The digital divide is the number one threat to community economic development in the 21st century. Public policy 101 argues that a problem needs to be defined before exploring potential solutions.
Update: percent disabled figure has been added to the SE component.
The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.
The INFA score groups four variables related to broadband infrastructure and adoption: (1) percentage of total 2010 population without access to fixed broadband of at least 25 Mbps download and 3 Mbps upload; (2) number of residential broadband connections with at least 10 Mbps download and 1 Mbps upload; (3) average maximum advertised download speeds; and (4) average maximum advertised upload speeds.
The SE score groups four variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; and (4) percent of noninstitutionalized civilian population with a disability. In other words, these variable indirectly measure adoption since they are potential predictors of lagging technology adoption.
These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.
The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather, it should help initiate important discussions among community leaders and residents.
The map below shows the DDI at the county and census tract level (to enable census tract level, zoom into a particular county). A darker color indicates a higher digital divide. Click on county or census tract to obtain more information.
Data for the digital divide index (DDI) was obtained from the 2011-2015 American Community Survey (ACS) and FCC Form 477. Since FCC data is available at the block level, it was aggregated to the Census tract level to match ACS Census tract data. County and state data was then aggregated from the Census tract level.
The DDI consists of two components. The first one is the infrastructure/adoption component (INFA) that includes percent of 2010 population without access to 25/3 fixed broadband (NBBND), average of maximum advertised download (DNS) and upload (UPS) speeds, and number of residential fixed broadband connections per 1,000 homes with speeds of at least 10 Mbps down and 1 Mpbs up (HHAD).
Within the INFA component, more weight was given to broadband access (0.4) and adoption (0.4) than to download (0.1) and upload (0.1) speeds. Although speeds are becoming more important, access and adoption precede speed and, thus, deserve more weight. Likewise, the download/upload speed range is more susceptible to extreme outliers, so assigning less weight helps minimize the impact of extreme outliers. Important to clarify is that multiple weight combinations were utilized and the results did not change drastically.
The second component is the socioeconomic (SE) component that includes the percent of the population age 65 and over (AGE65), percentage 25 and over with less than a high school degree (LTHS), individual poverty rate (POV), and percent of the noninstitutionalized civilian population with any disability (DIS). Equal weight was given to all indicators within this component.
Keep in mind that these socioeconomic variables indirectly measure adoption since they can be considered as potential predictors of lagging technology adoption. If a particular county scores high on these variables but low on broadband infrastructure, it may be better to focus on digital literacy and promoting the personal benefits of the technology.
Because these variables have different units and normal distributions, z-scores were calculated for each variable and geography. Z-scores standardize the data and indicate where a particular observation falls compared to the mean and standard deviation of the sample. Please note that these scores were calculated by looking at the geographic units (Census tracts, counties) and comparing them with their peers. For this reason, scores are not comparable across different geography tiers (Census tract versus counties versus states).
Since the DDI was designed to show a larger digital divide as the score increases, careful attention was paid to the signs in equations 1 and 2. The rationale behind the infrastructure/adoption (INFA) score (equation 1) was: as the z-scores of the percent of population without fixed 25/3 (NBBND) increases (+), the digital divide increases; while the z-scores of the average download (DNS), upload (UPS) speeds, and household adoption (HHAD) increase, the digital divide decreases (-).
A similar rationale was used to calculate the socioeconomic score (SE) in equation 2: as the z-scores of the percent population ages 65 and over (AGE65) increases (+), so does the potential lag in technology adoption; same as the z-scores of individual poverty rate (POV) increases (+), percent population 25 and over without a high school degree (LTHS) increases (+), and percent noninstutionalized population with any disability (DIS) increases (+), so does the digital divide.
Notice however that the SE components are given equal weight while INFA components are not. This may result in more variance in the SE score compared to the INFA score. This in turn gives SE more influence on the DDI score compared to the INFA score. For this reason, z-scores of the INFA and SE scores were calculated and then added up to calculate the final DDI score giving both components equal influence as shown in equation 3.
All scores were normalized to fall between a 0 to 100 range where the higher the number, the higher the digital divide.
Worth mentioning is that two important variables are lacking in the DDI: broadband cost and how the technology is being used. Without a doubt, these two variables would strengthen the DDI, but, unfortunately, nationwide data is not available.
On the other hand, access to cellular wireless was not included because most of the benefits of digital applications are undermined by mobile devices and limited data plans. It is much harder to complete a job application or complete a homework assignment using a smartphone that also has limited data. As broadband applications become more sophisticated and require more data, limited data plans undermine usage and can become very expensive.
In an effort to easily interpret and use the digital divide index (DDI) data, we have designed DDI profiles at the county and state level. These profiles incorporate the DDI data into an infographic format in order to jumpstart critical discussions around the topic. Narrative to better interpret the data is included, but please contact us if you have any questions or have some suggestions.