September 2023

1

170 Laurier Avenue, Suite 604
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CENTRE FOR THE
STUDY OF LIVING
STANDARDS

Literature Review of the Economic Impacts ofBroadband

Atakan Bakiskan and Sarah El Kaissi

September 2023
CSLS Research Report 2023-06

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Literature Review of the Economic Impacts of Broadband
Table of Contents
Abstract ...........................................................................................................................................4
Executive Summary ......................................................................................................................5
I. Key Concepts ..............................................................................................................................9
A. Definitions and Measurement .........................................................................................9
B. Applications of Broadband............................................................................................11
II. Impact of Broadband on Output and Labour Productivity ...............................................14
A. Cross-country Studies ...................................................................................................15i) Early Studies .......................................................................................................15ii) More Recent Studies ........................................................................................19
B. Within-country Studies ................................................................................................21
C. Firm-level Studies .........................................................................................................23
III. Impact of Broadband on Total Factor Productivity ..........................................................26
A. Cross-country Studies ...................................................................................................26
B. Firm-level Studies .........................................................................................................27
C. Industry Studies .............................................................................................................29
IV. Impact of Broadband on Employment ................................................................................31
V. Impact of Broadband Applications on Economic Outcomes .............................................34
Internet of Things & Industry 4.0 ......................................................................................35
VI. Other Impacts of Broadband ...............................................................................................36
A. Small & Medium Enterprises (SMEs) .........................................................................36
B. Human Capital...............................................................................................................36
C. Work from Home ..........................................................................................................37
D. Consumer Surplus .................................................................................................................. 38
E. Education ................................................................................................................................. 39
VII. Methodological Issues in Estimating the Impact of Broadband ......................................40
A. Discussion on Endogeneity in Measuring the Impact of Broadband ............................40

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B. Critique of the Use of Broadband Availability as an Instrument ..................................40
C. Shortcomings of Simultaneous Equation Models .........................................................42
VIII. Data Limitations in Estimating the Impact of Broadband .............................................44
A. Critique of the Use of Broadband Penetration Rate ......................................................44
B. Advantages and Disadvantages of Firm-level Data ......................................................44
IX. Conclusion ..............................................................................................................................46
X. References ................................................................................................................................48
XI. Appendix ...............................................................................................................................57
A. Further Details on Download Speeds............................................................................57
B. Summary Tables of Reviewed Studies ................................................................................ 58
C. Members of the Technical Advisory Committee ............................................................... 74
List of Tables
Table 1: Broadband Activity and Minimum Download Speed (Mbps) ........................................10
Table 2: Broadband Activity, Minimum Download Speed (Mbps) for Multiple Users ...............11
Table 3: Impact of Broadband Applications by Industry ..............................................................12
Table 4: Impacts of Broadband on Innovation in R&D ................................................................13
Table 5: Healthcare Applications and Broadband Speed..............................................................13
Table A1: Baseline Download Speed Requirements for Community Institutions .......................57
Table B1: Master Table of Early Output Studies ..........................................................................58
Table B2: Master Table of Recent Output Studies .......................................................................61
Table B3: Master Table of Early Total Factor Productivity Studies ............................................64
Table B4: Master Table of Recent Total Factor Productivity Studies ..........................................66
Table B5: Master Table of Early Other Impacts Studies ..............................................................69
Table B6: Master Table of Recent Other Impacts Studies............................................................72
List of Figures
Figure 1: Increasing Returns of Broadband Penetration on GDP Growth ....................................18
Figure 2: Differential Economic Effects by Region ....................................................................22

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Literature Review of the Economic Impacts of Broadband
Abstract
This comprehensive literature review examines the economic impacts of broadband, with a focus
on productivity and economic growth. Incorporating 55 quantitative economic papers coveringcross-country, firm-level, and industry studies, we provide detailed explanations of data choices,
methodologies, and key findings. We explore various measures used to quantify broadband
impacts, including penetration rates, speed, and technology adoption. We analyze studies on
broadband's effects on total output, total factor productivity (TFP), employment, and theapplications enabled by broadband, such as social media, e-commerce, and cloud computing.
Methodological challenges, such as endogeneity and data limitations, are discussed,
underscoring the need for improved methodologies and data to accurately assess broadband's
economic impact. Overall, findings indicate a generally positive association between broadband
penetration and output and labour productivity, despite some mixed results from earlier studies.
More recent studies with improved methodologies consistently demonstrate a positive impact.
Similarly, studies on TFP and employment reveal a positive influence of broadband.

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Literature Review of the Economic Impacts of Broadband
Executive Summary
This literature review provides a comprehensive analysis of the economic impacts of broadband,
focusing on productivity and economic growth. It includes 55 different quantitative economic
papers from cross-country studies to firm-level and industry studies, and provides a detailed
explanation of their data choices, methodology, and main findings.
We begin by discussing the various measures used in the literature to quantify the impacts ofbroadband. These measures include broadband penetration rates, broadband speed, and theadoption of broadband technologies by firms. We then look at four sets of studies.
First, we look at studies that measure the impact of broadband on total output. Although someearlier studies found mixed results, these studies have methodological shortcomings, and more
recent studies have general found a positive association between broadband penetration andoutput.
Second, we look at studies that attempt to isolate the impact of broadband on total factor
productivity (TFP), which essentially captures the extent to which investment creates spillovers
to the broader economy that are not captured by the companies investing in broadband. Thesestudies, particularly those the look across industries and countries rather than firms, also find apositive impact of broadband.
Third we look at the impact on employment, where the impact of broadband could be positive or
negative, depending on the extent to which broadband displaces existing jobs. Studies that lookat this area generally find positive impacts on employment, although this may not be true for lesseducated workers.
Finally, we emphasize that the economic impacts of broadband are not generated by broadband
alone, but by the applications it enables, such as social media, e-commerce, and cloudcomputing. We therefore also look at the literature focusing on the applications of broadband inareas like telehealth, education, telework, agriculture, and civic engagement, which have beenfound to positively affect GDP and employment.

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We then discuss some of the methodological problems that these studies can face. We discuss theendogeneity arising from the reverse causality between broadband and economic outcomes. This
arises because greater use of broadband can stimulate economic growth, while higher incomeslead to more demand for broadband. This makes disentangling the relationship between
broadband and economic growth less than straightforward. We also highlight potential issues
with the simultaneous equation model often used to identify the causal impact of broadband on
output and critique the use of broadband availability as an instrument for broadband adoption.
The review also discusses data limitations in estimating the impact of broadband, including thecritique of the use of broadband penetration rate as an instrument for broadband adoption, andthe advantages and disadvantages of firm-level data. A further challenge is disentangling theimpact of broadband per se from the applications, such as cloud computing, which it enables, andwhich are the proximate cause of productivity growth. Unfortunately, data on broadband
applications are extremely scarce.
While there are methodological challenges in estimating the impact of broadband, the literature
generally supports the view that broadband has significant positive impacts on economic
outcomes. Nevertheless, we emphasize the need for more robust methodologies and better datato accurately estimate the impact of broadband on the economy.

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Literature Review of the Economic Impacts of Broadband 1
The internet is a large part of everyday life in the 21st century, and the internet as we know it
would not be possible without broadband and its precursors. Broadband has spurred many
innovations, improved worker productivity and increased household consumer surplus (OECD,2021).
The internet began in the 1980s with the beginnings of computers communicating with eachother via USENET (Beckett, 2022). However, it was only available to those institutions with thecomputing power to access it, such as universities. In 1989, Tim Berners-Lee created the world
wide web, revolutionizing how people used the internet. In the 1990s, dial-up internet was
introduced, and internet service providers (ISPs) started providing connections to individual
households. The popularity of internet access led to the creation and introduction of broadband in
the early 2000s, which has since replaced dial-up. It allowed for much faster internet speeds but
was very expensive. This led to low initial penetration rates– just 9 per cent of UK households by
2001, for example. This period also saw the growth of online businesses and the beginning of
online entertainment: social media, such as Facebook emerged in 2004, followed closely by
YouTube and Reddit in 2005 (Beckett, 2022).
The increasing demand for internet data triggered the introduction of fibre-optic broadband
around 2008. This allowed for faster speeds of around 50Mbps – roughly double what wasoffered with standard broadband.2 The late 2000s also saw the emergence of 3G and mobile
phones. Since people could access the internet virtually anywhere, the 2010s were characterized
by a streaming, social media, and speed boom with the greatest broadband penetration rates.
Finally, the 2020s have shown that working from home is now possible thanks to broadband,
especially with the emergence of 5G and the potential for a fully fibre-based future (Beckett,2022). Therefore, there is no question that broadband significantly changed our lives, and how
1This report was undertaken by the Centre for the Study of Living Standards (CSLS). The research was conductedby Atakan Bakistan (atakan.bakiskan@csls.ca) and Sarah El Kaissi (sarah.elkaissi@mail.utoronto.ca) under thesupervision of CSLS Executive Director Andrew Sharpe and CSLS Deputy Executive Director Timothy Sargent.
We would like to thank TELUS for funding the project, and Joe Roswell of TELUS for his insightful and usefulcomments. We would also like to thank the technical advisory board for the insightful feedback that greatly
improved the report. A list of the members of the advisory committee can be found in Appendix C.
2Standard broadband was 25Mbps.

8businesses operate, yet there is no consensus on the exact impacts on productivity and economic
growth.
This survey paper contributes to the literature with a comprehensive and up-to-date analysis of
econometric studies on the impact of broadband on productivity, output, employment, andconsumer surplus. The literature review includes 55 different quantitative economic papers and adetailed explanation of their data choices, methodology, main findings, and measure ofbroadband. The review has a section discussing the endogeneity arising from the reverse
causality between broadband and economic outcomes. Moreover, we discuss the potential issues
in using broadband availability as an instrument for broadband adoption and a brief critique ofthe simultaneous equation model developed by Röller and Waverman (2001) that is often
adapted to understand the causal impact of broadband on output. Finally, the review has a section
on the impacts of broadband on other economic outcomes, such as consumer surplus, a
discussion on the definitions and measurements for broadband, a summary of broadband-enabledapplications, and the mechanisms through which broadband can impact economic outcomes.
The literature review consists of nine sections: 1) key concepts, 2) impact of broadband on
output and labour productivity, 3) impact of broadband on total factor productivity, 4) impact ofbroadband applications on employment, 5) impact of broadband on economic outcomes, 6)
impact of broadband on other economic outcomes, 7) methodological issues in estimating theimpact of broadband, 8) data limitations in estimating the impact of broadband and 9)
conclusion. The sections on employment and other economic outcomes are less detailed as thestudy focuses on understanding broadband's impact on productivity and output.

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I. Key Concepts
A. Definitions and Measurement
Broadband is the transmission of wide bandwidth data over a high-speed and high-quality
internet connection. There are six types of leading broadband transmission technologies: 1)
Digital Subscriber Line (DSL), 2) Cable Modem, 3) Fibre, 4) Mobile Wireless and Fixed
Wireless, 5) Satellite, and 6) Broadband over Powerlines (BPL). Throughout this survey, theterm broadband generally refers to total (fixed + mobile) broadband unless specified. DSL is awireline transmission that uses copper telephone lines to transmit data at high speeds. There are
two main DSL technologies: 1) Asymmetric DSL (ADSL), used primarily by residential
consumers, and 2) Very high data rate DSL (VDSL), which is offered to businesses (Federal
Communications Commission, 2014).
Broadband impacts productivity and economic growth by the degree of penetration and quality.
Quality includes speed, reliability, latency,3 and coverage.4 The speed varies considerably
between the internet provided by Fibre to The Home (FTTH), Fibre to The Node (FTTN), cable,
and ADSL. Generally, the speed order from fastest to slowest goes as follows: FTTH > FTTN >Cable > ADSL.
According to the definition set by Federal Communications Commission (FCC) in 2015,
broadband is any internet service with a minimum 25 Mbps download speed and a minimum
3Mbps upload speed (U.S. Department of Commerce, n.d.). However, there are efforts by thecurrent FCC Chair Jessica Rosenworcel to change the definition and raise the minimum
download speed to 100 Mbps and the minimum upload speed to 20 Mbps. For Canada, according
to the Canadian Radio-television and Telecommunications Commission (n.d.), the broadband
standards are a minimum of 50 Mbps for download speed and 10 Mbps for upload speed.
On the other hand, according to the International Telecommunication Union (2010), theminimum requirement for fixed broadband is 256 Kbps download speed. The fixed broadband
subscriptions include “cable modem, DSL, FTTH/FTTB,5 other fixed (wired)-broadband
The time it takes for data to travel from user’s device to a server and back.
How many areas or locations within a region are serviced by broadband infrastructure, regardless of whether or not
individuals or households in those areas have adopted the service.
5
Fibre to the Building.
3
4

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subscriptions, satellite broadband, and terrestrial fixed wireless broadband.” Similarly, theOECD (2019) defines broadband access as technologies providing access to the internet at
download speeds of a minimum of 256 Kbps.
There is no consensus on the definition of broadband; the discrepancy between the minimum
download speed of 256 Kbps and 25 Mbps is notable with the former being one-hundredth of thelatter. The OECD (2021) states one reason that OECD countries did not agree to raise thethreshold speed in 2012 is that some countries have the current definition incorporated into legal
instruments. Thus, changing the definition would have had "implications for the universal
services framework in those countries" (OECD, 2021).
The 25Mbps download and 3Mbps upload speeds are not required for many broadband-enabledapplications. However, this is different for 256 Kbps download speed, a minimum speed
definition of broadband embraced by ITU, OECD, and most econometric studies. The
Broadband Speed Guide by the FCC (2022a) provides the information in Table 1 that associates
broadband activities with their minimum download speed. The speeds in Table 1 assume that one
activity is running at a time. Higher speeds will be needed if more than one person runs theactivity. Table 2 describes the broadband activities and speeds needed to run them for more than
one user (FCC, 2022b). Community institutions with multiple users generally need a muchhigher speed than those in Table 1 (see Table B1).
Table 1: Broadband Activity and Minimum Download Speed (Mbps)
Activity
General Browsing and EmailStreaming Online RadioVoIP Calls
Student
Telecommuting
File Downloading
Social MediaStreaming Standard Definition Video
Streaming High Definition (HD) Video
Streaming Ultra HD 4K Video
Standard Personal Video Call
HD Personal Video Call
HD Video Teleconferencing

Minimum Download Speed (Mbps)
1
Less than 0.5
Less than 0.5
5 to 25
5 to 2510
1
3 to 45 to 8
25
1
1.5
6

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Game Console Connecting to the internet
Online Multiplayer

34Source: Federal Communications Commission (2022a)

Table 2: Broadband Activity, Minimum Download Speed (Mbps) for Multiple Users

1 user on 1 device
2 users or devices at atime
3 users or devices at atime
4 users or devices at atime

Light Use
(Basic functions:e-mail, browsing
basic video,
VoIP, internet
radio)Basic
Basic

Moderate Use(Basic functions plus one highdemand application: streaming
HD video, multiparty videoconferencing, online gaming,
telecommuting)
Basic
MediumHigh Use(Basic functions
plus more than one
high-demand
application runningat the same time)
MediumMedium/Advanced

MediumMediumAdvanced

MediumAdvanced

Advanced

Basic Service = 3 to 8 Mbps
Medium Service = 12 to 25 Mbps
Advanced Service = More than 25 Mbps
Source: Federal Communications Commission (2022b)

Most of the literature surveyed here focuses on measuring broadband adoption, quality, orinvestment. To measure adoption, authors tend to focus on the number of broadband lines per
100 inhabitants using either total broadband, only fixed, or only mobile. Alternatively, studies
focus on the use of various broadband connections by firms, specifying the technology (DSL,
cable, fibre, ultra-fast, etc.). The most common measure of quality used is broadband speed,using data such as the number of connections above a certain speed, mobile broadband speed,
and average download speed.
B. Applications of Broadband
It is not broadband itself that has productivity implications; it is the applications, such as e-mail,video streaming, and telehealth.6 Most studies use broadband penetration rate7 and speed data to
relate productivity to broadband. There are limitations to this approach because the productivity
6

Broadband can provide health care services through telehealth. The mobile health market - the provision of healthservices enabled by mobile communications - is expected to reach $102 billion globally by 2022, according to Zion
Market Research (2016).
7
Typically, this refers to broadband subscriptions per 100 people, which acts as a reasonable proxy for broadband
uptake by firms.

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implications of broadband applications are solely attributed to broadband adoption/availability.
The broadband penetration rate data does not specify which broadband applications are used.
Therefore, an increase in productivity due to an increase in broadband penetration rate might
overestimate the positive effect of broadband access, as some of that effect may be due to a new
broadband application that is unobservable in data.
Broadband speed is a prerequisite for applications; therefore, one should expect a highcorrelation between new applications and speed. Studies that isolate the economic impacts ofbroadband speed can indirectly address the benefits of broadband applications. The speed data
may serve as a good proxy since applications such as YouTube, Zoom, and Facebook could not
emerge if not for advancements in broadband speed.
The Australian Government (2018) compiled Table 3 to summarize the impact of broadband
applications on various industries. It shows how certain firms may use broadband technologies toimprove their productivity. One example is cloud computing, which allows firms to use
computing services over "the cloud." Such services include storage, analytics, databases, andnetworking. Many organizations are turning towards cloud computing due to its lower IT costs,
speed, global scale, and reliability (Microsoft Azure, n.d.).
Table 3: Impact of Broadband Applications by Industry
Industry
Information, media &telecommunications
Arts & entertainment

Education
Wholesale & retail

Finance & Insurance

Professional, scientific& research services

Potential 5G benefits and use cases
Supporting the Internet of Things
Increasing network capacity
Providing access to a fully wireless and mobile internet
Improved user experience with higher-quality videos
Mobile broadband in crowded areas that addresses issues with interference
and reliability
High quality of service despite challenging network conditions
Remote learning and teaching
Easier learning for students with special needs
Improved asset tracking experience
More interaction with consumers in a dynamic fashion
E-commerce, faster mobile payment
Managing larger volume of data, better analytics to address fraud detection
and customer segmentation
Faster transactions
Cloud computing, creation of new software
More outsourcing of business service functions

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Public administration
Health care and social

Supporting fast-response emergency services
Changes to systematic behaviours, easier processes
Internet of medical things
Health data mining and analytics
Predictive modelling to better anticipate risks to patients
Connected medicine to get better quality care

Source: Australian Government (2018)

The OECD (2008) highlights the potential impacts of broadband on innovation in R&D in Table
4.
Table 4: Impacts of Broadband on Innovation in R&D
Examples of the Potential Impacts of Broadband on Innovation in R&D
Enable instant sharing of knowledge and ideas
Lower barriers to product and process innovation via faster and less expensive communications
Accelerate start-ups
Improve business collaboration
Enable small businesses to expand their R&D and collaborate in larger R&D consortia
Reduce time from idea to final product
Foster greater networking
Promote "user-led innovation"
Source: OECD (2008)

Healthcare applications and corresponding qualities varying by speed are emphasized in Table 5
using information from Ericsson (2013).
Table 5: Healthcare Applications and Broadband Speed

Application
High-quality, non-real-timevideo imaging for diagnosis
Cardiology, neurology, andemergency room
consultations
Cineo-angiography andechocardiograms
3D interactive brain imaging
Clinical decision support
systems
Advanced clinical decision
support system
Professional tele-education
Source: Ericsson (2013)

Application
Technology
File transfer

1 MbpsBroadband speed
10 Mbps
100 Mbps

High quality

High quality

High quality

High quality

H.323 video

Low/medium
qualityHigh quality

High quality

High quality

H.323 video

High quality

High quality

High quality

SGI Vizserver

Low/medium
quality
Unsupportable

Unsupportable

High quality

Web browsing

High quality

High quality

MediumqualityHigh quality

Image transfer

Unsupportable

Unsupportable

High quality

MPEG 1 video

Low quality

Low quality

MediumqualityHigh quality

1 GbpsHigh quality

High quality

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II. Impact of Broadband on Output and Labour Productivity
The impact of broadband on output comes primarily from higher productivity. Rivlin and Litan(2001) list four ways broadband can enhance productivity. The first is reducing the transaction
costs necessary to produce and distribute existing goods and services. The AustralianGovernment (2018) argues that broadband will allow businesses to access information much
faster than older technologies, increasing production efficiency. Broadband will also enable new
goods and services to enter the market which can impact productivity. For example, using
autonomous vehicles enabled by broadband can be more efficient than the existing transportation
infrastructure if autonomous vehicles can navigate to remove congestion better than nonautonomous vehicles.
The second way broadband can increase productivity is through increasing management
efficiency. Firms will be able to optimize their supply chains better and communicate more
efficiently both within the firm and with clients. The third is effective marketing and pricing.
Firms with broadband access can optimize their marketing and pricing strategy by collecting datafrom customers and competitors. Broadband revolutionizes the print, movie, music, gaming, andadvertising industries as clients can reach digital content through high-speed download, reducing
transaction costs for both customer and producer, while high-quality video conferences can
reduce the number of business trips, which can help reduce firm expenses (Ezell et al, 2009).
The fourth is through increased competition. As broadband makes information more accessible,
the informational asymmetry in the market is reduced, and prices become more transparent. For
example, farmers can source materials at a lower cost through online price discovery, and there
will be an improved bargaining position by removing the informational asymmetry barrier(LoPiccalo, 2021). This brings more buyers and sellers to the market and increases thecompetition. Jensen (2007) shows how having a mobile broadband connection provides
fisherman in South India more bargaining power when pricing their fish in local markets. Usinga differences-in-differences analysis, the author shows that as more regions gain mobile phone
access, the price dispersion of fish across markets decreases.
In this section, we look at studies that look at either economic growth overall or labour
productivity (typically measured as GDP per worker or per hour). There is an in-depth discussion
on the data employed, the measure of broadband, the methodology, and the main findings for15every study. Studies are categorized according to whether the study is conducted acrosscountries, within a country, or at the firm-level. The cross-country studies are divided into those
focusing on early datasets (generally those terminating before 2010) followed by those using
newer datasets. Appendix tables B1 and B2 provide concise summaries of all the studies
discussed in this section.
A. Cross-country Studiesi) Studies on Earlier Datasets
In a cross-country analysis, Czernich et al. (2011) estimate the effect of broadband infrastructure
on economic growth for 25 OECD countries from 1996 to 2007. They use fixed-line voice
telephony and cable TV pre-existing networks as instruments for broadband and find that a 10
percentage point increase in broadband penetration raises annual per capita GDP growth by 0.91.5 percentage points per annum relative to no change in broadband penetration.8
Qiang et al. (2009) employ an endogenous growth model based on Barro (1991) to analyze theimpact of broadband penetration on economic growth. The authors use data based on 120
countries, of which most are classified as developing. Their cross-country analysis makes it
possible to distinguish the effects of growth on developing and developed countries. For
developed countries, the authors find that a 10 percentage point increase in fixed broadband
penetration between 1980 and 2006 is associated with a 1.2 percentage point increase in annual
per capita GDP.9 This is highly significant given that developed countries experienced an overall
average growth rate of only 2.1 per cent between 1980-2006. For developing countries, themagnitude is similar but slightly higher; an increase of 10 additional fixed broadband subscribers
per 100 people between 1980 and 2006 is associated with a 1.4 percentage point increase inannual per capita GDP growth during the same period. The authors note that the results fordeveloping countries are statistically significant at the 10 per cent level while results for

8

The authors give the following example of Germany. In 2003, Germany had a broadband penetration rate roughly
10 percentage points lower than leading OECD countries. Suppose Germany had increased broadband penetration
by 10 percentage points in 2003, then their GDP per capita would be 3.6-5.9 percentage points higher in 2007.
9This is a change in the growth rate - if the growth rate of GDP per capita was 2 per cent before the increase, itwould become 3.2 per cent annually after the change.

16

developed countries are significant at 5 per cent. They argue this is due to broadband penetrationin developing countries not yet reaching critical mass10 to generate robust aggregate effects.
Zaballos and López-Rivas (2012) focus on Latin American and Caribbean countries in theiranalysis of broadband. They analyze the impact of broadband on GDP per capita using a nonlinear model. Rather than using a production function approach where output is modelled as afunction of capital and labour, GDP is modelled as a function of expenditure components:
investment, consumption, public spending, and trade balance. To address a possible
multicollinearity problem, five significant independent variables are chosen that are thought toimpact GDP: 1) interest rate spread, 2) interest on new debt, 3) multilateral debt, 4) net official
development aid and 5) fixed broadband penetration per 100 inhabitants. They find that a 10
percentage point increase in broadband penetration in 2005 increases GDP per capita by 3.2
percentage points by 2009. Thus, broadband increases annual per capita GDP growth by 0.8
percentage points averaged over 2005-2009.
Like Zaballos and López-Rivas (2012), Bojnec and Fertö (2012) specify a model where GDP is
not a function of labour and capital, using data from 34 OECD countries from 1998 to 2009.
Their econometric specification models GDP as depending on investment, government
expenditure, inflation, labour productivity growth, trade openness, foreign direct investment, andbroadband availability. However, their overall results suggest no significant and positive
relationship between broadband availability per inhabitant and per capita GDP growth.
One potential problem with these studies is the potential endogeneity of broadband with respect
to economic growth. Arvin and Pradhan (2014) analyze broadband's impact on economic growthfor developed and developing countries by examining the Granger causality relationships
between broadband penetration and economic growth in 19 countries from 1998-2011. They
conduct a panel cointegration test11 and a Granger causality test.12 They find that for developed
countries, broadband penetration increases economic growth, but growth also increases
broadband penetration. For developing countries broadband penetration does not seem toincrease growth, but growth does increase broadband penetration.

10

Minimum level of participation that allows broadband's positive externalities to show their impact.
The panel cointegration test is used to see whether two times series have a stable, long-run relationship.
12
Granger causality tests are used to determine whether one time series is useful in forecasting another.
11

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Similarly, Lam and Shiu (2010) conducted a Granger causality study of 105 countries between
2002-2006 to investigate the relationships between teledensity (measured as the number of fixedline and mobile subscribers per 100 people) and GDP. Using a dynamic panel, the authors
investigate this for different subgroups of countries: divided by regions (Africa, Americas, Asiaand Oceania, and Europe) and by income (high, upper-middle, lower-middle, and low). Theyfind various significant Granger causalities, including that real GDP Granger causes teledensityand vice versa for European and high-income countries, teledensity Granger causes real GDP in
Asia and Oceania, and for the rest of the categories, real GDP Granger causes teledensity. These
relationships are important to consider as they could help explain the discrepancy of findings in
the literature.
These findings suggest that the two studies above that found positive impacts of broadband on
GDP may well have overestimated the impacts because they did not account for the partial
endogeneity of broadband penetration.
Koutroumpis (2009) uses data from 22 OECD countries from 2002 to 2007 to investigate thelink between broadband adoption and economic growth. He deals with the endogeneity issue by
using an often-cited simultaneous equation model by Röller and Waverman (2001).13 The modelis based on the joint estimation of a micromodel for broadband investment with a macro
production function. Koutroumpis (2009) transforms the equations for demand and supply fortelecommunications infrastructure in Röller and Waverman (2001) into demand and supply forbroadband infrastructure. The model is based on four equations: 1) aggregate production
function, 2) demand for broadband infrastructure, 3) supply of broadband infrastructure and 4)
broadband infrastructure production equation. The simultaneous equation approach ensures thatthe model incorporates feedback between broadband infrastructure and the aggregate economy.
Using supply and demand equations endogenizes the broadband infrastructure and addresses thetwo-way causality of broadband and output. The equations are jointly estimated including
country-fixed effects. He finds that a 10 per cent increase in broadband penetration rate generates
a 0.25 percentage point increase in the level of GDP growth.14 For the period studied, this

13

They find that approximately one-third of OECD Member Country growth between 1970 and 1990 can be
attributed to investment in telecommunications.
14
The author calculates the compounded annual growth effect for Spain to be 0.42% due to broadband penetration
increasing from 3.02% in 2002 to 17.70% in 2007.

18

translates into an average contribution to annual growth of OECD countries of 0.40 per cent per
annum.
In addition, the author considers the network externality property of broadband: as the number ofbroadband users increases, the value of broadband usage increases. Due to network externalities,
the impact of broadband penetration on growth will not be linear and can vary across different
penetration levels. The author finds that a 10 per cent increase in broadband adoption contributes
0.08 percentage points to annual GDP growth for countries with low broadband penetration (lessthan 20 per cent), 0.14 percentage points for countries with a medium level of penetration (20 per
cent - 30 per cent), and 0.23 percentage points for countries with a high level of penetration(greater than 30 per cent) (see figure 1). Thus, the impact on GDP is non-linear and is highest
after a critical mass of 30 per cent of the population with a broadband connection is reached until
saturation.
Figure 1: Increasing Returns to Broadband Penetration on GDP growth

Source: ITU (2012)

Finally, Thompson and Garbacz (2011) focus on the different impacts of broadband on lowincome and high-income countries while separating the effect of mobile broadband from fixed
broadband. The authors use panel data for 43 countries from 2005-2009 and apply fixed-effectIV regressions and include a dummy variable for low-income countries. The results show that19

mobile broadband is positive and significantly affects GDP high and low-income countries,
whereas fixed broadband's effect is insignificant in high-income countries, but negative andsignificant in low-income countries. In addition, the magnitude of the impact is found to belarger for low-income countries. The authors note that this may be due to near universal service
levels of fixed broadband in developed countries, and limited efforts to expand and use fixed
telecom investment in developing countries during this period.
ii) More Recent Studies
In an extension of his 2009 paper, Koutroumpis (2019) investigates the impact of broadband
speed on GDP for 35 OECD countries between 2002-2016. By adding a quadratic term of
broadband speed in the regression, speed is found to have a diminishing impact on GDP. The
annual GDP effect from broadband speeds between 2002 and 2016 varies between countries.
Mexico, Turkey, Slovakia, and Greece benefited the least with the impact of broadband speed on
annual GDP growth is less than 0.08pp. On the other hand, South Korea, Iceland, and Sweden
benefited the most, with an annual GDP effect of more than 0.12pp (see Koutroumpis (2019),
Figure 1). Overall, the author finds that the OECD region experienced a speed increase of
0.75Mbps in 2002 to 12.85Mbps in 2016, translating into a level increase in GDP of 1.32 percent – 0.09 per cent annually over the 15 year period.
In contrast, Kongaut and Bohlin (2014) use data for OECD countries from 2008 to 2012 and findthat broadband speed positively affects GDP per capita with greater effects for low-income
countries. Like many other studies, their model is based on the Cobb-Douglas production
function growth model, and they deal with the potential endogeneity problem by instrumenting
broadband speed using the percentage of fibre subscriptions of total fixed broadband
subscriptions. A comparison between high-income and low-income countries shows thatbroadband speed has a greater impact on low-income countries, consistent with the results in
Qiang et al. (2009). Similarly, Rohman and Bohlin (2012) apply the same approach using
broadband penetration, broadband price, and telecom revenue as instruments to estimate

20

broadband speed. They find that doubling the broadband speed in OECD countries increased
annual GDP growth by 0.3 percentage points.15
Edquist (2022) uses country-level panel data from 116 countries between 2014 and 2019 toestimate the impact of mobile broadband speed on labour productivity measured by GDP per
worker. The estimates are generated using fixed effects regression by modelling labour
productivity as a function of the capital-labour ratio, human capital, and mobile broadband
speed. While the author acknowledges the potential endogeneity, the only attempt to solve it is tolag mobile broadband speed by one year. However, this does not necessarily solve theendogeneity problem because the previous period's productivity will affect the previous period's
mobile broadband speed, and the evidence shows that previous productivity is correlated withthis period's productivity. Thus, endogeneity remains a problem. He finds that a 10 per centincrease in mobile broadband speed in the previous period is associated with a 0.2 per centincrease in the level of labour productivity.
Edquist et al (2018) use country-level data from 90 countries between 2002 and 2014 to estimate
the impact of broadband adoption on GDP. They instrument broadband adoption as the numberof mobile broadband subscriptions per 100 inhabitants in 2002 and find that a 10 percentage
point increase in broadband penetration increases GDP by 0.1-0.4 percentage points.
Mayer et al. (2019) investigate whether broadband network penetration is a determinant of
economic growth using quarterly data for 29 OECD countries from 2008 to 2012. They criticize
the model specification of Koutroumpis (2009) and Czernich et al. (2011) due to their static
nature and limited treatment of endogeneity. They argue that the change in broadband
infrastructure can affect current and future GDP. Therefore, in the regression specification, GDPneeds to depend on the explanatory variables' current and past values. Increased broadband
penetration and quality will affect future GDP due to learning, reorganization, and innovation(OECD, 2019). This creates a need for autoregressive models. However, the static fixed-effects
panel data models ignore the dynamics, and excluding lagged variables creates biased estimates
due to omitted variable bias. They find that the coefficient of the broadband penetration rate in
the dynamic growth equation is statistically insignificant. They conclude that the15

If the GDP growth rate was 2 per cent in 2008, doubling broadband speed would increase the growth rate to 2.3
per cent.

21

misspecification of the static models in other studies leads them to overstate the economic impactof broadband.
Moreover, Mayer et al. (2019) point out that explanatory variables in the growth model areendogenous, but the only endogeneity addressed in other studies is the potential endogeneity ofbroadband with respect to output. The threshold model is applied to allow parameters between
high-income and low-income countries to vary as it is thought that the impact of broadband on
economic growth is translated differently depending on the country's income level (see Qiang etal. (2009)). In contrast to other studies, Mayer et al. (2019) introduce an interaction variable
between speed and broadband coverage and find it to be negative, but speed alone is positive.
The negative interaction term means that the impact of broadband speed is higher for countries
with lower broadband penetration levels. Mayer et al. (2019) also find that the impact ofbroadband penetration on economic growth is greater for low-income countries than for highincome countries.
B. Within-country Studies
Several studies focused on the impact of broadband on growth using data from only one country.
Annual national data will provide an insufficient number of observations given the short periodof time over which broadband has been in existence. However, quarterly data or data based on
the country's administrative divisions can mitigate this problem (Minges, 2016). Furthermore,
the country-level analyses do not consider the heterogeneous impact of broadband on regions ofa country. There is no reason to assume that the impact is uniform across all territories of acountry.
Jung and López-Bazo (2020) use data from 27 Brazilian states for the period 2007-2011 to
analyze the regional impact of broadband on labour productivity. The initial OLS estimation
provides biased estimates for the coefficient of broadband infrastructure on labour productivity
due to reverse causality - investment in broadband can drive labour productivity but can also be
the result of productivity (Cardona et al., 2013). To solve this problem, the authors choose an IV
approach using the number of voice-telecommunication fixed access lines per 100 inhabitants
with a five-year lag and lagged population density as instruments. Overall, results suggest thatthe impact of broadband on productivity is positive but not uniform across provinces. Although22

the impact depended on connection quality and network effects, less developed regions benefited
more from broadband.
Crandall et al. (2007) investigate the effect of broadband penetration on output in the UnitedStates between 2003-2005. They generate estimates for the entire non-farm private sector and 2digit sectors such as manufacturing, business, and financial services. The aggregate results
suggest there is a positive but non-significant impact of early broadband on GDP. This could be
because their study period only covers the early implementation of broadband, and thus it is notpossible to measure the full impacts. As for the sector-specific findings, the only significant
effects are in the services sector.
Katz et al. (2010) focus on the impact of broadband on the German economy. The authors
estimate the total investment required to meet the government's broadband targets and analyze
whether the cost is justified by measuring the impact on economic output. They estimate that therequired investment will be €36 billion, generating €170 billion of additional GDP - 0.6 per centof GDP growth in Germany. Figure 2 shows that for regions with high broadband penetration(left), there is a large increase in output growth that diminishes over time, whereas areas withlow broadband penetration (right) experience more stable growth (catch-up effect).
Figure 2: Differential Economic Effects by Region

Source: Katz et al (2010)

23

Time series models at the national level include Katz and Callorda (2013), who use microdata
from Ecuadorian household surveys and find that households' average income in cantons thatwere unserved before 2009 increased by 3.7 per cent annually between 2009-2011 due tobroadband deployment. Using the simultaneous equation model initially developed by Röller andWaverman (2001), the authors conclude that every 10 per cent increase in fixed broadband
penetration is associated with a 0.5 percentage point increase in average annual GDP growth of
between 2008-2012.
C. Firm-level Studies
A study of the introduction of broadband by Bertschek et al. (2013) examines the expansion of
DSL's impact on German firms' productivity and innovation activity from 2001 to 2003 using
firm-level data from the ZEW ICT survey. The logarithm of sales per employee is used as ameasure of labour productivity. Innovation activity is a binary variable taking the value one if aninnovation has been realized, and its impact is analyzed using a recursive binary probit model.
Initially, the authors estimate the production function framework using OLS and find positive
results; however, the optimal choice of inputs (including broadband usage) may be impacted byfirm performance, creating an issue of reverse causality which renders OLS estimates biased.
Thus, they instrument firms' broadband usage using the regional availability of DSL. They alsouse control variables such as computerization of workplaces, firms' previous experience with
innovation, regional dummies, and GDP per head at the county level. The authors' main
conclusion is that broadband had no impact on firms' labour productivity but had a positive effecton firms' innovation activity.
DeStefano et al. (2023) reaffirm the findings of Bertschek et al. (2013) by applying a fuzzy
regression discontinuity design using plant- and firm-level panel data in the UK from 2000 to
2004. The geographical discontinuity they exploit arises from a historical accident resulting infirms on one side of a geographical boundary having broadband access and those on the other
having no access allowing broadband adoption to be considered exogenous. Sales per worker areused as a measure of labour productivity, as in Bertschek et al. (2013). The authors findbroadband use had no effect on firm performance.

24

Although these studies using early datasets found null effects, some newer literature has foundpositive effects. Gallardo et al. (2021) argue that the link between productivity and broadband
should be analyzed from a more extensive socioeconomic perspective. The authors criticize theprevious studies for focusing only on availability or adoption rather than on specific aspects ofbroadband, such as broadband at different download/upload speeds, digital divide index, anddigital distress. Using county-level data in the United States in 2017, Gallardo et al. (2021)
employ OLS models with spatial dependence that include ten unique broadband-related
variables. Labour productivity is measured as GDP per job and included in the estimation as thedependent variable. The authors find that broadband adoption and digital distress had a moresignificant impact on job productivity than broadband availability and speed. It is important tonote that Gallardo et al. (2021) did not account for the endogeneity between broadband adoption
and productivity; thus, their OLS estimates may be biased. This weakens the value of thesefindings compared to earlier studies, which all attempt to solve the endogeneity issue.
Grimes et al. (2012) use propensity score matching to control for factors that could determine afirm's broadband choice. The data comes from two sources: 1) unit record responses to Statistics
New Zealand's Business Operations Survey 2006 and 2) Statistics New Zealand's prototype
longitudinal business database (LBD). The extensive nature of data allows the authors to control
for numerous factors such as firm size, firm age, industry structure facing the firm, the quality of
ICT infrastructure in the firm's locality, being foreign-owned, etc. There are 6,051 firms withdetailed characteristics in their merged data, making propensity score matching an attractive
methodology. The authors estimate three average treatment effects: 1) broadband vs. nobroadband, 2) slow broadband vs. no broadband, and 3) fast broadband vs. slow broadband on
the firms' average productivity in 2005 and 2006. Results suggest that the labour productivity
effect of switching from no broadband to adopting broadband is a level increase of 10 per centfor the period studied. Finally, no productivity effect is observed for the third average treatment
effect. Therefore, switching to fast broadband from any other form of broadband is an
insignificant determinant of labour productivity growth.
In addition to looking at the productivity implications of firms having broadband access, one
should also investigate whether employees' access to mobile broadband impacts firm
productivity because mobile internet access can improve information flows and communication

25

and reduce associated costs. Bertschek and Niebel (2016) is the first micro econometric study
that explores the link between firms' labour productivity and the share of employees with mobileinternet access. The authors use data from 2143 German firms in 2014 and find that firms' labour
productivity (defined as sales per employee) increases as the share of employees with mobileinternet grows. The production function is augmented by the share of workers with internet
access, with mobile internet access, and predominantly using computers. Both OLS and IV
regressions are used to analyze the causal impact of mobile internet access on productivity
controlling for firm size and the share of young employees. Two instruments are used: theaverage mobile internet use at the industry level and the number of years the interviewee owns asmartphone. The IV estimation provides evidence of the positive causal impact of mobileinternet use on labour productivity. They find that the share of employees with mobile internet
devices is positively correlated with labour productivity.
Summary
In this section, we looked at numerous studies that examined the relationship between broadband
penetration and output and labour productivity at various levels (country, region, and firm).
Studies of the early implementation of broadband had mixed results, and anyway failed toconsider the potential impact of endogeneity on their results. Later studies which include more
recent data have generally found a positive association between broadband penetration andoutput.

26

III. Impacts of Broadband on Total Factor Productivity
This section reviews studies that investigate the impact of broadband on total factor productivity(TFP). TFP is a measure of all influences on output growth other than the contributions of capitaland labour through their private marginal products (Romer, 2019). By focusing on TFP rather
than labour productivity or GDP, these studies are trying to identify the broader benefits to theeconomy (spillover effects) that are not captured by the companies investing in the broadband
capital stock. Studies are categorized according to whether the study is conducted acrosscountries, at the firm, or industry level. Appendix tables B3 and B4 provide concise summaries
of all the studies discussed in this section.
A. Cross-country Studies
Bartelsman et al. (2019) use cross-country and industry-level data from 10 European countries
between 2002-2010 to estimate the impact of broadband on firm TFP. They measure broadband
adoption as the proportion of broadband internet-connected employees and obtain estimatesusing pooled OLS. This is one of few studies that try to disentangle the complementarity
between broadband adoption and the innovation process within firms. By including variables tomeasure the link between TFP and innovations, as well as a variable to measure broadband
adoption, the authors can ensure they do not attribute too much of the change in TFP to
broadband as opposed to the innovative process in firms. Nevertheless, they find that broadband
adoption is more important for determining TFP than the innovative process in firms. They findthat a 10 percentage point increase in broadband connected employees increases the level of TFP
by 3.6 per cent during the period studied, which is equivalent to an increase of 0.45 percentage
points to the growth of annual TFP.
Gal et al. (2019) combine cross-country firm-level data on productivity and industry-level dataon digital technology adoption to measure the effect on firm productivity while accounting forfirm heterogeneity. They use data from 20 OECD countries between 2010-2015. They measure
digital adoption by the share of firms using a specific technology (such as high-speed broadband
internet connection or cloud computing) and firm productivity as TFP growth. For high-speed
broadband, they find that a 10 percentage point increase in adoption leads to an instantaneous
increase in TFP of 1.4 percentage points. After 5 years, the level of TFP would be 5.8 per cent

27

higher. This implies an increase in annual TFP growth of 1.2 percentage points. Furthermore,they find that effects of high-speed broadband are stronger in manufacturing and routineintensive activities. They note that effects on TFP growth are also stronger for more productive
firms and weaker in the presence of skill shortages which provides insights into thecomplementarities between digital technology and other forms of capital.
B. Firm-level Studies
Haller and Lyons (2015) use DSL broadband availability as an instrument for broadband
adoption. Using firm-level panel data from 2002-2009 for 2290 Irish manufacturing firms, theauthors use fixed effects and instrumental variables (IV) regression and conclude that the effectof broadband adoption on a firm's TFP is not statistically significant. This finding holds for all
types of broadband and higher-speed DSL broadband.
Cambini et al. (2023) use a panel dataset of Italian firms for the 2013-2019 period to measure theimpact of firms' adoption of ultra-fast broadband (UFB) on TFP. The authors note that the main
issue in production function estimation is the endogeneity of inputs since they respond to afirm’s productivity level. They therefore opt for a control function approach, which uses a firm’s
demand for intermediate inputs to instrument for its unobserved productivity level, thenestimates the impact on TFP by IV regression using the firm’s municipality’s distance from theclosest backbone node as the instrument for broadband adoption. They estimate that that UFB
adoption increased the level of TFP by 2.9 per cent over the period 2013–2019, which isequivalent to an increase in annual TFP growth of 0.48 percentage points.
Dalgic and Fazlioglu (2020) find that switching from standard broadband to fast broadband plays
a significant role in improving firms' TFP. These findings are based on Turkish firms from 20122015. They find that firms that switch from normal broadband to faster broadband have a TFP
that is initially 5.3 percentage points higher than firms that do not switch, and TFP that thisdifference grows to 6.5 percentage points in the following year.
Zhang et al. (2022) exploit the "Broadband China" program which aims to speed up networks,
lower service fees and increase coverage in China. 120 cities have been selected for a pilot
program, and so it serves as a quasi-natural experiment in which changes in broadband
infrastructure can be treated as an exogenous shock to firm productivity. This allows the authors

28

to estimate the impact using differences-in-differences (DID). The authors measure broadband as
a binary variable equal to one if the registration place of the firm is in a "Broadband China" pilot
city and use TFP as the dependent variable. They conclude that TFP levels increased for pilot
city firms by 3.97 per cent, and the increase is more evident in capital-strapped firms, non-stateowned enterprises, and highly capital-intensive firms.
The Australian Government (2023) analyzed the impacts of switching from DSL to fibre or cablebroadband using firm-level data between 2009-2019. By focusing on the sample of firms thatswitch, they were able to estimate the effects of adopting better technologies without running arandomized controlled trial. They find that firms that switch technologies experience an
immediate increase in TFP of 4.2 per cent. Furthermore, they find that this difference isobservable and sustained over several years.
Giannini et al. (2022) analyze the impact of the digital economy on TFP in Italian firms between
2003-2018. They analyze the relationship between broadband adoption and TFP regionally
before and after the financial crisis in 2008. They find overall there is a strong positive
relationship between broadband adoption and TFP before 2008, but the relationship was lessevident after the crisis indicating a structural change that had different regional impacts. They
conclude that the digital economy played an important role as a shock absorber during therecession. Furthermore, policies that focus on digitalization of southern firms will help fill the
productivity gap between North and South Italy.
Finally, Fabling and Grimes (2021) also concentrate on the speed factor of broadband and see
whether there are TFP gains from UFB adoption. The data is drawn from Statistics New
Zealand's Longitudinal Business Database (LBD), which includes survey and administrative dataon business practices and performance. In New Zealand, the broadband infrastructure roll-out's
primary goal was to provide access to schools. Therefore, firms closer to schools are more likely
to access broadband. The proximity to schools is then used as an instrument for broadband
usage, and the results are estimated by OLS and IV regression. The authors find that UFB
adoption positively impacts TFP, finding that firms that adopt UFB in 2012 increase TFP by 0.38
per cent between 2010-2014. In addition, TFP growth is higher for UFB-adopting firms thatmake complementary investments.

29

C. Industry Studies
Nadiri et al. (2018) examine the impact of modern communication infrastructure characterizedby high broadband speed on different industries using data from 41 United States industries from1987–2008. The cost function for each industry is estimated, with cost depending on labour,
capital, material, output quantity, the flow of services from telecommunication infrastructure
capital (including broadband infrastructure capital), the flow of services from public
infrastructure capital, and the broadband penetration rate per 100 inhabitants. By taking a partial
derivative of the cost function with respect to broadband penetration, cost savings from a oneunit increase in broadband penetration can be derived. This can later be interpreted as themarginal benefit from broadband interpretation. The non-linear square method estimates thetranslog cost function with the factor-share and revenue-share equations. Results suggest that themarginal benefit is higher in service industries. The five industries with the highest marginal
benefit of broadband penetration are 1) other services, 2) health and social assistance, 3)banking, 4) construction, and 5) retail trade. Furthermore, the authors find that industries havehigher TFP when more modern communications infrastructure or broadband is used, but thedegree of impact varies between industries.
Duso et al. (2021) find similar industry-specific results in Germany. The authors use firm-level
data for 2010-2015 and measure broadband as the proportion of broadband available at 16Mbps
transmission speeds lagged by one year. They note that average proportion of broadband
available at 16Mbps speeds increased from 17 to 55 per cent during their study period. They findthat this increased broadband availability did not change the TFP of manufacturing firms but did
have a significant positive effect on service sector firms, with the estimated impact on the levelof TFP over the 6-year period ranging from 0.76-6.8 per cent. The largest impact of broadband
availability in the service sector is in advertising and market research.
LoPiccalo (2021) focuses on the impact of broadband on agricultural outcomes in the UnitedStates using county-level panel datasets for 2007, 2008, 2012, and 2017. The results provide
evidence of crop yield improvements (their measure of productivity) from increased broadband
penetration. Broadband allows farmers to discover prices online and receive materials at a lower
cost. Farmers can access information on weather, commodity markets, and services such as
online banking via broadband that would improve their agricultural outcomes. For estimation

30

techniques, both the fixed-effects and IV approaches are used. The instrument is chosen to be theaverage broadband penetration rates for the same speed threshold in all adjacent counties, thus
varying by each county. The authors find that doubling the number of broadband connections per
1,000 households is associated with a level increase of corn yields by 3.8 per cent.
Summary
By and large, the cross-country, firm-level, and industry-level analyses of the impact ofbroadband on TFP do find a positive impact. This finding is not universal though: one of thefirm-level studies finds no impact, while another does not find an impact for manufacturing.

31

IV. Impact of Broadband on Labour Market Outcomes
Investment in and use of broadband internet can also have impacts on the labour market, which
we will examine in this section. Appendix tables C5 and C6 provide concise summaries of all thestudies discussed in this section.
As highlighted in the Global Symposium for Regulators (2010) hosted by the International
Telecommunication Union (ITU), in the short term, broadband construction directly creates new
employment, such as telecommunications technicians, construction workers, and civil engineers,
throughout the deployment of network facilities. In addition, jobs such as metal product workers,
electrical equipment workers, and professional services are indirectly created from upstream
buying and selling between sectors. Income earned from the new employment translates into
household spending, which then induces new jobs for retail trade, consumer services, andconsumer durables.
New jobs are also created through recent applications enabled by broadband, such as
telemedicine, e-commerce, online education, social networks, and cloud computing. It is worthemphasizing that broadband is just an enabler, and the applications will have different
employment implications depending on its use.
It should also be noted that, at least in principle, broadband internet could have negative impacts
on employment in certain sectors, to the extent that it allows for firms to make efficiencies byreducing their workforce. Whether the employees who lose their jobs find work elsewheredepends on whether broadband can indeed lead to the creation of other jobs in the economy, as
well as other factors such as the state of aggregate demand. Several studies used input-output
analysis to forecast the employment effect of investment in broadband. For example, Katz et al.
(2010) concluded that approximately €36 billion of investment in broadband technology would
generate a total of 960,000 jobs in the German economy: 541,000 from the construction of the
network and 430,000 from the deployment of the network. The authors estimated that thebroadband network construction has a Type 1 multiplier effect16 of 1.5, meaning that for every
broadband job, 1.5 other jobs are created. In addition, the Type 2 multiplier effect17 is estimated16Type 1 multiplier effect = (Direct Employment + Indirect Employment)/Direct Employment
Type 2 multiplier effect = (Direct Employment + Indirect Employment + Induced Employment)/Direct
Employment
17

32

to be 1.9. It is unclear from the paper whether these jobs created are permanent or transitory;
however, they are likely transitory, especially those generated by the construction of the network.
Most of the studies use multivariate regression to analyze the impact of broadband on
employment. Crandall et al. (2007) use state-level data on 48 states in the United States for theperiod 2003-2005 and choose broadband lines per population as a proxy for broadband
penetration. Results suggest that a 10 percentage point increase in broadband penetration in astate leads to an employment increase of 2 to 3 per cent per year. In addition, the effect ofbroadband penetration on employment in different sectors is investigated, and the authors findthat broadband deployment has a significant positive impact on employment in service sectors,
such as finance, education, and healthcare.
Kolko (2012) finds that an increase in broadband providers (defined as "providers offering
broadband services at 200 kilobits per second or faster") positively affects employment growth.
The author finds that as the number of broadband providers increases, technology-intensive
sectors benefit much more than other sectors, such as mining and public administration.
The impact of broadband on employment will likely vary by the skill level of the workers. Jobs
in industries without broadband connection may only be affected by the spillover effects ofadopting broadband by other industries. Atasoy (2013), using 1999-2007 ZIP code and countylevel data for the United States, estimates OLS and fixed-effect regression models to find thatbroadband increases employment for workers with a college degree but decreases employment
for those without a college degree. Mack and Faggian (2013) confirm the skill-based
technological change of broadband using a spatial lag regression model with a positive
interaction term between broadband and workers' education level using United States data
between 2000-2007.
Ivus and Boland (2016) is the first study to provide an empirical analysis of the effect ofbroadband on employment in Canada. Using 1997-2011 municipality-level data, their static IV
regressions show that broadband availability increases employment and wage growth in rural
areas more than in urban areas, and the impact is more significant in industries with high IT
intensity.

33

Broadband impacts employment not just by the degree of penetration but also by its quality. One
proxy for the quality of broadband is the speed. Using data from 496 United States counties from2011 to 2014, Bai (2017) uses a first-differenced model to investigate the impact of broadband
speed on employment. Results suggest that faster broadband did not positively affect
employment more than normal-speed broadband.
Instead of using state-level data, some studies preferred more detailed data like ZIP code-level,county-level, municipality-level, and firm-level data. One example of using county-level andmunicipality-level data is Czernich (2014) where the impact of DSL-based broadband
availability on unemployment rates is examined for Germany from 2002-2006. The author findsthat DSL availability does not have a statistically significant effect on the unemployment rate.
On the other hand, Jayakar and Park (2013) use county-level data from the United States
between 2008-2011. They find that investment in broadband infrastructure reduces
unemployment. Their prediction suggests that increasing the national average proportion ofhouseholds with at least 3 Mbps in download speed by 7 per cent would reduce theunemployment rate by 0.49 percentage points.
Summary
As with the impacts on output, labour productivity and TFP, studies looking at the impact ofbroadband investment on employment generally find a positive impact on overall employment,
although there are exceptions. However, studies that look at the impact by skill level do find that
better educated workers are more likely to benefit and that employment for less educatedworkers can be negatively affected.

34

V. Impact of Broadband Applications on Economic Outcomes
As noted earlier, it is not the installation of broadband per se that affects GDP growth but theapplications that are enabled by its use. Other than Bartelsman et al. (2019) the studies discussed
thus far have not tried to disentangle the complementarities between broadband and its
applications. One application that is thought to improve the productivity of firms is Doodle,
which is an online calendar tool helpful for time management and coordinating meetings. Thissection presents the few studies that investigate the impact of broadband applications on output
and productivity. Appendix tables B5 and B6 provide concise summaries of all the studies
discussed in this section.
An application that could only exist with broadband is Facebook. To estimate the effectFacebook had on GDP growth in the United States, Brynjolfsson et al. (2018) used a discrete
choice experiment in which participants had to choose between keeping Facebook or giving it up
for a month and receiving a certain amount of money in return. Using the data collected, theycalculated that Facebook increased real GDP growth by 0.11 percentage points on average per
year between 2003-2017.
Colombo et al. (2013) focus on the change in productivity performance of small and medium
enterprises (SMEs) from broadband adoption by focusing on applications. The authors use firmand province-level data from a sample of 799 firms observed from 1998 to 2004 in Italy.
Initially, the authors group firms by the type of broadband application (basic, such as email oradvanced, such as supply chain management systems), then they estimate the production
functions using two-step GMM. Their main conclusion is that basic broadband applications do
not significantly affect productivity, but advanced applications can have a significant impact
under certain conditions. These conditions include the industry in which SMEs operate andwhether organizational changes are undertaken. For example, service firms can increase
productivity by adopting broadband-enabled applications such as file-sharing systems andsoftware applications that facilitate organizational changes.
Although broadband applications can have positive effects on productivity, studies also showthat the use of smartphones can have very negative effects on worker productivity (Zimmerman,2017). Using a questionnaire with 605 German respondents, Duke and Montag (2017) provides

35

empirical evidence on this issue. The questionnaire asked about smartphone ownership, private
and work-related smartphone use in hours per week, smartphone addiction and productivity, andprovided respondents individualized feedback on their smartphone addiction score. The authors
find that there is a positive relationship between smartphone addiction and a self-reported
decrease in productivity due to spending time on the smartphone during work.
Internet of Things & Industry 4.0
One key application of broadband internet that is likely to become more important in the years tocome is “Internet of Things” (IoT) - linking machines and objects to digital networks. Edquist etal. (2021) constructed a panel of 82 countries between 2010-2017 to measure the impacts of theInternet of Things (IoT) on TFP growth. Using a first differences technique, they find that a 10pp
increase in the number of IoT connections per inhabitant is associated with a 0.23pp growth in
TFP,18 and this relationship is still seen when the panel is divided into OECD and non-OECD
countries. Using a growth accounting framework, they estimate that the average IoT contribution
to growth will be 0.99 per cent per annum between 2018-2030, which translates to $850 billion
per annum in 2018 prices. These estimates are much more conservative than others in theliterature (see Baily and Manyika, 2015).
Internet of Things can also potentially impact employment. Using data from 107 countries
between 2010-2019, Clemente (2021) estimates the impact of IoT connections per 100
inhabitants on the employment within a country. The author finds that 1 additional IoT
connections per 100 inhabitants is associated with an increase in employment of 0.059 per centin OECD countries. However, the author also notes that the 10-year average number of IoT
connections in OECD countries was 12.9, and grew on average by 2.3 connections per year.
Thus, the result is economically negligible.

18

This is not a level change. If TFP was 1 per cent initially, then IoT connections increased by 10 per cent, the new
TFP growth rate would be 1.23 per cent.

36

VI. Other Impacts of Broadband
Although our focus is on the impact of broadband on productivity and economic growth, manystudies have focused on more specific impacts, including the impacts on SMEs, human capital,
work-from-home productivity, consumer surplus, and education. Furthermore, the discussion of
the pandemic is beyond the scope of this literature review, but it was a major driver in the workfrom home movement, thus we discuss the studies analyzing the impact of working from homeon productivity.
A. Small & Medium Enterprises (SMEs)
Nakavachara (2020) analyzes the effect of broadband adoption on micro-sized, small-sized, andmedium-sized enterprises using a dataset of approximately 100,000 manufacturing firms in
Thailand between 2004-2017. The authors address the potential endogeneity between broadband
and TFP by using the proportion of firms in the same group with a broadband connection as an
instrument for broadband adoption. The groups are formed as those of the same size, industry,
province, and industrial estate group. Estimation results suggest that broadband adoption can
increase the TFP of micro-enterprises by 54 per cent, small enterprises by 23 per cent, andmedium enterprises by 44 per cent.
Chaudhuri et al. (2018) use unit-level data for the informal manufacturing sector in India, andusing quantile treatment regressions, the authors conclude that the average effect of broadband
adoption on informal sector enterprise productivity is positive.
B. Human Capital
Mack and Faggian (2013) aim to investigate the link between broadband provision andproductivity, with a focus on evaluating the variability in broadband impacts due to differencesin the quality of human capital. They use data from 3,046 United States counties between 2000
and 2007. Due to the unavailability of productivity data at the county level, the change in thenatural logarithm of earnings is used as a measure of productivity. Such measures for
productivity may not be accurate since productivity gains only sometimes result in a positivechange in earnings. The authors develop spatial lag and error models to account for spatial
structure in the county-level data. They acknowledge that the impact of broadband depends on

37

the quality of human capital stock within counties and find that broadband has a positive effectonly when the level of human capital is high.
Other studies, such as Akerman et al. (2015) also examine the skill-related link between
broadband and productivity. The authors explore the connection between labour productivity andbroadband, focusing on worker's skill levels from firm-level data in Norway between 2000 and2008. To address the endogeneity of broadband adoption, they use a public program that rolled
out broadband access points and is believed to provide exogenous variation of broadband
adoption in firms. Their main result is that broadband adoption increases the relative productivityof skilled labour, especially college graduates, and lowers the marginal productivity of unskilled
labour. The explanation is based on broadband complementing skilled workers in non-routine
tasks and substituting unskilled workers in routine tasks.
C. Working from Home
Broadband allows employees to work from home, which provides a potential channel for
broadband to increase productivity. Etheridge et al. (2020) use data from the UK Household
Longitudinal Survey and measure self-reported productivity using the following survey question:“Please think about how much work you get done per hour these days. How does that compare to
how much you would have got done per hour back in January/February 2020?” to which
respondents choose from 5 choices ranging from “I get much more done” to “I get much lessdone”. They find that, on average, households report increased labour productivity whenworking from home. However, they observe labour productivity declines for those working
lower-paying jobs and for women.
The "Information and Communication for Development" report by the World Bank (2009) gives
the case of British Telecommunications in 2004 as an example of how working from home
affected productivity. On average, British Telecommunications employees that work from home
had a productivity increase of 15–31 per cent (Qiang et al., 2009). Through reduced office costs,
British Telecommunications saved more than £60 million.
Barrero et al. (2021) use the Survey of Working Arrangements and Attitudes (SWAA) data andconclude that, during the pandemic, people with better home internet service had higher
subjective well-being conditional on certain factors such as employment status and age. In38addition, there were earnings-weighted productivity gains from universal access to high-quality
home internet service. Broadband enables working from home, potentially making some workers
more efficient than working on business premises. When asked the question, "How does yourefficiency working from home during the COVID-19 pandemic compared to your efficiency
working on business premises before the pandemic?" approximately 40 per cent of the
respondents in the surveys collected from August 2020 to May 2021 by Inc-Query andQuestionPro replied as either "much more efficient," "substantially more efficient" or "moreefficient." Less than 15 per cent of the respondents replied as either "less efficient,""substantially less efficient," or "much less efficient."
D. Consumer Surplus
Much of the econometric analysis of broadband's impact on the economy is about capturing theeffect of broadband on employment, productivity, and economic growth. Less attention has been
given to quantifying the impact of broadband on consumer surplus. Consumer surplus is thedifference between the willingness to pay for a product and its price. It is not measured by GDP
and is difficult to estimate since preferences differ between people. Broadband can increase
consumer surplus through two channels: 1) increasing agents' willingness to pay for internet
services because the services are better and applications or 2) decrease their price.
Dutz et al. (2012) use a discrete choice demand model and direct survey method to measure thecontribution of home broadband to consumer surplus. The demand function is estimated basedon data on consumers' internet service choices when facing different prices. The willingness-topay and consumer surplus are then inferred from the estimated demand function. Another data
source used by Dutz et al. (2012) is a survey that asks consumers their maximum willingness topay for broadband services. The consumer surplus is then calculated by subtracting the price paid
from the willingness to pay. They estimate the net consumer benefits from home broadband in2008 in the United States to be $32 billion per year. Greenstein and McDevitt (2011) also use asurvey to infer consumer surplus. Using the 2002 survey of broadband users from Savage andWaldman (2004), they estimate the cumulative consumer surplus created by broadband to be$4.8-$6.7 billion (approximately 0.05 per cent of GDP) between 1999 and 2006 in the UnitedStates. Goolsbee and Klenow (2006) use the time people spend online as an indicator of

39

expenditure on internet-based technologies and estimate the consumer surplus for the UnitedStates to be $3,000 in 2015 per median United States resident (5.3 per cent of median income).
Using a different approach, Rosston et al. (2010) estimate a random utility model19 using datafrom a nationwide survey administered by Knowledge Networks Inc. in late 2009 and early
2010. They find that a representative household was willing to pay $79 per month for fast andreliable internet service (the average price for fast internet service was $44), $20 per month formore reliable service, and $45 per month for higher speed.
E. Education
Broadband technologies have found another useful application in the field of education, enabling
their widespread use in classrooms, and facilitating distance learning during challenging times
like the COVID-19 pandemic. Grimes and Townsend (2021) estimated the impacts of a school's
ultra-fast broadband (UFB) adoption on their National Standards passing rates in New Zealand
between 2012-2016. The New Zealand government funded a public policy program to develop
an UFB network, prioritizing connections for hospitals and schools. This allows the authors tocarry out a differences-in-differences study of the new fibre broadband network on academic
performance by exploiting the timing of broadband availability in schools. They find that fibre
broadband does increase students' passing rates on standardized tests by approximately 1
percentage point. Previous attempts to quantify the effects of broadband on school attainment
found no significant results; however, as the authors note, the previous studies did not analyze
the impacts of UFB networks, which plausibly explains the difference in findings.

19

A method that tries to model choices of individuals among a discrete set of alternatives.

40

VII. Methodological Issues in Estimating the Impact of Broadband
A. Discussion on Endogeneity in Measuring the Impact of Broadband
As noted above, there is an endogeneity problem linked to estimating the economic impacts ofincreased broadband penetration and speed. Higher use of broadband can stimulate economic
growth, and higher incomes arising from the economic growth can create more demand forbroadband20 (Jung and López-Bazo, 2020). The issue is reminiscent of the reverse causality
between public infrastructure spending and economic growth/productivity. This discussion isbeyond the scope of this review, so we refer the reader to Aschauer (1989) for more details.
State intervention in the telecommunication sector is also a source of endogeneity. If stateintervention depends on economic conditions, it will be difficult to separate the effect of policies
from the effect of broadband diffusion (Czernich et al., 2011). For example, in response to theeconomic crisis in 2008 and 2009, governments introduced economic stimulus plans that
included investments in broadband (OECD, 2008). If not considered, the decline in economic
growth from the financial crisis of 2008-09 can affect the estimates of broadband's economic
impact by wrongfully associating the decline with broadband penetration. The distorting effect of
the financial crisis can be avoided by re-estimating GDP for 2008 and 2009 as if the financial
crisis never happened or by introducing a dummy variable that equals one if economic growth isnegative (Minges, 2016).
At the firm level, endogeneity can arise if the optimal choice of inputs (including broadband
usage) is influenced by the firm performance (Bertschek et al., 2013). In addition, if capital andlabour are correlated with unobserved productivity and technology adoption, the OLS estimates
will be biased (Akerman et al., 2015).
B. Critique of the Use of Broadband Availability as an Instrument
One way to deal with this endogeneity problem is to use an instrumental variables approach
which attempts to isolate the exogenous aspect of the variable of interest. An instrument must be
exogenous, correlated with the endogenous variable (relevance), and only affect the dependent
variable through its relationship with the endogenous variable (exclusion restriction). Several
20

The issue of reverse causality also holds for ICT because investment in ICT can drive economic growth but canalso be the result of productivity and economic growth (Cardona et al., 2013).

41

studies utilize instruments derived from broadband infrastructure availability to examine thecausal impact of broadband on productivity. These instruments encompass various factors, fromproximity to schools to DSL availability at the postal code level.
Testing the instrument's validity is not always feasible unless certain instruments are assumed to
be exogenous, while others can be assessed using a Sargan test.21 However, it is crucial to notethat this still hinges on the assumption that some instruments are valid. Broadband infrastructure
availability is likely to predict adoption, fulfilling the relevance condition for instruments derived
from it. However, if the link between the instrument and endogenous variable is weak, IVestimation may perform even worse than OLS estimation, as discussed in Stock et al. (2002). In
such cases, both the estimator and standard errors can be biased.
Another potential problem is that the exogeneity condition is not necessarily fulfilled when using
broadband availability as an instrument for broadband adoption. DeStefano et al. (2023) pointout that employing broadband availability as an instrument is not ideal, particularly wheninfrastructure deployment is driven by profit motives, which is predominantly the case in theprivate telecommunication sector. Suppose infrastructure investments are concentrated in regionswith high-growth prospects. In that case, broadband infrastructure availability indicates a firm's
productivity based on location, thus correlating with the error term. Lagged values of broadband
availability can be utilized as instruments for broadband penetration, however, there isinsufficient evidence to suggest that they satisfy the exclusion restriction or exogeneity.
Therefore, caution should be exercised when using them.
An alternative approach that is limited in the literature is to measure the impact of broadband on
productivity is to consider the pathway through firm innovation. In this way, researchers canidentify the type of innovation brought about by installing broadband, such as process ororganizational innovation, which are more clearly linked to productivity. This could be apotential path forward to deal with the reverse endogeneity of broadband in future research.
Overall, while instruments derived from broadband infrastructure availability hold the potential
for measuring broadband penetration, it is crucial to acknowledge the limitations and challenges

21

Statistical test used to assess the validity of instruments.

42

associated with their validity. Further research and consideration of alternative instruments arenecessary to improve the accuracy and reliability of broadband penetration measurements.
C. Shortcomings of the Simultaneous Equations Model
Another way of dealing with the endogeneity problem is through a simultaneous equation model.
One such model, used for estimating the economic impact of telecommunications was initially
proposed by Röller and Waverman (2001). Essentially, they address the reverse causality issue
between income and telecommunications by including supply and demand equations fortelecommunications infrastructure. The model is adopted by numerous studies including Katz
and Callorda (2013), Koutroumpis (2009), and Koutroumpis (2019). Two issues can be
identified with the simultaneous equation model: 1) omitted variable bias in the determinants ofbroadband penetration and 2) lack of dynamics.
For an example of the first problem. we can use the model in Koutroumpis (2009) as an example.
The author defines an equation for the demand of broadband where broadband penetration is afunction of GDP per capita, broadband price, education spending (as a percentage of GDP),
R&D spending (as a percentage of GDP) and the degree of urbanization.
The challenge is that the determinants of broadband penetration in this simultaneous equation
model may ignore certain statistically significant variables in explaining broadband demand.
Leogrande et al. (2021) investigate the determinants of broadband penetration in Europe and findthat broadband penetration is positively associated with knowledge-intensive service exports andan innovation-friendly environment, negatively associated with government procurement ofadvanced technology products. These variables are not included in Koutroumpis’ (2009) demand
specification, which will lead to biased estimates.
Other examples of variables that could contribute to broadband demand that are not typically
included are found in the study by Zaballos and López-Rivas (2012). They formulate a nonlinear multivariate regression model where the dependent variable is the number of fixed
broadband subscriptions per 100 inhabitants and find seven independent variables that are all
statistically significant in determining broadband penetration. Some of the variables that are notincluded in Koutroumpis’ (2009) specification are: 1) cellular phones per 100 inhabitants, 2)
population aged between 15 to 64, 3) the percentage of households with a PC, 4) IP telephony

43

regulations, and 5) time to start a business. The variables are chosen from the four pillars
diagram that explains broadband penetration presented by the Inter-American Development
Bank (see Zaballos and López-Rivas, 2012). The four pillars are 1) development of public
policies and strategic view, 2) development of strategic regulation, 3) infrastructure deployment,
and 4) capacity building in the public and the private sector. None of these variables are includedin the simultaneous approach taken by various authors, which leads to omitted variables bias
(OVB), and renders their estimates biased. This reduces the validity of their studies and could
help explain the range of results found in the literature. Typically, studies that deal with omitted
variable bias find null results (e.g., Bertschek et al., 2013).
The second issue of the simultaneous equation model is the lack of dynamics. Mayer et al.
(2019) discuss in detail the need for dynamics in the formulation of broadband on economic
growth/productivity. The change in broadband infrastructure (or penetration) will affect current
and future GDP; therefore, lagged values should be included, and omitting them will result inbiased estimates. Due to the spillover effect of broadband infrastructure, it will take time for thecomplete impact of broadband to be realized (Crandall et al., 2007). Mayer et al. (2019) argue
that misspecification of the model, such as the static simultaneous equation, overestimates the
economic impact of broadband, providing another insight into the discrepancy of findings acrossstudies.

44

VIII. Data Limitations in Estimating the Impact of Broadband
A. Critique of the Use of Broadband Penetration Rate
There is uncertainty on what the most appropriate measure of broadband is. As mentioned in theapplications of the broadband section, it is not the broadband network that has productivity
implications but the applications. The available country-level data is on the number of broadband
subscribers per capita. Therefore, the broadband penetration rate is usually defined as the numberof broadband subscribers per population. Although there exists data on mobile broadband andfixed broadband, the data does not distinguish between firms and households and insteadmeasures broadband usage by the overall population. Productivity is a notion related solely to
producers and not households. Broadband usage by firms and households will have different
implications; only the firms contribute to productivity.
Nevertheless, in the literature, broadband usage per capita is a proxy for the broadband usage offirms. The use of broadband applications by firms would be a preferred measure for econometricanalysis of the importance of broadband for productivity. However, the data exists only on
broadband use but not on which broadband applications are used. While analyzing the economic
impacts of broadband, we assume that an increase in broadband penetration and speed results inhigher use of broadband applications and developments of new applications and therefore
impacts productivity. Thus, this measure of broadband usage may be overestimating thecontribution of broadband to firms' productivity.
B. Advantages and Disadvantages of Firm-level Data
Unlike studies on employment and consumer surplus, using firm-level data is more common
when understanding the impact of broadband on productivity. In the presence of an outside
macroeconomic shock, firms do not react homogeneously, which provides good reason to
analyze firm-level data, especially since more macro-level data, such as industry-level data, do
not capture the heterogeneity in firms. Moreover, only firm-level data can capture factors such as
the effect of firm size, age group of employees, and firm dynamism on productivity.
On the other hand, to understand broadband's aggregate labour productivity implications, firmlevel data may not necessarily be better than industry-level data, given that factors that affectaggregate labour productivity are at the national level, and the difference in productivity arising45

from heterogeneity can be offset across industries. Furthermore, firm-level data is not easily
accessible, costly, and often missing human capital data. Although both types of data have theirbenefits and drawbacks, each will provide different, essential insights, and thus it is useful tohave a mix of studies using different datasets.

46

IX. Conclusion
In this literature review, we have offered a comprehensive exploration of the economic impactsof broadband, with a focus on productivity and economic growth. We analyzed 55 different
quantitative economic papers, providing a detailed examination of their methodologies, data
choices, and main findings. While not all studies find statistically significant impacts, theliterature generally supports the conclusion that there are positive impacts of broadband on
economic outcomes such as output and productivity. This is particularly true for studies on more
recent periods which attempt to control for the potential endogeneity between output andbroadband investment. The fact that these more recent studies cover more recent time periods
may also be a factor in explaining the more positive results, as the positive economic impacts,
particularly the spillover effects, may well take time to have an impact on the overall economy.
For employment, where broadband investment could have negative or positive effects, studies
generally find that the creation of new jobs has significantly offset the loss of jobs from theintroduction of new products and process driven by the internet. However, there is evidence fromthe United States that it is better educated workers who have seen their employment increase:less educated workers. This is consistent with the idea that technological change in recent years
has been skilled-biased, benefit more educated workers.
There are also other important impacts discussed in the literature including the impacts ofbroadband on SMEs, human capital, working from home, consumer surplus and education. In
general, these impacts are positive: broadband improves the productivity of SMEs, allows
employees to work from home, increases consumer surplus, and improves educational
attainment. Furthermore, the applications that broadband has helped enable, such as Facebook,have contributed positively to GDP growth.
We also note several challenges in measuring the impact of broadband on economic outcomes.
One challenge is the potential simultaneity between output and broadband penetration, which not
all studies address. Another is disentangling the complementarities between broadband and its
applications, which can lead to overestimating the impact of broadband on its own. There is also
a debate about the best level of aggregation to use, with some studies focusing on cross-country
analyses, and others delving into firm-level analyses. Problems also arise in methodologies with47

some studies omitting variables shown to be important in explaining the relationship between
broadband and economic outcomes leading to biased results, and models lacking dynamics tounderstand how changes in broadband infrastructure can impact both current and future GDP. It
will be important for future work to deal effectively with these challenges to better understandthe economic impacts of broadband.

48

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Firm Productivity”
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https://doi.org/https://doi.org/10.1787/230450810820
OECD. (2019). “Measuring the Digital Transformation.”
https://doi.org/10.1787/9789264311992-en

55OECD. (2021). “Broadband policy and technology developments.” https://www.oecdilibrary.org/science-and-technology/broadband-policy-and-technologydevelopments_e273ff77-en
Qiang, C. Z.-W., Rossotto, C., & Kimura, K. (2009). “Economic Impacts of Broadband.”Information and Communications for Development. Washington D.C.: World Bank.
Rivlin, A. M., & Litan, R. E. (2001). “The economy and the internet: What lies ahead?”
Brookings Institute.
Rohman, I. K., & Bohlin, E. (2012). “Does broadband speed really matter as a driver of
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Röller, L.-H., & Waverman, L. (2001). “Telecommunications Infrastructure and Economic
Development: A simultaneous approach.” American Economic Review, 91(4), 909–923.
https://doi.org/10.1257/aer.91.4.909
Romer, D. (2019). Advanced Macroeconomics, fifth edition. McGraw Hill.
Rosston, G. L., Savage, S., & Waldman, D. M. (2010). “Household Demand for Broadband
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Savage, S. J., & Waldman, D. M. (2004). “United States demand for internet access.” Review of
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U.S. Department of Commerce. (n.d.). “Broadband Glossary.” Broadband USA
https://broadbandusa.ntia.doc.gov/sites/default/files/publicationpdfs/bbusa_broadband_glossary.pdf
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https://www.worldbank.org/en/topic/digitaldevelopment/publication/ic4d-2009
Zaballos, A. G., & López-Rivas, R. (2012). “Socioeconomic impact of broadband in Latin
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Zhang, L., Tao, Y., & Nie, C. (2022). “Does Broadband Infrastructure Boost firm productivity?
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https://doi.org/10.1016/j.frl.2022.102886
Zion Market Research. (2016). “mHealth Market.”https://www.zionmarketresearch.com/report/mhealth-market

57

Appendix A – Further Details on Download Speeds
Table A1: Baseline Download Speed Requirements for Community Institutions
Institution
Hospital

Typical Applications
sharing health records
performing virtual consultations
connecting first responders

Download Speeds
1 Gbps+Library

operating public computer centersmobile hotspot lending
enabling maker spaces

100 Mbps - 1 Gbps+

Schoolsharing educational material
online testing
accessing databases

100 Mbps - 1 Gbps+

Small
Businessmanaging inventory
operating point-of-sale terminals
coordinating shipping

50 Mbps+Homecompleting homework
streaming video
web browsing

25 Mbps+

Source: U.S. Department of Commerce (n.d.)

58

Appendix B – Summary Tables of Reviewed Studies
Table B1: Master Table of Earlier Period Studies on Output per capita and Labour Productivity
Reference

Arvin &
Pradhan(2014)Country& Period

Measure of
Broadband

Indicator
used (DV)G20
countries
1998-2011

Percentage of
total populationthat arebroadband users

Percentagechange inreal GDP per
capitaBertschek et
al (2013)

Germany
2001-03

Broadband usage
by firms

Bojnec &Ferto (2012)

34 OECD
countries
1998-2009

Total broadband
per
100 inhabitantsCzernich et al(2011)

25 OECD
countries
1996-2007

Number of fixedBB
subscribers per
100 inhabitants

Log sales per
employee

GDP growth

GDP per
capitaLevel of
Analysis

Methodology

Instrument

EffectMagnitude

SignificanceMarylandSMS

Deals with
Endogeneity

Country

Panelcointegration
test
Grangercausality test

N/A

Granger causality
exists bothways depending
on development
statusVarious results
see table 4

5%

3

Y

Firms

IV Regression
Recursive
Binary probit
model

DSL availability at
postal code level

None

N/A

None

4

Y

IV Regression

Fixed-line voice
telephonyand cable TV preexisting networks

None

N/A

None

4

Y

Positive

10pp increasein BB
penetration
increases GDP
per capita
growth rate by
0.9-1.5pp
annually

1%

4

Y

CountryCountry

IV Regression

Fixed-line voice
telephonyand cable TV preexisting networks

59

DeStefano etal (2023)

Grimes et al(2012)

UK
2000-04

New
Zealand2006

Use of broadband
by firms(speeds exceeding
128Kbps)

Firm Sales
LabourProductivity

Use of different
broadbandconnections

Average offirm’s ’05
and ’06
labourproductivity

Koutroumpis(2009)

22 OECD
countries
2002-07

Population with a
fixedbroadbandconnection per
100 population

Lam & Shiu
(2010)

105
countries
2002-2006

Number of fixedlines and mobile
subscribers per
100 peopleGDP
GDP per
capita

GDP

Firms
Plant

Fuzzy
regression
discontinuity

Firms

Propensity
score
matching

Country

Simultaneous
equationsmodel
using IV
GMM and
3SLS with
fixed effects

Country

Granger
Causality

N/A

N/A

N/A

N/A

None

N/A

None

4

N

PositiveBB
connectivity
increases LP
by 7-10%5%

4N

Positive

10% increasein BB
penetration
rate increase
GDP growthby 0.23%

1%

4

Y

Positive

Varioussignificant
results, seetable 3

1%

4

Y

60

120
countries
1980-2006

Number of fixedBB
subscriber per
100 people

GDP per
capitagrowthThompson &Garbacz(2011)

43
countries
2005-09

Fixed broadband
lines perhousehold
Mobile broadband
lines perhouseholdGDP perhousehold

Zaballos &Lopez-Rivas(2012)

26 Latin
AmericanandCaribbeancountries
2003-09

Fixed BB
penetration
per 100
inhabitants

Qiang et al(2009)

GDP per
capitaCountry

Endogenous
growthmodel

N/A

Positive

10pp increasein BB
penetration
increases GDP
per capita
growth rate by
1.21pp fordeveloped.
1.38pp fordeveloping

Country

Fixed effects
IV regression

N/A

Positive formobileNone for fixed

0.072

1%

4

Y

Positive

10pp increasein BB
penetration
increases GDP
per capita by
3.19%

1%

3

N

Country

Non-linear
multivariate
regression

N/A

5%(developed)
10%(developing)

3

N

61

Table B2: Master Table of Recent Period Studies on Output per capita and Labour Productivity
Reference

Country& Period

Bertschek &Niebel (2016)

Germany
2014

Edquist(2022)

116
countries
2014-19

Measure of
Broadband

Share of employeeswith mobileinternet access

Mobile broadband
speed

Indicator
used (DV)LabourProductivity

GDP

Edquist et al(2018)

90
countries
2002-14

Percentage ofmobile BB
connections
Mobile penetration
rate >= 1% (binary)GDP

Gallardo et al(2021)

US2017

10 broadband
indicators

LabourProductivity

Level of
Analysis

EffectMagnitude

SignificanceMarylandSMS

Deals with
Endogeneity?

Average mobileinternet use atindustry level
Number of years
interviewee owns asmartphone

Positive

10pp increase inshare ofemployees withmobile internet
access increases
LP by 9%1%

4

Y

Fixed Effects
PooledRegression

N/A

Positive
when
once
lagged10% increase inmobile BB speed
in t-1 increases
LP by 0.2% in t

5%

3

N

CountryFirst
differences
2SLS
Fixed effects

Mobile telephone
subscriptions per
100 inhabitants in
2002. Fixedinternet subscribers
per 100 inhabitantsin 2002

Positive

10pp increase inmobile BB
adoption leads tolevel increases
GDP by 0.1-0.4pp

1%

4

Y

CountyOLS withspatialdependence

N/A

Positive

Many results- see table 6

1%

2

N

Methodology

Instrument

Firms

IV regression

Country62

Brazil2007-12

Number ofconnections above
512Kbps per 100
inhabitants in theregionKatz &
Callorda(2013)

Ecuador2008-12Percentage ofindividuals
connected to fixed
broadband

Kongaut &Bohlin (2014)

OECD
countries
2008-12

Broadband speed

35 OECD
countries
2002-16

Fixed broadband
subscriptions per
100 people
Fixed broadband
speed in Mbit/s

Jung &
López-Bazo(2020)

Koutroumpis(2019)

LabourProductivity

GDP growth

GDP per
capitaGDP

Number of voice
telecom fixedaccess lines her 100
inhabitants
Lagged population
densityState

IV regression

Household

Simultaneous
equationsmodel
Fixed effects

N/A

Country

2SLS

Percentage of fiber
subscriptions of
total fixed
broadband
subscriptions

Country

Simultaneous
equations by
3SLS with
fixed effects

N/A

Positive

10% increase in
BB penetration
increases
productivity by
0.9% (level)

1%

4

Y

Positive

10% increase inBB
penetration
increases GDP
growth by 0.52%

5%

4

N

Positive

10% increase inBB
speed increases
GDP per capita
by 0.8% (level)

1%

4

Y

Positive

Increasing speed
from 2 to 8Mbps
increases GDP by
0.9%

1%

4

Y

63

Mayer et al(2019)

Rohman &Bohlin (2012)

29 OECD
countries
2008-12OECD
countries
2008-10

Fixed broadband
subscribers per 100
inhabitants
Actual broadband
download speed(Kbps)
Number of years
since introductionof BB technology

Average achieved
downlink speed

GDP per
capitaGDP growth

CountryCountry

Dynamic
panel with
fixed effects
using GMM

Lagged firstdifferences ofvarious
independent
variables

2SLS

Penetration rate,
broadband price,urban population,
density, andtelecom revenue

None

N/A

None

4

Y

Positive

Doubling BB
speed increases
GDP by 0.3%
(level)

1%

4

Y

64

Table B3: Master Table of Earlier Period Studies on Total Factor Productivity
Country &PeriodMeasure of
Broadband

Norway2000-08

BB subscription
rate forstratified random
sample of firms

10Europeancountries
2002-10

Proportion ofbroadbandinternetconnected
employees

Colombo etal (2013)

Italy
1998-2004

Principal
component
analysesdistinguishingbasic andadvanced BB
applications

TFP

Haller &Lyons(2015)

Ireland
2002-09

Use of different
broadbandconnections

TFP
TFP
growthReference

Akerman etal (2015)

Bartelsman
et al (2019)

Indicator
used (DV)

TFP

TFP

Level of
Analysis

Methodology

Instrument

EffectMagnitude

SignificanceMaryland SMS

Deals with
Endogeneity

Firms

Fixed effects
Naturalexperiment

Publicprogram thatrolled out BB
access points

Positive

10pp increasein BB
availability
increases TFP
by 0.4%1%

4Y

10pp increasein BB
connected
employees
increases TFP
by 3.6%(level)

1%

2

N

Firms
Industry

Pooled OLS

N/A

BB usage isa betterindicator
ofproductivity
thaninnovation

Firms
Provincial

Production
function
approachTwo-stepsystem GMM

N/A

None

N/A

None

4

N

Firms

Fixed effects
IV regression

GeographicalDSL
broadband
availability

None

N/A

None

4

Y

65

Nadiri et al(2018)

US
1987-2018

Broadband
penetration rate
per 100
inhabitants

TFP

IndustryNon-linear
least squares

N/A

Positive

Marginal
benefit of BB
is highest inservice sector

Not reported

4

Y

66

Table B4: Master Table of Recent Period Studies on Total Factor Productivity
Reference

Australian
Gov’t(2023)

Cambini etal (2023)

Chaudhuri etal (2018)

Dalgic &Fazlioglu(2020)Country& Period

Australia
2009-19

Measure of
Broadband

Number offirms
switching from
DSL to fibre

Adoption of
UFB by firmsIndia2010-11

Use ofbroadband andits applications
by firm(binary)

2012-15

Level of
Analysis

Broadbandtechnology
used by firm.

Italy
2013-19

TurkeyIndicator
used (DV)Firm’s use ofBB

Methodology

Instrument

Pooled OLS
TFP

Firms

N/A
Fixed Effects

TFP

TFP

TFP

Firms

Firms

Firm

2SLS

IV Quantile
treatment
regression

Propensity scorematching
DID

Magnitude

SignificanceMarylandSMS

Deals with
Endogeneity?

Firms thatswitch to fibreor cable BB
have annual TFP
growth 4.2%
higher thanfirms with DSL

5-10%

4

N

Positive

UFB adoption
increases
TFP by 2.9%
(level)5%

4

Y

Positive

Broadband
enhances
productivity
through basic
applications

5% for 45th
and 60th
quintiles

4

Y

Positive

Moving fromnormal to fast
broadband
increases TFP
5.3pp between
2012-2015

1.

4

N

EffectPositive forpooled OLS
None for FE

Municipality
distance fromclosestbackbone node

N/A

N/A

67

Germany
2010-15

Once lagged
16Mbpsbroadband
availability

82
countries
2010-17

Internet ofThingsconnections
per inhabitant
Mobile BB
connections
per inhabitant

Fabling &Grimes(2021)New
Zealand2008-18

Use ofultrafast
broadband byfirms

Gal et al(2019)

20 OECD
countries
22
industries
2010-15

Duso et al(2021)

Edquist et al(2021)

Share of firms
using aspecific digital
tech

TFP

TFP growth

TFP

TFP growth

Firms
Industry

Country

Firms

Firms
Industry

Control function
approach

Growthaccounting
First differences

IV regression

OLS

N/A

N/A

Proximity toschools

N/A

Positive inservices
None inmanufacturing

BB increase of
38pp
increased TFP
by 0.76-6.74%
in services

1%

4

N

Positive

10pp increasein IoT
connections
increases TFP
by 0.23pp(level)5%

2N

Positive

Adopting fibre
between 20102012 increases
TFP between
2010-2014

5%

4

Y

Positive

10pp increase inhigh-speed BB
leads toinstantaneous
increase in TFP
of 1.4pp

1%

2

N

68
Giannini etal (2022)

Italy
2002-18

LoPiccalo(2021)Nakavachara(2020)

Zhang et al(2022)

US
2007,2012
and 2017

Percentage offirms using a
PC

Ratio ofnumber ofconnections atgiven speed incounty overtotal numberof county
households

Thailand2017

Use ofbroadband byfirms (binary)China
2006-18

Whetherregistration offirm is part of“broadband
China” plot
city (binary)

TFP

Farm
expenses
Yield
measures

TFP

TFP

Firms

County

Firms

Firms

IV Regression

Percentage of
employees
using internet
facilities

Fixed effects
IV regression

Averagebroadband
penetration rate
for same speed
threshold in
adjacent
counties

2SLS

Proportion offirms in thesame groupwith abroadbandconnection

Diff-in-Diff

N/A

Positive

Increasing thepercentage of
employees using
internet facilities
increases TFP

10%

4

Y

Positive

Doubling BB
connections
increases cornyields by 3.8%

1%

4

Y

PositiveBB adoption
increases TFP
by 54%
for microenterprises, 23%
for small and
44% for medium

1%

4

Y

Positive"BB China"
strategy
increases TFP
by 3.97%

5%

4

Y

69

Table B5: Master Table of Earlier Period Studies on Other Impacts
Reference

Country& Period

Measure of
Broadband

Indicator used
(DV)

Level of
Analysis

Methodology Instrument

Atasoy(2013)

US
1999-2007

Ratio of population
living in a
broadband
available are in acountyEmployment
to population
ratio

ZIP CodeCountyOLS
Fixed Effects

Crandallet al(2007)

US
2003-05

Broadband lines
per population

GDP (nonfarm
private sector)State

Czernich(2014)

Germany
2002-2006

DSL-based BB
availability

Unemployment
rates

Country

Dutz et al(2012)

US
2005-08

Actual prices paid
for Internet services
Actual types ofinternet service
purchased

Consumer
Metropolitan
Surplusstatistical
Willingness-toarea
pay

EffectMagnitude

SignificanceMarylandSMS

Deals with
Endogeneity

N/A

Positive forcollegeeducated
Negative forthose withoutcollege degree

Gaining accessto BB services
in a county
increases
employment rateby 1.8pp

1%

2

N

OLS

N/A

None overall
Positive forsome sectors

10pp increase inBB
penetration
increases
employment by
2-3%

5%

2

N

IV
Regression

Distancefrom main
distribution
frame

None

N/A

None

4

Y

Discretechoice model

Marketvalues fromothergeographic
markets

BB
connections
are a strong
substitutefor dial-up
technology

Consumerbenefitsfrom BB in2008 isestimated to be$32bn per year

5%

3

Y

70
Etheridge
et al(2020)

UK
2020

Self-reported
response comparing
productivity before
and after pandemic

Change inlabourproductivity

HouseholdDirect Survey

N/A

On average
positive.
Negative forlow-wage andwomen-0.29 lowearners
0.07 highearners-0.09 women

1%

2

N

5%
5%

Goolsbee& Klenow(2006)

US
2005

Time people spend
onlineConsumer
SurplusIndividual

Demandcurve
estimation

N/A

Positive$3000 permedianresident

Not reported

3

N

Greenstein&
McDevitt(2011)

US
1999-2006

Total internet, BB,
and dial-up
adopters

Consumer
SurplusCountry

Direct Survey

N/A

Positive$4.8-6.7 billion

Not reported

3

N

Ivus &
Boland(2016)

Canada1998-2011

Deployment ratemeasured from BB
Index

Employment
growthWage growth

Municipal

IV
Regression

Variation inelevation
withinregionPositive inrural areas

Moving from no
BB to BB in
1997,
employment inservice industry
in rural areas
increase by
1.17pp

10%

4

Y

71
Katz et al(2010)

Germany
2000-06

Investment in
manufacturing,
construction, andtelecoms

Employment
rate

Rural

Input-Output
Analysis

N/A

Positive(withmultipliereffects)

36bn investment
generates
170.6bnadditional GDP
and 968,000
jobs
Type 1
multiplier effectof 1.45 andType 2 of 1.92

Not reported

3

N

Kolko
(2012)

US
1992-2006

Number of BB
providers

Change in
Employment

ZIP CodeCounty2SLSAverageslope ofterrain in an
area

Positive

0.0636

1%

4

Y

Mack &
Faggian(2013)

US
2000-07

Number of BB
providers in county
in 1999
Whether a county
had at least one BB
provider in 1999

Change inEarnings

CountySpatial lag
model
Spatial error
model

N/A

Positive forhighlyskilled humancapitalPresence of BB
ratherthan quantity iswhat affects
productivity

1%

3

Y

Rosston etal (2010)

US
2003-10

Responses tochoiceWillingness-topay for reliable
service andimproved
speedHousehold

Discretechoice model

N/A

Positive$79 per monthfor fast,
reliable Internet

Not reported

3

N

72

Table B6: Master Table of Recent Period Studies on Other Impacts
Country& Period

Measure of
Broadband

Indicator used (DV)Level of
Analysis

Methodology

Instrument

EffectMagnitude

SignificanceMaryland SMS

Deals with
Endogeneity?

US
2011-14

Percentage ofcounty population
with access todifferent
download speeds

Change in
employment rate

ZIP codeCountyFirstdifferences

N/A

None

N/A

None

2

N

US
2020

Internet accessqualityWork from home
efficiency
during pandemic
relative to officeefficiency prepandemic

N/A

Positive

40% ofrespondents
reported being
more efficient

Not reported

3

N

Brynjolfsson
et al (2018)US
2003-17

Choice between
keeping FB andgiving it up for
one month andgetting paid acertain amount

N/A

Positively
increases
GDP
growthIntro of FB
increased
GDP growthby 1.54pp

Not reported

3

N

Clemente(2021)

107
countries
2010-19

Internet of things
connections per
100 inhabitants

N/A

Positivecorr. inOECD
None forunemp.

10 additional
IoT connection
increases
employment
by 0.59%

1% forOECD

4

N

Reference

Bai (2017)

Barrero et al(2021)

Willingness toaccept price forgiving up Facebook
GDP growth

Unemployment rate
Total employment

HouseholdDirectSurveyIndividual

Incentive
compatible
discretechoiceexperiments
Binary logit

Country

Fixed effects
Dynamic
panel

73

Duke &
Montag(2017)

Germany
2016

Series ofquestions toprovide asmartphone
addiction score

Self-reported work
productivity

Percentage change
innumber of jobs, total
personal income, or
total labor earnings

National Standards
passing rates

Ford (2018)US
2013-15

Counties with at
least
80% coverage of
25Mbps service

Grimes &Townsend(2021)New
Zealand2012-16

School’s UFB
adoption

Jayakar &Park (2013)US
2008-11Percentage ofhouseholds withat least 3Mbps
download speed

Unemployment rate
Change inunemployment rate

Country

DirectSurveyN/A

Positive

Positivecorrelation b/w
smartphone
addiction anddecreases inreported
productivity

CountyAverage
Treatment
effect with
Coarsened
Exact
Matching

N/A

None

-0.30
0.32
-0.11

None are sig

3Y

Positive

Adopting fibre
BB increases
passing rates
by 1pp5%

4

N

Negative

Increasing # of
HH with atleast 3Mbps
in download
speed by 7pp
reduces
unemployment
rate by 0.49%

Not reported

2

N

Regional

CountyDiff-in-Diff

OLS

N/A

N/A

1%

3

N

74

Appendix C –Members of the Technical Advisory Committee
Catherine Beaudry
Professor of Engineering
Ecole Polytechnique
Université de Montréal
Montreal, Quebec
catherine.beaudry@polymtl.ca
Dany Brouillette
Senior Director
Innovation, Science and Economic Development
Ottawa, Ontario
Dany.Brouillette@ised-isde.gc.ca
Margaret Dalziel
Associate Professor
Conrad School of Entrepreneurship and Business
Vice-President, Evidence Network
University of Waterloo
Waterloo, Ontario
mdalziel@uwaterloo.ca
Harald Edquist
Senior Researcher
Ericsson
Stockholm, Sweden
harald.edquist@ericsson.com
Dennis Fixler
Chief Economist
US Bureau of Economic Analysis
Washington, DC
Dennis.Fixler@bea.gov
Kevin Fox
Professor of Economics
University of New South Wales
Sydney, Australia
k.fox@unsw.edu.au
Arthur Grimes
Senior Fellow

75Motu InstituteWellington, New Zealandarthur.grimes@motu.org.nz
Wulong Gu
Senior Advisor
Statistics Canada
Ottawa, Ontario
wulong.gu@statcan.gc.ca
Thomas Niebel
Senior Researcher
Research Department Digital Economy
ZEW- Leibniz Centre for European Economic Research
Mannheim, Germany
Thomas.Niebel@zew.de
Nelson Paterson
Director General
Economic Studies and Policy Analysis
Finance Canada
Ottawa, Ontario
Nelson.Paterson@fin.gc.ca
Dirk Pilat
Former Deputy Director
Science and Technology
OECD
Paris, France
dirkpilatparis@gmail.com
Bart van Ark
Managing Director
The Productivity Institute
University of Manchester
Manchester, United Kingdom
bart.vanark@manchester.ac.uk
Leonard Waverman
Professor, Business Economics and FinanceSpecial Advisor to the Provost and VP Research
DeGroote School of Business
McMaster University,

76

Hamilton, Ontario
waverl@mcmaster.ca
Joe Rowsell (ex officio)
Director of Regulatory Affairs
TELUS
Ottawa, Ontario
joe.rowsell@telus.com