In 2023, the employment rate of women with one or more disability in the United States stood at 20.5 percent. This was significantly lower than the rate of men with one or more disability, which was 24.8 the same year.

The disability employment gap is defined as the difference between the employment rates of people with no and those with some or severe limitation in their daily activities, aged 20-64. The employment rate is calculated by dividing the number of persons aged 20 to 64 in employment by the total population of the same age group. Source: EU Statistics on Income and Living Conditions (EU-SILC).

Labour market status of disabled people, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

Difference in employment rate between gender, ethnic groups and disability status. The following employment gaps are covered:

• Male - Female gap
• Non-disabled - Disabled gap (also split by gender)
• White - All ethnic minority gap (also split by gender)
• White - Mixed ethnicity gap (also split by gender)
• White - Indian gap (also split by gender)
• White - Pakistani/Bangladeshi gap (also split by gender)
• White - Black/Black British gap (also split by gender)
• White - Other ethnicities gap (also split by gender)

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

The disability employment gap is defined as the difference between the employment rates of people with no and those with some or severe limitation in their daily activities, aged 20-64. The employment rate is calculated by dividing the number of persons aged 20 to 64 in employment by the total population of the same age group. Source: EU Statistics on Income and Living Conditions (EU-SILC).

Difference in employment rate between gender, ethnic groups and disability status. The following employment gaps are covered: Male - Female gap Non-disabled - Disabled gap (also split by gender) White - All ethnic minority gap (also split by gender) White - Mixed ethnicity gap (also split by gender) White - Indian gap (also split by gender) White - Pakistani/Bangladeshi gap (also split by gender) White - Black/Black British gap (also split by gender) White - Other ethnicities gap (also split by gender) This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

This dataset contains disability pay gap estimates for all employees in London and the UK.

The disability pay gap is the difference in the average hourly wage of non-disabled employees and disabled employees across a workforce. If disabled employees do more of the less well paid jobs within an organisation than the non-disabled persons, the disablity pay gap is usually bigger.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

The Handicapped Placement Service market exhibits robust growth potential, driven by increasing awareness of disability inclusion, supportive government regulations, and a growing demand for skilled labor across various sectors. While precise market size figures for 2025 are unavailable, based on industry reports showing similar service markets reaching billions in value and considering a plausible CAGR (let's assume 7% for illustrative purposes), we can estimate the 2025 market size for Handicapped Placement Services at approximately $2.5 billion. This growth is projected to continue throughout the forecast period (2025-2033), with the 7% CAGR suggesting a significant expansion of the market. Key drivers include the rising prevalence of disabilities, corporate social responsibility initiatives prioritizing diverse workforces, and technological advancements enabling more effective job matching and accessibility solutions. Trends like remote work opportunities and flexible employment models are also contributing factors. However, challenges remain, including persistent biases against hiring individuals with disabilities, skill gaps in certain disability groups, and a lack of awareness among potential employers regarding the value and capabilities of this workforce. The market segmentation involves various service providers, including non-profit organizations (like Goodwill Industries and Morgan Memorial Goodwill), specialized employment agencies (Proact, employU, CVGT Employment), and government initiatives (like NC DHHS). Competition is expected to intensify as more players enter the market to cater to the growing demand. While precise regional breakdowns are absent, a reasonable assumption would suggest significant market presence in North America and Europe, with developing economies witnessing increasing participation over time. The continued success of the Handicapped Placement Service market will depend on addressing the challenges mentioned above and fostering a collaborative environment involving government agencies, businesses, and disability advocacy groups. This collaborative approach will be crucial in maximizing the positive impact of the sector and facilitating broader societal inclusion.

This report investigates how seven member countries (Bangladesh, Cambodia, Lao PDR, Mongolia, the Philippines, Sri Lanka, and Türkiye) are addressing disability-inclusive employment. Through policy analysis and interviews with key stakeholders, the study highlights gaps in legal implementation, societal barriers, and opportunities for reform. While most countries have established national policies aligned with the UN Convention on the Rights of Persons with Disabilities, enforcement challenges, and social stigma persist. The report provides actionable recommendations for improving access to employment, enhancing vocational training, strengthening public-private collaboration, and ensuring that persons with disabilities can fully participate in economic life.

Introduction: Persons with mental illness experience social life restriction and stigma that may have implications for their work ability. The aims of this study are (i) to report experienced and anticipated discrimination and social functioning in persons with mental disabilities in Kenya and (ii) to investigate the association between experienced and anticipated discrimination, social functioning, and employment in this population.Materials and Methods: Cross-sectional study design where we randomly recruited 72 persons with mental illness through two networks of persons with psychosocial disabilities in Kenya. Experienced and anticipated discrimination were measured using the Discrimination and Stigma Scale version 12 (DISC 12) while social functioning was measured using the Social Functioning questionnaire (SFQ).Results: Experienced discrimination was reported by 81.9% in making or keeping friends, 69.7 and 56.3% in finding or keeping job, respectively, and 63.3% in dating or having an intimate relationship. Anticipated discrimination stopped 59.2% from applying for work, 40.8% from applying for education or training courses, and 63.4% from having a close personal relationship. Females reported an overall higher experienced discrimination than males. Unemployed participants had slightly increased rates of experienced and anticipated discrimination (9.5 vs. 9.1 and 2.5 vs. 2.3, respectively) (p > 0.05), while there was a significant association between impaired social functioning and unemployment [14.0 vs. 11.2 (p = 0.037)].Conclusion: The rates of experienced and anticipated discrimination faced by persons with mental disabilities in Kenya is high and with significant gender disparity. Although no strong associations were observed between experienced and anticipated discrimination and employment, impaired social functioning of persons with mental disabilities seems to have implications for employment. Further research is essential to understand the predictors of the discrimination and measures to reduce them in persons with psychosocial disabilities.

In 2022, it was estimated that around 26 percent of people in the United States living with a disability were in poverty. In comparison, the poverty rate among people in the U.S. without a disability was 11.5 percent. A disability is any physical or mental condition that significantly impacts a person's ability to carry out daily tasks or life activities.

How many people in the United States are disabled? In 2022, around 14 percent of people in the United States were thought to be living with a disability. Types of disabilities include those that affect hearing, cognition, self-care, mobility, and vision. The most common type of disability in the United States is ambulatory disabilities, which impair a person’s ability to walk. In 2021, almost 30 percent of those aged 75 years and older in the U.S. had an ambulatory disability. However, disabilities are far less common among younger people, with less than five percent of those aged 21 to 64 suffering from an ambulatory disability.

Employment among the disabled The most obvious reason why the poverty rate among those with a disability is higher than those without a disability is because disabilities affect a person’s ability to work and be employed. In 2022, the employment rate for those with a disability was 44.5 percent, compared to an employment rate of 79 percent among those without a disability. Those with hearing disabilities are the most likely to be employed, with a rate of around 56 percent, compared to an employment rate of 27 percent among those with an ambulatory disability. Still, those with disabilities who do work have lower annual median earnings than those without disabilities. In 2022, the annual median earnings for U.S. adults without a disability was 55,208 U.S. dollars, compared to 46,887 U.S. dollars for those with a disability.

The Women, Minorities, and Persons with Disabilities in Science and Engineering dataset is an annual statistical report produced by the National Center for Science and Engineering Statistics (NCSES) under the U.S. National Science Foundation (NSF). It tracks the participation of these groups in science, technology, engineering, and mathematics (STEM) education and employment. The dataset includes data on enrollment in STEM fields, degree attainment, employment status, occupational distribution, and academic career progression, providing insights into demographic representation and trends over time. Its primary purpose is to serve as a policy-neutral statistical resource to inform discussions on diversity and inclusion in STEM. Key features include granular breakdowns by gender, race/ethnicity, disability status, and geographic region, enabling analysis of disparities and progress. The dataset draws from surveys like the National Science and Engineering Talent Pool and integrates data from federal agencies such as the U.S. Census Bureau. It is widely used by researchers, policymakers, and educators to assess workforce diversity, design equity initiatives, and monitor compliance with federal mandates. The report is publicly accessible via the NSF’s National Center for Science and Engineering Statistics (NCSES) and the U.S. government’s data portal. Unique aspects include its longitudinal scope and alignment with national goals like the NSF’s Broader Impacts criterion, which emphasizes inclusivity in STEM. (Word count: 198)

This statistic displays the number of disability pension recipients in Finland from 2008 to 2018, by gender. During the period under survey, the number of pension recipients has steadily decreased and the gap between genders has become smaller. In 2008, approximately 139 thousand men and 124 thousand women received disability pension, whereas in 2018, the figures were roughly 101 thousand and 98 thousand, respectively.

Research has shed light on the employment barriers faced by individuals with disabilities, and by racialized people. The challenges faced by people belonging to both marginalized groups are less well-understood. The purpose of this scoping review was to examine existing research on labour market and workplace experiences of racialized people with disabilities, and to identify how ableism and racism intersect to shape employment experiences and outcomes. Seven international databases were searched, covering the period from 2000 to April 2022. Four reviewers independently conducted the screening, and data extraction and analysis were performed on 44 articles that met our inclusion criteria. The findings highlighted rates of workplace ableism and racism (including discrimination allegations and perceived discrimination); types and forms of experiences arising from the intersection of ableism and racism (including unique individual stereotyping and systemic and institutional discrimination); and the role of other demographic variables. The intersection of ableism and racism impacted labour market outcomes, well-being in the workplace, and career/professional advancement. Our review highlights the need for greater in-depth research focusing explicitly on the intersection of ableism and racism (and of other forms of discrimination), to better understand and address the barriers that racialized people with disabilities face in employment.IMPLICATIONS FOR REHABILITATIONThe experiences of racialized people with disabilities have been under explored, and clinicians and rehabilitation specialists should consider incorporating intersectionality into their practices to better understand and serve these populations.Ableism and racism do not operate in isolation, and clinicians and other professionals need to be aware that racialized people with disabilities may face unique challenges and barriers as a result.Service providers should aim to address gaps and inequities in services faced by racialized people with disabilities which may prevent them from finding and/or maintaining meaningful employment. The experiences of racialized people with disabilities have been under explored, and clinicians and rehabilitation specialists should consider incorporating intersectionality into their practices to better understand and serve these populations. Ableism and racism do not operate in isolation, and clinicians and other professionals need to be aware that racialized people with disabilities may face unique challenges and barriers as a result. Service providers should aim to address gaps and inequities in services faced by racialized people with disabilities which may prevent them from finding and/or maintaining meaningful employment.

In today’s interconnected world, internet access is often considered critical for accessing work. However, reliance on the internet also poses challenges for some disabled people, who may be vulnerable to digital exclusion. This innovative project delivered by an interprofessional team of social scientists, voluntary sector organisations, vocational rehabilitation practitioners, disability researchers, disabled people, and digital engagement specialists examined digital inclusion of disabled adults in voluntary work.

The project aimed to: (1) advance understanding of how digital inclusion of disabled individuals fosters social inclusion in online and offline voluntary work; and (2) identify effective principles to boost participation, inclusivity, and leverage the potential of digital technologies in the voluntary sector organisations.

A mixed-methods approach is adopted for a more comprehensive understanding of the research problem. This included qualitative interviews and analysis that will provide deeper understanding and explanations for these patterns through analysis of the lived experiences of disabled adults and survey data analysis.

The key findings included: • Link Between Digital Inclusion and Volunteering: For disabled adults, being included in online and offline volunteering depends on how well they can access and use digital technology in general. The same devices, technologies, social support, and skills used in employment and other areas of life are often applied to volunteering. Because disabled adults often have less access to devices and the internet and use them less than non-disabled adults, they are more likely to miss out on volunteering opportunities that require it. • Digital Access and Divide: Most disabled adults in the UK are connected digitally, but the digital divide persists. Disabled adults face greater digital challenges and exclusion compared to non-disabled adults, with the disability digital gap remaining largely unchanged since 2018. • Digital exclusion: Nearly one million disabled adults don’t have Internet at home, 1.4 million don’t use the Internet, and about two million don’t own a smartphone or computer. • Frequency of Use and Online Exclusion: Disabled adults use the Internet less often than non-disabled adults and they are more likely to be left out of common online activities like browsing, emailing, social media, online banking, and streaming videos. • Double Disadvantage and Additional Challenges: Disabled people often come from groups that with already more limited access to the Internet and devices, such as older adults, those with low incomes, benefit recipients, renters from local authorities, people with less education and those living alone. On top of these existing barriers, being disabled means facing even more difficulties in accessing and using digital technology. • Individual Nature of Disability Experience with Digital Tools: Disabled adults’ experiences with digital tools are highly individual, shaped by factors such as impairment type, severity, presence of multiple impairments, and their social context. • Double-Edged Sword of Digital Technology and Online Volunteering: Digital technology is crucial for inclusion but can also be source of exclusion for those struggling with technology. Disabled adults are more inclined to engage in online volunteering, because of its accessibility. However, online volunteering also presents challenges that may deter some disabled volunteers. Despite some digital barriers, disabled adults show higher interest in online volunteering compared to non-disabled adults. • Importance of Internet Access for Engaging in Volunteering: Internet access and usage are crucial for enabling disabled individuals to engage in volunteering, including both online and in-person opportunities. Those who use the Internet more frequently are more likely to volunteer, even after considering their socioeconomic background. • Internet Access, Use, Devices and Volunteering Hours and Frequency: For disabled individuals, owning devices does not affect the number of volunteer hours. More frequent Internet use is linked to volunteering more hours, with exception of frequent Internet users (daily or weekly) who volunteer fewer hours. Device ownership and Internet use do not impact the frequency of volunteering. • Digital Barriers and Enablers of Volunteering: o Technology over-reliance: Over-reliance on technology can complicate volunteer journeys, especially when IT systems or support is unavailable or inadequate. o Technological assumptions: Organisations often assume people understand how to use technology and devices and may not recognise the need for support or training. o Variation in suitability of assistive devices: The effectiveness of assistive devices can facilitate or hinder participation, depending on their suitability and the level of support provided. Malfunctioning or unsuitable assistive devices can exacerbate challenges for disabled individuals. Disabled adults can experience a mismatched between requirements and devices available or provided that can lead to exclusion form volunteering opportunities. o Reliance of support networks: To be effective, use of assistive devices draws on the skills and experience of informal support networks. Organisations should remain attuned to prioritising supporting people rather than supporting technology. o Organisational culture: Organisations should encourage a culture of listening and providing empathetic support to address the specific needs of disabled volunteers and reduce digital barriers to volunteering. Support structures should be inclusive and allow volunteers to discuss their needs, ensuring parity with paid staff support. • Volunteering and Employment: Our findings suggest that, rather than volunteering serving as a direct path to paid work, employment helps disabled individuals overcome digital barriers to volunteering. The confidence, skills and resources acquired through paid employment, as well as the social capital that often comes from being part of being a part of more diverse networks that includes work colleagues as well as more personal relationships, means that those in employment might be better able to navigate some of the barriers to securing volunteering roles. Such resources are less easily initiated in volunteer roles - we heard frustrations that the Access to Work scheme, which supports paid employment, does not cover voluntary work. • Discrimination and Volunteering. Disabled people continue to experience indirect and direct discrimination. For some, the lack of appropriate devices, limited training, and organisational cultures that make people reluctant to seek support all imply a level of discrimination that makes it difficult for those in volunteering roles to always excel. Such discrimination was not always so indirect. There is evidence to indicate that those who have been in successful volunteering positions for some time suggested they are being denied employment opportunities because they are disabled.

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Disability, contains cross-sectional data from the year 2000. Based on the Decennial Census Special Table Series published by the Administration on Aging, this study contains a large number of disability measures categorized by age (55+), type of disability (sensory, learning, employment, and self-care), and poverty status.

The Labour Market Indicators spreadsheet for boroughs and regions will no longer be updated from March 2015. The final version from March 2015 will still be available to download at the bottom of this page. Most of the data is available within datasets elsewhere on the Datastore.

Workforce Jobs
Unemployment
Model based Unemployment for Boroughs
Claimant Count rates for Boroughs and Wards
Employment Rate Trends
Employment rates by Gender, Age and Disability
Number of Self Employed, Full and Part Time Employed
Employment by Occupation
Employment by Industry
Employment, Unemployment, Economic Activity and Inactivity Rates by Disability
Employment by Ethnicity
Economic Inactivity by Gender and Reason
Qualifications of Economically Active, Employed and Unemployed
Qualification levels of working-age population
Apprenticeship Starts and Achievements
Young People Not in Employment, Education or Training (NEET), Borough
19 year olds Qualified to NVQ Level 3
GCE A level examination results of 16-18 year olds
GCSE Results by Pupil Characteristics
People Claiming Out-of-Work Benefits
People Claiming Incapacity Benefit
Children Living in Workless Households
Gross Value Added, and Gross Disposable Household Income
Earnings by place of residence
Earnings by place of work
Business Demographics
Employment projections by sector
Jobs Density
Population Estimates
Population Migration

Core Indicators

Number of London residents of working age in employment
Employment rate
Number of male London residents of working age in employment
Male employment rate
Number of female London residents of working age in employment
Female employment rate
Workforce jobs
Jobs density
Number of London residents of working age who are economically inactive
Economic inactivity rate
Number of London residents aged 16+ who are unemployed (model based)
Proportion of London residents aged 16+ who are unemployed (model based)
Claimant unemployment
Claimant Count as a proportion of the working age population
Incidence of skill gaps (Numbers and rates)
GCSE (5+ A*–C) attainment including English and Maths
Number of working age people in London with no qualifications
Proportion of working age people in London with no qualifications
Number of working age people in London with Level 4+ qualifications
Proportion of working age people in London with Level 4+ qualifications
Number of people of working age claiming out of work benefits
Proportion of the working age population who claim out of work benefits
Number of young people aged 16-18 who are not in

2011–2023. The tobacco disparities dashboard data utilized the Behavioral Risk Factor Surveillance System (BRFSS) data to measure cigarette smoking disparities by age, disability, education, employment, income, mental health status, race and ethnicity, sex, and urban-rural status. The disparity value is the relative difference in the cigarette smoking prevalence among adults 18 and older in a focus group divided by the cigarette smoking prevalence among adults 18 and older in a reference group. A disparity value above 1 indicates that adults in the focus group smoke cigarettes at a higher rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value below 1 indicates that adults in the focus group smoke cigarettes at a lower rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value of 1 means there is no relative difference in the rate of adults who smoke cigarettes for the two groups compared.