ECON F241 / F244

ECON F241 / F244Akshay BhatKarthik MenonTushit Thakkar

What We Had Done BeforeIn A

YearGrowth InvestmentOpennessEducationLife Expectancy1970-197519.7527.3642.516.2970.71975-198018.9324.6849.296.86721980-19857.7227.1355.1337.2873.11985-199013.9823.5460.0657.6674.81990-19956.7525.9666.298.0276.11995-200014.5227.0676.768.5877.42000-20057.5125.8894.3238.9878.82005-20106.7324.61106.269.2780.1Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate811.9930.315.506.7319.75Investment825.781.941.3923.5427.36Openness868.83494.1722.2342.51106.26Education87.871.091.0446.299.27Life Expectancy875.3811.063.3370.780.1GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment0.04481Openness-0.673-0.23291Education-0.7083-0.22610.96771Life Expectancy-0.7022-0.24410.9730.99671Austria5YearGrowth InvestmentOpennessEducationLife Expectancy1970-1975-15.185.2326.0951.38461975-19808.216.9813.421.5753.21980-19851.514.6417.522.25561985-199010.2413.1217.932.7858.71990-19957.62212.6522.213.1561.31995-200011.0914.6732.983.7264.12000-200519.5919.7335.894.4866.42005-201026.2122.2544.715.2268.4Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate88.66150.9712.29-15.1826.21Investment813.6632.805.735.2322.25Openness826.34115.0410.7313.4244.71Education83.071.841.3561.385.22Life Expectancy859.2655.087.424668.4GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment0.8461Openness0.55140.7231Education0.86510.94110.84771Life Expectancy0.92330.9210.67260.9551BangladeshYearGrowth InvestmentOpennessEducationLife Expectancy1970-1975-15.5212.6446.5354.0146.71975-19802.1116.8641.724.5950.11980-1985-16.6410.6136.295.4753.91985-19902.5813.5542.936.3757.31990-19958.68.749.047.2660.11995-20005.839.7753.457.7862.12000-20056.2312.4657.3238.2963.92005-201013.0110.0464.459.3565.6Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate80.78120.0110.96-16.6413.01Investment811.836.862.628.716.86Openness848.9983.609.1436.2964.45Education86.643.471.864.019.35Life Expectancy857.4645.556.7546.765.6GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment-0.17671Openness0.7103-0.38911Education0.7891-0.57290.82681Life Expectancy0.7845-0.56560.75570.99041BoliviaYearGrowth InvestmentOpennessEducationLife Expectancy1970-197513.0837.054.1933.4364.61975-198017.2136.997.1423.9766.31980-198537.6736.9994.7567.71985-19901537.6313.0355.2568.91990-199548.9636.3820.615.62701995-20002437.1431.4566.4170.92000-200551.3135.4451.0557.1173.42005-201061.335.1568.127.6074.4Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate833.57351.6118.7513.0861.30Investment836.600.770.8735.1537.63Openness825.58535.3623.144.1968.12Education85.522.161.473.437.60Life Expectancy869.5311.333.3664.6074.40GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment-0.89251Openness0.7915-0.86941Education0.7728-0.72580.93831Life Expectancy-0.79780.7166-0.9242-0.99571ChinaYearGrowth InvestmentOpennessEducationLife Expectancy1990-19951990-1995-4.2121.6655.14710.821995-20001995-200010.0525.9783.11511.422000-20052000-200522.6125.3511411.782005-20102005-201014.98624.36135.64212.75Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate410.86127.6211.2967159239607-4.2122.61Investment424.343.621.9026034794459921.6625.97Openness497.981242.2235.245182621175355.15135.64Education411.690.650.80859445954075110.8212.75Life Expectancy474.633.711.9259196937221172.3076.80GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment0.79121Openness0.83820.53421Education0.68460.43410.96671Life Expectancy-0.7713-0.477-0.9932-0.98941Czech RepublicYearGrowth InvestmentOpennessEducationLife Expectancy1970-1975-6.518.1192.8751.31531975-19808.226.98124.161.6856.81980-19851.514.64101.032.6559.91985-199010.2413.1275.623.7563.61990-19957.6212.6566.0334.3865.51995-200011.0914.6755.7955.06682000-200519.619.7351.9435.91692005-201026.322.5579.1636.5469.9Number of observationsMeanVarianceStandard DeviationMinMaxGrowth Rate89.76101.7610.09-6.5126.30Investment814.0627.625.266.9822.55Openness880.83591.5324.3251.94124.16Education83.913.691.921.316.54Life Expectancy863.2137.566.1353.0069.90GrowthInvestmentOpennessEducationLife ExpectancyGrowth1Investment0.81521Openness-0.4341-0.5971Education0.88340.9115-0.76621Life Expectancy-0.9091-0.91270.6667-0.97131EgyptEconometric AnalysisRegression EquationGrowth-rate = a1+b1ln(initialgdp)+b2ln(invpercapita)+b3(openness)+b4ln(education)+ b5(lnlife)+ c2+c4+c5+t2+t3+t4+t5+t6+t7Where a1 , b1,b2,b3,b4,b5 are coefficients , and c2 ,c4,c5 are country dummy variables and t2,t3,t4,t5,t6,t7 are time dummy variables.We initially set our data set to cross-sectional by using the tsset command in STATA.

Test for Heteroschedasticity

Test is performed to check for heteroschedasticity. The probability of P > chi2 is 0. Hence the Null-hypothesis is rejected. Hence our model faces the problem of heteroschedasticity.

Test for Multicollinearity

Test for Multicollinearity

In statistics, the variance inflation factor (VIF) quantifies the severity of Multicollinearity, in an OLS analysis. It provides an index that measures how much the variance of an estimated regression coefficient is increased because of collinearity. For all practical purposes the VIF should be less than 10. In our model the VIF is 6.4 which means our model is good.A VIF of 6.4 implies that the standard errors are larger by a factor of 6.4 than would otherwise be the case, if there were no inter-correlations between the predictor of interest and the remaining predictor variables included in the multiple regression analysis.Test for Autocorrelation

Here the probability is greater than the significance level of 0.05, hence the null-hypothesis is not rejected .Which means there is no problem of autocorrelation.

Regression Diagnostic TestingOur model has the problem of heteroschedasticity hence we use the robust command in STATA to deal with it.

After performing the regression analysis on STATA it can be observed that only 2 variables are significant, which are:1) ln (initialgdp) (p-value 0.039 < 0.05)2) ln (life) (p value 0.006 < 0.05)

ECONOMIC AND STATISTICAL INTERPRETATION OF SIGNIFICANT VARIABLES.Ln (initialgdp)

The p-value of ln (initialgdp) is 0.039 which is less than 0.05 level of significance, hence it is significant. Also it is seen that its coefficient is negative, i.e. -.179(mild negative correlation).A 1% increase in initial gdp decreases the growth rate 0.179.Which implies that the higher the initial gdp the lower is the growth rate. This highlights the phenomenon of conditional convergence for economies. Which states that poorer countries per capita income, will tend to grow at faster rates than rich economies. As a result, all economies should eventually converge in terms of per capita income.Developing countries have a potential to grow at a faster rate than developed countries because of diminishing returns (in particular, capital) are not as strong as in capital-rich countries. Furthermore poorer countries can replicate the technologies and production methods of developed countries.Ln (life)

The variable ln (life) is also significant as its p value of 0.006 is less than the significance level of 0.05. It is seen that the coefficient is 0.894 .With 1% percent increase in life expectancy the growth rate increases by 0.894.The coefficient is positive implying that there is high positive correlation between life expectancy and the growth rate of an economy .This is synonymous with our ideology that as Life expectancy increases the growth rate also increases. This is because with increase in life expectancy the working population increases, hence the increased contribution would obviously increase the growth rate. As the labor force is the driving engine in any economy.

Interpretation of the F-value:

Ho: All slope coefficients are zero.H1: At least one slope coefficient is non-zero.The calculated F-value F (14, 33) is 11.95, the critical F-value at F (14, 33) is 2.09. So the calculated F is greater than the tabulated F. Therefore we reject the Null hypothesis: All slope coefficients are 0.This is true because as our t-statistic is significant we know in advance that our slope coefficients were non-zero. This is in accordance with the results obtained from the F-test. Hence we can say that our regression model has explanatory power as it turns out that growth rate is related to the explanatory variables.

Interpretation of the R^2 results:

The R-square value ranges from 0 to 1. An R-square of 1 means a perfect fit. On the other hand an R-square of 0 means there is no relationship between the regressand and the regressor whatsoever. The R-square value in our case is 0.6393. Which means that our model is satisfactorily explaining the relationship between the regressor and the regressand.

Using the model for policy purposes:

In our model the life expectancy is highly positively correlated with the gdp growth rate. Hence policies pertaining to economic growth have to take into account life expectancy as a major factor .So the government has to use its fiscal policy through government expenditure on medical facilities and sanitation provided to its citizens for better economic performance. The quantity rather than quality of health services has been the focus historically in developing countries, ample evidence suggests that quality of care (or the lack of it) must be at the center of every discussion about better health.

As the labor force of the country is a major factor in its growth, the workforce should be provided with services of medical insurance as well as life insurance at an affordable rate as this would provide a sense of social security through policies of the same.Ensuring Health care for children as children are the future of a country. Female infanticide needs to be checked and vaccinations up to a certain age for diseases like polio are a basic necessity and needs to be implemented in all parts of the country.Support for Broad based Medical Research. Monetary support for incurable diseases like Cancer should be provided as any progress in this sector would save thousands of lives.

Using the model for policy purposes:

Are There Any Questions?