copyright 2016, theeradej suabtrirat

THREE ESSAYS ON HIGH-SPEED INTERNET: HOME AND MOBILE

INTERNET ADOPTION, THE EFFECT OF INTERNET’S PRICE AND SPEED,

AND WELFARE EVALUATION

by

THEERADEJ SUABTRIRAT, BBA

A DISSERTATION

IN

ECONOMICS

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Robert McComb, PhD

Chair of Committee

Terry Von Ende, PhD

Masha Rahnamamoghadam, PhD

Benaissa Chidmi, PhD

Mark Sheridan, PhD

Dean of the Graduate School

May 2016

Texas Tech University, Theeradej Suabtrirat, May 2016

ii

ACKNOWLEDGMENTS

Completing a dissertation is a long journey, and I have reached the destination

by the strong support of many people around me. First, I would like to thank Dr.

McComb for his chairing my dissertation. He has given me encouragement, valuable

guidance, and marketable research topics. I will remember him as a role model when I

become a professor in the near future. Second, I would like to thank Dr. Chidmi for his

help in verifying the validity of my econometric results. Third, I would like to thank

Dr. Von Ende and Dr. Rahnamamoghadam for serving as my dissertation committee

and offering useful insights, as well as Dr. Khan for serving as the graduate school

representative of my dissertation. Fourth, I would like to thank Dr. Boonsaeng, Dr.

Carpio, and Dr. Noel for valuable comments to improve my dissertation.

Next, I would like to thank Dr. Becker, Dr. Al-Hmoud, and the Department of

Economics for the opportunity to teach an undergraduate class and for a teaching

assistantship for the completion of my PhD study. Moreover, I would like to thank the

Snyder Communication Skills Center of the Rawls College of Business for editing and

revising my dissertation. Furthermore, I would like to thank my parents, my brother,

and my loved ones for motivating me when I was tempted to give up. Lastly, I would

like to dedicate this work to my late grandmother who raised me when I was a little

naughty boy.


iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .................................................................................................. ii

ABSTRACT ...................................................................................................................... v

LIST OF TABLES ............................................................................................................ vi

LIST OF FIGURES .......................................................................................................... xi

CHAPTER I: INTRODUCTION ......................................................................................... 1

CHAPTER II: HOME AND MOBILE INTERNET ADOPTION ............................................. 4

Introduction ................................................................................................................ 4

Home Internet Adoption ............................................................................................ 5

Literature Review ................................................................................................... 9

Model ................................................................................................................... 12

Data Sources......................................................................................................... 14

Results .................................................................................................................. 15

Mobile Internet Adoption ......................................................................................... 19

Literature Review ................................................................................................. 22

Model ................................................................................................................... 25

Data Sources......................................................................................................... 25

Results .................................................................................................................. 26

Conclusion ............................................................................................................... 31

CHAPTER III: THE EFFECT OF INTERNET’S PRICE AND SPEED ................................. 32

Introduction .............................................................................................................. 32

Literature Review ..................................................................................................... 33

Model ....................................................................................................................... 37

Data Sources............................................................................................................. 40

Results ...................................................................................................................... 45

Results on the Effect of Internet’s Monthly Price and Download Speed ............ 46

Results on Own and Cross Price Effect ............................................................... 52

Results on Own and Cross Price Elasticity .......................................................... 54

Conclusion ............................................................................................................... 57


iv

CHAPTER IV: WELFARE EVALUATION ....................................................................... 59

Introduction .............................................................................................................. 59

Literature Review ..................................................................................................... 60

Model ....................................................................................................................... 64

Data Sources............................................................................................................. 67

Results ...................................................................................................................... 71

Results on Welfare Change from Decreased Monthly Price ............................... 71

Results on Welfare Change from Faster Download Speed .................................. 82

Conclusion ............................................................................................................... 92

Appendix .................................................................................................................. 94

CHAPTER V: POLICY IMPLICATIONS ......................................................................... 110

Solutions Supported by the Results of this Dissertation ........................................ 110

Overcoming Digital Literacy Barriers ............................................................... 110

Overcoming Cost Barriers.................................................................................. 111

Overcoming Irrelevance Barriers ....................................................................... 112

Overcoming Language Barriers ......................................................................... 112

Overcoming Accessibility Barriers .................................................................... 113

Solutions Supported by the Findings of Previous Literature ................................. 114

Overcoming Low Internet Adoption in Low Income Areas .............................. 114

Overcoming Insufficient Infrastructure in Rural Areas ..................................... 114

Expanding Availability of Information Technology Employment .................... 115

Conclusion ............................................................................................................. 115

BIBLIOGRAPHY .................................................................................................... 117


v

ABSTRACT

The Internet brings substantial benefits to American households. This

dissertation studies the internet adoption of American households and divides its

presentation into three essays as follows.

The first essay analyzes the effect of demographic and geographic

characteristics of American households on the probability of home and mobile internet

adoption. The data source of this essay is the Current Population Survey Computer

and Internet Use Supplement July 2013. Using the binary logit model, the results

indicate that 1) the internet adoption rates tend to be higher for households with better

education, higher family income, younger adults, school-age children, and urban

residence; and 2) the home and mobile internet models have different sizes of

marginal effects for several household characteristics.

The second essay investigates the effect of the internet service’s monthly price

and download speed and the household’s characteristics on the choice of home

internet service of the households in four U.S. cities (Los Angeles, San Francisco,

Washington, DC, and New York City). This essay merges the Current Population

Survey Computer and Internet Use Supplement July 2013 with the Cost of

Connectivity 2013 dataset. Using the mixed logit model, the results indicate that 1)

households tend to purchase the internet package that offers faster download speed at a

more affordable price; 2) the demand for internet services is price-elastic; and 3)

households with better education, higher family income, and younger adults are more

likely to purchase high-speed internet service than households with opposite qualities.

The third essay quantifies the amount of welfare improvement and predicts an

increase in internet adoption rate from cheaper internet price and faster internet speed.

The data sources of this essay are the same as those of the second essay. Using the

log-sum difference method, the results demonstrate that households with better

education, higher family income, and younger adults tend to have higher internet

adoption rates and earn larger amounts of compensated variation than households with

opposite characteristics.


vi

LIST OF TABLES

Table 1: Home Computer, Internet, and Broadband Adoption by

Demographic Characteristics. .............................................................. 10

Table 2: Logistic Regression of Home Internet Adoption on

Household's and Householder's Characteristics ................................... 16

Table 3: Smartphone Owners in 2014 by Demographic Characteristics ..................... 23

Table 4: Marginal Effects of Demographics on Selected Mobile Phone

Activities. ............................................................................................. 24

Table 5: Logistic Regression of Mobile Internet Adoption on

Household's and Householder's Characteristics ................................... 27

Table 6: The List of Important Variables. .................................................................... 42

Table 7: The List of Internet Packages in Los Angeles ............................................... 44

Table 8: The List of Internet Packages in San Francisco ............................................. 44

Table 9: The List of Internet Packages in Washington, DC ........................................ 45

Table 10: The List of Internet Packages in New York City ......................................... 45

Table 11: The Result of the Mixed Logit Regression of Households'

Choice on Internet Package's and Household's

Characteristics in Los Angeles and San Francisco Area. ..................... 48

Table 12: The Result of the Mixed Logit Regression of Households'

Choice on Internet Package's and Household's

Characteristics in Washington, DC and New York City

Area. ..................................................................................................... 50

Table 13: Own and Cross Price Effect of Internet Packages in Los

Angeles (in Probability Unit). .............................................................. 53

Table 14: Own and Cross Price Effect of Internet Packages in San

Francisco (in Probability Unit)............................................................. 53

Table 15: Own and Cross Price Effect of Internet Packages in

Washington, DC (in Probability Unit). ................................................ 54

Table 16: Own and Cross Price Effect of Internet Packages in New

York City (in Probability Unit). ........................................................... 54

Table 17: Own and Cross Price Elasticity of Internet Packages in Los

Angeles (in Rate of Change of Probability). ........................................ 56


vii

Table 18: Own and Cross Price Elasticity of Internet Packages in San

Francisco (in Rate of Change of Probability). ..................................... 56

Table 19: Own and Cross Price Elasticity of Internet Packages in

Washington DC (in Rate of Change of Probability). ........................... 57

Table 20: Own and Cross Price Elasticity of Internet Packages in New

York City (in Rate of Change of Probability). ..................................... 57

Table 21: Group of Variables for Welfare Evaluation by Household’s

and Internet Package’s Characteristics................................................. 67

Table 22: The List of Internet Packages in Los Angeles (after Speed

Group Assignment) .............................................................................. 69

Table 23: The List of Internet Packages in San Francisco (after Speed


Table 24: The List of Internet Packages in Washington, DC (after

Speed Group Assignment) ................................................................... 70

Table 25: The List of Internet Packages in New York City (after Speed


Table 26: The Means of Compensated Variation (CV) and Expected

Adoption Increase (Percentage in Parenthesis) from a

$10 Decrease in Monthly Price for Los Angeles

Households Categorized by Family Annual Income

Levels. .................................................................................................. 72




Households Categorized by Education Levels of

Householders. ....................................................................................... 75




Households Categorized by Age Levels Of

Householders. ....................................................................................... 76



$10 Decrease in Monthly Price for San Francisco


Levels. .................................................................................................. 77


viii





Householders. ....................................................................................... 78




Households Categorized by Age Levels of

Householders. ....................................................................................... 78


Adoption Increase (Percentage In Parenthesis) from a

$10 Decrease in Monthly Price for Washington, DC


Levels. .................................................................................................. 79


Adoption Increase (Percentage In Parenthesis) from a

$10 Decrease In Monthly Price for Washington, DC


Householders. ....................................................................................... 79



$10 Decrease in Monthly Price for Washington, DC


Householders. ....................................................................................... 80



$10 Decrease in Monthly Price for New York City


Levels. .................................................................................................. 80




Households Categorized by Education Levels ..................................... 81





Householders. ....................................................................................... 81


ix


Adoption Increase (Percentage in Parenthesis) from a 10

Mbps Increase in Download Speed for Los Angeles


Levels. .................................................................................................. 83





Householders. ....................................................................................... 85





Householders. ....................................................................................... 86



Mbps Increase in Download Speed for San Francisco


Levels. .................................................................................................. 87





Householders. ....................................................................................... 88





Householders. ....................................................................................... 88



Mbps Increase in Download Speed for Washington, DC


Levels. .................................................................................................. 89





Householders. ....................................................................................... 89


x





Householders. ....................................................................................... 90



Mbps Increase in Download Speed for New York City


Levels. .................................................................................................. 90





Householders. ....................................................................................... 91





Householders. ....................................................................................... 91


xi

LIST OF FIGURES

Figure 1 Computer, Internet, and Broadband Adoption Rates of U.S.

Households ............................................................................................. 6

Figure 2: Home Internet by Connection Technology .................................................... 7

Figure 3: Proportion of Cell Phone Owners Who Goes Online Using

Their Cell Phone. ................................................................................. 20

Figure 4: Activities Americans Conduct on Mobile Phone, Percent of

Mobile Phone Users Age 25+, 2011-2012. .......................................... 21

Figure 5: Time Spent with the Internet by Device. ...................................................... 22

Figure 6: Median Prices of DSL, Cable, and Fiber Internet Service in

the United States By Speed Tier. ......................................................... 35

Figure 7: Wired Broadband Speed by Technology. ..................................................... 37

Figure 8: The Means of Compensated Variation (CV) from a $10

Decrease in Monthly Price for Los Angeles Households

Categorized by Family Annual Income Level. .................................... 94



Categorized by Education Level of Householders. .............................. 94



Categorized by Age Level of Householders. ....................................... 95

Figure 11: Expected Adoption Increase from a $10 Decrease in

Monthly Price for Los Angeles Households Categorized

by Levels of Family Annual Income, Education of

Householders, and Age of Householders. ............................................ 95


Decrease in Monthly Price for San Francisco


Level. .................................................................................................... 96


Decrease in Monthly Price For San Francisco

Households Categorized by Education Level of

Householders. ....................................................................................... 96


Decrease in Monthly Price for San Francisco

Households Categorized by Age Level of householders. .................... 97


xii


Monthly Price for San Francisco Households

Categorized by Levels of Family Annual Income,

Education of Householders, and Age of Householders. ...................... 97


Decrease in Monthly Price for Washington, DC


Level. .................................................................................................... 98


Decrease in Monthly Price for Washington, DC


Householders. ....................................................................................... 98


Decrease in Monthly Price For Washington, DC

Households Categorized by Age Level of Householders. ................... 99


Monthly Price for Washington, DC Households


Education of Householders, and Age of Householders. ...................... 99


Decrease in Monthly Price for New York City


Level. .................................................................................................. 100




Householders. ..................................................................................... 100



Households Categorized by Age Level of Householders. ................. 101


Monthly Price for New York City Households


Education of Householders, and Age of Householders. .................... 101

Figure 24: The Means of Compensated Variation (CV) from a 10 Mbps

Increase in Download Speed for Los Angeles


Level. .................................................................................................. 102


xiii




Householders. ..................................................................................... 102




Figure 27: Expected Adoption Increase from a 10 Mbps Increase in

Download Speed for Los Angeles Households




Increase in Download Speed for San Francisco


Level. .................................................................................................. 104




Householders. ..................................................................................... 104





Download Speed for San Francisco Households


Education of Householders, and Age Of Householders..................... 105


Increase in Download Speed for Washington, DC


Level. .................................................................................................. 106

Figure 33: The Means of Compensated Variation (CV) From a 10



Householders. ..................................................................................... 106


Increase in Download Speed for Washington, DC



Download Speed for Washington, DC Households




xiv


Increase in Download Speed for New York City


Level. .................................................................................................. 108




Householders. ..................................................................................... 108





Download Speed for New York City Households




1

CHAPTER I

INTRODUCTION

The Internet offers tremendous benefits to its users. It enables people to search

for the information they want, communicate over email and social networking

websites, do online shopping, and download video and entertainment content. This

dissertation contains five chapters: 1) introduction, 2) the first essay on home and

mobile internet adoption, 3) the second essay on internet service’s monthly price and

download speed, 4) the third essay on welfare evaluation, and 5) policy implications.

This dissertation follows the three-essay format; each essay analyzes internet

use and adoption in different aspects. The first essay studies how household’s

characteristics affect home and mobile internet adoption. The second essay

investigates how internet service’s monthly price and download speed influence the

choice of home internet service. The third essay evaluates the amount of welfare

change and an increase in internet adoption rate of American households resulting

from cheaper internet price and faster download speed. The summary of each essay is

provided as follows.

The first essay analyzes the effect of demographic and geographic

characteristics of American households on the probability of home and mobile internet

adoption. Encouraging American households to adopt (purchase) internet service is the

most important step to transform America into a digital nation. The latest surveyed

adoption rates (as of 2013) for mobile internet (63.6%) and home internet (73.4%) are

far short of the target adoption rate of the National Broadband Plan (90%+ by 2020).

This essay predicts home and mobile internet adoption rates by the binary logit model

and households’ demographic data. The results indicate that adoption rates tend to be

higher for households with better education, higher family income, younger adults,

school-age children, and urban residence. The marginal effects of home and mobile

internet models have different size and significance levels for several household

characteristics. The econometric results of this essay confirm the validity of previous


2

literature’s results and substantiate the federal government’s National Broadband Plan

to boost internet adoption of American households.

The second essay combines two datasets on internet service’s monthly price

and download speed and household’s characteristics, and examines how these factors

influence the household’s choice of home internet service. The data merging between

monthly price and download speed is the major contribution of this essay to the

existing literature. Previous studies analyzed the demand for high-speed internet

service using data on monthly prices, not on internet download speed. Such an

exclusion may result in omitted variable bias since households are very likely to

consider internet speed when deciding which internet package to purchase. This essay

uses the mixed logit model to study the demand for high-speed internet in four U.S.

cities (Los Angeles, San Francisco, New York City, and Washington, DC). The results

on these four U.S. cities share similar patterns: 1) households tend to purchase the

internet package that offers faster download speed at a more affordable price; 2) the

demand for internet services is price-elastic; and 3) households with better education,

higher family income, and younger adults are more likely to purchase high-speed

internet than households with less education, lower family income, and older adults.

The third essay quantifies the amount of welfare improvement and predicts an

increase in internet adoption of American households resulting from cheaper internet

price and faster download speed. Internet services in the United States have had these

improvements since 2004. Welfare evaluation from these changes has important

policy implications. Faster internet speed increases the enjoyment of internet surfing;

cheaper internet prices encourage internet adoption and improve digital literacy of

American households. Econometric estimates from the second essay are used to

quantify welfare change under two scenarios: 1) a decrease of $10 in monthly price

while keeping download speed unchanged, and 2) an increase of 10 Mbps in download

speed while keeping monthly price unchanged. This essay quantifies welfare change

into compensated variation by the log-sum difference method and predicts an increase

in adoption rate by comparing utility from purchasing a high-speed internet package

versus utility from not purchasing one. The results demonstrate that households with


3

better education, higher family income, and younger adults tend to have higher

internet adoption rates and larger amounts of compensated variation than households

with less education, lower family income, and older adults. The four U.S. cities have

similar patterns of results but differ in the percentage of internet adoption rates and the

amount of compensated variation.

The last chapter of this dissertation presents policy implications related to the

overall results of the three essays. Solutions supported by the econometric results of

this dissertation suggest: 1) offering digital literacy education to low education

households; 2) decreasing the monthly price of internet service to low income

households; 3) showing the benefits of the Internet to senior citizens; 4) delivering

computer trainings in learners’ native language; and 5) supplying accessibility devices

to people with disabilities. On the other hand, solutions supported by the findings of

previous literature suggest: 1) awarding ISPs that successfully recruit new customers

in low income areas; 2) raising fees of E-rate program and investing additional internet

infrastructure in rural areas; and 3) magnifying the availability of information

technology employment. These solutions are proposed to shrink the digital divide

among non-internet using households.


4

CHAPTER II

HOME AND MOBILE INTERNET ADOPTION

Introduction

The Internet dramatically improves daily lives of American households. Those

who purchase (a.k.a. adopt) an internet connection can take advantage of the Internet

in many ways. With home internet, they can stream movies to their computer and

television on demand, instead of driving out to rent a DVD from a video store. With

mobile internet, they can use mobile apps to get directions to shops and restaurants

compare prices of goods sold in local and online stores. The Internet brings

convenience to their lives and save them a substantial amount of money and time.

Despite the great benefits of the Internet, the latest surveyed adoption rates (as

of 2013) for mobile internet (63.6%) and home internet (73.4%) of American

households largely falls behind the target adoption rate of the National Broadband

Plan (90%+ by 2020), according to File and Ryan (2014, 3) and the Federal

Communications Commission: FCC (2010, 7). Limited access to the Internet forces

non-internet using households to lose access to useful information, to give up

participation in governmental activities, and to miss a number of employment

opportunities. Encouraging American households to purchase (or adopt) an internet

connection is the important step to enable them to fully learn about the benefits of the

Internet.

This paper intends to 1) identify factors that best predict internet adoption

decisions of various demographic groups of American households, 2) compare the

marginal effect of household’s demographic variables of home and mobile internet

adoption, and 3) formulate public policies that effectively boost internet adoption rates

of American households. This paper contains three sections: (1) home internet

adoption, (2) mobile internet adoption, and (3) the conclusion.


5

Home Internet Adoption

Home internet has been the traditional form of internet connection since the

availability of 56 kbps (kilobits per second) dial-up internet in the mid-1990s

(Christensson 2009). Today’s home internet has a much larger data bandwidth than

dial-up internet and is called high-speed or broadband internet. The download speed

of 3 Mbps (megabits per second) is equivalent to 3,000 kbps, or 55 times faster than

dial-up. The large data bandwidth enables an internet user to view a website with a

handful of web objects in few seconds.

Although internet infrastructure geographically covers 98% of American

households’ locations, only 72% of American households use high-speed internet with

download speeds of at least 3 Mbps and upload speeds of at least 768 Kbps (National

Telecommunications and Information Administration: NTIA and Economics and

Statistic Administration: ESA 2013, 2). This fact shows that the internet use of

American households is lower than it should be.

Figure 1 shows that the home internet adoption of American households in

2012 is about 72% (high-speed internet only) or 75% (if includes low-speed dial-up

internet) (NTIA 2015). The adoption gap is found to be correlated to demographic

factors (such as income, educational attainment, age, and race) and geographic factors

(such as population density and state) (NTIA and ESA 2013, 3).


6

Figure 1: Computer, Internet, and Broadband Adoption Rates of U.S. Households.

Source: National Telecommunications and Information Administration, 2015.

Figure 1 illustrates that internet use tends to be positively correlated with

computer ownership. File and Ryan (2014, 2) have a similar finding. As of 2013,

73.4% of American households use high-speed internet, while 83.8% of American

households own a computer (such as a desktop computer, a laptop computer, or a

handheld computer). At the state level, out of 25 states with high rates of computer

ownership, 22 of them also have high rates of internet adoption. Moreover, as of 2012,

94.8% of households with a computer use it to connect to the Internet (U.S. Census

Bureau 2014, 1). Having a computer encourages households to go online since

households have more enjoyment from their computer when using it to access and

utilize online services (such as watching free videos on YouTube). In addition, the

FCC suggests that subsidizing the cost of a computer, a modem, and an air card could

be an effective way to boost internet adoption rates among low-income households

(Federal Communication Commission 2010, 173). This paper plans to investigate the

effect of computer ownership on the internet adoption rate in the future.

Home internet of American households is based on several connection

technologies. The American Community Survey (ACS) Computer and Internet Use


7

2013 asks households about the type of internet technology they are using (File and

Ryan 2014, 3). The survey allows multiple responses. For example, a household may

reply that it is using cable modem internet and mobile broadband internet. Figure 2

below shows that the popular internet technologies include a cable modem (42.8%),

mobile broadband (33.1%), and DSL: Digital Subscriber Line (21.2%). It should be

noted that mobile broadband users may purchase mobile broadband internet from a

cell phone carrier. Mobile broadband internet users may insert a mobile WiFi device, a

USB dongle, or a SIM card into their computers. On the other hand, about 25% of

households have access to the Internet but do not pay for it. Many apartment

complexes may provide free WiFi internet to their residents.

Figure 2: Home Internet by Connection Technology.

Source: File and Ryan (2014, 3).

It is worthwhile to discuss how a household chooses its internet service

provider (ISP). Factors influencing its decision include, but are not limited to, (1)

available ISPs in the household’s area, (2) word of mouth, (3) monthly price, (4)

connection speed, (5) data allowance, and (6) contract terms.

First, available ISPs in the household’s area basically determine the set of

household’s choices. Unfortunately, ISP choices are very limited for American

households (Beede 2014, 1). While about 88 percent of populations have at least two

ISPs offering a download speed of 3 Mbps or greater, only 37 percent of populations


8

have at least two ISPs offering a download speed of 25 Mbps or greater. On the other

hand, Crawford (2013, 1) suggests that too few of ISPs enable them to charge

customers an unfairly high price.

Second, a household may choose an ISP based on word of mouth of its

neighbors (Lifetips 2015). A household is likely to ask current customers of an ISP 1)

whether the internet connection is reliable and 2) whether ISP’s technical support is

responsive to customers’ problems. Moreover, Consumer New Zealand (2015) asserts

that households tend to have a positive perception toward an ISP when receiving (1) a

reliable connection, (2) consistently fast internet speed, and (3) good customer service.

On the contrary, households who do not receive good internet service reply that they

are very likely to switch to a new ISP.

Third, monthly price plays an important role in a household’s decision. Dutz,

Orszag and Willig (2009, 23) find that the demand for broadband service is price-

elastic; a fall in the price of internet service induces a great boost in home internet

adoption. Moreover, a household may lower its internet bill by bundling an internet

service with television or phone service (Price 2015). Bundling may induce a

household to choose an ISP who offers a wide range of services rather than an ISP

who offers only internet service.

Fourth, a household tends to choose an ISP that provides a sufficient

connection speed for its usage. Households doing basic web-browsing may feel

satisfied at the download speed of 1-2 Mbps, while households streaming a high-

definition movie may need a download speed of 15 Mbps or greater (FCC 2015).

Fifth, a household tends to choose an ISP that supplies enough data allowance.

Data allowance is currently well above the need of home internet users (Yu 2012). An

average home internet user is predicted to have a monthly data consumption of 84 GB

or more in 2016, according to a study by Cisco System (Yu 2012). Data allowance for

home internet service generally ranges from 150GB (AT&T) to 300GB (Comcast)

(Singleton 2014). However, data allowance for mobile internet users is quite


9

restrictive (Fung 2015). The author finds that the majority of mobile internet users

carefully keep track of their usage to avoid paying extra to cellular companies.

Finally, a household tends to choose an ISP that offers reasonable contract

terms. An ISP may (1) require a minimum contract length of 12 months, (2) charge a

one-time equipment fee, or (3) stop a promotional price after 12 months. Websites that

enable internet users to compare contract terms across ISPs include DSL Report

(http://www.dslreports.com/search), White Fence (http://www.whitefence.com), and

ISPProvidersinMyArea (http://www.ispprovidersinmyarea.com/) (Pinola 2013).

Although these six factors play an important role in an ISP choosing of

American households, this paper focuses only on the effect of the household’s

characteristics on the probability of internet adoption. On the other hand, three of the

six factors (namely available ISPs, monthly price, and connection speed) will be

analyzed in the second essay of this dissertation.

Literature Review

Previous studies on internet adoption (such as those of NTIA and ESA, the

Pew Research Center, and the Current Population Survey) were frequently done by

demographic tabulation. Table 1, displayed on the next page, shows the tabulation of

several demographic characteristics against adoption rates (NTIA and ESA 2013, 26).

The adoption rates vary greatly by demographic characteristics. For example,

households with family income less than $25,000, $25,000-$49,999, and $50,000-

$74,999 have broadband adoption rates of 43%, 65%, and 84%, respectively.

Moreover, adoption rates also vary greatly by education levels. Households with no

high school diploma, with high school diploma, and with college degree or more, have

broadband adoption rates of 35%, 58%, and 88%, respectively.

http://www.dslreports.com/search

http://www.whitefence.com/

http://www.ispprovidersinmyarea.com/


10

Table 1: Home Computer, Internet, and Broadband Adoption by Demographic

Characteristics.

Source: NTIA and ESA (2013, 26).

However, demographic tabulation has several drawbacks; it produces only a

simple statistical summary and cannot predict the probability of internet adoption by a

given set of household characteristics. On the other hand, the regression analysis has

great ability to predict the probability of internet adoption from a household’s

demographic characteristics. Such ability has important policy implications since

previous literature finds that non-adoption is persistent in certain groups of the U.S.


11

population, such as households with low family income, less education, ethnic

minorities, senior citizens, rural residents, and people with disabilities (FCC 2010,

168).

The regression analysis better predicts differences in broadband adoption by

isolating household characteristics (i.e. by letting only one factor change and holding

other factors constant). The regression analysis is capable of estimating the marginal

effect of a change in a predictor variable. For instance, it can estimate the marginal

effect of living in urban vs rural areas by letting rural households be the reference

category and estimating how much more likely urban households would be to adopt

the home internet. Historically, the regression analysis of broadband adoption on

household characteristics was carried out mainly by a linear probability model (NTIA

and ESA 2011, 48). These authors may have chosen a linear probability model for its

simplicity and ease of result interpretation. For example, a regressor’s coefficient can

be instantly read as a change in internet adoption probability from a change in

regressor’s value.

However, a linear probability model has several serious weaknesses such as (1)

heteroskedastic error term, (2) an inability to constrain predicted probabilities to be

between 0 and 1, and (3) a constant effect of an increase in the value of explanatory

variables (Hill et.al. 2007, 421). The non-linear probability model (such as the binary

logit and mixed logit model used in this paper) is more attractive since the problem of

heteroskedastic error term and unconstrained predicted probabilities are greatly

subdued. This improvement is possible since the logit function is used to produce an

S-shaped cumulative probability curve. Moreover, the effect of an increase in the

value of explanatory variables (marginal effect) is no longer constant. The marginal

effect is greatest when an individual is making a decision on the borderline (i.e. when

the probability is a 50% chance of deciding yes or no). The marginal effect is modest

when an individual is “set” in his or her way (i.e. when the probability is near 0.01%

or 99%). The S-shaped probability curve better models a household’s decision than

does a linear probability model.


12

Model

The paper implements the binary logit model to predict the probability of

adopting home internet service as shown in equation 1. The model is adopted from

Cameron and Trivedi (2010, 460).

(1)

The binary variable 𝑦𝑖 is 1 if a household decides to adopt home internet and 0

if a household decides not to. The vector X refers to the household’s demographic and

geographic characteristics, which are expected to have a strong explanatory power on

the adoption decision. The vector β refers to unknown parameters which show the

impact of the change in value of X on the probability of adopting (𝑦𝑖=1). The function

Λ(𝑥′𝛽) represents the cumulative probability function (CDF) of the logistic

distribution. The value of Λ(𝑥′𝛽) is therefore constrained between 0 and 1.

The parameter estimation for β is accomplished by maximum likelihood

estimation as shown in equation 2.

(2)

For a single observation, the density function is . For a

sample of N independent observations, the maximum likelihood estimation (in

equation 2) chooses the vector β that maximizes the log-likelihood function, LL(β|xi)

for a given sample data.

The estimated parameters in β vector are in log-odd unit and called logit

(shown in equation 3) (University of California at Los Angeles’s Statistical Consulting

Group 2015).

(3)

In equation 3, the logit tells an increase in log-odd unit resulting from a one

unit increase in the regressor’s value, holding all other regressors constant. A positive

coefficient in β indicates the increased likelihood of internet adoption from an increase


13

in value of X, and vice versa for a negative coefficient. However, coefficients in β are

not in probability units since they are in log-odd units.

The marginal effect, which is in probability units and has straightforward

interpretation, is shown in equation 4 (Cameron and Trivedi 2010, 460).

(4)

In equation 4, the marginal effect from an increase in the value of continuous

regressor, 𝜕p

𝜕𝑥𝑗 is not constant. It depends on 𝜆(𝑥′𝛽), the probability distribution

function of the logistic distribution and 𝛽, logit coefficient as shown in equation 4.

The direction of marginal effect depends solely on the sign of 𝛽 since the terms

Λ(𝑥′𝛽) and 1 − Λ(𝑥′𝛽) are always positive.

The marginal effect from an increase in the value of a discrete regressor is

shown in equation 5 (Greene 2008, 775).

(5)

In equation 5, xd refers to a discrete regressor and 𝜕p

𝜕𝑥𝑑 refers to the marginal

effect from an increase in the value of a discrete regressor. For example, xd may be a

dummy variable for marital status (one for married and zero otherwise). It is not

appropriate to apply equation 4 to a discrete regressor since the derivative approach

works only for a small change in a continuous variable. A large change in the

regressor’s value, such as a value change from zero to one, should be taken care of by

equation 5. This equation computes the difference in probability when a discrete

regressor has a value of one versus when a discrete regressor has a value of zero, while

holding �̅�𝑑 (regressors other than xd) at their means.


14

Data Sources

The data source of this paper is the Current Population Survey (CPS)

Computer and Internet Use July 2013. The survey contains two parts: (1) the Main

CPS and (2) the Computer and Internet Use Supplement (NTIA and ESA 2013, 43).

The Main CPS collects data on labor force participation, earnings, and demographic

characteristics of households from civilian non-institutional populations, while the

Computer and Internet Use Supplement asks approximately 56,000 households

whether a household member accesses the Internet using what devices (such as computer,

tablet, or cellphone) and from what locations (such as home, work, or public library). The

survey asks the household about the type of home internet technology: dial-up, digital

subscriber line, cable modem, fiber optics, satellite, mobile broadband, or other

technology.

Home internet using households refers to households who access the Internet

from home by computers (such as laptop, netbook, and desktop computer) or mobile

devices (such as internet-enabled cellular phone or smartphone). The mobile devices

also include non-cellular, WiFi-only tablets such as iPad and Samsung Galaxy Note.

Additionally, mobile broadband is also considered as a type of home internet

technology if a household uses it to connect to the Internet from home. On the

contrary, households who own computers or mobile devices but do not access the

Internet are not considered home internet using households.

NTIA and ESA (2013, 43) suggests using characteristics of the householder

(also known as the “head of household” or “reference person”) as proxies for

household member’s characteristics such as race, ethnicity, age, education,

employment status, disability status, and foreign-born status. Householder refers to the

person that the housing unit is owned by or rented to. Householder is also the

reference person to whom the relationship of all other household members is recorded.

NTIA and ESA (2013, 43) caution that the respondents are not evenly distributed

across the sample based on age. The data analysis should only include the population

ages 25 and older. This paper follows recommendations of NTIA and ESA (2013, 43)

to ensure the reliability of econometric results.


15

Results

The results of the logistic regression of home internet adoption on household's

and householder's characteristics are presented in Table 2. Home internet using

households are defined as households who access the Internet from home by

computers or mobile devices (including non-cellular tablets). Explanatory variables of

the model are (1) metropolitan status, (2) census region, (3) family income in last 12

months, (4) number of household’s members, (5) having own children, (6) interaction

term between number of household’s members and having own children, (7) race, (8)

age, (9) education, (10) employment status, (11) foreign-born status, and (12)

disability status. The results should be read from the marginal effect column; marginal

effect has straightforward interpretation since it is in probability unit.

The parameter estimates of Table 2 have expected signs, supported by results

of previous literature such as NTIA and ESA (2013, 26). The result discussion is

divided into two groups: (1) one for household’s characteristic, and (2) another one for

householder’s characteristic.


16

Table 2: Logistic Regression of Home Internet Adoption on Household's and Householder's

Characteristics.

Coef Std Err p-value Sig dy/dx Std Err p-value Sig

Household's Characteristics:

Metropolitan Status

Non-Metropolitan Omitted Omitted Omitted Omitted Omitted Omitted

Metropolitan 0.2427 0.0342 0.000 *** 0.0323 0.0046 0.000 ***

Unidentified -0.0168 0.1539 0.913 -0.0023 0.0212 0.913

Census Region

NorthEast Omitted Omitted Omitted Omitted Omitted Omitted

MidWest -0.1290 0.0440 0.003 *** -0.0167 0.0057 0.003 ***

South -0.2156 0.0414 0.000 *** -0.0282 0.0054 0.000 ***

West 0.0050 0.0456 0.912 0.0006 0.0058 0.912

Family Income

Less than $10K Omitted Omitted Omitted Omitted Omitted Omitted

Between $10K and $20K 0.2228 0.0547 0.000 *** 0.0419 0.0103 0.000 ***

Between $20K and $35K 0.5396 0.0525 0.000 *** 0.0982 0.0098 0.000 ***

Between $35K and $60K 1.1437 0.0545 0.000 *** 0.1918 0.0099 0.000 ***

Greater than $60K 1.8231 0.0583 0.000 *** 0.2723 0.0100 0.000 ***

Members

No. of Household Members 0.3528 0.0189 0.000 *** 0.0377 0.0020 0.000 ***

Children

No Children Omitted Omitted Omitted Omitted Omitted Omitted

One Child or more 0.9016 0.1058 0.000 *** 0.0294 0.0070 0.000 ***

Interaction

Members*Children -0.3061 0.0297 0.000 *** N/A N/A N/A

Householder's Characteristics:

Race

White, NH Omitted Omitted Omitted Omitted Omitted Omitted

Black, NH -0.7177 0.0448 0.000 *** -0.0989 0.0066 0.000 ***

American Indian/Alaskan, NH -0.9760 0.1338 0.000 *** -0.1388 0.0211 0.000 ***

Asian, NH -0.2193 0.0855 0.010 ** -0.0282 0.0113 0.013 **

Hawaiian/Pacific Islander, NH -1.0178 0.2252 0.000 *** -0.1455 0.0358 0.000 ***

Mixed Race, NH -0.2423 0.1314 0.065 * -0.0312 0.0175 0.075 *

Any Race of Hispanic Origin -0.8300 0.0509 0.000 *** -0.1160 0.0076 0.000 ***

Age

Age 0.0554 0.0060 0.000 *** -0.0037 0.0002 0.000 ***

Age squared -0.0008 0.0001 0.000 *** N/A N/A N/A

Education

No High School Diploma Omitted Omitted Omitted Omitted Omitted Omitted

High School Diploma or GED 0.5379 0.0452 0.000 *** 0.0940 0.0082 0.000 ***

Some College or Assoc. Degree 1.2841 0.0482 0.000 *** 0.2044 0.0083 0.000 ***

Bachelor's Degree 1.6581 0.0585 0.000 *** 0.2492 0.0091 0.000 ***

Master's, Doctorate or Prof. 1.9560 0.0733 0.000 *** 0.2796 0.0097 0.000 ***

Employment

Unemployed, or Not in Labor Force Omitted Omitted Omitted Omitted Omitted Omitted

Employed 0.1613 0.0361 0.000 *** 0.0213 0.0048 0.000 ***

Citizenship

Native, or Foreign-born & Citizen Omitted Omitted Omitted Omitted Omitted Omitted

Foreign-Born & Non-Citizen -0.1680 0.0650 0.010 *** -0.0224 0.0088 0.011 **

Disabled

No Disablity Omitted Omitted Omitted Omitted Omitted Omitted

Has a Disability -0.3623 0.0575 0.000 *** -0.0496 0.0082 0.000 ***

Constant

Constant -2.0576 0.1759 0.000 *** N/A N/A N/A

No. of Observations: 38,264 Sig Significance level, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.10.

Log Likelihood: -15,639 Omitted No estimate for base-l ine category.

McFadden R-sq: 0.2767 N/A No appl icable result for squared, interaction, and constant terms.

NH Abbreviation for Non-Hispanic

Parameter Estimate Marginal Effect


17

The result discussion for household’s characteristics is as follows. First,

metropolitan status is statistically significant and has a positive sign when the rural

area is chosen as the reference category. Households in an urban area are 3.23% more

likely to have a home internet than ones in a rural area. NTIA and ESA (2013, 26) find

that the home internet adoption gap between urban and rural areas is about 14%.

Second, census region (the region where a household is located) is statistically

significant for (1) the Northeast region vs. the Midwest region, and (2) the Northeast

region vs. the South region. Several states in the Northeast region (such as New

Jersey, New York, Pennsylvania, and Massachusetts) have a number of cities with

dense population and a greater degree of economic development (Hobbs 2008, 647).

More wealthy cities in the Northeast Region may naturally have a higher rates of

home internet adoption. Third, family income in the last 12 months is found to strongly

prescribe home internet adoption rates. When the lowest family income level (less

than $10,000 annually) is chosen to be the reference category, all other income levels

are found to have a statistically higher adoption rates. For example, households with a

family income between $10,000 and $20,000 are 4.19% more likely to adopt a home

internet than households with a family income less than $10,000. The marginal effects

are increasingly stronger at higher income levels. For example, households with

family income between $20,000 and $35,000 are 9.82% more likely to adopt a home

internet than households with family income less than $10,000. Fourth, the number of

household members has a statistically significant positive sign. An additional

household member increases the probability of home internet adoption by 3.77%.

Once a home broadband is purchased, it can be shared to all household’s members by

a home WiFi router. More members of the households make “internet cost per person”

cheaper and may encourage home internet adoption. Alternatively, an additional

household member may imply the existence of young adults who wishes to use the

Internet. Fifth, households with own children are 2.94% more likely to have a home

broadband than households without a child. The children variable is coded as 1 when

having at least one child who is less than 18-year-old living in the household. A

possible explanation is that a household may adopt a home internet for its children’s


18

schooling needs. Surveys find that 71% of teens need the Internet for school projects,

and 65% of teens are assigned Internet-related homework (FCC 2010, 178).

The result discussion for householder’s characteristics is as the following.

First, race strongly prescribes differences in home internet adoption rates among racial

groups. Race is broadly classified as Non-Hispanic and Hispanic. Non-Hispanic (NH)

refers to householders in various races without Hispanic origin, while Hispanic refers

to householders of any race with Hispanic origin (for example, White with Hispanic

origin is classified into this group, not in Non-Hispanic White. The classification of

Hispanic origin is supported by previous literature. For example, Livingston (2010, 7)

finds that Hispanics fall behind Non-Hispanics in terms of cell phone use (76% vs.

86%) and internet use (64% vs. 78%). In the model, Non-Hispanic White is chosen as

the reference category and found to have a statistically higher internet adoption rate

than all other races (namely Non-Hispanic Black, Non-Hispanic American Indian and

Alaskan, Non-Hispanic Asian, Non-Hispanic Hawaiian and Pacific Islander, Non-

Hispanic mixed race, and any race of Hispanic Origin). Second, age of householder

has a negative sign and is statistically significant; as individuals get older, they are less

likely to adopt home internet. On average, an increase in one year of age decreases the

probability of home broadband by 0.37%. Third, education level of householder

predicts a difference in home internet adoption very well. When the lowest education

level (no high school diploma) is chosen as the reference category, all other education

levels have a statistically higher adoption rates. The marginal effects are increasingly

stronger at higher education levels. Schooling may introduce householders to the

benefits of the Internet and encourage them to purchase an internet connection in the

home. Fourth, having employment may encourage householders to purchase home

internet since employment provides income for householders to afford the cost of

home internet. Householders with employment is 2.13% more likely to have home

internet than Householders without it. This result is supported by the finding of

International Labour Organization (2001, 115), which states that employment growth

and internet use is positively-related in OECD (Organization for Economic

Cooperation and Development) countries. The labor market may benefit from an


19

online job search; the Internet may speed up the matching process over wider

geographic areas and reduce unemployment. Fifth, householders who are foreign-born

and non-U.S. citizens is 2.24% less likely to have home internet than native or foreign-

born and U.S. citizens. This result is consistent with that of Livingston (2010, 1),

which asserts that the Internet use of foreign-born Latinos falls behind that of native,

U.S. born Latinos. The difference in internet use is potentially related to age and

English proficiency: 1) U.S. born Latinos tend to be younger than foreign-born

Latinos; and 2) 87% and 77% of English-dominant and bilingual Latinos use the

Internet, while only 35% of Spanish-dominant Latinos do so. Lastly, householders

with disability are less likely to own a home internet connection since they face higher

costs of internet access than householders without disability. Householders with

disability generally need accessibility-supported websites, hardware, and software to

compensate their visual or physical disability (FCC 2010, 174). Compared to

householders without disability, householders with disability are 4.96% less likely to

have a home internet.

Mobile Internet Adoption

Mobile Internet has blended into the lifestyle of American households. It

enables them to connect with their friends and families, get a direction to a restaurant

or a shopping place, and search for the latest information on the go. Mobile Internet is

the combination of a handy cellular phone and an internet connectivity. Mobile

Internet is increasingly more popular among American households (NTIA and ESA

2014, i).

The Pew Research Center finds that 91% of Americans own a cellular phone,

and 63% of them use it to access the Internet (Duggan and Smith 2013). The red line

in Figure 3 shows that the proportion of cellular phone owners with internet access has

skyrocketed. The proportion of cellular phone owners who go online was 31% in

2009, 47% in 2011, and 63% in 2013.


20

Figure 3: Proportion of Cell Phone Owners Who Goes Online Using Their Cell Phone.

Source: Duggan and Smith (2013).

Smith (2015, 3) proposes that about 10% of Americans are highly dependent

on mobile internet; they access the Internet mostly by a cellular phone and not by

home internet. Americans who are highly dependent on mobile internet tend to be

young adults, non-whites (African Americans and Latinos), and those with low

income and educational attainment. Moreover, mobile internet dependent Americans

tend to have economic hardships; they sometimes cancel their internet service due to

financial constraints or exceeding the data allowance on their smartphone plan. This

fact points out that expensive prices of mobile internet and limited data allowance may

prevent some Americans from learning about the benefits of the Internet.

NTIA and CPS (2014, 7) suggest that non-voice, internet-based activities are

more popular among American cellular phone users. Figure 4 shows that proportions

of American households engaging in such activities (namely taking photos, checking

email, browsing the web, downloading apps, and social networking) grew by about 8-

10% between July 2011 and October 2012.


21

Figure 4: Activities Americans Conduct on Mobile Phone, Percent of Mobile Phone Users

Age 25+, 2011-2012.

Source: NTIA and CPS (2014, 7).

Murtagh (2014) finds that internet users spend more time on a cellular phone

than on a desktop computer. Figure 5 shows that this phenomenon occurred in January

2014. In the opinion of this dissertation’s author, internet users may spend more time

on cellular phones since it is very handy (easily taken out from pants’ pocket or

handbag) and can be used anywhere (for example, look for a recipe while cooking). A

desktop computer is less convenient since it is on a working table and not easily

moved to other places. This fact shows that mobile internet is increasingly important

in the daily lives of internet users.


22

Source: Murtagh (2014).

Literature Review

Mobile internet is a new topic for internet adoption study in the United States.

Two major investigators in this topic are (1) the National Telecommunications and

Information Administration (NTIA) and the Economics and Statistics Administration

(ESA), and (2) the Pew Internet Research. Similar to home internet adoption,

demographic tabulation remains the traditional way to study mobile internet adoption.

Fox and Rainie (2010, 16) show that smartphone use varies greatly by demographics.

Table 3 shows that smartphone use tends to higher among young adults (age 18-29

years old and 30-49 years old), adults with college education or better, adults with

higher household income (household income $50,000-$74,999 and $75,000 or more)

and adults living in urban and suburban area).

Figure 5: Time Spent with the Internet by Device.


23

Table 3: Smartphone Owners in 2014 by Demographic Characteristics.

Source: Fox and Rainie (2010, 16).


24

The regression analysis of mobile internet activities on household

demographics was carried out by ESA (2014, 45-48) using a linear probability model

as shown in Table 4 below. The internet-related activities are categorized into four

types: (1) using email, (2) web browsing, (3) downloading apps, and (4) social

networking. The numbers shown in the table are in percentage points.

Table 4: Marginal Effects of Demographics on Selected Mobile Phone Activities.

Source: NTIA & ESA (2014, 12).

In Table 4, many demographics (such as family income, education,

employment status, census region, gender, and metropolitan status) are statistically

significant at 95% confidence level. Results on gender and census region are very

interesting. First, the model predicts that female cellular phone users were 5

percentage points more likely to use social networks than their male counterparts,

while there is no statistically significant relationship between gender and other

internet-related activities. Second, the model predicts that mobile internet users in the

Midwest, the South, and the West are more likely to perform internet-related activities

on their cellular phone than users in the Northeast. These geographic disparities are

worthy of investigation.


25

However, a linear probability model suffered from many weaknesses as

previously mentioned in the literature review section of home internet adoption.

Therefore, this paper suggests using a non-linear, binary logistic regression to predict

mobile internet adoption.

Model

The model for mobile internet adoption is binary logistic regression and is

identical to that for home internet adoption. While both models use the same set of the

household’s characteristics as explanatory variables, the predicted variable for mobile

internet adoption is whether a householder performs internet-related activities on his

or her smartphone.

Mobile internet adoption variable is coded as Yes or 1 if a householder (1)

owns a smartphone and (2) performs at least one internet-related activity (namely

using email, web browsing, downloading apps, and social networking). Households

owning a cellphone but not performing the aforementioned activities are considered

non-mobile internet using households; the mobile internet adoption variable is coded

as No or 0. A reader who wishes to review the binary logistic regression may visit the

model section of home internet adoption.

Data Sources

The data source for mobile internet adoption is the Current Population Survey

(CPS) Computer and Internet Use July 2013. In addition to gathering data on home

internet use, the CPS surveys about mobile internet use on a cellular phone. It asks

households whether they own a cellular phone and what activities they perform on

their cellular phone. The activities could be non-internet-related (such as making a

phone call or getting text message) or internet-related (such as using email, web

browsing, downloading apps, and social networking).


26

Results

The results of the logistic regression of mobile internet adoption on

household's and householder's characteristics are summarized in Table 5. Mobile

internet using households are defined as households who both own a cellular phone

and use it to engage in internet-related activities. Explanatory variables of the model

are similar to those of home internet adoption model and include the following

variables: (1) metropolitan status, (2) census region, (3) family income in last 12

months, (4) number of household’s members, (5) having own children, (6) the

interaction term between number of household’s members and having own children,

(7) race, (8) age, (9) education, (10) employment status, (11) foreign-born status, and

(12) disability status.


27

Table 5: Logistic Regression of Mobile Internet Adoption on Household's and Householder's

Characteristics.

Coef. Std. Err. p-value Sig. dy/dx Std. Err p-value Sig.

Household's Characteristics:

Metropolitan Status

Non-Metropolitan Omitted Omitted Omitted Omitted Omitted Omitted

Metropolitan 0.4363 0.0311 0.000 *** 0.0767 0.0055 0.000 ***

Unidentified 0.2398 0.1359 0.078 * 0.0424 0.0239 0.076 *

Census Region

NorthEast Omitted Omitted Omitted Omitted Omitted Omitted

MidWest 0.0476 0.0375 0.204 0.0083 0.0065 0.204

South 0.1582 0.0357 0.000 *** 0.0274 0.0062 0.000 ***

West 0.1590 0.0381 0.000 *** 0.0275 0.0066 0.000 ***

Family Income

Less than $10K Omitted Omitted Omitted Omitted Omitted Omitted

Between $10K and $20K 0.0395 0.0607 0.515 0.0074 0.0113 0.515

Between $20K and $35K 0.2548 0.0565 0.000 *** 0.0480 0.0106 0.000 ***

Between $35K and $60K 0.7196 0.0558 0.000 *** 0.1367 0.0105 0.000 ***

Greater than $60K 1.2590 0.0560 0.000 *** 0.2361 0.0105 0.000 ***

Members

No. of Household Members 0.2965 0.0152 0.000 *** 0.0382 0.0021 0.000 ***

Children

No Children Omitted Omitted Omitted Omitted Omitted Omitted

One Child or more 0.8329 0.0855 0.000 *** 0.0257 0.0076 0.001 ***

Interaction

Members*Children -0.2827 0.0239 0.000 *** N/A N/A N/A

Householder's Characteristics:

Race

White, NH Omitted Omitted Omitted Omitted Omitted Omitted

Black, NH -0.0646 0.0423 0.127 -0.0112 0.0073 0.127

American Indian/Alaskan, NH -0.1218 0.1316 0.355 -0.0211 0.0229 0.357

Asian, NH -0.1079 0.0666 0.105 -0.0187 0.0115 0.106

Hawaiian/Pacific Islander, NH -0.3948 0.2090 0.059 * -0.0688 0.0366 0.060 *

Mixed Race, NH 0.1442 0.1158 0.213 0.0247 0.0197 0.210

Any Race of Hispanic Origin -0.1228 0.0469 0.009 *** -0.0213 0.0081 0.009 ***

Age

Age -0.0383 0.0059 0.000 *** -0.0091 0.0002 0.000 ***

Age squared -0.0001 0.0001 0.011 ** N/A N/A N/A

Education

No High School Diploma Omitted Omitted Omitted Omitted Omitted Omitted

High School Diploma or GED 0.2995 0.0494 0.000 *** 0.0550 0.0091 0.000 ***

Some College or Assoc. Degree 0.7515 0.0503 0.000 *** 0.1376 0.0092 0.000 ***

Bachelor's Degree 0.9738 0.0545 0.000 *** 0.1772 0.0100 0.000 ***

Master's, Doctorate or Prof. 1.1520 0.0597 0.000 *** 0.2080 0.0108 0.000 ***

Employment

Unemployed, or Not in Labor Force Omitted Omitted Omitted Omitted Omitted Omitted

Employed 0.3136 0.0308 0.000 *** 0.0561 0.0057 0.000 ***

Citizenship

Native, or Foreign-born & Citizen Omitted Omitted Omitted Omitted Omitted Omitted

Foreign-Born & Non-Citizen -0.2469 0.0588 0.000 *** -0.0429 0.0103 0.000 ***

Disabled

No Disablity Omitted Omitted Omitted Omitted Omitted Omitted

Has a Disability -0.1961 0.0603 0.001 *** -0.0342 0.0106 0.001 ***

Constant

Constant -0.0538 0.1627 0.741 N/A N/A N/A

No. of Observations: 38,264 Sig Significance level, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.10.

Log Likelihood: -19,900 Omitted No estimate for base-l ine category.

McFadden R-sq: 0.2480 N/A No appl icable result for squared, interaction, and constant terms.

NH Abbreviation for Non-Hispanic

Parameter Estimate Marginal Effect


28

The parameter estimates of Table 5 have expected signs, supported by results

of previous literature such as NTIA and ESA (2014, 45-48). The result discussion is

divided into two groups: (1) one for household’s characteristic and (2) another one for

householder’s characteristic.

The result discussion for household’s characteristics is as follows. First,

metropolitan status is statistically significant and has a positive sign when the rural

area is chosen as the reference category. Households in an urban area are 7.67% more

likely to have a mobile internet than ones in a rural area. This marginal effect is much

stronger than that of home internet adoption (3.23%). Fox and Rainie (2010, 16) find

that about 64% of adults in an urban area are mobile internet user, while only 43% of

adults in a rural area are. This 21% adoption gap of mobile internet is larger than that

of home internet (14% adoption gap, as previously shown in Table 1). Scola (2014)

extends that smartphones depend heavily on internet network and gear toward city-

center customers. It is normal to have higher mobile internet use in an urban area than

in a rural area. The mobile internet use in rural areas tend to be lower since their

infrastructure have limited capacities. Second, census region is statistically significant

for (1) the Northeast region vs. the South region and (2) the Northeast region vs the

West region. It is surprising to see that households in the South region and in the West

region are 2.74% and 2.75% more likely to use mobile internet than ones in the

Northeast region, respectively. NTIA and ESA (2014, 45-48) have the same finding

and suggest that these geographic disparities are worthwhile for further investigation.

Third, family income in the last 12 months is found to strongly prescribe mobile

internet adoption rates. When the lowest family income level (income less than

$10,000) is chosen to be the reference category, all other income levels are found to

have statistically higher adoption rates. The marginal effect from family income of

mobile internet adoption is weaker than that of home internet adoption. This result

may imply that mobile internet is more prevalent among low-income households than

home internet is. It is possible that: (1) low income households consider a cellular

phone as necessity while consider a computer as a luxury, and (2) households are

encouraged by cellular salespeople to add a mobile internet as bundled service when


29

purchasing a cellular phone. Fourth, the number of household members has a

statistically significant positive sign. An additional number of household member

increases the probability of mobile internet adoption by 3.82%. This marginal effect is

approximately equal in size to that of home internet (3.77%). Since many cellular

phones can work as mobile hotspot for other devices (Segan 2015), the more number

of household members makes “internet cost per household member” cheaper and

increases the likelihood of mobile internet adoption. Fifth, households with own

children are 2.57% more likely to have a mobile internet than ones without own

children. The marginal effect from having own children of mobile internet is

approximately as strong as that of home internet. Lenhart (2015, 1) suggests that

teenagers consistently access the Internet by mobile devices. The author finds that

about 92% of teenagers (age 13 to 17) go online daily, and 24% of them go online

almost consistently.

The result discussion for householder’s characteristics is as follows. First, race

of householders no longer suggests significant difference among racial groups as it did

in home internet adoption, except the differences between (1) Non-Hispanic White vs.

Non-Hispanic Hawaiian and Pacific islanders, and (2) Non-Hispanic White vs. any

race of Hispanic origin. The smaller adoption gap among races is supported by

previous literature: the adoption gap of mobile internet between White vs. Hispanic is

about 8% (Fox and Rainie 2010, 16), while the adoption gap of home internet between

White vs. Hispanic is about 17% (NTIA and ESA 2013, 26). These results confirm

that mobile internet has smaller adoption gap among races than home internet does.

Second, age of householder has a negative sign and is statistically significant. As

householders get older, they are less likely to adopt a mobile internet. On average, an

increase in one year of age decreases the probability of mobile internet adoption by

0.91%. This marginal effect from age of mobile internet adoption is stronger than that

of home internet. This result confirms the finding of Fox and Rainie (2010, 16) that

young adults are more likely to adopt mobile internet than old adults. Third, education

level of householder predicts difference in mobile internet adoption very well. When

the lowest education level (no high school diploma) is chosen as reference category,


30

all other education levels have statistically higher adoption rates. The marginal effects

are increasingly stronger at a higher education levels. However, the magnitude of

these marginal effects of mobile internet adoption is smaller than those of home

internet adoption. In other words, mobile internet may penetrate less-educated

households better than home internet. The smaller adoption gap is supported by

previous literature: the adoption gap of mobile internet between high school education

or less vs. college education is about 27% (Fox and Rainie 2010, 16), while the

adoption gap of home internet between high school diploma vs. college degree or

more is about 30% (NTIA and ESA 2013, 26). Fourth, having employment

encourages a householder to purchase a cellular phone and mobile internet. The

marginal effect for employment status of mobile internet (5.61%) is stronger than that

of home internet (2.13%). Hansen (2016, 3) suggests that 1) about 43% of its survey

respondents reply that mobile devices are necessary for getting their job done; and 2)

44% of them reply that they work on their mobile devices more than 20 times a day

(for checking email, taking notes, and joining a conference call). Thanks to substantial

productivity from mobile devices, it is not surprising to see a positive relationship

between mobile internet and employment. Fifth, householders who are foreign-born

and non-U.S. citizen are 4.29% less likely to use mobile internet than ones who are

native or foreign-born with U.S. citizenship. The marginal effect of being a non-U.S.

citizen on mobile internet adoption is stronger than that of home internet adoption

(2.24%). Lopez, Gonzalez-Barrera, and Patten (2013, 22) find that, among Latino

cellphone owners, about 54% of native-born Latinos use smartphone, while only 46%

of foreign-born Latinos do so. This finding confirms that being a non-U.S. citizen tend

to decrease the probability of being a mobile internet user. Lastly, householders with

disability are less likely to adopt mobile internet. Compared to householders without

disability, householders with disability is 3.42% less likely to have a mobile internet.

This marginal effect is weaker than that of home internet (4.96%). However, the

barrier between disability and mobile devices is expected to fade away since more

mobile devices have become accessible for users with disability (Avila 2014). For


31

example, screen reader is readily available to assist people with visual impairment to

use mobile devices comfortably.

Conclusion

The results of this paper indicate that the internet adoption rates tend to be

higher among households with better education, higher family income, younger adults,

school-aged children, and urban residents. Conversely, the adoption rates tend to be

lower among households with people with disabilities, who are of Hispanic origin, and

who are non-U.S. citizenship. The marginal effects of home and mobile internet

models have different size and significance level. Explanatory variables showing

differences between the two models are metropolitan status, census region, family

income, race, and education level. The econometric results of this paper confirms the

validity of demographic tabulation results produced by previous literature (such as

those of CPS, NTIA and ESA, and Pew Internet Research). These results

econometrically substantiate the solutions to overcome internet adoption barriers

proposed by the National Broadband Plan.


32

CHAPTER III

THE EFFECT OF INTERNET’S PRICE AND SPEED

Introduction

Previous literature studies the demand for high-speed internet only by data on

monthly prices, but not internet speed. This limitation is likely to result from a data

scarcity problem. There is no publicly available survey that collects both price and

speed within a single dataset. The Current Population Survey Internet and Computer

Use Supplement (CPS Internet Use) 1984 – 2013 knows only how much households

pay for internet services monthly but not the internet speed that households subscribe

to. This limitation is consistent with the finding of the Federal Communications

Commission (FCC) that 80 percent of internet users in the United States do not know

the speed of their internet connection (FCC 2015c).

One should not analyze the demand for high-speed internet without

information on internet speed. Such an exclusion may result in omitted variable bias

since households are likely to consider internet speed when deciding which internet

package to purchase. This paper enriches the existing literature by using both internet

price and speed data to predict the household’s choice of internet package. This

approach properly reflects household’s willingness to pay for a faster internet

connection.

In addition to internet price and speed, this paper includes household’s

demographics (such as family annual income, education of householders, and age of

householders) into its analysis. The household’s demographics are likely to contain

information about their attitude toward the benefit of the Internet and their willingness

to pay for internet service. This assumption is supported by the finding of previous

literature that households with higher education and income are more likely to

purchase a home internet than households with lower education and income.

This paper studies the demand for high-speed internet in the four U.S. cities

(Los Angeles, San Francisco, Washington, DC, and New York City) using 2013 data

from the CPS Internet Use and the Cost of Connectivity dataset. The results from these


33

cities have consistent and similar patterns: 1) the demand for internet services is price-

elastic, 2) the monthly price and the download speed are very influential in the

household’s decision, and 3) households with better education, higher family income,

and younger householders are more likely to purchase internet services than

households with less education, lower family income, and older householders.

This paper contains five major sections: (1) literature review, (2) model, (3)

data source, (4) results, and (5) the conclusion.

Literature Review

Previous literature finds that the household’s demographics have very strong

predictive power on the internet choice of American households. For example, NTIA

and ESA (2011, 48) perform a linear logistic regression and conclude that households

with high school diploma or better or those with family income greater than $25,000

have significantly higher internet adoption rates than their counterparts.

On the other hand, internet package’s characteristics are also useful in

predicting household’s choice of internet package. Important characteristics of internet

packages include, but are not limited to, (1) available internet service providers (ISPs)

in household’s area, (2) internet monthly price, (3) internet speed, (4) the cost of

equipment and installation, (5) word of mouth of current users, (6) monthly data

allowance, and (7) short-term promotions and contract agreements. However, this

paper focuses only on the first three factors: (1) the available ISPs, (2) monthly price,

and (3) download speed. Data on these three factors are well collected and suitable to

model the household’s choice of internet package.

First, the available ISPs in the household’s area determine the possible choices

of internet packages. Wallsten and Mallahan (2010, 2) use a new FCC dataset on

residential broadband subscribership and speeds at the census tract level to study about

residential broadband competition in the United States. The authors assert that the

number of wireline providers is positively correlated to the highest available internet

speed. The internet speed of DSL, cable, and fiber speeds is significantly higher when

there is more than one provider than when there is only one provider. On the other


34

hand, the number of wireline providers is negatively correlated to the monthly prices

of the internet package, especially at lower speeds (such as 768 kbps or lower).

Second, the prices of internet packages are very influential in the internet

adoption decision of American households. First, Dutz, Orszag and Willig (2009, 23)

assert that the demand for high-speed internet is price-elastic; a small degree of price

decrease induces a large increase in the demand on high-speed internet. Moreover, the

majority of U.S. broadband adoption research (such as those by the National

Telecommunications and Information Administration: NTIA, the Current Population

Survey, and the Pew Research Center) discover that non-adopting households cite “too

expensive subscription price” as an important reason for not purchasing a home

internet. For example, NTIA (2014, 9) observes that 29% of non-adopting households

claims “too expensive” as a reason for no home internet use. Third, a national survey

conducted by the FCC finds that 38% of internet users changed their internet service

providers (ISPs) in the last three years, and 47% of them replied that price was a major

reason for their ISP switching (Gurin 2010). These previous studies agree that the

prices of internet packages play an important role in internet adoption among the U.S.

population.

Figure 6 shows the median prices for seven speed tiers of wired internet

service in the United States with DSL, cable, and fiber technologies (Wallsten and

Mallahan 2010, 19). The authors combine cross‐sectional data on advertised prices

and internet packages from the Telogical Systems with the FCC’s Form 477 data. The

authors note that wired internet service is usually bundled with other services (such as

voice and video), causing a difficulty in isolating its standalone price.


35

Figure 6: Median Prices of DSL, Cable, and Fiber Internet Service in the United States by

Speed Tier.

Source: Wallsten and Mallahan (2010, 19).

The figure compares the price of internet service by speed tiers across

connection technologies. The figure shows that: 1) the prices of internet service with

faster speed tend to be more costly than those with slower speed; 2) the weighted

average price across all technologies and speed tiers is about $45 per month; 3) cable

internet tends to be more expensive than DSL internet for typical speed tiers; and 4)

fiber internet tends to be more expensive than DSL and cable internet.

Third, internet speed has mixed findings about its influence in a household’s

decision. While several studies assert that internet speed is very influential in a

household’s internet choice, some studies refute this finding. Several researchers

suggest that households value internet speed highly when choosing an internet

package. Gurin (2010) finds that 49% of internet users stated that faster speed is the

major reason of their ISP switching. MO Broadband Now (2013, 4) conducted a

survey on residential broadband and found that about 64% of households rank internet

speed as the most important feature of their internet package.


36

On the contrary, several studies indicate that internet speed has small weight in

a household’s decision. For example, the FCC finds that 80% of high-speed internet

users neither know the speed of the internet package they subscribe to, nor attempt a

speed test to see whether they are getting the speed they are paying for (FCC 2015c).

Moreover, Janke and Trechter (2014, 13) have a similar finding; about 40% of their

residential respondents reply “I am not sure” about the upload speed or the download

speed of the internet package. In addition, Tomer and Kane (2015, 8) find that faster

internet’s download speed is not associated with higher internet adoption rate. Rather,

the internet adoption rates are positively correlated with the share of population living

in urban areas. This may result from greater internet infrastructure in urban areas and

the networking effect (a household benefits more from the Internet when its neighbors

are also using the Internet.

According to Figure 7, download speed of at least 3 Mbps is currently

available to about 96% of American households. In January 2015, the FCC voted to

raise the minimum download speed from 4 Mbps to 25 Mbps and upload speed from 1

Mbps to 3 Mbps (Singleton 2015). This voting decreased the broadband availability

from 96% to 80% of the U.S. population (see blue line in Figure 7), and excluded DSL

from being considered as a broadband internet since DSL on telephone lines generally

cannot deliver a download speed of at least 25 Mbps (see green line in Figure 7).

Cable internet becomes the main source of broadband internet since it can deliver a

minimum download speeds of at least 25 Mbps to about 83% of population. On the

other hand, fiber optic is the only technology that can deliver a download speed of at

least 100 Mbps to about 22% of the population, or at least 1 Gbps to about 9% of the

population.


37

Figure 7: Wired Broadband Speed by Technology.

Source: NTIA and FCC (2015, 1).

Model

This paper implements the mixed logit model to predict the household’s choice

of internet packages. The mixed logit model is the combination of the multinomial

logit model and the conditional logit model. Its explanatory variables include both

household’s demographics (such as family income, education, and age of householder)

and alternative’s characteristics (such as internet package’s price and download

speed). The probability that a household chooses a particular internet package

available in its area is shown in equation 6. This equation is adopted from So and

Kuhfeld (1995, 667).

Π𝑖𝑗 = 𝑃(𝑦𝑖

= 𝑗) = 𝑒𝑥𝑝(𝛽𝑗

′𝑋𝑖+𝜃′𝑍𝑖𝑗)

∑ 𝑒𝑥𝑝(𝛽𝑙′𝑋𝑖+𝜃′𝑍𝑖𝑙)𝑚

𝑙=1

(6)

Let Π𝑖𝑗 represent the probability that household i chooses internet package j,

𝑃(𝑦𝑖 = 𝑗). The internet package j ranges from 1 to m (j = 1, 2, 3,…, m). The first

internet package j=1 is “no internet purchase” (a.k.a. an outside good) and serves as

the reference category. Households who choose not to purchase any internet package

will be assigned the value of zero to their monthly price, download speed, and ordinal

utility (Berry and Haile 2009, 10). On the contrary, households who purchase an


38

internet package are considered choosing an inside good. The inside good refers to any

type of internet package (j ≠ 1) such as dial-up, digital subscriber line, cable, and fiber

internet.

Let Xi represent demographic characteristics of household i. The matrix Xi

contains 1) family annual income of the household, 2) education level of the

householder, and 3) age of the householder. Let Zij represent characteristics of internet

package j that household i encounters. Parameter estimates are carried out by the

maximum likelihood estimation as shown in equation 7.

max 𝐿𝐿(𝛽𝑗 , 𝜃|𝑋𝑖, 𝑍𝑖𝑗) = ∑ ∑ 𝑑𝑖𝑗𝑙𝑛𝑃(𝑦𝑖 = 𝑗)𝑚𝑙=1

𝑛𝑖=1 (7)

For a sample of n independent observations, the maximum likelihood

estimation determines the vector 𝛽𝑗 and θ that maximizes the log-likelihood function

for a given sample data, LL(𝛽𝑗,θ|Xi, Zij). The indicator dij is one if household i chooses

internet package j and zero otherwise (SAS Institute Inc. 2014, 1025).

Let 𝛽𝑗 = {𝛽1, 𝛽2, … , 𝛽𝑚} represent the vectors of parameter associated to

household demographic characteristics. By normalizing β1 to be zero, the vector βj

represents coefficients associated to household characteristics and estimates the

probability that a household chooses internet package j over the reference internet

package.

Let θ represent the vector of parameters related to the internet package’s

characteristics, Zij. Let Zij = {Pij, Sij }; this vector contains the internet package’s

monthly price, Pij and the internet package’s download speed, Sij. Let θ = {θ𝑝, θ𝑠};

this vector contains the coefficient associated with monthly price, θ𝑝 and the

coefficient associated with the download speed, θ𝑠.

The upload speed is excluded from the model since ISPs generally do not

allow households to customize the upload speed. For example, households could not

choose a faster upload speed if they do not upgrade to another internet package with a

faster download speed and more expensive monthly price.

The effect of the internet package’s monthly price is important since previous

literature finds that the demand on high-speed internet is price-elastic (Dutz, Orszag


39

and Willig 2009, 23). This paper adopts methodology for computing own price effect,

own price elasticity, cross price effect, and cross price elasticity from Ang (2007, 4).

The own price effect is shown in equation 8 below.

𝜕Π𝑖𝑗

𝜕Pj= θ𝑝Π𝑖𝑗(1 − Π𝑖𝑗) (8)

The coefficient for monthly price is expected to be negative (θ𝑝 < 0). If an

internet package becomes more expensive, a household will be less likely to choose it.

The own price effect shows a decrease in probability (in percentage unit) of internet

package j being chosen if its monthly price increases by $1. The own price elasticity is

shown in the equation 9 below.

𝜀𝑗 = 𝜕Π𝑖𝑗

𝜕Pj∗

Pj

Π𝑖𝑗 = θ𝑝Pj(1 − Π𝑖𝑗) (9)

The own price elasticity is expected to be negative, like the own price effect.

The own price elasticity shows the decrease in the rate of probability (not in

percentage unit) of internet package j being chosen if its monthly price increases by

1%.

According to Saeedi (2015, 14), price elasticities estimated under the mixed

logit model may be inappropriate for empirical application since the size of own price

elasticity is largely dictated by the price of the product (Pj). Suppose that the market

has many products and Pj is large (i.e. expensive price), the market share (Π𝑖𝑗) of the

product j will be small. The sum of the other products’ market share (1 − Π𝑖𝑗) would

be large and approximately equal to Pj. The large size of Pj and (1 − Π𝑖𝑗) imply that

expensive products tend to have higher elasticity and smaller markup. In other word,

cheaper products tend to have lower elasticity and larger markup. This prediction goes

against the general observation that cheaper products tend to have a smaller markup

than expensive products in the reality. This paper acknowledges the major weakness

of the mixed logit model at this point of discussion.

The cross price effect is shown in the equation 10 below.

𝜕Π𝑖𝑗

𝜕Pk= −θ𝑝Π𝑖𝑗Π𝑖𝑘 (10)

The cross price effect is expected to be positive. If the other internet package k


40

becomes more expensive, a household will be likely to switch to internet package j.

The cross price effect shows the increase in probability (in percentage unit) that

internet package j being chosen if the monthly price of internet package k increases by

$1. The cross price elasticity is shown in the equation 11 below.

𝜀𝑗𝑘 = 𝜕Π𝑖𝑗

𝜕Pk∗

Pk

Π𝑖𝑗= −θ𝑝PkΠ𝑖𝑘 (11)

The cross price elasticity is expected to be positive, like the cross price effect.

The cross price elasticity shows the increase in the rate of probability (not in

percentage unit) of internet package j being chosen if the monthly price of internet

package k increases by 1%.

The coefficient for download speed is expected to be positive (θ𝑠 > 0). The

own speed effects are expected to be positive since households are likely to choose an

internet package with faster speed (see equation 12). The effects and elasticities for the

download speed are not computed since they are much less meaningful than those for

the monthly price.

𝜕Π𝑖𝑗

𝜕Sij = θ𝑠Π𝑖𝑗(1 − Π𝑖𝑗) (12)

Data Sources

Currently, there is no publicly available dataset that contains both household’s

characteristics and internet package’s characteristics. This paper merges the Current

Population Survey Computer and Internet Use Supplement 2013 (CPS Internet 2013)

and the Cost of Connectivity 2013 (COC 2013) dataset to combine the household’s

and the internet package’s characteristics into a single dataset.

The basic CPS collects primarily labor force data about the civilian

noninstitutionalized population living in the United States (all 50 states and the

District of Columbia). The CPS Supplement on Computer and Internet Use annually

surveys more than 55,000 households and asks about: 1) the manner that households

use computers and the Internet in their daily lives; 2) their demographic information

such as the city of residence, education of householders, family annual income, and

age of householders (only householders of 25 years old or older are included in the


41

analysis as suggested in the technical note of the CPS Internet Use); 3) the monthly

price and network technology of their internet package; and 4) an indicator variable

denoting whether the monthly price paid is for internet service only or bundled with

other services (such as television and telephone). Households that subscribe to internet

services bundled with other services are deleted the during data cleaning process.

On the other hand, the Cost of Connectivity (COC) 2013 dataset contains the

list of home broadband and mobile internet service providers (ISPs) that are qualified

according to the authors’ metrics. The dataset gathers internet service offered in 22

cities around the world (9 cities are in the United States) during July to September of

2013. The dataset collects data on internet services utilizing DSL, cable, and fiber-

optic technology. Satellite services are excluded since the authors focus on urban areas

only.

In the COC 2013 dataset, several internet packages’ characteristics are

recorded, such as the name of ISP, network technology, download and upload speeds,

monthly prices (in U.S. dollars using the World Bank’s purchasing power parity), data

caps, penalties if data caps are exceeded (overage fees or throttled speeds), activation

and installation fees, equipment rental or purchase fees, and contract lengths.

This paper merges the CPS Internet 2013 and the COC 2013 datasets by the

area that a household and an internet package occurs. The area merging is the best

possible matching, not the exact one. For example, internet packages in Los Angeles

in the COC 2013 dataset are matched to households in the core based statistical area

(CBSA) no. 31100 of the CPS Internet 2013, covering Los Angeles and adjacent areas

(such as Long Beach and Santa Ana). Such limitation results from the fact that

households in the CPS Internet 2013 are identified based on their CBSA, not on the

individual city.

Moreover, the merging is based on the type of network technology since the

CPS Internet 2013 does not survey the name of internet service provider from

households. For example, households using cable internet in the CPS Internet 2013 are

assumed to have purchased the service from one of the cable companies listed in the

COC 2013.


42

Additionally, the monthly price in the CPS Internet 2013 serves as the merging

key for the speed matching to the COC 2013 since households in the CPS Internet

2013 do not report the speed of their home internet. For example, a household paying

a higher price is assumed to have purchased a faster speed, and vice versa. This paper

creates price ranges for internet packages to assign households into appropriate speed

tiers. The price ranges are 33% lower and upper from the monthly prices listed in the

COC 2013. Households with too high or too low monthly prices are removed from the

speed matching. Besides, the midpoint prices are created to classify households into

different speed of internet packages offered by the same ISP and network technology.

After data cleaning, the number of households in Los Angeles, San Francisco,

Washington, DC and New York City is 657, 202, 627, and 1,009 households,

respectively.

Table 6 shows the lists of important variables after data merging and cleaning.

Ordinal variables such as education and family annual income of householders are

treated as if they were continuous so that they can be included in the mixed logit

model. Characteristics of the householder (also known as the “head of the household”

or “reference person”) are used as proxies for household members’ characteristics.

Table 6: The List of Important Variables.

Variable Variable Definition Note Source

ID 15-digit household identifier sample value:

560234071607296

CPS Internet

2013

CBSA 5-digit core based statistical

(CBSA) area code

CBSA of Los Angeles = 31100

CBSA of New York City = 35620

CBSA of San Francisco = 41860

CBSA of Washington DC = 47900

CPS Internet

2013

Tech Technology of internet service

(nominal variable)

1 = No internet

2 = Dialup

3 = Digital Subscriber line

4 = Cable

5 = Fiber

CPS Internet

2013


43

Table 6 (Continued): The List of Important Variables.

Variable Variable Definition Note Source

Chosen Indicator for the chosen internet

package (a set of nine dummy

variables).

1 = Chosen packages

0 = Non-chosen packages

Nine internet packages in each

city are different.

COC 2013

and the

author's data

merging.

Price Monthly price of internet

service in dollars (continuous

variables)

Households who subscribed to

internet services bundled with

other services such as television

and telephone are excluded.

CPS Internet

2013

Download Download speed in Mbps

(continuous variables)

0 = no internet packages

0.056 = dialup internet

Positive number for high-speed

internet packages.

COC 2013

Age Age of householder in years

(continuous variables)

Only householders of age 25

years or older are included in

the analysis.

CPS Internet

2013

Educ Highest education of

householder (ordinal variables)

1 = Less than high school

2 = High school

3 = Associate degree

4 = Bachelor’s degree

5 = Master’s/Professional/PhD

CPS Internet

2013

Income Family annual income (ordinal

variables).

1 = Less than $10,000

2 = $10,000-$20,000

3 = $20,000-$35,000

4 = $35,000-$60,000

5 = More than $60,000

CPS Internet

2013


44

Table 7-10 shows the list of internet packages in Los Angeles, San Francisco,

Washington, DC, and New York City, respectively. This paper deletes several internet

packages that are suspected of data recording errors, not chosen by households (after

price and speed data merging), or not prevalently available in the cities’s area.

Table 7: The List of Internet Packages in Los Angeles.

Choice ISP Technology Package

Name

Download

Speed

(Mbps)

Upload

Speed

(Mbps)

Monthly

Price ($)

1_LA None None No internet 0 0 0.00

2_LA Unknown DialUp DialUp 0.056 0.033 21.00

3_LA AT&T DSL U-verse 6 1 46.00

4_LA AT&T DSL U-verse 12 1 51.00

5_LA Time Warner Cable Standard 15 1 44.99

6_LA Time Warner Cable Turbo 20 2 54.99

7_LA Time Warner Cable Extreme 30 5 64.99

8_LA Verizon Fiber Fios 15 15 5 74.99

9_LA Verizon Fiber Fios 75 75 35 94.99

Table 8: The List of Internet Packages in San Francisco.

No ISP Technology Package

Name

Download

Speed

(Mbps)

Upload

Speed

(Mbps)

Monthly

Price ($)

1_SF None None No internet 0 0 0

2_SF Unknown DialUp DialUp 0.056 0.033 21.00

3_SF i-Step DSL i-Step 6 6 0.768 69.00

4_SF i-Step DSL i-Step 10 10 1 91.00

5_SF Comcast Cable Xfinity 30 30 6 59.99



8_SF Astound Fiber Wave 50 15 2 30.00

9_SF Astound Fiber Wave 75 30 3 50.00


45

Table 9: The List of Internet Packages in Washington, DC.


Name

Download

Speed

(Mbps)

Upload

Speed

(Mbps)

Monthly

Price ($)

1_DC None None No internet 0 0 0.00

2_DC Unknown DialUp DialUp 0.056 0.033 21.00

3_DC Verizon DSL Enhanced 15 0.8 29.99

4_DC Comcast Cable Xfinity 6 6 1 49.95



7_DC Verizon Fiber Fios 15 15 5 59.99



Table 10: The List of Internet Packages in New York City.

No ISP Technology Package

Name

Download

Speed

(Mbps)

Upload

Speed

(Mbps)

Monthly

Price ($)

1_NY None None No internet 0 0 0.00

2_NY Unknown DialUp DialUp 0.056 0.033 21.00

3_NY Verizon DSL Enhanced 8 0.60 29.99

4_NY Time Warner Cable Standard 15 1.00 34.99

5_NY Time Warner Cable Turbo 20 2.00 54.99

6_NY Time Warner Cable Extreme 30 5.00 64.99

7_NY Verizon Fiber Fios 15 5.00 74.99



Results

Results are separated into three sections: (1) result on the effect of internet’s

monthly price and download speed, 2) result on own and cross price effect, and 3)

result on own and cross price elasticity. Each section presents results of four U.S.

cities (Los Angeles, San Francisco, Washington, DC, and New York City).


46

Results on the Effect of Internet’s Monthly Price and Download Speed

Table 11 shows the result of the mixed logit regression of the households'

choice on internet packages and household's characteristics for the Los Angeles and

San Francisco areas, while Table 12 does the same for the Washington, DC and New

York City areas.

The results of these four cities share similar patterns: 1) the coefficients for

monthly price are negative and statistically significant for all four cities but vary in the

size of coefficients; 2) the coefficients for download speed are positive and

statistically significant but vary in the size of coefficients; 3) the demand for internet

services is price-elastic; and 4) households with better education, higher family

income, and younger adults are more likely to purchase high-speed internet than

households with less education, lower family income, and older adults.

The model has five important variables: (1) monthly price, (2) download

speed, (3) age of householder, (4) education of householder, and (5) family annual

income. Ordinal variables such as education and family annual income of

householders are treated as if they were continuous so that they can be included the

mixed logit model. The discussion for each variable is as follows.

First, all coefficients for monthly price are negative and statistically

significant. The result shows that, if the monthly price of an internet package rises up

while holding other variables constant, households will be less likely to choose that

internet package. The general finding of previous literature (such as Dutz, Orszag and

Willig [2009, 23], NTIA [2014, 9] and Gurin [2010]) confirms that higher monthly

prices of internet packages tend to discourage internet service purchase. The negative

price coefficient leads to negative own-price effect and positive cross-price effect,

which will be further discussed in the result section on own and cross price effect.

Second, all coefficients for download speed are positive and statistically

significant. This result implies that, if the download speed of an internet package rises

up while holding other variables constant, households will expect more enjoyment

from faster internet and are more likely to choose that internet package. This result is

consistent with several previous studies (such as Gurin [2010] and MO Broadband


47

Now [2013, 4]). These studies assert that the download speed is an important feature

of internet packages that households consider when deciding which internet package

to purchase. Moreover, the statistical significance of download speed in the four cities

is indirectly confirmed by the findings of Tomer and Kane (2015, 8), which contend

that internet adoption rates are positively linked to the share of population living in

urban areas. These four cities are obviously urban areas.

Third, most coefficients of householder’s age are negative and statistically

significant; few coefficients are positive but statistically insignificant. This result

indicates that as householders get older, they are less likely to purchase an internet

package. This result is supported by FCC (2010, 174), which finds that the average

age of non-internet users is around 61 years old; senior citizens tend to feel that the

Internet is irrelevant to their daily lives and may not wish to purchase home internet

service.

Fourth, all coefficients of the householder’s education are positive and

statistically significant. This result shows that the schooling is likely to introduce

householders to the benefits of the Internet and encourages them to purchase an

internet service. This result is consistent with the finding of FCC (2010, 178), which

states that 71% of teenagers need the Internet for their school projects, and 65% of

them are assigned Internet-related homework. Familiarity with the Internet during

schooling is likely to encourage a householder to purchase internet service.

Fifth, most coefficients of family annual income are positive and statistically

significant; few coefficients are negative but statistically insignificant. This result

reveals that wealthier households have a greater ability to afford the monthly cost of

internet connection than poorer ones. This result is in line with that of NTIA and ESA

(2011, 48), which find that households with family income greater than $25,000 are

more likely to purchase an internet connection than households with family income

lower than $25,000.

The additional information section provides several measures of fit such as the

log likelihood of the full vs null models. The p-value of the likelihood ratio test is less

than 0.0001, indicating that the explanatory power of the full model is considerably


48

stronger than that of the null model. The McFadden R-square for four cities have

typical values (ranging from 34.32% for Washington, DC to 55.60% for San

Francisco).

Table 11: The Result of the Mixed Logit Regression of Households' Choice on Internet

Package's and Household's Characteristics in Los Angeles and San Francisco Area.

Los Angeles

San Francisco

Coef. Std.Err. Sig.

Coef. Std.Err. Sig.

Internet package's

characteristics

Monthly Price ($) -0.1732 0.0158 ***

-0.2532 0.0363 ***

Download Speed (Mbps) 0.2192 0.0486 *** 0.2180 0.0460 ***

Household's characteristics

Nine internet packages:

1st package (no internet)

(no internet)

Age of householder Omitted Omitted

Omitted Omitted

Education level of householder Omitted Omitted

Omitted Omitted

Family annual income Omitted Omitted Omitted Omitted

2nd package (dialup 0.056 Mbps)

(dialup 0.056 Mbps)

Age of householder -0.0239 0.0089 ***

-0.0074 0.0125

Education level of householder 0.6010 0.1871 ***

0.2784 0.4312

Family annual income -0.2383 0.1450 0.6178 0.3509 *

3rd package (U-verse 6 Mbps)

(i-Step 6 Mbps)

Age of householder 0.0064 0.0072

0.0608 0.0309 **


0.8789 0.3581 **

Family annual income 0.8748 0.1093 *** 1.7872 0.5135 ***

4th package (U-verse 12 Mbps)

(i-Step 10 Mbps)

Age of householder -0.0182 0.0101 *

0.0390 0.0316


3.6089 0.6379 ***

Family annual income 0.9835 0.1522 *** -0.4511 0.3794

5th package (TWC 15 Mbps)

(Xfinity 30 Mbps)


-0.0058 0.0160


1.0053 0.2591 ***



(Xfinity 50 Mbps)


-0.0110 0.0159


0.8102 0.2070 ***



49

Table 11: (Continued) The Result of the Mixed Logit Regression of Households' Choice on

Internet Package's and Household's Characteristics in Los Angeles and San Francisco Area.

Los Angeles

San Francisco

Coef. Std.Err. Sig.

Coef. Std.Err. Sig.


(Xfinity 105 Mbps)


-0.0246 0.0163


0.4524 0.2280 **


8th package (Fios 15 Mbps)

(Wave 15 Mbps)


-0.0415 0.0141 ***


0.3123 0.2158



(Wave 30 Mbps)


-0.0347 0.0168 **

Education level of householder 0.5252 0.2808 *

0.6229 0.1542 ***


Additional Information

Number of observations 657

202

McFadden R-squared 44.16% 55.60%

Log likelihood of null model -1,444.00

-443.83

Log likelihood of full model -806.05

-197.08

p-value of likelihood ratio test <0.0001 <0.0001

Significance: *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.10.

Note: Household ordinal variables are treated as if they were continuous.


50

Table 12: The Result of the Mixed Logit Regression of Households' Choice on Internet

Package's and Household's Characteristics in Washington, DC and New York City Area.

Washington, DC

New York City

Coef. Std.Err. Sig

Coef. Std.Err. Sig

Internet package's

characteristics

Monthly Price ($) -0.1428 0.0146 ***

-0.1087 0.0171 ***

Download Speed (Mbps) 0.0425 0.0198 ** 0.0716 0.0293 **

Household's characteristics

Nine internet packages:

1st package (no internet)

(no internet)

Age of householder Omitted Omitted

Omitted Omitted

Education level of householder Omitted Omitted

Omitted Omitted

Family annual income Omitted Omitted Omitted Omitted

2nd package (dialup 0.056 Mbps)

(dialup 0.056 Mbps)

Age of householder -0.0237 0.0103 **

-0.0490 0.0133 ***

Education level of householder 0.0444 0.1994

0.4782 0.1682 ***

Family annual income 0.4240 0.1307 *** 0.1964 0.1841

3rd package (Enhanced 15 Mbps)

(Enhanced 8 Mbps)


-0.0378 0.0089 ***


0.4887 0.1567 ***

Family annual income 0.4869 0.1126 *** 0.2911 0.1346 **

4th package (Xfinity 6 Mbps)

(TWC 15 Mbps)


-0.0333 0.0047 ***


0.5262 0.0801 ***



(TWC 20 Mbps)


-0.0180 0.0069 ***


0.5925 0.0941 ***



(TWC 30 Mbps)


-0.0282 0.0075 ***


0.5667 0.1146 ***



51

Table 12 (Continued): The Result of the Mixed Logit Regression of Households' Choice on

Internet Package's and Household's Characteristics in Washington, DC and New York City

Area.

Washington, DC

New York City

Coef. Std.Err. Sig.

Coef. Std.Err. Sig.


(Fios 15 Mbps)

Age of householder -0.0051 0.0084

-0.0164 0.0135


0.5352 0.1470 ***



(Fios 50 Mbps)

Age of householder -0.0064 0.0111

-0.0685 0.0225 ***


0.7752 0.1463 ***



(Fios 75 Mbps)


-0.0378 0.0129 ***

Education level of householder 0.3667 0.1713 **

0.5154 0.1548 ***


Additional Information

Number of observations 627 1,009

McFadden R-squared 34.32% 35.91%

Log likelihood of null model -1,378.00

-2,217.00

Log likelihood of full model -904.86

-1,421.00

p-value of likelihood ratio test <0.0001 <0.0001

Significance: *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.10.

Note: Household ordinal variables are treated as if they were continuous.


52

Results on Own and Cross Price Effect

Tables 13-16 show the results on own and cross price effects from a $1

increase in the monthly price of internet packages in Los Angeles, San Francisco,

Washington, DC, and New York City, respectively. The numbers shown on these

tables are in probability units, not in dollar units.

The tables have own price effect on their diagonal elements and cross price

effect on their off-diagonal elements. The upper triangle elements of the matrix are

omitted since cross price effects are symmetric. Consistent with Equations 8 and 10,

all own price effects are negative and all cross price effects are positive.

In Tables 13-16, vertical columns represent the current internet package of

households, and horizontal rows show the effect of the $1 increase in the monthly

price of internet packages. When the vertical columns and horizontal rows coincide on

the same internet package, the number shown on the table is own price effect. For

example, the top left number in Table 13 (-0.00126) illustrates the negative own price

effect; the $1 increase in the monthly price of the dial-up internet package decreases

the probability that households will choose this internet package by 0.126%. On the

other hand, when the vertical columns and horizontal rows coincide on different

internet packages, the numbers shown on the table are the cross price effects. For

example, the number below the top left number in Table 13 (0.00012) illustrates the

positive cross price effect; the $1 increase in the monthly price of the third internet

package increases the probability that households will choose the dial-up internet

package by 0.012%.

On average, own and cross price effects tend to be smaller if households are

currently paying expensive monthly prices. For example, Table 13 shows that

households subscribing to the ninth internet package with the monthly price of $94.99

perceive smaller price effects from the $1 increase in monthly price than households

subscribing to the eighth internet package with the monthly price of $74.99.

The substitutability among internet packages solely depends on probability of

related choices (see Equation 8 and 10). The mixed logit model could not reflect

functional substitutability of internet packages. For example, some households may


53

have only a cable connector in their home and cannot connect to the Internet by other

technologies at all. In reality, such households have zero cross price effect to other

internet technology. Moreover, the cross price effects depend on the probability of two

related choices and are unaffected by the addition of outside choices (see equation 10).

This limitation is common to the mixed logit model and is generally known as

independence of irrelevant alternatives (IIA).

Table 13: Own and Cross Price Effect of Internet Packages in Los Angeles (in Probability

Unit).

Current Package

Other

Package 2_LA 3_LA 4_LA 5_LA 6_LA 7_LA 8_LA 9_LA

2_LA -0.00126

3_LA 0.00012 -0.01725

4_LA 0.00006 0.00193 -0.01006

5_LA 0.00025 0.00597 0.00333 -0.02591

6_LA 0.00006 0.00161 0.00097 0.00287 -0.00898

7_LA 0.00003 0.00060 0.00043 0.00124 0.00041 -0.00378

8_LA 0.00002 0.00056 0.00031 0.00093 0.00025 0.00010 -0.00292

9_LA 0.00001 0.00016 0.00013 0.00035 0.00014 0.00010 0.00002 -0.00127

Table 14: Own and Cross Price Effect of Internet Packages in San Francisco (in Probability

Unit).

Current Package

Other

Package 2_SF 3_SF 4_SF 5_SF 6_SF 7_SF 8_SF 9_SF

2_SF -0.00471

3_SF 0.00026 -0.00932

4_SF 0.00003 0.00005 -0.00105

5_SF 0.00087 0.00314 0.00023 -0.02554

6_SF 0.00048 0.00163 0.00007 0.00560 -0.01643

7_SF 0.00063 0.00161 0.00004 0.00590 0.00335 -0.01878

8_SF 0.00012 0.00018 0.00001 0.00087 0.00051 0.00067 -0.00414

9_SF 0.00012 0.00024 0.00002 0.00107 0.00063 0.00072 0.00013 -0.00419


54

Table 15: Own and Cross Price Effect of Internet Packages in Washington, DC (in

Probability Unit).

Current Package

Other

Package 2_DC 3_DC 4_DC 5_DC 6_DC 7_DC 8_DC 9_DC

2_DC -0.00298

3_DC 0.00030 -0.01163

4_DC 0.00054 0.00295 -0.02061

5_DC 0.00017 0.00124 0.00279 -0.00921

6_DC 0.00017 0.00114 0.00264 0.00126 -0.00869

7_DC 0.00023 0.00145 0.00370 0.00154 0.00149 -0.01155

8_DC 0.00005 0.00029 0.00070 0.00029 0.00027 0.00036 -0.00260

9_DC 0.00009 0.00041 0.00112 0.00035 0.00036 0.00054 0.00010 -0.00397

Table 16: Own and Cross Price Effect of Internet Packages in New York City (in Probability

Unit).

Current Internet Package

Other

Package 2_NY 3_NY 4_NY 5_NY 6_NY 7_NY 8_NY 9_NY

2_NY -0.00183

3_NY 0.00007 -0.00295

4_NY 0.00050 0.00083 -0.01920

5_NY 0.00026 0.00044 0.00513 -0.01300

6_NY 0.00016 0.00028 0.00340 0.00199 -0.00892

7_NY 0.00005 0.00009 0.00118 0.00068 0.00046 -0.00334

8_NY 0.00002 0.00002 0.00025 0.00014 0.00010 0.00003 -0.00071

9_NY 0.00006 0.00010 0.00110 0.00062 0.00042 0.00014 0.00003 -0.00321

Results on Own and Cross Price Elasticity

Tables 17-20 show the results on own and cross price elasticity from a 1%

increase in the monthly price of internet packages in Los Angeles, San Francisco,

Washington, DC, and New York City, respectively. The numbers shown on these

tables are the rates of change in probability, not in probability units.


55

The tables have own price elasticity on their diagonal elements and cross price

elasticity on their off-diagonal elements. The upper triangle elements are not omitted

since cross price elasticities are asymmetric. Consistent with Equations 9 and 11, all

own price elasticities are negative and all cross price elasticities are positive.

In Tables 17-20, vertical columns represent the current internet package of

households, and horizontal rows show the effect of the 1% increase in the monthly

price of internet packages. When the vertical columns and horizontal rows coincide on

the same internet package, the number shown on the table is own price elasticity. For

example, the top left number in Table 17 (-3.61492) is own price elasticity; the 1%

increase in the monthly price of the dial-up internet package decreases the rate of

probability that households will choose this internet package by 3.61492. On the other

hand, when the vertical columns and horizontal rows coincide on different internet

packages, the numbers shown on the table are cross price elasticities. For example, the

number below the top-left number in Table 17 (0.98130) is the cross price elasticity;

the 1% increase in the monthly price of the third internet package increases the rate of

probability that households will choose dial-up internet package by 0.98130.

All own price elasticities are greater than 1, indicating that the demand for

internet services is price-elastic. Own price elasticities are large since internet service

is a highly differentiated product. Internet service of an ISP can be easily substituted

by internet service of other ISPs. On the other hand, while several cross price

elasticities are greater than 1, many cross price elasticities are less than 1 (or price-

inelastic). These results correspond to those of Dutz, Orszag and Willig (2009, 24),

who find own price elasticities to be large, and negative and cross price elasticities to

be small and positive.

On average, own price elasticities tend to be larger if households are currently

paying expensive monthly prices for internet service. For example, Table 17 shows

that the own price elasticity of the ninth internet package (-16.334903) with the

monthly price of $94.99 is larger than the own price elasticity of the eighth internet

package (-12.65537) with the monthly price of $74.99. The explanation for this result

pattern is previously discussed in the model section (Saeedi 2015, 14).


56

Table 17: Own and Cross Price Elasticity of Internet Packages in Los Angeles (in Rate of

Change of Probability).


Other

Package 2_LA 3_LA 4_LA 5_LA 6_LA 7_LA 8_LA 9_LA

2_LA -3.61492 0.02702 0.02702 0.02702 0.02702 0.02702 0.02702 0.02702

3_LA 0.98130 -6.83853 0.98130 0.98130 0.98130 0.98130 0.98130 0.98130

4_LA 0.57203 0.57203 -8.28357 0.57203 0.57203 0.57203 0.57203 0.57203

5_LA 1.88176 1.88176 1.88176 -5.66422 1.88176 1.88176 1.88176 1.88176

6_LA 0.54500 0.54500 0.54500 0.54500 -9.01601 0.54500 0.54500 0.54500

7_LA 0.26292 0.26292 0.26292 0.26292 0.26292 -11.01052 0.26292 0.26292

8_LA 0.25570 0.25570 0.25570 0.25570 0.25570 0.25570 -12.65537 0.25570

9_LA 0.12660 0.12660 0.12660 0.12660 0.12660 0.12660 0.12660 -16.33490

Table 18: Own and Cross Price Elasticity of Internet Packages in San Francisco (in Rate of



Choice 2_SF 3_SF 4_SF 5_SF 6_SF 7_SF 8_SF 9_SF

2_SF -5.23149 0.06977 0.06977 0.06977 0.06977 0.06977 0.06977 0.06977

3_SF 0.73843 -16.11678 0.73843 0.73843 0.73843 0.73843 0.73843 0.73843

4_SF 0.07931 0.07931 -15.68532 0.07931 0.07931 0.07931 0.07931 0.07931

5_SF 2.59132 2.59132 2.59132 -10.56469 2.59132 2.59132 2.59132 2.59132

6_SF 1.05408 1.05408 1.05408 1.05408 -17.37729 1.05408 1.05408 1.05408

7_SF 2.23708 2.23708 2.23708 2.23708 2.23708 -25.69853 2.23708 2.23708

8_SF 0.08797 0.08797 0.08797 0.08797 0.08797 0.08797 -7.50500 0.08797

9_SF 0.14816 0.14816 0.14816 0.14816 0.14816 0.14816 0.14816 -12.45594


57

Table 19: Own and Cross Price Elasticity of Internet Packages in Washington DC (in Rate of



Choice 2_DC 3_DC 4_DC 5_DC 6_DC 7_DC 8_DC 9_DC

2_DC -2.94096 0.07844 0.07844 0.07844 0.07844 0.07844 0.07844 0.07844

3_DC 0.47754 -3.91813 0.47754 0.47754 0.47754 0.47754 0.47754 0.47754

4_DC 1.81342 1.81342 -5.39005 1.81342 1.81342 1.81342 1.81342 1.81342

5_DC 0.71313 0.71313 0.71313 -7.30321 0.71313 0.71313 0.71313 0.71313

6_DC 0.82212 0.82212 0.82212 0.82212 -8.99967 0.82212 0.82212 0.82212

7_DC 1.07496 1.07496 1.07496 1.07496 1.07496 -7.49370 1.07496 1.07496

8_DC 0.22626 0.22626 0.22626 0.22626 0.22626 0.22626 -9.80463 0.22626

9_DC 0.40285 0.40285 0.40285 0.40285 0.40285 0.40285 0.40285 -11.11371

Table 20: Own and Cross Price Elasticity of Internet Packages in New York City (in Rate of



Other

Package 2_NY 3_NY 4_NY 5_NY 6_NY 7_NY 8_NY 9_NY

2_NY -3.5819 0.0630 0.0630 0.0630 0.0630 0.0630 0.0630 0.0630

3_NY 0.1455 -5.0475 0.1455 0.1455 0.1455 0.1455 0.1455 0.1455

4_NY 1.6039 1.6039 -4.4594 1.6039 1.6039 1.6039 1.6039 1.6039

5_NY 1.3883 1.3883 1.3883 -7.9697 1.3883 1.3883 1.3883 1.3883

6_NY 1.0674 1.0674 1.0674 1.0674 -10.2371 1.0674 1.0674 1.0674

7_NY 0.4161 0.4161 0.4161 0.4161 0.4161 -12.4699 0.4161 0.4161

8_NY 0.0976 0.0976 0.0976 0.0976 0.0976 0.0976 -14.6193 0.0976

9_NY 0.5073 0.5073 0.5073 0.5073 0.5073 0.5073 0.5073 -16.0127

Conclusion

The contribution of this paper to previous literature is that it includes both

monthly price and download speed to model and predict household’s choice of

internet service. While previous literature excludes download speed from its analysis,

this paper takes this factor into consideration. This paper merges the Current

Population Survey Computer and Internet Use Supplement 2013 (CPS Internet 2013)


58

and the Cost of Connectivity 2013 (COC 2013) dataset to combine household’s and

internet package’s characteristics into a single dataset.

Data on householder’s characteristics (age, education, and family annual

income) and internet package’s characteristics (monthly price and download speed) is

used to model household’s choice of internet service in four U.S. cities: Los Angeles,

San Francisco, Washington, DC, and New York City. The econometric results of these

four U.S. cities have similar and consistent patterns.

Results from the householder’s characteristics indicate that older householders

or those with less education are less likely to purchase home internet service. These

householders need digital literacy education to familiarize with and get themselves

start using the Internet. In addition, households with low family income tend to

consider internet service not worth the money. Subsidizing the cost of internet service

is a good way to encourage their internet adoption.

Results from internet package’s characteristics indicate that the coefficient of

monthly price is found to be negative and statistically significant in household’s

choice of internet service. The demand for internet service is found to be price-elastic.

A decrease in monthly price should convert a large number of non-internet-using

households to internet-using ones. On the other hand, the download speed of internet

packages is found to be positive and statistically significant in household’s choice of

internet service. This paper shows that the download speed is a feature of internet

packages that households care about when choosing internet packages.


59

CHAPTER IV

WELFARE EVALUATION

Introduction

The Internet is integrated into our daily lives. Many American households

subscribe to a high-speed (a.k.a. broadband) internet service, but how much welfare

improvement do they earn from going online? This paper wishes to provide a useful

suggestion to the National Broadband Plan, which intends to make high-speed internet

available everywhere in the United States. Its investment cost ranges from $23 billion

to $350 billion to install network infrastructures with a download speed of 4 Mbps to

100 Mbps or faster, respectively (Federal Communication Commission: FCC, 2010

and DSL Report 2009).

Some criticize that the investment cost of the National Broadband Plan is too

large compared to a small growth in the gross domestic product (GDP) in rural or low

population density areas. However, the economic benefit of high-speed internet is not

totally captured by GDP growth since a large proportion of the economic benefit is in

the form of welfare improvement. Welfare improvement is not measured in GDP

calculation but results in a better quality of life for internet users. For example,

students may access academic materials on the Internet and save money by purchasing

fewer physical textbooks. The money saving is an example of welfare improvement

from high-speed internet that is not measured in GDP calculation.

Previous literature estimates welfare improvement by data on households’

demographic and internet package’s monthly price, but not internet speed. Such an

exclusion may result in omitted variable bias since many households consider speed

an important feature of the internet package they purchase (FCC 2010b, 10). This

paper enriches existing literature by including download speed in the estimation of

welfare improvement.


60

This paper focuses on welfare evaluation under various scenarios of price and

download speed since strong evidence of a decline in quality-adjusted price has been

found by previous literature (Greenstein and McDevitt 2011, 5 and FCC 2015b).

Several interesting research questions can be addressed. First, how much does a price

decline encourage non-internet and dialup users to become high-speed internet users?

Second, how much welfare improvement would households earn from a

complementary speed upgrade? Third, does the amount of welfare improvement vary

across household demographics? For example, from a decline in quality-adjusted

price, do high-income households earn more in welfare improvement than low-income

households?

This paper contains six sections: (1) literature review, (2) model, (3) data

source, (4) result, (5) the conclusion, and (6) the appendix.

Literature Review

Households are willing to pay for internet service with faster speed and

additional useful features (Rosston, Savage, and Waldman 2011, 2). The authors

collect answers to a national questionnaire survey (6,271 observations) in 2009-2010

to estimate household demand for high-speed internet service. Each respondent

answers four choice experiment questions from two sequential choice tasks. First, a

respondents are asked to indicate their preference for alternatives A or B, which differ

by three important internet features (cost, speed, and reliability) and one of five

internet activities (accessing the Internet on laptop computers, watching high-

definition movies, designating download priorities, interacting with healthcare

specialists, and making free video calls). The speed features have three rankings: 1)

slow: similar to dialup, and good for light email and web surfing; 2) fast: much faster

downloads and uploads, which are good for photo sharing and video watching; and 3)

very fast: blazing fast downloads and uploads which are good for gaming and

watching high-definition movies. Second, respondents are asked to state their

preference for their current internet service (status quo) versus the choice A or B that

they chose in the first task.


61

Rosston, Savage, and Waldman (2011, 29) estimate marginal utility and

willingness to pay from observed choices by a random utility model and a bivariate

discrete choice model. Their important findings are: 1) households are willing to pay

$45 for a speed upgrade from slow to fast and $48 for a speed upgrade from slow to

very fast, respectively (in other words, very fast service is worth $3 more than fast

service); 2) the willingness to pay for speed increases with education, income and

online experience but decreases with age; and 3) the valuations for internet service

increase substantially with online experience. The third finding has important

implications; the provision of digital literacy education, free access to internet service,

and discounted internet service have a great potential to encourage internet adoption

by American households.

Internet users are willing to pay for faster internet speed in order to utilize

online services more effectively (Computer and Communications Industry

Association: CCIA 2013, 1). CCIA asserts that about 43% of internet users in Italy,

France, and Germany purchase speed upgrades for a better internet experience.

Moreover, the likelihood to upgrade increases when internet users are more

experienced in internet using. For example, about 31% of internet users who use more

demanding services (such as downloading video clips, uploading large files, and

making a voice over internet protocol) reply that they are willing to pay for faster

internet speed if they receive a good deal from their internet service provider.

Analyses at brand levels (such as Skype, YouTube, Play Station, and Xbox Live)

show that at least 61% of internet users are willing to purchase a speed upgrade to

ensure flawless calls, smooth video streaming, and an uninterrupted gaming

experience.

On the other hand, new online applications are also beneficial for internet

service providers (CCIA 2013, 8). About 85% of internet users who upgrade their

speed reply that they are fairly or very satisfied with their internet service purchase,

compared to 77% of overall internet users (both upgraded and non-upgraded users)

who reply to the same question. CCIA suggests that ISPs who advertise their internet


62

service alongside popular online applications tend to be more successful in attracting

new customers and retaining existing customers.

Faster internet speed has positive socioeconomic effects on household income

(Ericsson, Arthur D. Little, and Chalmers University of Technology 2013, 21). The

authors claim that a faster broadband speed boosts personal productivity, enables more

flexible work arrangements, and facilitates home-based businesses as a replacement or

a complement to salaried jobs. The authors use the Propensity Score Matching method

to compare the income of people who have access to high-speed internet (broadband)

and the income of people without broadband access. People with highly similar

characteristics, but who differ by access to broadband, are matched and compared

regrading their difference in income. Their importance findings are 1) people in

OECD countries with 4 Mbps of broadband have an increase in household income by

$2,100 per year; 2) people in BIC countries with 0.5 Mbps of broadband have an

increase in household income by $800 per year; 3) people upgrading from 0.5 Mbps to

4 Mbps have an increase in household income by around $322 per month, and 4)

people upgrading from 0.5 to 4 Mbps have an increase in household income by $46

per month.

Previous literature in U.S. high-speed internet finds evidence of a decline in

quality-adjusted price. Greenstein and McDevitt (2011, 5) assert that U.S. internet

service providers have been competing by offering faster internet speed to their

customers from 2004 through 2009. The authors create a consumer price index for

standalone and bundled high-speed internet services using data from 1,500 contracts

offered by DSL and cable providers. With a mix of matched-model methods and

hedonic price index estimations, the authors conclude that the decline in quality-

adjusted price is modest, ranging from 3% to 10% for the five-year period. The

authors argue that their index is more useful than the index created by the Bureau of

Labor Statistics (BLS). The BLS index declines too slowly because it does not break

down into separate indexes for different technologies (for example, DSL vs. cable) or

regions (for example, urban vs. rural), which are central in policy evaluation.


63

The Federal Communications Commission (2015b, 12) finds evidence of a

decline in quality-adjusted price from 2011 through 2013. The agency collected data

on advertised price for residential broadband services in 40 countries (including the

United States), most of which are members of the OECD. For unlimited fixed

broadband, the United States ranked 21st least expensive out of 34 countries in 2012

and 31st least expensive out of 33 countries in 2013, respectively. However, the

average advertised speed of the U.S. plans increased from 7.59 Mbps in 2012 (28th of

34 countries) to 10.73 Mbps in 2013 (19th of 33). On the other hand, for plans with

usage limits, U.S. plan prices divided by the number of gigabytes (GB) of data

allowance tend to be the least expensive among countries surveyed. The United States

was the fifth least expensive in 2012 with a price of $1.25 per GB. However, the

United States was the fourth least expensive in 2013 with a price per GB of $1.65.

In the FCC’s report, the cost per unit of speed is used to compare broadband

prices across different countries. Ookla’s Home Value Index, created from test results

from speedtest.net (its web-based service), ranks countries by the median cost in U.S.

dollars per Megabit per second (Mbps). The average weighted price per Mbps

($/Mbps) in the United States fell from $6.14 in 2011 to $5.39 in 2012, and to $4.30 in

2013. By this metric, the United States shows its improvement in the least expensive

ranking (25th least expensive out of 35 countries in 2011, 21st least expensive out of

37 countries in 2012, and 23rd least expensive out of 37 countries in 2013).

Considering together the finding of Greenstein and McDevitt (2011, 5) and the FCC

(2015b, 12), U.S. high-speed internet has shown a decline in quality-adjusted price

since 2004.

Questions relevant to welfare changes of internet users should involve

decreased monthly price and increased internet speed. The decreased price of internet

packages not only raises the welfare of current internet users but also encourages some

of non-internet users to start purchasing an internet connection. Predicting an increase

in internet adoption rate from monthly price reduction is a worthwhile investigation.

On the other hand, the faster speed directly raises the welfare of internet users since it


64

shortens internet user’s waiting time. Quantifying welfare improvement from faster

speed is also an interesting research question.

Constructing appropriate monetary measure is an important problem in welfare

economics (Nicholson and Snyder 2008, 165). The expenditure function is the starting

point to study the relationship between price and welfare. Technological advancement

in internet service (so-called good x) is analogous to a price decline from 𝑝𝑥0 to 𝑝𝑥

1.

Initially, an internet user requires expenditure of 𝐸(𝑝𝑥0, 𝑝𝑦, 𝑈0) to reach a utility level

𝑈0 (where y refers to composite goods other than internet service). Thanks to the price

decline, an internet user would require a lower amount of expenditure 𝐸(𝑝𝑥1, 𝑝𝑦, 𝑈0) to

reach the same level of utility, 𝑈0. Compensated variation (CV), shown in equation

13, is the amount of income to be taken away from an internet user to make him or her

feel indifferent between original price and decreased price situations.

𝐶𝑉 = 𝐸(𝑝𝑥0, 𝑝𝑦, 𝑈0) − 𝐸(𝑝𝑥

1, 𝑝𝑦, 𝑈0) (13)

Nicholson and Snyder (2008, 165) note that individuals’ utility function and

their associated indifference curves needed to estimate CV are not directly observable.

This paper adopts empirical methods to estimate CV from Karlstrom and Morey

(2004, 4) and Nevo (2000, 517). The method will be discussed in detail in the model

section.

Model

This paper uses econometric estimates from the second essay to quantify

welfare change from cheaper monthly price and faster download speed. This paper

focuses on two scenarios: 1) a decrease in the monthly price while keeping the

download speed unchanged; and 2) an increase in the download speed while keeping

the monthly price unchanged. This paper quantifies the benefits from these scenarios

into measurable dollars using the methods of Karlstrom and Morey (2004, 4) and

Nevo (2000, 517). The indirect utility function is shown in equation 14.

𝑈𝑖𝑗 = 𝑉𝑖𝑗 + 𝜀𝑖𝑗 (14)


65

The utility of individual i from internet package j (𝑈𝑖𝑗) is a function of

predicted utility from observable characteristics of internet package and household

(𝑉𝑖𝑗), and random error term (𝜀𝑖𝑗). The random error term is not observable by a

researcher and is assumed to be drawn from a joint density function 𝑓𝜀(𝜀).

To obtain empirical estimates of compensated variation, this paper proposes a

linear specification for the predicted utility (𝑉𝑖𝑗) as shown in equation 15.

𝑉𝑖𝑗 = 𝛽𝑗𝑎𝑎𝑔𝑒𝑖 + 𝛽𝑗

𝑖𝐼𝑛𝑐𝐿𝑒𝑣𝑒𝑙𝑖 + 𝛽𝑗𝑒𝐸𝑑𝑢𝑐𝐿𝑒𝑣𝑒𝑙𝑖 + 𝜃𝑝𝑃𝑟𝑖𝑐𝑒𝑖𝑗 + 𝜃𝑠𝑆𝑝𝑒𝑒𝑑𝑖𝑗 (15)

The predicted utility (𝑉𝑖𝑗) is a linear function of five observable characteristics,

which include 1) age of householders (𝑎𝑔𝑒𝑖); 2) family income level of households

(𝐼𝑛𝑐𝐿𝑒𝑣𝑒𝑙𝑖); 3) education level of householders (𝐸𝑑𝑢𝑐𝐿𝑒𝑣𝑒𝑙𝑖); 4) monthly price of

internet packages (𝑃𝑟𝑖𝑐𝑒𝑖𝑗); and 5) download speed of internet packages (𝑆𝑝𝑒𝑒𝑑𝑖𝑗).

The coefficients of household characteristics are subscripted j, indicating that there are

j parameter estimates for each household characteristic (as shown in the Result section

of the second essay). On the other hand, the coefficients of internet packages do not

have any subscript, indicating that there is only one parameter estimate for each

internet package characteristic (as shown in the Result section of the second essay).

Equation 15 shows that the derivative of the predicted utility with respect to

the monthly price is simply the constant 𝜃𝑝, which shows that the marginal utility of

money is constant. The marginal utility of income is proxied by the coefficient of

monthly price since a one-dollar decrease in the monthly price is analogous to a one-

dollar increase in the household’s income. If a researcher assumes that the error term

𝜀𝑖𝑗 in equation 14 has a Gumbel extreme value distribution, the expected compensated

variation, 𝐸(𝐶𝑉) can be computed as shown in equation 16 (Karlstrom and Morey

2004, 4).

𝐸(𝐶𝑉) = 1

𝛼𝑖[𝐸[𝑈(𝑦𝑖

0, 𝑝𝑖𝑗0 , 𝑍𝑖𝑗

0 )] − 𝐸[𝑈(𝑦𝑖0, 𝑝𝑖𝑗

1 , 𝑍𝑖𝑗1 )]]

= 1

𝛼𝑖[ln ∑ 𝑒𝑉(𝑦𝑖

0,𝑝𝑖𝑗0 ,𝑍𝑖𝑗

0 )9𝑗=1 − ln ∑ 𝑒𝑉(𝑦𝑖

0,𝑝𝑖𝑗1 ,𝑍𝑖𝑗

1 )9𝑗=1 ] (16)

The expected compensated variation could be computed as the product of the

inverse of marginal utility of money and the difference between the two expected

utility levels, where 𝐸[𝑈(𝑦𝑖0, 𝑝𝑖𝑗

0 , 𝑍𝑖𝑗0 )] and 𝐸[𝑈(𝑦𝑖

0, 𝑝𝑖𝑗1 , 𝑍𝑖𝑗

1 )] represent the expected


66

utility level in the initial state and the expected utility level in the new state,

respectively. The expected utility level is mathematically summarized into the log-sum

term in the well-accepted paper of McFadden (1995, 17). The log-sum term is defined

as the log of the sum of the exponentiated utilities across all internet packages

available to a household. The marginal utility of income (𝛼𝑖) is proxied by the

coefficient of monthly price (𝜃𝑝) as previously stated above.

Karlstrom and Morey (2004, 4) argue that equation 16 is the classic no

income-effect model when the utility is a linear function of the income. Household

income is dropped out from the equation, and the compensated variation is not a

function of household income. Nevo (2010, 517) has a similar argument; he notes that

“this [linear] form of the indirect utility can be derived from a quasilinear utility

function, which is free of wealth effects.” The linear specification is reasonable when

the product’s price is a small portion of the consumer’s income (such as a $10

decrease in the monthly price of internet packages), while it is unreasonable when the

product’s price is a large portion of the consumer’s income (such as automobile and

housing purchases).

In addition to quantifying compensated variation, this paper also estimates an

expected increase in internet adoption rate. It is evaluated under the two situations: 1)

a decrease in the monthly price while keeping the download speed unchanged; and 2)

an increase in the download speed while keeping the monthly price of internet

packages unchanged. The utility of households for each internet package choice is re-

calculated after one of the two favorable changes is applied. The criteria for

classifying a non-internet using household to be a newly high-speed internet using

household are shown in the equation 17 below.

𝑉𝑖𝑘′ > 𝑉𝑖1

′ and 𝑉𝑖𝑘′ > 𝑉𝑖2

′ , where 𝑘 ≠ 1,2 (17)

The prime superscript (such the one on 𝑉𝑖𝑘′ ) represents the household’s

predicted utility after one of the two favorable changes is applied. If the predicted

utility from any k high-speed internet package is greater than the predicted utility from

no internet purchase (𝑉𝑖𝑘′ > 𝑉𝑖1

′ ) and is also greater than the predicted utility from dial-

up internet (𝑉𝑖𝑘′ > 𝑉𝑖2

′ ), such household is predicted to purchase a high-speed


67

(broadband) internet package after it experiences a favorable change in internet’s

monthly price or download speed.

Data Sources

This paper shares the same data source as the second essay. Data sources are

from the Current Population Survey Computer and Internet Use Supplement 2013

(CPS Internet 2013) and the Cost of Connectivity 2013 (COC 2013). However,

additional variables are created by this paper’s author to estimate a varying amount of

compensated variation and different rates of internet adoption by household’

demographics (AgeGr, EducGr, IncomeGr) and internet speed (SpeedGr), as shown in

Table 21.

Table 21: Group of Variables for Welfare Evaluation by Household’s Characteristics and

Internet Package’s Characteristics.

Variable Variable Definition Note

AgeGr Group of householder's

age (ordinal variables)

1 = 25-44 years old (1st to 33rd

percentile)

2 = 45-60 years old (34th to

66th percentile)

3 = 61-85 years old (67th to

99th percentile)

EducGr Group of householder's

highest education (ordinal

variables)

1 = High school or lower

2 = Associate or Bachelor's

degree

3 = Master's or Professional or

PhD

IncomeGr Group of family annual

income (ordinal variables)

1 = Income less than $20,000

2 = Income $20,000-$60,000

3 = Income greater than

$60,000


68

Table 21 (Continued): Group of Variables for Welfare Evaluation by Household’s

Characteristics and Internet Package’s Characteristics.

Variable Variable Definition Note

SpeedGr Group of internet

download speed (ordinal

variables)

1 = 0 or 0.056 Mbps (no

internet or dialup internet)

2 = 1-24 Mbps (below FCC

broadband standard)

3 = 25 Mbps or faster (meeting

FCC broadband standard)

CVAvg Compensated variation in

dollars (continuous

variables)

The average of compensated

variation is computed for each

group of variables: AgeGr,

EducGr, IncomeGr, and

SpeedGr.

Adopt Predicted internet

adoption decision after a

change in monthly price

or download speed

(binary variables)

1 = a household is predicted to

adopt an internet connection.

0 = a household is predicted

not to adopt an internet

connection.

AdoptAvg Predicted internet

adoption rate in

percentage (continuous

variables)

The average adoption rate is

computed for each group of

variables: AgeGr, EducGr,

IncomeGr, and SpeedGr.

Internet packages for each city are classified into three speed groups: 1) 0 or

0.056 Mbps (no internet or dialup internet); 2) 1-24 Mbps (speed below FCC

broadband standard); and 3) 25 Mbps or faster (speed meeting FCC broadband

standard). Tables 22-25 show the list of internet packages (after speed group

assignment) in Los Angeles, San Francisco, Washington, DC, and New York City,

respectively.


69

Table 22: The List of Internet Packages in Los Angeles (after Speed Group Assignment).


Name

Monthly

Price ($)

Download

Speed

(Mbps)

Download

Speed

Group

1_LA None None No internet 0.00 0 1

(<0.056M) 2_LA Unknown DialUp Unknown 21.00 0.056

3_LA AT&T DSL U-verse 46.00 6

2

(1-24M)

4_LA AT&T DSL U-verse 51.00 12

5_LA Time Warner Cable Standard 44.99 15

6_LA Time Warner Cable Turbo 54.99 20

8_LA Verizon Fiber Fios 15 74.99 15

7_LA Time Warner Cable Extreme 64.99 30 3

(>=25M) 9_LA Verizon Fiber Fios 75 94.99 75

Table 23: The List of Internet Packages in San Francisco (after Speed Group Assignment).


Name

Monthly

Price

Download

Speed

(Mbps)

Download

Speed

Group

1_SF None None No internet 0.00 0 1

(<0.056M) 2_SF Unknown DialUp DialUp 21.00 0.056

3_SF i-Step DSL i-Step 6 69.00 6 2

(1-24M) 4_SF i-Step DSL i-Step 10 91.00 10

8_SF Astound Fiber Wave 50 30.00 15

5_SF Comcast Cable Xfinity 30 59.99 30 3

(>=25M)

6_SF Comcast Cable Xfinity 50 74.95 50

7_SF Comcast Cable Xfinity 105 114.95 105

9_SF Astound Fiber Wave 75 50.00 30


70

Table 24: The List of Internet Packages in Washington, DC (after Speed Group Assignment).


Name

Monthly

Price

Download

Speed

(Mbps)

Download

Speed

Group

1_DC None None No internet 0.00 0 1

(<0.056M) 2_DC Unknown DialUp Unknown 21.00 0.056

3_DC Verizon DSL Enhanced 29.99 15 2

(1-24M) 4_DC Comcast Cable Xfinity 6 49.95 6

7_DC Verizon Fiber Fios 15 59.99 15

5_DC Comcast Cable Xfinity 25 54.99 25 3

(>=25M)

6_DC Comcast Cable Xfinity 50 67.50 50



Table 25: The List of Internet Packages in New York City (after Speed Group Assignment).


Name

Monthly

Price

Download

Speed

(Mbps)

Download

Speed

Group

1_NY None None No internet 0.00 0 1

(<0.056M) 2_NY Unknown DialUp DialUp 21.00 0.056

3_NY Verizon DSL Enhanced 29.99 8 2

(1-24M)

4_NY Time Warner Cable Standard 34.99 15

5_NY Time Warner Cable Turbo 54.99 20

7_NY Verizon Fiber Fios 74.99 15

6_NY Time

Warner

Cable Extreme 64.99 30 3

(>=25M) 8_NY Verizon Fiber Fios 84.99 50

9_NY Verizon Fiber Fios 94.99 75


71

Results

The result is separated into two smaller sections: 1) result on welfare change

from a $10 decrease in monthly price and 2) result on welfare change from a 10 Mbps

increase in download speed. Each section contains results on predicted internet

adoption and compensated variation by household demographics.

Results on Welfare Change from Decreased Monthly Price

This section analyzes how much a $10 decrease in monthly price raises welfare

of internet using households (while keeping the monthly price of dial-up internet and

the download speed of all internet packages constant). This paper chooses $10 since it

is a convenient number and close to the $9.25 monthly discount of the Lifeline

program. The Lifeline program is currently providing a voice service on mobile phone

for $24.74 per month (regular price of $33.99) and a service on home phone for

$10.74 per month (regular price of $19.99) to eligible American households (Verizon

2015). The FCC is currently suggesting the Congress to extend this program to home

internet service (FCC 2010, 168) in order to encourage internet adoption of American

households.

A $10 decrease in monthly price is applied only to the prices of high-speed

internet packages (not on the price of dial-up internet package) since this paper wishes

to predict the effect of price decrease on high-speed internet adoption. Tables 26-28,

29-31, 32-34, 35-37 show the results for Los Angeles, San Francisco, New York City,

and Washington, DC households respectively. In these tables, the means of

compensated variation (CV) are negative since the decrease in monthly price is a

favorable change for households. Money must be taken away from households to

bring them to their original utility level (the original situation of no price decrease).

The larger absolute value of CV indicates the better level of welfare improvement.


72

Results on Welfare Change from Decreased Monthly Price for Los Angeles

Households

Table 26 shows the mean of compensated variation (CV) and expected

adoption increase (percentage in parenthesis) from the $10 decrease in monthly price

for Los Angeles households categorized in four groups of family annual income: 1)

any family annual income level; 2) family annual income less than $20,000; 3) family

annual income between $20,000 and $60,000; and 4) family annual income greater

than $60,000. As described in the data source section, the computation is performed

for three speed groups: 1) 0 or 0.056 Mbps (no internet or dialup internet); 2) 1-24

Mbps (download speed below FCC broadband standard); and 3) 25 Mbps or faster

(download speed meeting FCC broadband standard). However, it should be noted that

the means of CV of the first speed groups is computed from households that become

high-speed internet users after price decrease only. These means exclude households

that continue to be non-internet users despite price decrease. The CV of non-

purchasers is zero according to general microeconomic theory.

Table 26: The Means of Compensated Variation (CV) and Expected Adoption Increase

(Percentage in Parenthesis) from a $10 Decrease in Monthly Price for Los Angeles

Households Categorized by Family Annual Income Levels.

Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_LA 0.00 0.00 1

(<0.056M)

-7.31 -5.27 -6.74 -8.89

2_LA 21.00 21.00 (46.52%) (14.42%) (54.4%) (100%)

3_LA 46.00 36.00

2

(1-24M) -8.33 -4.86 -7.77 -9.26

4_LA 51.00 41.00

5_LA 44.99 34.99

6_LA 54.99 44.99

8_LA 74.99 64.99

7_LA 64.99 54.99 3

(>=25M) -8.81 -4.52 -7.77 -9.41

9_LA 94.99 84.99


73

The general result of Table 26 is that means of CV tend to be larger with the

higher level of family annual income. For example, means of CV by speed groups of

households with family annual income greater than $60,000 (-8.89, -9.26, and -9.41)

are greater than means of CV by speed groups of households with family annual

income between $20,000 and $60,000 (-6.74, -7.77, and -7.77). Mathematically,

difference in mean of CVs could be explained by the way that log-sum is calculated.

Since most coefficients of family annual income in the mixed logit model are positive,

the log-sum of high income households tends to be larger than the log-sum of low

income households, and the larger log-sum would finally lead to larger CV.

Intuitively, high income households earn more welfare improvement since they can

take advantage from the Internet better than low income households. Jansen (2010, 4)

finds that high income households are much more active than low income households

in many online activities (such as using email, reading news on the Internet, paying

bills online, and conducting research on product or service). Moreover, Table 26

shows that among households with family annual income less than $20,000, means of

CV tend to be smaller as speed group increases since few number of households with

family annual income less than $20,000 are using broadband internet with download

speed greater than 25 Mbps. This may confirm that low income households have less

need for faster internet since they are less active in online activities.

In Table 26, the expected adoption increase is shown as the percentage in

parenthesis. The overall expected adoption increase (any family annual income level)

is estimated to be 46.52%. The expected adoption increase is increasing with higher

levels of family annual income. The expected adoption increase for households with

family annual income less than $20,000, between $20,000 and $60,000, and greater

than $60,000 is estimated to be 14.42%, 54.4%, and 100%, respectively. This result

supports the FCC’s suggestion to boost internet adoption rates by subsidizing the

monthly price of internet package for low-income households (FCC 2010, 168).

Table 27 shows the means of compensated variation (CV) and expected

adoption increase (percentage in parenthesis) from a $10 decrease in monthly price for

Los Angeles households categorized in four groups of education level: 1) any


74

education level; 2) high school diploma or lower; 3) associate’s or bachelor’s degree;

and 4) master’s, professional, or PhD degree. The general result of Table 27 is that

means of CV tend to be larger with the higher level of education. For example, means

of CV by speed groups of householders with master’s, professional, or PhD degree (-

8.30, -9.54, and -9.66) are greater than the means of CV by speed groups of

householders with associate’s or bachelor’s degree (-7.64, -8.71, and -8.51) and the

means of CV by speed groups of householders with high school diploma or lower

(-6.48, -6.44, and -8.50). Mathematically, the difference in mean of CVs could be

explained by the way that log-sum is calculated. Since most coefficients of education

levels in the mixed logit model are positive, the log-sum of high education households

tend to be larger than the log-sum of low education households, and larger log-sum

would finally lead to larger CV. Intuitively, high education households earn more

welfare improvement since they can take advantage from the Internet better than low

education households. Zickuhr and Smith (2009, 13) find that households with some

college education are more active in online activities than households with a high

school diploma. About 74%, 73%, and 66% of households with some college

education shop online, use social network websites, and bank online, respectively. In

contrast, there are only 59%, 60%, and 47% of households with high school education

that perform the aforementioned online activities.


75



Households Categorized by Education Levels of Householders.

Choice Original

Monthly Price

($)

Decreased

Monthly

Price ($)

Download

Speed Group

Mean of CV

(Any

Education

Level)

Mean of CV

(High

School or

Lower)

Mean of CV

(Associate/

Bachelor)

Mean of

CV

(Master/

PhD/Profe

ssional)

1_LA 0.00 0.00 1

(<0.056M)

-7.31 -6.48 -7.64 -8.30

2_LA 21.00 21.00 (46.52%) (24.41%) (82.22%) (100%)

3_LA 46.00 36.00

2

(1-24M) -8.33 -6.64 -8.71 -9.54

4_LA 51.00 41.00

5_LA 44.99 34.99

6_LA 54.99 44.99

8_LA 74.99 64.99

7_LA 64.99 54.99 3

(>=25M) -8.81 -8.50 -8.51 -9.66

9_LA 94.99 84.99


parenthesis. The overall expected adoption increase (any education level) is estimated

to be 46.52%. The expected adoption increase is increasing with higher education

levels. The expected adoption increase for householders with high school diploma or

lower; associate’s or bachelor’s degree; and master’s or professional degree or PhD is

estimated to be 24.41%, 82.22%, and 100%, respectively. This result is consistent with

the finding of FCC (2010, 178), which states that familiarity with the Internet during

schooling encourages householders to purchase home internet service.


adoption increase (percentage in parenthesis) from a $10 decrease in monthly price for

Los Angeles households categorized in four groups of age level: 1) any age level, 2)

ages 25-44 years, 3) ages 45-60 years, and 4) ages 61-85 yrs. The general result of

Table 28 is that means of CV tend to be shrinking with higher age levels. For example,

the means of CV by speed groups of householders with ages 61-85 years (-7.32, -7.66,

N/A) are smaller than the means of CV by speed groups of householders with ages 45-

60 years (-7.15, -8.33, and -7.75) and smaller than means of CV by speed groups of

householders with ages 25-44 years (-7.50, -8.75, and -9.19). It should be noted that

“N/A” refers to the non-applicable result since none of the householders with ages 61-


76

85 years purchases internet services faster than 25 Mbps. Mathematically, the

difference in mean of CVs could be explained by the way that log-sum is calculated.

Since most coefficients of householder’s age in the mixed logit model are negative,

the log-sum of older householders tend to be smaller than the log-sum of younger

householders. And a smaller log-sum would finally lead to smaller CV. Intuitively, old

householders earn less welfare improvement than young householders since they are

less likely to take advantage from the Internet. For example, Zickuhr and Smith (2009,

13) find that only 56%, 29%, and 44%, of 65 years or older householders shop online,

use social network websites, and engage in online banking, respectively. In contrast,

there are about 73%, 68%, and 68% of 30-49 years old householders who perform the

aforementioned online activities.



Households Categorized by Age Levels of Householders.

Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Age

Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85

Yrs)

1_LA 0.00 0.00 1

(<0.056M)

-7.31 -7.50 -7.15 -7.32

2_LA 21.00 21.00 (46.52%) (56.66%) (50.63%) (39.55%)

3_LA 46.00 36.00

2

(1-24M) -8.33 -8.75 -8.33 -7.66

4_LA 51.00 41.00

5_LA 44.99 34.99

6_LA 54.99 44.99

8_LA 74.99 64.99

7_LA 64.99 54.99 3

(>=25M) -8.81 -9.19 -7.75 N/A

9_LA 94.99 84.99


parenthesis. The overall expected adoption increase (any age level) is estimated to be

46.52%. The expected adoption increase is decreasing with higher age levels. The

expected adoption increase for householders with ages 25-44 years, ages 45-60 years,

and ages 61-85 years is estimated to be 56.66%, 50.63%, and 39.55%, respectively.

This result is consistent with the finding of the FCC (2010, 174), which states that


77

senior citizens tend to feel that the Internet is irrelevant to their daily lives and may not

wish to purchase home internet service.

Only the results of Los Angeles households are discussed in detail; results for

other cities have similar patterns to that of Los Angeles households. The general

pattern of all results is that compensated variation (CV) and expected adoption

increase tend to be increasing with family annual income and education of

householders but decreasing with age of householders. This finding is similar to that

of Rosston, Savage, and Waldman (2011, 29), who find that willingness to pay for

high-speed internet increases with education, income, and online experience but

decreases with age. Tables 29-31, 32-34, 35-37 show the results for San Francisco,

Washington, DC, and New York City households, respectively. Figures 8-11, 12-15,

16-19, and 20-23 which depicts information in Tables 26-28, 29-31, 32-34, and 35-37,

respectively, are shown in the appendix section.

Results on Welfare Change from Decreased Monthly Price for San Francisco Households


(Percentage in Parenthesis) from a $10 Decrease in Monthly Price for San Francisco


Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_SF 0.00 0.00 1

(<0.056M)

-7.39 -7.22 -6.68 -9.05

2_SF 21.00 21.00 (45.45%) (6.06%) (67.64%) (100%)

3_SF 69.00 59.00 2

(1-24M) -8.42 -4.81 -8.17 -9.54 4_SF 91.00 81.00

8_SF 30.00 20.00

5_SF 59.99 49.99 3

(>=25M) -9.17 -6.67 -8.27 -9.63 6_SF 74.95 64.95

7_SF 114.95 104.95

9_SF 50.00 40.00


78




Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

education

level)

Mean of

CV (High

School or

Lower)

Mean of

CV

(Associate/

Bachelor)

Mean of

CV

(Master/

PhD/Prof

essional)

1_SF 0.00 0.00 1

(<0.056M)

-7.39 -6.20 -7.74 -8.64

2_SF 21.00 21.00 (45.45%) (23.40%) (76.00%) (100%)

3_SF 69.00 59.00 2

(1-24M) -8.42 -4.34 -9.15 -9.11 4_SF 91.00 81.00

8_SF 30.00 20.00

5_SF 59.99 49.99 3

(>=25M) -9.17 -7.50 -9.41 -9.57 6_SF 74.95 64.95

7_SF 114.95 104.95

9_SF 50.00 40.00




Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Age Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85 Yrs)

1_SF 0.00 0.00 1

(<0.056M)

-7.39 -7.22 -7.76 -7.29

2_SF 21.00 21.00 (45.45%) (63.15%) (50.00%) (35.00%)

3_SF 69.00 59.00 2

(1-24M) -8.42 -7.75 -9.51 -8.26 4_SF 91.00 81.00

8_SF 30.00 20.00

5_SF 59.99 49.99 3

(>=25M) -9.17 -9.18 -8.91 -9.43 6_SF 74.95 64.95

7_SF 114.95 104.95

9_SF 50.00 40.00


79

Results on Welfare Change from Decreased Monthly Price for Washington, DC Households


(Percentage in Parenthesis) from a $10 Decrease in Monthly Price for Washington, DC


Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_DC 0.00 0.00 1

(<0.056M)

-7.82 -6.15 -7.24 -8.56

2_DC 21.00 21.00 (38.32%) (3.09%) (48.31%) (100%)

3_DC 29.99 19.99 2

(1-24M) -8.57 -3.98 -7.43 -9.42 4_DC 49.95 39.95

7_DC 59.99 49.99

5_DC 54.99 44.99 3

(>=25M) -8.64 -3.13 -7.73 -9.30 6_DC 67.50 57.50

8_DC 69.99 59.99

9_DC 79.99 69.99


(Percentage In Parenthesis) from a $10 Decrease In Monthly Price for Washington, DC


Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Education

Level)

Mean of

CV (High

School

Or

Lower)

Mean of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD/

Prof)

1_DC 0.00 0.00 1

(<0.056M)

-7.82 -7.01 -7.99 -9.24

2_DC 21.00 21.00 (38.32%) (20.00%) (66.16%) (100%)

3_DC 29.99 19.99 2

(1-24M) -8.57 -6.74 -8.72 -9.66 4_DC 49.95 39.95

7_DC 59.99 49.99

5_DC 54.99 44.99 3

(>=25M) -8.64 -5.22 -8.65 -9.57 6_DC 67.50 57.50

8_DC 69.99 59.99

9_DC 79.99 69.99


80


(Percentage in Parenthesis) from a $10 Decrease in Monthly Price for Washington, DC


Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Age Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85 Yrs)

1_DC 0.00 0.00 1

(<0.056M)

-7.82 -8.00 -7.78 -7.75

2_DC 21.00 21.00 (38.32%) (36.84%) (41.09%) (37.11%)

3_DC 29.99 19.99 2

(1-24M) -8.57 -8.79 -8.57 -8.29 4_DC 49.95 39.95

7_DC 59.99 49.99

5_DC 54.99 44.99 3

(>=25M) -8.64 -9.10 -8.65 -7.57 6_DC 67.50 57.50

8_DC 69.99 59.99

9_DC 79.99 69.99

Results on Welfare Change from Decreased Monthly Price for New York City Households


(Percentage in Parenthesis) from a $10 Decrease in Monthly Price for New York City


Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_NY 0.00 0.00 1

(<0.056M)

-7.55 -6.24 -7.07 -8.79

2_NY 21.00 21.00 (41.77%) (8.18%) (59.00%) (100%)

3_NY 29.99 19.99 2

(1-24M) -8.08 -4.69 -7.38 -9.23 4_NY 34.99 24.99

5_NY 54.99 44.99

7_NY 74.99 64.99

6_NY 64.99 54.99 3

(>=25M) -8.42 -5.05 -7.44 -9.18 8_NY 84.99 74.99

9_NY 94.99 84.99


81



Households Categorized by Education Levels.

Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean Of

CV (Any

Education

Level)

Mean Of

CV (High

School or

Lower)

Mean Of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD

/Prof)

1_NY 0.00 0.00 1

(<0.056M)

-7.55 -6.94 -8.10 -8.26

2_NY 21.00 21.00 (41.77%) (29.36%) (68.08%) (85.00%)

3_NY 29.99 19.99 2

(1-24M) -8.08 -6.56 -8.55 -9.43 4_NY 34.99 24.99

5_NY 54.99 44.99

7_NY 74.99 64.99

6_NY 64.99 54.99 3

(>=25M) -8.42 -7.12 -8.57 -9.49 8_NY 84.99 74.99

9_NY 94.99 84.99




Choice Original

Monthly

Price ($)

Decreased

Monthly

Price ($)

Download

Speed

Group

Mean of

CV (Any

Age Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60 Yrs)

Mean of

CV (Age

61-85

Yrs)

1_NY 0.00 0.00 1

(<0.056M)

-7.55 -7.59 -7.54 -7.51

2_NY 21.00 21.00 (41.77%) (61.90%) (54.90%) (26.39%)

3_NY 29.99 19.99 2

(1-24M) -8.08 -8.53 -8.12 -7.23 4_NY 34.99 24.99

5_NY 54.99 44.99

7_NY 74.99 64.99

6_NY 64.99 54.99 3

(>=25M) -8.42 -8.87 -8.44 -7.67 8_NY 84.99 74.99

9_NY 94.99 84.99


82

Results on Welfare Change from Faster Download Speed

This section analyzes how much a 10 Mbps increase in download speed raises

the welfare of internet using households (while keeping the monthly prices of internet

packages and the download speed of dialup internet constant). The 10 Mbps increase

in download speed is applied only to the download speed of high-speed internet

packages since the download speed of dial-up internet cannot exceed 56 Kbps. Faster

internet speed greatly enhances satisfaction from internet surfing; many households

are willing to pay for faster speed in order to utilize online services more effectively

(Computer and Communications Industry Association 2013, 1). Popular online

services include downloading video clips, uploading large files, making video calls,

and online gaming. Tables 38-40, 41-43, 44-46, and 47-49 show the results for Los

Angeles, San Francisco, New York City, and Washington, DC households,

respectively. In these tables, the means of compensated variation (CV) are negative

since the increase in download speed is a favorable change for households. Money

must be taken away from households to bring them to their original utility level (the

original situation of no speed increase). The larger absolute value of CV indicates the

greater level of welfare improvement.

Result on Welfare Change from Faster Download Speed for Los Angeles Households

Table 38 shows the mean of compensated variation (CV) and expected

adoption increase (percentage in parenthesis) from 10 Mbps increase in download

speed for Los Angeles households categorized in four groups of family annual

income: 1) any family annual income level; 2) family annual income less than

$20,000; 3) family annual income between $20,000 and $60,000; and 4) family annual

income greater than $60,000. As described in the data source section, the computation

is performed for three speed groups: 1) 0 or 0.056 Mbps (no internet or dialup

internet); 2) 1-24 Mbps (download speed below FCC broadband standard); and 3) 25

Mbps or faster (download speed meeting FCC broadband standard). It should be noted

that the means of CV of the first speed groups are computed from households who


83

become high-speed internet users after download speed increase only. These means

exclude households who continue to be non-internet users despite the download speed

increase. The CV of non-purchasers is zero according to general microeconomic

theory.


(Percentage in Parenthesis) from 10 Mbps Increase in Download Speed for Los Angeles


Choice Original

Download

Speed (Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_LA 0 0 1

(<0.056M)

-8.76 -6.44 -8.30 -11.45

2_LA 0.056 0.056 (61.90%) (31.73%) (73.60%) (100%)

3_LA 6 16

2

(1-24M) -10.79 -6.76 -10.17 -11.85

4_LA 12 22

5_LA 15 25

6_LA 20 30

8_LA 15 25

7_LA 30 40 3

(>=25M) -11.34 -6.40 -10.19 -12.02

9_LA 75 85

The general result of Table 38 is that means of CV tend to be increasing with

the higher level of family annual income. For example, means of CV by speed groups

of households with family annual income greater than $60,000 (-11.45, -11.85, and

-12.02) are greater than means of CV by speed groups of households with family

annual income between $20,000 and $60,000 (-8.30, -10.17, and -10.19) and greater

than means of CV by speed groups of households with family annual income less than

$20,000 (-6.44, -6.76, and -6.40). Mathematically, the difference in mean of CVs

could be explained by how the log-sum is calculated. Since most coefficients of family

annual income in the mixed logit model are positive, the log-sum of high income

households tend to be larger than the log-sum of low income households. A larger log-

sum would finally lead to a larger CV. Intuitively, high income households earn more

CV since they can take advantage of faster internet speed better than low income


84

households since they are more active than low income households in many internet

activities (Jansen 2010, 4).

In Table 38, the expected adoption increase from faster speed is shown as the

percentage in parenthesis. The overall expected adoption increase (any family annual

income level) is estimated to be 61.90%. The expected adoption increase is increasing

with higher levels of family annual income. The expected adoption increase for

households with family annual income less than $20,000, between $20,000 and

$60,000, and greater than $60,000 is estimated to be 31.73%, 73.60%, and 100%,

respectively. However, this result has limited interpretation. Non-broadband using

households may not appreciate faster internet speed if they are not using the Internet in

their daily lives. For example, Tomer and Kane (2015, 8) find that faster internet’s

download speed is not associated with a higher internet adoption rate. Non-broadband

using households need digital literacy education to learn how to benefit from faster

internet speed.


adoption increase (percentage in parenthesis) from a 10 Mbps increase in download

speed for Los Angeles households categorized in four groups of education level: 1)

any education level; 2) high school diploma or lower; 3) associate’s or bachelor’s

degree; and 4) master’s, professional degree, or PhD. The general result of this table is

that means of CV tend to be larger with the higher levels of education. For example,

means of CV by speed groups of householders with master’s, professional degree, or

PhD (-10.76, -12.16, and -12.30) are greater than the means of CV by speed groups of

householders with associate’s or bachelor’s degree (-9.44, -11.24, and -11.00) and the

means of CV by speed groups of householders with high school diploma or lower (-

7.46, -8.85, and -11.02). Mathematically, the difference in mean of CVs could be

explained by the way that the log-sum is calculated. Since most coefficients of

education levels in the mixed logit model are positive, the log-sum of high education

households tends to be larger than the log-sum of low education households. A larger

log-sum would finally lead to a larger CV. Intuitively, high education households earn


85

more welfare improvement since they are more active in online activities than

households with high school diploma or lower (Zickuhr and Smith 2009, 13).




Choice Original

Download

Speed (Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Education

Level)

Mean of

CV (High

School or

Lower)

Mean of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD

/Prof.)

1_LA 0 0 1

(<0.056M)

-8.76 -7.64 -9.44 -10.76

2_LA 0.056 0.056 (61.90%) (41.86%) (95.55%) (100%)

3_LA 6 16

2

(1-24M) -10.79 -8.85 -11.24 -12.16

4_LA 12 22

5_LA 15 25

6_LA 20 30

8_LA 15 25

7_LA 30 40 3

(>=25M) -11.34 -11.02 -11.00 -12.30

9_LA 75 85


parenthesis. The overall expected adoption increase (for any education level) is

estimated to be 61.90%. The expected adoption increase is increasing with higher

education levels. The expected adoption increase for householders with high school

diploma or lower; associate’s or bachelor’s degree; and master’s or professional

degree or PhD is estimated to be 41.86%, 95.55%, and 100%, respectively. Again, this

result has limited interpretation. Non-broadband using households may not appreciate

faster internet speed if they are not using the Internet in their daily lives.


adoption increase (percentage in parenthesis) from a 10 Mbps increase in download

speed for Los Angeles households categorized in four groups of age level: 1) any age

level, 2) age 25-44 years, 3) age 45-60 years, and 4) age 61-85 yrs. The general result

of Table 40 is that means of CV tend to be shrinking with higher age levels. For

example, the means of CV by speed groups of householders with age 61-85 years (-

8.38, -10.02, and N/A) are smaller than the means of CV by speed groups of


86

householders with age 45-60 years (-9.24, -10.79, and -10.13) and smaller than means

of CV by speed groups of householders with age 25-44 years (-8.76, -11.27, and -

11.78). It should be noted that “N/A” refers to the non-applicable result since none of

the householders with age 61-85 years purchases internet services faster than 25 Mbps.

Mathematically, the difference in mean of CVs could be explained by the way that the

log-sum is calculated. Since most coefficients of householder’s age in the mixed logit

model are negative, the log-sum of old householders tend to be smaller than the log-

sum of young householders. A smaller log-sum would finally lead to a smaller CV.

Intuitively, old householders earn less welfare improvement than young householders

since they are less active in online activities (Zickuhr and Smith 2009, 13).




Choice Original

Download

Speed (Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Age

Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85

Yrs)

1_LA 0 0 1

(<0.056M)

-8.76 -8.98 -9.24 -8.38

2_LA 0.056 0.056 (61.90%) (71.66%) (55.96%) (61.19%)

3_LA 6 16

2

(1-24M) -10.79 -11.27 -10.79 -10.02

4_LA 12 22

5_LA 15 25

6_LA 20 30

8_LA 15 25

7_LA 30 40 3

(>=25M) -11.34 -11.78 -10.13 N/A

9_LA 75 85


parenthesis. The overall expected adoption increase (any age level) is estimated to be

61.90%. The expected adoption increase is decreasing with higher age levels. The

expected adoption increase for householders with ages 25-44 years, ages 45-60 years,

and ages 61-85 years is estimated to be 71.66%, 55.96%, and 61.19%, respectively.

Even though the expected adoption increase should be lowest for householders with

ages 61-85 years, this rate is greater than that of householders with ages 45-60 years


87

due to estimation error. The general pattern (that the expected adoption increase is

decreasing with higher age levels) is consistent with the finding of FCC (2010, 174),

which states that senior citizens tend to feel that the Internet is irrelevant to their daily

lives and may not wish to purchase home internet service. Only the results of Los Angeles households are discussed in details; results for

other cities have similar patterns to that of Los Angeles households. The general

pattern of results is that compensated variation (CV) and expected adoption increase

tend to be increasing with family annual income and education of householders but

decreasing with age of householders. This finding is similar to that of Rosston,

Savage, and Waldman (2011, 29), who find that willingness to pay for high-speed

internet increases with education, income, and online experience but decreases with

age. Tables 41-43, 44-46, 47-49 show the results for San Francisco, Washington, DC,

and New York City households, respectively. Figures 24-27, 28-31, 32-35, and 36-39,

which depict information in Tables 38-40, 41-43, 44-46, 47-49, respectively, are

shown in the appendix section.

Result on Welfare Change from Faster Download Speed for San Francisco Households


(Percentage in Parenthesis) from 10 Mbps Increase in Download Speed for San Francisco


Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_SF 0 0 1

(<0.056M)

-6.29 -5.97 -5.65 -7.70

2_SF 0.056 0.056 (42.85%) (6.06%) (61.76%) (100.00%)

3_SF 6 16 2

(1-24M) -7.15 -3.89 -6.92 -8.17 4_SF 10 20

8_SF 15 25

5_SF 30 40 3

(>=25M) -7.82 -5.55 -6.98 -8.26 6_SF 50 60

7_SF 105 115

9_SF 30 40


88




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Education

Level)

Mean of

CV (High

School or

Lower)

Mean of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD

/Prof.)

1_SF 0 0 1

(<0.056M)

-6.29 -5.05 -6.79 -7.32

2_SF 0.056 0.056 (42.85%) (23.40%) (68.00%) (100.00%)

3_SF 6 16 2

(1-24M) -7.15 -3.54 -7.80 -7.77 4_SF 10 20

8_SF 15 25

5_SF 30 40 3

(>=25M) -7.82 -6.28 -8.05 -8.20 6_SF 50 60

7_SF 105 115

9_SF 30 40




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Age

Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85

Yrs)

1_SF 0 0 1

(<0.056M)

-6.29 -5.99 -6.50 -6.44

2_SF 0.056 0.056 (42.85%) (63.15%) (50.00%) (30.00%)

3_SF 6 16 2

(1-24M) -7.15 -6.56 -8.14 -6.99 4_SF 10 20

8_SF 15 25

5_SF 30 40 3

(>=25M) -7.82 -7.84 -7.59 -8.07 6_SF 50 60

7_SF 105 115

9_SF 30 40


89

Result on Welfare Change from Faster Download Speed for Washington, DC Households


(Percentage in Parenthesis) from 10 Mbps Increase in Download Speed for Washington, DC


Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_DC 0 0 1

(<0.056M)

-2.41 N/A -2.38 -2.42

2_DC 0.056 0.056 (22.9%) N/A (16.85%) (90.24%)

3_DC 15 25 2

(1-24M) -2.41 -0.89 -1.96 -2.72 4_DC 6 16

7_DC 15 25

5_DC 25 35 3

(>=25M) -2.44 -0.68 -2.08 -2.67 6_DC 50 60

8_DC 50 60

9_DC 75 85




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Education

Level)

Mean of

CV

(High

School or

Lower)

Mean of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD

/Prof.)

1_DC 0 0 1

(<0.056M)

-2.41 -2.04 -2.47 -2.76

2_DC 0.056 0.056 (22.9%) (9.65%) (39.43%) (90.90%)

3_DC 15 25 2

(1-24M) -2.41 -1.74 -2.45 -2.82 4_DC 6 16

7_DC 15 25

5_DC 25 35 3

(>=25M) -2.44 -1.30 -2.41 -2.79 6_DC 50 60

8_DC 50 60

9_DC 75 85


90




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Age

Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60

Yrs)

Mean of

CV (Age

61-85

Yrs)

1_DC 0 0 1

(<0.056M)

-2.41 -2.54 -2.42 -2.35

2_DC 0.056 0.056 (22.9%) (17.54%) (23.28%) (25.77%)

3_DC 15 25 2

(1-24M) -2.41 -2.49 -2.41 -2.30 4_DC 6 16

7_DC 15 25

5_DC 25 35 3

(>=25M) -2.44 -2.61 -2.42 -2.07 6_DC 50 60

8_DC 50 60

9_DC 75 85

Result on Welfare Change from Faster Download Speed for New York City Households


(Percentage in Parenthesis) from 10 Mbps Increase in Download Speed for New York City


Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Income

Level)

Mean of

CV

(Income

<$20K)

Mean of

CV

(Income

$20K-

$60K)

Mean of

CV

(Income

>$60K)

1_NY 0 0.00 1

(<0.056M)

-5.19 -4.29 -4.80 -5.71

2_NY 0.056 0.056 (31.33%) (2.92%) (40.37%) (98.00%)

3_NY 8 18.00 2

(1-24M) -5.18 -2.82 -4.66 -6.00 4_NY 15 25.00

5_NY 20 30.00

7_NY 15 25.00

6_NY 30 40.00 3

(>=25M) -5.42 -3.06 -4.70 -5.96 8_NY 50 60.00

9_NY 75 85.00


91




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Education

Level)

Mean of

CV (High

School or

Lower)

Mean of

CV

(Associate

/Bachelor)

Mean of

CV

(Master

/PhD

/Prof.)

1_NY 0 0.00 1

(<0.056M)

-5.19 -4.77 -5.33 -5.95

2_NY 0.056 0.056 (31.33%) (18.58%) (61.70%) (60.00%)

3_NY 8 18.00 2

(1-24M) -5.18 -4.10 -5.51 -6.15 4_NY 15 25.00

5_NY 20 30.00

7_NY 15 25.00

6_NY 30 40.00 3

(>=25M) -5.42 -4.48 -5.52 -6.19 8_NY 50 60.00

9_NY 75 85.00




Choice Original

Download

Speed

(Mbps)

Increased

Download

Speed

(Mbps)

Download

Speed

Group

Mean of

CV (Any

Age Level)

Mean of

CV (Age

25-44

Yrs)

Mean of

CV (Age

45-60 Yrs)

Mean of

CV (Age

61-85

Yrs)

1_NY 0 0.00 1

(<0.056M)

-5.19 -5.08 -5.24 -5.15

2_NY 0.056 0.056 (31.33%) (51.19%) (40.19%) (18.27%)

3_NY 8 18.00 2

(1-24M) -5.18 -5.50 -5.20 -4.64 4_NY 15 25.00

5_NY 20 30.00

7_NY 15 25.00

6_NY 30 40.00 3

(>=25M) -5.42 -5.74 -5.43 -4.88 8_NY 50 60.00

9_NY 75 85.00


92

Conclusion

Welfare evaluation of this paper focuses on cheaper monthly prices and faster

download speed. This research idea is motivated by the findings of previous literature,

which observes strong evidence of a decline in quality-adjusted price of U.S. internet

service (Greenstein and McDevitt 2011, 5 and FCC 2015b). This desirable change has

important policy implications; cheaper internet leads to a higher level of internet

adoption and alleviates digital divide among American households, while faster

internet speed decreases wait time and enables households to utilize online services

more effectively.

Econometric estimates from the second essay are used to quantify welfare

changes under two scenarios: 1) a decrease of $10 in the monthly price while keeping

download speed unchanged, and 2) an increase of 10 Mbps in the download speed

while keeping monthly prices unchanged. This paper quantifies welfare changes into

compensated variation (CV) by multiplying the inverse of marginal utility of money

with the difference between two log-sums. All CVs are negative since price decrease

or speed increase is a favorable change for households. On the other hand, among non-

internet using households, a household is predicted to be a new internet adopter if its

utility from purchasing a high-speed internet package is greater than its utility from

not purchasing one.

The results demonstrate that households with higher family annual income,

better education, and younger adults tend to have higher internet adoption rates and

larger amounts of compensated variation than households with lower family annual

income, less education, and older adults. CVs for a given demographic variable tend to

be larger as the download speed group increases; however, this pattern is less

prevalent in the lowest level of family annual income and education and in the highest

level of age. The four U.S. cities (namely Los Angeles, San Francisco, Washington,

DC and New York City) have similar patterns of results but differ in the amount of

compensated variation and the percentage of expected adoption increase. These

differences are direct results from varying coefficient sizes of monthly price and


93

download speed estimated in the second essay. Their results are graphically shown by

the figures in the appendix section.


94

Appendix

Figures Accompanying the Results of Tables 25-27

Figure 9: The Means of Compensated Variation (CV) from a $10 Decrease in Monthly Price

for Los Angeles Households Categorized by Education Level of Householders.

7.316.48

7.64 8.30 8.336.64

8.71 9.54

8.81 8.50 8.51 9.66

0

2

4

6

8

10

12

CV

($

)

Speed 1 (<0.056 Mbps) Speed 2 (1-24 Mbps) Speed 3 (>25 Mbps)

7.31

5.27

6.74

8.898.33

4.86

7.77

9.268.81

4.52

7.77

9.41

0

1

2

3

4

5

6

7

8

9

10

Any income level Income <$20K Income $20K-$60K Income >$60K

CV

($

)


Figure 8: The Means of Compensated Variation (CV) from $10 Decrease in Monthly

Price for Los Angeles Households Categorized by Family Annual Income Level.


95


for Los Angeles Households Categorized by Age Level of Householders.

Figure 11: Expected Adoption Increase from a $10 Decrease in Monthly Price for Los

Angeles Households Categorized by Levels of Family Annual Income, Education of

Householders, and Age of Householders.

7.31 7.50 7.15 7.32

8.338.75

8.33 7.66

8.819.19

7.75

N/A0

1

2

3

4

5

6

7

8

9

10

Any age level Age 25-44 yrs Age 45-60 yrs Age 61-85 yrs

CV

($

)


14.42%

24.41%

56.66%

54.40%

82.22%

50.63%

46.52%

100.00%

100.00%

39.55%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall

Demographic Level 1 Demographic Level 2 Demographic Level 3


96



For San Francisco Households Categorized by Education Level of Householders.

7.39 6.20

7.74 8.64 8.42

4.34

9.15 9.11 9.17 7.50

9.41 9.57

-

2.00

4.00

6.00

8.00

10.00

12.00

CV

($

)


7.39 7.226.68

9.058.42

4.81

8.17

9.549.17

6.67

8.27

9.63

0

2

4

6

8

10

12


CV

($

)


Figure 12: The Means of Compensated Variation (CV) from $10 Decrease in Monthly

Price for San Francisco Households Categorized by Family Annual Income Level.


97


for San Francisco Households Categorized by Age Level of householders.

Figure 15: Expected Adoption Increase from a $10 Decrease in Monthly Price for San

Francisco Households Categorized by Levels of Family Annual Income, Education of


7.39 7.227.76

7.29

8.427.75

9.51

8.26

9.17 9.18 8.919.43

0

1

2

3

4

5

6

7

8

9

10


CV

($

)


14.42%

24.41%

56.66%

54.40%

82.22%

50.63%

45.45%

100.00%

100.00%

39.55%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



98



for Washington, DC Households Categorized by Family Annual Income Level.


for Washington, DC Households Categorized by Education Level of Householders.

7.82

6.15

7.24

8.56 8.57

3.98

7.43

9.42 8.64

3.13

7.73

9.30

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00


CV

($

)


7.827.01

7.999.248.57

6.74

8.729.66

8.64

5.22

8.659.57

0

2

4

6

8

10

12

CV

($

)



99


For Washington, DC Households Categorized by Age Level of Householders.

Figure 19: Expected Adoption Increase from a $10 Decrease in Monthly Price for

Washington, DC Households Categorized by Levels of Family Annual Income, Education of


7.82 8.00 7.78 7.75 8.57 8.79 8.57 8.29

8.64 9.10

8.65

7.57

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00


CV

($

)


3.09%

20.00%

36.84%

48.31%

66.16%

41.09%

38.32%

100.00%

100.00%

37.11%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



100



for New York City Households Categorized by Family Annual Income Level.


for New York City Households Categorized by Education Level of Householders.

7.55

6.247.07

8.798.08

4.69

7.38

9.238.42

5.05

7.44

9.18

0

1

2

3

4

5

6

7

8

9

10


CV

($

)


7.55 6.94

8.10 8.26 8.08

6.56

8.55 9.43

8.42 7.12

8.57 9.49

- 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

10.00

CV

($

)



101


for New York City Households Categorized by Age Level of Householders.

Figure 23: Expected Adoption Increase from a $10 Decrease in Monthly Price for New York

City Households Categorized by Levels of Family Annual Income, Education of Householders,

and Age of Householders.

7.55 7.59 7.54 7.518.08

8.538.12

7.23

8.428.87

8.447.67

0

1

2

3

4

5

6

7

8

9

10


CV

($

)


8.18%

29.36%

61.90%

59.00%

68.08%

54.90%

41.77%

100.00%

85.00%

26.39%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



102


Figure 24: The Means of Compensated Variation (CV) from a 10 Mbps Increase in Download

Speed for Los Angeles Households Categorized by Family Annual Income Level.


Speed for Los Angeles Households Categorized by Education Level of Householders.

8.76

6.44

8.30

11.45 10.79

6.76

10.17

11.85 11.34

6.40

10.19

12.02

-

2.00

4.00

6.00

8.00

10.00

12.00

14.00


CV

($

)


8.76 7.64

9.44 10.76 10.79

8.85

11.24 12.16

11.34 11.02 11.00 12.30

- 2.00 4.00 6.00 8.00

10.00 12.00 14.00

CV

($

)



103


Speed for Los Angeles Households Categorized by Age Level of Householders.

Figure 27: Expected Adoption Increase from a 10 Mbps Increase in Download Speed for Los

Angeles Households Categorized by Levels of Family Annual Income, Education of


8.76 8.98 9.248.38

10.7911.27

10.7910.02

11.3411.78

10.13

N/A0

2

4

6

8

10

12

14


CV

($

)


31.73%

41.86%

71.66%

73.60%

95.55%

55.96%

61.90%

100.00%

100.00%

61.19%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



104



Speed for San Francisco Households Categorized by Family Annual Income Level.


Speed for San Francisco Households Categorized by Education Level of Householders.

6.29 5.97 5.65

7.707.15

3.89

6.92

8.177.82

5.55

6.98

8.26

0

1

2

3

4

5

6

7

8

9


CV

($

)


6.29

5.05

6.79 7.32 7.15

3.54

7.80 7.77 7.82

6.28

8.05 8.20

- 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

CV

($

)



105


Speed for San Francisco Households Categorized by Age Level of Householders.

Figure 31: Expected Adoption Increase from a 10 Mbps Increase in Download Speed for San

Francisco Households Categorized by Levels of Family Annual Income, Education of


6.29 5.99 6.50 6.44

7.15 6.56

8.14

6.99

7.82 7.84 7.59 8.07

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00


CV

($

)


6.06%

23.40%

63.15%

61.76%

68.00%

50.00%

42.85%

100.00%

100.00%

30.00%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



106



Speed for Washington, DC Households Categorized by Family Annual Income Level.


Speed for Washington, DC Households Categorized by Education Level of Householders.

2.41

N/A

2.38 2.42 2.41

0.89

1.96

2.72

2.44

0.68

2.08

2.67

-

0.50

1.00

1.50

2.00

2.50

3.00


CV

($

)


2.41 2.04

2.47 2.76

2.41

1.74

2.45 2.82

2.44

1.30

2.41 2.79

-

0.50

1.00

1.50

2.00

2.50

3.00

CV

($

)



107


Speed for Washington, DC Households Categorized by Age Level of Householders.

Figure 35: Expected Adoption Increase from a 10 Mbps Increase in Download Speed for

Washington, DC Households Categorized by Levels of Family Annual Income, Education of


2.41 2.54

2.42 2.35 2.41 2.49 2.41 2.30

2.44 2.61

2.42

2.07

-

0.50

1.00

1.50

2.00

2.50

3.00


CV

($

)


N/A

9.65%

17.54%

16.85%

39.43%

23.28%

22.90%

90.24%

90.90%

25.77%

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%

Income

Education

Age

Overall



108



Speed for New York City Households Categorized by Family Annual Income Level.


Speed for New York City Households Categorized by Education Level of Householders.

5.19

4.29 4.80

5.71 5.18

2.82

4.66

6.00 5.42

3.06

4.70

5.96

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00


CV

($

)


5.19 4.77 5.33

5.95 5.18

4.10

5.51 6.15

5.42 4.48

5.52 6.19

- 1.00 2.00 3.00 4.00 5.00 6.00 7.00

CV

($

)



109


Speed for New York City Households Categorized by Age Level of Householders.

Figure 39: Expected Adoption Increase from a 10 Mbps Increase in Download Speed for New

York City Households Categorized by Levels of Family Annual Income, Education of


5.19 5.08 5.24 5.15 5.18 5.50

5.20 4.64

5.42 5.74

5.43 4.88

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00


CV

($

)


2.92%

18.58%

51.19%

40.37%

61.70%

40.19%

31.33%

98.00%

60.00%

18.27%

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

Income

Education

Age

Overall



110

CHAPTER V

POLICY IMPLICATIONS

This chapter focuses on policy implications from the econometric results of

this dissertation. Several solutions are proposed to encourage internet adoption and

improve digital literacy among certain groups of the U.S population who fall behind in

computer and internet use. The solutions are categorized into two groups: 1) solutions

supported by the results of this dissertation, and 2) solutions supported by the findings

of previous literature.

Solutions Supported by the Results of this Dissertation

This dissertation concludes that several factors increase the probability of

internet adoption: earning higher family income, attaining higher education levels,

having a residence in an urban area, and being employed. On the contrary, the factors

decreasing the probability of internet adoption include high prices of internet service,

older age, having disabilities that obstruct internet use, and lack of U.S. citizenship.

Solutions presented in this section aim at overcoming the following barriers: 1) digital

literacy, 2) cost, 3) irrelevance, 4) language, and 5) accessibility.

Overcoming Digital Literacy Barriers

Digital literacy is the ability to effectively use computers, communication

tools, and the Internet to find, evaluate, use, and create information (US Digital

Literacy 2015). This dissertation finds that households with low education attainment

tend to lag behind in digital literacy. Visser (2013, 106) finds that individuals lacking

digital literacy tend to be the ones who have less formal education, earn a low family

income, and live in underserved rural areas. The author further suggests that 1) public

libraries have been successful in offering digital literacy education for individuals with

the aforementioned characteristics; 2) successful training does not teach skills in

isolation but links them to certain specific outcomes (such as job seeking, financial

literacy, and legal issues research); and 3) formal classes and in-person assistance


111

should be given in combination so that individuals learn new skills in a formal class

and practice them in personal assisting sessions.

The FCC (2010, 174) finds that public libraries and community centers have

been providing digital literacy training to people with limited educational

opportunities. However, many public libraries and community centers report a lack of

computer equipment to serve their patrons. About 80% of libraries claimed that

patrons are often put on waiting lists and given a time limit of 30 or 60 minutes. Such

restrictions hinder patrons from using computers and the Internet at their leisure. The

FCC suggests supplying additional computer equipment to public libraries and

community centers to alleviate the problem.

Overcoming Cost Barriers

Householders who refuse to purchase home and mobile internet service

popularly cite “too expensive price” as their primary reason (NTIA 2014, 9).

Similarly, this dissertation concludes that 1) an increase in monthly price of internet

service tends to decrease the probability of home internet adoption; and 2) the demand

for home internet service is highly price-elastic. Reducing the monthly price of

internet connection should greatly boost internet adoption rates of low income

households.

The FCC plans to boost internet adoption among low-income households by

offering them a low-cost internet connection through the Lifeline Assistance and the

Link-Up America programs (FCC 2010, 168). These two programs are currently

providing phone services to low-income households at very affordable prices. For

example, the Lifeline Assistance by Verizon offers a mobile phone service for $24.74

per month and a landline service for $10.74 per month (Verizon 2015). The

implementation of these programs was highly successful; it boosted telephone

subscription of low-income households from 80% in 1984 to 89.7% in 2008. The FCC

suggests that the Congress should extend these two programs to home internet service

since they should be able to greatly boost home internet adoption among low-income

households, as they did with telephone service subscriptions.


112

Alternatively, the FCC suggests that a free internet connection could be made

available to American households through advertising-supported programs (FCC

2010, 172). Broadcast television (or free-to-air TV) is an example of successful

implementation of such program, providing American households with news,

information, shows, and programming. The National Broadband Plan suggests

licensing a frequency though an auction and requiring the licensees to offer a free or

very low cost internet service to households. However, the FCC is currently searching

for a successful business model for an advertising-supported internet connection.

Overcoming Irrelevance Barriers

Householders who refuse to use home and mobile internet cite “irrelevance to

daily lives” as their primary reason (FCC 2010, 174). The agency finds that such

householders have an average age of 61 years old. Similarly, this dissertation

concludes that the older age of householders are less likely to adopt home and mobile

internet services.

The FCC (2010, 179) suggests three ways to bring senior citizens into the

digital world: (1) show them how to use the Internet to discover tips for staying

healthy and locating nearby healthcare providers; 2) notify them about telemedicine

programs that enable health management at lower costs; and 3) introduce them to

social networking websites so that they can reconnect with their old friends and make

new friends without travelling from home. Previous studies find that senior citizens

are more likely to go online after seeing practical benefits of the Internet.

Overcoming Language Barriers

This dissertation concludes that there is an adoption gap among different races,

and that Hispanic households are found to fall behind other races in both home and

mobile internet adoption. This result is confirmed by previous literature such as

Livingston (2010, 7). The author finds that Hispanics fall behind Non-Hispanics in

terms of cell phone use (76% vs. 86%) and internet use (64% vs. 78%).

To fight against language barriers, the FCC (2010, 174) suggests that public

libraries and community centers should hire instructors with foreign language skills to


113

provide computer training to non-English speakers. Previous literature shows that the

lack of fluency with English and the scarcity of non-English websites are associated

with low levels of internet adoption. For example, only 20% of Hispanics who

participated in the FCC’s survey in Spanish replied that they have home internet. The

training provided in the learners’ native language (such as Spanish) or tailored for

learners who use English as their second language would effectively reduce cultural

and linguistic barriers between instructors and learners.

Overcoming Accessibility Barriers

People with disabilities are less likely to access the Internet since they face

many disability-related obstacles (FCC 2010, 181). This finding is similar to that of

this dissertation, which concludes that people with disabilities have lower probability

of home and mobile internet adoption.

The FCC makes three recommendations to make accessibility-supported

websites, hardware, and software available for people with disabilities. First, the

federal websites should be accessibly designed for people with various types of

disabilities. Second, the FCC plans to extend its Section 255 rules to require

manufacturers of end-user equipment to make their products compatible with

accessibility devices. For example, Apple has made its iOS to support hearing aid

devices through a Bluetooth connection (Apple Computer 2015). Samsung has created

the Eyecan+, an eye-ball tracking mouse that enables its users to click an object by

blinking their eyes (Wood 2014). Third, the Universal Service Fund should be

appropriated to projects with the following purposes: 1) projects to create assistive

technology that would enable individuals who are deaf or blind to use the Internet (up

to $10 million per year); and 2) projects to fund competitive awards for developers

who create innovative devices, software, and applications that promote internet

adoption (up to $10 million per year).


114

Solutions Supported by the Findings of Previous Literature

The following solutions are not directly supported by the results of this

dissertation but are relevant to the emphasis of this dissertation. These solutions are

strongly supported by the findings of previous literature for their ability to encourage

home and mobile internet adoption. Solutions presented in this section aim at: 1)

overcoming low internet adoption in low income areas, 2) overcoming insufficient

infrastructure in rural areas, and 3) expanding availability of information technology

employment.

Overcoming Low Internet Adoption in Low Income Areas

Despite prevalent availability of internet infrastructure, home and mobile

internet adoption tends to be too low in low income communities situated in urban and

suburban areas (Atkinson 2009, 3). Many ISPs choose not to recruit new customers in

such areas since the cost of doing so sometimes exceeds expected revenue. The cost

may involve digital literacy training and marketing expenses to explain the benefits of

the Internet to consumers. The author recommends that the National

Telecommunications and Information Administration (NTIA) should establish a

competition among ISPs and reward those that successfully recruit new customers. For

example, the winning ISP might be awarded $250 per new subscriber and a public

recognition at the White House. Cooperation from ISPs is very important since their

marketing campaign and customer service could persuade non-internet-using

households to purchase and start using internet service.

Overcoming Insufficient Infrastructure in Rural Areas

About 15 million Americans in rural areas do not have access to high-speed

internet service in their homes (Wheeler 2014). Moreover, about 41% of American

rural schools do not have access to high-speed internet. Modernizing the FCC’s E-rate

program will make high-speed internet available in rural communities. The FCC

proposes to increase the cap on American households’ contribution to the E-rate

program via telephone bill by 16 cents a month. The funding from a hike in the E-rate

will be spent on a one-time infrastructure investment needed by high-cost rural


115

libraries and schools. The FCC contends that an increase in E-rate is justified since the

E-Rate budget has not received an inflation adjustment for 13 years. Moreover, the

FCC has been remodeling the Universal Service Fund and the new Connect America

Fund to finance construction of high-speed internet networks in underserved rural

areas. These funds will invest about $20 billion over the next five years to create

internet connections with minimum download speeds of 10 Mbps for rural households.

Expanding Availability of Information Technology Employment

Information technology employment and telecommuting (work from home) are

found to be positively correlated with higher internet adoption rates (Tomer and Kane

2015, 7). Using a regression model, the authors show that internet adoption rates have

a positive relationship with: 1) the share of population living in urban area, 2) the

share of telecommuter (work-from-home workers), and 3) the share of workers in

service, technology, management, and education industries. These relationships are

statistically significant at 5% level. Therefore, creating telecommuting employment

and job opportunities in the aforementioned industries could be a good way to

encourage internet adoption since households will have an opportunity to earn income

by accumulating information technology skills.

Conclusion

This chapter relates the econometric results of this dissertation to the solutions

to encourage internet adoption and improve digital literacy of American households.

Solutions supported by the results of this dissertation suggests: 1) providing digital

literacy education to low education households; 2) reducing the monthly price of

internet service to low income households; 3) pointing out the benefits of the Internet

to senior citizens; 4) furnishing computer trainings in learners’ native language; and 5)

providing accessibility devices to people with disability. On the other hand, solutions

supported by the findings of previous literature suggest: 1) rewarding ISPs who

successfully recruit new customers in low income areas; 2) raising the fee of the E-rate


116

program and spending these funds on internet infrastructure in rural areas; and 3)

expanding availability of information technology employment. These solutions should

be able to effectively close the digital divide among non-internet using households.


117

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