Broadband Adoption Correlated With Income. Surprised?

Recently, several articles have featured studies on mobile broadband adoption and why more people aren't subscribing to wireline broadband. I thought it would be interesting look a little at these studies and revisit the CPUC's own study on wireline broadband adoption.

Telephone Landlines Are Disappearing

• In December, the Centers for Disease Control found that as many as 47% of American adults lived in homes with only mobile phones for telephone service (no traditional telephone line).
• More than 2/3 of adults aged 25–29 (71.3%) and aged 30-34 (67.8%) lived in households with only wireless telephones.
• The percentage of adults living with only wireless telephones decreased as age increased beyond 35 years: 56.6% for those 35–44; 40.8% for those 45–64; and 19.3% for those 65 and over.

Broadband Not Relevant To Low Income Americans?

• The Benton Foundation’s blog has a post by three researchers that suggests one of the key barriers often mentioned in broadband adoption, “relevance,” may mask other fundamental issues such as price and ability to pay. The researchers suggest in response to broadband adoption surveys that show people “not interested in getting online,” adding follow-up questions that focus on cost and digital literacy.

Income and Broadband Adoption

• The CPUC's California Broadband Report based on June 2011 wireline subscription data looked at seven variables to see if there might be any correlation with wireline adoption rates. The table below summarizes relationships between the wireline adoption rates of California census tracts and a series of demographic variables.
•  This list is not intended to be exhaustive, but rather includes variables with some of the highest explanatory power, such as median household income and educational attainment indicators, along with variables that would be expected to hold a high degree of explanatory power, but in fact do not, such as density.

 It is necessary to note that these variables are highly correlated not only with adoption rates but also to one another. This limits the ability to accurately determine the contribution of each individual variable to overall changes in adoption rates within a statistical model that includes more than one explanatory variable. Multivariate models can improve overall explanatory power, but interpreting the results becomes increasingly complex.