December 2023

170 Laurier Avenue, Suite 604
Ottawa, Ontario K1P 5V5
613-233-8891 info@csls.ca

CENTRE FOR THE
STUDY OF LIVING
STANDARDS

An Econometric Analysis of the Impact ofBroadband Internet on Productivity

Timothy Sargent
CSLS Research Report 2023-08

December 2023
Prepared for TELUS Corporation

An Econometric Analysis of the Impact of Broadband Internet on
Productivity
Abstract
In this paper we look at whether increases in broadband internet penetration have a significant
impact on productivity. We use data on a panel of 116 countries over the period 2009–2019 toestimate the impact of increases in fixed broadband penetration rates and high-speed broadband
penetration rates on labour productivity, measured as output per person. We find a significant
impact on productivity from increases in fixed broadband penetration rates. Our estimatesindicate that broadband internet increased labour productivity growth by an average of 0.20
percentage points annually in developed countries and 0.26 percentage points in developing
countries: this meant that broadband internet accounted for around 16 per cent of productivity
growth in developed countries and 20 per cent of productivity growth in developing countries.
For Canada we find that broadband internet contributed an average of 0.15 percentage points
annually to Canada’s overall productivity growth of 0.91 per cent, about 17 per cent of total
productivity growth. However, we did not find significant impacts for high-speed broadband
over the period studies: it is possible that this is because applications that require higher speeds,
such as videoconferencing, had not been widely deployed over our time period. This study
emphasizes the crucial impact of broadband internet investment on boosting Canada's economywide productivity. This research indicates an important role for the government in facilitating the
expansion of broadband access, optimizing regulatory frameworks, and stimulating private sector
investment, especially in underserved regions, to enhance the social and economic benefits
throughout Canada.

2

An Econometric Analysis of the Impact of Broadband Internet on
Productivity
Table of Contents
Executive Summary ........................................................................................................................ 4
I. Introduction ................................................................................................................................. 6
II. Related Literature ....................................................................................................................... 7
III. Methodology ........................................................................................................................... 10A.

Theoretical Framework................................................................................................................... 10

B.

Empirical Implementation............................................................................................................... 11

IV. Data and Descriptive Statistics................................................................................................ 12A.

Data on Broadband Internet ........................................................................................................... 13

B.

Data on Labour Productivity and the Capital–Labour Ratio ......................................................... 18

C.

Data on the other Drivers of Total Factor Productivity.................................................................. 20

V. Results ...................................................................................................................................... 23
A.

Developed Countries ....................................................................................................................... 23

B.

Developing Countries ..................................................................................................................... 25

C.

Robustness checks ........................................................................................................................... 27

VI. Contributions of Broadband to Labour Productivity .............................................................. 28VII. Policy Implications ................................................................................................................ 33
VIII. Conclusion ........................................................................................................................... 35
IX. Directions for Further Work ................................................................................................... 37
References ..................................................................................................................................... 40
Appendix 1: List of Developing and Developed Countries in the Dataset ................................... 42
Appendix 2: Indicator Variables and Sources of Data for Components of the PCI ...................... 443

An Econometric Analysis of the Impact of Broadband Internet on
Productivity
Executive Summary
This report looks at the impact of broadband internet on labour productivity for a panel of 116
countries over the period 2009 to 2019. We estimate a standard economic growth model using abroad range of variables that potentially affect productivity, as well as measures of broadband
internet penetration, in order to identify the specific contribution of broadband internet to
productivity growth. We use both overall broadband penetration and a measure of high speed (orfast) internet penetration in our estimations.
We find that overall broadband internet penetration has had a significant impact on productivity
in both developed and developing countries. For developed countries, broadband internet has
added an average of 0.20 percentage points to productivity growth every year, whereas fordeveloping countries the impact is 0.26 percentage points every year. These impacts explain
around 15 to 20 per cent of overall productivity growth over the sample period.
Canada’s experience is similar to the average: broadband internet penetration is estimated tocontribute an average of 0.16 percentage points to productivity growth annually, responsible for17 per cent of total productivity growth over the sample period.
In contrast to these results, we find little impact of high-speed broadband on productivity: we
speculate that the applications specifically enabled by high-speed broadband did not have thesame productivity impact as those enabled by regular broadband; however, this may be changing
as bandwidth intensive applications such as videoconferencing have become more prevalent.
While there are some caveats to our empirical results, overall they indicate that broadband
internet has had a very significant impact on productivity across both developed and developing
countries over the decade of the 2010s: it remains to be seen whether high speed broadband will
have the same impact.
This study emphasizes the crucial impact of broadband internet investment on boosting Canada's
economy-wide productivity. This research indicates an important role for the government in4

facilitating the expansion of broadband access, optimizing regulatory frameworks, andstimulating private sector investment, especially in underserved regions, to enhance the socialand economic benefits throughout Canada.

5

An Econometric Analysis of the Impact of Broadband Internet on
Productivity
I. Introduction1
The widespread availability of broadband internet has allowed fast and reliable internet
connection across the developed world, with individuals now able to engage in a multitude ofactivities—from telemedicine to e-learning, from videoconferencing to streaming movies—that
would have been difficult if not impossible with the dial-up internet access technology thatpreceded broadband.
However, it is one thing for a technology to enrich people’s lives, and another for it to have apervasive impact on productivity across the economy. For this to happen the technology needs tosignificantly boost the ability of firms in every major sector to produce more goods and services
than before from the same amount of labour. Certainly the potential for such a boost is there:
business has adopted broadband internet as enthusiastically as individuals have, and broadband
has allowed for productivity-improving enhancements such as real-time supply chain andinventory management that better matches production to demand, improved data sharing andcommunication that makes it easier for teams in different locations to collaborate on projects,
and e-commerce platforms that allow companies to dispense with traditional physical retailchannels and to deal directly with customers across the globe. The question though is whether
these productivity improvements, important though they may have been in specific sectors, arelarge enough to have made a measurable impact on economy-wide productivity.
In this report we attempt to answer this question. We look at whether and to what extent
investments in broadband internet have boosted economy-wide productivity over the period

This research report was prepared by Timothy Sargent, who is CSLS Deputy Executive Director andholds a Ph.D. in Economics from the University of British Columbia. Able research assistance wasprovided by two CSLS staff members: Sarah El Kaissi and Adriana Suuronen. We would like to thank
TELUS for funding the project, and Joe Roswell and his team at TELUS for their insightful comments.
We would also like to thank the technical advisory board for the insightful feedback. Their names andprofessional affiliations are to be found in Appendix 3. The opinions in this paper are solely the author’s,
and not those of the technical advisory board members. The database used to produce our results isavailable online at www.csls.ca. Comments are welcome and may be sent to Tim.Sargent@csls.ca.
1

6

2009–2019 for 116 developed and developing countries. We look at the impacts of all fixed
broadband penetration, as well as high-speed broadband penetration, which in our data sets isdefined as broadband with speeds over 10Mbps. This period saw significant investment inbroadband internet across the world, and we use different countries’ experiences to identify theoverall impact of broadband internet on productivity.
We begin with a brief literature survey and indicate how our paper contributes to this literature.
We then go on to outline our theoretical framework, which is based on the standard neoclassicaleconomic growth model, and its empirical implementation in an econometric model. We then go
on to present descriptive statistics for the data series that we use in the estimations. One novel
feature of our work is that we use a dataset compiled by the United Nations Conference on Tradeand Development’s that has a broad variety of explanatory variables that reflect everything froma country’s natural capital to its education levels to the quality of its institutions. This allows us
to better model the cross-country and time series variation in productivity and thus to better
distinguish the impact of broadband internet from other factors that affect productivity. We thenreport our estimation results and use these to calculate the impact of broadband internet on
productivity in different countries, both developed and developing. We conclude with a summaryof our results and their implications, as well as some caveats about the robustness of the results.

II. Related Literature
There is now a significant literature on the impacts of broadband infrastructure on the economy.
This literature is surveyed at length in Bakiskan and El Kaissi (2023), and so we do not propose
to provide a comprehensive account in this report. Rather, we shall provide a brief sketch, andhighlight those papers that are closest to the approach we adopt here, which is to use time series
data from a panel of countries to provide the broadest possible perspective on the issue.
This literature on broadband largely focuses on the impact on economic indicators of two key
variables: penetration, often measured by the proportion of households with broadband internet
access; and speed, which is sometimes measured directly, or is measured as the proportion ofhouseholds with access to some measure of fast broadband. The main economic indicators that
researchers focus on are output, labour productivity (output per hour or per worker), and total
7

factor productivity (the contribution to output that comes from factors such as technology or how
production is organized, rather than inputs of human or physical capital). Some studies use firm
or industry level data, whereas other use data on a cross section of countries over time.
Most studies find economically and statistically significant impacts of increases in both speed
and penetration on economic indicators of interest. However, there are some studies that findlittle impact; conversely some studies find impacts that seem implausibly large. One challenge inestimating the impacts of broadband internet, particularly on output, is endogeneity: while
increases in speed and penetration can certainly increase output, higher output and thus income
may increase consumers’ willingness and ability to pay for broadband internet, so that causation
between economic indicators such as output and measures of broadband internet may run in both
directions. A number of studies try to deal with this issue using different econometric techniques.
The studies that come closest to ours use a panel of countries over time to examine the impact ofbroadband internet on productivity. Kongaut and Bohlin (2014) analyzed the effects of speed on
GDP per capita in the 33 OECD countries between 2008 and 2012. They use a two-stage leastsquares (2SLS) approach to deal with potential endogeneity, where broadband speed is measured
in the first stage as a function of fixed broadband penetration, the percentage of fibre
subscriptions, GDP growth, population density and telecommunications revenue. In the second
stage, they estimate GDP per capita as a function of gross fixed capital formation, labour force,broadband speed, the degree of economic freedom, urban population, and an income dummy todivide the analysis into high- and low-income countries. They find that a 10 per cent increase inbroadband speed increases GDP per capita by 0.8 per cent. When they divide the sample into
low- and high-income countries, they find that the impact of increasing broadband speed isgreater in lower-income countries. Specifically, in low-income countries, they find that a 10 per
cent increase in broadband speed increases GDP per capita by 1 per cent, while the increase in
GDP per capita in high-income countries is 0.6 per cent.
Koutroumpis (2019) uses a panel of OECD countries to model the impact of broadband
penetration and speed on output. He used an econometric approach similar to but more elaborate
than that of Kongaut and Bohlin (2014), with four equations: an aggregate production function,
demand and supply equations for broadband internet, and a broadband infrastructure production

8

function. He found that broadband contributed 0.4 percentage points to annual OECD output
growth over the period 2002–2016.
One challenge with the approach adopted by Kongaut and Bohlin (2014) and Koutroumpis(2019) is that the instrumental variable techniques that underlie their econometric methodologies
requires good instruments for broadband penetration: that is, variables that are strongly
correlated with broadband, but not with output. Unfortunately, it is hard to come up withvariables that are truly exogenous with respect to output: for example, Kongaut and Bohlin(2014) use fixed broadband penetration rates and mobile penetration rates as instrumental
variables for broadband speed; however, if output affects speed (because people with higher
incomes can afford faster internet coverage) then it presumably affects penetration rates as well.
In a recent study, Edquist (2022) examines the impact of mobile broadband internet speed overthe period 2014–2019. He models labour productivity (measured as GDP per worker) as afunction of the capital-labour ratio, human capital index, and mobile broadband speed. The
author estimates the relationship using pooled OLS and fixed effects estimation to remove theeffects of country-specific components. He finds a positive association between speed and labour
productivity: a 10 per cent increase in the previous year’s broadband speed is associated with a
0.2 per cent increase in labour productivity in the current period. He does not correct for
simultaneity, arguing that is very difficult to find appropriate instruments with which to correct
for simultaneity.
In our study we follow the approach of Edquist (2022) by modelling labour productivity rather
than output per capita: as discussed below, we think broadband internet demand is likely to be
somewhat less sensitive to productivity than to output per capita, which is also affected bychanges in employment rates. However, we use a broader list of explanatory variables, and we
focus on the impact of fixed broadband penetration and high speed fixed broadband penetration
rather than median speed.

9III. Methodology
A. Theoretical Framework
The economic model underlying the analysis is based on the standard neoclassical growth model
made famous by Solow (1956). In this model, output (Y) is generated by capital (K) and labour(L) according to a constant returns to scale Cobb-Douglas production function2:
𝑌 = 𝐴𝐾 𝛼 𝐿1−𝛼(1)where 𝛼 is capital's share of output. The parameter A is total factor productivity (TFP): it
captures influences—such as the level of technology, the efficiency of markets, and the quality of
the workforce—that are separate from the quantities of capital and labour employed, and which
increase the contributions of these two factors to output.
By taking natural logarithms, the production function can be expressed as:ln⁡ 𝑌 = ln 𝐴 + 𝛼 ln 𝐾 + (1 − 𝛼) ln 𝐿

(2)

Subtracting the natural logarithm of labour gives:𝑌𝐾
ln ( ) = ln 𝐴 + 𝛼 ln ( )
𝐿
𝐿

(3)

so that labour productivity (Y/L) is a function of an economy's capital-labour ratio (K/L) and its
total factor productivity (A).
The equation provides for two channels for broadband internet to have an impact on labour
productivity. The first is through an increase in the capital stock K, as telecommunications
companies invest resources in constructing broadband networks. The second is through totalfactor productivity A, as broadband internet makes possible new kinds of products and businessmodels, and increases the flow of information in ways that improves productivity across the
economy. These latter gains do not accrue to the telecommunications companies: they “spillover”
across the economy. In our empirical work below it is this latter impact of broadband internet on
TFP that we shall seek to measure.

Constant returns to scale: increasing the inputs by a certain proportion will result in an increase in output of thesame proportion.
2

10

B. Empirical Implementation
We implement empirically the equation determining labour productivity (3) using the following
equation:ln(𝑌/𝐿)𝑖𝑡 = 𝛽0 +⁡𝛽1 𝑙𝑛(𝐾/𝐿)𝑖𝑡 + 𝛽2 𝑙𝑛(𝑝𝑒𝑛𝑒𝑡𝑟𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽3 𝑋𝑖𝑡 + 𝛽4 𝑡 + ⁡ 𝜖𝑖𝑡(4)

where 𝑋𝑖𝑡 is function of factors other than broadband internet (such as human capital or thequality of institutions) that affect total factor productivity, t is a time dummy and 𝜖𝑖𝑡 ⁡is anormally-distributed error with mean zero and constant variance. The time dummy is intended topick up global macroeconomic shocks, such as the Great Financial Crisis, that have common
impact across countries. This equation is estimated for a panel of 116 countries over between
2009 and 2019. We estimate two versions of the equation: one using overall broadband internet
penetration; and one using high-speed broadband internet penetration.
We chose to estimate the equation separately for developed and developing countries, in order tosee if there were significant differences in the impacts of broadband internet across the twocategories of country.
One issue in using panel data of the kind we have here is whether to use a fixed effects estimator,
which controls for constant differences in cross country productivity levels over time by giving
each country its own dummy variable, and a random effects estimator, which assumes that all thevariation is attributed to the explanatory variables or to the random error. Generally speaking the
fixed effects estimator is usually preferred when using cross-country macroeconomic data, as it
is likely that however complete the list of independent variables, they will not pick up all theidiosyncratic factors (such as access to the sea, climate, cultural influences) that determine
productivity. If this is true, and these idiosyncratic factors are correlated with independent
variables, then the random effects estimator may overestimate the impact of the independent
variables. We use both estimators in our analysis, and use a Hausman test (see Torres-Reyna
2007) to decide which estimator is the most appropriate.
As discussed in the literature review, authors have approached in different ways the possibility
that the dependent variable may affect penetration rates and therefore bias the estimates. Thispossibility arises in the present case because higher productivity increases incomes which canincrease the demand for broadband internet. One approach is to use instrumental variables
11methods to correct for the bias: however, this approach relies on finding good instruments—
variables that are strongly correlated with the potentially endogenous explanatory variable(penetration in this case), but not with the dependent variable. As noted by Edquist (2022), this isdifficult to do, as variables that might affect broadband internet penetration are likely to beaffected by income as well.
An alternative approach is to simply lag the potentially endogenous regressor one period. While
this approach addresses endogeneity in the time series dimension, it does not address
endogeneity in the cross-section dimension. Furthermore, it assumes uncorrelated errors: if theerrors in the equation are correlated, then the lagged regressor will be correlated with
contemporaneous regressor, and the endogeneity problem remains.
There does not therefore seem to be a good solution to the problem of endogeneity. We do think
it will be less a problem in our case because we are using productivity, and not income percapita. While productivity does ultimately affect incomes, it is not the only factor: changes in
employment rates or the terms of trade, for example, will also affect household incomes. We
come back to this issue in the conclusion.

IV. Data and Descriptive Statistics
Our data set comprises information on 116 countries for the period 2009–2019. Of these 116
countries, 41 are developed countries and 75 are developing countries. (See Appendix 1 for thelist of countries in each category). The number of countries and the time period is limited bytelecommunications data that we use, which is taken from the International Telecommunications
Union (ITU) database. Nonetheless, virtually all major developed and developing countries arecovered: the countries missing are largely smaller developing countries. The time period covers
significant changes in both overall broadband penetration and high speed penetration. The data
set can be divided into three categories: telecommunications data on broadband internet;
economic data on productivity and the capital–labour ratio; and the components of the
productive capacities index. These are described in greater detail below, along with descriptive
statistics.

12

A. Data on Broadband Internet
Data on fixed broadband subscriptions, which will be our proxy for broadband penetration, andfixed broadband subscriptions above 10Mbps, our proxy for broadband speed, are collected fromthe World Telecommunication/ICT Indicators database (International Telecommunication Union,2023). Both variables are reported per 100 inhabitants.
Ideally, since our focus is on productivity, we would prefer to have a measure of broadband
internet penetration that measures penetration amongst businesses. Unfortunately, those data are
not available for most of the countries in our sample, and so we use broadband internet
penetration per person as a proxy. This is an approach that has been adopted by other authors,
and we think it is justifiable as both household and business adoption of broadband internet arelikely to be closely related to the availability of broadband internet infrastructure in a given area.
In Table 1 below we show these data for both developed and developing G20 countries, for 2009
(the start of our estimation period), for 2019 (the end of our estimation period), and thepercentage point change for the 2009–2019 period. We also show the unweighted averages forboth G20 and all countries. (We do not show data for all countries for reasons of space).

13

Table 1: Descriptive Statistics on Broadband Infrastructure
Overall Broadband Penetration

High-speed Penetration

Fixed Broadband Subscriptions (per
100 inhabitants)

Subscriptions Above 10Mbps (per
100 inhabitants)

2009

2019

2009-2019(absolute change)

2009

2019

2009-2019(absolute change)

Developed G20 Countries
Australia
CanadaFranceGermany
Italy
Japan
Republic of Korea
RussiaUnited Kingdom
United States

26.1
30.6
32.0
31.4
20.3
25.7
33.6
9.0
28.7
25.9

34.7
40.4
46.2
42.3
29.3
33.8
42.0
22.5
40.3
34.2

8.6
9.7
14.2
10.9
9.0
8.1
8.4
13.5
11.6
8.3

11.4
5.8
33.0
9.5
1.6
24.0
31.3
2.6
9.6
5.4

26.0
36.1
43.9
38.8
23.3
31.0
42.0
17.8
39.0
31.5

14.6
30.3
10.9
29.3
21.7
7.0
10.7
15.2
29.4
26.0

Unweighted Average:
Developed G20

26.3

36.6

10.2

13.4

32.9

19.5

23.6

34.1

10.5

5.2

30.7

25.5

8.6
6.0
7.8
0.6
0.8
8.7
5.0
0.9
8.9

19.7
15.5
31.6
1.4
3.8
15.5
19.0
2.2
17.0

11.0
9.5
23.8
0.8
3.0
6.8
13.9
1.2
8.1

1.2
1.2
1.1
0.1
1.0
1.0
0.3
0.3
0.0

12.7
9.9
31.2
0.9
3.1
14.3
16.6
1.0
12.7

11.5
8.7
30.2
0.8
2.1
13.3
16.3
0.7
12.7

5.3

14.0

8.7

0.7

11.4

10.7

Unweighted Average: All
Developed
Developing G20 Countries
Argentina
BrazilChina
India
Indonesia
MexicoSaudi Arabia
South Africa
TurkeyUnweighted Average:
Developing G20
Unweighted Average: All
Developing
Unweighted Average: All
G20

4.2

9.1

4.9

0.3

5.6

5.3

16.4

25.9

9.5

7.4

22.7

15.3

Unweighted Average: All
Countries

11.2

17.9

6.7

2.0

14.3

12.3

Source: World Telecommunication Indicators Database by the International Telecommunication Union (ITU),
https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx

14Notes: Asterix (*) indicates that the first year of data is from later than 2009. First year of data is: 2011 for Japan andRussia; 2012 for France; 2013 for Brazil and Mexico; 2014 for Argentina and South Africa; 2015 for India; 2017 forIndonesia.

Beginning with data on fixed broadband subscriptions, we see that developed countries (with the
exception of Russia), had much higher penetration rates than developing countries in 2009, witha range of 20–34 subscriptions per 100 inhabitants, compared to 1–9 for developing countries.
By 2019, the penetration rate in developed countries had risen by 8–14 subscriptions per
hundred, so that penetration rates (except for Russia), were in the range of 29 per cent to 46 percent. Some developing countries, particularly China, saw penetration rates rise as fast if not
faster than in developed countries (in China the rate rose by 23.8 to a level higher than Italy’s).
However, poorer developing countries such as India and Indonesia saw only a small rise inabsolute terms, although in percentage terms the increase was significant. India for example saw
penetration rates more than double, but because penetration rates in 2009 were very low at 0.6,
the penetration rate in 2019 was only 1.4 subscriptions per inhabitant.
The picture for high-speed penetration is somewhat different, as few subscribers, even indeveloped countries, had subscriptions above 10Mbps in 2009. This changed substantially overthe period: by 2019 90 per cent of subscriptions (30.7 percentage points out of 34.1 percentage
points) were high-speed on average in developed countries. Among G20 countries, Australia(only 75 per cent high speed) and Russia (only 79 per cent) were outliers on the negative side,
whereas Korea (100 per cent) and the UK (97 per cent) were outliers on the positive side. Canada
was around average, with 89 per cent of subscriptions being high speed in 2019. Interestingly,
Canada had the largest increase in high-speed subscriptions between 2009 and 2019 among theseven G20 countries for which we have complete data.
For developing countries the picture for high speed penetration is mixed. China was in a class of
its own, going from very little high-speed penetration in 2009 to a level comparable to Japan or
the U.S. by 2019. Middle income countries such as Argentina, Brazil, Mexico, Saudi Arabia andTurkey had achieved penetration rates of 9–17 per cent, roughly a third to a half of developed
country levels. Low income developing G20 countries—India, Indonesia and South Africa—still
had very low high-speed penetration rates in 2019.

15

Canada’s relative position in the G20 remained high during the period. Canada was fourth in theG20 for all broadband subscriptions in 2009 and retained that position in 2019. Canada was fifth
in the G20 for high-speed penetration in 2019 and above the G20 developed country average andthe total developed country average in that year. Interestingly, Canada had higher penetration
rates—both overall, and high speed, than other geographically large countries such as Australia
and the United States.
Below we show the time series behaviour for both overall fixed broadband (Chart 1) and highspeed broadband penetration (Chart 2), for developed and developing countries and for Canada.

Chart 1. Fixed Broadband Penetration 2009–2019

Source: World Telecommunication Indicators Database by the International Telecommunication Union (ITU),
https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx

16Chart 2. High-Speed Broadband Penetration 2009–2019

Source: World Telecommunication Indicators Database by the International Telecommunication Union (ITU),
https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx

The time series behaviour for fixed broadband penetration is fairly smooth, with a gradualupwards trend, on average, for both the developed and developing country aggregates, as well as
for Canada. The pattern for high-speed broadband is somewhat different: while there is a smooth
upwards trend for developed countries, the pace of change for developing countries picked up
after 2015. In Canada the pace of adoption of high-speed broadband picked up significantly
between 2011 and 2013; since then, high-speed broadband adoption in Canada has remained
significantly above the developed country aggregate. Overall it is notable that the pace ofbroadband adoption, both overall and for high speed, did not seem to be slowing, at least up to
2019.

17

B. Data on Labour Productivity and the Capital–Labour Ratio
Data on output, employment and the capital stock were collected from the Penn World Table
10.01 (Feenstra et al., 2015). We calculate labour productivity as output per person employed,
where output is measured in 2017 U.S. dollars, with national currency values converted usingpurchasing power parity (PPP) exchange rates. The capital–labour ratio is measured as capitalper person employed, with capital also measured using 2017 U.S. dollars converted using PPP
exchange rates.

18

Table 2: Descriptive Statistics on Labour Productivity and the Capital-Labour Ratio for
G20 Countries, 2009 and 2019
Labour Productivity (2017 US$ perperson employed)

Capital-Labour Ratio (2017 US$ perperson employed)

2009

2019

2009-2019(average annual
growth rate)

96,06388,701
96,387
88,19596,51367,946
66,832
48,20686,038
115,999

102,287
97,115
103,928
96,306
96,41372,871
81,83856,53991,464
129,903

0.63
0.91
0.76
0.88
-0.01
0.70
2.05
1.61
0.61
1.14

420,039
380,129
538,940
427,809650,768
346,610311,669
234,366
412,438
428,314457,293438,544586,230
439,007672,448
341,799398,248248,364
431,113436,258

0.85
1.44
0.84
0.26
0.33
-0.14
2.48
0.58
0.44
0.18

85,08892,8660.88

415,108

444,930

0.70

76,42786,8051.28

383,942

419,518

0.89

48,87532,01816,175
10,376
17,36940,721
143,074
38,692
59,90647,25932,37825,754
18,41423,71343,758120,01139,31079,853

-0.34
0.11
4.76
5.90
3.16
0.72
-1.74
0.16
2.92

141,888
118,81744,38436,62889,043
181,150
418,521
134,837
232,338

155,265
135,651
124,697
68,731
132,390
188,333
482,593
150,797344,243

0.91
1.33
10.88
6.50
4.05
0.39
1.43
1.12
4.01

45,24547,8280.56

155,289

198,078

2.46

32,40966,21536,86471,5321.30
0.78

109,212
292,036

141,619
328,000

2.63
1.17

47,96754,515
1.29
206,315
Source: Penn World Tables 10.01, https://www.rug.nl/ggdc/productivity/pwt/?lang=en

239,842

1.52

Developed G20 Countries
Australia
CanadaFranceGermany
Italy
Japan
Republic of Korea
RussiaUnited Kingdom
United States
Unweighted Average:
Developed G20
Unweighted Average: All
Developed
Developing G20 Countries
Argentina
BrazilChina
India
Indonesia
MexicoSaudi Arabia
South Africa
TurkeyUnweighted Average:
Developing G20
Unweighted Average: All
Developing
Unweighted Average: All G20
Unweighted Average: All
Countries

2009

2019

2009-2019(average annual
growth rate)

In Table 2 above we show labour productivity and the capital–output ratio for G20 countries for2009 and 2019, as well as the average annual growth rate. The U.S. has the highest labour
productivity, at close to $130,000 in 2019, with most other developed G20 countries in the19

$72,000 to $103,000 range (Russia is the exception). Interestingly, Japan and Korea lag the three
EU countries, the UK, the US, Canada and Australia significantly in labour productivity, which
likely reflects lower productivity in their services sectors. Average annual productivity growth
was in the range of 0.60 to 1.1 per cent, with the exceptions of Russia and Korea, which grewfaster, and Italy which saw no productivity growth at all. Capital–labour ratios were largely
correlated with productivity; the main exception was the U.S., which had a capital–labour ratiothat was in the middle of the ten developed G20 countries, even though its productivity was by
far the highest.
There is much more disparity in productivity among developing countries. Saudi Arabia has by
far the highest level of productivity, owing to its oil resources. Excluding Turkey, the otherdeveloping countries were in the range of $18,000 to $47,000. The countries with the lowest
productivity in 2009—China, India and Indonesia—had the fastest growth in productivity,
between 3 and 6 per cent per annum, whereas the better off countries of Brazil, Argentina, South
Africa and Saudi Arabia had little or even negative productivity growth. Only Mexico had
similar productivity growth to developed countries. As with developed countries, capital–labour
ratios are correlated to productivity levels, and generally less than half of those of developed
countries.
Canada ranked relatively highly in the ranking of productivity in the G7. In 2009 Canada’s
productivity was fourth highest in the G7; by 2019 that ranking had risen to third highest, behind
the U.S. and France. Canada had the sixth highest capital–labour ratio in the G7 in 2009, rising
to fifth highest in 2019. Canada’s productivity growth rate was respectable, averaging 0.91 per
cent: lower than the U.S. and Korea, but higher than most other developed countries.
C. Data on the other Drivers of Total Factor Productivity
For the determinants of total factor productivity other than broadband internet penetration, welargely rely on the components of the United Nations Conference on Trade and Development’s
Productive Capacity Index (PCI) (UNCTAD 2021). This index is made up of eight componentsthat are equally weighted to construct the overall index. These components are:
1. Human Capital: education, skills and health and R&D
2. Natural Capital: availability of extractive and agricultural resources20

3. Energy: availability, sustainability and efficiency of power sources
4. Transport: capacity of the roads and railways network, and air connectivity
5. Information and Communication Technology: extent of IT infrastructure
6. Institutions: political stability and efficiency
7. Private Sector: extent of regulatory and trade barriers
8. Structural Change: ability of resources to move from low-productivity to highproductivity economic activities.
Each component is itself based on three to nine indicator time series (See Appendix 2 for a list ofthese indicator time series). Principal component analysis is used to weight these constituent
indicator time series in order to construct a single time series each of the eight components. Thisis done for each country, and the resulting measures are bounded between 0 and 100.
In our analysis we do not use the ICT component, as it contains measures of broadband internet
use that we model separately (the other components are highly correlated with the measures weuse). We also do not use the Human Capital component: we prefer the index available from thePenn World Tables in order to be more comparable with other studies. This human capital index
uses data on average years of schooling that are weighted by an assumed rate of return on
education that declines with additional years of schooling to produce a measure of how much
more productive an average worker is as result of his or her education3. To illustrate, if a country
that has a human capital index twice that of another country, that means that an average worker is
twice as productive, everything else being equal, as a result of more years of schooling.
Table 3 below provides descriptive statistics for the seven variables that we use to model TFP for
the G20 countries. Developed countries score somewhat better on their energy infrastructure;
however, they generally score lower on the measure of natural capital, as developing countries
tend to be more reliant on natural resource extraction. Developed countries also score a little
higher on transportation infrastructure, although some developing countries such as Turkey andSouth Africa are approaching developed country levels.

3

See https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf for further explanation.

21

Table 3: Descriptive Statistics for Productive Capacities Indices and for PWT IHC for G20
Countries, 2009-2019
Average Index Level, 2009-2019
Natural
Structural
Index ofEnergyCapitalPrivate Sector Change Index Transport
Institutions Human CapitalIndex (PCI) Index (PCI) Index (PCI)(PCI)
Index (PCI) Index (PCI)(per person)
Developed G20
Countries
Australia
CanadaFranceGermany
Italy
Japan
Republic of Korea
RussiaUnited Kingdom
United States
Unweighted Average:
Developed G20
Unweighted Average:
All Developed
Developing G20
Countries
Argentina
BrazilChina
India
Indonesia
MexicoSaudi Arabia
South Africa
TurkeyUnweighted Average:
Developing G20
Unweighted Average:
All Developing
Unweighted Average: All
G20
Unweighted Average: All
Countries

76.7
74.8
72.2
77.1
72.3
78.0
80.9
70.5
74.4
77.3

42.8
27.9
21.3
22.1
20.4
8.2
13.3
36.1
31.2
27.0

81.2
83.0
74.0
79.2
66.7
94.1
93.1
45.4
75.7
88.1

73.0
77.8
91.7
93.0
90.6
87.6
80.8
61.2
88.2
95.0

61.7
61.8
52.8
56.9
44.3
51.0
51.5
47.2
57.6
61.5

91.7
92.8
82.2
89.5
67.6
85.4
73.8
39.2
88.0
83.9

3.48
3.68
3.13
3.66
3.07
3.54
3.60
3.36
3.25
3.72

75.4

25.0

78.1

83.9

54.6

79.4

3.45

71.4

27.5

68.3

73.2

53.2

78.5

3.34

62.0
52.8
65.1
41.6
55.5
61.6
79.5
59.0
64.4

39.2
37.4
44.0
46.5
37.1
37.9
63.1
52.4
35.7

43.3
45.5
71.8
50.9
46.0
55.3
51.4
59.5
57.7

56.9
65.9
96.8
75.7
69.8
70.4
53.5
70.3
78.9

41.4
42.9
38.1
29.3
34.3
40.0
44.2
45.2
48.1

50.8
54.1
45.2
50.3
48.8
49.5
49.0
59.0
50.7

2.95
2.75
2.57
2.06
2.36
2.68
2.60
2.68
2.35

60.2

43.7

53.5

70.9

40.4

50.8

2.55

49.0

43.6

47.6

53.0

36.9

48.9

2.38

68.2

33.9

66.4

77.8

47.9

65.9

3.03

57.0

37.8

55.0

60.2

42.7

59.5

2.72

Source:PCI - United Nations Conference on Trade and Development (UNCTAD), https://unctad.org/topic/least-developedcountries/productive-capacities-index
Index of Human Capital- Penn World Tables 10.01, https://www.rug.nl/ggdc/productivity/pwt/?lang=en

Bigger disparities between developed and developing countries are evident in the indices thatreflect the strength of institutions, the freedom of the private sector, and the ease of moving
22resources (structural change). One outlier though is China, which has a level for the Structural
Change index that is higher than any developed country, but which has the second lowest level in
the G20 for the quality of institutions. This can be explained by the totalitarian nature of China’s
political and economic system, which allows the government to direct resources in ways thatwould be more difficult in more democratic economics that adhere more to the rule of law.
Finally, the level of the human capital index is uniformly higher in developed countries, withlevels highest in the U.S., Canada and Germany, and lowest in India, Indonesia and Turkey.
Although this is changing, China’s education levels were lower than G20 developing countries in
Latin America, as well as South Africa. Human capital levels take much longer to change thanphysical capital levels, because people have multi decade working lives and most human capital
accumulation takes place before they begin working. Thus China is still feeling the impacts ofvery low human capital accumulation several decades ago.
Overall Canada does quite well across the seven potential measures of the drivers of total factor
productivity. Canada has the highest levels of institutional quality, transportation infrastructureand human capital, and is above the average for all developed countries for all measures.

V. Results
In this section we will present estimates based on the empirical specification outlined in section
III.B. We present estimates using both overall penetration rates and high-speed penetration rates,
and for both random and fixed effects estimators.
A. Developed Countries
Table 4 below shows results using the 40 developed countries in our data set for the period
2009–2019, where Penetration denotes fixed broadband subscriptions per inhabitant and HighSpeed Penetration denotes broadband subscriptions of over 10Mbps.

23

Table 4: Regression results for Developed Countries

Dependent Variable:(1)ln Productivity(2)
ln Productivity(3)
ln Productivity(4)
ln Productivity

Estimator

Random Effects

Fixed Effects

Random Effects

Fixed Effects

ln Human Capital Index-0.165(0.123)-0.265**(0.132)0.0448(0.148)-0.054(0.165)ln Capital–Labour Ratio

0.503***(0.028)0.524***(0.031)0.505***(0.031)

0.522***(0.03)

ln Penetration

0.058***(0.016)

0.054***(0.016)-0.007**(0.003)-0.006**(0.003)ln High Speed Penetration

Energy0.006***(0.001)0.006***(0.001)0.006***(0.001)0.006***(0.001)Natural Capital

0.001(0.001)0.001(0.001)-0.000(0.001)0.001**(0.001)

Private Sector-0.001(0.001)-0.002**(0.001)-0.001(0.001)-0.002(0.006)Structural Change

0.006***(0.001)0.006***(0.001)0.005***(0.001)0.006***(0.001)

Transport-0.001*(0.001)-0.001*(0.001)-0.001*(0.000)-0.001**(0.000)Institutions

0.003***(0.00)

0.002***(0.001)0.004***(0.001)0.002*(0.001)

Constant

3.763***(0.40)

3.737***(0.44)

3.752***(0.44)

3.744***(0.49)

Observations

451

451

395

395

R2

0.6942

0.63580.69750.6300Standard errors are in parentheses*** p<0.01, ** p<0.05, * p<0.1

24

The Hausman test for which estimator should be preferred indicates that the fixed effects
estimator is preferred for both overall penetration rates and high-speed penetration rates. We
therefore focus on the fixed effects models in columns (2) and (4) as our preferred specifications.
We can see from column (2) that the overall broadband internet penetration rate is positive andsignificant at the one per cent level. The coefficient estimates indicate that a one per centincrease in the overall penetration rate leads to a 0.054 per cent increase in the level ofproductivity. These estimates are economically quite significant: as we shall see below, theseestimates imply that increases in penetration rates increased annual productivity growth by 0.2
percentage points on average over the time period. However, the high-speed penetration rate is
insignificant, and negative when year dummies are used.
Looking at the other explanatory variables, the capital–labour ratio is significant and thecoefficient estimates of around a half are only a little higher than capital’s share of output (what
we would expect from the theoretical model). The estimates for human capital are generally
insignificant. Of the other explanatory variables, Energy, Structural Change and Institutions are
positive and significant across all specifications (except Institutions in the high-speed
regression), whereas Natural Capital, Private Sector and Transport are either insignificant orincorrectly signed. The R-squared statistics for the preferred equations, which measure theproportion of total variation in the dependent variable “explained” by the regressors, are in therange of 0.63–0.64, which is reasonable for a panel with a lot of cross section variation.
B. Developing Countries
We next go on to estimate the same four specifications using our sample of 75 developing
countries over the same time period. The results are shown in Table 5 below.

25

Table 5: Regression results for Developing Countries(1)ln Productivity(2)
ln Productivity(3)
ln Productivity(4)
ln Productivity

Random Effects

Fixed Effects

Random Effects

Fixed Effects

ln Human Capital Index

0.0551(0.087)-0.154(0.100)-0.096(0.131)0.118(0.155)ln Capital–Labour Ratio

0.468***

0.405***

0.335***

0.253***(0.030)(0.035)(0.042)(0.053)0.011*(0.005)0.011*(0.005)-0.001(0.002)-0.001(0.003)

Dependent Variable:
Estimator:ln Penetration

ln High Speed Penetration

Energy0.013***

0.013***

0.020***

0.021***(0.001)(0.001)(0.002)(0.002)Natural Capital-0.002(0.001)-0.003*(0.002)-0.007**(0.002)-0.013***(0.003)

Private Sector-0.001(0.001)-0.001(0.001)-0.001(0.002)0.000(0.002)

Structural Change-0.003***(0.001)-0.003***(0.001)-0.001(0.001)-0.000(0.001)

Transport

0.000(0.001)

0.002(0.001)0.007***(0.001)0.008***(0.001)

Institutions

0.008***(0.001)0.008***(0.001)0.010***(0.002)0.011***(0.002)

Constant

3.983***(0.33)

4.793***(0.40)

5.028***(0.48)

5.822***(0.63)

Observations

779

779

423

423

R2

0.85220.84580.73350.6790Standard errors are in parentheses*** p<0.01, ** p<0.05, * p<0.1

We again performed a Hausman test to see whether the fixed effects or random effects estimator
was preferred. For overall penetration, the null hypothesis of a random effects estimator was
rejected at the ten per cent but not the five per cent level. However, the coefficient estimates

26

were almost identical. For high-speed penetration the random effects estimator was rejected infavour of the fixed effects estimator at the one per cent level. We therefore focus, as we did fordeveloping countries, on the fixed effects estimators.
The results for developing countries are quite different. The coefficient on penetration rate isabout a fifth what it is for developed countries, and only statistically significant at the ten percent level. High-speed penetration is not significant, consistent with developing country results.
The estimate of the impact of the capital–labour ratio is positive and significant at the one percent level, the coefficient is between 0.4 and 0.25, lower than for developed countries but
reasonable from a theoretical standpoint. The human capital index is not significant. Looking at
the other explanatory variables, Energy and Institutions are both significant and positive, as theylargely were for the developed countries, as is Transport in the high-speed equation, unlike
developed countries. Natural capital is negative and significant, unlike for developed countries
where it was insignificant, perhaps indicative of the well-known “resource curse”, whereby
countries with significant mineral rents often have slower growth partly as result of conflicts
over these rents (See Auty 1993). Structural change is negative although not significant, whereas
for developed countries it was positive.
Overall the equations for developing countries perform a little better than those for developed
countries. The R-squared for the two specifications is in the range of 0.68 to 0.85, higher than the
0.63-0.64 range for developed countries.
C. Robustness checks
We performed some tests to test the robustness of the results. We tested to see if the year
dummies are jointly significant: we found that they were, for both developed and developing
countries. We also tested to see if lagged values of the overall penetration and high speed
penetration were significant. We found that they were not in any of the four specifications,
whether or not the contemporaneous values of these variables were included. We also used thehuman capital indicator from the Productive Capacities Index as an alternative to that from Penn
World Tables; however, the results were very similar.

27

VI. Contributions of Broadband to Labour Productivity
In order to understand the economic significance of our results we calculate the implied
contribution of broadband internet to productivity growth for G20 countries over the period
2009–2019. We then calculate what proportion of actual labour productivity growth is accounted
for by the growth in broadband internet penetration. We use specification (2), which uses fixed
effect. These results are shown in Table 6 below.

28

Table 6: Contribution of Broadband Internet Penetration to Productivity Growth: G20
Countries

Developed G20 Countries
Australia
CanadaFranceGermany
Italy
Japan
Republic of Korea
RussiaUnited Kingdom
United States
Unweighted Average:
Developed G20
Unweighted Average: All
Developed
Developing G20 Countries
Argentina
BrazilChina
India
Indonesia
MexicoSaudi Arabia
South Africa
TurkeyUnweighted Average:
Developing G20
Unweighted Average: All
Developing
Unweighted Average: All G20
Unweighted Average: All
Countries

Average Productivity
Growth 2009-2019(%)

of which: Growth due
to Broadband Internet
Penetration (p.p.)

Proportion of
Productivity Growth
Accounted for by
Broadband Internet

0.63
0.91
0.75
0.88
-0.01
0.70
2.03
1.59
0.61
1.13

0.16
0.15
0.20
0.18
0.20
0.15
0.12
0.50
0.19
0.15

0.25
0.17
0.27
0.20
n.a.
0.22
0.06
0.32
0.30
0.13

0.92

0.20

0.22

1.27

0.20

0.16

-0.34
0.11
4.65
5.74
3.11
0.72
-1.76
0.16
2.87

0.09
0.10
0.15
0.09
0.18
0.06
0.15
0.09
0.07

n.a.
0.93
0.03
0.02
0.06
0.09
n.a.
0.57
0.02

1.70

0.11

0.06

1.28
1.29

0.09
0.16

0.07
0.12

1.28

0.13

0.10

Note: n.a. indicates that productivity growth was negative and so we do not show the contributions of broadband
internet because they are negative.

The simulations show that growth in broadband internet penetration increased productivity
growth by an average of 0.20 percentage points per year across the ten G20 developed countries
over the period 2009 to 2019. Because total labour productivity growth was 0.92 per cent per29

year for these countries, on average, this means that broadband internet penetration accounts for
22 per cent of the average annual productivity growth observed in these ten countries. For thebroader sample of 41 developed countries, broadband internet penetration can explain 16 percent of total labour productivity growth over the 2009–2019 period.
The greatest contribution of broadband internet to productivity growth was in Russia, where
broadband internet growth added 0.5 percentage points to productivity growth every year on
average, reflecting the large percentage increase in broadband internet penetration in thatcountry. Fully a third of Russia’s productivity growth is estimated to have come from theincrease in penetration over the 2009–2019 period. The lowest contribution, 0.15 percentage
points per year was observed in Korea, which started the period with the highest broadband
internet penetration, and so did not experience as big an increase in penetration as some otherG20 developed countries. Because Korea’s overall productivity growth was quite high over thisperiod—averaging 2.05 per year, the highest among the developed G20 nations—only six per
cent of Korea’s productivity growth can be attributed to broadband internet penetration over the
sample period.
Canada was in the middle of the pack: increases in broadband internet penetration added 0.15
percentage points to productivity growth over the period, 17 per cent of Canada’s average annual
productivity growth of 0.9 per cent. Canada was quite similar to the U.S. in the contribution ofbroadband internet to productivity growth. In Italy, productivity growth fell very slightly; theresults indicate that productivity growth would have fallen by 0.21 per cent annually had it notbeen for increases in broadband penetration.
For developing countries, the contribution is somewhat smaller, given the lower estimates of the
impacts of broadband internet in our regression results. Broadband internet penetration added
between 0.18 (Indonesia) and 0.06 (Mexico) percentage points to annual average productivity
growth between 2009 and 2019. Excluding countries with negative productivity growth,broadband internet accounted for between 2 per cent (India) and 93 per cent (Brazil) of total
productivity growth. This greater dispersion than for developed countries is accounted for by thegreater dispersion in productivity performance among G20 developing countries. Countries like
China and India had much faster productivity growth, and so broadband internet is relatively lessimportant. Overall the average contribution of broadband internet was 0.11 percentage points for30

G20 developing countries and 0.09 for all developing countries, about half the developed country
figure of 0.20 percentage points. This contribution of broadband internet accounted for 6 per centof average G20 developing country productivity growth (this number is pulled down by fastgrowing countries like India and China, as noted above), and 11 per cent of average productivity
growth for all developing countries in our sample.
These estimated contributions of broadband internet to productivity, and thus to economic
growth and income, are significant, given all the other factors that affect productivity, fromhuman capital and physical capital intensity to factors such as infrastructure and institutions that
we measure using the PCI.
That the average results for developed countries are about half of that of developing countries
may come as a surprise, as recall from Table 5 that the estimated coefficient of the impact ofbroadband internet in developing countries is only a fifth of that for developed countries in Table
4. However, the percentage increase in penetration was much larger, on average, for developing
countries, so the net effect on productivity is larger than estimated coefficient would suggest.
Chart 3 below shows the time path of the contribution of broadband internet penetration to
overall increases in productivity growth for Canada. Between 2009 and 2019, labour productivity
in Canada grew by $8,414 (in 2017 US dollars, adjusted for purchasing power parity). $1,426 ofthis increase, or 17 per cent, can be attributed to increases in broadband internet penetration.

31

Chart 3: Productivity Increases in Canada 2010–2019 and the Contribution of Broadband
Internet

Interestingly, while overall productivity growth declined in Canada in 2015, partly as a result ofa slowdown in the energy sector, broadband internet penetration continued to rise, and so thecontribution of broadband internet to productivity also kept rising, ameliorating but not offsetting
the overall decline of productivity in the economy in that year.
It is important to note that the estimated impacts on productivity from broadband internet
penetration are in addition to the direct impact on productivity from capital investment inbroadband internet. These effects are picked up separately through an increase in the capital–
labour ratio.

32

VII. Policy Implications
Our finding that investment in broadband internet increases total factor productivity across the
economy has important implications for public policy. This is because, as noted above, increases
in TFP represent spillover benefits to the broader economy that are over and above those
captured by the company making the investments. This makes broadband internet infrastructure
similar to other public infrastructure, such as roads and highways, where the benefits are
significant but diffused across the economy in ways that make it hard to ensure that the ultimate
economic beneficiaries are paying a market price equal to the marginal benefit they receive fromit. This is why governments have tended to provide these networks for free to users. (Theexceptions include toll highways where access is easy to monitor; however, this represents asmall fraction of the overall road network, and non-toll alternatives almost always exist). The
existence of sizable spillovers implies that, like roads and highways, we should think ofbroadband internet as critical infrastructure for the modern economy, facilitating other sectors’
growth and efficiency.
These spillovers mean that there is strong case for government to encourage broadband access,as these broader economic benefits will not be taken into account by the telecommunicationcompanies when making investment decisions. This is because in a free market, companies
invest only up to the point that their private return (from charging customers) exceeds the cost of
making the investments. As a result, without any government intervention, the provision ofbroadband internet will be below the socially optimal level.
Policies to encourage broadband internet investment can take many forms. One approach would
be for government could simply provide the service directly, as with the roads and highway
example above. However, government provision is unlikely to be the best approach in the case ofbroadband internet. Building out a broadband network, especially with next-generation
technologies, requires enormous capital investment that fiscally constrained governments willfind difficult to afford. Even more importantly, given the rapidly evolving technology in thisarea, private companies are much better placed to respond to market incentives to innovate.
Alternative approaches to stimulate investment would include subsidies and tax incentives totelecommunications companies to encourage them to build out their network. This is theapproach adopted by a number of countries, particularly for rural and remote areas. In the United
33

States, for example, the Federal Communications Commission’s Connect America Fund (FCC
2012) is designed to ensure that consumers in non-urban areas have access to broadband at rates
that are comparable to urban areas. In Canada the federal Government launched the Universal
Broadband Fund in 2020, designed to connect 98 per cent of Canadians to high-speed internet by2026 and 100 per cent by 2030 (see Canada (2023)).
Subsidies are not the only tool available to governments to incent investment in broadband
internet. As noted above, broadband internet requires large amounts of capital investment;
however, corporate income tax systems in most developed countries, including Canada,discourage capital investment, particularly when it is financed by equity rather than debt.
Enabling more favourable tax treatment for broadband internet investment could therefore be
another important tool for encouraging investment. This could take the form of accelerated
depreciation allowances, a tool that has been used by a number of governments, including
Canada, to promote capital investment in specific areas (see Canada 2018).
Government policy can also act on the demand side, by incenting adoption of broadband internet
technology by users, particularly small businesses. Policies could include subsidies, such as the
U.K.’s Gigabit Voucher scheme (UK 2021) that provides grants directly to business andindividuals to adopt high speed broadband, or tax measures, such as eliminating sales taxes oninternet services.
Finally, and as discussed further below, it is hard to separate the impacts of broadband internet
from impacts of the software applications that it makes possible. To the extent that theseapplications also contribute to the significant TFP benefits that we have found, it will be
important for governments to ensure that barriers and impediments to the adoption of new
software applications are minimised.

34

VIII. Conclusion
In this report we have estimated the impact of broadband internet penetration on labour
productivity using a panel of 116 countries over the time period 2009–2019. We looked at the
impact of all fixed broadband internet, as well as high-speed broadband, defined as broadband
internet with speeds above 10Mbps. Our conceptual model is the standard Solow model ofgrowth accounting, which allows us to apportion increases in productivity growth between
growth in inputs of physical capital, and growth in total factor productivity, or the efficiency withwhich factors of production are used. This allows us to separate out the gains whichtelecommunications companies receive directly from investment in broadband internet, whichshow up as gains to capital investment, and the broader benefits to the economy, which do notaccrue to the companies making the investment, but which accrue to other businesses and theirworkers, through increases in total factor productivity.
Our results show statistically and economically significant impacts of fixed broadband internet
on productivity growth for both developed and developing countries. We find that broadband
internet increased labour productivity growth by an average of 0.20 percentage points annually indeveloped countries and 0.26 percentage points in developing countries from 2009 to 2019: thismeant that broadband internet accounted for around 16 per cent of productivity growth indeveloped countries and 7 per cent of productivity growth in developing countries. The very low
rates of broadband penetration in countries such as India, Indonesia and South Africa means thatthese countries have a very significant potential for a boost in productivity growth if they canboost broadband internet infrastructure, as China, for example, has done.
The results for Canada were fairly typical for developed countries, with broadband internet
contributing 0.15 percentage points annually, on average, to Canada’s overall productivity
growth of 0.91 per cent. This contribution was similar to other large developed countries,
reflecting similar trends in broadband adoption.
Overall, the results suggest significant spillovers to the rest of the economy from the adoption ofbroadband internet, attesting to the role of broadband internet as critical infrastructure for the
economy, underpinning productivity across the economy. This suggests an important role forgovernment in encouraging the building out of broadband internet: appropriate policies could
include subsidies and tax incentives directed at both telecommunications companies and business35

users, especially small businesses. Indeed, many countries, including Canada, have adopted thesekinds of policies.
While the results for overall broadband internet penetration showed very significant effects, theresults for high-speed broadband internet penetration showed no additional impact. Taken at facevalue, this would suggest that while the applications associated with lower speed internet access,such as surfing the Web, have very positive impacts on productivity, more speed-intensive
applications, such as gaming or videoconferencing do not, or at least did not over the sample
period. Of course, the importance of videoconferencing did increase massively during and after
the COVID 19 lockdowns: it is possible that with more recent data we would see much moresignificant differences between those countries that had the broadband internet infrastructure to
support widespread videoconferencing and those that did not.
The result that increases in high-speed penetration do not seem to add to labour productivity is
consistent with much of the recent literature, at least for developed countries. Edquist (2022),
who looks at mobile and not fixed broadband internet, does not find robust effects for developed
countries. Koutroumpis (2019) also finds that speed increases above 9.8 Mbps do not add toGDP for OECD countries.
None of this is to say that higher speeds might not be important for productivity in the future.
Indeed, it is symptomatic of information technology that advances in hardware capacity that
seemed well in advance of customers’ need when introduced soon became essential for software
applications that were developed to use the new capacity: hence the saying “What Intel giveth,
Microsoft taketh away.” (OSNews, 2007). Indeed, a recent OECD study (Calvino and Fontanelli2023) finds that AI use is correlated with ultra-fast broadband: it may be that once firms start
reaping the benefits of technologies like AI we will see a positive link between high speed
broadband and productivity.
How robust are the results? Simultaneity is a potential problem that may bias the results upwards
if higher productivity causes higher broadband internet penetration instead of the other way
around. Another possibility that might bias the results upwards is omitted variables: while we
included a broad array of variables that might affect labour productivity, it is possible that wehave omitted a variable that matters for productivity and that is correlated with broadband
internet penetration, exaggerating the role of the latter.
36

The other important caveat to make is that the productivity benefits from broadband
infrastructure require accompanying software applications if these benefits are actually to berealised in practice. Disentangling the productivity impacts of these two factors would be quite
challenging: we would need time series data on what software was being used by businesses
across different countries, and we are not aware of the existence of such a dataset for a wide
range of countries (although it is available for Canada—see below). That said, while not a
sufficient condition, infrastructure investment is certainly a necessary condition for the
productivity gains that we have estimated, without it they could not have occurred.
With these caveats in mind, we nonetheless believe that the data support the hypothesis thatbroadband internet penetration—although not high-speed penetration—had economicallysignificant impacts on labour productivity during the estimation period by increasing the
productivity of capital and labour across the economy. This conclusion reinforces the idea thatinvestment in broadband infrastructure has very important network externalities across the
economy and that the benefits to society go well beyond the returns captured by thetelecommunications companies. Whether investment in higher speed broadband internet will
have the same impact will depend on the evolution of applications such as cloud computing, data
analytics and artificial intelligence, as ultimately the benefits of advances in hardware and insoftware are inextricably linked.

IX. Directions for Further Work
Our analysis has used country-level data to estimate the impact of broadband penetration on
productivity. However, there is likely considerable regional and sectoral variation in broadband
internet penetration that is not reflected in country-level aggregates. Furthermore, because
business-level data was not available for many of the countries in our sample, we were forced touse broadband internet penetration for households as a proxy for the use of broadband internet byfirms.
An obvious direction for further work would therefore be to use data on firms rather thanhouseholds, broken down by region and sector. Not only might this give use more precise
estimates of the overall impact of broadband, because we would be looking at firms not37households, this disaggregated analysis would also be very relevant for policymakers who would
be better able to target policies towards regions and sectors where the spillover impacts on
productivity are the greatest.
Canada does have a dataset that could be used for this kind of study. This is the Survey of Digital
Technology and Internet Use, which surveyed more than 10,000 firms across the Canadian
economy, and covers 2013, 2014, 2019 and 2021, albeit with some changes in methodology.
Data is available by firm size, industry and province, as well as on the activities that firms use
internet access for, including e-commerce, cloud computing and the internet of things. Matching
up changes in industry-level internet use over this period with changes in productivity would
allow one to better understand how internet use, not simply availability, affects productivity at
the industry level. Industry-level data may also be able to help us understand why we do not see
more impact on productivity of high-speed broadband: it may be that high-speed broadband does
boost productivity for those firms that use applications that require high speed, but that the
number of firms using these applications may still be small. This would be very relevant topolicymakers, as encouraging greater use of high-speed applications through greater availabilityof infrastructure might be an important avenue for raising productivity at the industry level, if
indeed there is a connection by applications that require high-speed internet and productivity.
Another direction for further work would be to perform a more comprehensive cost benefit
analysis of investments in broadband. This infrastructure is expensive, and governments would
want to know the social rate of return on investment before committing funds. Key
considerations would include the government’s marginal cost of funds, and the extent to whichinvestments might pay for themselves through increased tax revenues.
Finally, we have focussed in this paper on fixed broadband internet connections, and so have notconsiderable the very significant expansion of mobile broadband internet for many countries inour dataset. Indeed, one reason why we find weaker results for developing countries might bethat in many of these countries consumers rely less on fixed connections, which are often
expensive and not always reliable, and more on mobile connections. Another direction for further
work could look at whether this expansion of mobile broadband has affected productivity in bothdeveloped and developing countries.

38

39

References
Auty, R.M. (1993). Sustaining development in mineral economies: the resource curse thesis(London: Routledge).
Bakiskan, A. and El Kaissi (2023). Literature review of the economic impacts of broadband;with a focus on productivity and economic growth, CSLS Research Report 2023-06.
Canada (2018). Fall Economic Statement, https://www.budget.canada.ca/feseea/2018/docs/nrc/2018-11-21-en.html
Canada (2023). High-speed Internet for all Canadians, https://ised-isde.canada.ca/site/highspeed-internet-canada/en
Calvino, F. and L. Fontanelli (2023), A portrait of AI adopters across countries: Firmcharacteristics, assets’ complementarities and productivity, OECD Science, Technology andIndustry Working Papers, No. 2023/02, OECD Publishing, Paris,
https://doi.org/10.1787/0fb79bb9-en
Connolly, E. and K. J. Fox (2006). The impact of high-tech capital on productivity: evidence
from Australia. Economic Inquiry, 44(1), 50–68. https://doi.org/10.1093/ei/cbj004
Czernich, N., Falck, O., Kretschmer, T., & Woessmann, L. (2011). Broadband infrastructure andeconomic growth. The Economic Journal, 121(552), 505–532.
Edquist, H. (2022). The economic impact of mobile broadband speed. Telecommunications
Policy, 46(5), 102351.
Edquist, H., Goodridge, P., Haskel, J., Li, X., & Lindquist, E. (2018). How important are mobile
broadband networks for the global economic development? Information, Economics andPolicy, 45, 16–29. https://doi.org/10.1016/j.infoecopol.2018.10.001
Feenstra, R. C., R. Inklaar and M. P. Timmer (2015), The Next Generation of the Penn World
Table American Economic Review, 105 (10), 3150–3182, available for download atwww.ggdc.net/pwt
40

Kongaut, C., & Bohlin, E. (2014). Impact of broadband speed on economic outputs: An
empirical study of OECD countries. In 25th European Regional ITS Conference, Brussels
2014 (p. 101415).
Koutroumpis, P. (2019). The economic impact of broadband: Evidence from OECD Countries.
Technological Forecasting and Social Change, 148, 119719.
https://doi.org/10.1016/j.techfore.2019.119719
Mayer, W., Madden, G., & Wu, C. (2019). Broadband and economic growth: A reassessment.
Information Technology for Development, 26(1), 128–145.
https://doi.org/10.1080/02681102.2019.1586631
OSNews (2007). https://www.osnews.com/story/18931/what-intel-giveth-microsoft-taketh-away/
Rohman, I. K., & Bohlin, E. (2012). Does broadband speed really matter as a driver of economic
growth? Investigating OECD countries. International Journal of Management and Network
Economics, 5(2), 336–356.
Solow, R. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of
Economics, 70(1), 65-94
Torres-Reyna, O. (2007). Panel data analysis: fixed and random effects using Stata.
https://www.princeton.edu/~otorres/Panel101.pdf
UK (2021). https://www.gov.uk/government/news/government-launches-new-5bn-projectgigabit
UNCTAD (2021). UNCTAD Productive Capacities Index: Methodological Approach andResults. Geneva: United Nations Conference on Trade and Development
https://unctad.org/system/files/official-document/aldc2020d3_en.pdf

41

Appendix 1: List of Developing and Developed Countries in the Dataset
Developed Countries:
Australia

FranceLithuania

RussiaAustria

Germany

Luxembourg

SerbiaBelgium

GreeceMalta

Slovakia

Bulgaria

Hungary

Netherlands

Slovenia

CanadaIceland

New Zealand

Spain

Croatia

Ireland

NorwaySwedenCyprusIsraelPolandSwitzerland

Czech Republic

Italy

Portugal

UkraineDenmarkJapan

Republic of Korea

United Kingdom

Estonia

LatviaRomania

United States

AngolaDominican Republic

Laos

Qatar

Argentina

EcuadorLesothoRwandaArmenia

Egypt

Macau

Saudi Arabia

BahrainEswatini

Malaysia

SenegalBarbadosFiji

Mauritania

Sierra Leone

Benin

Gabon

Mauritius

Singapore

Bolivia

GuatemalaMexicoSouth Africa

Bostwana

Honduras

Moldova

Sri Lanka

BrazilHong Kong

Mongolia

Sudan

Burkina Faso

India

MoroccoTaiwanBurundiIndonesia

Mozambique

Tanzania

Cameroon

Iran

Namibia

ThailandCentral African

Iraq

Nicaragua

Togo

FinlandDeveloping Countries:

42

Republic

Jamaica

Niger

Trinidad and Tobago

Chile

JordanNigeria

Tunisia

China

Kazakhstan

PanamaTurkeyColombia

Kenya

Paraguay

Uruguay

Costa Rica

KuwaitPeru

Venezuela

Cote D'Ivoire

Kyrgyzstan

Philippines

Zimbabwe

43

Appendix 2: Indicator Variables and Sources of Data for Components of the PCIEnergyShare of people with access to electricity
Transmission and distribution losses as share of primary supply
Renewable energy consumption as share of total final energy consumption
GDP per kg of oil consumption
Total primary energy supply per capita
Total energy consumption per capita
Institutions
Control of corruption
Government effectiveness
Political stability and absence of violence/ terrorism
Regulatory quality
Rule of law
Voice and accountability
Natural Capital
Agricultural land as share of land area
Forest area as share of land area
Share of all extraction flows in GDP
Material intensity (total extraction flows over industrial value added)
Total natural resources rent as share of GDP
Private sector
Domestic credit to private sector as share of GDP
Cost to export a container
Time to export (days)
Cost to import a container
Time to import (days)

44

Enforcing of contracts (days)
Starting a business (days)
Trademark applications
Patent applications
Structural change
Export concentration index
Economic complexity index
Gross fixed capital formation as share of GDP
Industrial ratio (industry and services over total GDP)
Transport
Air transport, registered carrier departures worldwide per 100 people
Air transport, freight (million ton-km)
Air passengers per capita
Logarithm of km of roads/100km2 land
Logarithm of total km of rail lines per capita
Source: United Nations Conference on Trade and Development (UNCTAD), UNCTAD Productive Capacities
Index

45

Appendix 3: Members of the Technical Advisory Committee
Catherine Beaudry
Professor of Engineering
Ecole Polytechnique
Université de Montréal
Montreal, Quebec
Jonathan Barr
Senior Director
Innovation, Science and Economic Development
Ottawa, Ontario
Margaret Dalziel
Associate Professor
Conrad School of Entrepreneurship and Business
Vice-President, Evidence Network
University of Waterloo
Waterloo, Ontario
Harald Edquist
Senior Researcher
Ericsson
Stockholm, Sweden
Dennis Fixler
Chief Economist
US Bureau of Economic Analysis
Washington, DC
Kevin Fox
Professor of Economics
University of New South Wales
Sydney, Australia
Arthur Grimes
Senior Fellow
Motu InstituteWellington, New Zealand
Wulong Gu
Senior Advisor
Statistics Canada
Ottawa, Ontario

46

Thomas Niebel
Senior Researcher
Research Department Digital Economy
ZEW- Leibniz Centre for European Economic Research
Mannheim, Germany
Nelson Paterson
Director General
Economic Studies and Policy Analysis
Finance Canada
Ottawa, Ontario
Dirk Pilat
Former Deputy Director
Science and Technology
OECD
Paris, France
Bart van Ark
Managing Director
The Productivity Institute
University of Manchester
Manchester, United Kingdom
Leonard Waverman
Professor, Business Economics and FinanceSpecial Advisor to the Provost and VP Research
DeGroote School of Business. McMaster University
Hamilton, Ontario
Dany BrouilletteInnovation, Science and Economic Development
Ottawa, Ontario

47