Factors Hindering Technological Development in African Countries
Technological inventions and digitalization play a role in today’s ever-changing lifestyle and living standards. Technological advancements can be used to evaluate the growth and development of countries. Merely, the African countries have lagged in technological development and improvement. Most developed countries are only observed in the English-speaking counties, which has triggered the World Bank to lay strategies on how to initiate Digital development in the top ten poorest states in the African continent. The factors that hinder developments within the African countries include a shortage of learning institutions, high standards of living, and severe economic hazards. The World Bank is interested in determining the specific development that needs to be implemented as the factors that contribute to low digital effects vary from one country to another. The paper below analyses previous historical data of poorly developed countries, such as the measures of central tendency and the measures of deviation. The descriptive statistic shows the earlier data of the 39 poorest countries extracted from the World Bank website, examining the population, GDP, GNI, school enrolment rate, and Birth rate in the respective countries to determine the specific areas that need improvement.
The journal, The Digitization of Africa by Adeola, argues that the African Counties’ slugged digitalization and globalization are much more experienced. Fewer technological inventions and achievements characterize the countries, and such innovations are only evident in European countries (Adeola and Evans, 2022). As the journal argues, the factors that have accelerated the swift Digitization include poor infrastructure and corruption. According to the journal Africa, by Macdonald Brookes, The shortage of resources has contributed to slowness in Digitization. The state GNI and GDP are pretty low compared to the GDP and GNI of the most developed countries and require a little financial support to smoothen Economy and Technology(Adesina and Ayorinde, 2022).
The dataset is extracted from the World Bank platform. The website has enlisted listed several datasets available for data analysis. Still, the dataset selected for data analysis is the dataset partnering with the highly indebted developing country in the world. The website has analyzed a total of thirty-nine countries in the world and amongst the 39 counties that are poor in the world, 39 countries out of 54 countries in Africa are classified as developing countries. The other six developing countries come from outside the African continent. To categorize a country as a poor state, several considerations were evaluated. The country’s Gross National Product in USA dollars, The Gross National Income, and the country’s population size were also taken into account. Among the other variables considered during classification includes the country’s life expectancy rate.
The primary task was to come up with a list of the ten poorest countries in Africa. The Data is stored in excel as a comma Delimited File and includes the 39 poorest counties globally. Since R statistical package will be used most in data analyses, the six poorest countries that do not belong to the African continent will be eliminated. The data shall then be exported for continued data analysis and study. The GDP of the ten poorest countries will be classified together vector during data analysis. The observations are typically for the year 2022 February. To come up with the top ten poorest countries in Africa, we shall consider the Gross National Income. The counties that are more deserving of Digital Development are the most impoverished countries. To rank the counties from the most excellent to the least deserving, we shall consider the Gross National Income. The country with the lowest Gross National income is the country that will be the poorest country and the country that thus requires Digital Development first.
World Bank’s Strategy to Initiate Digital Development in the Poorest African Countries
The data above belongs to the year 2022. In the future, we might decide to know the value of the Gross national income. Thus, we shall build a Multiple linear Regression Model That will help us predict the Gross National Income of the poorest countries in the year 2023 concerning the available measures of The population, The school enrolment rate, and the Birth rate. The Gross National Income will be treated as the Dependent variable, while the other variables will be the set of predictor variables. To ensure that the model performs best, we shall determine the correlation within the predictor variables. The variables that will produce a high degree of correlation will be eliminated from the model. The higher the correlation between the predictor variable, the poorer the regression model performs. The model fitness of the model will be evaluated first before a decision rule is made to say that this model can be used to make future predictions. To evaluates its effectiveness; we shall split our dataset into two sets, the training data, which we shall use to fit the model, and the test data, which we shall rely on to evaluate its fitness. The mean squared error that results in providing the above model will then be computed and will help us know the model’s effectiveness. Additionally, the residual plots shall also be evaluated to test whether all Linear Regression assumptions have been met.
To analyze the past historical data, we shall consider the measures of the central tendency and the measures of deviation. Also, a table and graphical representation of the data will be regarded as essential during the analysis stages.
The data frame above gives the list of the ten poorest African countries based on their Gross National income. The country with the lowest GNI is Burundi, and its GNI is $230. Followed by Somalia, the two countries need to take the first digital development actions compared to the rest of the other African countries. The country needs digital development in different fields of life. Does it have the highest school enrolment compared to the rest of the ten poorest states in Africa?
The output above indicates the mean, the median l, the minimal value, and maximum observation for each GDP, GNI, population, school enrolment, year, and birth rate. The maximal birth rate amongst the top ten poorest countries is 67, while the minimal birth rate in the ten poorest countries in Africa is 53. The country with the highest population has a population of 89561404, is the Democratic Republic of Congo. The country with the lowest number of people in the Central African Republic (Bright, 2022). Moreover, the highest school enrolment rate is 144, and the country with this enrolment is Sierra Leon, whereas a minimal school enrolment rate is in Somalia. Their school enrolment rate is 23. The state with the highest GDP in The Democratic Republic of Congo has a GDP of 48716961, whereas the state with the lowest GDP in the Central African Republic. The highest GNI is in Liberia, GNI of 570, and the lowest GNI is in Burundi, with a GNI of $230.
Data Analysis of The Poorest Countries in Africa
The mean GNI, school enrolment rate, and GDP in the top 10 poorest countries are $479, 198.6, and 1303.461. Also, the mean birth rate is 60.7, and the mean population is 26221219.
The correlation between the independent variable is quite tolerable. The correlation ranges from -0.09195254 to 0.35844635, except only the correlation between the Gross Domestic Product and the population variables has a high correlation coefficient. The above will be tolerated as the two values are pretty significant.
The resulting model for predicting the GNI of the ten poorest countries is.
The value of the adjusted R squared measures how well a multiple regression model is well fit for a given data. The value of the adjusted R squared coefficient of the above model is 0.6394, higher than 0.5. Hence the model can predict the future value of GNI given the set of the predictor variables population, birth rate, school enrolment, and the GDP.
GNI=-1.5265e+03 -3.015e-05(Population)+3.202e+00(school enrolment ) +3.286e-05(GDP) 3.534e+01(Birth rate)
The resulting value of the mean squared error after fitting the model sample in 17 observations and testing its effectiveness in the remaining 16 observations is 183.8119. This value is tolerable compared to how the GNI values are distributed within the data.
The assumption of a linear relationship is met. The points are evenly distributed below and above the blue line. Even the issues might not be clustered around the blue line. The pre-existing distribution is entirely reasonable to conclude that a linear relationship between the independent and independent variables is satisfied.
The residuals are well aligned toward the blue dotted line, and the assumption that the errors follow a normal distribution is satisfied.
The dots are well spaced throughout the dataset. This indicates that a uniform variance distribution is evident within the data.
There are no points outside the cook’s distance. Therefore the assumption of the linear model that there are no outliers while fitting the regression model is satisfied.
Conclusion
The descriptive statistics have aided in tracking the development in the African countries. The Descriptive analysis has helped us determine the countries in Africa which require Digital Development. The output has also helped clarify the specific areas that need improvement in Africa’s least developed countries. For example, Burundi, the least developed country in terms of GNI, has the highest school enrolment rate amongst these top 10 developing countries, which means that we should rule out initiating school development in the state of Burundi when we think of Digital development in the African countries. The descriptive statistics have also aided in determining the aggregate GDP, GNI, school enrolment rate, and the mean population in the state of Africa. Besides that, we have also been able to establish the country in the least developed countries in Africa, which country has the lowest population and the countries with the highest population. Moreover, the data has helped us build a regression model that we can always use to predict if we have variations in the other set of predictor variables defined initially. The GNI and GDP values of the least developed countries in Africa are so low compared to the GNI and GDP of the highly developed counties; thus, a lot needs to be done to initiate Digital Development in the least developed counties.
The predictive model, the multiple linear regression model, fit the entire subset of the 59 countries in the poorest states in AFRICA. When the model was included in Africa’s top ten most impoverished countries, the model’s output was statistically insignificant. The statistical model often performs well in a large dataset, but work is usually negligible when the model is fit in a small portion of data.
Furthermore, the data used in the analysis were collected in the year 2022 in the months of February which might not reflect the True population, GDP, GNI, and school enrolment rate amongst the African least developed African countries.
The descriptive statistics were evaluated on a small dataset; therefore, it might sound deceptive that the output above took place only on a small dataset when concluding and reporting the statistics.
Reference List
Adeola, O. and Evans, O., 2022. ICT, infrastructure, and tourism development in Africa. Tourism Economics, 26(1), pp.97-114.Bright, M., 2022. Heavily indebted developing countries (HIPC) | Data. [online] Data.worldbank.org. Available at: <https://data.worldbank.org/region/heavily-indebted-poor-countries-hipc> [Accessed 6 April 2022]..
Adesina, M.A. and Ayorinde, T., 2022. From Africa to the World: Reimagining Africa’s research capacity and culture in the global knowledge economy. Journal of Global Health, 10(1).
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