Introduction:

In their article, “Linking public housing, employment, and disability benefits for working-age people with disabilities,” researchers Brucker and Scally document the many issues which prevent disabled individuals from accessing government benefits like Supplemental Security Income. 1 They point out barriers like employment status—as benefits often require individuals to prove they cannot work—and bureaucratic limitations. Notably, disabled individuals are more likely to be low-income and members of marginalized groups, meaning they may not have the resources or time to fill out lengthy benefits applications. Using data from the Current Population Survey (CPS)’s Annual Social and Economic Supplement (ASEC), this study examines the rates of disability and SSI benefits across the 50 US states. We want to know: Is there a discrepancy between the percentage of disabled individuals and those receiving Supplemental Security Income in each state?

The percentage of residents receiving SSI benefits in each US state

Within this Static Map, each color represents the percentage of the population in each state receiving Supplemental Security Income (SSI), which is calculated from the number of surveyed SSI recipients divided by the total number of individuals surveyed within each state. Knowledge about SSI receipt offers key insights into the state’s accessibility policies and quality of life it offers to disabled individuals. For example, as Maine has one of the highest rates of SSI receipt in the US, we may interpret that Maine has good policies that make SSI easier for residents to access or that Maine is a state that prioritizes accessibility.

We can also consider what factors may lead to the development of disabilities; in addition to Maine, some states in the Rust Belt have relatively high percentages of SSI recipients. This may be a lasting effect of industry-related unsafe working conditions, which increase the likelihood of work-related accidents and lead to higher rates of disabilities and subsequently, government benefits.

The percentage of disabled residents in each US state

This choropleth map shows the percentage of individuals living with a disability in each state. The four colors on the map represent the quantiles of the data, with darker colors representing higher rates of disability.

The percentage of disabled residents receiving SSI by state

The map reveals that states in the Rust Belt have the highest percentages of disabled individuals; this may result from the prevalence of high-risk jobs like coal mining and industrial manufacturing in the region. Likewise, states like Oregon and Maine are known for their high-risk logging industries. Notably, we do not see any strong partisan patterns in the data. For example, New York and California, both liberal states are placed in different quantiles.

However, this may point to a major limitation of choropleth maps: the delineation of colors doesn’t fully capture the nuance of the data. Two states that are colored differently may have similar percentages of disabled residents, each on different sides of the quantile cutoff.

The percentage of disabled residents recieving SSI by state

All parts of this static map are identical to the first static map, barring the calculation of the SSI percentage. Here, SSI percentage represents the percent of disabled individuals receiving SSI in each state, calculated by dividing the number of surveyed SSI recipients by the total number of disabled individuals in each state. Interestingly, the Rust Belt—which had the highest percentage of SSI recipients and disabled individuals among the general population—does not have high rates of SSI recipients among its disabled population. This indicates that the first map may be misleading when considering policy implications, as, despite high rates of benefits, fewer disabled individuals are being helped than we would have assumed.

This pushes us to consider reasons why these states are providing insufficient proper income supplements, especially when such a high proportion of their state population lives with disabilities.

Summary

Ethical Concerns

At least one ethical concern that should be considered when analyzing this data includes how the data is collected (ex. If census, then considering the unaccounted homeless population), as well as the assumptions that the dataset holds that disabled people are willing to accurately identify themselves or can receive a diagnosis/recognition as a disabled person.

In addition, the limited, binary data collected means that researchers might not consider other factors beyond socioeconomic/employment status and group identity, such as the social environments that surround disabled individuals which could impact their employment and the opportunities they are offered (or lack thereof). Likewise, the nature of the interactive map lends itself toward simplicity, potentially guiding viewers to overlook quantile differences or cutoffs.

Policy Implications

One policy implication for disabled people based on the results of our data analysis includes analyzing the dangers of hard labor work and the potential impact they have in raising the disabled population in areas, such as those seen along the Rust Belt in the interactive and static maps. In these instances, there are areas for policy improvement in both workplace protections that can decrease the number of disability-causing accidents and in government financial support individuals with disabilities.

Additionally, when observing the static map, we may consider rethinking the markers that qualify a disabled individual for Supplemental Security Income, as many people are categorized as “having a disability”, but do not receive any supplements to improve their quality of life. However, a difficulty with this policy implication is that we lack data on the quality of life for the individuals surveyed.

  1. Brucker, D. L., & Scally, C. P. (2015). Linking public housing, employment, and disability benefits for working-age people with disabilities. Housing and Society, 42(2), 126–147. https://doi.org/10.1080/08882746.2015.1076130↩︎