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.

Graph and download economic data for Expenditures: Health Insurance by Type of Area: Urban (CXUHLTHINSRLB1802M) from 1984 to 2020 about health, insurance, expenditures, urban, and USA.

Key Table Information.Table Title.Private Health Insurance Coverage by Type and Selected Characteristics.Table ID.ACSST1Y2024.S2703.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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.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.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, count...

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and 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 population, 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 2015 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..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see http://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 http://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 http://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, 2015 American Community Survey 1-Year Estimates

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, 2018 American Community Survey 1-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. 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..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..While the 2018 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..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:..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, or the margin of error associated with a median was larger than the median itself..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....

United States - Personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type price index) was 122.72100 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type price index) reached a record high of 122.72100 in January of 2024 and a record low of 2.75900 in January of 1959. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type price index) - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Expenditures: Health Insurance by Type of Area: Rural (CXUHLTHINSRLB1805M) from 1984 to 2020 about rural, health, insurance, expenditures, and USA.

United States - Real personal consumption expenditures: Net health insurance: Income loss (chain-type quantity index) was 132.91300 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Real personal consumption expenditures: Net health insurance: Income loss (chain-type quantity index) reached a record high of 138.77000 in January of 2022 and a record low of 28.35700 in January of 1959. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real personal consumption expenditures: Net health insurance: Income loss (chain-type quantity index) - last updated from the United States Federal Reserve on November of 2025.

United States - Real personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type quantity index) was 105.55500 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Real personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type quantity index) reached a record high of 111.30400 in January of 2018 and a record low of 14.35600 in January of 1959. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real personal consumption expenditures: Net health insurance: Medical care and hospitalization (chain-type quantity index) - last updated from the United States Federal Reserve on November of 2025.

In 2023, family coverage insurance for fully insured employees cost on average ****** U.S. dollars, whereas employees who funded their own health insurance paid ****** U.S. dollars. Both these figures have increased every year since 2000, with the values being ***** and ***** U.S. dollars respectively in 2000.

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..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..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.

Adjusted Income and Poverty in the US

Changes in People and Health Insurance

By U.S. Census Bureau [source]

About this dataset

The U.S. Bureau of the Census' Current Population Survey, Annual Social and Economic Supplements, presents an insightful look into American society at any given time period. Through it's annual data, one can understand the makeup of a nation across a multitude of parameters--including income level distribution measures, poverty status characteristics and health insurance coverage broken down by age, race/ethnicity and gender.

This chart series is like a snapshot into America's past--allowing us to monitor both current progress made in regards to economic stability while also reflecting on the growth (or lack thereof) achieved over different decades in terms of racial discrepancies in poverty levels as well as an individual's ability present etc to maintain financial health. The series looks at data collected from 1959-2015; providing information on number/percentage all noninstitutionalized population (15+ years old) who are below or above poverty thresholds as well as median earnings for male/female earners adjusted for real inflation values (based on current dollars). Insights such as these enable us to gain key information about how economic disparities have fared during each year throughout this half century time span and how policy changes have impacted the overall wellbeing on a national level since then

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How to use the dataset

Introduction

This dataset contains information on the equivalence-adjusted income and poverty in the US from 1967 to 2015. It includes information on the population without health insurance coverage by state, total workers and full-time, year-round workers by sex and female-to-male earnings ratio, selected measures of equivalence-adjusted income dispersion, people in poverty by selected characteristics, and measures of income inequality. This guide will explain how to use this dataset effectively for analysis.

Data Overview

The datasets contain both summary statistics and detailed breakdowns for different categories throughout the years 1967 to 2015. In Table A1 you can find data on population without health insurance coverage by state during that time period. Table A4 contains total numbers of workers as well as real median earning details organized by sex and male/female earning ratios over time period in question. The tables A3 through 5 include more specific details related to measurements of Equivalence Adjusted Income Dispersion such as Gini Coefficient values.. Both table 2 & 3 provides detail breakdowns relating to Income distribution measurements between 2014 & 2015 along with other related statistical figures regarding individuals below poverty line during this time period based upon age , race , Hispanic Origin factors.

Data Cleaning/Preparation Specifics

This dataset follows a similar notation used throughout each table so it shouldn't be difficult understand what is being represented .However representing individual components like Gini Coefficient (TableA3) or Female ratio Vs Male earnings remains abstract in comparison especially when attempting visualization techniques (Charting). In order for users not familiar with certain terms like “Equivalence -Adjusted Income Dispersion” it would need explaining thoroughly or these terms should at least be highlighted & avoid confusing readers . Level out Missing Data that is within range statistically makes sense according “Census Technical Docs” . For example missing value data pertaining Individual Poverty estimates have based upon qualification requirements where numbers are rounded up after exchange calculations ( See official Raw Data column Notes available under Sources ).

Visualization Strategies

For effective visualization there needs be understanding between what counts supplied are actually representing For example: Column such as Difference Between Female & Male Earnings shown TableA4 helps gauge pay gap but difference between % Measures significantly important when charting any changes overtime diagrams or identifying movements visually from various bar /line graphs dealing this type data set . Other numerical aspects such Gender Ratio

Research Ideas

• Tracking changes in poverty levels over time by state and ethnicity
• Examining the impact of government programs like the EITC and CTC on pov...

United States - Real personal consumption expenditures: Services: Net health insurance (chain-type quantity index) was 108.32000 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Real personal consumption expenditures: Services: Net health insurance (chain-type quantity index) reached a record high of 111.31000 in January of 2018 and a record low of 1.66800 in January of 1933. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Real personal consumption expenditures: Services: Net health insurance (chain-type quantity index) - last updated from the United States Federal Reserve on November of 2025.

Graph and download economic data for Personal consumption expenditures: Services: Net health insurance (chain-type price index) (DHINRG3A086NBEA) from 1929 to 2024 about chained, health, insurance, PCE, Net, consumption expenditures, consumption, personal, services, GDP, price index, indexes, price, and USA.

Identity Graph Vendor Liability Insurance Market Outlook

According to our latest research, the global Identity Graph Vendor Liability Insurance market size reached USD 1.12 billion in 2024, demonstrating robust expansion driven by the increasing complexities of data privacy and heightened risk management needs across digital ecosystems. The market is expected to advance at a CAGR of 11.5% from 2025 to 2033, reaching a projected value of USD 3.08 billion by 2033. This growth is primarily fueled by the rising adoption of identity graph solutions, evolving regulatory landscapes, and the escalating frequency of cyber threats impacting vendors and their clients.

The primary growth factor for the Identity Graph Vendor Liability Insurance market is the intensifying regulatory environment concerning data privacy and consumer protection. As global data privacy laws such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and similar regulations in Asia Pacific and Latin America become more stringent, identity graph vendors are increasingly exposed to legal liabilities. These regulations require vendors to ensure robust data management practices and transparency, making liability insurance not only a risk mitigation tool but also a business necessity. The heightened risk of non-compliance, coupled with the potential for significant financial penalties, is compelling vendors and their partners to seek comprehensive insurance coverage tailored to their unique operational risks.

Another significant growth driver is the proliferation of digital marketing and customer data platforms that rely on identity graphs for cross-device and omnichannel consumer insights. As organizations across sectors such as retail, financial services, healthcare, and media intensify their digital transformation efforts, the demand for sophisticated identity graph solutions has surged. This expansion, however, exposes vendors to new liabilities, including data breaches, misattribution, and errors in identity resolution. Liability insurance tailored for identity graph vendors has thus become a critical safeguard, enabling these companies to operate confidently while fostering trust with enterprise clients and consumers. The insurance market is responding with innovative products that address emerging risks, further accelerating adoption.

The growing sophistication and frequency of cyberattacks targeting data aggregators and identity graph providers is also catalyzing market growth. As cybercriminals employ advanced tactics to exploit vulnerabilities in identity resolution processes, the financial and reputational risks for vendors have escalated. Insurers are developing specialized cyber liability products that address the unique exposures of identity graph vendors, including coverage for regulatory investigations, breach remediation, and third-party claims. This evolving threat landscape is prompting even smaller vendors and startups to prioritize liability insurance as a core component of their risk management strategies, thereby broadening the market base.

From a regional perspective, North America continues to dominate the Identity Graph Vendor Liability Insurance market, accounting for over 40% of global revenue in 2024, followed by Europe and Asia Pacific. The United States, in particular, benefits from a mature insurance sector, advanced digital infrastructure, and a dynamic regulatory environment. Meanwhile, Asia Pacific is emerging as the fastest-growing region, with a CAGR of 14.2% projected through 2033, fueled by rapid digitalization, expanding e-commerce, and rising awareness of data privacy risks. Europe also maintains a strong presence, driven by stringent data protection regulations and robust demand from the financial and healthcare sectors.

Coverage Type Analysis

The Coverage Type segment in the Identity Graph Vendor Liability Insurance market is highly diversified, reflecting the complex risk landscape faced by identity gr

This graph shows the breakdown by type of health insurance expenditure for the care of Parkinson's disease in France in 2015. In the year in question, more than *** million euros were allocated to hospital expenses.

United States - Personal consumption expenditures: Services: Net health insurance (chain-type price index) was 115.68600 Index 2009=100 in January of 2024, according to the United States Federal Reserve. Historically, United States - Personal consumption expenditures: Services: Net health insurance (chain-type price index) reached a record high of 115.68600 in January of 2024 and a record low of 2.69400 in January of 1932. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Personal consumption expenditures: Services: Net health insurance (chain-type price index) - last updated from the United States Federal Reserve on November of 2025.

This healthcare dataset, covering 55,500 patient records from 2019 to 2024, provides insights into patient demographics, medical conditions, hospital admissions, and billing trends. The average patient age is 51.54 years, with an equal gender distribution and O+ as the most common blood type. Obesity, Cancer, and Arthritis are the most frequent diagnoses, with Diabetes having the highest total billing amount. Emergency admissions are the most common, and the average billing amount is $25,539.32, ranging from - $2,008.49 (possible data error) to $52,764.28. Visualizations include histograms, pie charts, bar graphs, bubble charts, and treemaps, highlighting trends in admission types, medical conditions, and costs. The data can be used for predictive healthcare analytics, hospital resource planning, insurance cost analysis, and public health insights.

Identity Graph Vendor Liability Insurance Market Outlook

According to our latest research, the global Identity Graph Vendor Liability Insurance market size reached USD 1.14 billion in 2024, and it is expected to grow at a CAGR of 18.2% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 5.45 billion. This remarkable growth trajectory is primarily driven by the escalating demand for robust data privacy and security frameworks, the proliferation of identity graph technologies across industries, and the increasing complexity of regulatory compliance requirements worldwide. As businesses increasingly rely on identity graph solutions to unify customer data, the necessity for comprehensive liability insurance coverage has become more pronounced, fueling the expansion of this specialized market segment.

A key growth factor for the Identity Graph Vendor Liability Insurance market is the exponential rise in data-driven marketing and analytics initiatives across sectors such as retail, e-commerce, finance, and healthcare. Organizations are leveraging identity graph solutions to create unified customer profiles, enhance personalization, and drive targeted engagement. However, this increased reliance on sensitive data aggregation and processing introduces significant exposure to privacy breaches, data misuse, and regulatory violations. As a result, vendors and their clients are seeking advanced liability insurance policies tailored to cover risks unique to identity graph operations, including cyber liability, professional liability, and product liability. The surge in high-profile data breaches and stringent enforcement of data protection regulations such as GDPR, CCPA, and others further underscores the critical need for specialized insurance products, driving market growth.

Another significant driver is the rapid evolution of regulatory landscapes globally. Governments and regulatory bodies are continuously updating and tightening data protection laws to address emerging threats and safeguard consumer privacy. This dynamic environment compels identity graph vendors to not only invest in advanced security measures but also secure comprehensive liability coverage to mitigate the financial and reputational risks associated with non-compliance. Insurance providers are responding with innovative solutions that address the nuances of identity graph technologies, offering coverage for legal defense costs, regulatory fines, and third-party claims. The growing awareness among enterprises regarding the potential liabilities associated with identity graph deployment is accelerating the adoption of such insurance policies, fostering robust market expansion.

Technological advancements and the increasing sophistication of cyber threats are also propelling the demand for Identity Graph Vendor Liability Insurance. As identity graphs become more integral to digital transformation strategies, the attack surface for cybercriminals expands, heightening the risk of data breaches, identity theft, and unauthorized access. Insurance providers are developing specialized products that address these emerging risks, including coverage for ransomware attacks, system downtime, and business interruption. The integration of artificial intelligence and machine learning in identity graph solutions further complicates risk profiles, necessitating adaptive insurance offerings. This convergence of technology and insurance is creating new growth opportunities for both vendors and insurers, reinforcing the upward trajectory of the market.

From a regional perspective, North America currently dominates the Identity Graph Vendor Liability Insurance market, accounting for the largest share due to the presence of leading technology companies, a highly developed insurance sector, and a mature regulatory environment. However, Europe and the Asia Pacific regions are witnessing accelerated growth, driven by increasing digitalization, stringent data protection laws, and rising awareness of cyber risks among enterprises. Latin America and the Middle East & Africa are also emerging as promising markets, supported by expanding digital economies and regulatory reforms. The global nature of data flows and the interconnectedness of digital ecosystems ensure that demand for identity graph liability insurance will continue to rise across all major regions in the coming years.

Coverage Type Analysis

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In the United States, average employee premium contributions and deductibles as a percentage of median household income have risen in the past decade. In 2020, an employee’s total potential out-of-pocket medical costs (premium and deductible) amounted to 11.6 percent of median income. This included 6.9 percent in employee premium contributions and 4.7 percent in deductibles. However, states varied greatly in median income spent on premiums and deductibles, with workers in Mississippi having to spend on average 19 percent of their income on potential out-of-pocket medical costs.

Employer sponsored health insurance In 2020, over half of the U.S. population has some type of employment-based health insurance coverage. The Affordable Care Act penalizes large employers (with 50 or more full-time employees), if they do not provide health insurance to their employees. Nevertheless, of the uninsured aged under 65 years, the large majority worked either full or part-time (or someone in their household did).

Out-of-pocket medical costs Despite having insurance coverage, most plans have a deductible, the amount an insured must pay themselves that year before their insurance starts covering for them. The average annual deductible for single coverage amounted to roughly 1,700 U.S. dollars in 2021. Even after reaching their deductible, most insured have other forms of out-of-pocket health costs in the form of co-payments and co-insurance for health services or prescription drugs.