To see the impact of population growth on gdp growth

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To see the impact of population growth on GDP growth of economy, a variable log of population in millions is used in the model. Descriptive statistics of the data indicate that the distribution of population is negatively skewed with extreme values of 4.76 and 5.11. These values of proportionate change of population describes that there are not high variation is the population trend. The value of the measure of kurtosis of proportionate change of population shows that the distribution of this variable is platykurtic having most of the values dispersed around the average. Correlation Matrix of the model The correlation matrix Table 2 explains the association of GDP growth (GDPG) with some other desired variables. The estimated results are almost according to expectations of the study. Our main focus is on GDPG in relation to other relevant variables. The results of the time series data indicate that GDPG is not correlated at the extreme level with any attempted variable. It means it is not having perfect or zero correlation with the independent variables of the model. Table 2 Correlation Matrix of the Variables Regarding GDP Growth and CPI Inflation in Pakistan Variables GDPG CINF OPNS INVG LFPR LPOPM GDPG 1.00 CINF -0.08 1.00 OPNS 0.17 -0.16 1.00 INVG 0.18 0.54 -0.27 1.00 LFPR 0.06 0.15 -0.51 0.00 1.00 LPOPM -0.31 -0.31 0.17 -0.38 0.08 1.00 Note: All the calculations are carried out by E-views The results describe that there is a weak negative association between GDPG and CPI inflation (CINF). This explains that both the variables are depending upon each other in a negative and less sensitive way. Any increase in the CPI inflation has an inverse impact on the GDP growth. Another negative interdependence has been found between GDPG and proportionate change in the total population of the economy (LPOPM). The result implies that level of negative association between GDPG and LPOPM is moderate. It indicates that any increase in the population will cause GDPG to decrease and vice versa. The remaining explanatory variables are positively correlated with GDPG. The Table: 2 shows that openness (OPNS), investment growth rate (INVG), and labor force participation rate (LFPR) are positively associated with GDPG. LFPR is weakly correlated with GDPG, as the correlation co-efficient between these two variables is just 0.06.
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60 Pakistan Journal of Social Sciences Vol. 31, No. 1 B). Econometric Analysis The results of the estimated model (Equation 1 and Equation 2) are arranged in Table 3, which explain that our specified model performed quite well in terms of F- statistic. On the basis of our hypothesis that all the variables are jointly significant, the results describe that our model is highly significant .Though the size of R 2 is moderate in the present model, but we just not look at the size of R 2 . It is a co-efficient of determination that explains how much linear relationship has the dependent variable with independent variables. The value of R 2 in Equation 1 is 0.33, which explains that 33 percent variations in the GDP growth are explained by the attempted independent variables, whereas its value is 0.35 in Equation 2. These moderate values of R
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