Tag Archives: Roberto Gallardo

Growth of Mobile-Dependent Households

Roberto Gallardo, an Associate professor at Purdue University, has released an interesting policy paper, An Evolving Digital Divide: Measuring and Understanding Mobile-Dependent Households (MDH). The paper summarizes Gallardo’s research, using U.S. Census data, to compare households that rely on cellular for broadband connectivity in homes before and after the pandemic.

The paper cites several interesting statistics. Data from the U.S. Census American Community Survey (ACS) shows that the percentage of homes with no Internet access decreased from 17.6% in 2017 to 6.5% in 2025, meaning that 12.5 million homes gained broadband over that period. However, over the same period, the percentage of homes that only access the Internet with a smartphone increased from 7.5% in 2017 to 11.5% in 2024, an increase of six million households. The study also looked at device ownership. The percentage of homes that only have mobile devices (no laptops or desktops) in 2017 was 9% of 2017 and increased to 15.2% by 2024, an increase of 9 million homes. These statistics show that, while home broadband usage has increased dramatically by 12.5 million homes from 2017 to 2024, a lot of that gain was from the growth of mobile-dependent homes.

Over the years, I recall seeing articles in the popular press that speculated about these trends and concluded that people are increasingly happy with mobile broadband and don’t need a big-screen broadband option. Gallardo cites other studies that come to a different conclusion. He cites a study by Napoli and Obar in 2014 that coined the term “mobile Internet underclass” to describe the inferior nature of broadband access through a cellphone using the small screen and data plans with small data caps. That study concluded that treating mobile access as equivalent to fixed broadband risks rendering the growing mobile-only population invisible. He also cites a study by Quaglione, Matteucci, Furia, Marra, and Pozzi in 2020 that says that mobile broadband is a good complement to having home broadband, but is not a substitute for home broadband.

Gallardo goes on to undertake a deep data dive into the Census data for 2019 and 2024. He looked to see if he could discern any differences in mobile-dependent households that are related to metrics like metropolitan versus rural, race, those between 18 and 34, those over 65, households employed in blue-collar occupations, household incomes, home ownership, and homes with no Internet access available.

Some of the most interesting findings from the data related to mobile-only households include:

  • Metropolitan areas saw a significant increase in the percentage of MDH households.
  • There was no statistical change for those between 18 and 34.
  • There was a decrease in the percentage of MDH households over 65
  • There was an increase in the percentage of MDH for homes with no broadband availability.
  • There was a drop in the percentage of MDH in blue-collar households.
  • There was a higher percentage of MDH in low-income households.

Gallardo’s research didn’t look at the causes of the shifts, but it’s not hard to speculate about the reason behind some of the statistics. The increase in mobile-dependent homes in urban areas and in low-income homes is likely mostly linked to affordability. The decrease in mobile-dependent homes in those over 65 and blue-collar workers might be due to increased home broadband availability,

Gallardo reaches some interesting conclusions based upon the statistics. First, infrastructure alone does not solve the digital divide issues that push homes to use mobile broadband instead of home broadband. He concludes that if the goal is to improve access to broadband, then infrastructure construction should be paired with demand-side intervention programs like providing devices, digital literacy training, and affordability programs. At least for now, much of the digital equity funding is in limbo, and we saw Congress allow the ACP program to die on the vine. Gallardo says, “Without affordability support, the path of least resistance for many households is a mobile plan.”

Remote Work Is Here to Stay

Working from home is still one of the most important uses of broadband, particularly in rural communities. Roberto Gallardo, who writes in Substack as the “Data Whisperer”, recently wrote an article discussing the change over time of those who work from home. For those who don’t know Roberto, he’s an Associate Professor and the Vice President for Engagement in the Agricultural Economics Department at Purdue University.

Everybody remembers the explosion of working from home during the COVID-19 pandemic. A lot of people decided that they liked working from home, and the percentage of those doing so has not dropped to anywhere near pre-pandemic levels.

The article starts out by comparing the percentage of those who worked from home in 2014 and 2024. Overall, the percentage of those who are working from home more than tripled over the decade. In 2014, 4.4% of the U.S. labor force worked from home, and in 2024 that had grown to 15.1%.

There is still a big disparity in the percentage of home workers in urban versus rural areas. In 2024, 16.1% of urban residents work from home while around 8.4% of those living in rural areas work from home. Gallardo credits the difference to two reasons. First, there is a higher percentage of jobs in urban areas that can be done from home. He says that over 36% of jobs in urban areas could be done from home, while only around 28% of jobs in rural areas can be done remotely.

But there is also still a difference due to the lack of good broadband in rural areas. While there has been a lot of grant funding aimed at rural America, a lot of it hasn’t been built yet. Even where there is broadband that supposedly meets the FCC definition of broadband, there are many rural ISPs that are not delivering a good enough upstream path to reliably work from home. For example, a recent Ookla report said that only about 45% of Starlink customers have speeds that meet that FCC speed definition.

One of the most interesting parts of his article is an interactive map that shows the percentage of people who work at home in each county in 2014 and 2024 compared to the number of jobs that could be done remotely. Gallardo acknowledges that a lot of remote workers work for somebody in another county or state, but this is still an intriguing map that shows the level at which remote work has become an important part of local economies. The map shows that remote work has been more accepted in New England, the Pacific Northwest, Colorado, and the Mid-Atlantic. Interestingly, the states with the highest level of remote work in 2014 had bigger gains by 2024 than other states.

As most people will do, I looked at my own county in North Carolina first. The county had 15.4% of eligible workers working remotely in 2014, which grew to 34.8% in 2024. I can attest that remote work is common here, and there are numerous people on my own block who work from home. But examining the map shows a lot of counties where the percentage of those working from home is below 10%, some at only half that level.

I’ve heard stories about how getting good broadband can transform communities. I talked to a cooperative in Alabama who told me that there were practically no rural home workers before fiber was built, but that many homes in the coop territory now have people working remotely. They said that higher incomes from remote work are transforming the economics for families and the local community, and that the fiber is also attracting some people who already work from home to move to the county.

Broadband and Demographics

In August, Roberto Gallardo, the Director of the Purdue Center for Regional Development wrote an article that takes a quantitative look at measuring the digital divide. Gallardo has created what he calls the Digital Divide Index (DDI) that allows for the mapping of various socioeconomic factors that can limit households from buying broadband. I obviously want you to read the rest of my blog first, but it’s then worth popping out and playing with the DDI map.

The Digital Divide Index creates a value for every county in the country, with values ranging from 1 to 100, with 100 meaning the highest level of digital divide. The DDI map also plots an infrastructure/adoption (INFA) score and a Socioeconomic score (SE) for every county.

The  Infrastructure/Adoption (INFA) score includes five variables that are related to broadband adoption:

  • The percentage of homes that are not receiving 100/20 Mbps broadband as measured by the Ookla speed test.
  • Percentage of homes without a computer.
  • Percentage of homes that don’t have a broadband subscription.
  • Download speeds.
  • Upload speeds.

The Socioeconomic Score (SE) includes the following five variables:

  • Percentage of the population over age 65.
  • The percentage of the population age 25 and older that didn’t complete high school.
  • The individual poverty rate.
  • The percentage of the population with a disability.
  • A newly calculated digital inequality or internet income ratio.

The DDI is the first attempt I’ve seen to bring together such a broad range of variables and relate them to the digital divide. Gallardo uses FCC 2020 data in creating the DDI. He also grabbed information from the U.S. Census Bureau’s 5-year American Community Survey, the Bureau of Economic Analysis, Lightcast (formerly known as Economic Modeling Specialists, Inc. or EMSI), and Venture Forward by GoDaddy.

Gallardo assigned values to each county using the many variables. Gallardo then separated counties into three equal groups by the values. The average DDI score for the lowest third of counties was 17.33, while the score of the top third was 36.58. Numerically, this infers that the third of the counties with the most digital divide issues have twice the digital divide issue, on average than counties with the least amount of digital divide issues. This is not a surprising finding, but I’ve never seen it expressed numerically.

The article goes on to create charts that compare the variables between the lowest and highest-ranking counties. For example, the report compared urban and rural. Not surprising, the percentage of counties with the worst DDI scores is almost four times more rural than urban. It turns out that other variables don’t differ between the highest and lowest DDI rankings, such as the percentage of children in the population.

This kind of report is useful for those interested in fixing the digital divide in a community. The report provides some interesting clues about how to find homes that are on the wrong side of the digital divide. For example, households that include somebody with a disability look to be far more likely to be on the wrong side of the digital divide.