Introduction
The urgency of the topic
Although well-being lacks a singular definition, the OECD (2013) suggests that both experts and the general public agree that economic well-being encompasses the fulfillment of various human needs, including essential aspects like good health, the pursuit of personal goals, and overall life satisfaction Recognizing the complexity of well-being and the strong interconnections among its determinants, the OECD emphasizes the need for a comprehensive framework to assess well-being effectively This multi-dimensional perspective is reflected in the OECD’s Better Life Initiative (2011), which aims to measure and understand well-being Ultimately, economic well-being is a fundamental component of overall human well-being, and while no single measurement method can fully capture it, GDP per capita remains a widely used and effective indicator for analyzing economic well-being.
Gross Domestic Product (GDP) per capita is a key indicator of economic performance and serves as a broad measure of average living standards and economic well-being By dividing a country's economic output by its total population, GDP per capita effectively reflects the standard of living and prosperity experienced by each citizen Thus, it aligns closely with the concept of Economic Well-being, making it an essential metric for assessing a nation's overall quality of life.
Economic well-being is crucial for advancing social progress, enhancing well-being, and promoting equity, rather than merely focusing on increasing economic output As economies grow, governments can leverage tax revenues to provide essential public goods and services such as healthcare, education, and social protection Inclusive growth not only benefits government initiatives but also leads to broader material gains, creating wealth that directly benefits employers and workers Higher incomes allow individuals to escape poverty and improve their living standards Economic growth theories explore how current activities and various factors can influence future economic developments, identifying potential sources for sustained growth Recognizing the importance of economic growth for human evolution and well-being, our research group has undertaken a study on the social and economic factors affecting global economic development.
Objective of the study
This study examines the social and economic factors that impact GDP per capita globally, highlighting their significant role in shaping overall economic well-being It identifies seven key factors that contribute to these dynamics.
● Manufacturing Contribution to the Economy
Following the Econometrics course, we acknowledge the significance of this discipline in societal contexts To deepen our understanding of Econometrics and its real-world applications, our team intends to create a report under the mentorship of PhD Dinh Thi Thanh Binh Utilizing the econometric analysis tool STATA, we aim to elucidate and examine the discussed topic in detail.
Literature review
Economic growth theory
Economic growth refers to the rise in the inflation-adjusted market value of goods and services produced by an economy over time, typically measured annually It is expressed as the percentage increase in real gross domestic product (GDP), often on a per capita basis Economists utilize both theoretical frameworks and empirical research to analyze the factors driving economic growth.
The neoclassical growth theory, initially introduced by Ramsey in 1928 and popularized by Solow in 1956, is characterized by several key assumptions, including exogenous technological change, constant returns to scale, substitutability between capital and labor, and diminishing marginal productivity of capital These models make three significant claims regarding economic growth.
An increase in the capital-to-labour ratio, reflecting higher investment and savings, is crucial for driving economic growth However, economies will ultimately reach a steady state where additional capital does not lead to further growth unless technological advancements allow for more efficient production Moreover, less advanced economies are likely to experience faster growth compared to their more advanced counterparts until they reach this steady state, facilitating economic convergence.
Overview of researches related to factors affecting GDP per capita
Numerous studies have explored the factors influencing economic performance, employing diverse conceptual and methodological approaches These investigations highlight various explanatory parameters and provide valuable insights into the drivers of economic growth.
Trade is essential for eradicating global poverty, as countries engaged in international trade experience faster growth, increased innovation, and improved productivity, leading to higher incomes and more opportunities for their citizens Open trade also benefits low-income households by providing access to more affordable goods and services By integrating into the world economy through trade and global value chains, nations can stimulate economic growth and alleviate poverty both locally and globally Empirical evidence supports the notion that trade positively influences economic growth; a one percent increase in the trade-to-GDP ratio correlates with a 0.47 percentage point rise in GDP per capita growth Notably, exporting has a more pronounced effect on growth (1.02) compared to importing (0.76).
Investment is a key driver of economic growth, as highlighted by neoclassical growth theories, leading to extensive empirical research on its relationship with growth Despite varied findings, Foreign Direct Investment (FDI) has emerged as a vital factor in global economic activity, serving as a primary source of technology transfer and growth Endogenous growth theories emphasize FDI's significant positive impact on economic growth, supported by consistent empirical evidence (Borensztein et al 1998; Hermes and Lensink 2000; Lensink and Morrissey 2006) Thus, increasing investment is essential for enhancing economic growth.
Daniel L Thornton, Vice President and Economic Adviser, notes that while a higher saving rate typically leads to reduced consumption, it can also foster increased capital investment, ultimately driving economic growth Notably, the average growth rate of real GDP tends to be higher during periods of rising personal saving rates compared to times when saving rates decline.
The first chapter of Industrialization as the driver of sustained prosperity
Manufacturing is a crucial driver of economic growth, as evidenced by its role in enhancing productivity and transforming economies Historically, the rise of manufacturing in the 18th and 19th centuries reshaped Europe's and the United States' economic landscapes, while more recently, East Asian economies have experienced significant growth since the 1960s through industrialization Key advantages of manufacturing include economies of scale, where increased production lowers per-unit costs, and strong linkages to other economic sectors that stimulate demand for skills, inputs, and services Furthermore, the manufacturing sector is a primary source of innovation and technological advancements that benefit the broader economy International data reveals a consistent correlation between the proportion of manufacturing in an economy and rising GDP across various income levels, with this trend reversing only in high-income countries where services begin to dominate.
Okun's Law is a fundamental principle in economics that illustrates the relationship between GDP growth and unemployment rates, suggesting that an increase in GDP correlates with higher employment and lower unemployment This theory has been validated across various countries, both developed and developing, emphasizing its significance in macroeconomic discussions Specifically, Okun's Law posits that a 1% decrease in unemployment can lead to an approximate 3% increase in output, while a 1% rise in unemployment results in a more than 3% decline in GDP growth This enduring theory continues to be relevant in economic research and policy-making.
Neoclassical growth theory posits that less developed economies will experience faster growth rates compared to their more advanced counterparts until they reach a steady state, leading to economic convergence This implies that developing countries tend to grow at a quicker pace than developed nations, allowing them to catch up economically However, it is important to acknowledge that developed countries will still maintain a higher GDP per capita than developing nations.
Set up Economic and Econometric models
Economic model
After analysing variables, our model includes:
GDPp.c (GDP per capita, US$)
Trade: sum of exports and imports/GDP (%GDP)
Investment: Net in FDI net inflow (%GDP)
Savings: Gross National Savings/GDP (%GDP)
Manufacturing: value contributed in GDP (%GDP)
Developed: 1 if the observe is developed country, otherwise is 0
=> We have the economic model as for our interest:
GDPp.c = + Trade + Net in FDI + Savings + Unemployment +
Econometric model
To exactly assess the relationship between GDPp.c and other variables, we also need to calculate about the effect of random factors (u)
Then we have Sample Regression Model:
GDPpc = + Trade + Net in FDI + Savings + Unemployment +
General Data Description
A review of the literature reveals that while numerous factors influence economic well-being, six key elements—Trade, Investment, Savings, Unemployment Rate, Manufacturing Sector Contribution, and Countries’ Development Level—play a significant role in global economic participation To enhance the analysis of interconnected economies on a global scale, we have selected relevant data from a diverse range of countries, avoiding classification by region or other unrelated factors The six independent variables representing these factors will be incorporated into a multiple linear regression model to mitigate omitted variable bias.
To measure economic well-being, the dependent variable used is GDP per capita
An increase in GDP per capita signifies improved economic well-being, while a decrease indicates the opposite Trade, defined as the total of imports and exports as a percentage of GDP, is hypothesized to have a positive correlation with economic well-being Similarly, Foreign Direct Investment (FDI), measured by net inflows of equity capital and long-term capital as a percentage of GDP, is expected to positively influence economic well-being, as net inflows reflect direct investment into an economy Furthermore, personal savings, calculated as the difference between disposable income and consumption, are anticipated to correlate positively with economic well-being In contrast, a lower unemployment rate is theorized to align with higher GDP, indicating a negative relationship with economic well-being The manufacturing sector's contribution, represented by net output as a percentage of GDP, varies by economy, making predictions about its relationship with economic well-being less certain Lastly, a dummy variable differentiates developed countries, expected to have higher GDP per capita, from developing economies and economies in transition.
This research utilized data sourced from the World Bank and the United Nations, focusing on 2019 statistics across 176 countries, detailed in the Appendix The interpretation and sources of all variables employed in the analysis are illustrated in the accompanying figure.
Factor Variable name in Stata Interpretation Expect
Well-being GDPpc Percent of GDP per capita Worldban k
Trade Trade The sum of the imports and exports of goods and services as a percentage of a country’s GDP
Investments NetinFDI FDI measured by the sum of the net inflows of equity capital, reinvestment of earnings, and other long-term capital as a percentage of GDP
Savings Savings The difference between disposable income and consumption, including net transfers
Unemploymen t Rate Unemploymen t The unemployment rate in the total labor force - Worldban k
Contribution Manufacturing The Net output of the manufacturing sector after adding up all outputs minus intermediate inputs as a percentage of GDP
Level Developed Dummy variable equals 1 with developed economy and 0 with developing and in- transition economy
Statistics Dercription of Variables
The analysis conducted using the 'des' command from Stata software reveals key insights into several variables, including GDP per capita (GDPpc), trade, net foreign direct investment (NetinFDI), savings, unemployment, and manufacturing The results provide a comprehensive overview of these variables, detailing their types, formats, and labels, which are essential for understanding the economic landscape of developed countries.
NetinFDI double %10.0g Net.in.FDI (%GDP)
In our analysis of economic well-being, we examined data from 176 countries in 2019, as detailed in the Appendix We selected GDP per capita (GDPpc) as the dependent variable, while identifying six independent variables: Trade, Net Foreign Direct Investment (FDI), Savings, Unemployment, Manufacturing, and Development status.
Afterwards, we use sum command to have further information about min, max, mean and standard deviation of these variables: sum GDPpc Trade NetinFDI Savings Unemployment Manufacturing Developed
Variable | Obs Mean Std Dev Min Max
The analysis reveals a significant disparity in GDP per capita, with the maximum value being eight times greater than the minimum, highlighting the stark contrast between developed and developing nations Additionally, the presence of negative values in the Savings and Net Foreign Direct Investment (FDI) variables indicates considerable variability within the dataset, enhancing its relevance for global economic analysis.
With dummy variable developed, we also use tab command to check the development level of the countries chosen in our dataset: tab Developed
The result shows that, in our dataset, 32 developed countries (18.18%) were chosen for our study, while other 144 undeveloped and developing countries account for 81.82% on a whole.
Quantitative analysis
Estimation of econometric model
Economic model: linear regression model
Data type: Cross-sectional and dependent variables are quantitative and continuous
Method to estimate coefficients: OLS
Check the correlation of the dependent variable (GDP per capita) and independent variables (NetinFDI, Savings, Unemployment, Manufacturing, and Developed)
We use a correlation matrix by the below command statement on Stata to test As result: corr GDPpc Trade NetinFDI Savings Unemployment Manufacturing Developed
| GDPpc Trade NetinFDI Savings Unemployment Manufa~g Develo~d -+ -
From the figure above, it is evidently depicted that:
So, we can conclude that all listed independent variables including Trade,
NetinFDI, Savings, Unemployment, Manufacturing, and Developed exhibit a significant correlation with GDP per capita (GDPpc), allowing the inclusion of all six independent variables in the model Among these, Developed and Trade demonstrate the strongest correlations with GDPpc, recorded at 0.6074 and 0.413, respectively Additionally, three independent variables—Trade, Savings, and Developed—show a positive correlation with GDPpc.
GDPpc, 3 other independent variables (NetinFDI, Unemployment,
Manufacturing) have a negative correlation with GDPpc.
The correlation matrix indicates that the correlation coefficients between independent variables, r(XJ, XK), do not exceed 0.8, suggesting that multicollinearity is not yet evident A formal assessment of multicollinearity will be conducted in the subsequent section, titled "Diagnosing Model Problems."
Using the command statement in Stata to operate a regression model We have:
reg GDPpc Trade NetinFDI Savings Unemployment Manufacturing Developed
Source | SS df MS Number of obs = 176 -+ - F(6, 169) = 30.34 Model | 3.9211e+10 6 6.5351e+09 Prob > F = 0.0000 Residual | 3.6405e+10 169 215413685 R-squared = 0.5186 -+ - Adj R-squared = 0.5015 Total | 7.5615e+10 175 432088096 Root MSE = 14677
- GDPpc | Coef Std Err t P>|t| [95% Conf Interval] -+ - Trade | 101.9403 21.53721 4.73 0.000 59.42371 144.4569 NetinFDI | -335.6789 116.1891 -2.89 0.004 -565.0479 -106.3099 Savings | 482.8761 120.6021 4.00 0.000 244.7953 720.9568 Unemployment | -439.4177 243.1653 -1.81 0.073 -919.4505 40.61501 Manufacturing | -496.3073 209.7789 -2.37 0.019 -910.4319 -82.18273 Developed | 28727.64 3030.141 9.48 0.000 22745.83 34709.44 _cons | 1017.494 4391.756 0.23 0.817 -7652.273 9687.261 -
From the Stata’s estimation above, we have:
Variable Coefficient Estimated Coefficient Value
From that, We can build up the sample regression model:
GDPpc = 1017.494 + 101.9403Trade - 335.6789 NetinFDI + 482.8761 Savings - 439.4177 Unemployment - 496.3073 Manufacturing + 28727.64 Developed + u^ Or:
Some analysis from the above regression estimation:
There are 176 observations available for the regression model, with
SST = 7.5615e+10 or 7.5615 x 10^10 is the sum of the squared difference between and the mean of y(GDPpc) It means the total sample variation in the
SSR = 3.6405e+10 or 3.6405 x 10^10 is the sum of the squared difference between the predicted value of y and the mean of Y It means the sample variation in the residual ui
SSE = 3.9211e+10 or 3.9211 x 10^10 ías the sum of the squared difference between the observed and predicted value of y It means the sample variation in the
P-value of F = 0.0000 < 5% It means that estimated coefficients in the population regression model can not equal 0 at the same time.
Coefficient of Determination: R-squared = 0.5186 means that 6 independent variables can explain 51.86% of sample variation of GDPpc, while the other 48.14% is included in u (residual)
The standard deviation of residuals: Root MSE = 14677
The standard deviation of estimators seems to be large, with nearly
P-value of Unemployment is the one and only that is higher than 5%
Diagnosing model problem
The first assumption of Ordinary Least Squares (OLS) is that the model is linear in parameters, illustrated by the simple linear regression equation y = β0 + β1x + u This indicates a linear relationship between the x and y values Our model adheres to this assumption, as there are linear relationships between GDP per capita and the independent variables.
Assumption 2 of the OLS states that we pick a sample size from the population n. Our model does not violate this assumption since we picked data of 176 countries in 2019 (these countries have a wide range of economic metrics).
2.3 Assumption 3 - Sample variation in the explanatory variable:
To ensure the validity of our model, we verified that the sample outcomes of our independent variables are not uniform Throughout the modeling process, we employed various methods, including correlation analysis, descriptive statistics, frequency tables, and summary statistics, to examine our dataset The results from these tests confirmed that our dataset aligns with the necessary assumptions, thereby validating the model's integrity.
Assumption 4 of the OLS states that in the sample, there are no exact linear relationships among independent variables To ensure that our variables do not violate the assumption, we use command corr to check the correlation between variables: corr Trade NetinFDI Savings Unemployment Manufacturing Developed
| Trade NetinFDI Savings Unempl~t Manufa~g Develo~d
The result given by this command from Stata software shows that there are no exact linear relationships among our variables If the model violates any of these
To ensure the validity of our model, it is essential to adhere to the four fundamental assumptions of Ordinary Least Squares (OLS) If these assumptions are not met, we cannot proceed with our analysis In the subsequent section of our study, we will conduct further analysis and make necessary adjustments to enhance the accuracy of our estimations.
To see whether our model has problems with misspecification of functional form or not, we run the ovtest command on Stata software (Ramsey test), here is the result: ovtest
Ramsey RESET test using powers of the fitted values of GDPpc
Ho: model has no omitted variables
H0: Model has no omitted variables
We have P-value = 0.0001 < 5% => Reject at = 5%
=> Conclusion: Our current model has misspecification of functional form.
To solve this problem, we decided to generate a new variable: logGDP log(GDPpc) gen logGDP = log(GDPpc)
In the next step, we decided to replace our dependent variable GDPpc with our new variable logGDP, here is the new econometrics model:
= + Trade + Net in FDI + Savings + Unemployment +
Manufacturing + Developed logGDP = + Trade + Net in FDI + Savings + Unemployment +
Running OLS regression with dependent variable logGDP: reg LogGDP Trade NetinFDI Savings Unemployment Manufacturing Developed
Source | SS df MS Number of obs = 176 -+ - F(6, 169) = 22.13 Model | 25.7266667 6 4.28777778 Prob > F = 0.0000 Residual | 32.7464024 169 193765695 R-squared = 0.4400 -+ - Adj R-squared = 0.4201 Total | 58.4730691 175 334131823 Root MSE = 44019
- LogGDP | Coef Std Err t P>|t| [95% Conf Interval] -+ -
Trade | 0020407 0006459 3.16 0.002 0007656 0033159 NetinFDI | -.00214 0034847 -0.61 0.540 -.0090192 0047392 Savings | 0151233 0036171 4.18 0.000 0079829 0222638 Unemployment | 0066938 007293 0.92 0.360 -.0077032 0210909 Manufacturing | -.0009485 0062916 -0.15 0.880 -.0133688 0114718 Developed | 7866881 0908792 8.66 0.000 6072834 9660929 _cons | 3.148559 1317164 23.90 0.000 2.888537 3.40858 -
After having new coefficients of regression, we build our new SRF:
= 3.148559 + 0.0020407Trade - 0.00214Net in FDI + 0.0151233Savings + 0.0066938Unemployment - 0.0009485Manufacturing + 0.7866881Developed logGDP = 3.148559 + 0.0020407Trade - 0.00214Net in FDI + 0.0151233Savings + 0.0066938Unemployment - 0.0009485Manufacturing + 0.7866881Developed +
Running Ramsay test with our new model, here is what we have: ovtest
Ramsey RESET test using powers of the fitted values of LogGDP
Ho: model has no omitted variables
With p-value = 0.7695 > 5%, can’t reject H0 at = 5%.
=> Conclusion: Our new SRF doesn’t have any misspecification of functional form.
Multicollinearity can lead to significant issues in regression models, causing coefficient estimates to fluctuate dramatically depending on the inclusion of other independent variables This sensitivity to minor changes in the model undermines the reliability of the coefficients Additionally, multicollinearity diminishes the precision of p-values, adversely impacting the accuracy of hypothesis testing.
To make sure that our model doesn’t violate the theory we use two methods:
Method 1: Use command corr in the Stata software to check the correlation of variables: corr LogGDP Trade NetinFDI Savings Unemployment Manufacturing Developed
| LogGDP Trade NetinFDI Savings Unempl~t Manufa~g Develo~d
After analysing the result, we can clearly see that almost all values given are below 0.8 (except correlation of 2 exact same variables).
Method 2: Run command vif on independent variables: vif
All the values on vif column given by the test are F = 0.0000 R-squared = 0.4400 Root MSE = 44019
LogGDP | Coef Std Err t P>|t| [95% Conf Interval] -+ - Trade | 0020407 0006086 3.35 0.001 0008394 0032421 NetinFDI | -.00214 0020721 -1.03 0.303 -.0062305 0019506 Savings | 0151233 003758 4.02 0.000 0077046 0225421 Unemployment | 0066938 0064731 1.03 0.303 -.0060847 0194724 Manufacturing | -.0009485 0063969 -0.15 0.882 -.0135767 0116796 Developed | 7866881 0659169 11.93 0.000 6565616 9168147 _cons | 3.148559 127925 24.61 0.000 2.896022 3.401095 -
With Robust regression, we still have the same coefficients, but the standard errors became lower and the precision of the hypothesis tests (next step) was maximized.
There are two methods that we used to check the distribution of our residuals: graph and Jacque - Bera test.
The graph given by Stata software shows the distribution of our residuals, it fits the shape of normal distribution.
In order to confirm that our model’s residuals have normal distribution, Jacque - Bera test was performed in the Stata software: sktest u
Skewness/Kurtosis tests for Normality
Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
=> Jacque - Bera test confirmed that u has normal distribution
Hypothesis
So, after the diagnosing problems step above, we have the sample regression function after fix as:
= 3.148559 + 0.0020407Trade - 0.00214Net in FDI + 0.0151233Savings + 0.0066938Unemployment - 0.0009485Manufacturing + 0.7866881Developed logGDP = 3.148559 + 0.0020407Trade - 0.00214Net in FDI + 0.0151233Savings +Or
0.0066938Unemployment - 0.0009485Manufacturing + 0.7866881Developed + with regression model after robust:
reg LogGDP Trade NetinFDI Savings Unemployment Manufacturing Developed, robust
Linear regression Number of obs = 176 F(6, 169) = 47.19 Prob > F = 0.0000 R-squared = 0.4400 Root MSE = 44019
LogGDP | Coef Std Err t P>|t| [95% Conf Interval] -+ - Trade | 0020407 0006086 3.35 0.001 0008394 0032421 NetinFDI | -.00214 0020721 -1.03 0.303 -.0062305 0019506 Savings | 0151233 003758 4.02 0.000 0077046 0225421 Unemployment | 0066938 0064731 1.03 0.303 -.0060847 0194724 Manufacturing | -.0009485 0063969 -0.15 0.882 -.0135767 0116796 Developed | 7866881 0659169 11.93 0.000 6565616 9168147 _cons | 3.148559 127925 24.61 0.000 2.896022 3.401095 -
Test overall significance of the model
We apply F-Test as below:
Calculated F-statistics on Stata we have Fs = 47.19 ( )
So, we have F-statistics (G.19) > F(=2.16) ⇒ Reject H0⇒ The model is significant
Hypothesis test to check independent variable has a statistically significant effect on the dependent variable (LogGDP) or not
P-value of Trade variable = 0.001 < 5% ⇒ Reject H0 at confidence interval 5%
P-value of Trade variable = 0.303 > 5% ⇒ Can not Reject H0 at confidence interval 5%
P-value of Savings variable = 0.0000 < 5% ⇒ Reject H0 at confidence interval 5%
P-value of Unemployment variable = 0.303 > 5% ⇒ Can not Reject H0 at confidence interval 5%
P-value of Unemployment variable = 0.882 > 5% ⇒ Can not Reject H0 at confidence interval 5%
P-value of Developed variable = 0.000 < 5% ⇒ Reject H0 at confidence interval 5%.
Estimated results analysis
Trade (The sum of the imports and exports of goods and services as a percentage of a country’s GDP) has a statistically significant effect on economic well-being The effect is positive
According to our analysis, a 1% increase in the total of imports and exports relative to a country's GDP is associated with a 0.2% rise in average GDP per capita, assuming other factors remain constant.
Foreign Direct Investment (FDI), calculated as the total net inflows of equity capital, reinvested earnings, and other long-term capital as a percentage of GDP, does not have a statistically significant impact on economic well-being.
Savings (The difference between disposable income and consumption, including net transfers) has a statistically significant effect on economic well-being.
Our analysis indicates that a 1% increase in Gross National Savings relative to the country's GDP can lead to a 1.5% rise in average GDP per capita, assuming other factors remain constant.
Unemployment Rate (The unemployment rate in the total labor force) has no statistically significant effect on economic well-being.
The manufacturing sector's contribution to the economy, defined as the net output after subtracting intermediate inputs from total outputs and expressed as a percentage of GDP, does not significantly impact economic well-being.
A country’s Development Rate has a statistically significant effect on economic well-being
Developed countries have higher GDP per capita on average in comparison with developing and in-transition countries.
The analysis indicates that developed countries exhibit an average GDP per capita that is 78.67% higher than that of developing and in-transition countries, when controlling for other factors.
Conclusion with Policy implication
The analysis revealed that our initial estimation of the strong relationship between Foreign Investments, Unemployment rate, Manufacturing sector share, and GDP per capita, which reflects economic well-being, was rejected The regression model indicated that Trade, represented by the sum of exports and imports, does influence economic well-being, albeit to a limited extent National Savings showed similar results Notably, the final model demonstrated that a country's level of development has a significant, direct, and robust impact on its economic well-being.
Improving a country's economic well-being is fundamentally linked to advancing its development level, a complex concept that is challenging to define and categorize Countries are often classified as developed or developing based on their social and economic outcomes, with developed economies exhibiting high levels of growth and security However, a high per capita GDP alone does not qualify a country as developed if it suffers from poor infrastructure and significant income inequality Additionally, social factors such as the human development index, standard of living, and social equality are crucial in determining a nation's overall development level.
To build a sustainable economy that promotes well-being, it is essential for governments and society to consider a broader range of factors beyond just economic metrics Balancing social and human-driven advantages with economic benefits is crucial, as relying solely on economic growth does not ensure overall well-being Ultimately, sustainable development is the key to establishing a robust economy that supports the welfare of its citizens.
To enhance the development level of countries, particularly in the developing world, we propose five straightforward steps focused on economic factors that aim to improve trade and savings.
Reducing resource consumption at the family level directly lowers a nation's ecological footprint While developing countries may lack access to electric or semi-electric vehicles, individuals can still contribute to environmental conservation by carpooling, biking, and reusing grocery bags, which also helps save money and preserve oxygen.
Influential figures, such as Lord Nicholas Stern, advocate for the connection between poverty alleviation and climate change mitigation He cautions against using high-carbon resources to support impoverished nations, highlighting the global underinvestment in infrastructure, particularly in developing countries with significant unmet needs Stern emphasizes that climate and environmental funds should not be separated from foreign aid, as integrating these efforts is essential for achieving sustainable, long-term benefits.
Education at all levels, from kindergarten to university, plays a crucial role in personal and societal development Each educational stage should focus on enhancing quality of life and driving economic growth Furthermore, education is vital in preventing the rise of extremist groups and in preparing professionals, such as doctors and scientists, to research and address diseases effectively.
It is one of the primary movers that help impoverished nations to help themselves
Education is crucial for the most vulnerable groups in developing countries, particularly women, who represent a significant demographic among farmers, small-scale producers, and victims of crises While both boys and girls face educational challenges, impoverished boys often have better opportunities for social mobility and education, unlike girls In the least educated African nations, such as Somalia, Niger, Liberia, and Mali, over 70 percent of girls aged seven and older are not receiving an education, highlighting the urgent need for targeted educational initiatives.
Investing in schools near rural areas can empower women and equalize academic opportunities, ultimately increasing average incomes By reducing the long travel times for children of farmers, families will no longer have to choose between farm work and education, allowing the poorest populations to make significant progress.
The involvement of big businesses and lobbyists with politicians often leads to detrimental consequences for the poorest citizens, particularly in developing countries, resulting in violent uprisings with numerous casualties This reality underscores the importance of fields like international relations and politics, as aligning with unscrupulous political powers rarely benefits disadvantaged nations Therefore, it is crucial for the educated to carefully select their political allies to foster significant advancements in ecological, economic, and humanitarian development.
5 Reform thesystems of food and aid distribution
Millions of people continue to experience hunger globally, not due to a lack of generosity from foreign taxpayers, but because of inefficient distribution systems The focus should be on empowering Africans rather than relying on inexperienced aid workers Developed countries should invest in local African businesses, enabling communities to enhance their own situations without depending on potentially corrupt or incompetent leadership.
Economic well-being is influenced by numerous factors, and this research paper focuses on those deemed most relevant To accurately assess the impact of social and economic elements on economic well-being, it is essential to refine the model's scope By narrowing the analysis—potentially by sector—it may be possible to reveal a more detailed and significant relationship between trade and economic growth.
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A List of Countries used in research:
Central Europe and the Baltics
Latin America & Caribbean (excluding high income) Lebanon
Liberia Latin America & Caribbean Least developed countries: UN classification
Sri Lanka Lower middle income Low & middle income Lesotho
Moldova Maldives Middle East & North Africa Mexico
Middle income North Macedonia Malta
MontenegroMongoliaMozambiqueMauritiusMalaysiaNorth America
East Asia & Pacific (excluding high income)
Fragile and conflict affected situations
Heavily indebted poor countries (HIPC)
Namibia Niger Nigeria Nicaragua Netherlands Norway Nepal OECD members OmanOther small states Pakistan
Panama PeruPhilippines Poland Pre-demographic dividend Portugal
Paraguay West Bank and Gaza Post-demographic dividend Qatar
South Asia Saudi Arabia Singapore Sierra Leone
El Salvador Serbia Sub-Saharan Africa (excluding high income)
Sub-Saharan Africa Small states
Sweden Eswatini East Asia & Pacific (IDA & IBRD) Europe & Central Asia (IDA & IBRD)
TogoThailandTajikistanLatin America & Caribbean (IDA