Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).

In 2024, around ********** households in Japan were households in which both husband and wife were employees. The rise in dual-income households indicated an increasing participation of women in the labor market.

The total number of dual-earner families in Canada increased by 0.2 million numbers (+3.91 percent) in 2022. Therefore, the total number in Canada reached a peak in 2022 with 5.34 million numbers.

In 2023, approximately **** percent of households in South Korea were dual-earner families, slightly increased from around **** percent in the previous year. The share of dual-income households fluctuated in the past years, but gradually increased overall.

The median annual family income of dual-earner families in Canada increased by 5,520 dollars (+4.99 percent) in 2022 in comparison to the previous year. With 116,110 dollars, the median annual income thereby reached its highest value in the observed period.

Average Family Income: Philippines: Two Persons data was reported at 192,000.000 PHP in 2015. This records an increase from the previous number of 167,000.000 PHP for 2012. Average Family Income: Philippines: Two Persons data is updated yearly, averaging 179,500.000 PHP from Dec 2012 (Median) to 2015, with 2 observations. The data reached an all-time high of 192,000.000 PHP in 2015 and a record low of 167,000.000 PHP in 2012. Average Family Income: Philippines: Two Persons data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H021: Family Income and Expenditure Survey: Average Annual Income: By Family Size and Income Group.

This dataset contains interview data for a phenomenological study as part of the doctoral thesis “Parents’ Unsociable Work Schedules and Children’s Well-Being: A Mixed Methods Study of Dual-Earner Households in Mainland China.” The study aims to explore the lived experiences of children living in Chinese dual-earner households where parents are exposed to unsociable work schedules, defined as work scheduling practices that are not conducive to direct ad stable parental involvement, such as long work hours, night shifts, weekend work, inflexible scheduling, and on-call duties. Fifteen children from dual-earner households in mainland China were recruited to participate in semi-structured interviews. The interviews were conducted remotely via WeChat video calls in December 2023, with all participants joining from their homes. Each interview lasted between 35 and 60 minutes and was conducted in Mandarin Chinese. The sessions were audio-recorded and subsequently transcribed verbatim. To ensure the privacy and confidentiality of participants, all data were anonymized, removing any identifying information about the individuals included in the dataset. The data files comprise fifteen verbatim transcripts of the interview data.

In South Korea in 2018, at least ** percent of households with children under the age of ** were dual-earner families, meaning both parents worked for a living. The share of dual-earner families increased as the children grew older, with both parents working in nearly ** percent of families with teenaged children.

The Survey of Consumer Finances (SCF) is conducted annually to obtain work experience and income information from Canadian households. The Survey provides up-to-date information on the distribution and sources of income, before and after taxes, for families and individuals. With this file, users may identify specific family types, such as two-parent and lone-parent families. Information is also provided on earnings, transfers, and total income for the head and the spouse of the census family unit, as well as personal and labour-related characteristics. The refernce year for this file is 1982. Commencing with the 1998 microdata files, annual cross-sectional income data will be sourced from the Survey of Labour and Income Dynamics (SLID).

About this dataset

Context

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

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Two Rivers did not include 7-person households. Across the different household sizes in Two Rivers the mean income is $72,462, and the standard deviation is $22,057. The coefficient of variation (CV) is 30.44%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $37,407. It then further increased to $59,282 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/two-rivers-wi-median-household-income-by-household-size.jpeg" alt="Two Rivers, WI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

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

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Two Rivers median household income. You can refer the same here

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

Both estimated median household income and estimated median family income were higher in 2024 than in 2005. The change in estimated median household income between 2023 and 2024 was not statistically significant. However, the increase in estimated median family income between 2023 and 2024 was statistically significant. Estimated median family income is consistently higher than estimated median household income, largely due to the definitions of each term, and the types of household that are measured and are not measured in each category.

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

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

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

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

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

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

Sources: U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (2 December 2025).; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (18 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (3 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).

Abstract

Household Income and Expenditure Survey (HIES) collects a wealth of information on HH income and expenditure, such as source of income by industry, HH expenditure on goods and services, and income and expenditure associated with subsistence production and consumption. In addition to this, HIES collects information on sectoral and thematic areas, such as education, health, labour force, primary activities, transport, information and communication, transfers and remittances, food expenditure (as a proxy for HH food consumption and nutrition analysis), and gender.

The Pacific Islands regionally standardized HIES instruments and procedures were adopted by the Government of Tokelau for the 2015/16 Tokelau HIES. These standards were designed to feed high-quality data to HIES data end users for:

1. deriving expenditure weights and other useful data for the revision of the consumer price index;
2. supplementing the data available for use in compiling official estimates of various components in the System of National Accounts;
3. supplementing the data available for production of the balance of payments; and
4. gathering information on poverty lines and the incidence of poverty in Tokelau.

The data allow for the production of useful indicators and information on the sectors covered in the survey, including providing data to inform indicators under the UN Sustainable Development Goals (SDGs). This report, the above listed outputs, and any thematic analyses of HIES data, collectively provide information to assist with social and economic planning and policy formation.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The universe of the 2015/16 Tokelau Household Income and Expenditure Survey (HIES) is all occupied households (HHs) in Tokelau. HHs are the sampling unit, defined as a group of people (related or not) who pool their money, cook and eat together. It is not the physical structure (dwelling) in which people live. The HH must have been living in Tokelau for a period of six months, or have had the intention to live in Tokelau for a period of twelve months in order to be included in the survey.

Household members covered in the survey include: -usual residents currently living in the HH; -usual residents who are temporarily away (e.g., for work or a holiday); -usual residents who are away for an extended period, but are financially dependent on, or supporting, the HH (e.g., students living in school dormitories outside Tokelau, or a provider working overseas who hasn't formed or joined another HH in the host country) and plan to return; -persons who frequently come and go from the HH, but consider the HH being interviewed as their main place of stay; -any person who lives with the HH and is employed (paid or in-kind) as a domestic worker and who shares accommodation and eats with the host HH; and -visitors currently living with the HH for a period of six months or more.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015/16 Tokelau Household Income and Expenditure Survey (HIES) sampling approach was designed to generate reliable results at the national level. That is, the survey was not designed to produce reliable results at any lower level, such as for the three individual atolls. The reason for this is partly budgetary constraint, but also because the HIES will serve its primary objectives with a sample size that will provide reliable national aggregates.

The sampling frame used for the random selection of HHs was from December 2013, i.e. the HH listing updated in the 2013 Population Count.

The 2015/16 Tokelau HIES had a quota of 120 HHs. The sample covered all three populated atolls in Tokelau (Fakaofo, Nukunonu and Atafu) and the sample was evenly allocated between the three atoll clusters (i.e., 40 HHs per atoll surveyed over a ten-month period). The HHs within each cluster were randomly selected using a single-stage selection process.

In addition to the 120 selected HHs, 60 HHs (20 per cluster) were randomly selected as replacement HHs to ensure that the desired sample was met. The replacement HHs were only approached for interview in the case that one of the primarily selected HHs could not be interviewed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for this Household Income and Expenditure Survey (HIES) are composed of a diary and 4 modules published in English and in Tokelauan. All English questionnaires and modules are provided as external resources.

Here is the list of the questionnaires for this 2015-2016 HIES: - Diary: week 1 an 2; - Module 1: Demographic information (Household listing, Demographic profile, Activities, Educational status, Communication status...); - Module 2: Household expenditure (Housing characteristics, Housing tenure expenditure, Utilities and communication, Land and home...etc); - Module 3: Individual expenditure (Education, Health, Clothing, Communication, Luxury items, Alcohonl & tobacco); - Module 4: Household and individual income (Wages and salary, Agricultural and forestry activities, Fishing gathering and hunting activities, livestock and aquaculture activities...etc).

Cleaning operations

All inconsistencies and missing values were corrected using a variety of methods: 1. Manual correction: verified on actual questionnaires (double check on the form, questionnaire notes, local knowledge, manual verifications) 2. Subjective: the answer is obvious and be deducted from other questions 3. Donor hot deck: the value is imputed based on similar characteristics from other HHs or individuals (see example below) 4. Donor median: the missing or outliers were imputed from similar items reported median value 5. Record deletion: the record was filled by mistake and had to be removed.

Several questions used the hotdeck method of imputation to impute missing and outlying values. This method can use one to three dimensions and is dependent on which section and module the question was placed. The process works by placing correct values in a coded matrix. For example in Tokelau the “Drink Alcohol” questions used a three dimension hotdeck to store in-range reported data. The constraining dimensions used are AGE, SEX and RELATIONSHIP questions and act as a key for the hotdeck. On the first pass the valid yes/no responses are place into this 3-dimension hotdeck. On the second pass the data in the matrix is updated one person at a time. If a “Drink Alcohol” question contained a missing response then the person's coded age, sex and relationship key is searched in the “valid” matrix. Once a key is found the result contained in the matrix is imputed for the missing value. The first preferred method to correct missing or outlying data is the manual correction (trying to obtain the real value, it could have been miss-keyed or reported incorrectly). If the manual correction was unsuccessful at correcting the values, a subjective approach was used, the next method would be the hotdeck, then the donor median and the last correction is the record deletion. The survey procedure and enumeration team structure allow for in-round data entry, which gives the field staff the opportunity to correct the data by manual review and by using the entry system-generated error messages. This process was designed to improve data quality. The data entry system used system-controlled entry, interactive coding and validity and consistency checks. Despite the validity and consistency checks put in place, the data still required cleaning. The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database, consisting of: Person level record - characteristics of every (household) HH member, including activity and education profile; HH level record - characteristics of the dwelling and access to services; Final aggregated income - all HH income streams, by category and type; Final aggregated expenditure - all HH expenditure items, by category and type.

The cleaning was a two-stage process, which included manual cleaning while referencing the questionnaire, whereas the second stage involved computer-assisted code verification and, in some cases, imputation. Once the data were clean, verified and consistent, they were recoded to form a final aggregated database.

Response rate

Overall, 99% of the response rate objective was achieved.

Sampling error estimates

Refer to Appendix 2 of the Tokelau 2015/2016 Household Income and Expenditure Survey report attached as an external resource.

About this dataset

Context

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

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, only 2-person households were found in Two Buttes. The coefficient of variation (CV) is 65.93%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 2-person households, with an income of $31,250. Additionally, the Census Bureau did not report a median household income for larger household sizes.

Content

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

Household Sizes:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Two Buttes median household income. You can refer the same here

Spain Household Annual Net Income: Avg: Two Adults with One or More Dependent Children data was reported at 32,731.000 EUR in 2015. This records an increase from the previous number of 31,558.000 EUR for 2014. Spain Household Annual Net Income: Avg: Two Adults with One or More Dependent Children data is updated yearly, averaging 32,731.000 EUR from Dec 2007 (Median) to 2015, with 9 observations. The data reached an all-time high of 34,858.000 EUR in 2009 and a record low of 30,936.000 EUR in 2012. Spain Household Annual Net Income: Avg: Two Adults with One or More Dependent Children data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Spain – Table ES.H017: Households Net Income.

This survey shows the results of a survey in China on the reasons for dual-income households without children (DINKS*) in China in 2011. In 2011, 17 percent of respondents in China thought couples with a double income prefer enjoying their life as a couple.

Italy AAHI: Number of Income Earner: Two data was reported at 30,486.000 EUR in 2015. This records an increase from the previous number of 29,736.000 EUR for 2014. Italy AAHI: Number of Income Earner: Two data is updated yearly, averaging 29,736.000 EUR from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 30,486.000 EUR in 2015 and a record low of 24,826.000 EUR in 2003. Italy AAHI: Number of Income Earner: Two data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.H014: Average Annual Household Income.

Indirect pathways from work demands to partner's fatigue through partner's family demands.

Toddler Learning Towers Market Outlook

According to our latest research, the global toddler learning towers market size reached USD 1.16 billion in 2024, with a robust year-on-year growth driven by increasing parental focus on child safety and developmental learning tools. The market is projected to expand at a CAGR of 7.2% from 2025 to 2033, reaching an estimated USD 2.16 billion by 2033. This impressive growth trajectory is primarily attributed to rising awareness about early childhood development, the proliferation of dual-income households, and the growing trend of Montessori-inspired home environments.

One of the primary growth factors for the toddler learning towers market is the increasing emphasis on child safety and developmental engagement within the home environment. Parents are becoming more aware of the benefits of hands-on learning, particularly in the formative years between ages one and four. Learning towers, which allow toddlers to safely participate in kitchen and household activities at counter height, are rapidly gaining favor as essential household equipment. The market is also benefiting from the alignment with Montessori principles, which advocate for independence and self-directed activity in young children. This educational philosophy has influenced product design and marketing, making learning towers a must-have for parents seeking to foster autonomy and skill-building in their children.

Another significant driver is the surge in dual-income families and the resultant demand for products that enable productive parent-child interaction in limited timeframes. As more parents work from home or follow hybrid work models, integrating children into daily routines has become a practical necessity. Toddler learning towers bridge this gap by allowing children to safely observe and participate in cooking, cleaning, and other household tasks, which not only enhances their cognitive and motor development but also strengthens family bonds. Additionally, the market is witnessing innovation in product design, with features such as adjustable heights, foldability, and convertible functionalities, further enhancing the appeal and utility of these products across diverse household settings.

The proliferation of e-commerce and the increased availability of toddler learning towers through online retail channels have also significantly contributed to market growth. With the convenience of online shopping and the ability to access a wide range of brands and models, parents are more inclined to invest in quality learning towers. Social media platforms and parenting blogs play a pivotal role in product discovery and peer recommendations, accelerating market penetration. Moreover, the COVID-19 pandemic has heightened the focus on home-based learning and engagement, leading to a sustained increase in demand for educational furniture and accessories designed for toddlers.

From a regional perspective, North America currently dominates the toddler learning towers market, accounting for over 35% of the global revenue in 2024, followed by Europe and Asia Pacific. The high adoption rate in North America is attributed to greater disposable incomes, a strong culture of early childhood education, and the widespread influence of Montessori and Waldorf educational philosophies. Europe, with its focus on child welfare and innovation in educational products, is also a significant market, while Asia Pacific is emerging as a high-growth region due to increasing urbanization, rising middle-class incomes, and growing awareness of early childhood development. The market landscape is characterized by both established international brands and a growing number of regional players catering to specific consumer preferences and regulatory standards.

Product Type Analysis

The toddler learning towers market is segmented by product type into adjustable learning towers, foldable learning towers, convertible learning towers, and others. Adjustable learning towers are witnessing the highest demand, accounting for nearly 40% of the total market share in 2024. These products offer flexibility in height adjustment, making them suitable for children of varying ages and sizes. The ability to adapt the tower as the child grows extends the product’s usability, providing better value for money for parents. Manufacturers are focusing on innovative mechanisms that allow for easy and safe height adjustments, which enhances bo

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Two Harbors: 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 21(1.40%) households where the householder is under 25 years old, 534(35.55%) households with a householder aged between 25 and 44 years, 461(30.69%) households with a householder aged between 45 and 64 years, and 486(32.36%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of Two Harbors, 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 Two Harbors median household income by age. You can refer the same here