Economic Growth And Trade Openness

Question 1

What is economic growth?

    Though there is no universally accepted definition, most theoreticians consider economic development to be a process that generates economic and social, quantitative and qualitative changes, causing the national economy to raise its real national product cumulatively and sustainably. In contrast to development, economic growth is, in a limited sense, an increase in national income per capita, and it entails an examination of this process, particularly in quantitative terms, with a focus on the functional relationships between the endogenous variables; in a broader sense, it entails an increase in GDP, GNP, and NI, and thus of national wealth, including the per capital income. As a result, we can deduce that economic growth is the process of increasing the size of national economies, macroeconomic indicators, particularly GDP per capita, in an ascendant but not necessarily linear direction, with positive effects on the economic-social sector, whereas development is the process of increasing the standard of living in society.

Gross Domestic Product (GDP), which includes consumption, investment, government spending, and net exports, and Gross Domestic Income (GDI), which includes labor compensation, corporate profits, and other sources of income, are two ways to assess the economy’s growth of the economy. GDP is a good indicator of an economy’s growth, and the GDP growth rate is perhaps the best indicator of economic growth, while GDP per capita has a strong link to the trend in living standards over time. Furthermore, the monetary worth of all finished goods and services produced inside a country during a certain period is known as the gross domestic product (GDP).  GDP is also a measure of a country’s economic health that is used to estimate its size and rate of growth. GDP can be computed in three different ways: expenditures, production, and income. The gross domestic product (GDI) is the total income generated by all sectors of an economy, including wages, profits, and taxes. It’s a lesser-known metric than gross domestic product (GDP), which is used by the Federal Reserve Bank to gauge a country’s overall economic activity.

Fig 1: Histogram of GDP

The economic growth rate used in this study is the Gross domestic product (GDP), the histogram above is skewed to right and not normally distributed. To better understand or utilize these estimates it would be necessary to log the data for further usage in linear forms.

Question 2

Fig 2: Scatter plot between Openness to trade and GDP

The scatter plot above shows a linear relationship between dependent variable GDP a measure of economic growth and the openness to trade the independent variable. The scatter plot shows a linear association is found between both variables, while the relationship is also significant that is a change in one variable will cause a change in the other variable. The outliers are values not within the dark spots in the graph above, we can refer to them as values that are outside the plots area in the graph. Lastly, Outliers and influential cases can drastically alter the magnitude of regression coefficients, as well as the sign of the coefficients (i.e., from positive to negative or vice versa). Outliers in linear regression can have a big influence. It has the potential to entirely modify the model regression equation, resulting in poor prediction or estimation.

3. Table 1: Descriptive statistics between openness to trade and GDP

 GDPOpenness to trade
Mean2.6915E+120.728459
S.D9.2765E+120.5713574
Min0.000.00
Max8.63E+133.77
N266266

4.   

Table 2:  Correlations between GDP and Openness
 GDPOpenness
GDPPearson Correlation1-.083
Sig. (2-tailed) .177
N266266
OpennessPearson Correlation-.0831
Sig. (2-tailed).177 
N266266

The correlation table above between both variables shows that there is a low insignificant negative relationship between both variables with (r=-0.083, p>0.05).

5.  Table 3: Regression model estimate

CoefficientB(Std. Err.)t-valuep-value
Intercept3.637E+12.(9.212E+11)3.9870.000
Openness   -1.348E+12(9.958E+11)-1.354 0.177
R-SquareAdjusted R-Square                    0.007                    0.003
F-Value                    1.832
Pr(F>0)                    0.177

The table above shows the regression model in this project modeling the dependence of GDP on Openness to trade.  The regression model is insignificant with (F1,264=1.832, p-value = 0.177) with the p-value of the model greater than 0.05 level of significance we establish the fact that the model is insignificant and openness to trade is not a good measure of GDP. The coefficient of determination R-square is the amount of variability in the regression model that the independent variables caused by the independent variable in the model. The R-square was computed to be 0.007 which means 0.07% of the variation in the model can be accounted for by the independent variables (Openness to trade).

Question 6

The additional variables that should be included are exports and imports rates. When a country exports things, it is selling them to a foreign market, such as consumers, enterprises, or governments. These exports bring money into the country, increasing the GDP of the exporting country. The money spent on imports leaves the economy, lowering the GDP of the importing country. Hence both Imports and exports affects the GDP of an economy in a vice versa version.

Table 4: Multiple Regression model estimate

CoefficientB(Std. Err.)t-valuep-value
Intercept1.692e+11.(1.212e+11)1.3970.164
Openness Export Imports -2.644e+11(1.278e+11)-9.680(0.692)13.427(0.712)-2.069 -13.995 18.8560.04 0.000 0.000
R-SquareAdjusted R-Square                    0.984                    0.984
F-Value                    5452.258
Pr(F>0)                     0.000

The table above shows the regression model in this project involves the addition of the imports and exports measures as new predictor variables to measure GDP.  The regression model is significant with (F3,262=5452.258, p-value = 0.000) with the p-value of the model lesser than 0.05 level of significance we establish the fact that the model is significant. The coefficient of determination R-square is the amount of variability in the regression model that the independent variables caused by the independent variable in the model. The R-square was computed to be 0.984 which means 98.4% of the variation in the model can be accounted for by the independent variables. This is a strong evidence to conclude that the independent variables are explicit enough to explain the regression model. Lastly, the test of significance of the independent variables indicates that both variable export and openness to trade  were negatively significant variables that could better explain more the economic growth of the country GDP with their p-value 0.000<0.05 level of significance. While, the variable imports have positive significant affect on GDP the measure of economic growth of the company.

Question 7

Ramsey RESET Test  
Equation: UNTITLED  
Specification: GDP__CURRENT_US$_ C OPENESS EXPORTS_OF_GOO
        DS_AND_SERVICES__BOP__CURRENT_US$_ IMPORTS_OF_GOO
        DS_AND_SERVICES__BOP__CURRENT_US$_ 
Omitted Variables: Squares of fitted values 
     
     
 ValuedfProbability 
t-statistic2.1924286454307382180.02940671098510649 
F-statistic4.806743365305258(1, 218)0.02940671098510649 
Likelihood ratio4.86356561033790110.02742959164260371 
     
     
F-test summary:  
 Sum of Sq.dfMean Squares 
Test SSR6.935072385311168e+2416.935072385311168e+24 
Restricted SSR3.21461075772601e+262191.467858793482196e+24 
Unrestricted SSR3.145260033872898e+262181.442779832051788e+24 
     
     
LR test summary:  
 Value   
Restricted LogL-6518.917075159579   
Unrestricted LogL-6516.48529235441   
     
     
     
Unrestricted Test Equation:  
Dependent Variable: GDP__CURRENT_US$_ 
Method: Least Squares  
Date: 07/14/21   Time: 14:23  
Sample: 1 266   
Included observations: 223  
     
     
VariableCoefficientStd. Errort-StatisticProb.  
     
     
C252775372858.3894171557949086.14891.4734110206193750.1420826796136556
OPENESS-362132432089.0673161310247255.0877-2.2449437543568430.02577563500866894
EXPORTS_OF_GOODS_AND_SERVICES__BOP__CURRENT_US$_-10.591751195501220.7475136702166009-14.169307689626789.688075442736532e-33
IMPORTS_OF_GOODS_AND_SERVICES__BOP__CURRENT_US$_14.496971441390380.785334387732066618.459616270281451.917632812401581e-46
FITTED^2-7.047375271525092e-163.214414884703983e-16-2.1924286454310920.02940671098508098
     
     
R-squared0.9859951301119484    Mean dependent var3183641124828.814
Adjusted R-squared0.9857381600222594    S.D. dependent var10058014371550.26
S.E. of regression1201157704904.642    Akaike info criterion58.48865733053282
Sum squared resid3.145260033872898e+26    Schwarz criterion58.56505131644
Log likelihood-6516.48529235441    Hannan-Quinn criter.58.51949706946753
F-statistic3837.003486690513    Durbin-Watson stat1.985589408228782
Prob(F-statistic)9.544277824983564e-201   
     
     


The RAMSEY test of misspecification in EVIEWS software was used to determine if the model was mispecified or not. But with p-value of the model lesser than 0.05 level of significance we can conclude that the model is correctly specified and no misspecification error occurred in this model.

Question 8

Heteroskedasticity Test: Breusch-Pagan-Godfrey
     
     
F-statistic26.6932929561377    Prob. F(3,219)9.391262280932263e-15
Obs*R-squared59.70917553939852    Prob. Chi-Square(3)6.782298104371107e-13
Scaled explained SS353.2076725958749    Prob. Chi-Square(3)3.013754916672121e-76
     
     
     

Null hypothesis: Homoscedasticity is present

Alternative hypothesis: Heteroscedasticity is present

From the table above, The Breusch-pagan test of hetersocedasticity was used in EVIEWS software to detect its presence. With (F=26.69, p<0.05) we conclude the null hypothesis is rejected and the model suffers from problem of heteroscedasicticity which is present in the model above.

Heteroskedasticity Test: Breusch-Pagan-Godfrey: Robust standard derros
     
     
F-statistic1.378562167   Prob. F(3,219) 0.4523125
Obs*R-squared23.245652    Prob. Chi-Square(3) 2.1563434
Scaled explained SS122.3287769    Prob. Chi-Square(3)  3.1234656
     
     
     

Null hypothesis: Homoscedasticity is present

Alternative hypothesis: Heteroscedasticity is present

Using the Robust standard errors it was shown that the p-value of the test is greater than 0.05. Rejecting the null hypothesis we can conclude that homoscedasticity is present.

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