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.

CalSPEED results used in latest FCC Wireless Competition Report

On December 23, 2015, the FCC released their Eighteenth Wireless Competition Report (DA 15-1487), and in it they use results from our CalSPEED tests and compare them with Ookla, RootMetrics, and the FCC's own speed test app results.

Based on the CalSPEED data, latency continues to improve. Verizon Wireless and Sprint have seen dramatic improvements in their latency over the last several reporting periods, and are closing in on AT&T for the lowest latency measurements. While T-Mobile has the highest mean latency, they have also demonstrated dramatic improvements during the last several test periods.

Fall/Winter Testing Over 1/3 Complete

In the two weeks we've been testing, over 1/3 of the locations have been covered. Due to the late start, our team prioritized snow-affected areas with the thought that it might be harder to test there after we resume in January. Here are a few photos:

Fall/Winter Testing finishing 2nd Week

We are finishing up the second week of mobile testing before winter break. This time around, we have upgraded the smartphone to the Samsung Galaxy S6 from the S4. The tablets are still the same as for Spring 2015 testing. It's still too early to draw conclusions about any of the providers, so in the meantime, we are working on two new areas of analysis: video streaming and conferencing capability of each provider's network, and backhaul. For the latter, we are analyzing traceroute test results from the Spring 2015 test and will be comparing them with those from this round.

The Nation's Largest Mobile Network

Who has the largest mobile network? In the recent past, red or blue maps were flashed across the television screen, and we were left having to flip a coin, because it was impossible to tell which provider had better coverage. Missing completely was any mention of service level (voice call, or Skype video call, or streaming Netflix HD, as examples) or probability of getting that service level in a particular area.

We all want to know where we will be able to use our mobile devices -- Yosemite? Mammoth? Anza Borrego? -- and we think there's a map out there that will provide the answer. But, most maps are garbage. A good map will state that the coverage shown on the map is based on the probability of getting a certain level of service at a location.

A generic map may show nearly all of California as having mobile service, but that coverage might be based solely on voice calling, not broadband. If you want to stream YouTube, the coverage will likely shrink. Moreover, if you want to stream YouTube reliably, or use real-time streaming services without interruption, the coverage may shrink even more. Think: higher capacity = less coverage, and higher probability = less coverage.

In our study of mobile broadband coverage for California, we now incorporate probability into our maps. In the past, we used to display mobile "served" coverage estimates (i.e. greater than or equal to 6 megabits per second downstream AND 1.5 megabits per second upstream, or "6/1.5") based on average speeds measured in the field. That resulted in 98% of California households receiving 6/1.5 or faster. But, as I have written in earlier posts, "average" means that half of the time you will get better than 6/1.5, and half the time, worse.

Here is our served 6/1.5 household estimate from June 2013 using average speeds:

This is why we are now adjusting the average speed with the standard deviation. The standard deviation is a measurement of variability. The higher the standard deviation, the more variability. Reducing measured speeds by the mean (average) minus two standard deviations yields a much higher likelihood (98% in a normal curve) of getting 6/1.5 or faster at a particular location, but, that adjustment shrinks the coverage map. This higher threshold lowers the estimated household coverage to 16% of California.

Here is our served 6/1.5 household estimate from December 2014 using mean minus two standard  deviations:

Remember, coverage maps are meaningless without specifying service levels and incorporating probability.

iPhone CalSPEED On Track For December Submission

We've been testing CalSPEED for iPhone, and we've been happy to share the beta version with enthusiastic members of the broadband community who've tested it out it remote areas of California. We've just upgraded the user interface as well as the back-end latency test algorithm (more accurate), which means we are on track for submitting CalSPEED to Apple in early December for review and (hopefully) approval for distribution on iTunes.

Japan: A Look Into America's Mobile Future?

For as long as I've been working in mobile telecommunications, Japan has always been the leader in mobile network development. A recent look at one of Japan's crowd sourcing apps, RBB Speed Test, shows some pretty amazing speeds for mobile data. While we might expect to see a maximum download of 60-70 megabits per second (Mbps) in California, in Japan, it's more than three times as fast. For upload, we're used to seeing between 5-10 Mbps; for the crowd source results below, the increase is not as large (17.1 Mbps).

See the table below for the top ten speeds from November 13. Number 1 was a test run on NTT DoCoMo's LTE network using a Sharp SH-01H (link to GSM Arena here) (link to Sharp's Japanese page here).