Introduction
Theory and empirical study have both been utilized by economists to understand economic growth’s origins. Most later works are built on the theoretical frameworks established by earlier researchers. Wang’s (2018) work has resulted in substantial empirical research testing the validity of the theories they developed. Accordingly, this study will use the theoretical framework developed in 1996 by Barro (who offered an empirical model of economic growth) and updated data to examine whether it holds for a group of emerging countries.
In recent years, there has been a lack of empirical research that focuses on the elements that drive growth in developing economies. The dynamic nature of growth means that research conducted hundreds of years ago may not be as relevant now (Wang, 2018). Many countries’ economic growth has been changed by technological advancements that have taken place during the past few decades. This study will encourage additional research-based studies on developing economies to learn and benefit from the experiences of developed countries. There are several possible policy consequences to this study.
Research Question
This research aimed to identify the variables that influence economic growth in emerging economies. This study also looked at how growth in developed countries compares to that in underdeveloped countries. According to an Ordinary Least Squares regression, the factors that influence economic growth are often constant (Combes, 2019). As a result, it was found that the process was cumbersome because of a lack of reliable data. There should be more empirical studies done on developing countries.
Data and Methodology
Data was gathered based on the GDP per capita of 76 developing nations as of 2019. A cross-section of each year’s data was used to generate the results. Africa, Asia, Australia, and the Americas were all included in this dataset. Multiple Ordinary Least Squares regressions were utilized after the data had been gathered. This allowed researchers to discover the relationship between financial development and other characteristics that had previously been linked to economic growth (Hu, 2018). To see if the results from each year were similar, they were compared to the results from the previous year.
INITIALGDP was the initial control variable in the model, which represented the initial level of GDP per capita. It is the year-on-year GDP per capita. Purchasing power parity (PPP) dollars were used in this calculation. Price disparities between nations were taken into account using purchasing power parity dollars. Controlling the economy’s size by including this variable in the statistical model was an important consideration. According to the idea of conditional convergence, larger economies tend to grow more slowly than smaller economies, so we included this variable in our model to account for this effect (Combes, 2019). The INITIALGDP was projected to have a negative sign.
In addition to EXPORT, the second variable was the volume of exports. The number of products and services that the country exported in constant 2005 purchasing power parity dollars during the year. According to economic theories, higher exports lead to greater access to foreign markets and, thus, greater profits. The coefficient was expected to have a positive sign.
RESOURCE was used to describe the country’s ability to develop natural resources for its consumption or export. This variable’s proportion of GDP served as its unit of measurement. As an illustration, if a country’s natural resources were worth $10,000 and its GDP was $100,000, RESOURCE would be worth 10%. Whether natural resource extraction and sales positively or negatively impact economic growth has been the subject of conflicting arguments. Since a prior study suggested that the export of commodities could positively or negatively impact a country’s growth, the estimated coefficient was uncertain.
Data were analyzed using descriptive statistics after it had been gathered and filtered. The 2010 descriptive statistics provided a sample of the data’s behavior. The sample’s average annual growth rate was about 5%, with the highest at just under 14% for Chad and the lowest at about 5.5% for Haiti. The debt-to-GDP ratio of Eritrea was 144%, making it the only state on this list with a ratio higher than 100%. Tonga’s GDP benefited the least from its natural resources. Only Angola had a foreign direct investment trade deficit at a -4 percent annual rate.
Results and Discussions
The test had a total of 57 participants. Initially, 76 countries were included in the test. However, incomplete data cut the size of the sample to 57. The beginning GDP, export volume, foreign debt, resource extraction yield, help received, life expectancy, economic investment, and FDI were significant independent variables in this experiment.
The growth rate was negatively correlated with the initial GDP per capita. Earlier, we discussed a theory known as convergence. There was a correlation between the coefficient’s magnitude and the growth rate, but the correlation was so weak that it might be construed as meaningless. Because the p-value of INITIALGDP was so close to zero, it signified a high degree of statistical significance. There is little difference in our sample countries’ GDP per capita levels because they are all emerging countries. This argument does not indicate that increasing GDP per capita slows down economic growth (Le, 2019). Wouldn’t it have been nice if the richer economies had been included? Countries with a greater GDP per capita at the start of the study would have grown more slowly than those with lower GDP per capita, even when the increase in their GDP per capita was much larger.
These findings show that many of the same effects on the growth of industrialized countries also affect the growth of emerging countries. However, this was not the case for the production of natural resources, which did not suffer from Dutch Disease. The Dutch Disease effect, which suggests that natural resources have a detrimental influence, was incorrect.
The rate of government borrowing and the amount of foreign help a country received were also statistical significance coefficients in this conclusion. Because of the negative correlation between these variables and economic growth, the findings of this study were not surprising. Coefficients for government debt and foreign aid were both -0.04 and -0.09. These findings were also in line with theories that suggest that while large levels of debt and foreign aid can aid a country’s long-term development, they can have the opposite effect in the near term.
Conclusions
To find out what causes economic growth, this research relies on Ordinary Least Squares regressions. After then looks at how the variables have changed over time to see if any patterns emerge. There is a strong correlation between the volume of trade and the production of natural resources in developing countries and economic growth throughout time. Higher life expectancy and an increase in investment are also positively affecting economic growth. However, this outcome only applied to two of the three-time periods. Some of these factors have a similar impact on economic growth in industrialized and developing countries. The Dutch Disease Effect was also not discovered.
It is possible that this research does not consider the impact of some parameters on the growth of the economy. In the first place, the study shows conflicting conclusions about the effect of capital structure on economic growth. In one time period, foreign investment had a favorable impact on economic growth, whereas it had a negative impact in another. There needs to be more research into foreign direct investment’s role in emerging countries.
It also fails to support the conditional convergence theory, according to which large economies grow at a slower pace. It is possible to explain this result by the fact that most developing countries have the same GDP per capita level at the outset. It is possible to assess if developing economies with similar GDP per capita levels demonstrate convergence in the long run through additional research. Government debt and development aid inflows have inconsistent effects on economic growth, as well, according to the models. Government debt and high levels of foreign aid have a detrimental impact on the economy, according to the first model. However, adding more models doesn’t contribute anything to the findings of this study, which means that more research will be needed to fill the gap.
Even though this study has important political consequences for underdeveloped countries, additional research is needed. Better information is needed. Some of the growth-related questions can be answered by studying specific case studies in developing economies that are faster. To confirm the findings of this work, future researchers should conduct time series or panel analyses with a similar dataset. Developing countries could benefit greatly from the policy recommendations offered by this research.
References
Combes, J. L., Kinda, T., Ouedraogo, R., & Plane, P. (2019). Financial flows and economic growth in developing countries. Economic Modelling, 83, 195-209.
Hu, H., Xie, N., Fang, D., & Zhang, X. (2018). The role of renewable energy consumption and commercial services trade in carbon dioxide reduction: Evidence from 25 developing countries. Applied energy, 211, 1229-1244.
Le, T. H., & Nguyen, C. P. (2019). Is energy security a driver for economic growth? Evidence from a global sample. Energy Policy, 129, 436-451.
McClelland, D. C. (2019). The achievement motive in economic growth (pp. 53-69). Routledge.
Wang, S., Li, G., & Fang, C. (2018). Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renewable and Sustainable Energy Reviews, 81, 2144-2159.
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