: reproductive health, empowerment, and economic activity. Reproductive health is a significant dimension of the GII, encompassing indicators such as maternal mortality rates and adolescent birth rates. These indicators reflect the disparities in health outcomes experienced by women, especially in terms of maternal health and reproductive rights.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, GII Rank (2021) representing the Gender Inequality Index Rank for 2021 and Gender Inequality Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• GII Rank (2021) - Gender Inequality Index Rank for 2021
• Gender Inequality Index from 1990 to 2021 - Gender Inequality Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/E64Y2Be.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by pikisuperstar on Freepik

Thumbnail by: Equality icons created by Freepik - Flaticon

CR: Income Share Held by Lowest 10% data was reported at 1.600 % in 2023. This records a decrease from the previous number of 1.700 % for 2022. CR: Income Share Held by Lowest 10% data is updated yearly, averaging 1.400 % from Dec 1981 (Median) to 2023, with 37 observations. The data reached an all-time high of 1.700 % in 2022 and a record low of 1.000 % in 1991. CR: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;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).

CO: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 54.800 % in 2022. This records a decrease from the previous number of 55.100 % for 2021. CO: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 53.600 % from Dec 1980 (Median) to 2022, with 28 observations. The data reached an all-time high of 59.100 % in 1980 and a record low of 49.700 % in 2017. CO: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;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).

Austria AT: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 30.700 % in 2021. This records an increase from the previous number of 29.800 % for 2020. Austria AT: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 30.350 % from Dec 1994 (Median) to 2021, with 26 observations. The data reached an all-time high of 31.500 % in 2009 and a record low of 28.700 % in 2005. Austria AT: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Austria – Table AT.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;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).

apportionments_pop_2021_pred_2024.xlsx This is a dataset containing prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules we used for calculation. Note: We recommend readers who are not so well informed about apportionment problems and rounding rules see https://www.census.gov/library/video/2021/what-is-apportionment.html or https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html.

Data interpretations for this dataset are as follows. 4 worksheets: all: prediction apportionment results of all configurations under the assumption that the membership remains unchanged and the total number of seats is between 705 (current total number of seats) and 750 (statutory threshold). no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. increase_no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. response: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats while any Member State with a decreasing population does not gain seats; (3) and the total number of seats is between 705 and 750. Meanings of column names: State: name of Member State of the European Union p_2011: population data from the 2011 census (data source: https://ec.europa.eu/eurostat/web/population-demography/population-housing-censuses/database) p_2021: population data from the 2021 census (data source: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_and_housing_census_2021_-_population_grids&stable=1#Distribution_of_European_population) stat_2020: current distribution of seats in the EP (data source: https://www.europarl.europa.eu/news/en/headlines/eu-affairs/20180126STO94114/infographic-how-many-seats-does-each-country-get-in-in-the-european-parliament) other columns: composed in the order of "a", "gamma", "d-rounding rule", and "the total number of seats (S)".

indexes_pop_2021_pred_2024.csv This is a dataset presenting the extent of the PSI-based inequality index (index based on Population Seat Index) and the conventional PSP-based index (index based on the proportion of seats to population) of all prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules used for calculation and the PSI-based index and PSP-based index used for evaluation. Data interpretations for this dataset are as follows. Meanings of column names: a: configuration of the standard function gamma: configuration of the standard function rounding: d-rounding rule used for obtaining a whole number S: the total number of seats in the prediction x_min: the minimum number of seats in the prediction apportionment x_max: the maximum number of seats in the prediction apportionment inequality index: maximum of PSI divided by minimum of PSI psp_max/psp_min: maximum of PSP divided by minimum of PSP

Exclusionary attitudes towards out-groups are often justified by historical narratives of conflict. A large body of literature explores how making in-group victimhood narratives salient can affect attitudes towards out-groups. Much less, however, has been done to study how in-group perpetrator narratives may reduce or exacerbate animosity towards a (historically victimized) out-group. This dataset helps to fill this gap by studying islamophobia in Spain. It can be paired with a mirrored dataset focused on antisemitism submitted by the same research team. It measures islamophobia and various intergroup attitudes, while also including a survey experiment that randomly assigns respondents into one of three treatment arms related to historical perpetrator-hood and out-group victimization.

Understanding how global rises in economic inequality are affecting governance regimes across the world is a critical question to the social sciences today. Historically, sharp increases in inequality have generated drastic changes in political and social order (Gurr 1970, Skocpol 1979, 1994). However, existing knowledge about the causal effects of inequality on governance is surprisingly limited. At the macro-level, studies show in general a negative association between economic inequality and the quality of governance institutions (Acemoglu and Robinson 2006, Rothstein 2011), but have not reached a consensus about the causal mechanisms that may explain this relationship. At the micro-level, the evidence so far reveals mixed effects of inequality on a number of factors that shape governance institutions, including voting behaviour, attitudes towards democracy and the rule of law, civic participation, collective mobilisation and political violence (Alesina and La Ferrara 2005, Bardhan 2005, Solt 2008). These mixed effects are not surprising because the ways in which citizens participate in the political arena and mobilise collectively to drive change are moulded by inequality itself. One case in point - which we currently observe in many parts of the world - is when rises in inequality allow the capture of political decision-making processes by those that benefit from them, eroding social relations between groups and trust in institutions (Piketty 2013, Stiglitz 2013). However, despite the enormous consequences of these complex relationships for global stability and democratic values, there is to date limited knowledge about the factors that may mediate the relationship between rising inequalities and governance outcomes. When this evidence exists, it is restricted to a handful of countries. The main aim of this project is to explore how one key factor -trust- mediates the relationship between inequality and governance in settings where democratic institutions may be unstable or under threat.

The project is organised around three thematic areas: (i) how trust within and between social groups and towards governance institutions emerges and evolves in contexts of rising inequality; (ii) how trust in unequal societies shapes governance outcomes through two intervening factors - political behaviour and social mobilisation; and (iii) the pathways through which changes in such intervening factors may sometimes result in inclusive governance outcomes, but in the breakdown of governance at other times. Each of these areas will incorporate detailed theoretical and empirical analyses at the subnational level in four countries -Colombia, Ethiopia, Pakistan and Spain- affected by rising inequalities and characterised by unstable or strained democratic institutions.

The absence of systematic qualitative, quantitative and behavioural data has hindered progress in understanding the links between inequality, trust and governance in countries outside North America and Western Europe. The project seeks to compile a number of unexplored data sources and collect new data comparatively across these other countries in order to fulfil this critical gap. This data collection will involve: (i) comparative individual-level surveys to understand contemporaneous levels of trust, and attitudes towards formal and non-formal local governing institutions, (ii) behavioural experiments under different inequality and political contexts to better understand the formation of trust under different scenarios, (iii) indepth interviews with key political actors in government, members of social movements and citizen organisations to understand how inequalities affect perceptions of governance and strategies of political mobilisation, and (iv)detailed compilation of archival data that will allow us to better understand how inequalities and attitudes have evolved across time and how different historical junctures may shape the governance outcomes we observe today.

We utilize the reports compiled by the Medical Officer of Health (MOH) to gather information on mortality from different respiratory diseases, focussing on Influenza, Pneumonia and Bronchitis. We catalogue annual mortality rates for different diseases for Belfast, Birmingham, Cardiff, Glasgow, Liverpool, London, Manchester and Sheffield. The dataset covers the years 1895 to 1956.

This research aims to assess the medium-run implications of COVID-19 on income and health inequality, and possible policies that aim to mitigate these effects. The medium run is important because the impacts of COVID-19 on inequality are expected to persist for many years. Understanding how inequality changes over the medium run, and assessing mitigation policies beyond the short term, requires information on the evolution of income and health inequalities several years after an outbreak. To achieve this, we will combine models typically applied to modern datasets with quantitative data from historical periods that, unlike contemporary data, cover extended post-outbreak periods. We will use records from Glasgow since the end of the 19th century, covering a period of intense and volatile economic activity, as well as multiple disease outbreaks. We choose Glasgow because it is a large city demonstrating similar inequalities to those seen today, and because administrative records for Glasgow provide detailed relevant information.

Our approach is the following. We will use a modelling framework that has been shown to be effective in capturing income inequality and the effects of recessions on this inequality. We will extend the modelling approach to also include health inequalities and ensure that both income and health inequalities are represented accurately using recent datasets. To set up the model so that it captures the effects of outbreaks on inequalities, we will use historical data from earlier times that include large disease outbreaks. The model will then allow us to examine the effects of different policy interventions for households with different socioeconomic characteristics.

The Gross Domestic Product (GDP) in Israel expanded 2.75 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - Israel GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

ABSTRACT The international debate on wealth taxation has been subject to renewed interest amid new proposals coming out of the US electoral cycle and the salience of wealth inequality. This article reviews the case for taxing wealth and its transfer across generations (wealth and inheritance taxes), analyzing their design from an international comparative perspective, and extracting lessons for Brazil. The long-debated “Tax on Large Fortunes” has never been implemented and the state-level “Tax on Inheritances” has been watered down over time. We propose a framework for the progressive implementation and reform of both taxes in the country. We argue, given the historical record and current research, that they are technically and administratively feasible propositions, notwithstanding important political economy considerations.

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 Story County. 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 2022

Based on our analysis ACS 2022 1-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Story County, the median income for all workers aged 15 years and older, regardless of work hours, was $32,478 for males and $25,601 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 21% between the median incomes of males and females in Story County. With women, regardless of work hours, earning 79 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecounty of Story County.

- Full-time workers, aged 15 years and older: In Story County, among full-time, year-round workers aged 15 years and older, males earned a median income of $67,630, while females earned $52,134, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Story County, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/story-county-ia-income-by-gender.jpeg" alt="Story County, IA gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-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 Story County median household income by gender. You can refer the same here