Assignment-3A ECON1193- Group-11 Team-4-3 PDF

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Student DistributionFIRSTNAMESTUDENTIDPARTDISTRIBUTIONCONTRIBUTION% SIGNATUREMAI SPart 1, 3, 4 (Conclusion for Part 3), Part 2 (Box and Whisker), Video Powerpoint100%TAM S3877398 Part B), 5,6,7 Script (Region 100%ANH S3880774 Part A), 5,6,7 Script (Region 100%DUNG SPart 2 (Median), Part 4 (Conclusio...

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ECON1193B – Semester A, 2021

Student Distribution

FIRST STUDENT PART CONTRIBUTION NAM ID DISTRIBUTION % E MAI

S3878898

Part 1, 3, 4 (Conclusion for Part 3), Part 2 (Box and Whisker), Video Powerpoint

TAM

S3877398

Part 5,6,7.2 (Region B), Video Script

100%

ANH

S3880774

Part 5,6,7.2 (Region A), Video Script

100%

DUNG

S3877053

Part 2 (Median), Part 4 (Conclusion for Part 2), Part 7.1 & 7.3, Video Presenter

100%

HUY

S3877033

Part 2 (IQR), Part 4 (Conclusion for Part 2), Part 7.1 & 7.3

100%

100%

SIGNATURE

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ECON1193B – Semester A, 2021

Table of Contents I. DATA COLLECTION...............................................................................................................4 II. DESCRIPTIVE STATISTICS.................................................................................................4 2.1 Central Tendency.................................................................................................................4 2.2 Variation................................................................................................................................5 2.3 Box-and-Whisker plot..........................................................................................................6 III. MULTIPLE REGRESSION..................................................................................................6 3.1 America.................................................................................................................................6 3.2 Africa...................................................................................................................................10 IV. REGRESSION CONCLUSION...........................................................................................11 V. TIME SERIES.........................................................................................................................12 5.1 Significant trend model......................................................................................................12 5.2 Recommended model to predict the regional GDP per capita growth rate..................18 5.3 Prediction for GDP per capita growth rate in 2021, 2022 and 2023..............................19 VI. TIME SERIES CONCLUSION...........................................................................................20 6.1 Line chart............................................................................................................................20 6.2 Recommended model to predict the world GDP per capita growth rate......................21 VII. OVERALL CONCLUSION................................................................................................21 6.1 Other factors that affect GDP per capita growth rate....................................................21 6.2 The predicted world GDP per capita growth rate in 2030.............................................21 6.3 Recommendations..............................................................................................................22 VIII. REFERENCES...................................................................................................................23 IX. APPENDIX............................................................................................................................24

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ECON1193B – Semester A, 2021 I.

Data Collection

Our assigned regions are America (Region A) and Africa (Region B), and our assigned year is 2015. The report includes 9 variables. Data of 9 variables is collected from The World Bank. We collect 45 countries in America and 52 countries in Africa. However, because of some missing values of some variables, we have to eliminate some countries. Consequently, the final number of countries included in this report are 27 and 49 countries in America and Africa, respectively. II.

Descriptive Statistics: 1. Measurement of Central Tendency

To check the outliers, Q1, Q3 and IQR are calculated:

There is one upper outlier (5.76% > Q3+1.5*IQR) and one lower outliers (-4.351% < Q11.5*IQR) in America. In Africa, there are five lower outliers (-5.609%, -6.884%, -9.661%, -12.131% and -22.312% < Q1-1.5*IQR).

Due to the evidence of outliers in both datasets of America and Africa GDP per capita growth rate, the mean cannot be used to compare in this situation because of its sensitivity to outliers. 4

ECON1193B – Semester A, 2021 Moreover, the mode might not exist in the datasets, which is correct to the GDP per capita growth in America and Africa, there is no appearance of mode. Therefore, the median is the most appropriate measure to analyze owing to its resistance to outliers. The median of GDP per capita growth rate in America is 1.963%, meaning 50% of observations (countries) have a growth rate higher than this number. Similarly, 50% of observations (countries) in Africa have the GDP per capita growth rate higher than 0.974%. Additionally, the median of America is higher than Africa, showing that GDP per capita growth rate in 2015 of America is higher than that figure of Africa. 2. Measurement of Variation

In general, all the figures of Africa are excessively higher than that of America. Due to the sensitivity to outliers, sample variance, standard deviation, coefficients of variation, standard deviation, and range are not applicable. IQR is not susceptible to outliers, hence, it is the most suitable measure to compare these GDP per capita growth rates in both regions. The IQR of GDP per capita growth rate in Africa (3.445%) is larger than the IQR in America (2.08%), which is about 1.656 times greater. This figure shows that the GDP per capita growth rate in Africa is more fluctuated than that in America.

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ECON1193B – Semester A, 2021 3. Box-and-Whisker Plot

Both GDP per capita growth rates in America and Africa had right-skewed shape (mean > median). There are 5 lower outliers in Africa, and the lowest number is even -22.312%, indicating that there are many countries with extremely low GDP per capita growth rates. Meanwhile, the lowest number in America is only -4.351%. Additionally, the left-whisker of Africa (-3.186% to -0.345%) is located in the lower position than the left-whisker of America (1.507% to 0.53%). Therefore, it can be concluded that the GDP per capita growth rate in Africa is extremely lower than that in America. III.

Multiple Regression: 1. America

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ECON1193B – Semester A, 2021

P-value of Exports of goods and services (% of GDP) and Imports of goods and services (% of GDP) have #NUM! error. This problem occurs because the independent column is linearly dependent on the others (multicollinearity).

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ECON1193B – Semester A, 2021 Therefore, we need to exclude either exports of goods and services or imports of goods and services. After applying backward elimination (Appendix A): Final regression output when eliminating Exports of goods and services:

Final regression output when eliminating Imports of goods and services:

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ECON1193B – Semester A, 2021 Both p-value of imports of goods and services and trade are lower than 0.05. However, in comparison, R-square of Imports of goods and services (19.46%) is lower than R-square of Trade (20.224%). Additionally, P-value of Imports of goods and services (0.021) is higher than P-value of Trade (0.019). Therefore, Trade is considered as the most significant independent variable. Regression equation: shows that when Trade = 0%, the predicted GDP per capita growth rate of GDP per capita is – 0.3%. Nevertheless, this does not make sense because X=0% is out of the observation range. Slope indicates that GDP per capita will increase 0.029% for every 1% increase in Trade. Positive value of indicates that the linear relationship between Trade and GDP per capita growth rate is the positive relationship. R-square = 20.224% indicates that 20.224% of variation of GDP per capita growth rate can be explained by variation of Trade.

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ECON1193B – Semester A, 2021 2. Africa

P-value of Imports of goods and services (% of GDP) and Trade (% of GDP) have #NUM! error. This problem occurs because the independent column is linearly dependent on the others (multicollinearity).

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ECON1193B – Semester A, 2021 Therefore, we need to exclude either trade or imports of goods and services. After eliminating either Imports of goods and service or Trade, Excel shows the same final

output:

After applying the backward elimination method (Appendix A), the final regression result indicates no linear relationship between GDP per capita growth rate and 8 independent variables. P-value of Life expectancy at birth (0.24) is still higher than 0.05, therefore, it is not the significant independent variable. IV.

Regression Conclusion

America and Africa have different significant independent variables. While the GDP per capita growth rate of America is affected by only one independent variable (Trade (% of GDP)), Africa's GDP per capita growth rate is not affected by any independent variables. GDP per capita growth rate of America was affected by one independent variable (Trade (% of GDP)). However, the R-squared is small, which is only 20.22%. When the R-squared is too small, it means that the prediction is less likely to be accurate (Israeli 2007). Therefore, the relationship between America GDP per capita and Trade (% of GDP) is weak and may not be 11

ECON1193B – Semester A, 2021 accurate. Additionally, the value of is only 0.029, it shows that America Region GDP per capita will increase only 0.029% for every 1% increase in Trade. Therefore, the variation of Trade (% of GDP) impacts the variation of America GDP per capita growth rate, but the impact is not too much. The GDP per capita growth rate shows whether the GDP is rising or falling. Since the median of America GDP per capita growth rate is higher than that of Africa (part 2), 50% of countries in America dataset have the GDP rising and better than 50% of countries in Africa dataset. Additionally, the lowest value of GDP per capita growth rate in Africa is -22.312% while that figure in America is only -4.351%. Therefore, it can be concluded that the American economy is more sustainable than African economy. V.

Time Series

In our collected datasets, there is no low-income (LI) country in America during 1990-2015. Besides, among four income categories, the numbers of middle-income countries are the highest in this region in 2014 (Appendix B). This is the reason why our report chooses the upper- and lower-middle-income (UMI and LMI) countries in the analysis for America. Meanwhile, there is only one high-income (HI) country in Africa, which is Seychelles. However, there are no significant trend models applied for this country (Appendix D.1). Moreover, World Bank (2018) reported that low-income inhabitants in Africa grew from 278 million in 1990 to 413 million in 2015, making Africa the poorest continent worldwide. Amid 54 countries in this region, the most major income group is low income, at 34 countries, following by lower-middleincome countries. Reasonably, low and lower-middle-income (LI and LMI) countries are chosen to conduct this analysis. 1. Significant Trend Model

Because of some negative values of the GDP per capita growth rate, log(Y) function cannot be calculated. Therefore, the regression of exponential trend models of all countries chosen cannot be produced. 12

ECON1193B – Semester A, 2021

A. America Regarding Appendix C.1 and Appendix C.2, the average GNI of Bolivia is $1310.385, between $1,000 and $4,000 per capita. Therefore, Bolivia is listed as the lower-middle-income (LMI) country in America. Besides, that figure of Brazil is $5896.538, ranging between $4,001 and $12,250 per capita. Hence, Brazil is classified as an upper-middle-income (UMI) country. 

Lower-Middle Income country (LMI) – Bolivia

According to Appendix D.2, it can be deduced that linear is significant trend model of Bolivia’s GDP per capita growth rate (annual %) during 1990-2015. a. Regression output

b. Formula and Coefficient explanation

means that when T=0 year (in 1989), Bolivia’s GDP per capita growth rate is estimated to be approximately 1.087%. However, this does not make sense since 0 is an out-of-observationscope value of T. shows that with every year (T) increases, Bolivia’s GDP growth rate is predicted to increase by 0.086%. Hence, there is positive direction and an upward trend of this linear model. 13

ECON1193B – Semester A, 2021 

Upper-Middle Income country (UMI) – Brazil

Based on Appendix D.3, there is a quadratic trend model of Brazil’s GDP per capita growth rate (annual %) during 1990-2015. a. Regression output

b. Formula and Coefficient explanation

shows that when T=0 (in 1989), Brazil’s GDP per capita growth rate is estimated to be approximately -2.908%. However, since 0 is an out-of-observation-scope value, this does not make sense. indicates that for every year (T) increase, the Brazil’s GDP growth rate is predicted to change by . B. Africa As observed in Appendix C.3 and Appendix C.4, Ethiopia's average GNI per capita in 1990-2015 is $246,154, less than $1,000 per capita, regarding as low-income nation (LI). Meanwhile, that figure of Cameroon ranges between $1,000 and $4,000 per capita, at $1,000, which constitutes lower-middle-income country (LMI).

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ECON1193B – Semester A, 2021 

Low-Income country (LI) – Ethiopia

a. Regression output Based on Appendix D.4, it can be deduced that linear is a significant trend model of Ethiopia’s GDP per capita growth rate (%) during 1990-2015.

b. Formula and Coefficient explanation

shows that when T=0 year (in 1989), Ethiopia’s estimated GDP per capita growth rate is approximately -3.242%. However, this does not make sense since 0 is out of the observation range of T. means that with every year (T) increases, Ethiopia’s GDP per capita growth rate is predicted to increase by 0.496%. It indicates the positive direction and the upward trend of this linear model.



Lower-Middle Income country (LMI) – Cameroon

As illustrated in Appendix D.5, the significant model of Cameroon’s GDP per capita growth rate (%) during 1990-2015 has linear and quadratic trend. 15

ECON1193B – Semester A, 2021 -

LIN:

a. Regression output

b. Formula and Coefficient explanation

shows that when T=0 year (in 1989), Cameroon's estimated GDP per capita growth rate is approximately -4.218%. However, this does not make sense since 0 is out of the observation range of T. means that with every year (T) increases, Cameroon's GDP per capita growth rate is predicted to increase by 0.331%. It indicates the positive direction and the upward trend of this linear model. -

QUA:

a. Regression output

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ECON1193B – Semester A, 2021

b. Formula and Coefficient explanation

shows that when T = 0 year (in 1989), the Cameroon’s estimated GDP per capita growth rate is %. However, this does not make sense since 0 is out of the observation range of T. means that with every year (T) increases, Cameroon's GDP per capita growth rate is predicted to change by (%).

2. Recommended trend models to predict the GDP per capita growth rate (annual %)

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ECON1193B – Semester A, 2021

A. American It can be seen from Figure 18 that the linear trend model of Bolivia has the smallest SSE and MAD values (47 & 1.083), which means it has the fewest errors in future estimation. Hence, the linear will be the most applicable trend model to predict America's GDP per capita growth rate. B. Africa Among three significant trend models of Africa, the smallest SSE and MAD values (131.103 and 1.755) are both observed in Cameroon's quadratic model, depicting that this model would generate the least error compared to two remaining models. Therefore, it becomes the most preferred model to predict Africa's GDP per capita growth rate (annual %).

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ECON1193B – Semester A, 2021 3. Prediction for GDP per capita growth rate (annual %) in 2021, 2022, 2023

In evaluation, it can be concluded that in 2021, 2022 and 2023, the American GDP per capita growth rates are 3.867%, 3.953% and 4.04%, respectively. These numbers illustrate that America will witness an upward trend of the GDP per capita growth rate in the future. Meanwhile, a reverse trend in the predicted GDP per capita growth rate is observed in Africa, with the figures steadily fall by approximately 1% every year, from –3.789% in 2021 to -4.743% in 2022 and – 5.764% in 2023.

VI.

Time Series Conclusion 19

ECON1193B – Semester A, 2021 1. Line chart

Figure 21 demonstrates the trend of the GDP per capita growth rate in Bolivia, Brazil (America), Ethiopia, Cameroon (Africa), over 26-year period (1990-2015). Overall, GDP per capita growth rates of four countries above experienced a wild fluctuation; however, the speed of changes varies differently from each country. The figures of two American countries were more stable than their African counterparts, which fluctuate much more wildly, especially Cameroon with the most significant fluctuation. Despite all fluctuations, GDP per capita growth rates of two African countries witnessed an upward trend. Conversely, that figures for American ones seemed to remain unchanged until the end of this period. Specifically, the Bolivia’s GDP per capita growth rate varies considerably in the first 10 years before an unexpected fall in 2015. Compared to Bolivia, the figure of Brazil moved more unstably; however, it continuously declined and ended as the lowest one in 2015 with nearly -5%. Ethiopia’s GDP per capita growth rate fluctuated wildly in the first 15 years before reaching a peak in 2004 and ultimately ranked highest among four countries in 2015. Regarding Cameroon, the figure started to skyrocket in 1993 and afterward remained relatively stable. 20

ECON1193B – Semester A, 2021 Based on the conducted calculation in Part 5, these countries follow different trend models. Despite different degrees of fluctuation, both Bolivia and Ethiopia’s GDP per capita growth rates follow the linear trend during this period, whereas that figure of Brazil has the quadratic trend model. Both linear and quadratic trends are observed in Cameroon’s figure; however, it tends to have quadratic trend since this trend model has the lowest errors in future estimation. 2. Recommended trend model to predict the world’s GDP per capita growth rate

Both SSE and MAD values of America’s linear trend model are the smallest, indicating the fewest errors in estimation. Consequently, to ensure accuracy, using this America’s linear trend is most preferable model to predict the global GDP per capita growth rate (annual %). Formula of the world’s GDP per capita growth rate: VII.

Overall Conclusion

1. Other factors that affect GDP per capita growth rate (annual %) Another factor that can affect the GDP per capita growth rate is the population's level of education. The investment in human capital education would enhance labor quality, contributing to greater proportion of employees in working-age and higher quantity of well-educated workers ((Jose et al. 2018). As a result, this phenomenon can positively impact the GDP per capita growth rate (Elizabeth 2017). Besides, ...