Leasing of Medical Equipment in Africa-Kenya

Leasing of Medical Equipment in Africa-Kenya

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Leasing of Medical Equipment in Africa-Kenya

In most cases, social scientists and economists are interested in studying the impact of particular events or policy interventions in different speculation points. These policies and events always have diverse effects on aggregate entities like the geographic or administrative areas, industries, or learning institutions (Xu, 2017). In this case, estimating the impact of those particular events and interventions, the concerned require comparative case studies, which involves assessing cluster outcomes belonging to one specific unit (Xu, 2017). A case study centralizes on a particular occurrence of an event of interest. For instance, in this study, the possibility of interest in Kenya’s Vision 2030. So the primary purpose is to determine the impacts of a given policy intervention on some outcome of interest.

This study will also be based on some of the GDP determinants, including economic activities and other related factors (Xu, 2017). This information is vital in understanding the operation reliance of the Synthetic Control Method (SCM). By definition, the synthetic control method is a statistical technique used to examine the impacts of a particular intervention in comparative case studies (Xu, 2017). In this method, a treatment group is required, determined, and finally examined to know what would have happened to it in the absence of its treatment. It becomes a control tool for the experiment being conducted.

In comparative case studies, units affected by the events are compared with another unaffected set of units. As a result, comparative case studies will only be practical and effective if some units are exposed to a particular treatment while others are not (Xu, 2017). The artificial method has the impacts or significance of confounders which are dynamic. Can do it by weighting the control group to ensure that the treatment group is much better than before (Xu, 2017). Additionally, Expanding the confounding approach, the data-generating model can be used to explain the basics of confounding. The following simple example will serve as an illustration of crushing. Let X be some independent variable representing a particular African regional leadership policy and Y be a dependent variable representing GDP contribution by a specific region in Africa (Xu, 2017). An economist can use a technique of suppressing extraneous variables that influence both the policy made by authority and regions’ GDP generation (Xu, 2017). In this case, I will say that another variable confounds both GDP generation and the policy. This method involves combining elements from both matching and difference-in-difference techniques. These techniques are statistic approaches used to evaluate policies made on different dynamics.

It is using the artificial mode for measuring the outcome of the approach in Kenya’s Vision 2030. Vision 2030 is a long-term development program to enhance Kenya’s upgraded economy and a better society (Kazimierczuk, 2019). The pillar of the Vision is economic upgrading which has a robust target of making Kenya a well-settled middle-income country by providing higher quality life to Kenyans in a friendly environment under all the aspects. Once this objective is achieved, it will be the county’s economic, social and political pillar (Kazimierczuk, 2019). Moreover, by examining the effects of policy interventions, the synthetic control method can draw conclusions and explanations.

Research Objective

This study aims to find whether the Kenya Vision2030 policy effectively uses the synthetic control method (SCM) on the GDP per capita. Therefore, the analysis of Kenyan GDP will be done while relating the output with the general GDP for the eastern countries, including Tanzania, Uganda, Somalia, Djibouti, Ethiopia, and Eritrea.

Research Question

In comparison to the developing countries in Eastern Africa, is Kenya’s Vision 2030 policy effective?

Research Problem

The research problem of this particular research study is whether the Kenya Vision2030 policy is effective compared to eastern countries.

Literature Review

Over the past decades, Kenya had shown a relative shift to a better face of industrialization than in the distant past when factors like low civilization barred industrialization. The government at the national and county levels has worked together towards achieving the goal of industrializing this middle-income country. This process has been progressing due to the higher rate of civilization among the Kenyans, with many people shifting to the digital world. Shift to the digital world has a more significant impact on developing an industrialized Kenya of Vision 2030. Due to the reliance on the internet, can easily borrow technological advancement ideologies from the developed nations like the United States of America and China. According to the study conducted by Chege et al. (2020), Kenya’s GDP is proliferating compared to other Eastern Africa Countries. On the other hand, the survey conducted by Kigunda (2018) shows that Kenya’s GDP declines slowly due to a significant magnitude of economic challenges.

Similarly, the channel can assist Kenyans technocrats in implementing Kenya’s Vision 2030; this can best work if the technocrats rely on the internet to study the behavior and advancements achieved by some African and eastern countries making their countries better. For instance, over the last two years, data projected to explain how digitalization is evolving in Kenya showed that by January 2020, over 22.8 million Kenyans are internet users—constituting a penetration rate of 43%. Mobile connections in Kenya seem to be almost equivalent to the country’s population by standing at 98% of the total population, which amounts to 52.06 million mobile connections (Chege et al., 2020). Based on the above data on the digitalization rate in Kenya, Kenya’s Vision 2030 remains promising and encouraging. Today, learning centers have introduced internet-based class attendance, a translation that Kenya has made to achieve the 2030 Vision.

However, several challenges have encountered this country and almost brought a collapse of the entire plan. The working of these three pillars of Vision 2030 converges to a common objective in the year 2006. The aim is to boost the country’s GDP. The sitting president by then (Mwai Kibaki) was concerned with the economy of his country. So, he developed a technique whose purpose was to stabilize Kenya’s economy (Kigunda, 2018). In four years since 2002, the country’s GDP growth raised from 0.6% to 6.1%. This development plan rotates on ten key sectors. These are agriculture, tourism, infrastructure, education & training, financial services, trade, Business Process Outsourcing & ICT, public sector reforms, and science, technology, and innovation (Kigunda, 2018). Several signs of progress have been made for the past twenty years. However, several challenges have been witnessed, forcing the policy developers to respond to the problems better.

Economic analysts have been at the forefront to examine the effects of the policy on Vision 2030 based on the trend shown by the country’s GDP relative to the economic growth reflected by some eastern and African countries. The Covid-19 pandemic has slowed the economic developments across the globe, with African nations feeling the most significant impact on the economy. The IMF data on economic growth in Africa projected 2020 explained the possible outcomes of the continent’s economic decline by 3.3% (Anwar &Graham, 2021). The reason is that the pandemic has ravaged both the supply and demand chains across the continent. The latest data projected in April 2021 shows that Africa has an annual economic value of 4.9%.

On the other hand, in the study conducted by 2021, economic value for the Middle East region is at 2.5%, with an advancement rate of 3.7%. The Sub-Sahara region has projected a monetary value of 3.3% with an advancement rate of 3.4% in April 2021 (Anwar &Graham, 2021). The trend shown in these different economies seems to be similar to the Kenyan GDP trend per capita since 2004. However, the rate has not been consistent according to the data projected on the same.

The following features can as well explain the attributes of the Kenyan economy. First, the percentage of the labor force by occupation data in which agricultural labor force has 61.1%, industrial labor force rate of 6.7 and services with 32.2% (Anwar&Graham, 2021). Significantly, Kenya majorly relies on agricultural products. The poverty rate in the country is the second feature that can satisfactorily explain Kenya’s economy. The percentage of the population below the poverty line according to 2020 data is 16%, with the country’s inflation rate positioned at 5.1% (Anwar&Graham, 2021). Finally, the measure of GDP contribution by sector is a bold feature. The latest data show that the agricultural sector contributes 34.5%, the industrial sector raising 17.8%, while services take the lead with 47.5%. Industrial GDP contribution is delivering a good performance since 2017.

Methodology

This research study uses secondary data concerning the GDP fluctuation in different developing countries in Eastern Africa. The data used to carry out the analysis of the GDP for the various countries was from the reports prepared by World Bank Organization. The synthetic research of the collected data in terms of multiple line graphs with forecasting trends. The artificial analysis was also conducted using the simple linear regression and multiple regression model. Hence, the synthetic analysis of the secondary data obtained from the World Bank Organization was analyzed using Microsoft Excel.

Data Analysis

The analysis in this particular research will be conducted using secondary data concerning the GDP in Kenya researched by the world-bank organization. On the other hand, the data analysis will involve synthetic control models, which will be relevant in analyzing the GDP growth of various countries. Therefore, we will compare the growth of the Kenyan GDP with the different eastern countries in Africa and the Sub-Saharan countries, which include: Kenya, Tanzania, Uganda, Somalia, Djibouti, Ethiopia, and Eritrea. Hence, these eight countries also belong to the Sub-Saharan part of Africa. Therefore, the analysis of the GDP from the Eastern countries will be conducted using forecast techniques such as linear regression models and line graphs. Thus, the various form of data analysis using the GPD per capita in different countries are as follows:

The Gross Domestic Product Per the Capital Analysis of Kenya

Figure1 (Documents & Reports – All Documents | the World Bank, 2013).

The graph in figure 1 shows that the GDP of Kenya will increase by a significant percentage in 2030. I am providing an explicit clarification that the Vision 2030 policy of Kenya is effective.

Table 1

When considering the Linear Regression model in table 1, the F-significance value is 0.23, below 0.05 (Significance level), implying that agriculture, forestry, and GDP have no association with growth in Kenya.

Table 2

As per the Simple Linear Regression model in table 2, the value of the F-significance value is 0.237, which is above 0.05 (Significance level), thus indicating that the aspect of doing with GDP deflator has a significant impact on the GDP growth in Kenya. This conclusion is also supported by the value of the adjusted considerable value of 0.37, below 0.5 (r > 0.5). Therefore, there is a significant relationship between the Kenyan GDP and forestry, agriculture, and fishing.

Table 3

When considering the Simple Linear Regression model in table 3, the value of F significance is 0.039, which is below 0.05 (Significance level), implying that the element to do with the export of goods and services has a significant impact on the GDP growth in Kenya. This clarification is also supported by the value of the adjusted considerable value of 0.884, which is above 0.5 (r > 0.5). Therefore, there is a significant relationship between the growth of the Kenyan GDP and the export of goods and services.

Table 4

When considering the Simple Linear Regression model in table 4, the value of F significance is 0.827, which is above 0.05 (Significance level), implying that fishing, agriculture, and forestry do not significantly impact the GDP growth in Kenya. This clarification is also supported by the value of the adjusted significant value of 0.884, which is below 0.5 (r > 0.5). Therefore, there is no meaningful relationship between the two variables under study.

The Gross Domestic Product per Capital of Eritrea

Figure.2: The GDP per capital forecast for Eritrea using a line graph (Documents & Reports – All Documents | the World Bank, 2013).

When considering the result of the graph in figure 2 above, the GDP of Eritrea in 2030 will zero. Therefore, when comparing Kenya and Eritrea, the economic growth for Kenya will be more effective.

Table 5:

As per the output of the regression model in table 5, the value of significance is 0.561, which is above 0.05 (significance level). Therefore, this result output clarifies no significant relationship between the GDP growth and the deflator in Eritrea.

Table 6:

As per the output of the regression model in table 6, the value of significance is 0.581, which is above 0.05 (significance level). Hence, this result output clarifies no significant relationship between the inflator and GDP growth in Eritrea.

Table 7

When considering the result of the regression model in table 5, the significance value is 0.37, which is above 0.05 (significance level). Therefore, this output provides a clear clarification to conclude that there is no significant relationship between the GDP growth and the exports of goods and services in Eritrea.

Table 8:

As per the output of the regression model in table 8, the value of significance is 0.004, which is below 0.05 (significance level). Therefore, this result output provides a clear clarification to conclude a significant relationship between the GDP growth and agriculture and forestry in Eritrea.

The Gross Domestic Product per the Capital analysis of Djibouti

Figure 3(Documents & Reports – All Documents | the World Bank, 2013).

When considering figure 3 above, the prediction shows that the GDP of Djibouti will be improved by a considerable percentage when in 2030.

When considering the various forms of Simple Linear regression models in Djibouti, the prediction shows that the GDP growth will increase by a considerable percentage in 2030. But when the comparison is made, the GDP of Kenya will grow by a more considerable rate when compared to Djibouti

The Gross Domestic Product per the Capital analysis of Uganda

Figure 4

The Gross Domestic Product per the Capital analysis of Ethiopia

Figure 5(Documents & Reports – All Documents | the World Bank, 2013).

As per the various forms of Simple Linear regression models in Ethiopia, the prediction shows that the GDP growth will increase by a considerable percentage in 2030. Although, when the comparison is made, the GDP of Kenya will grow by a more significant percentage compared to Ethiopia.

The Gross Domestic Product per the Capital analysis of Somalia

Figure 6(Documents & Reports – All Documents | the World Bank, 2013).

As per the outputs of the chart and the various forms of regression models, the prediction shows that the GDP of Somalia will remain at zero levels when 2030 comes. Therefore, the GDP growth of Kenya is more effective when compared to Somalia.

Discussion

The Synthetic analysis in this particular study was done using the line graphs characterized by forecast line from 1990 to 2030. The line graph is essential since they provide a clear visual impression of the variation of different GDP determinants from 1990 to 2020. When comparing the various graph outputs in Tables 1, 2, 3, and 4 for other countries in Eastern Africa, there is sufficient evidence to conclude that Kenya has an adequate Vision 2030 policy—moreover, Conducting the synthetic analysis on the GDP determinants in terms of Simple regression analysis. When the significance value of the given regression model is below 0.5 (significance level), it indicates that there is a significant relationship between the two variables and the study. Therefore, when considering the various regression models, there is sufficient evidence to conclude that the determinant to do with exports has a significant impact on the GDP growth of a given country.

Conclusion

As per the various sets of line graphs, the prediction of the GDP data of Kenya shows that its total GDP in terms of billion dollars will increase by the year 2030. Hence, there is sufficient evidence to conclude that the Kenya Vision 2030 policy is effective compared to the GDP of other countries. On the other hand, when comparing the other forecast graphs of Ethiopia, Eritrea, Uganda, Somalia, and Djibouti, Kenya has got the highest prediction of GDP when it comes to the year 2030.

References

Anwar, M. A., & Graham, M. (2021). Between a rock and a hard place: Freedom, flexibility, precarity, and vulnerability in the gig economy in Africa. Competition & Change, 25(2), 237-258.https://journals.sagepub.com/doi/full/10.1177/1024529420914473

Chege, S. M., Wang, D., &Suntu, S. L. (2020). Impact of information technology innovation on firm performance in Kenya. Information Technology for Development, 26(2), 316-345. https://www.tandfonline.com/doi/abs/10.1080/02681102.2019.1573717

Kazimierczuk, A. H. (2019). Wind energy in Kenya: A status and policy framework review. Renewable and Sustainable Energy Reviews, 107, 434-445.https://www.sciencedirect.com/science/article/pii/S136403211830861X

Kigunda, E. G. (2018). Effect of Vision 2030 Development Strategies on Strategic Planning at Kenya Pipeline (Doctoral dissertation, University of Nairobi).http://erepository.uonbi.ac.ke/bitstream/handle/11295/105190/Kigunda_Effect%20of%20Vision%202030%20Development%20Strategies%20on%20Strategic%20Planning%20at%20Kenya%20Pipeline.pdf?sequence=1

Xu, Y. (2017). Generalized synthetic control method: Causal inference with interactive fixed effects models. Political Analysis, 25(1), 57-76.https://www.cambridge.org/core/journals/political-analysis/article/generalized-synthetic-control-method-causal-inference-with-interactive-fixed-effects-models/B63A8BD7C239DD4141C67DA10CD0E4F3