In 2024, **** percent of the U.S. population had employment-based health insurance coverage. This statistic depicts the percentage of the U.S. population with employment-based health insurance from 1987 to 2024.

The percentage of people in the United States with health insurance has increased over the past decade with a noticeably sharp increase in 2014 when the Affordable Care Act (ACA) was enacted. As of 2024, around ** percent of people in the United States had some form of health insurance, compared to around ** percent in 2010. Despite the increases in the percentage of insured people in the U.S., there were still over ** million people in the United States without health insurance as of 2024. Insurance coverage Health insurance in the United States consists of different private and public insurance programs such as those provided by private employers or those provided publicly through Medicare and Medicaid. Almost half of the insured population in the United States were insured privately through an employer as of 2021, while **** percent of people were insured through Medicaid, and **** percent through Medicare . The Affordable Care Act The Affordable Care Act (ACA), enacted in 2014, has significantly reduced the number of uninsured people in the United States. In 2014, the percentage of U.S. individuals with health insurance increased to almost ** percent. Furthermore, the percentage of people without health insurance reached an all time low in 2022. Public opinion on healthcare reform in the United States remains an ongoing political issue with public opinion consistently divided.

In 2020, around **** percent of the U.S. population had private health insurance coverage. This share slightly decreased to **** percent in 2024. Medicare and Medicaid together provided healthcare coverage to approximately ** percent of the population in the United States. U.S. population with and without health insurance In 2022, over half of the U.S. population had health insurance coverage through their place of employment, around 54.5 percent. Approximately 35 percent had coverage through some form of government plan in the same year. While still low, the U.S. population without health insurance has decreased slightly from the previous year. A large portion of those without health insurance are between 19 and 25 years of age. Approximately ** percent of adults in this age group did not have health insurance in 2021. Health expenditure The United States spent approximately ****** U.S. dollars per capita on health in 2022 while in comparison, the Canadian government expended some ***** U.S. dollars per capita in the same year. However, higher health spending did not equate to a better health system or outcomes and when ranked with other comparable high-income countries, the U.S. came in last on nearly all health performance categories from access of care to health outcomes.

In 2024, approximately ******** percent of the Hispanic population in the United States did not have health insurance, a historical low since 2010. In 2024, the national average was *** percent. White Americans had a below-average rate of just ***** percent, whereas *** percent of Black Americans had no health insurance.Impact of the Affordable Care ActThe Affordable Care Act (ACA), also known as Obamacare, was enacted in March 2010, which expanded the Medicaid program, made affordable health insurance available to more people and aimed to lower health care costs by supporting innovative medical care delivery methods. Though it was enacted in 2010, the full effects of it weren’t seen until 2013, when government-run insurance marketplaces such as HealthCare.gov were opened. The number of Americans without health insurance fell significantly between 2010 and 2015, but began to rise again after 2016. What caused the change?The Tax Cuts and Jobs Act of 2017 has played a role in decreasing the number of Americans with health insurance, because the individual mandate was repealed. The aim of the individual mandate (part of the ACA) was to ensure that all Americans had health coverage and thus spread the costs over the young, old, sick and healthy by imposing a large tax fine on those without coverage.

Table from the American Community Survey (ACS) B27011 health insurance coverage status and type by employment status. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): B27011Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

In the United States, children and young adults aged between 18 and 25 with employer-sponsored health insurance (ESI) were more likely to be enrolled as dependents on a plan sponsored by someone within the household. As of 2024, ** percent of adults aged 24 had healthcare coverage through ESI.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Employer Health Plan Cyber Insurance Market Outlook

According to our latest research, the global Employer Health Plan Cyber Insurance market size was valued at USD 4.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 22.6% projected from 2025 to 2033. By the end of 2033, the market is anticipated to reach USD 32.1 billion. This exceptional growth is primarily driven by the escalating frequency and sophistication of cyberattacks targeting sensitive healthcare data, as well as the increasing regulatory requirements for data protection across the globe.

One of the primary growth factors propelling the Employer Health Plan Cyber Insurance market is the exponential rise in cyber threats targeting employer-sponsored health plans and healthcare providers. As digitalization accelerates across the healthcare sector, vast amounts of sensitive employee and patient data are stored and transmitted electronically, creating lucrative targets for cybercriminals. Ransomware, phishing, and data breach incidents have become alarmingly common, prompting organizations to seek comprehensive cyber insurance solutions to mitigate financial and reputational losses. The increasing reliance on interconnected systems, cloud-based platforms, and telehealth services further amplifies vulnerabilities, making robust cyber insurance coverage an essential risk management tool for employers and health plan providers.

Another significant driver is the tightening regulatory landscape governing data privacy and cybersecurity in the healthcare domain. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and similar frameworks in other regions have imposed stringent requirements on organizations to safeguard personal health information. Non-compliance can result in severe penalties and legal liabilities, compelling employers and health plan administrators to invest in cyber insurance policies that offer both first-party and third-party coverage. These policies not only provide financial protection in the event of a breach but also offer access to specialized incident response services and legal counsel, further enhancing their value proposition.

Moreover, the increasing awareness among organizations regarding the multifaceted risks associated with cyber incidents is fueling market growth. As cyberattacks become more sophisticated and damaging, employers are recognizing the need for tailored insurance solutions that address the unique risks inherent in managing health plans. Insurers are responding by developing innovative products that cover a broad spectrum of cyber risks, including business interruption, data restoration, regulatory fines, and liability claims. The rise of remote work, digital health platforms, and third-party service providers has added new layers of complexity, making comprehensive cyber insurance coverage a strategic imperative for organizations of all sizes.

Regionally, North America continues to dominate the Employer Health Plan Cyber Insurance market, accounting for the largest share in 2024. This leadership position is attributed to the region's advanced digital infrastructure, high incidence of cyberattacks, and stringent regulatory environment. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid digital transformation, increasing cyber threats, and evolving regulatory frameworks. Europe also represents a significant market, supported by robust data protection laws and growing adoption of cyber insurance among health plan providers and employers. Latin America and the Middle East & Africa are emerging markets, with increasing investments in digital health and cybersecurity solutions.

Coverage Type Analysis

The Employer Health Plan Cyber Insurance market by coverage type is segmented into First-Party Coverage, Third-Party Coverage, and Others. First-party cov

Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

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How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Embedded Health Benefits Platform Market Outlook

According to our latest research, the global Embedded Health Benefits Platform market size reached USD 9.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.1%. This dynamic market is on track to achieve a value of USD 41.5 billion by 2033, propelled by the rising demand for integrated digital health solutions across enterprises, insurance providers, and healthcare organizations. The primary growth factor driving this expansion is the increasing adoption of digital platforms to streamline employee health benefits, enhance claims management, and improve overall wellness outcomes, as per our latest research findings.

The growth trajectory of the Embedded Health Benefits Platform market is significantly influenced by the ongoing digital transformation in the healthcare and insurance sectors. Organizations are increasingly recognizing the value of embedding health benefits directly within their operational ecosystems to simplify processes, reduce administrative burdens, and deliver a more seamless employee experience. Additionally, the proliferation of cloud-based solutions and the integration of artificial intelligence and machine learning technologies have further accelerated the adoption of these platforms. These innovations not only enable real-time data analytics and personalized health recommendations but also support proactive health management, ultimately resulting in cost savings and improved employee satisfaction.

Another critical growth factor is the evolving regulatory landscape and the emphasis on compliance and data security. Governments and regulatory bodies worldwide are mandating stricter data protection and transparency standards, compelling organizations to invest in secure and compliant health benefits platforms. This shift is particularly evident in regions such as North America and Europe, where regulatory frameworks like HIPAA and GDPR play a pivotal role in shaping technology adoption. As a result, vendors are focusing on enhancing platform security, interoperability, and compliance features, thereby driving market growth and fostering trust among enterprises and end-users.

Furthermore, the increasing focus on employee wellness and preventive healthcare is reshaping the way organizations approach benefits administration. Employers are seeking comprehensive solutions that not only manage claims and benefits but also promote holistic well-being through wellness programs, mental health support, and personalized care pathways. This trend is further amplified by the rise of remote and hybrid work models, which necessitate flexible and accessible health benefits solutions. As businesses strive to attract and retain top talent in a competitive labor market, the demand for advanced embedded health benefits platforms is expected to surge, fueling sustained market expansion over the forecast period.

From a regional perspective, North America currently dominates the Embedded Health Benefits Platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high adoption rate in North America can be attributed to the presence of leading technology providers, a mature healthcare infrastructure, and favorable regulatory policies. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing healthcare investments, and a burgeoning workforce. Latin America and the Middle East & Africa are also witnessing steady growth, supported by rising awareness and gradual improvements in healthcare delivery systems. This regional diversity underscores the global appeal and adaptability of embedded health benefits platforms across different market landscapes.

Component Analysis

The Embedded Health Benefits Platform market by component is segmented into software and services, each playing a crucial role in shaping the market’s overall value proposition. The software segment encompasses a wide array of platforms and applications designed to automate and optimize benefits administration, claims processing, and employee wellness initiatives. These solutions leverage advanced technologies such as cloud computing, artificial intelligence, and data analytics to deliver real-time insights, personalized recommendations, and seamless integration with existing enterprise systems. As organizations increasingly prioritize digital transformation, the demand for robust, sca

The number of unemployed people who have been claiming benefit continuously for more than 12 months divided by the total number of unemployed people claiming benefits, expressed as a percentage. Claimant count, age and duration data are based on computerised claims. Therefore while total unemployed claimants is available including clerical claims, for the purpose of this calculation both the numerator and denominator are restricted to computerised claims. To monitor the policy aim of tackling unemployment, focusing on long term unemployment. The indicator is based on the claimant count, which includes the number of people who are claiming Jobseekers Allowance (JSA) or National Insurance Credits. Unemployment is a significant risk factor for poor physical and mental health and therefore a major determinant of health inequalities. It is associated with morbidity, injuries, and premature mortality, especially through increased risk of coronary heart disease. It is also related to depression, anxiety, self-harm and suicide. In addition, unemployment reinforces inequalities in health by social class. Legacy unique identifier: P01081

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled ."Health Insurance Table Updates".. for further details..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see .https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html.. for more details. The 2008 data table in American FactFinder does not incorporate these edits. Therefore, the estimates that appear in these tables are not comparable to the estimates in the 2009 and later tables. Select geographies of 2008 data comparable to the 2009 and later tables are available at .https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html... The health insurance coverage category names were modified in 2010. See .https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18.. for a list of the insurance type definitions..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates

Workers’ Compensation Insurance Market Outlook

According to our latest research, the global Workers’ Compensation Insurance market size reached USD 67.4 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 5.8% from 2025 to 2033, reaching a forecasted value of USD 112.4 billion by 2033. The primary growth driver for this market is the increasing emphasis on workplace safety and employee welfare regulations across developed and emerging economies, compelling organizations to invest in comprehensive insurance solutions. This outlook is based on the most recent industry data and reflects the dynamic evolution of workers’ compensation insurance in response to regulatory, technological, and social trends.

One of the principal growth factors for the Workers’ Compensation Insurance market is the intensifying regulatory landscape worldwide. Governments in regions such as North America, Europe, and Asia Pacific are continually tightening labor laws and mandating robust employee compensation schemes to protect workers from occupational hazards. This regulatory push has led to higher insurance penetration rates, especially among small and medium enterprises (SMEs) that previously operated without adequate coverage. Additionally, increased awareness regarding employee rights and workplace safety has prompted organizations to proactively seek out comprehensive workers’ compensation insurance policies. These factors are not only driving market growth but also reshaping the competitive dynamics as insurers innovate to meet evolving compliance requirements.

Technological advancements are also playing a pivotal role in shaping the growth trajectory of the Workers’ Compensation Insurance market. The integration of digital platforms, artificial intelligence, and predictive analytics into insurance operations has streamlined claim processing, risk assessment, and fraud detection. These innovations are enabling insurers to offer more personalized products, reduce administrative costs, and enhance customer experience. Moreover, digital transformation is facilitating the expansion of distribution channels, making insurance products more accessible to a broader audience, including previously underserved sectors such as gig economy workers and remote employees. This ongoing digitalization is expected to further boost market adoption and efficiency over the forecast period.

Another significant growth driver is the evolving nature of work itself. The rise of remote work, the gig economy, and new forms of employment have created additional complexities in risk management and insurance coverage. Employers are increasingly seeking insurance products that can adapt to these changing workforce dynamics, ensuring comprehensive protection regardless of where or how employees perform their duties. This shift is prompting insurers to develop flexible, modular policies that cater to diverse occupational risks and employment arrangements. As a result, the Workers’ Compensation Insurance market is witnessing a surge in demand for innovative solutions that address the unique challenges of the modern workplace, further fueling market expansion.

Regionally, North America continues to dominate the Workers’ Compensation Insurance market, accounting for the largest share in 2024, driven by stringent regulatory frameworks, high awareness levels, and a mature insurance ecosystem. Europe follows closely, with strong growth in countries such as Germany, the United Kingdom, and France, where government mandates and corporate responsibility initiatives are propelling market demand. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid industrialization, expanding labor forces, and increasing government focus on occupational health and safety. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as regulatory reforms and economic development spur insurance adoption.

Type Analysis

The Type segment of the Workers’ Compensation Insurance market comprises Medical Benefits, Disability Benefits, Rehabilitation Benefits, and Death Benefits. Among these, Medical Benefits represent the largest share, as they cover the immediate and ongoing healthcare expenses resulting from workplace injuries or illnesses. The rising cost of medical care and the prevalence of workplace accidents have made medical cov

Long-Term Disability Insurance Market Outlook

According to our latest research, the global long-term disability insurance market size reached USD 19.7 billion in 2024, reflecting a steady and consistent expansion in demand for income protection solutions. The market is expected to grow at a CAGR of 6.1% from 2025 to 2033, with projections indicating the market will achieve a value of USD 33.6 billion by 2033. This robust growth is primarily driven by rising awareness of financial security against prolonged illnesses or injuries, evolving workforce dynamics, and the increasing prevalence of lifestyle-related health conditions.

A significant growth factor for the long-term disability insurance market is the increasing recognition among both employers and individuals of the financial vulnerabilities posed by long-term disabilities. As the global workforce becomes more diverse and includes a greater proportion of freelancers, gig workers, and self-employed professionals, the need for comprehensive income protection has intensified. Traditional government safety nets often fall short in providing sufficient coverage, prompting more people to seek private insurance solutions. Additionally, the COVID-19 pandemic has heightened awareness of unforeseen health risks, further accelerating demand for long-term disability insurance products. Insurers are responding by developing more flexible policies tailored to various employment types and health profiles, thereby broadening the market’s reach.

Another key driver is the aging global population and the associated rise in chronic health conditions that can lead to extended periods of disability. As people live and work longer, the probability of experiencing a disabling event during their working years has increased, making long-term disability insurance an essential component of financial planning. Employers, particularly in developed regions, are increasingly offering disability insurance as part of comprehensive employee benefits packages to attract and retain talent. This trend is especially pronounced in sectors with physically demanding roles, where the risk of occupational injuries is higher. Furthermore, advancements in underwriting technologies and data analytics have enabled insurers to better assess risk, streamline claims processing, and offer more competitive premiums, thereby enhancing the overall value proposition for policyholders.

The digital transformation of the insurance industry is also playing a pivotal role in the market’s growth trajectory. The proliferation of online platforms and digital distribution channels has made it easier for consumers to compare, purchase, and manage long-term disability insurance policies. Insurtech innovations, such as AI-driven risk assessments and automated claims processing, are improving customer experience and reducing operational costs for insurers. This digital shift is particularly attractive to younger, tech-savvy consumers who prioritize convenience and transparency in their financial transactions. As a result, insurers that invest in digital capabilities are well-positioned to capture a larger share of the growing market.

From a regional perspective, North America continues to dominate the long-term disability insurance market, accounting for the largest share due to high consumer awareness, strong employer-sponsored insurance programs, and a well-established regulatory framework. Europe follows closely, driven by robust social security systems that are increasingly being supplemented by private insurance. The Asia Pacific region, while still emerging, is experiencing rapid growth fueled by expanding middle-class populations, rising healthcare costs, and increasing employer adoption of disability benefits. Latin America and the Middle East & Africa are also witnessing gradual market penetration, supported by economic development and evolving insurance regulations. Each region presents unique opportunities and challenges, shaping the competitive strategies of market participants.

Coverage Type Analysis

The coverage type segment of the long-term disability insurance market is primarily categorized into individual and group policies. Individual long-term disability insurance is tailored for self-employed professionals, freelancers, and those whose employers do not offer group coverage. This segment is gaining traction as more people seek personalized solutions that address their unique financial needs

The American Community Survey 5-year Data Profile (DP03) of Selected Economic Characteristics was downloaded from the U.S. Census Bureau for state, county, place, reservation, house district, senate district and tract geographies in the state of Montana.Selected economic characteristics in this data set include: EMPLOYMENT STATUS, COMMUTING TO WORK, OCCUPATION, INDUSTRY, CLASS OF WORKER, INCOME AND BENEFITS, HEALTH INSURANCE COVERAGE, POVERTY - PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL. Source: U.S. Census Bureau, 2014-2018 American Community Survey 5-Year Estimates.Downloaded January 2020.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2017 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Beginning in 2017, selected variable categories were updated, including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables. See user note entitled "Health Insurance Table Updates" for further details..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see https://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. The 2008 data table in American FactFinder does not incorporate these edits. Therefore, the estimates that appear in these tables are not comparable to the estimates in the 2009 and later tables. Select geographies of 2008 data comparable to the 2009 and later tables are available at https://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See https://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2017 American Community Survey 1-Year Estimates

Gig Economy Insurance Market Outlook

According to our latest research, the global gig economy insurance market size reached USD 10.7 billion in 2024, with a robust compound annual growth rate (CAGR) of 16.4% observed over the past year. This market is driven by the rapid expansion of the gig workforce, the increasing need for tailored insurance solutions, and the growing digitalization of insurance services. By 2033, the gig economy insurance market is forecasted to reach USD 45.7 billion, underscoring the sector's dynamic growth trajectory and the critical role of insurance in supporting the evolving nature of work.

The primary growth factor for the gig economy insurance market is the exponential rise in the number of gig workers globally. As more individuals opt for flexible work arrangements—whether as freelancers, rideshare drivers, delivery workers, or independent contractors—the demand for customized insurance products that address their unique risks is surging. Traditional insurance offerings often fail to meet the needs of gig workers, who typically lack employer-provided benefits and face irregular income streams. Consequently, insurers are innovating to provide health, liability, income protection, and vehicle insurance policies that are adaptable, affordable, and accessible, thereby fueling market expansion.

Another significant driver is the increasing digitalization of insurance distribution channels. The proliferation of online platforms and mobile applications has revolutionized how gig workers access and purchase insurance products. These digital avenues offer enhanced transparency, streamlined policy management, and personalized recommendations, making it easier for gig workers to find suitable coverage. Additionally, partnerships between gig platforms and insurance providers are resulting in bundled insurance offerings, further simplifying the process for end-users. The integration of artificial intelligence and data analytics in underwriting and claims processing is also improving efficiency and customer experience, contributing to the marketÂ’s sustained growth.

Changing regulatory frameworks and growing awareness about the vulnerabilities faced by gig workers are also pivotal in driving the gig economy insurance market. Governments and regulatory bodies in various regions are increasingly recognizing the importance of social protection for non-traditional workers. This has led to the introduction of mandates and incentives for gig platforms to provide insurance coverage for their workforce. Moreover, as gig workers become more aware of their exposure to financial, health, and liability risks, there is a heightened willingness to invest in comprehensive insurance solutions. These factors collectively create a fertile environment for the expansion of gig economy insurance offerings worldwide.

Regionally, North America dominates the gig economy insurance market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of major gig platforms, high digital literacy rates, and favorable regulatory developments have made North America a fertile ground for innovative insurance solutions. Europe is experiencing steady growth due to increased regulatory attention and the rising popularity of gig work in urban centers. Meanwhile, the Asia Pacific region is witnessing rapid adoption driven by the expanding gig workforce and increasing smartphone penetration, indicating significant future growth potential.

The rise of the Creator Economy has introduced new dynamics into the gig economy insurance market. As more individuals turn to platforms like YouTube, TikTok, and Patreon to monetize their content, there is a growing need for specialized insurance solutions that cater to the unique risks faced by digital content creators. These creators often operate as independent contractors, lacking traditional employment benefits and facing potential income volatility. Insurers are increasingly recognizing the importance of offering Creator Economy Insurance, which provides coverage for intellectual property, liability, and income protection. This emerging segment represents a significant opportunity for insurers to expand their portfolios and address the evolving needs of the gig workforce.

Medical Oxygen Supply Interruption Insurance Market Outlook

According to our latest research, the Global Medical Oxygen Supply Interruption Insurance market size was valued at $1.2 billion in 2024 and is projected to reach $3.6 billion by 2033, expanding at a robust CAGR of 12.9% during the forecast period of 2025–2033. The primary growth driver for this market is the increasing dependency on uninterrupted medical oxygen supply, especially in critical healthcare settings such as hospitals and emergency medical services. The COVID-19 pandemic has heightened awareness about the catastrophic consequences of oxygen supply disruptions, prompting healthcare providers to seek comprehensive insurance solutions that can mitigate financial losses and ensure continuity of care. This heightened risk perception, coupled with regulatory mandates for patient safety, is propelling the adoption of medical oxygen supply interruption insurance globally.

Regional Outlook

North America currently holds the largest share of the global Medical Oxygen Supply Interruption Insurance market, commanding approximately 38% of the total market value in 2024. The region’s dominance is underpinned by its mature healthcare infrastructure, advanced insurance ecosystem, and stringent regulatory requirements for patient safety and business continuity in medical facilities. The United States, in particular, boasts a well-established network of healthcare providers and insurance companies that offer specialized policies tailored to the unique risks associated with medical oxygen supply interruptions. Moreover, frequent investments in healthcare technology and risk management solutions have enabled North American providers to mitigate operational risks effectively, driving higher adoption rates of these insurance products. The presence of major market players and a strong culture of insurance awareness further reinforce the region's leading position.

The Asia Pacific region is projected to be the fastest-growing market, with a forecasted CAGR of 15.2% from 2025 to 2033. This dynamic growth is primarily fueled by rapid healthcare infrastructure expansion, increasing investments in hospital and clinic networks, and growing awareness of risk management in emerging economies such as China, India, and Southeast Asia. The pandemic experience has accelerated the adoption of insurance solutions to safeguard against supply chain vulnerabilities and prevent service disruptions in critical care settings. Additionally, government initiatives aimed at improving healthcare quality and resilience are encouraging hospitals and clinics to invest in business interruption and supply chain insurance. The influx of international insurance providers, coupled with the digital transformation of the insurance sector, is making coverage more accessible and affordable, further stimulating market growth in the region.

Emerging economies in Latin America, the Middle East, and Africa are experiencing gradual but steady adoption of medical oxygen supply interruption insurance. While these regions collectively represent a smaller market share—estimated at around 16% in 2024—their growth potential is significant due to increasing healthcare investments and ongoing policy reforms aimed at enhancing patient safety. However, challenges such as limited insurance penetration, fragmented healthcare systems, and varying regulatory standards impede rapid adoption. Localized demand is often driven by large urban hospitals and private healthcare providers seeking to align with international best practices. As governments and industry stakeholders work to improve healthcare infrastructure and insurance literacy, these regions are expected to witness an uptick in demand, albeit at a more measured pace compared to developed markets.

Report Scope

The number of people claiming key benefits (JSA, IB, SDA, IS) as a percentage of the population of working age. Please note that although people can claim more than one benefit, these data have been modified to count each person only once, therefore they are a measure of the number of people claiming key benefits. This indicator measures the number of people who are claiming any of the following benefits: Job Seekers Allowance (JSA), Incapacity Benefit (IB), Severe Disablement Allowance (SDA) or Income Support (IS). Benefit claims are often used as a proxy measure to identify those on the lowest incomes, as reliable measures of income are not available in this country. The level of income has been found to be correlated with a wide range of health indicators and is therefore an important determinant of health. Improvements in income result in improvements in living standards and are clearly associated with improvements in health. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01085