Digital Distress: What is it and who does it affect? Part 2.
By: Roberto Gallardo & Cheyanne Geideman
(This is the second of a two-part series)
Digital distress was defined and understood geographically speaking in our first post. Now, a deeper look is taken at the socioeconomic characteristics of these digitally distressed areas.
Remember that digital distress refers to Census tracts (neighborhoods) that have a harder time using and leveraging the internet to improve their quality of life due to the type of internet subscription or devices owned. Four indicators were taken from the 2013–2017 U.S. Census Bureau American Community Survey (ACS) to measure digital distress for the nation’s 72,400+ Census tracts:
- percent of homes with a cellular data only subscription
- percent of homes with no internet access (not subscribing)
- percent of homes relying only on mobile devices
- percent of homes not owning a computing device
Less than six percent of tracts were identified as digitally distressed, and of these, close to 70 percent were completely urban neighborhoods (refer to first post for an explanation of the metropolitan categories used).
When all is said and done, about 4.87 million households were in digitally distressed neighborhoods accounting for 4.4 percent of the nation’s 2017 population, or about 14.1 million people.
Regarding age groups, note on the figure below that the share of millennials living in digitally distressed neighborhoods was higher than the U.S. share (21.5 versus 20.7 percent, though this difference may fall in the margin of error of the data).
The fact that one-fifth of residents in digitally distressed neighborhoods were millennials — considered the first “digital native” generation or those who were born into the digital age and did not have to adapt like older generations — highlights the importance of addressing this issue. Otherwise, it makes it harder to leverage digital applications, wasting their digital “potential.”
Also, the homework gap is noticeable. About 28 percent of people living in digitally distressed neighborhoods were children, a higher share compared to the nation. Children in digital distress areas are being left behind through no fault of their own, impacting future generations and their ability to contribute to the digital economy and society.
As expected, educational attainment was lower in digitally distressed areas. A little more than 30 percent of residents did not finish high school, compared to less than 13 percent in the U.S. overall. Likewise, less than 10 percent of digitally distressed residents had a bachelor’s degree or higher compared to almost 31 percent in the U.S. overall.
Unemployment rate among prime working age workers (ages 25 to 54) in digitally distressed areas was higher than the national average (7.9 percent versus 4.6 percent, though this difference may fall in the margin of error of the data) as shown in the figure below. Also, the share of prime age workers not in the labor force was also higher in digitally distressed areas compared to the U.S. average (30.7 percent versus 18.3 percent).
While digital distress may not be the sole reason why almost one-third of prime age workers are not in the labor force, it does limit their options to learn skills and credentials online and become more employable or start their own businesses.
Regarding industry type, a slightly lower share in digitally distressed areas worked in service industries while a higher share worked on production industries, compared to the U.S. average. Note that the share of those working from home (non-agriculture) was lower in digitally distressed areas compared to the nation (though this difference may fall in the margin of error of the data).
When looking at type of workers, there is no large difference between the U.S. and digitally distressed areas. Do note however, that the entrepreneur share (self-employed) of all workers ages 16 and over is slightly lower in digitally distressed areas compared to the U.S. average (9.4 versus 7.5 percent, though this difference may fall in the margin of error of the data). Also note that the share of workers that worked as employees for private for-profit businesses was higher in digitally distressed areas (73.6 percent versus 68.1 percent).
Individual poverty was twice as high in digitally distressed areas compared to the U.S. average (14.6 versus 36.2 percent). Likewise, the share of population with disabilities was also higher in digitally distressed neighborhoods compared to the nation.
A similar, if not identical pattern, is seen regarding median household income. The figure below shows that the median household income in digitally distressed areas was half of the U.S. median income.
Again, what is causing what could be argued both ways: low-income leads to digital distress or digital distress leads to low-income. Regardless, having a harder time leveraging digital applications that in turn could lead to improvements in education or employability does impact the residents’ ability to earn more.
Lastly and regarding race & ethnicity, digitally distressed areas had a higher share of minorities compared to the nation, as shown in the figure below. The share of white non-Hispanics in digitally distressed areas was less than half the national average (24.8 percent in digitally distressed areas versus 61.5 percent overall) while the share of black non-Hispanic was three times the national share (12.3 versus 36.4 percent). Also note, the share of Hispanics in digitally distressed areas was almost double the share found in the nation overall (17.6 versus 32.6 percent).
To conclude, there are multiple points worth discussing. First, digital distress is a phenomenon that exists putting families and children at a disadvantage. This phenomenon affects 4.4 percent of the total population and cuts across urban and rural areas.
Second, the socioeconomic characteristics of those in digital distress denote a higher share of minorities, less educated, poorer, and younger residents. Ironically, these same groups could benefit greatly from digital applications to improve their quality of life. However, being in digital distress places them at a disadvantage.
Targeted digital inclusion efforts must be any community’s priority to reduce these inequalities. Case studies and best practices exist of device refurbishing and loan programs, federal/state/local programs incentivizing internet subscriptions, and digital literacy programs and workshops to ensure these populations benefit from the digital age.
While the outcome of any digital inclusion strategy is far from defined, a lack of one will definitely leave your community behind!
This article reflects our views, not those of our employers.
