Recommended model to predict the regional GDP per capita growth rate Prediction for GDP per capita growth rate in 2021, 2022 and 2023

Additionally, themedian of America is higher than Africa, showing that GDP per capita growth rate in 2015 ofAmerica is higher than that figure of Africa.. Therefore, Trade is considered

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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

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 Wecollect 45 countries in America and 52 countries in Africa However, because of some missingvalues 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% < 1.5*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)

Q1-Due to the evidence of outliers in both datasets of America and Africa GDP per capita growthrate, the mean cannot be used to compare in this situation because of its sensitivity to outliers

Moreover, the mode might not exist in the datasets, which is correct to the GDP per capitagrowth in America and Africa, there is no appearance of mode Therefore, the median is the mostappropriate 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, themedian of America is higher than Africa, showing that GDP per capita growth rate in 2015 ofAmerica 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 thesensitivity to outliers, sample variance, standard deviation, coefficients of variation, standarddeviation, and range are not applicable IQR is not susceptible to outliers, hence, it is the mostsuitable 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 growthrate in Africa is more fluctuated than that in America

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 ofAfrica (-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

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

Therefore, we need to exclude either exports of goods and services or imports of goods andservices.

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:

Both p-value of imports of goods and services and trade are lower than 0.05 However, incomparison, 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 growthrate is the positive relationship

R-square = 20.224% indicates that 20.224% of variation of GDP per capita growth rate can beexplained by variation of Trade

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)

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 resultindicates 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 thesignificant independent variable

IV Regression Conclusion

America and Africa have different significant independent variables While the GDP per capitagrowth 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 (% ofGDP)) However, the R-squared is small, which is only 20.22% When the R-squared is toosmall, it means that the prediction is less likely to be accurate (Israeli 2007) Therefore, therelationship between America GDP per capita and Trade (% of GDP) is weak and may not be

accurate Additionally, the value of is only 0.029, it shows that America Region GDP per capitawill 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 toomuch

The GDP per capita growth rate shows whether the GDP is rising or falling Since the median ofAmerica GDP per capita growth rate is higher than that of Africa (part 2), 50% of countries inAmerica 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 thatfigure in America is only -4.351% Therefore, it can be concluded that the American economy ismore sustainable than African economy

413 million in 2015, making Africa the poorest continent worldwide Amid 54 countries in thisregion, the most major income group is low income, at 34 countries, following by lower-middle-income 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 becalculated Therefore, the regression of exponential trend models of all countries chosen cannot

be produced

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’sGDP 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 beapproximately 1.087% However, this does not make sense since 0 is an out-of-observation-scope value of T

shows that with every year (T) increases, Bolivia’s GDP growth rate is predicted to increase by0.086% Hence, there is positive direction and an upward trend of this linear model

 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 beapproximately -2.908% However, since 0 is an out-of-observation-scope value, this does notmake 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, thatfigure of Cameroon ranges between $1,000 and $4,000 per capita, at $1,000, which constituteslower-middle-income country (LMI)

 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’sGDP 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 isapproximately -3.242% However, this does not make sense since 0 is out of the observationrange of T

means that with every year (T) increases, Ethiopia’s GDP per capita growth rate is predicted toincrease 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

- 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 isapproximately -4.218% However, this does not make sense since 0 is out of the observationrange of T

means that with every year (T) increases, Cameroon's GDP per capita growth rate is predicted toincrease by 0.331% It indicates the positive direction and the upward trend of this linear model

- QUA:

a Regression output

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 tochange by (%)

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

A American

It can be seen from Figure 18 that the linear trend model of Bolivia has the smallest SSE andMAD values (47 & 1.083), which means it has the fewest errors in future estimation Hence, thelinear 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 and1.755) are both observed in Cameroon's quadratic model, depicting that this model wouldgenerate the least error compared to two remaining models Therefore, it becomes the mostpreferred model to predict Africa's GDP per capita growth rate (annual %)

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 capitagrowth rates are 3.867%, 3.953% and 4.04%, respectively These numbers illustrate that Americawill witness an upward trend of the GDP per capita growth rate in the future Meanwhile, areverse trend in the predicted GDP per capita growth rate is observed in Africa, with the figuressteadily 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

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 growthrates of four countries above experienced a wild fluctuation; however, the speed of changesvaries differently from each country The figures of two American countries were more stablethan their African counterparts, which fluctuate much more wildly, especially Cameroon with themost significant fluctuation Despite all fluctuations, GDP per capita growth rates of two Africancountries witnessed an upward trend Conversely, that figures for American ones seemed toremain unchanged until the end of this period

Specifically, the Bolivia’s GDP per capita growth rate varies considerably in the first 10 yearsbefore an unexpected fall in 2015 Compared to Bolivia, the figure of Brazil moved moreunstably; 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 RegardingCameroon, the figure started to skyrocket in 1993 and afterward remained relatively stable

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 ratesfollow the linear trend during this period, whereas that figure of Brazil has the quadratic trendmodel Both linear and quadratic trends are observed in Cameroon’s figure; however, it tends tohave 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 thefewest errors in estimation Consequently, to ensure accuracy, using this America’s linear trend ismost 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 ofeducation 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 growthrate (Elizabeth 2017)

Besides, the GDP per capita growth rate can be influenced by the technology advancement(Alberio 2015) Applying innovation in technology helps businesses achieve economies of scalemore efficiently, in which they can increase production at lower costs to enhance productivity(Krugman & Wells 2008) The increase in productivity makes the economic growth rate higher,

in other words, the GDP per capita growth rate is positively affected by technology innovation(Korkmaz & Korkmaz 2017)

2 The predicted world GDP per capita growth rate in 2030 (annual %).

As mentioned in Part 6.2, Bolivia’s linear trend model is the most suitable trend to predict theworld's GDP per capita growth rate Therefore, in 2030, T=41 is used to predict the world’s GDPper capita growth rate by the formula Consequently, by plugging into the estimated formula, weconclude that the predicted world’s GDP per capita growth rate will be 4.648% in 2030 Thisnumber indicates that the world’s living standards are significantly improved, takingopportunities to devote more resources to important fields like healthcare and education (Stone2017)

However, this predicted figure is only an estimation Although Bolivia’s linear trend model hasthe smallest SSE (47) and MAD (1.083), depicting the most reliable model with the fewest error,the unexpected outcome of estimation could happen The four chosen countries could notproperly demonstrate the actual GDP per capita growth rate of the whole world since the datawas collected within the 1990-2015 period, considering the outmoded figures Besides, the GDPper capita growth rate can be affected by many factors such as youth employment and populationbelow poverty (Ilter 2017), which needs to well-researched to reach the final conclusion

3 Recommendations

Technology is the primary driver of economic growth, followed by labor investment (Alberio2015) To increase the GDP per capita growth rate, advanced technologies should be applied,especially processes related to Information Technology (IT) IT is regarded as an essential factoraffecting productivity IT features, namely data storage or processing, can help managersimprove their organizational performances IT users who used such tools to provide services tocustomers reported significantly better job completion, timely data recovery, and informationretrieval than when the IT system was not used (Alberio 2015) Therefore, investment in IT canlead to high productivity, resulting in higher economic growth, in other words, higher GDP percapita growth rate (Korkmaz & Korkmaz 2017)

Additionally, to achieve long-term economic growth, countries should invest in education toimprove productivity For example, new teaching methods should be examined, or the countries

can seek support from other nations with better education systems Consequently, whenproductivity levels increase, the economy grows more quickly, increasing the country's GDP percapita growth rate (Erdem & Tugcu 2012) Moreover, it is supposed that higher educationconnects with low levels of unemployment, which can reduce poverty and significantly increaseeconomic growth (Calmfors & Holmlund 2000; Nunez & Livanos 2010).

VIII References

Albeiro, PB 2015, Technology Trends for Business Productivity Increase, INGE CUC, vol 11,

no 2, pp 84-96, viewed 22 May 2021, <http://dx.doi.org/10.17981/ingecuc.11.2.2015.09>.Calmfors, L & Holmlund, B 2000, 'Unemployment and economic growth: a partial survey',

Swedish Economic Policy Review, vol 7, no 1, pp 107-154.

Elizabeth, NA 2017, ‘The Effect of Education Expenditure on Per Capita GDP in DevelopingCountries’, International Journal of Economics and Finance, vol 9, no 10, pp 136-144

Erdem, E & Tugcu, CT 2012, ‘Higher Education and Unemployment: A cointegration and

causality analysis of the case of Turkey’, European Journal of Education, vol 47, no 2, pp

299-309 Viewed 22 May 2021, ResearchGate database

Ilter, C 2017, ‘What Economic and Social factor affect GDP per capita? A study on 40 countries’,

Journal of Global Strategic Management, vol 11, no 2, pp 51-62

Krugman, PR & Wells, R 2008, Microeconomics, 2 edn, New York: W H Freeman, New York.ndNunez, I & Livanos, I 2010, ‘Higher education and unemployment in Europe: An analysis of theacademic subject and national effects’, Higher Education, vol 59, no 4, pp 475-487, viewed 22May 2021, SpringerLink database

Israeli, O 2007, 'A Shapley-based decomposition of the R-Square of a linear regression', Journal

of economic inequality, vol 5, no 2, pp 199-212, ProQuest database.

José, MP, Carlos, P, Lorenzo, S & Ángel, S 2018, ‘Higher education institutions, economic

growth and GDP per capita in European Union countries’, European Planning Studies, vol 26,

no 8, pp 1616-1637