Investigating the Factors of Growth Within the Commonwealth of Nations: An Empirical Analysis

8/17/2019 Investigating the Factors of Growth Within the Commonwealth of Nations: An Empirical Analysis

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Investigating The Factors Of Growth Within The

Commonwealth Of Nations: An Empirical Analysis

TAYLOR, Walter Terence David

Lancaster University Management School


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Investigating The Factors Of Growth Within The Commonwealth Of Nations: An Empirical Analysis

Lancaster University Management School Page | 1

Introduction

During recent times there has been debate on whether the United Kingdom should alter their focus from

the European Union and start trade with Commonwealth countries. Some within this country have been

sceptical of the benefits of EU membership and for whatever reason feel the economic benefits areoutweighed by the loss of sovereignty. Some have called for a return to commonwealth preference trade

(Lea, 2012) or even a commonwealth union but until now this has been pure fantasy as the

commonwealth was nowhere near the EU in terms of GDP. Slowly but surely we are seeing the

commonwealth catch up to the EU, especially during the financial crisis in Europe, but there must be

other factors for this growth. In this piece we will investigate the factors that affect growth within the

commonwealth, measuring how its GDP growth is consistently high and the variables that meant the

Commonwealth percentage of world Gross Domestic Product (GDP) exceeded that of European Union

for the first time last year (Northcott, 2012).

The graphs above (Waterson, 2012) show GDP growth from 1970 to the present day with the

commonwealth growing on average at 5% a year compared to 2.5% for the European Union.


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Literature review

To investigate the growth of the commonwealth, we must investigate other empirical studies of growth in

order to help form our hypothesis. The topic of growth within the commonwealth, whilst being a

common debate within Britain, doesn’t have many empirical studies so we will have to look at a broaderrange of material to determine factors of growth. As this is a cross sectional analysis of 54 different

countries, we must not get the subject confused with time series analysis, we are looking for factors of

growth within the given years of 1990 and 2010 as two separate analyses.

One empirical study which is related is “Economic Growth In A Cross Section Of Countries” by Barrow

(1991) where the GDP per capita growth was inversely linked to the government consumption of GDP

in percentage terms. He also noted that Political stability being key to growth, with instability leading to

market distortions.

Cer vellati & Sunde (2011) is an interesting study titled “Life Expectancy And Economic Growth: The

Role Of The Demographic Transition” where they speculate that high life expectancy is associated with

high income per capita. They did this by accounting for demographic transition using variables such as

life expectancy, population growth and a measure of individuals education against a countries GDP.

Whilst there results were inconclusive, another study called “Death And Development” (Lorentzen et al.,

2008) goes further to “exploit exogenous variation in morality across countries” and finds that increased

life expectancies casually lead to faster economic growth.

“Sources Of Growth In African Economies” by Sachs and Warner (1997) investigates into sub-Saharan

Africa to determine why GDP growth is so low compared to other economic regions. This piece is useful

as a significant proportion of our variable will come from African countries. They look at physical

features of a country as well as political factors, noting that countries near the sea or with a proportion of

water around them have grown quicker than other African countries in the last thirty years. They also

make an interesting albeit obvious point that countries with liberal economies seem to grow at a quicker

rate, this is interesting as there is evidence that Foreign Direct Investment normally increases a countries

growth rate, as we discussed earlier.

Robert Lucas (1988) discussed “On The Mechanics Of Economic Development” and looked into how

the accumulation of human capital lead to a rise in per capita growth. He stated that schooling could

have a positive effect on growth, which whilst being theoretically correct is difficult to prove using

statistical analysis. He also studied the advancement of technology and noted the “rapid physical capital

growth” associated with countries which used advanced equipment.

Fernandez et al. (2001) stated in “Model Uncertainty In Cross-Country Growth Regressions” that the

variables used in a regression analysis can be anything from religion to blackmail, with the need for

numeric data only part of an analysis. They used this wider scope to form a Bayesian model which charted


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the growth of former European colonial countries, with results that showed variables such as military

revolution and coups, religion and blackmail as statistically significant. Whilst there model may have been

successful in some respects, the amount of regressions preformed was into the million mark, and the

weightings of these variables are unknown.


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Data Acquisition

To retrieve our data we have used the World Bank database which has a large selection of variables which

could prove useful in this study. The world bank is one of the only data collectors who translate

information in terms of percentages instead of monetary dollar values, this was the primary advantageover the International Monetary Fund (IMF). Whilst monetary values are good for determining GDP

factors in other empirical studies, we are looking at growth and don’t want inflation or deprecation of

currencies to undermine our results. By excluding monetary values and determining growth on GDP

percentage changes, we will be able to chart the change in variables from different time periods in our

cross sectional analysis.

When selecting the commonwealth countries for our dataset we encountered two problems, one being

that both the World Bank and IMF databases did not have any data for the country of Nauru, which is

the smallest nation on earth covering only eight square miles. As this nation is only small with less than

10000 inhabitants, we have omitted this country from the study as it will have minimal significance on our

final results. The second problem encountered was the nation of Fiji, which is currently suspended from

the commonwealth because of a military coup (Campbell, 2006). A country under military dictatorship is

very unlikely to join a commonwealth union and its data may also be unreliable because of the widespread

corruption within the country, thus Fiji has also been omitted. The fifty-two remaining countries included

are listed in the table below.

Commonwealth Countries Included In Dataset Antigua and Barbuda Kenya Singapore

Australia Kiribati Solomon Islands

Bahamas, The Lesotho South Africa

Bangladesh Malawi Sri Lanka

Barbados Malaysia St. Kitts and Nevis

Belize Maldives St. Lucia

Botswana Malta St. Vincent and the Grenadines

Brunei Darussalam Mauritius Swaziland

Cameroon Mozambique Tanzania

Canada Namibia Tonga

Cyprus New Zealand Trinidad and Tobago

Dominica Nigeria Tuvalu

Gambia, The Pakistan United Kingdom

Ghana Papua New Guinea Uganda

Grenada Rwanda Vanuatu

Guyana Samoa Zambia

India Seychelles Jamaica Sierra Leone


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The data variables that I have used below are from the world bank with the only exceptions being the

“corruption index ratings” and the “near water” variables. The corruption index ratings were found using

the corruption perception index at the Transparency International website. This data was taken for 2010

and charts the perceived corruption within a government from 1 being the most corrupt and 10 being the

least amount of corruption. The data for the near water variable collected by myself using Google world

maps. This was only a measure to see if countries were near a considerable amount of water (calculated by

having 15% of countries border with water.

Variable Description

GDPgrow Growth of GDP in percentage terms for 2010.

Pop Population of each country for 2010.

FDI FDI into chosen country as percentage of GDP.

GDPcapgrow Growth of GDP per capita in percentage terms for2010.

CorrupInd Corruption index taken from Transparency

International. Scale from 1 to 10 with 1= Most

corrupt and 10= Least corrupt.

DevCoun Whether the chosen country is developed is decided

by being an high income country from the world

bank (data for 2010).

LogPop Log of population for 2010.NrWater Whether a country has 15% of its land near water.

Lifeexp Life expectancy in years for 2010.

spendeduc Public Spending as a percentage of government

expenditure for 2010.

Healthexpen Health expenditure as a percentage of government

expenditure for 2010.

One of the first things i need to detect is whether my model shows signs of multicollinearity.

Multicollinearity is where two or more variables are highly correlated with each other, so in theory we

could be using the same information twice unknowingly with the model (Pindyck and Rubinfeld, 1998).

This can be problematic as we need to obtain least square estimates later in our analysis and even thought

we could still obtain these values with multicollinearity, they would prove to be statistically insignificant as

there is little or no variance in the variable used.

Variance inflation factor (VIF) asses the severity of multicollinearity in our OLS Regression (Koop, 2005).

This is calculated using Stata and we will be looking for a number greater than the formula of:


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We will also have to check the mean VIF to discount serious multicollinearity, this value

has to be smaller than 5.

A key element to look out for in this piece of cross sectional data is Heteroskedasticity. This could be a

key issue for our research because heteroskedasticity normally indicates that while the smaller values of

the model may be correct (those at the beginning of the scatter plot) as the value of Y (GDP Growth Per

Capita) increases, the accuracy of the plot is becoming weaker as the constant variance of the coefficients

cause OLS to calculate inaccurate estimates of standard error of coefficients (Studenmund, 2010, P99).

This means that while our model is performing well at generating coefficients for smaller GDP growth, it

would be experiencing large problems for those with proportionately larger GDP growth.

To address this problem before I run my regression, I have chosen to use natural logarithms to reduce the

difference between lower and higher population bands. This would make my data easier to analyse whilst

also clearing some possibility of heteroskedasticity. Another way we may choose to approach this is by

using Weighted Least Squares (WLS) as opposed to Ordinary Least Squares (OLS) which we are already

using. By using Weighted Least squares method, it will take into account non-constant variance, meaning

that all of the residuals will be given an equal weighting which as all the variables will be multiplied by a

particular number of weights (Xiohong & Yanqin2004).


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Y(GDPCapGro) = β0 + β1(Ln(Pop)i) - β2(CorrupIndi) + β3(FDIi) + β4(NrWateri) +

β5(LifeExpi) + β6(HealthExpeni) – β7(DevCouni) + β8(SpendEdui) + εi

H0: β1 ≤ 0 HA: β1 > 0 H0: β2 ≥ 0 HA: β2 < 0 H0: β3 ≤ 0 HA: β3 > 0 H0: β4 ≤ 0 HA: β4 > 0

H0: β5 ≤ 0 HA: β5 > 0 H0: β6 ≤ 0 HA: β6 > 0 H0: β7 ≥ 0 HA: β7 < 0 β8 ≤ 0 HA: β8 > 0

Motivation for variables

Above is the model we will use for this empirical study along with the null hypotheses of the model. I

have chosen these variables based on the research done in my literature review. The first variable

“GDPgrow” is the growth of GDP in commonwealth countries. Whilst this is a good indicator of

growth, results can be skewed by the large difference in population sizes of different countries. This is

why the variable “GDPCapGrow” is being used, with Van Den Bergh (2009) stating that it’s a fairer

comparison of countries GDP although there is a point where minute populations start to harm GDP per

capita too. This is true for countries as large countries tend to produce more manufactured goods

whereas smaller islands generate GDP through agriculture or tourism. This won’t matter in our study as

we are analysing growth throughout the commonwealth, although there is a fear that variables of smaller

countries may suffer a “crowding out” effect.

“Pop” is the variable for population which will help inform us whether a larger population has a positive

effect on GDP per capita growth. Referring to the previous section where we mention heteroskedasticity,

Log of population or “LogPop” will be used to give smaller values of populations, removing thedominance of this variable over others within the model whilst still having accurate data.

Foreign Direct Investment is an important variable as it indicates trust within a country as well as possible

incentives for growth. “FDI” means that either the population or infrastructure of a country is growing

(Berensztein et al., 1998) with a positive effect expect for our model. The corruption index is also an

interesting variable, with the idea developed from the study by Fernandez et al. (2001). There work

inspired me to implement the corruption data, although the accuracy of this data could be contested as a

matter of opinion, it does make the results interesting. We would expect “CorrupInd” to have a severe

negative impact on growth per capita but must remind ourselves that corruption is not widespread within

commonwealth countries.

“DevCoun” is a dummy variable for whether a country is considered “developed” by World Bank

standards. These high income countries are expected to experience less economic growth due to both the

financial crisis and the saturation of markets where as developing countries are expected to grow at a

quicker rate, catching up to developed countries. We will also have another dummy variable for near

water “NrWater”. This is another idea from my literature review as the African case study showed that

nations with water grew quicker economically.


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Life Expectancy “LifeExp” is another variable which we expect to have a positive effect on growth

although the effects of this variable along with educational spending “SpendEduc” may not effect growth

this year much (Levine & Renelt, 1992), but will have lasting effects in the future. Health Expenditure is

another variable which could be misleading as we expect a government that spends more as a percentage

of GDP on health care to have greater rates of growth, although “HealthExpen” could also be hard to

measure (Quah, 1993).

There were other variables I wished to use within my model such as Trade deficits, exports and foreign

direct aid but the data wasn’t available via World Bank, IMF or other resources. Trade Deficit data was

available for developed countries but I decided against implementing it in our study because there would

only be 10 countries in our data set.


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Empirical Analysis

To conduct this empirical analysis we will be using Stata software which will allow us to analyse our

datasets quickly, automatically calculating variables with great accuracy.

One of the first problems I encountered was with the spending on education variable, this was due to the

lack of information for most countries which left us with only 12 observations compared to the full 52

observations for all other variables. This meant that I had to drop this variable from my model. When we

run the regression for our model, we find that the r-squared was 0.4195 or 42%, meaning 42 % of all

squared deviations from the mean can be explained by this model. This is a little disappointing for cross-

sectional data as a good percentage is normally 80% + but because of our variables been loosely related,

it’s difficult to find accuracy.

We also find that only two of our variables are greater than 1.96, meaning that the dummy variables of

near water and developed country are statistically significant. One of the surprises here is that being near

to water seems to have a negative impact on GDP growth per capita, which undermines our research that

countries near the sea grew quicker.

The coefficients also show that LogPop was 0.8231, which means that population had an 83% effect on

the model, proving our theory that a greater population will help to some extent for GDP per capita.

Foreign direct investment also had some effect with a beta score of 0.2382 or 22% effect on growth per

capita. This number is not as high as we expected but does show that foreign direct investment can

induce growth per capita.

We adjust our model to take out life expectancy, this is because the life expectancy variable, like an

educational variable is one that does not have a serious impact on cross-sectional data, with the variables


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needing to be lagged throughout a time series analysis, this is unfortunate but was considered in the data

review.

We then ran the regression to find VIF scores, indicating if there was any multicollinearity within the

model. As you can see there is little multicollinearity between the variables within our model because all

the values are all less than 2. All countries have a degree of correlation between each independent

variable, but it is not until the VIF value approaches 5 that action should be taken.

We also ran a test for heteroskedasticity which found constant variance, thus not violating the 5th classical

assumption.

Below are graph of key variables to our study, the first being GDP Growth in relation to FDI. Our model

gave us a coefficient of 0.2382 when we first regressed, and this graph show the slight relation between


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the independent and dependent variables. Obviously this line of best fit only has a narrow tilt, indicating

that the relation between the two variables isn’t that strong.

Life expectancy seems to have a negative impact on per capita growth, this could be put down to the cost

of increased life expectancy, with more older residents needing welfare support whilst not contributing

statistically to GDP growth.

Our final graph shows the impact of corruption

compared to growth rates with growth rates higher

in those countries which are more corrupt. This is

shocking as all evidence should point towards less

corrupt economies growing faster. I have two

hypothesis for this, the first being that developed

countries are less corrupted and at the same time

have less excess capacity to grow. The second

theory is that the data itself suffers an element of

corruption because of the corrupt states, thus the results are inconclusive.

Finally we ran the same regression based on data found from 1990, with this model only have an R-

squared value of 11.85 or 12% accuracy. Whilst this may be unreliable in some respects, the coefficients

show that both Near Water and Developed Country variables are statistically significant at 95%

confidence interval. The values of these two variables are higher than those of the 2010 analysis, possibly

showing that the difference between developed and developing countries is getting smaller.


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One thing that is surprising for the 1990 dataset is that population has a negative effect on GDP per

capita growth, whereas in 2010 it is positive. FDI also seemed to have more of an effect in 1990

compared to today, although this data may not be the most accurate.


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Conclusions

In summary we found only two variables that were statistically significant, those of “near water” and

“developed country”. Both had negative impacts on our GDP per capita growth model, with the near

water dummy variable being expected to have a positive impact on growth from our literature review. We

also found that the other variables such as population log and FDI as a percentage of GDP had some

positive impact on growth per capita, but not enough to be considered statistically significant. The model

itself was disappointing with only an r-squared of 0.4195 which doesn’t represent the greatest of models,

although it was far more accurate than our 1990 model, which either indicates that statistics are improving

or that factors of growth between countries are less loosely correlated than before.

The regret with this model w as that I couldn’t use numeric values as much as I would like because of my

insistence of percentages from the start to discount for any exchange risk or other factors such as

inflation. It is also equally disappointing that data for certain variables such as education or amount of

foreign aid weren’t available for all commonwealth countries and thus were omitted from the final model.

This empirical analysis shows how the commonwealth is growing, with factors such as sea meaning less,

possibly due to air travel as an increasing mode of transportation for exports. Whilst my data may say

otherwise, the sheer population of the commonwealth and the amount of FDI should be significant, with

the increasing abundance of human capital also playing a key role in consistent growth


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Investigating the Factors of Growth Within the Commonwealth of Nations: An Empirical Analysis