an be examined over a longer period of time. Secondary data includes information from the census, a company’s financial position and safety records such as their injury rates, or other government statistical information such as the number of workers in different sectors.
In secondary data, information relates to a past period and as such, it lacks aptness and has unsatisfactory value. The drawback is that often the reliability, accuracy and integrity of the data is uncertain. However, it is easier to collect such data and longitudinal study may be possible.
4.3 Model Specification
A simple log-linear Cobb-Douglass production function is used to measure the impact of tourism on economic growth in Mauritius. The equation is as follows:
GDP = f (INV, TRP, CPI, EXP)
Consider the following model, known as an exponential regression model:
GDPt = β0 INVt β1 TRPt β2 CPIt β3 EXPt β4 e εt (4.2.1)
which may be expressed alternatively as
lnGDPt = lnβ0 + β1lnINVt + β2lnTRPt + β3lnCPIt + β4lnEXPt + εt (4.2.2)
where ln is the natural log (i.e log to the base e, and where e = 2.7183)
Equation 4.2.2 can be written as:
lnGDPt = C + β1lnINVt + β2lnTRPt + β3lnCPIt + β4lnEXPt + εt (4.2.3)
where C = lnβ0
Therefore, the transformed model is:
ln GDPt = C + β1 ln INVt + β2 ln TRPt + β3 ln CPIt + β4 ln EXPt + εt
Where ln GDP: Log of real gross domestic product per capita
ln INV : Log of investment
ln TRP : Log of tourism receipts per capita
ln CPI : Log of consumer price index used as a proxy for inflation
ln EXP : Log of exports
C : Constant term
εt : White noise disturbance term
In the above log-linear model, the dependent variable, GDP, is expressed as a linear function of four other independent variables, also known as the explanatory variables, namely INV, TRP, CPI and EXP. It is often assumed for such log-linear model that the causal relationships which may exist, flow only in one direction, namely from the explanatory variables to the dependent variable.
The parameters of the model can be estimated by using the Ordinary Least Square method, if the assumptions of the classical linear regression model are fulfilled. As such,
GDPt* = C + β1 INVt* + β2 TRPt* + β3 CPIt* + β4 EXPt* + εt
where GDPt* = ln GDPt, INVt* = ln INVt, TRPt* = ln TRPt, CPIt* = ln CPIt, EXPt* = ln EXPt
The coefficient of each of the four explanatory variables measures the partial elasticity of the dependent variable GDP with respect to that variable. As such, each of the partial regression coefficient β1, β2, β3 and β4 are the partial elasticities of GDP with respect to variables INV, TRP, CPI and EXP respectively.
4.4 Explanation of Variables
4.4.1Gross Domestic Product (GDP)
Gross Domestic Product is used to assess the market value of all final goods and services produced during a given period of time within an economy. It also measures the total income of an economy and as such, it is often correlated with standard of living. GDP is used as a reference variable in order to assess the impact of tourism on economic growth in Mauritius. GDP is an important factor used to analyse the development of the tourism sector. As such, in case the tourism sector brings huge foreign earnings, there will be an increase in GDP, suggesting that the economy is flourishing. The GDP figures that are used for the regression have been adjusted for inflation using the GDP deflator.
4.4.2 Investment (INV)
Investment, which is a major component of the gross domestic product of an economy, refers to the acquisition of new capital goods. A positive change in investment may lead to a positive change in income and output of an economy in the short run. Higher level of investment may contribute to aggregate demand while higher level of income may indirectly impact on consumer demand. Investment, which is an injection in the circular flow of income, is a useful tool to analyse the impact of tourism on the economy of Mauritius.
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