Introduction:

Disability impacts individuals in varied ways and often overlaps with critical demographic factors such as gender and geographic location. This research seeks to investigate the complex interactions between disability, gender, and geographic distinctions using data from the 2018 American Community Survey (ACS). The aim is to pinpoint the unique challenges that people face at these identity intersections to enhance support services and policy initiatives. This study will provide an in-depth analysis of the disparities and barriers encountered, emphasizing the importance of inclusive practices that consider the cumulative effects of multiple identity elements. Ultimately, this research aims to help build a fairer society where everyone, regardless of disability status or gender, has the support needed to succeed.

Research Questions:

1. How does the prevalence of self-care disabilities differ between individuals of various sexes residing in New York City compared to those living outside of New York City within the state?
2. How do education and income outcomes differ for individuals with self-care disabilities across various sex groups and between New York City residents and those living outside of New York City within the state?

Research Audiences:

This research is important for many people who are working to improve the lives of individuals with disabilities. It provides useful information for policymakers and government officials to create detailed and effective policies that consider disability, gender, and race together. Health service providers, social workers, and support staff will find the data on how people are doing in different areas of life very helpful for making programs that meet people’s real needs. Academics and researchers can use this data to deepen their understanding of society and how people are grouped. Advocacy groups can use clear and powerful charts and stories from the data to push for fair access to resources. The general public, especially those affected by disability, will see a clear picture of the challenges people face through these visualizations.

Data:

I sourced the data from the Integrated Public Use Microdata Series (IPUMS), which provides samples of anonymized data from the U.S. Census Bureau’s American Community Survey (ACS). Specifically, I used the 5-Year dataset for 2018 in New York State, which compiles data collected over a five-year period to improve statistical reliability for smaller geographic areas and demographic groups.

The dataset can be accessed at IPUMS USA, where researchers can select, and extract data based on their specific requirements. The data is available in a micro-data format, where each row represents an individual or household, depending on unit of analysis, with variables as columns.

Variables Description:

• Statefip: State Federal Information Processing Standards code, useful for geographic specific analyses.
• Sex: sex of the respondent
• Age: Age of the respondent.
• Educ, Educational Attainment
• Vetdisab (VA Service-Connected Disability Rating): Indicates if the respondent has a VA-rated service-connected disability.
• Diffcare: Self-Care Difficulty
• inctot = total personal income

Cleaning and Recoding the Data:

My analysis began with the necessary data cleaning and descriptive analysis to ensure that the variables were suitable for addressing my research questions. One of the initial challenges I encountered after obtaining the data from IPUMS was the need to label the variables I used in my analysis. For instance, I labeled variables such as SEX, Self-care Disability, and Level of Disability. I also recorded

some variables, such as education, reducing it from seven categories to four, which I found more useful. These categories included: No High School, Some High School, Some College, and College Graduate and Above. Additionally, I removed negative values for income, as they were not logical for my analysis. To address outliers in income data, specifically those earning more than $500K, I applied top coding methods, capping the upper income value at $500K. Furthermore, as geographic data was unavailable for all counties across New York State and was only available for about 15 counties, I created a categorical variable that included the five boroughs in the city and labeled the rest as outside the city.

Analysis:

The map illustrates average income data across the five boroughs of New York City, with each borough color-coded to indicate varying income levels among residents with self-care disabilities. Staten Island, represented in dark green, has the highest average income, amounting to $25,434. Manhattan, Queens, and Brooklyn, each shown in different shades of yellow, display middle-income levels, with figures ranging from $19,388 to $19,631. The Bronx is depicted in shades of orange and red, signifying the lowest income levels. This visualization provides essential insights into the economic conditions faced by the population with self-care disabilities in various parts of the city, which is vital for the formulation of targeted social programs and policies.

The map filters data for New York City residents without disabilities, showing Manhattan with the highest average income at $95,920. Following closely are Staten Island, Brooklyn, and Queens, depicted in shades of yellow, with average incomes of $56,922, $52,855, and $47,872, respectively, indicating they are middle-income boroughs. The Bronx, shown in red, registers the lowest average income at $35,109, marking it as the borough facing the most significant economic difficulties. This visualization highlights the substantial economic disparities within New York City and offers critical insights that could shape future economic and social policies.

Urban vs. Rural Discrepancies: Individuals with self-care disabilities in both urban and rural regions earn less than their non-disabled peers. Notably, the income disparity between those with and without disabilities is more pronounced in rural areas than in urban ones.

Gender Disparities: Across both urban and rural environments, males consistently out-earn females, regardless of disability status. This gender-based income gap is observed universally across various locations and disability conditions.

Effects of Disability on Income: Self-care disabilities significantly affect incomes for both genders. Notably, urban females with disabilities report the lowest income levels among all groups, underscoring a dual disadvantage rooted in both gender and disability.

Comparative Insights: In urban areas, females with disabilities earn significantly less than their male counterparts, highlighting a severe gender disparity within this group. Conversely, in rural settings, while males with disabilities still earn more than females, the income differences are less extreme.

This analysis provides essential perspectives on how factors like location, gender, and disability status intersect to influence economic results. It emphasizes the importance of creating specific policies and initiatives that tackle these disparities, promoting equal opportunities for everyone, especially those encountering multiple disadvantages.

Educational Attainment and Income:As educational levels increase, average incomes rise for both males and females, regardless of disability status.

Impact of Disability on Income :For every educational category, individuals with a self-care disability generally earn less than their non-disabled counterparts.

Gender Disparities

• Males tend to earn more than females within the same educational level and disability status, with the gap widening at higher levels of education.
• At the highest educational level (“College Graduate and Above”), males without disabilities earn the most ($100,944), while the earnings decrease slightly for those with disabilities ($65,471).
• For females, the highest earners are those without disabilities at the same education level ($44,728), with a marked reduction in earnings for those with disabilities.

This chart highlights the intersectionality of disability, education, and gender in determining economic outcomes. It underscores the additional financial challenges faced by individuals with self-care disabilities, which is crucial for informing policy measures aimed at reducing these disparities.

Findings:

This study looked at how disabilities, gender, and where people live affect how much money they make in New York State. The researchers used data from the 2018 American Community Survey (ACS) to find big differences in income and education levels. This shows the challenges faced by people with these different identities.The study found that in both cities and rural areas, people with disabilities make less money than those without disabilities. The gap is even bigger in rural areas. Men make more money than women in all places, and disabilities affect women’s income more, especially in cities like New York City.These findings show that policies need to consider all the different parts of a person’s identity. Policymakers, health workers, and social workers should work together to create programs that help everyone get the resources they need. This research helps us understand the challenges faced by people with disabilities. It also guides future work to build a society where everyone can succeed, no matter their disability, gender, or where they live. By looking at how these identities intersect, we can create better solutions to help all communities.

Suggestions:

Future research can build on this study by:

• Expanding the geographic coverage beyond New York to incorporate more diverse socioeconomic and cultural contexts.
• Examining a wider range of disabilities and additional demographic factors like race, ethnicity, and age to deepen the analysis of intersectional impacts.
• Conducting longitudinal studies to track the long-term effects of policies on these populations over time.
• Integrating qualitative research methods to provide deeper insights into personal experiences behind the statistical data.

This multifaceted approach can lead to a more holistic understanding of the complex, intersecting challenges faced by individuals with disabilities across gender and geographic lines. The resulting insights can then inform more targeted and effective policy interventions to address these disparities.