About this dataset

Context

The dataset tabulates the Bad Axe population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Bad Axe. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,739 (57.77% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Bad Axe population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Bad Axe is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Bad Axe is shown in the following column.

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 Bad Axe Population by Age. You can refer the same here

Abstract

The Department of Social Development (DSD) commissioned a set of socio-economic and demographic baseline studies in the 22 nodes that make up the Integrated Sustainable Rural Development Programme (ISRDP) and Urban Renewal Programme (URP), coupled to a management support programme, that ran from 2006 to 2008. The nodes – 14 of which fall under the ISRDP and 8 of which fall under the URP – were selected because of the deep poverty in which many of their citizens live. The ISRDP and URP aimed to transform their respective nodes into economically vibrant and socially cohesive areas initially through anchor projects to kick-start the programmes, and then through better co-ordination between departments geared to providing an integrated suite of services to all citizens, especially those living in poverty. The point of both programmes is the more efficient and effective use of existing government resources, rather than operating as standard, stand-alone programmes with a dedicated budget. Two surveys were commissioned: a larger baseline in 2006 and a smaller measurement survey in 2008. In the interim, the Department implemented a national, provincial and nodal support programme while considering and reacting to the findings of the first phase of background reports and qualitative nodal-level evaluations. The second survey sought to detect changes (good or bad) that occurred in the interim period.

Analysis unit

Units of analysis included households and individuals

Universe

The survey covered all adult household members, that is, those aged 18 and above

Kind of data

Sample survey data [ssd]

Sampling procedure

The Baseline Survey The 2006 baseline survey sought to conduct 400 interviews in each of the 14 ISRDP nodes and the 8 URP nodes. In order to allow for comparisons with the ISRDS (as it then was) baseline statistics published by Statistics South Africa in 2002, the 400 interviews for Maluti-a-Phofung were spread across the whole district municipality of Thabo Mofutsanyane. The adult population aged 18 and older according to the Census 2001 was used as the sample frame. For the ISRDP nodes, the sample was stratified by local municipalities to ensure sufficient interviews were conducted in each municipality. According to the principles of probability proportional to size sampling (PPS), a list of place names in each of the local municipalities was then generated as a starting point for the fieldwork. At each starting point in the ISRDP nodes five interviews were conducted. For the URP nodes, detailed maps at a ward level were generated from the Municipal Demarcation Board website. Again using the principles of probability proportional to size sampling (PPS), starting points across the different wards were identified on the maps. At each starting point in the URP nodes four interviews were conducted. At the end of the fieldwork phase a total of 8 387 interviews across the 22 nodes had been conducted. Once the information from each interview had been coded and captured on computer, the realised samples in each of the ISRDP nodes were weighted back to the actual population figures across each local municipality. It should be noted that on the one hand, 8 400 is a very large sample with a margin of sampling error of only 1.1%. However, when the data are analysed at nodal level, each of the 22 samples of 400 have a larger sampling error of 4.9%.

The Measurement Survey The 2008 measurement survey sought to conduct 250 interviews in each of the 14 ISRDP nodes (except in Bushbuckridge and Maruleng, where 250 interviews were divided across the two nodes according to population size) and the 8 URP nodes. In order to allow for comparisons with the 2006 baseline survey, the 250 interviews for Maluti-a-Phofung were spread across the whole district municipality of Thabo Mofutsanyane. For comparative purposes, the sample frame (the adult population aged 18 and older according to the Census 2001) and list of starting points from the 2006 baseline survey was used. For the ISRDP nodes, the following steps were followed: The sample for each node was firstly stratified by local municipalities (to ensure sufficient interviews were conducted in each municipality). Within each municipality, the sample was then stratified by settlement type (rural versus urban). According to the principles of probability proportional to size sampling (PPS), a random list of place names in each municipality was then generated. At each place name, the fieldworkers were instructed to find a school (if multiple starting points at one place, subsequent starting points were at different schools or crèches). From the school, they then walked in the direction of dwellings and started at first dwelling - thereafter, every fifth dwelling was selected. The birthday rule was used to select the respondent at each selected dwelling - this random process seeks to interview the adult in the household whose birthday is next. For the ISRDP nodes, five interviews were conducted per starting point. For the URP nodes, the following steps were followed: ?. The sample for each node was firstly stratified by wards. Within each ward, the sample was then stratified by settlement type (formal versus informal types). Detailed maps at a ward level were generated from the Municipal Demarcation Board website. According to the principles of probability proportional to size sampling (PPS), a random series of starting points in each ward were then generated using a random grid of points. From the identified starting point, the fieldworkers proceeded in the direction of the centre of the node and interviewed at the first dwelling they came to - thereafter, every fifth dwelling was selected. The birthday rule was again used to select the respondent at each selected dwelling. For the URP nodes, four interviews were conducted per starting point. At the end of the fieldwork phase a total of 5 232 interviews across the 22 nodes had been conducted. Note, while 5 250 is a large sample with a margin of sampling error of only 1.4%, a nodal sample of 250 has a far larger sampling error of 6.2%. For both surveys, sampling and weighting was undertaken by Ross Jennings of Strategy & Tactics.

Mode of data collection

Face-to-face [f2f]

Research instrument

The baseline survey questionnaire covered the following topics: Poverty, development awareness, social capital, health status, service delivery, gender inequality and sustainable Livelihoods. Each of these included a set of key indicator questions, as well as sub-sets of questions.

Cleaning operations

Fieldwork quality control was undertaken by Strategy & Tactics and Dikarabong. Data punching was undertaken by Strategy & Tactics and coding by OmniData.

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Bad Axe, MI, 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 Bad Axe, MI 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 Bad Axe households based on income levels.

Key observations

• For Family Households: In Bad Axe, the majority of family households, representing 18.51%, earn $60,000 to $74,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $100,000 to $124,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Bad Axe, the majority of non-family households, accounting for 21.9%, have income $20,000 to $24,999, 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 0.0%, earn $100,000 to $124,999, 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 Bad Axe, MI (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 Bad Axe, MI
• 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 Bad Axe, MI
• 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 Bad Axe, MI

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 Bad Axe median household income. You can refer the same here

Abstract

The ultimate objective of the BMTHS is to have a high frequency programme of household surveys that is predictable, flexible and amenable to the ever increasing and changing data needs for government, private sector, planners and researchers. The BMTHS provides a permanent platform for the collection of socio-economic data. This is in contrast to the inter-censal programme of surveys, which is, to a large extent adhoc in nature in that the surveys are infrequent, and the emerging stakeholder needs which have not been planned for are done on adhoc basis.

Specific Survey objectives

It is imperative that a well-coordinated, predictable data provision framework is put in place in the form of BMTHS, as compared to the inter-censal programme of surveys. The BMTHS will provide more frequent, stakeholder specific data that enable policy makers and planners to use real time data in formulation of policies and programmes. The continuous (yearly) nature of the BMTHS allows for close monitoring of programmes, ensuring timely interventions and programme/policy fine tuning. This will lead to robust, responsive relevant programmes that would ultimately improve on the livelihoods of Batswana and the economy. Savings on development budget would be realized due to effective informed policies and programmes.

The BMTHS is set out to;

· provide socio-demographics of the Botswana population; · Provide poverty datum line (PDL) in the country · Provide a list of indicators that monitor poverty
· Provide disaggregated information on poverty levels for monitoring and evaluation of eradication programmes on more regular basis · Continuously provide profiles of poor households. · Profile the poor to assist stakeholders identify the poor among the population · Provide household expenditure information to be used in re-basing of Consumer Price Index · Measures of both current and usual economic activity · Obtain a measure of the size of employment in both formal and informal sectors · Measure of unemployment and underemployment · Determine the size of economically active and inactive population · Provide information on education attainment, occupation and employment status · Determine the impact of education and health among on poor population; · Determine the impact of agriculture among poor population.

Survey Methodology

The Survey methodology outlines the sampling, data collection, processing, publicity and analysis methodologies and strategies employed in the conduct of the BMTHS.

Survey Sampling The Botswana Multi-Topic Household Survey like most national surveys, employed a two stage stratified sampling design. The procedure was made plausible by the existing stratification of twentyseven (27) census districts which are heterogeneous in nature and are aligned to administrative districts. In this structure, the census districts were further grouped into three (3) domains, being; cities/ towns, urban villages and rural areas. The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level.

Geographic coverage

The coverage- nation-wide using administrative district and sub-districts that are usually used by Statistics Botswana in most surveys and censuses

Analysis unit

individuals, households, and communities.

Universe

The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level.

Sampling procedure

The Botswana Multi-Topic Household Survey like most national surveys, employed a two stage stratified sampling design. The procedure was made plausible by the existing stratification of twenty seven (27) census districts which are heterogeneous in nature and are aligned to administrative districts. In this structure, the census districts were further grouped into three (3) domains, being; cities/ towns, urban villages and rural areas. The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level. Botswana Multi-Topic Household Survey Report 2015/16 Statistics Botswana In light of the above, the first stage was the selection of Enumeration Areas (EAs) as Primary Sampling Units (PSUs) with Probability Proportional to Size (PPS) where measure of size is the number of households in an EA as defined in the 2011 Population & Housing Census. This yielded 599 Enumeration Areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphan hood status.

Cleaning operations

The data editing should contain information on how the data was treated or controlled for in terms of consistency and coherence. This item does not concern the data entry phase but only the editing of data whether manual or automatic. - Was a hot deck or a cold deck technique used to edit the data? - Were corrections made automatically (by program), or by visual control of the questionnaire? - What software was used?

If materials are available (specifications for data editing, report on data editing, programs used for data editing), they should be listed here and provided as external resources.

Example:

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

Response rates for the survey

Variable Estimates Response Response rate
Enumeration Areas (PSU) 599 598 99.8
Households (SSU) 7,188 7,060 98.2
Persons Participation 25,130 24,720 98.4

Sampling error estimates

For sampling surveys, it is good practice to calculate and publish sampling error. This field is used to provide information on these calculations. This includes: - A list of ratios/indicators for which sampling errors were computed. - Details regarding the software used for computing the sampling error, and reference to the programs used (to be provided as external resources) as the program used to perform the calculations. - Reference to the reports or other document where the results can be found (to be provided as external resources).

Example:

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

This estimate of the percent of distressed housing units in each Census Tract was prepared using data from the American Community Survey and the Allegheny County Property Assessment database. The estimate was produced by the Reinvestment Fund in their work with the Allegheny County Department of Economic Development.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Bad Axe. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Bad Axe. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Bad Axe, the median household income stands at $68,681 for householders within the 25 to 44 years age group, followed by $49,844 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $28,173.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Bad Axe median household income by age. You can refer the same here

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 Bad Axe. 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 Bad Axe, the median income for all workers aged 15 years and older, regardless of work hours, was $39,432 for males and $21,875 for females.

These income figures highlight a substantial gender-based income gap in Bad Axe. Women, regardless of work hours, earn 55 cents for each dollar earned by men. This significant gender pay gap, approximately 45%, underscores concerning gender-based income inequality in the city of Bad Axe.

- Full-time workers, aged 15 years and older: In Bad Axe, among full-time, year-round workers aged 15 years and older, males earned a median income of $43,588, while females earned $36,250, leading to a 17% gender pay gap among full-time workers. This illustrates that women earn 83 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.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Bad Axe.

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

About this dataset

Context

The dataset tabulates the Bad Axe median household income by race. The dataset can be utilized to understand the racial distribution of Bad Axe income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Bad Axe, MI median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Bad Axe, MI (2021, 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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Bad Axe median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Bad Axe by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Bad Axe. The dataset can be utilized to understand the population distribution of Bad Axe by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Bad Axe. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Bad Axe.

Key observations

Largest age group (population): Male # 65-69 years (183) | Female # 30-34 years (195). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Bad Axe population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Bad Axe is shown in the following column.
• Population (Female): The female population in the Bad Axe is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Bad Axe for each age group.

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 Bad Axe Population by Gender. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Bad Axe, 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 9,601, while the mean income for the highest quintile (20% of households with the highest income) is 129,086. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 176,504, which is 136.73% higher compared to the highest quintile, and 1838.39% 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 Bad Axe median household income. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Bad Axe: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 51(4.04%) households where the householder is under 25 years old, 417(33.04%) households with a householder aged between 25 and 44 years, 392(31.06%) households with a householder aged between 45 and 64 years, and 402(31.85%) households where the householder is over 65 years old.
• In Bad Axe, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

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

Income brackets:

• 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
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Bad Axe median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Bad Axe by race. It includes the population of Bad Axe across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Bad Axe across relevant racial categories.

Key observations

The percent distribution of Bad Axe population by race (across all racial categories recognized by the U.S. Census Bureau): 87.28% are white, 0.70% are Black or African American, 1.79% are Asian, 4.98% are some other race and 5.25% are multiracial.

Content

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

Racial categories include:

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

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Bad Axe
• Population: The population of the racial category (excluding ethnicity) in the Bad Axe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Bad Axe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Bad Axe Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Bad Axe by race. It includes the distribution of the Non-Hispanic population of Bad Axe across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Bad Axe across relevant racial categories.

Key observations

Of the Non-Hispanic population in Bad Axe, the largest racial group is White alone with a population of 2,608 (94.66% of the total Non-Hispanic population).

Content

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

Racial categories include:

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

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Bad Axe
• Population: The population of the racial category (for Non-Hispanic) in the Bad Axe is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Bad Axe total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Bad Axe Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Bad Axe: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 80(5.86%) households where the householder is under 25 years old, 407(29.82%) households with a householder aged between 25 and 44 years, 447(32.75%) households with a householder aged between 45 and 64 years, and 431(31.58%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Bad Axe, showcasing varying income levels among different age demographics.

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:

• 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
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Bad Axe median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Bad Axe. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Bad Axe, the median household income for the households where the householder is White increased by $3,826(10.01%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $38,225 in 2013 and $42,051 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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

Racial categories include:

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

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Bad Axe.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-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 Bad Axe median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the Bad Axe Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Bad Axe, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Bad Axe.

Key observations

Among the Hispanic population in Bad Axe, regardless of the race, the largest group is of Mexican origin, with a population of 249 (97.65% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Bad Axe
• Population: The population of the specific origin for Hispanic or Latino population in the Bad Axe is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Bad Axe total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Bad Axe Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Bad Axe. 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 Bad Axe population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 87.28% of the total residents in Bad Axe. Notably, the median household income for White households is $42,051. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $85,865. This reveals that, while Whites may be the most numerous in Bad Axe, Two or More Races households experience greater economic prosperity in terms of 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.

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 Bad Axe.
• Median household income: Median household income, adjusting for inflation, presented in 2023-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 Bad Axe median household income by race. You can refer the same here

About this dataset

Context

The dataset illustrates the median household income in Bad Axe, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Bad Axe increased by $9,419 (24.71%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

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 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous 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 Bad Axe median household income. You can refer the same here

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 Bad Axe. The dataset can be utilized to gain insights into gender-based income distribution within the Bad Axe population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Bad Axe, among individuals aged 15 years and older with income, there were 1,005 men and 1,428 women in the workforce. Among them, 471 men were engaged in full-time, year-round employment, while 329 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 16.77% fell within the income range of under $24,999, while 28.88% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 3.82% of men in full-time roles earned incomes exceeding $100,000, while 9.73% 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 Bad Axe median household income by race. You can refer the same here