1

Chapter 1

Introduction

Time and again, economic growth has been considered as one of the surest signs of a country’s

overall health. As per definition economic growth is a long term expansion of the productive

potential of an economy. While economists, government and individual may all have their own

opinions about what should constitute economic growth, the truth is all of these things working

together help create an overall healthy economy. Without one piece, the entire puzzle may fall

apart.

Unfolding the economy of Mauritius, it can be seen that a lot of development took place for the

past decades. Indeed, the island had witnessed various changes when it comes to the economic

activities. From a mono-crop economy depending mainly on sugar, Mauritius has been

diversified in such a way that it is known worldwide for the services it provides. Indeed,

Mauritius observed an economic growth of -10.1% in the year 1980 while in 2009 the growth

was estimated at 3.1%. This rise in the Gross Domestic Product (GDP) of the country can be

explained by a number of factors.

Some factors such as changes in consumer and business confidence, aggregate demand

conditions and monetary and fiscal policy, tend to have a mainly temporary effect on growth.

Other factors such as unemployment rate and productivity growth have more enduring effects

and help to determine the economy’s average growth rate over a longer period of time.

Using time series data for the Mauritian economy for the period 1976-2009, the relationship of

the determinants of the economic growth shall be modeled. To reiterate further, factors such as

openness, investment, inflation rate, literacy rate and financial intermediation will be considered.

The data will be tested whether in Mauritius context, the relationship of these determinants

mentioned above is significant. Subsequently nominal GDP per capita was used to represent

economic growth, primary enrollment ratio was used to represent literacy rate, GDFCF was used

to represent investment and CPI to represent inflation.

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Below there shall be an analysis of the effects that each factor has on economic growth and their

respective trends throughout the period 1976-2009. Furthermore, various tests will be carried out

to determine the relationship between the independent and the dependent variable and these

include the Link test, a Regression analysis, a Correlation analysis, test for Multicollinearity, and

finally a test for Serial Correlation will be undertaken.

This introduction will be followed by Chapter 2 where past research will be reviewed. Chapter 3

consists of an overview of the Mauritian economy. The methodology will subsequently be

considered in Chapter 4. Chapter 5 consists of the interpretation and analysis part of the model.

The last steps that will be undertaken are the policy implications of the model and finally the

conclusion in Chapter 6.

3

Chapter 2

Literature Review and Empirical Evidence

Economists have long been interested in the factors which cause different countries to grow at

different rates and achieve different levels of wealth. This issue is especially relevant today. The

1990 World Bank World Development Report highlights the scale of global poverty and the

importance of economic growth in alleviating poverty. The historical record shows a broad range

of outcomes in achieving su3tained economic growth. Some countries have achieved high

incomes while many remain at lower levels. Determining the reasons for these differences

remains an important theoretical and empirical task. We review recent theoretical advances in

growth theory which are potentially relevant to development policymakers and the existing

empirical literature on the determinants of economic growth.

Data limitations complicate generalizing these findings to periods before the twentieth century

and to geographical areas beyond the United States. However, the rise of modern economic

growth over the last few centuries seems to roughly coincide with the rise of mass schooling

throughout the world. The sustained growth in income per capita evidenced in much of the world

over the past two to two and a half centuries is a marked divergence from previous tendencies.

Bloom and others confirm the findings of Tilak (2003) that one possible channel through which

higher education can enhance economic development in poor/ developing countries is through

technological catch-up. In knowledge economy, tertiary education can help economies gain

ground on more technologically advanced societies as graduates are likely to be more aware of

and better able to use new technologies.

Keynesians postulate that the impact of money in an economy depends the ability of money to

influence interest rate, rate of interest to influence demand for investment fund and for

investment fund to influence national income. In line with this, the Harrod-Dommar postulates

that changes in national income depend linearly on change in capital stock. Investment or change

in capital stock if financed out of domestic savings. In summary the Harrod-Dommar growth

model summaries as follows, economic growth will proceed at the rate which society can

mobilize domestic savings resources coupled with the productivity of the investment (Somoye,

4

2002). The creation of a pool of investment fund is the objective of bank financial

intermediation. Banks through credit creation provide a pool of investment funds for borrowers.

But the ability of banks to create credit to a large extent depends on the development of a

nation’s banking system.

Fischer (1993) results indicate that inflation reduces growth by reducing investment and

productivity growth. He further notes that, low inflation and small fiscal deficits are not

necessary for high growth even over long periods; likewise, high inflation is not consistent with

sustained economic growth. Ghosh and Phillips (1998), using large panel dataset, covering IMF

member countries over 1960 to 1996, found that at very low inflation rates (less than 2-3 per

cent) inflation and growth are positively correlated. However, they are negatively correlated at

high level of inflation. Similarly, the empirical results of Nell (2000) suggest that inflation within

the single-digit zone may be beneficial, while inflation in the double-digit zone appears to

impose slower growth.

International trade indicators can be of two categories: price-based and quantity-based indicators.

The most commonly used measure is the degree of openness which is defined by exports plus

imports divided by GDP

Trade Openness = (Exports + Imports) / GDP

Where X is the value of exports, M is the value of the imports, and the GDP is the total Gross

Domestic Product.

Economic growth is defined by the increase of goods and services produced by the country over

time. It is measured as the percentage of the increase of the real Gross Domestic Products (GDP)

(Sullivan & Steven, 2003). The relationship between globalization and economic growth has

been extensively researched and investigated. Dollar (1992) analyzed the relationship between

economic performance and trade openness, while Frankel and Romer (1996) studied the

relationship between growth and actual trade flows. Their results show that openness and trade

flows are significantly related to the economic growth.

5

Chapter 3

Overview of the Economic Growth history in Mauritius

Despite its several weaknesses, Mauritius has transformed itself from a poor mono-crop

economy into one of the most successful economies in Africa in recent decades. Using

purchasing power parity (PPP) data for 44 Sub-Saharan African countries, Arbache and Page

(2008), for example, examined country-level dynamics of long-run growth between 1975 and

2005 and conclude that Mauritius was one of the best performers, both in terms of per capita

growth performance and in terms of low growth volatility.

Real GDP growth averaged more than 5 percent between 1970 and 2009, while GDP per capita

has increased more than tenfold over the same period. In its heyday in the 1970s, the sugar sector

in Mauritius accounted for close to one-third of employment, one-third of export earnings, and

one-quarter of GDP. Through smart negotiations and building on a preexisting relationship with

the United Kingdom, Mauritius succeeded in obtaining preferential treatment from the European

Economic Community (EEC) through the Sugar Protocol of the Lomé Convention in 1975,

under which it received more or less free access for its sugar exports to the EEC.

In 1970, Mauritius passed the Export Processing Zone Act, which provided powerful incentives

to manufacturers to cater for foreign markets. Key components of the new legislation included

protective import duties and quotas for infant industries, suspension of import duties on materials

and equipment for industrial use rebates of import duties on other raw materials and components

for specified industries, duty drawback schemes, and favorable long-term loans. The granting of

duty-free inputs for manufactured exports was a key policy in expanding Mauritius’ export

competitiveness on world markets, while tax incentives provided to the exporting firms helped

subsidize exports.

Though Mauritius benefited from the sugar protocols, the government also recognized early on

the advantages of diversification. As the sugar and EPZ sectors in Mauritius struggled, the

tourism sector has expanded rapidly; according to the Mauritius Chamber of Commerce, tourist

arrivals reached 240,000 in 1988, 400,000 in 2000, and 900,000 in 2008.

6

Efforts at economic diversification have been successful, allowing the country to move from

sugar to textiles to a broader service economy, embracing new sectors, particularly light

manufacturing, offshore banking and financial services, and service-related information and

communication technology (ICT). Development of the information technology sector was

intended to transform Mauritius into a “cyber-island” by creating a high-tech multi-storeyed

tower with strong technological capabilities that provides a home to companies from all over the

world to set up operations, manage data, facilitate e-commerce, and establish call centers.

Though a variety of explanations have been developed to explain Mauritius’ growth

performance, there is no doubt that the country’s focus on international trade has been a critical

element of that performance; open trade policies have been important in sustaining growth.

Simultaneously, Mauritius pursued a very liberal investment regime and used incentives to

attract foreign direct investments (FDI). The island has also developed a plethora of strong

institutions and good governance.

Fiscal policy in Mauritius has focused on ensuring that spending remains linked to the resource

availability. On the other hand, monetary policy has helped anchor economic growth and ensured

competitiveness. Since its creation in 1967, the Bank of Mauritius has been concerned with

ensuring the competitiveness of the country’s export sectors and, secondly, with price stability.

Furthermore, another major reason for Mauritius’ economic success has been its business climate

and incentives for foreign companies to locate therein. Mauritius has no capital controls, a

relatively stable currency, a low flat corporate tax rate of 15 percent, and a large number of

double taxation avoidance agreements; together, these attributes sometimes make Mauritius

more attractive than larger financial sectors for businesses. The World Bank’s Doing Business

2010 ranks Mauritius as the best country in which to do business in Africa. Overall, Mauritius is

ranked 17th out of 183 countries ranked in the 2010 survey.

During and in the aftermath of the biggest exogenous shocks to its economy in recent times—the

phasing out of the Multifibre Arrangement governing textiles, significant reductions in EU sugar

protocol prices, the 2008 food and fuel crisis, and the 2008–09 global financial crisis—

Mauritius’s economy has displayed strong resilience. Morisset, Bastos, and Rojid (2010) find

7

that the country’s resilience to the shocks was a combination of four factors: reforms to sustain

long-term growth, which accelerated in 2006; timely, targeted, and temporary short-term

response to the crisis; institutional arrangements to face the crisis that promoted private sector

collaboration; and strong relationships.

8

Chapter 4

METHODOLOGY

In this assignment, the effect of the above mentioned macroeconomic factors on the economic

growth of Mauritius will be tested. In this context, a very simple economics function has been

adopted as follows:

Economic growth = f (literacy rate, financial intermediation, inflation, trade

openness, GDFCF)

For analytical purposes and to conduct various tests using STATA, we have transformed the

function into an econometric model which can be described as follows:

Lngdp = α + β1lnlit+ β2lnfin + β3lninfl + β4lnopen + β5lngdfcf + ut

Where the dependent variable is economic growth (GDP) and the explanatory variables are the

constant (α), the literacy rate(β1), financial intermediation (β2), inflation (β3), trade openness (β4)

and investment (β 5) and µt, the error term.

I. Model Used: Log-Log

The Log-Log model has been used for its simplicity and ease of analysis. Indeed, it measures the

percentage change of economic growth (Y variable) given a percentage change in the

macroeconomic factors(X variables). The slope coefficients on the other hand assess the

elasticity of economic growth in respect with the other macroeconomic determinants.

The table below illustrates the variables used and how they have been measured.

Variables Proxy How measured

Lngdp Economic growth Nominal GDP per capita

Lnlit Literacy Rate Primary enrolment ratio

Lngdfcf Investment Gross domestic fixed capital

formation ( as a % of GDP)

LnOpen Openness Openness Index

Lnfin Financial intermediation Financial intermediation (in

RsM) as a % GDP

Lninfl Inflation CPI as a % GDP

9

The primary data has been obtained from the Central Statistics Office annual report for period’s

1976 to2009 and IMF. Based on the data collected, the relationship which exists between the

values of the aforesaid macroeconomic variables and the economic growth of Mauritius will be

determined by using regression analysis. The result will enable in validating one of these

hypothesis:

H0: Null hypothesis – the values of each macroeconomic factor is not related to the economic

growth of Mauritius.

H1: Alternate hypothesis- the values of each macroeconomic factor is related at any point to the

economic growth of Mauritius.

ASSUMPTIONS:

» Over the periods all Government expenditure that inject into the economy are held

constant.

» Activities of other financial institution in the economy are held constant.

» Monetary/legal policies over these periods are uniform.

II. Testing for stationarity

To prevent spurious regression, it is vital to test for stationarity. The economic variables are

initially expected to be non-stationary- I(1). The latter have therefore been subject to the

Augmented Dickey-Fuller (ADF) test, after determining the optimal number of lags that should

be used.

Ho: variable is non-stationary

H1: variable is stationary

The null hypothesis about the unit root is rejected if the variable is I(0) and will not be rejected if

it is assumed to be non-stationary[I(1)].

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III. Testing for stationarity after first differences

If in step 1, the data is I(1), meaning that if there is indication of non-stationary series, to avoid

spurious regression, we have to transform the non-stationary series to make them stationary. In

such cases, ADF test has to be run on the first difference of the original time series. By so doing,

the null hypothesis will be rejected and hence can be concluded that the data becomes I (0).

IV. Establishing the long run relationship

In this stage, the cointegrating regression is estimated using the Ordinary least squares (OLS)

approach, and generate the long run relationship as follows:

Lngdp = α + β1lnlit+ β2lnfin + β3lninfl + β4lnopen + β5lngdfcf + ut

V. Testing for stationarity for the unit roots of the residuals

The regression residuals are then saved and an ADF test has been carried out on them. The

residuals are expected to be stationary (I (O)) to conclude that the variables are cointegrated.

VI. Error Correction Model

If the variables are cointegrated, then we use the residuals obtained in step 1 to build up the error

correction model. This mechanism corrects for disequilibrium and will create the following

short run equation:

∆ gdpt = β1∆lnlitt + β2∆linfint + β3∆lninflt + β4∆lnopent+ β5∆lngdfcft + α4ût-1 + vt

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Chapter 5

Data Presentation, Regression Analysis and Interpretation

5.1 Analysis of Data

A. Literacy Rate

In economics, the literacy rate is the proportion of the population over age fifteen that can read

and write.

The highest value of public spending on education over the past 29 years was 5.84 in 1980, thus

explaining the increase in enrollment for primary education in the following three years. The

government has made an effort to provide adequate funding for education, occasionally straining

tight budgets. In 1991-92, reflecting the trend of earlier budgets, the government allocated 13

percent for education, culture and art. Nonetheless, facilities in rural areas tended to be less

adequate than those in Port Louis and other cities. Literacy in 1990 for the population over

fifteen years of age on the island of Mauritius was 80 percent overall, 85 percent for males, and

75 percent for females. The year 1993 marked the advent of the Basic compulsory education law

which caused a drastic fall in child labour and an increase in the enrollment rate in secondary

education.

According to the new educational reform, introduced by the new government who came into

power in 2000, and the regionalisation, four educational zones were created. This new

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development also had a positive impact as children who previously could not attend school due

to traveling expenses would now be able to as their assigned school would be closer to them.

Due to the competition and the ranking system at CPE there was a substantial amount of failures

which resulted in the fact that 50% were excluded from secondary education some ten years ago

and consequently when the ranking system was abolished in 2002, there was a positive impact on

the enrollment rate in secondary education. In 2005, there was the amendment of the Education

Act which stated that education was compulsory up until the age of 16. This further caused a fall

in the dropout rate and an increase in enrollment both primary and secondary education

B. Financial Intermediation

Over the past few years, Mauritius has carved out a comfortable niche in the world of

international financial services due to its expanding tax treaty network, a reputable offshore

jurisdiction, its strategic location between the Far East and the European time zones, and its

membership of major regional trading blocs such as the SADC and the COMESA. Financial

Intermediation, led by offshore banking, is the fourth pillar of the economy.

The sector is expected to grow at an annual average rate exceeding 5% over the years to come. It

currently consists of a number of key components including banking, global business, insurance

& re insurance, capital markets, international legal services and other non-banking financial

intermediaries. The Stock Exchange of Mauritius (SEM) was set up in 1989 and is today

renowned as one of the leading exchanges in Africa. Since 1989 annualised total return of 17.3

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% in USD terms supported by compelling incentives in the form of no withholding taxes on

dividends and no taxes on capital gains, have attracted strong foreign investment inflows on

many listed companies.

In the early 1990s, the then newly launched global business sector, supported by a growing

banking sector and a profitable Stock Exchange, gave a strong boost to the Mauritius

International Financial Centre (MIFC). The official launch of the Mauritius offshore centre was

carried out in 1992. The new millennium brought in a plethora of laws such as the Trust Act

2001, Financial Intelligence Act and AML Act 2002 which will help in preventing money

laundering activities. This subsequently led to the sector’s growth by 14.6 per cent in 2000 and

13.5 per cent in 2001. The OECD classified Mauritius in 2009 as a jurisdiction which has

implemented internationally agreed tax and transparency standards. Investors’ confidence and

trust in Maritius as an international financial center has increased, thus allowing for more use of

the platform by the international business community. However in that same year, the rate of real

growth for the financial intermediation sector has slowed sharply in 2009 due to the global

economic downturn In 2010, the Mauritius Financial Services Commission had registered more

than 25 000 global business companies and around 750 global funds.

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C. Inflation

Inflation is a condition, when cost of services coupled with goods rise and the entire economy

seems to go haywire. Inflation has never done good to the economy. Inflation can lead to

uncertainty about the future profitability of investment projects.

The second oil crisis in 1979 which led to 100% increase in the prices of petroleum products, hit

the world and Mauritius and unfortunately there was no corresponding increase in sugar prices to

cushion the damaging economic and financial effects of this second oil price shock. Furthermore

the rate of inflation which was 42% in 1980 was mainly due to the first rupee devaluation

coupled with the after effects of the 1979 rise in petroleum prices and subsequently fell to 14.5%

in 1981 and 5.6% in 1983. In 1983, a new sales tax at the rate of 5% was introduced for the first

time in the economy and this explains why the inflation rate rose from 5.6% to 7.3% as

Mauritians had to pay a higher price to buy the same goods and they may even have been

hoarding certain staple goods just before the implementation of the sales tax thus causing a more

rapid rise in prices.

In 1991, nearly 300,000 tourists visited the island, creating in its wake much employment and

bringing into the country substantial foreign exchange as gross earnings reached the level of Rs.4

billion. With the advent of tight labour markets, inflation had again made its appearance on the

economic scene, reaching double-digit figures in 1989 and 1990 although it had been brought

down to 7% in 1991. The introduction of the VAT in 1998 explains the slight increase in the

inflation rate. The increase in the VAT in 2001 led to a subsequent rise in energy prices and

15

utilities and thus causing a rise in the inflation rate. In the wake of the mid-2006 liberalization,

major price increases were observed for a large number of goods including food, soft drinks,

cigarettes, and alcoholic beverages. The price of government imported flour and kerosene almost

doubled, while that of items such as bread, rice, diesel oil, and gasoline rose by around 50

percent. The advent of the 2008 economic meltdown did nothing to improve the situation with

the inflation rate continuing to increase.

D. Trade Openness Ratio

Using time series data for the Mauritian economy from 1976 to 2009, we have noticed the

complex relationship between the trade openness ratio and economic growth.

An analysis of local trade openness ratio reveals that there has been a significant increase from

97.97 in 1976 to 133.99 in 2009.The trade openness ratio has reduced by 5.99% between 1976

and 1979. This is due to the Mauritian economic deterioration in the late 1970s. Petroleum prices

rose, the sugar boom ended, and the balance of payments deficit steadily rose as imports

outpaced exports. There has been a rise of 33.46% in trade openness ratio between 1980 and

1990. In 1986 Mauritius had its first trade surplus in twelve years. This good performance can be

explained by the elimination of the quantitative restrictions in Mauritius. Price controls were

lifted on most items and the export tax on sugar was reduced. The early 1990s witnessed a

removal of import licensing for the majority of products and a liberalization of the credit policy.

Mauritian trade policies have grown extensively less restrictive and less discriminatory. The

16

trade openness ratio was also affected by Europe’s common agricultural policy and the potential

effects on textiles of the General Agreement on Tariffs and Trade (GATT).

Mauritius’ trade openness ratio dropped slightly from an average rate of 1.04 over the 2005-07

periods to 0.93 in 2008. Export growth decelerated marginally in 2008 relative to 2005-07, from

5.9% to 5.4%, but import experienced a steeper decline, going from 6.5% to 2.9%. Exports were

hurt by little growth in the textile sector (a major export sector) owing to reduced consumer

demand in their main EU (primarily the UK) and U.S. markets following the global financial

crisis, and a fall in sugar exports. Tourism, which accounted for 55% of services exports in 2007,

also exhibited weak performance in 2008 as a result of a decline in tourist arrivals due to the

global economic downturn. As illustrated in the graph above, trade openness ratio was at its

highest point, amounting to 168.55 in 2006 followed by a continuous slowdown till 2009.

E. Investment

Economic theory reveals that there is a direct relationship between GDFCF and GDP. This

relationship further stresses the importance of investment to the economy. As shown below, alike

any other component of the economic growth it is subject to violent fluctuations.

An analysis of local GDFCF as a percentage of GDP reveals that there has been a significant

decreased significantly from 30.90 in 1976 to 21.54 in 1984. The ratio has slightly increased by

4.27% from 1976 to 1978 which slumped continuously till 1984. There has then been a sharp

increase of 55.77% between 1987 and 1994. This upward trend can be explained by the growth

of 18% in investment in machinery and equipment. In addition, there was a rise in buildings and

17

construction work. The GDFCF ratio had declined to 22.94% in 2000 and this is partly due to the

fall amounting to 5.8% in investment in the EPZ. This situation coincides to some extent with the

persistent weakness of the euro which has compounded the existing financial duress of less

performing firms already weakened by rising unit labor costs. Although the subdued investment

level can partly be attributable to some exogenous factors, it is equally true that business

confidence has not as yet fully recovered. . As for 2007, the GDFCF ratio amounting to 25.10 is

largely attributable to the investment by the government, particularly in the education sector

5.2 Summary of Data

Variable Obs Mean Std.Dev. Min Max year 34 1992.5 9.958246 1976 2009

lngdp 34 10.61824 1.147958 8.557526 12.29246

lnlit 34 4.656358 .0368989 4.589581 4.744133

lnfin 34 1.842316 .4202326 1.267164 2.575689

lninfl 34 .9407482 .4135601 .3179288 1.566017

lnopen 34 4.855539 .1864807 4.522837 5.127262

lngdfcf 34 3.791176 .5500841 2.82 4.92

The above represents data for 34 consecutive years starting from 1976 to 2009. For each of the

variables the following can be observed:

DEPENDENT VARIABLE

Economic Growth ( GDP)

Over the last 34 years it has been observed that the lowest value for economic growth

was registered in 1980 and the highest value was in 2009. The mean value was found to

be 10.61824 and it has been observed that there was a diversion of +1.147958 from the

mean value.

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Independent variable

Literacy Rate

Over the period of observation it has been seen that the lowest value for literacy rate was

registered in 2007 and 2008 and the highest value was in 1982. The mean value was

found to be 4.656358 and it has been observed that there was a diversion of +0.0368989

from the mean value.

Financial intermediation

Over the period of observation it has been seen that the lowest value for financial

intermediation was registered in 1976 and the highest value was in 2009. The mean value

was found to be 1.842316 and it has been observed that there was a diversion of

+0.4202326 from the mean value.

Inflation

Over the period of observation it has been seen that the lowest value for inflation was

registered in 2008 and the highest value was in 1981. The mean value was found to be

0.9407482 and it has been observed that there was a diversion of +0.4135601 from the

mean value.

Openness

From 1976 to 2009 it has been observed that the lowest value for openess was registered

in 1979 and the highest value was in 2006. The mean value was found to be 4.855539

and it has been observed that there was a diversion of +0.1864807 from the mean value.

Investment (Gross domestic fixed capital formation)

From 1976 to 2009 it has been observed that the lowest value for investment was

registered in 1987 and the highest value was in 1978. The mean value was found to be

3.791176 and it has been observed that there was a diversion of +0.5500841 from the

mean value.

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5.3 Tests for Model Specification

SOURCE SS DF MS NUMBER OF OBS = 34

Model 42.6132162 2 21.3066081 F( 2, 31) = 755.35

Residual 0.874432449 31 0.028207498 Prob > F = 0.0000

Total 43.4876487 33 1.31780754 R-squared = 0.9799

Adj R-squared = 0.9786

Root MSE = 0.16795

lngdp Coef. Std. Err. t P> ǀtǀ [95% Conf. Interval]

_hat 1.806359 0.5994913 3.01 0.005 0.5836885 3.02903

_hatsq -0.0382683 0.0284245 -1.35 0.188 -0.0962404 0.0197038

_cons -4.199576 3.1314 -1.34 0.190 -10.58611 2.186957

The linktest can be used to detect a specification error in a sample. The idea behind linktest is

that if the model is properly specified, one should not be able to find any additional predictors

that are statistically significant except by chance. The variable _hat should be a statistically

significant predictor, since it is the predicted value from the model. This will be the case unless

the model is completely misspecified.

On the other hand, the variable _hatsq is insignificant as p-value is greater than 0.05(p-

value=0.188), i.e. the linktest is insignificant. This means that there is no omission of relevant

variable(s) and the function has been correctly specified.

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5.4 Stationarity of variables

Augmented Dickey Fuller (ADF) Test

NON-STATIONARY VARIABLES AFTER FIRST DIFFERENCE

VARIABLES P-VALUE VARIABLES P-VALUE CONCLUSION

gdp 0.0668 gdp 0.00321 I(1)

lit 0.4305 lit 0.05310 I(1)

fin 0.9374 fin 0.0004 I(1)

infl 0.8882 infl 0.0209 I(1)

open 0.4462 open 0.0042 I(1)

gdfcf 0.3672 gdfcf 0.0023 I(1)

The ADF test which was carried out at a 90% confidence level to test for stationarity on the

individual variables mentioned above in the table. It has been assumed that if the p-value is less

than 0.05, then the variables will be considered to be stationary.

Therefore, from the above table, we can see that all the variables were at first non-stationary. The

non-stationary time series was transformed by taking their first difference, we conclude that the

first-differenced time series data are stationary of order 1.

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5.5 Cointegrating Relationship (Long Run Relationship)

Variables Coefficient P value Beta Adjusted R2

97.49 % Lnlit 8.413385 0.000 0.2704321

Lnfin 0.5270708 0.021 0.1929446

Lninfl -2.067108 0.000 -0.7446904 No=34

LnOpen 1.3442661 0.003 0.2181092

Lnfgdfcf 0.3392551 0.000 0.1625659

Lngdp = 8.413385Lnlit +0.5270708Lnfin – 2.067108Lninfl + 1.3444661Lnopen

+ 0.3392551Lngdfcf + ut

The table above is the analysis of variance from the regression viewpoint. It demonstrates a

decomposition of the total sum of squares of the dependent variable. The total variance is

partitioned into the variance which can be explained by the independent variables (Model) and

the variance which is not explained by the independent variables (Residual, sometimes called

Error).

R-squared - R-Squared is the proportion of variance in the dependent variable (lngdp)

which can be explained by the independent variables (trade openness, investment,

literacy rate, inflation, financial intermediation). This is an overall measure of the

strength of association and does not reflect the extent to which any particular

independent variable is associated with the dependent variable. In this case the model

explains 97.87% of the variance in Ln GDP. The R-squared is relatively high since it is

greater than 0.8.

22

P-value

The P value is referred as significance level of 5%. In this case all variables have a significant

impact on LnGDP since they are inferior to 0.05.It must be noted that the size of the P value of t-

statistics says nothing about the size of the effect that variable is having on a dependent variable

- it is possible to have a highly significant result (very small P-value) for a miniscule effect.

95% Confidence Interval

We can be 95% confident that the real, underlying value of the coefficient that we are estimating

falls somewhere in that 95% confidence interval, so if the interval does not contain 0, your P

value will be .05 or less. For instance, none of our variables have a confidence interval

containing 0 and consequently their P value is less than 0.05.

Coefficient

The coefficient tells you how much the dependent variable is expected to increase when that

independent variable increases by one, holding all the other independent variables constant.

The coefficient of literacy rate (β1) is 8.41. This illustrates a direct positive relationship between

the level of economic growth and literacy rate whereby 1% increases in the level of education in

Mauritius will expand the level of economic growth by 8.41%.

The coefficient of financial intermediation (β2) is 0.52. It can be inferred that the link between

financial intermediation and economic growth in Mauritius is a positive one. Thus, 1% rise in the

financial intermediation ratio will result in an increase of 0.52% in economic growth. This can be

backed by the fact that Mauritius is having a blooming financial sector.

The coefficient of inflation (β3) is -2.07. This shows a negative relationship between inflation

rate and economic growth implying any decrease in inflation rate will have a positive impact on

the level of economic growth. This is asserted by various economics theories. For instance,

according to the Philips curve, there is an indirect relationship between inflation and

unemployment. Thus, an increase in rate of inflation will cause a decrease in unemployment

23

resulting to an increase in economic growth. In other words, there exists a direct relationship

between inflation rate and economic growth in accordance to economic theories.

The coefficient of openness (β4) is 1.34. This means that there is a positive relationship between

economic growth and openness. Hence, an increase of 1% in openness, holding other variables

constant will result to a rise of 1.34% in economic growth.

The coefficient of GDFCF (β5) is 0.34. In this case as well there exists a positive relationship

between economic growth and literacy rate. Thus, a rise of 1% in GDFCF will lead to an

increase of 0.34% in economic growth.

5.5 Cointegration (Testing for residuals to be stationary)

Consider the following regression:

Lngdp = 8.413385Lnlit +0.5270708Lnfin – 2.067108Lninfl + 1.3444661Lnopen +

0.3392551Lngdfcf + ut

where Lngdp, Lnlit, Lnfin, Lninfl, Lnopen and Lngdfcf are I(1). We can write the above equation as

follows:

ut = Lngdp – 8.413385Lnlit – 0.5270708Lnfin + 2.067108Lninfl – 1.3444661Lnopen - 0.3392551Lngdfcf ---

---------------- Equation (1)

After saving the estimated residuals from the cointegrating regression, firstly the optimal number

of lags has been determined so as to perform the Augmented Dickey Fuller test in order to test

for stationarity of the residuals.

24

Optimum Number of lags

Optimum Lags for residuals

Sample: 1980-2009 Observation:

30

Lag LL LR df p FPE AIC HQIC SBIC

0 21.2907 0.015138 -1.35271 -1.33777 -1.30601

1 29.9084 17.235 1 0.000 0.009111 -1.86056 -1.83068 -1.76715

2 36.1867 12.557* 1 0.000 0.006412* -2.21245* -2.16762* -2.07233*

3 36.1906 0.00776 1 0.930 0.006858 -2.14604 -2.08627 -1.95921

From the above table, it has been estimated that the number of lag to be used to test stationarity

of the residuals to be zero.

Testing for stationarity of the residuals

ADF test on residuals

Variables p-value Conclusion

R 0.0005 I(0)

After running the unit root test, it has been found that ut is I(0) that is they are stationary.

Therefore, although Lngdp, Lnlit, Lnfin, Lninfl, Lnopen and Lngdfcf are I(1), their linear

combination that is Equation (1) above is I(0), and it can be concluded that there is a

cointegrating relationship between the variables.

25

5.6 Error Correction Model (ECM)

It has just been shown that gdp, lit, lit, fin, infl, open and gdfcf are cointegrated; that is there is a

long term or equilibrium relationship between the variables. In the short run there may be

disequilibrium. Therefore, on e can treat the error term in Equation 1 as the ‘’equilibrium error’’.

This error term can be used in the short run behavior of of gdp to its long run vale. The Error

Correction Model (ECM)[Engel & Granger Approach] corrects for disequilibrium. If variables

are cointegrated, then the relationship between them can be expressed as an ECM. In our model,

this will be represented as follows:

∆ gdpt = β1∆lnlitt + β2∆linfint + β3∆lninflt + β4∆lnopent+ β5∆lngdfcft + α4ût-1 + vt

Where, ∆ denotes the first difference operator

vt is a random error term

Using Stata, we have generated a regression of the above model using,

i. Linear Regression

ii. Prais-Winsten Regression

i. Linear Regression(short run)

ût-1 = gdpt-1 - 0.4902289 lnlitt-1 - 0.018698 lnfint-1 + 0.2152635 lninflt-1 -0.0898744 lnopent-1 - -

0.0162135 lngdfcft-1 ; that is, the one period lagged value of the error term from the

cointegrating regression.

ii. Prais-Winsten Regression-The Durbin-Watson (d) resulted as follows:

Durbin-Watson statistic (original): 1.818886

Durbin-Watson statistic (transformed): 1.89903

Since d is closer to 2 in the second case (transformed), the Prais-Winsten Regression model will

therefore be interpreted for our model.

26

REGRESSION COEFFICIENTS FROM ANOVA TABLE

Variables Coefficients p-value Number of observations = 32

Lnlit 0.4874272 0.050 F(6,26) = 0.56

Lnfin 0.0281949 0.394 Prob > F = 0.0000

Lninfl -0.419384 0.000 R-squared = 0.6253

Lnopen 0.774905 0.176 Adj R-squared = 0.5559

Lngdfcf 0.143098 0.149 Root MSE = 0.02275

ût-1 -0.0632752 0.0072

Therefore the short run equation summarises to:

∆ gdpt = 0.0.4874272 ∆lnlitt + 0.0281949 ∆lnfint - 0.419384 ∆lninflt + 0.774905∆lnopent

+0.143098∆lngdfcft -0.0632752 ût-1 + vt ------ Equation 2

From above, it can be concluded that the model is overall significant given a p-value 0.0032

which is less than 0.1 (10% significance level) and also may be said to be a good fit given an

adjusted R2

of 0.6253 which is relatively high. The model has been regressed using first

differences [I(0) series] so there is no such risk as spurious correlation occurring as a result of

non-stationary series.

Moreover, from the above it can be seen that in the short run there are only 2 variables which

GDP. These are literacy rate and inflation since there p-values are less than 0.1.

Hence, it is concluded that the model does not suffer from any autocorrelation. This means that

the error terms associated with observations of different time periods are not related or are

independent of each other. This also satisfies the assumption of the OLS estimator that E (ut) = 0

and COV (ui uj, xi xj) = 0

27

The graph below shows that the error terms are independent upon time, that is, the absence of

autocorrelation.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Residual, u

Residual, u

28

5.7 Correlation Analysis

Correlation is a measure that determines the degree to which two variables’ movements are

associated. It measures the direction and strength of the linear relationship which exists between

two random variables and can take both positive and negative values. Moreover, the correlation

coefficient can also be zero which indicates no relationship exists between the two variables. A

positive one illustrates perfect direct relationship and a negative one shows an inverse

relationship.

Variable Literacy

Rate

Financial

Intermediation

Inflation Openness Investment

Literacy rate 1 Negative Positive Negative Negative

Financial

Intermediation

Negative 1

Negative Positive Positive

Inflation Positive Negative 1 Negative

Negative

Openness Negative Positive Negative 1

Positive

Investment Negative Positive Negative Positive 1

From the above table, it can be inferred that the correlation coefficient among most of the

variables are negative. This implies that some variables have a perfect inverse relationship with

the others. For instance, as the level of inflation falls, a rise in investment is expected and vice

versa.

On the other hand, certain variables have a positive correlation coefficient, it can be implied that

the variables have a direct relationship and move in the same direction. For example, from the

above analysis, we conclude that as the level of financial intermediation rises, the level of

investment also rises.

29

5.8 Test for Multicollinearity

Interconnectedness of independent variables is an important issue when considering models.

Often variables are so highly correlated with each other that it is difficult to come up with

reliable estimates of their individual regression coefficients and such a problem is referred to as

Multicollinearity. A high correlation proves to be a problem to determine the true relationship

between the independent variables. Thus, the Variance Inflation Factor (VIF) test will be used to

test for the presence of Multicollinearity between explanatory variables.

Long Run Short Run

Variables VIF 1/VIF Variables VIF 1/VIF

lninfl 14.47 0.069105 lninfl 14.3 0.069930

lnfin 8.24 0.121316 lnfin 7.95 0.125786

lnopen 6.10 0.164043 lnopen 5.12 0.195313

lnlit 2.85 0.350433 lnlit 2.05 0.487805

lngdfcf 1.57 0.636186 lngdfcf 1.03 0.970204

Mean VIF 6.646 Mean VIF 6.09

An ideal VIF is to ensure that the problem of Multicollinearity should be less than 10 or a

tolerance of less than 0.1. From the VIF test above, it can be concluded that the independent

variables of the model are not linearly dependent since the VIF is 6.65 which is below 10 in

average. This illustrates that the extent of interconnectedness between variables is to a lesser

extent. This means that the independent variables used are not linearly dependent, neither in the

short run nor in the long run. The model used is therefore reliable.

30

Chapter 6

A. POLICY IMPLICATIONS

It can be seen that all the determinants of economic growth are statistically and economically

significant and as a result it can be seen that they all have certain policy implications which can

be implemented in the context of Mauritius.

The direct positive relationship with economic growth implies that much emphasis must be given

to literacy rate. Due to free education and other facilities Mauritius has made a lot of progress in

the educational sector and as such the government as well as the private sector can contribute to

improve the quality of the labour force so as to make it up to international standards. Investment

in education should take place at all levels, formal and non-formal. Non-formal education is an

important area for further investment. However, basic skills programmes should complement,

rather than replace, funding for formal education.

As the level of financial intermediation increases this causes economic growth to move in the

same direction. Aggressive monetary policy can help in mitigating the negative impact of shocks

to banking sector in line with recent economic disturbances. Monetary and macro prudential

policies should closely co-operate. Moreover, the benefits of macro prudential policy can be

sizeable when an economy is hit by financial shocks. Thus macro prudential policy can be

effective in leaning against financial cycles.

The negative relationship between inflation rate and economic growth will in the long run

hamper the economic prosperity of the island and as such certain remedial measures need to be

adopted. Labour market reforms can be adopted so as to nip the problem at the bud and thus try

to decrease cost push inflationary pressures. Furthermore, fiscal policies may be reinforced such

as an increase in the direct taxes or even a reduction in government spending or in government

borrowing.

The positive relationship between trade openness and economic growth can be further sustained

if Mauritius consolidates new trade agreements with other countries than the EU and the African

countries as these countries have exhausted most of their potential. In addition, there needs to be

more bilateral tax treaties amongst countries to help boost the trade openness ratio.

31

The investment of Mauritius increases with its economic growth. Investment has got to be

carried out strategically so as to make the most of the expenditure. Investment has got to be

carried out in such a way so as to create value. Investment in infrastructure and in the

improvement of the transportation sector is important so as to give Mauritius a certain

competitive edge.

B. Conclusion

The sample data chosen was used to carry out analysis. Multiple regression was constructed to

determine whether the macro economic factors really have an impact on economic growth. After

the regression, it was asserted that the Mauritian economy is representative of economic theories

relating to economic growth and other macroeconomic factors. The H0 that states the values of

each macroeconomic factor are not related to the economy has been rejected as the p-value of all

coefficients of various variables are significant.

Various tests have been performed such as the Test for Model Specification, Test for

Multicollinearity, the Shapiro Wilk Test, test for Heteroscedasticity and Serial Correlation test to

strengthen the above mentioned hypothesis. The model is shown shown below:

Lngdp = α + 8.4134Lnlit+ 0.5271Lnfin -2.0671Lninfl + 1.3427Lnopen + 0.3393Lngdfcf + ut

Thus using STATA, it has been confirmed that the data model opted is relevant. However, it is to

be noted that in reality there are other economic and non economic factors which impact on

economic growth.

32

References:

Book: Gujarati, Basic Econometrics, 4th Edition

Report: Mauritius. Bank of Mauritius- Annual report, 30 June 2011

Journals and Working Papers:

Helen V. Milner and Keiko Kutoba (Winter, 2005). ‘Why the move to Free Trade? Democracy and Trade Policy in the Developing Countries’ International Organization, 59(1): 107-143

Ricardo Hausmann, Lant Pritchett, Dani Rodrik (Dec 2005). ‘Growth Accelerations’ Journal of Economic Growth, 10(4): 303-329

Anupam Basu, Evangelos A. Calamitsis, Dhaneshwar Ghura (Aug 2010). ‘Promoting Growth in Sub-Saharan Africa’ International Monetary Fund

Cesar calderon, Norman Loayza, Klaus Schmidt-Hebbel (October 2005). ‘Does Openness imply greater Exposure’ World Bank Policy Research Working Paper 3733

Ali Zafar (January 2011). ‘Mauritius: An Economic Success Story’

Arvind Subramanian and Devesh Roy (August 2001). ‘Who can Explain the Mauritian Miracle: Meade, Romer, Sachs, or Rodrik?’ International Monetary Fund working Paper

Data source:

International Monetary Fund - http://www.imf.org/external/index.htm

World Bank - http://www.worldbank.org/

Stata -http://data.princeton.edu/stata/Introduction.html

-http://www.stasoft.com

33

Appendix

year lngdp lnlit lnfin Lninfl lnopen lngdfcf

1976 8.557526 4.664148 1.515704 1.469506 4.58464 2.92

1977 8.687454 4.677786 1.360134 1.431145 4.598897 2.99

1978 8.810431 4.704891 1.347698 1.408961 4.524136 3.08

1979 8.993998 4.676577 1.267164 1.302545 4.522837 3.24

1980 9.105285 4.683409 1.416088 1.476237 4.689207 3.45

1981 9.250756 4.725658 1.422839 1.566017 4.584557 3.58

1982 9.376986 4.744133 1.412333 1.565541 4.5685 3.88

1983 9.452616 4.725124 1.425431 1.561248 4.541208 3.93

1984 9.560055 4.670934 1.425238 1.508301 4.624187 4.01

1985 9.697922 4.666276 1.391884 1.450153 4.711192 4.05

1986 9.860409 4.670079 1.371479 1.329691 4.767187 4.92

1987 10.05957 4.639958 1.758632 1.137548 4.88022 4.34

1988 10.22173 4.593254 1.822412 0.990226 4.94323 4.34

1989 10.36254 4.589581 1.928331 0.997931 4.960684 4.17

1990 10.52542 4.638028 1.488278 0.93663 4.977911 4.22

1991 10.64033 4.660238 1.519829 0.942216 4.922571 4.34

1992 10.74215 4.670822 1.63595 0.867994 4.884025 2.82

1993 10.86806 4.677964 1.631442 0.827351 4.901383 2.93

1994 10.95809 4.679216 1.767647 0.827152 4.912597 2.94

1995 11.04408 4.675155 1.864453 0.800375 4.901004 3.28

1996 11.15592 4.673742 1.848337 0.744916 4.976817 3.34

1997 11.24902 4.666709 1.914302 0.727895 4.988568 3.39

1998 11.36442 4.658324 2.025944 0.665038 5.033233 3.63

1999 11.42895 4.650421 2.146015 0.676584 5.039521 3.52

2000 11.5261 4.648338 2.304888 0.63104 4.986422 3.79

2001 11.6092 4.633611 2.235769 0.591004 5.057587 3.88

2002 11.67598 4.631382 2.289584 0.585342 4.985983 3.99

2003 11.76511 4.624568 2.374616 0.545952 4.914929 4.04

2004 11.86595 4.620485 2.346687 0.483447 4.914234 4.19

2005 11.91203 4.621935 2.420246 0.491829 5.051966 4.24

2006 12.01169 4.616816 2.437744 0.44191 5.127262 4.22

2007 12.13878 4.609041 2.447154 0.326734 5.08149 4.31

2008 12.24924 4.612459 2.49882 0.317929 5.032352 4.44

2009 12.29246 4.61512 2.575689 0.359054 4.89778 4.49

34

Commands in STATA

summarise

linktest

tsset year, yearly

varsoc lngdp

dfuller lngdp, lag (1)

dfuller D . lngdp, lag (1)

varsoc lnlit

dfuller lnlit,lag(3)

dfuller D.lnlit, lag(3)

varsoc lnfin

dfuller lnfin, lag(1)

dfuller D.lnfin, lag(1)

varsoc lninfl

dfuller lninfl, lag(1)

dfuller D.lninfl, lag(1)

varsoc lnopen

dfuller lnopen, lag(1)

dfuller D.lnopen, lag(1)

varsoc gdfcf

dfuller lngdfcf, lag(1)

35

dfuller D.lngdfcf, lag(1)

dfuller D.labparticipatingrate, lags (0)

regress lngdp lnlit lnfin lninfl lnopen lngdfcf

predict r, res

varsoc r

dfuller r, lags(2)

regress D.lngdp D.lnlit D.lnfin D.lninfl D.lnopen D.lngdfcf L.r, noconstant

prais D.lngdp D.lnlit D.lnfin D.lninfl D.lnopen D.lngdfcf L.r, noconstant

correlate

vif