Digital Divide Index

2015

Roberto Gallardo, Ph.D.March 2017

About the Report

The Mississippi State University (MSU) Extension Service Intelligent

Community Institute helps rural communities—through outreach and

research—transition to, plan for, and prosper in the digital age

resulting in inclusive and sustainable communities. The Institute is

part of the worldwide Intelligent Community Forum network. For

more information on the Institute, please go to

http://ici.msucares.com.

About the Author

Dr. Roberto Gallardo is an Associate Extension Professor with the

Mississippi State University Extension Service. Has an undergraduate

engineering degree, a master’s degree in Economic Development

from the University of Southern Mississippi, and a Ph.D. in Public

Policy and Administration from Mississippi State University. His

outreach and research interests include local and regional community

economic development as well as the impact of technology in

community economic development. He oversees the MSU Extension

Service Intelligent Community Institute.

1

The digital divide is the number-one threat to community and economic development in the 21st century. Public policy 101 argues that, first and foremost, the problem needs to be defined and agreed upon in order to explore potential solutions. This report introduces a county-level digital divide index (DDI). The DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. This report presents findings for the 2014 updated1 version as well as the 2015 version.

Some key findings include:

First DDI component measures infrastructure/adoption and includes percent population without

access to fixed broadband 25/3, number of residential fixed broadband connections at a

minimum download of 10 Mbps and upload of 1 Mbps2, and average advertised

download/upload fixed broadband speeds.

Second DDI component measures socioeconomic (SE) characteristics that are known to affect

technology adoption, including percent population age 65 and over, percent population 25 and

over with less than high school, and individual poverty rate. This component was included in an

effort to identify counties at higher risk of lagging in technology adoption.

Not included in the DDI due to data constraints include broadband cost and Internet use. Also,

mobile wireless access was not included because of the impact of limited data plans on the use

of this technology and its increasingly sophisticated applications.

Results indicate that the percent of people without access to fixed broadband 25/3 decreased

significantly between 2014 and 2015. However, this decrease took place mainly among counties

with an already low digital divide. The number of people living in counties where the digital

divide was higher (two higher quartiles) had a slight increase from 39.3 million in 2014 versus

39.5 million in 2015.

The 2013–14 and 2014–15 population change (looking only at mobility, not natural) overall and

in those ages 20 to 34 (also known as digital natives) was positive in the lowest (better) DDI

quartile but flat or negative in the highest (worse) DDI quartiles. In other words, the digital

divide may be impacting population mobility.

Efforts to reduce the digital divide will require public–private partnerships that deal with

broadband infrastructure and digital literacy at the same time. Otherwise, residents may not

subscribe to recently upgraded broadband connectivity, or those who increased their digital

skills may run into lack of connectivity, expensive plans, and/or inadequate speeds.

1 The first 2014 edition omitted two counties due to lack of data and some errors were found regarding average download/upload speeds. This updated version includes all counties (n=3,104). Keep in mind Virginia independent cities and counties were merged. 2 Household adoption for this edition was measured with the number of residential fixed broadband connections at a minimum of 10 Mbps download and 1 Mbps upload compared to the 2014 edition of a minimum of 3 Mbps download and 768 Kbps upload.

Executive Summary

2

The social and economic landscape is drastically changing as the digital age unfolds. Digital technologies

and applications are transforming the way we access information, search/apply for work, conduct work,

engage with government, communicate with friends and relatives, obtain quality education, and access

quality health care, among others. The assumptions are that, with access (including affordability) to the

technology (specifically broadband) and know-how of digital applications, society will benefit3.

But what happens if the access and/or know-

how are not there? In that case, the individual,

households, organization, or community miss

out on the community economic development

opportunities of the digital age. This does not

bode well in an age where information and

computing technologies are the primary means

of producing content and knowledge4.

Some scholars and practitioners alike refer to this issue—lack of access and/or know-how—as the

“digital divide.” The main implication is that those on the wrong side of the divide are falling farther and

farther behind. As with any public policy issue, defining and measuring the problem is of paramount

importance. This report attempts to measure the digital divide at the county level through a combined

metric called the digital divide index (DDI) relying on both socioeconomic indicators as well as

broadband infrastructure and adoption.

The DDI introduced here was designed to be used as a descriptive and pragmatic tool to help

policymakers, community leaders, and residents better understand this complex topic and rank their

counties along a digital divide continuum.

The term digital divide has been used in multiple ways: to label advocates and detractors of information

technology; to discuss whether the technology is good or bad; to describe interoperability issues

between analog and digital cellular networks; and to illustrate the unequal distribution of information

technology in schools5.

In addition, the definition of the digital divide evolves, as does the information technology to which it is

associated. Existing digital divide indicators—DIDIX, Network Readiness Index, and the Digital Access

3 All images used in this report were tagged with the Creative commons license. No copyright infringement was intended. 4 (Rogers, 2016) 5 (Gunkel, 2003)

Background

Introduction

3

Index, to name a few—define the concept similarly, yet operationalize it in different ways, mostly

constrained by data availability. These national indices have been criticized for assigning weights

arbitrarily and overlooking subnational or local contexts6.

There is no doubt that at the root of the digital divide concept resides an equality issue. Although it is

beyond the scope of this report to delve deeply into the equality issue, a brief discussion is warranted.

Some of the equality issues related to the digital divide include technological (access to computers,

devices, broadband), immaterial (life chances, freedom), material (capital, resources), social (position,

power, participation), and educational (capabilities, skills) issues7. The DDI focuses primarily—due to

data constraints—on the technological equality issue.

The technological equality issue needs to be understood as a process, rather

than a simple yes/no situation. This process consists of motivation, physical

access, skills, and usage. In other words, strong motivation to use the

technology, physical access to the technology, skills to manage the

technology, and ability to use the technology (diverse applications and

creative use)8 play a role in the digital divide.

In general terms, the digital divide consists of two broad elements: access

(including affordability) and adoption. Focusing on one and overlooking the

other can provide a skewed perception of the issue.

The DDI measures primarily physical access and socioeconomic characteristics that may limit motivation,

skills, and usage. More importantly, the digital divide is not bridged simply by subscribing to broadband

or having physical access to the technology. Rather, the digital divide is a continuum.

The following physical access (infrastructure) indicators were obtained from the Federal

Communications Commission (FCC) Form 477 to calculate the DDI:

Percent of population without access to fixed9 broadband (NBBND)—defined as speeds of at

least 25 Mbps down and 3 Mbps up.

Average fixed broadband download (DNS) and upload (UPS) advertised speeds. Since advertised

speeds are only available at the block level, county averages were calculated using pivot tables.

Fixed broadband residential connections per 1,000 households with a minimum download

speed of 10 Mbps and 3 Mbps upload (HHAD).

6 (Barzilai-Nahon, 2006) 7 (van Dijk, 2006) 8 (van Dijk, 2006) 9 Includes fixed wireless and satellite; does not include mobile wireless

Data & Methodology

4

Speeds are important to include for two reasons. First, since broadband access and subscription rates

are reaching saturation levels in some areas, speed is becoming an issue10. Second, as web applications

become more sophisticated and demand faster transmission of data, simply having access to inadequate

speeds is not enough.

Regarding broadband adoption, the variable used provides the number of residential fixed broadband

connections per 1,000 homes grouping counties into five groups: group zero had no residential fixed

broadband connections; group 1 had more than zero but less than 20 percent; group 2 had 20 percent

to less than 40 percent; group 3 had 40 percent to less than 60 percent; group 4 had 60 percent to less

than 80 percent; and group 5 had 80 percent or more homes with fixed broadband connections.

Important to note is that, for this particular variable, broadband was defined at much lower speeds

(compared to the percent not having access to broadband) of at least 10 Mbps down and 1 Mbps up.

Although broadband adoption is far more complex than residential fixed broadband connections, this

variable was used assuming a household has a fixed broadband connection because it has a use for it,

finds it relevant, and can afford it. Or put another way, it can be assumed that counties with a high rate

of residential broadband connections have a higher motivation, have better access, and can afford it.

Specific socioeconomic characteristics typically lag when it comes to technology adoption and use.

According to the Pew Research Center, adults age 65 and over, those with less than high school

education, and those with low income are lagging when it comes to using the Internet11. For this reason,

it is important to include in the DDI the percent of the population age 65 and over (AGE65), percentage

25 and over with less than a high school degree (LTHS), and individual poverty rate (POV).

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 technology12.

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, data at the county level 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.

Lastly and regarding weights, more weight was given to broadband access (0.4) and adoption (0.4) than

to download (0.1) and upload (0.1) speeds in the infrastructure/adoption component. Within the

socioeconomic component, equal weight (1/3) was given to each indicator mainly because there is no

empirical research to help assign appropriate weights to these variables.

10 (Hilbert, 2016) 11 (Perrin & Duggan, 2015) 12 (LaRose, Gregg, Strover, Straubhaar, & Inagaki, 2008)

5

Unfortunately, there are no scientific studies that can be used to more objectively weight the DDI

variables. 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 (notice the range between the minimum and maximum values in Table 2), 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. Table 1 provides a summary of the

DDI variables and their weights.

Table 1. DDI Variables Name, Description, and Source.

Name Description Source Weights

Infrastructure 1.0

NBBND Per. pop. with no access to fixed broadband FCC Form 477 0.4

DNS Avg. advertised fixed download speed FCC Form 477 0.1

UPS Avg. advertised fixed upload speed FCC Form 477 0.1

HHAD Fixed residential connections per 1,000 homes FCC Form 477 0.4

Socioeconomic 1.0

pAGE65 Per. pop. ages 65 and over Census 5YR ACS 0.3

pLTHS Per. pop. 25+ with less than high school Census 5YR ACS 0.3

pPOV Individual poverty rate Census 5YR ACS 0.3 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

As shown in Table 2, the total number of counties analyzed was 3,104 (counties and independent cities

in Virginia were merged). The average percent of population without access to fixed broadband 25/3

was 51.1 percent in 2014 compared to 35.5 percent in 2015. In other words, the number of people

without fixed broadband decreased significantly between 2014 and 2015. However, most of this

decrease took place in the two quartiles with the lowest digital divide. Average download speeds

increased from 25.4 Mbps in 2014 to 32.6 Mbps in 2015 while average upload speeds also increased

from 8.2 Mbps in 2014 to 12.7 Mbps in 2015. Note that no summary table is provided for the

socioeconomic variables. The reason for this is that since the source is the five-year American

Community Survey, 2014 data and 2015 data are not comparable.

Table 2. Statistical Summary of Infrastructure County Variables (n = 3,104)

Infrastructure Average St. Dev. Minimum Maximum

Year 2014 2015 2014 2015 2014 2015 2014 2015

NBBND (%) 51.1 35.5 36.5 31.3 0.0 0.0 100.0 100.0

DNS (Mbps) 25.4 32.6 26.2 38.1 4.4 4.0 322.9 378.5

UPS (Mbps) 8.2 12.7 20.2 30.9 0.8 1.0 318.3 361.9

HHAD (Ordinal)* 2.8 2.3 0.9 1.0 0.0 0.0 5.0 5.0 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

*Note: Variable not comparable between years due to change in measurement.

Because these variables have different units and normal distributions, z-scores were calculated for each

variable and county. Z-scores standardize the data and indicate where a particular observation falls

compared to the mean and standard deviation of the sample. Since the DDI was designed to show a

larger digital divide as the score increases, careful attention was paid to the signs in the equations.

6

The rationale behind equations 1–3 was: as NBBND increases (+), digital divide increases, while higher

average DNS and UPS (-) and HHAD (-) decrease the digital divide. Similarly, as AGE65 increases (+),

technology adoption likely will lag increasing the digital divide; the same is true for LTHS (+) and POV (+).

The resulting INFA and SE components were calculated adding the z-scores using equations 1 and 2,

while the overall DDI score was calculated adding the z-scores using equation 3. All scores were then

transformed to a 0 to 100 range, where the closer the number to 100, the higher the digital divide.

Equation 1: INFA = NBBND*0.4 – DNS*0.1 – UPS*0.1 – HHAD*0.4

Equation 2: SE = pAGE65*0.33 + pLTHS*0.33 + pPOV*0.33

Equation 3: DDI = INFA + SE

To clearly identify which component has a larger impact on

different counties, this section divided the results by

component. The DDI, if calculated annually, can also help

document the impact of investments in infrastructure/adoption.

Again, the objective is to increase awareness and provide a

consistent measure to compare counties over time. Due to

space limitations, only the top and bottom 10 counties were

listed13 ranked by 2015 scores.

Infrastructure/Adoption Component

As shown in Table 3, the top 10 counties with the largest INFA score are in multiple states. Not

surprising, all top ten counties were noncore, or most rural, according to the Core-Based typology14.

Table 3. Top Ten Counties with the Highest Infrastructure/Adoption (INFA) Digital Divide Score.

2015 Rank

County Name

State OMB INFA Score

Change 2014 2015

1 Yakutat City AK Noncore 99.65 100.00 +0.35

2 Roger Mills OK Noncore 91.92 99.95 +8.03

3 North Slope AK Noncore 82.59 99.94 +17.35

4 Haines AK Noncore 91.01 99.94 +8.93

5 Wheeler OR Noncore 91.73 99.84 +8.11

6 Loving TX Noncore 74.44 99.76 +25.32

7 Daggett UT Noncore 91.56 99.72 +8.16

8 Aleutians West AK Noncore 91.12 99.68 +8.55

9 Dillingham AK Noncore 91.06 99.67 +8.61

10 Lake & Peninsula AK Noncore 91.10 99.66 +8.56 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

13 To access a list of all counties and other variables, please go to http://ici.msucares.com/ddi 14 Click here for more information on this typology.

Results

7

The county with the highest infrastructure/adoption digital divide score was Yakutat City in Alaska,

which obtained a score of 100 shown in Table 3. Its infrastructure DDI score increased slightly between

2014 and 2015. In 2015, these top ten counties had 100 percent of their population without access to

fixed broadband 25/3 while their average download and upload speeds were 7.448 Mbps and 1.567

Mbps respectively.

On the other hand, Table 4 shows the top 10 counties with the lowest INFA score. Surprisingly, eight out

of the top 10 are nonmetropolitan (micropolitan or noncore). As of 2015, these counties had an average

of 2.6 percent of their population without access to fixed broadband 25/3 and a stunning 316.483 and

284.399 Mbps average download and upload speeds respectively. The top county—Sully County in

South Dakota—went from having 100 percent of its population without access to fixed broadband 25/3

in 2014 to 0 percent in 2015. For the complete list of indicators and counties click here.

Table 4. Top Ten Counties with the Lowest Infrastructure/Adoption (INFA) Digital Divide Score.

2015 Rank

County Name

State OMB INFA Score

Change 2014 2015

1 Sully SD Micropolitan 54.06 0.00 -54.06

2 Hyde SD Noncore 52.84 7.51 -45.33

3 Potter SD Noncore 54.11 8.69 -45.42

4 Marshall SD Noncore 59.28 15.98 -43.30

5 Graham KS Noncore 0.00 16.88 +16.88

6 Williamson TX Metropolitan 44.36 17.23 -27.13

7 Warren TN Micropolitan 63.13 18.97 -44.17

8 White TN Noncore 66.81 21.26 -45.55

9 Travis TX Metropolitan 22.48 21.79 -0.69

10 Candler GA Noncore 64.77 24.37 -40.40 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Regarding geographic location of the different INFA quartiles, Figure 1 shows that counties with the

highest infrastructure/adoption (dark green) are located mostly on the east and west coasts as well

North Dakota and Michigan. Counties with the worse infrastructure/adoption (red) are located primarily

in rural areas, especially larger counties where population density is lower.

8

Figure 1. Infrastructure/Adoption (INFA) Score by Quartiles, 2015

Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Socioeconomic Component

This component is incorporated into the overall digital divide index because it shows the counties at a

higher risk of lagging in technology adoption—counties that may need more investment in digital

literacy and exposure to the benefits of the technology. This is very important because some carriers

argue that, in many cases, even if the technology is built or upgraded, residents do not subscribe.

It is important to note that not one of the top 10 counties with the highest INFA score repeated in the SE

score. This implies that the digital divide is complex and including both components makes the overall

DDI score more robust.

Table 5 shows the top 10 counties with the highest SE score. Not surprisingly, eight out of the 10

counties were nonmetropolitan (noncore and micropolitan). Texas had three of the top 10 spots while

Kentucky held two. In 2015, these counties had an average of 18.9 percent population age 65 and over,

35.5 percent poverty, and 36.8 percent population 25 and over with less than high school.

9

Table 5. Top Ten Counties with the Highest Socioeconomic (SE) Digital Divide Score.

2015 Rank

County Name

State OMB SE Score

Change 2014 2015

1 Hudspeth TX Metropolitan 100.00 100.00 0.00

2 Issaquena MS Noncore 75.50 96.33 +20.83

3 Starr TX Micropolitan 92.71 93.18 +0.46

4 Wolfe KY Noncore 90.83 87.41 -3.42

5 Zapata TX Micropolitan 80.80 87.12 +6.32

6 Stewart GA Noncore 78.73 86.01 +7.28

7 East Carroll LA Noncore 82.32 84.16 +1.84

8 Sumter FL Metropolitan 84.95 83.22 -1.73

9 McDowell WV Noncore 81.28 83.18 +1.89

10 Bell KY Micropolitan 72.83 82.67 +9.84 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Table 6 shows the top ten counties with the lowest SE component score. Notice all of the top 10 spots

were metropolitan counties. In 2015, these counties had an average of 8.8 percent population age 65

and over, 5.4 percent poverty, and 4.7 percent population 25 and over with less than a high school.

Table 6. Top Ten Counties with the Lowest Socioeconomic (SE) Digital Divide Score.

2015 Rank

County Name

State OMB SE Score

Change 2014 2015

1 Douglas CO Metropolitan 0.00 0.00 0.00

2 Loudoun VA Metropolitan 21.80 1.87 -19.92

3 Hamilton IN Metropolitan 2.78 3.61 +0.83

4 Lincoln SD Metropolitan 2.71 3.67 +0.96

5 Carver MN Metropolitan 2.34 3.72 +1.38

6 Morgan UT Metropolitan 2.33 3.79 +1.46

7 Kendall IL Metropolitan 5.05 4.05 -1.00

8 Delaware OH Metropolitan 4.58 4.59 +0.01

9 Scott MN Metropolitan 3.61 4.62 +1.01

10 Chattahoochee GA Metropolitan 0.73 4.93 +4.20 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

A different geographic pattern can be seen regarding socioeconomic indicators affecting technology

adoption. Figure 2 shows that the counties with socioeconomic characteristics conducive to technology

adoption (dark green) are again mostly located in the northeast and Silicon Valley as well as the majority

of counties in Utah, Colorado, Wyoming, and Iowa. On the other hand, counties with socioeconomic

characteristics affecting technology adoption (red) are mostly located in the south including New

Mexico, Texas, Mississippi, Alabama, and West Virginia.

10

Figure 2. Socioeconomic (SE) Score by Quartiles, 2015

Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Overall Digital Divide Index

Now that we have looked at both components separately, the digital divide index score combines both

scores into one. The objective is to identify counties that are both lacking in broadband infrastructure

and at a higher risk of lagging in technology adoption due to above-average specific socioeconomic

characteristics. Table 7 shows the overall digital divide index score. Not surprising, nine of the top 10

counties were nonmetro (micropolitan and noncore).

Six counties listed in Table 7 did not appear in the INFA or SE top 10 rankings. This showcases that, when

infrastructure/adoption and socioeconomic characteristics are combined, a different digital divide

picture emerges. Mississippi ranked four counties in the top ten with the highest digital divide followed

by two counties in Texas. Important to note is that it is not clear from the index score if the technology-

lagging socioeconomic characteristics result in inadequate broadband infrastructure or vice versa.

11

Table 7. Top Ten Counties with the Highest Digital Divide Index (DDI) Score.

2015 Rank

County Name

State OMB DDI Score

Change 2014 2015

1 Hudspeth TX Metropolitan 100.00 100.00 0.00

2 Issaquena MS Noncore 88.11 97.89 +9.78

3 Stewart GA Noncore 89.15 91.19 +2.04

4 Humphreys MS Noncore 92.08 90.10 -1.98

5 East Carroll LA Noncore 88.46 89.18 +0.73

6 Jefferson MS Noncore 87.51 88.35 +0.83

7 Noxubee MS Noncore 88.53 88.13 -0.41

8 Culberson TX Noncore 81.47 86.61 +5.14

9 Greene AL Noncore 81.23 86.40 +5.17

10 Hickory MO Noncore 87.42 85.48 -1.95 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Table 8 shows the top 10 counties with the lowest digital divide index score. These counties not only

have more than adequate broadband infrastructure and adoption, but their socioeconomic

characteristics don’t put them at a higher risk of lagging in technology adoption. Again, not surprisingly,

eight of the 10 counties were metropolitan. In 2015, South Dakota placed three counties in the top ten

nationwide, including a rural (noncore) county, with the lowest digital divide as defined by this study.

Table 8. Top Ten Counties with the Lowest Digital Divide Index (DDI) Score.

2015 Rank

County Name

State OMB DDI Score

Change 2014 2015

1 Sully SD Micropolitan 29.99 0.00 -29.99

2 Williamson TX Metropolitan 16.93 4.31 -12.62

3 Johnson KS Metropolitan 1.15 10.00 +8.85

4 Hyde SD Noncore 37.71 11.86 -25.85

5 Lincoln SD Metropolitan 4.62 12.55 +7.93

6 Anchorage AK Metropolitan 3.71 13.28 +9.57

7 Travis TX Metropolitan 10.51 13.32 +2.81

8 Hamilton IN Metropolitan 4.83 13.41 +8.59

9 Stafford VA Metropolitan 51.98 13.46 -38.51

10 Williamson TN Metropolitan 8.54 14.50 +5.96 Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Overall, 26.4 percent of counties in the lowest digital divide quartile in 2014 and 26.5 percent in 2015

were non-metropolitan counties. On the other hand, 86.6 percent of counties in 2014 and 88.5 percent

in 2015 ranked in the highest digital divide quartile were non-metropolitan. In 2015, about 230 million

or 71.7 percent of total population lived in counties with the lowest digital divide. On the other hand,

15.4 million or 4.8 percent of total population in 2015 lived in counties with the highest digital divide.

As shown in Figure 3, the group of counties with the lowest digital divide (dark green) are mostly located

around urban areas and on the east and west coasts. There was not much geographical difference

compared to 2014. North Dakota has several counties on the lowest quartile as does Wyoming, Iowa,

and Utah. On the other hand, New Mexico, Texas, Mississippi, and Alabama have a high share of

counties in the highest digital divide quartile (red).

12

Figure 3. Digital Divide Index (DDI) Score by Quartiles, 2015

Source: MSU Extension Service Intelligent Community Institute Digital Divide Index

Regarding population change (looking only at mobility, not natural increase), Figure 4 shows the percent

change in overall population by quartiles. The counties with the lowest digital divide (1) grew as a group

about 0.68 percent between 2013-2014 and 0.69 percent between 2014-2015 while counties with the

highest digital divide (4) experienced no change during both time periods.

13

Figure 4. 2013-2014; 2014-2015 Percent Population Change by DDI Quartiles.

Source: US Census Bureau; MSU Extension Service Intelligent Community Institute

Rural areas struggle to retain their youth, mostly those younger than 35, who are considered “digital

natives.” Figure 5 shows that, in fact, between 2013-2014 and 2014-2015, the counties with the lowest

digital divide (1) grew about 1.5 percent during both time periods in those ages 20 to 34, compared to a

loss of about 0.3 percent for both time periods in counties with the highest digital divide (4).

Figure 5. 2013-2014; 2014-2015 Percent Population Change Ages 20 to 34 by DDI Quartiles.

Source: US Census Bureau; MSU Extension Service Intelligent Community Institute

0.68

0.59

0.42

-0.01

0.69

0.61

0.42

0.00

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1 2 3 4

2013-2014 2014-2015

1.51

0.74

0.36

-0.23

1.48

0.68

0.27

-0.26-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4

2013-2014 2014-2015

14

As shown throughout this report, both infrastructure/adoption and specific socioeconomic

characteristics shape the digital divide. Therefore, strategies to reduce the digital divide should focus on

both upgrading and deploying broadband infrastructure/adoption, as well as increasing digital literacy

and technology relevance for those groups that are known to lag in technology adoption. Granted,

broadband-related investments will not make an older population younger. However, this index score

can help making efforts to reduce the digital divide more strategic.

More importantly, both strategies will have to be implemented at the same time. Otherwise, carriers

making the broadband infrastructure investment may find that people are not subscribing, while those

who find the technology relevant may face problems with technology availability, cost, or speed.

The DDI explained in this report was designed 1) with data availability in mind, but also 2) for pragmatic

and descriptive purposes. Not only does it include broadband availability and adoption at the county

level—two measures used extensively for research purposes—but also average advertised speeds. In

addition, the DDI also includes several socioeconomic variables that may potentially indicate that certain

counties will struggle more when adopting technology.

Several other indicators are lacking—specifically cost of broadband subscriptions, digital devices, and

technology use—that future research should attempt to measure at the county level. Once this data

becomes available, they can be included in the DDI. Similarly, mobile wireless access was not included

due to the impact limited data plans have on how the technology is used.

It is hoped that future technology—that may not necessarily include burying fiber-optic cables—will

help close the availability gap so prevalent in sparsely populated and isolated areas. In addition,

however, other areas that do have certain availability are falling behind regarding speeds. As the web

becomes more sophisticated, adequate speeds will be critical. Without a doubt, public–private

partnerships will be required not only to improve availability, but also to upgrade inadequate speeds.

Lastly, it is critical to frame the discussion differently regarding the importance of broadband

connectivity and applications. Broadband began as an entertainment tool. The explosion of e-commerce

and social media applications were possible in part because they successfully leveraged the technology.

However, more sophisticated applications are starting to leverage the technology, as well, and these

have a more direct impact on quality of life than entertainment. As health care relies more on telehealth

and smartphones, and telework job opportunities—stimulated in part by the exploding “gig” economy—

continue to increase, health and jobs will be tied to broadband connectivity and know-how.

Communities and individuals on the wrong side of the divide will miss out on these opportunities.

Young people leave their rural communities mainly due to lack of education and jobs. Older rural

residents leave due to lack of quality health care. Both age groups and their rural communities can

benefit tremendously from digital-age applications—if only they were on the right side of the divide.

Conclusions

15

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LaRose, R., Gregg, J. L., Strover, S., Straubhaar, J., & Inagaki, N. (2008). Closing the Rural Broadband Gap.

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