2. 2
Table of Contents
I. Introduction ……………………………………………………………………………… 2
II. The Process…………………………………………………………………….………... 3
III. The Results …………………………………………………………………………….. 3
i. ASIA………………………………………………………………………….………. 4
ii. AFRICA……………………………………………………………………………... 5
iii. EUROPE……………………………………………………………………………. 6
iv. NORTH AMERICA………………………………………………………………… 7
v. SOUTH AMERICA…………………………………………………………………. 8
vi. WORLD………………………………………………………………………..…… 9
IV. Conclusion……………………………………………………………………………… 10
V. Bibliography …………………………………………………………………………… 11
I. Introduction
It would seem logical that there is there a causal relationship between the GDP per capita of a
country and its broadband penetration rate. In our world today where access and speed to
information is key, one would expect broadband penetration to have a lot of explanatory power
for GDP - the higher the amount of people connected to the internet would imply a better
infrastructure to support it, an increase of information, trade and commerce. But is this
necessarily true?
As time advances, technology and our ideologies change as well. We begin to think of matters in
a different way as we create innovative tools which create major breakthroughs in the way we do
things. In the contemporary world, technology, specifically information technology, has become
an integrated part of our society. Since the invention of the world wide web, businesses,
governments and people all around the world have become increasingly dependent upon this
revolutionary technology which connects us all throughout the globe. Thinking of this
technology in economic terms, raises the interesting topic of the internet’s role in the economy;
that is to say, how the economic welfare of a country is affected by its level of internet
availability to its citizens, also known as broadband penetration. This paper aims to explore this
relationship by comparing several countries from five continents from around the world.
3. 3
II. The Process
Our model for regressing internet penetration on GDP per capita and explore the relationship
between the two will be: . As
formerly mentioned, the model will be estimating how broadband penetration rates affect GDP
per capita. would be GDP per capita , our explained variable. It is also measured in
international dollars and is constant for all of the countries we used in the analysis. Our
explanatory variables would include , , and which are
broadband penetration rates, population density, balance of payments and unemployment rates
respectively. Besides broadband penetration rate (BPR) these are all the variables which we
considered to have a significant enough impact on GDP that ought to be included in the model
for accurately measuring the relationship between BPR and GDP.
The data to execute this model was gathered from several internet sources. Data was obtained for
each of the separate variables for each country that we considered in our research. The data
includes the GDP per capita of the country, measured in international dollars, and adjusted to
purchasing power parity to give a fairer account in living standards between countries (especially
poorer ones). The broadband penetration rate is measured in the percentage of people with access
to fixed broadband in the country. Generally speaking the number of people with access to the
internet divided by the total population. Another data variable we added was population density
as theoretically it should be cheaper and easier to install broadband infrastructure to a population
that is conglomerated near each other. This can easily be seen in countries such as South Korea
where high rise apartments gave way to easier installation of VDSL exchanges, thus connecting
more people. Population density is measured in the amount of people per square kilometer. And
finally, our last variable is the unemployment rate. We chose the unemployment rate as it is an
easy indicator of the overall health of an economy, something that can easily affect the GDP. The
unemployment rate measured in the percentage of the labor force who are out of work.
It should be noted that there is a difference in the years of when the data was collected which
might affect the results our thesis. This was unavoidable as some data for the indicators were not
updated while some were. The data for GDP per capita (PPP), balance of payments as well as the
broadband penetration rate is measured from 2013. However, the data for the unemployment rate
as well as population density was pulled from 2012. We believe that that the difference in results
from this one year difference would not be that significant as it is not a sustained increase but
only one year. The data for the explanatory variables was assembled and regressed on the data
collected for the respective GDP’s. Regressions were run separately by continent, using data
collected for 10 countries in that specific continent. Afterwards, a regression was run for all
countries put together as a whole; thus using a bigger sample size and obtaining an output for the
world rather than a specific continent
4. 4
III. The Results
ASIA
As seen in our regression, it seems as though the broadband penetration rate and the amount of
people per square kilometer are significant when explaining the GDP in our model of Asia.
According to our model, for every percentage of people who acquire access to fixed broadband,
GDP will increase by 43204.77. For every additional person living in Asia per square kilometer,
GDP will on average increase by about four international dollars. As one of the most populated
places on earth, this is rather surprising that this figure wasn’t that much higher - if we look at
the top 3 countries by GDP and broadband penetration, we can see it’s the most populated
density wise. At the same time, the significance level of population density points out how
important it is to GDP. As citizens live closer to each other, this impacts trade and thus a higher
GDP. Balance of payments and unemployment are not very significant to the model but seem to
suggest a slight inverse relationship in regards to GDP. This could be the result of not having
enough samples to explain this unique continent: besides Cambodia, all have a trade surplus and
low unemployment rate. Broadband penetration seems to account for a very large percentage of
GDP when compared to the other factors however. Our R-squared for the model is pretty high as
well, indicating a strong relationship for our model.
5. 5
AFRICA
Our model suggest that for Africa, none of our variables are significant enough to explain change
in Africa’s GDP at at 5% significance level. The most significant variable in our model is the
unemployment rate. This would suggest that Africa is in need of employing a larger proportion
of workforce to increase its GDP. This is especially true of South Africa with an unemployment
rate of 25%. Furthermore, broadband penetration rates are only the second highest significant
variable but still suggests a highly positive relationship with GDP. Africa probably has many
other factors when compared to other countries in terms of what affects its GDP which is why
our variables are not significant enough when trying to determine Africa’s explained variation of
GDP. This is probably the result of other factors we have not considered at all in our model,
including already existing infrastructure, political stability, education levels, and mortality rate as
well as HIV rates (Botswana and South Africa have one of the highest in the world, with
between 1 in 3 to 1 in 4 infected). These factors may contribute as to why our variables do not
explain as much as desired and Africa probably has many other variables to explain its GDP.
Africa, unlike Asia, has a much lower population density yet it seems to yield the same results
that population density doesn’t have a significant effect on GDP.
6. 6
EUROPE
The output of the regression for the European continent indicates that, besides broadband
penetration, all the variables are insignificant at a 5% level of significance. The most significant
of all the variables, is broadband penetration rate. This suggests that at least in Europe, out of
broadband penetration, population density, balance of payments, and unemployment, broadband
penetration is the factor that accounts for changes in GDP per capita the most; in fact, the output
suggests that it is the only explanatory variable that has any explanatory power on GDP
variation. Broadband penetration rate and GDP have a positive relationship, as hypothesized
formerly, meaning that a higher broadband penetration rate results in a higher GDP per capita.
Each percentage increase in broadband penetration results in an increase of 65225 dollars in
GDP per capita The R-squared for this regression suggests a strong explanatory power of our
model since according to the value, about 93% of the variation in GDP per capita is explained by
our model and, as discussed with regards to the significance of the explanatory variables,
broadband penetration rate is the variable that accounts for most of this explanatory power.
7. 7
NORTH AMERICA
The continent of North America has a very strong relationship with our model, with an R-
squared of 0.9871. Moreover, broadband penetration rates have a P-value of 0, meaning that it is
very significant for our model when explaining what affects GDP. Our regression indicates that a
one percent increase of people who have access to broadband connections in North America
would increase the GDP of that country by 48440.08 international dollars. Another significant
variable is the balance of payments. It seems that if the balance of payments increased, then GDP
would decrease slightly, more so than any of our other continents. This could be the result of the
interesting dynamic that is the United States. Consumption accounts for a large proportion of the
economy of the United States, which in turn accounts for a very high majority of the North
American GDP overall. Assuming a positive balance of trade for the United States means a
slightly lower consumption, ceteris paribus, this could harm the GDP. Population density and
unemployment rates do not seem to be as significant when explaining GDP as compared to
broadband penetration rates and balance of payments. It is noted however, that Canada is a
significant outlier in this data analysis (high GDP per capita, very low population density) and it
may affected the model significantly.
8. 8
SOUTH AMERICA
The continent of South America, like Africa, also has a weak R-squared implying that our model
does not have a high explanatory power; only 68% of the variation in GDP per capita is
explained by our regression model. Our variables are additionally not very significant
considering a 5% level of significance. However once again, out of all the explanatory variables,
broadband penetration rates proved to be the strongest. In this model, broadband penetration
rates again seem to be positively correlated to GDP at a much larger scale when compared to the
other explanatory variables. Here, we see a significant shift in ranking in our graphs whereas the
other continents showed a relatively similar path.; The GDP per capita graph in this model are
not exactly correlated with the broadband penetration graph, for example, besides Brazil and
Paraguay, all the other countries are shifted around more so than the other continents.
9. 9
WORLD
In this regression, we regressed all of our data collected for the explanatory variables on our data
collected for the GDP per capita for each country. Unlike our previous regressions, which were
done per continent and included a sample size limited to 10 countries (with Africa being an
exception at 11), this regression combines all the countries considered in our study, regardless
continent and thus increases the sample size to a total number of observations equal to 51. In this
regression, all the explanatory variables are significant at a 5% significant level, except for
unemployment. This indicates that, other than unemployment, all the explanatory variables
considered in the model have explanatory power on variation in GDP. We saw this because
Africa was the only continent where employment was a larger factor in GDP growth.
Increasing the sample size definitely appears to have improved the model. Both the broadband
penetration model and population density variables are very significant to our model in this
regression. Each percentage increase in broadband penetration results in an increase of 45173
dollars in GDP per capita while each unit increase in population density results in an increase of
about 5 dollars in GDP per capita. Balance of payments is not as significant as broadband
penetration and population density, but it is still significant. The R-squared for the model is also
decent, with a value of 0.8899 indicating that 89% of the variation in GDP per capita is captured
by our model, thus implying that our model is efficient in explaining GDP per capita based on
our explanatory variables.
10. 10
IV. Conclusion
In the final analysis, we can see that broadband penetration has a huge impact on GDP in
countries all around the world. If anything, broadband penetration rates are definitely a positive
force on GDP. Our data suggests that broadband penetration rates explain more variation of the
GDP than factors such as unemployment rate, balance of payments, and perhaps even population
density; all which are conventional determinants of a country’s GDP. Population density seemed
to be the second most influencing variable we had as seen on the Asia model and world model.
When looking at the GDP graphs and broadband penetration graphs side by side, the pattern of
countries being at the top and bottom of the graphs is consistent; the country with the highest
broadband penetration rate turned out to also be the country with the highest GDP per capita.
Thus, we can see that the positive relationship between broadband penetration rates and GDP is
visually eminent. Of course, the regression results varied from country to country. Some showed
broadband penetration as being more significant than others. However in the final regression,
when all countries/observations were combined, the general trend was shown and we concluded
the previously mentioned findings which consequently support the claim which was also
formerly mentioned, of the internet growing not only in popularity but in importance as well.
If we were to do something like this in the future, there would be a few things that we would like
to change. First of all, if it can be avoided, we would like to have all the data be from one year.
As we stated in the introduction, we could not avoid using some data from 2012 and 2013.
Although the changes in between the years for the data is most likely minimal, it would be in our
best interest for all the data to be from the same time period. Furthermore, adding more countries
to our data would improve the accuracy of our model as well. The more countries added, the
more accurate of a picture could be drawn about what affects that countries’ GDP. Our model
not be accurate for a country such as Russia since Russia has a very low population per density
value. Another way to ensure that our model could have been more accommodating for extreme
cases of countries would be to add more relevant variables to our model. Our models for Africa
and South America did not have high R-squared values, nor did they have any significant
variables. Adding other variables could have given us a more accurate representation of what
affects their GDP and how broadband penetration relates to that country's’ GDP. Perhaps
variables such as education, human capital per worker, investment, savings, natural resources,
etc. could all have been included to better represent what a country’s explained variation of
GDP. However, in the end, the relationship between broadband penetration rates and GDP is
obvious and very significant. With this information we can suggest that countries try to increase
their broadband penetration rates. If a country is capable of such things, perhaps having the
government providing a subsidized access to the internet would act as a way to increase the
country’s share of information and eventual output, wealth and GDP.
11. 11
V. Bibliography
"Country Comparison :: Current Account Balance." Central Intelligence Agency. Central
Intelligence Agency, n.d. Web. 06 May 2014.
"Country Comparison :: GDP - per Capita (PPP)." Central Intelligence Agency. Central
Intelligence Agency, n.d. Web. 06 May 2014.
"Population Density (people per Sq. Km of Land Area)." Data. N.p., n.d. Web. 06 May 2014.
"Unemployment, Total (% of Total Labor Force) (modeled ILO Estimate)." Data. N.p., n.d.
Web. 06 May 2014.
"World Internet Users Statistics Usage and World Population Stats." World Internet Users
Statistics Usage and World Population Stats. N.p., n.d. Web. 03 May 2014.
