About this dataset

Context

The dataset presents the median household income across different racial categories in Omaha. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Omaha population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 73.61% of the total residents in Omaha. Notably, the median household income for White households is $75,474. Interestingly, despite the White population being the most populous, it is worth noting that Native Hawaiian and Other Pacific Islander households actually reports the highest median household income, with a median income of $109,183. This reveals that, while Whites may be the most numerous in Omaha, Native Hawaiian and Other Pacific Islander households experience greater economic prosperity in terms of median household income.

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Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-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 Omaha.
• Median household income: Median household income, adjusting for inflation, 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 Omaha median household income by race. You can refer the same here

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This dataset tracks annual diversity score from 1991 to 2023 for Omaha Public Schools vs. Nebraska

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Omaha. The dataset can be utilized to gain insights into gender-based income distribution within the Omaha population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Omaha, among individuals aged 15 years and older with income, there were 175,073 men and 173,005 women in the workforce. Among them, 103,007 men were engaged in full-time, year-round employment, while 81,316 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 6.36% fell within the income range of under $24,999, while 9.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 25.25% of men in full-time roles earned incomes exceeding $100,000, while 15.67% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

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Supplementary material 10 from: Aust S, Ahrendsen D, Kellar P (2015) Biodiversity assessment among two Nebraska prairies: a comparison between traditional and phylogenetic diversity indices. Biodiversity Data Journal 3: e5403. https://doi.org/10.3897/BDJ.3.e5403

Global biodiversity is declining rapidly as a consequence of anthropogenic changes to the environment. Traditional diversity indices such as species richness have been used to assess biodiversity, but recent arguments call for a more comprehensive assessment that includes both phylogenetic and functional diversity (PD and FD, respectively). Many PD metrics have been developed, but few empirical studies have compared metrics across sites with the goal of understanding their application to characterizing biodiversity. In this study, 17 PD metrics, four traditional diversity indices, and one measure of FD were calculated and compared between two Nebraska grasslands. PD metrics were calculated from robust phylogenies estimated from next-generation sequencing data of 45 species. Traditional indices were calculated using species abundance data, and FD was quantified by measuring the phylogenetic signal, K, of specific leaf area (SLA). Results showed that PD metrics and traditional indices were not always correlated, and various PD metrics characterized biodiversity differently. In addition, phylogenies estimated from >80 genes were more robust than single- or dual-gene phylogenies resulting in more reliable PD metrics. K of SLA indicated random trait assembly in all sites. Results suggested that metrics that identify phylogenetic structure and relatedness can provide information to conservation planners about the ability of a community to persist in an unpredictable future. A combination of these results with those of future investigations applying PD and FD metrics to varying communities will support concrete recommendations to conservation planners about how to incorporate these metrics into the selection of priority regions.

This dataset tracks annual diversity score from 1991 to 2023 for Northwest High School vs. Nebraska and Omaha Public Schools

Maximum likelihood (ML) tree (-ln L=30809.97) inferred from the concatenation of rbcL + matK (Suppl. material 9); matching one most parsimonious (MP) tree except were dagger (†) is shown. Tree includes 62 asterid species and 3 outgroups (Comandra umbellata, Silene vulgaris, and Silene antirrhina). Numbers above branches indicate branch lengths used to calculate various Phylogenetic Diversity (PD) metrics. Numbers below the branches indicate MP/ML bootstrap support values resulting from 1000 replicates each. Low branch support (<50) is indicated by an asterisk (*). Missing bootstrap values are denoted by a dash (-).

Maximum likelihood (ML) tree (-ln L=10645.92) inferred from rbcL only (Suppl. material 7); matching 1 of 68 most parsimonious (MP) trees except were dagger (†) is shown. Tree includes 62 asterid species and 3 outgroups (Comandra umbellata, Silene vulgaris, and Silene antirrhina). Numbers above branches indicate branch lengths used to calculate various Phylogenetic Diversity (PD) metrics. Numbers below the branches indicate MP/ML bootstrap support values resulting from 1000 replicates each. Low branch support (<50) is indicated by an asterisk (*). Missing bootstrap values are denoted by a dash (-).

GenBank accession numbers for each gene/region by organelle.

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