Analyzing Social and Economic Factors Affecting Global Economic Well-being

Introduction

The urgency of the topic

Despite the absence of a single definition of well-being, OECD (2013) argues that most experts and ordinary people around the world would agree that economic well-being requires meeting various human needs, some of which are essential (e.g being in good health), and includes the ability to pursue one’s goals, to thrive and feel satisfied with their life OECD (2013) also argues that since well-being is a complex phenomenon and many of its determinants are strongly correlated with each other, assessing well-being requires a comprehensive framework that includes a large number of components and that, ideally, allows gauging how their interrelations shape people’s lives Reflecting this multi-dimensional approach, the OECD’s Better Life Initiative, presented in OECD (2011), identifies for understanding and measuring people’s well-being Therfore, we can conclude that economic well-being is of the most important fundamental to the human well-being There is no individual method of measurement or seperated factor that can 100% reflect an economy’s well-being One of the most popular and effcient factor researchers use for analysing economic well-being is through GDP per capita

Gross Domestic Product (GDP) per capita is a core indicator of economic performance and commonly used as a broad measure of average living standards or economic wellbeing (OECD, 2013) The fact that the GDP per capita divides a country's economic output by its total population makes it a good measurement of a country's standard of living, especially since it tells you how prosperous a country feels to each of its citizens Therefore, GDP per capita is the one metric that perfectly meets the same definition with Economic Well-being

According to the Effective States and Inclusive Development (ESID) Research Centre, economic well-being is important as a means to fuel progress in social terms – including increasing well-being and equity – rather than increasing economic output as an objective in itself When economies enhance, governments can tax that revenue and gain the capacity and resources needed to provide the public goods and services that their citizens need, like healthcare, education, social protection and basic public services Further to benefits provided by the governments, inclusive growth brings wider material gains Economic well-being creates wealth, some of which goes directly into the pockets of employers and workers As people earn higher incomes and spend more money, this enables people to exit poverty and gain improved living standards Economic growth theories highlight the different ways in which the present economic activities and other factors can have an influence on future economic developments and can also identify sources that may lead to continued economic growth Understanding the need for economic growth for the evolution and well being of the human race, our group decided to do some research about the topic “ Analyzing Social and Economic Factors Affecting Global Economic

Objective of the study

The study focuses on the social and economic factors influencing the GDP per capita all over the world, then implicating the overall effect on global economic well-being The study consists of seven factors:

● Manufacturing Contribution to the Economy

After the Econometrics course, we recognize the importance of this subject to social life as a whole In order to have a better understanding aboutEconometrics and its application in real life, our group would like to develop a report under the guidance of PhD Dinh Thi Thanh Binh In this report, with the help of econometrics analysis tool STATA, we will clarify and analyze the topic as mentioned above.

Literature review

Economic growth theory

In simple terms, economic growth is the increase in the inflation- adjusted market value of the goods and services produced by an economy over time, which is most cases a year It is measured as the percent rate of increase in real gross domestic product (GDP), usually in per capita terms Economists have used both theory and empirical research to explain the cause of economic growth.

The neoclassical growth theory was introduced by Ramsey (1928) but it was Solow (1956) who put forth its most popular model Assuming exogenous technological change, constant returns to scale, substitutability between capital and labour and diminishing marginal productivity of capital, the neoclassical growth models have made three important claims

First, an increase in the capital-to-labour ratio (investment and savings ratio) is the key source of economic growth Second, economies will eventually reach a state at which no new increase in capital will create economic growth (steady state), unless there are technological improvements to enable production with fewer resources Third, for the same amount of capital available, the less advanced economies would grow faster than the more advanced ones until steady state is reached, and as such economic convergence is to be achieved.

Overview of researches related to factors affecting GDP per capita

A wide range of studies has investigated the factors underlying economic performance Using differing conceptual and methodological frameworks, these studies have placed emphasis on a different set of explanatory parameters and offered various insights to the sources of economic growth

Trade is central to ending global poverty Countries that are open to international trade tend to grow faster, innovate, improve productivity and provide higher income and more opportunities to their people Open trade also benefits lower-income households by offering consumers more affordable goods and services Integrating with the world economy through trade and global value chains helps drive economic growth and reduce poverty—locally and globally In general, trade has a positive and significant impact on economic growth, which is consistent with the evidence in the empirical literature A one percent rise in the average trade to GDP ratio leads to an increase in the average GDP per capita growth by about one-half (0.47) percentage point However, exporting has a higher impact on growth (1.02) than importing (0.76) (Maureen Were, 2015)

Investment is the most fundamental determinant of economic growth identified by neoclassical growth theories The importance attached to investment has led to an enormous amount of empirical studies examining the relationship between investment and economic growth Nevertheless, findings are not conclusive Foreign Direct Investment (FDI) has recently played a crucial role in internationalizing economic activity and it is a primary source of technology transfer and economic growth This major role is stressed in several models of endogenous growth theories The empirical literature examining the impact of FDI on growth has provided more-or-less consistent findings affirming a significant positive link between the two factors (Borensztein et al 1998; Hermes and Lensink 2000; Lensink and Morrissey 2006) An increase in investment should be a boost to economic growth.

According to Daniel L Thornton, Vice President and Economic Adviser, A higher saving rate does mean less consumption, but it could also result in more capital investment and, ultimately, a higher rate of economic growth In this respect, it is interesting that the growth rate of real GDP has been higher on average when the personal saving rate is rising than when it is falling

The first chapter of Industrialization as the driver of sustained prosperity

(UNIDO, 2020) states that manufacturing drives economic growth There is clear evidence that a thriving manufacturing sector is key to increased productivity, and thereby economic growth The advent of manufacturing in the eighteenth and nineteenth centuries revolutionized the productive structure of Europe and the United States, and industrialization has been the driving force behind more recent economic miracles, such as the transformation of East Asian economies since the 1960s This is because manufacturing offers several productive advantages First, mass production entails economies of scale: the more units produced, the lower the per-unit cost, and thereby increasing the value of outputs per input Second, manufacturing tends to have strong linkages to other parts of the economy, creating demand for skills, inputs, manufacturing components, transportation and storage This means that growth in manufacturing boosts growth throughout a broader set of activities, including in the service sector Third, most innovation and technological advances originate in the manufacturing sector, which can then feed into other economic sectors, making them more productive as well The relationship between industry and growth generally holds across countries and income levels International data confirms that the proportion of manufacturing in the economy rises as gross domestic product (GDP) increases for low, lower-middle and upper-middle- income countries This correlation only reverses once a country becomes a high- income economy, where services start to take a relatively higher share than manufacturing.

It is a widely accepted view in economics that the growth rate of the GDP of an economy increases employment and reduces unemployment This theoretical proposition relating output and unemployment is called “Okun’s Law” This relation is among the most famous in macroeconomics theory and has been found to be held for several countries and regions mainly, in developed and developing countries (Lee, 2000; Farsio and Quade, 2003; Christopoulos, 2004; Daniels and Ejara, 2009) Okun’s study remains an important theory It has been discussed and updated by much economic research this law states that a 1% reduction in the unemployment rate would reduce approximately 3% more output In fact, Okun postulated that a 1% increase in the growth rate above the trend rate of growth would lead only to 0.3% in the reduction of unemployment Reversing the causality, a 1% increase in unemployment will mean roughly more than 3% loss in GDP growth.

According to neoclassical growth theory, the less advanced economies would grow faster than the more advanced ones until steady state is reached, and as such economic convergence is to be achieved This means poorer (developing) countries (or regions of countries) grow faster than richer ones (developed) and therefore catch-up on them However, it is no deny that developed countries will have higher GDP per capital than developing countries.

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 brief review of the literature above indicates that while there are seemingly infinite factors that may affect economic well-being, 6 factors, including Trade, Investment, Savings, Unemployment Rate, Manufacturing Sector Contribution to The Economy as a whole, Countries’ Development Level, account for largest global economic well-being participation Attempting to expand the analysis of interconnected economies to the global extent, not only an individual economy, we have chosen to take relevant data of a large number of countries in the world without any classification due to region or any irrelevant factors Six independent variables representative for 6 factors above, included in the multiple linear regression, are expected to decrease omitted variable bias.

To measure economic well-being, the dependent variable used is GDP per capita

It means that GDP per capita increasing will indicate positive economic well- being and vice versa Trade is measured as the sum of the imports and exports of goods and services as a percentage of a country’s GDP Based on the literature, we created this simple model to test that a positive relationship existed between Trade activity and Economic Well-being In terms of the Investment as an independent variable, it is recorded as Foreign Direct Investment measured by the sum of the net inflows of equity capital, reinvestment of earnings, and other long-term capital as a percentage of GDP This factor is quantitatively shown by net inflows rather than outflows because net inflows directly reveal investment into an economy As the same with Trade, this analysis we aim for is also to test the positive link between Investment and Economic Well-being As for Savings, we calculate the difference between disposable income and consumption, including net transfers to display as the representative independent variable Through analysis, we expected a higher rate of personal Savings means higher rate of Economic Well-being Next to the Unemployment factor, is measured by the unemployment rate in the total labor force In contrast to Trade, Investment, or Savings, it is theoretically predicted that a lower unemployment rate goes with a higher GDP With regard to Manufacturing Sector contribution, this independent variable is reported by the Net output of the manufacturing sector after adding up all outputs minus intermediate inputs as a percentage of GDP The relationship between Manufacturing and Economic Well-being, as discussed in The Literature Review, depends on the individual economy So, we do not make the prediction about that Last but not least, we also use a dummy variable which is valued by 1 if this is a Developed country and 0 with developing economies and economies in transition Based on reference research, we expect that developed countries will have higher GDP per capita

All of the data for this research was gathered from the World Bank database and the United Nations The interpretation and source of all of the variables used can be seen in the Figure below All data used was reported for 2019 with 176 country observations used in this analysis; they are listed in the Appendix

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

Using des command from Stata software to analyse variables, we have the following results: des GDPpc Trade NetinFDI Savings Unemployment Manufacturing Developed storage display value variable name type format label variable label

NetinFDI double %10.0g Net.in.FDI (%GDP)

These are data about factors that have effect on economic well-being We gathered data of 176 countries in 2019 (detailed source in Appendix) After judging carefully, we decided to choose GDPpc (GDP per capital) as dependent variable and 6 other variables (Trade, Net in FDI, Savings, Unemployment, Manufacturing, Developed) as independent variables of our model.

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

Looking at the results, we can see a big difference between min GDPpc and max GDPpc (max GDPpc"8 times min GDPpc), proving a big gap between developed countries and in-transition, developing countries Beside that, there were also negative values given in Savings and Net in FDI variables Therefore, the dataset has a large variety, optimizing the analysis for global implication.

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 have a correlation with GDPpc Therefore, we can include all 6 independent variables in the model Besides, Developed and Trade variables have the highest correlation with GDPpc, with 0.6074 and 0.413 respectively Moreover, while 3 independent variables, namely Trade, Savings, Developed have a positive correlation with

GDPpc, 3 other independent variables (NetinFDI, Unemployment,

Manufacturing) have a negative correlation with GDPpc.

Pre-checking the correlation between independent variables to each other, with r(XJ, XK) shown on the correlation matrix above, there is no r(XJ, XK) higher than 0.8 Therefore, it is seemingly presented that multicollinearity is still not revealed at least till this time There will be the step of official checking for the multicollinearity problem, later (on 2 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 the OLS states that the model is linear in parameters This can be shown by the simple linear regression equation and determines that the x and y values have a linear relationship, as in this equation: y = β0 + β1x + u Our model meets this assumption since there are linear relationships between GDPp.c and our 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:

This assumption requires the sample outcomes on our independent variables are not all the same values During the process of building the model, we checked our dataset with multiple methods (corr, des, tab, sum, ) All the results of these tests do not violate this assumption, ensuring that our dataset and model fit the assumption.

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

4 assumptions of the OLS, we can not run our model, that proves the suitability of our model with 4 basic OLS assumptions In the next part of our study, further analysis and correction will be performed to optimize our estimation.

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.

There are multiple consequences of multicollinearity if our model violates this theory Firstly, the coefficient estimates can swing wildly based on which other independent variables are in the model, then the coefficients become very sensitive to small changes in the model Beside that, it also reduces the precision of the p-values, which affect the precision of hypothesis tests.

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

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

 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

In particular, with the sample we have, the estimated result shows that one more percent of the sum of the imports and exports compared to the country’s GDP will increase average GDP per capita by 0.2%, holding other factors fixed.

 Investment (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) has no statistically significant effect on economic well-being.

 Savings (The difference between disposable income and consumption, including net transfers) has a statistically significant effect on economic well-being.

In particular, with the sample we have, the estimated result shows that one more percent of the Gross National Savings compared to the country’s GDP will increase average GDP per capita by 1.5%, holding other factors fixed.

 Unemployment Rate (The unemployment rate in the total labor force) has no statistically significant effect on economic well-being.

 Manufacturing Sector Contribution to the Economy (The Net output of the manufacturing sector after adding up all outputs minus intermediate inputs as a percentage of GDP) has no statistically significant effect on 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.

In particular, with the sample we have, the estimated result shows that Developed countries have higher average GDP per capita compared with Developing and In-transition countries by 78.67%, holding other factors fixed.

Conclusion with Policy implication

The analysis overall led to a rejection to our estimation of the tight relationship between Foreign Investments, Unemployment rate, Manufacturing sector share in the economy and the GDP capita, which is the representative for economic well-being Furthermore, according to the regresion model, Trade, mearsured by the sum of export and import, does have influence on the economic well-being but with a little share The case of National Savings does not differ Most interestingly, the final model proved that the development level of a country definitely have weighty, direct and tight-nit impact to the economic well-being of that nation

Therefore, we can reach to a conclusion that the question how to improve a country’s economic well-being is majorly directed to the question how to advance the development level Development is a concept that is difficult to define; it is inevitable that it will also be challenging to construct development taxonomy Countries are placed into groups to try to better understand their social and economic outcomes The most widely accepted criterion is labeling countries as either developed or developing countries Countries with relatively high levels of economic growth and security are considered to have developed economies However, If per capita gross domestic product is high but a country has poor infrastructure and income inequality, it would not be considered a developed economy Social, or noneconomic factors, such as the human development index, the standard of living, social equality in particular, also play a significant role in the national development level

People in general, or the governments in particular who want to build up an economy with sustainable base of well-being, need to enlarge the question to the wider extent of the society Not only all about economic factors, but we have to balance the social, humans-driven advantages with economic benefits which will not guarantee the well-being for the economy In other words, it is not deniable that sustainab development is the only means of establishing a strong economy with well-being

Stick with econmic factors with an aim to improve Trade, Savings, etc, we have founded5 easy steps to enhance the countries’ development level in all over the world, especially developing countries:

Obviously, the fewer resources an average family uses, the lower the nation’s ecological footprint Developing countries may not be able to afford electric or semi-electric cars, but their people can conserve both money and oxygen by carpooling, riding bikes and reusing grocery bags.

At the level of foreign advocacy, there are already influential notables arguing for the synergy between alleviating poverty and quelling climate change Lord Nicholas Stern, chairman of the Grantham Research Institute on Climate Change and the Environment, warned against resorting to high-carbon-intensive resources to help impoverished countries “The world is underinvesting in infrastructure, especially in developing countries where there are the largest unmet needs,” he wrote recently For this reason, he encouraged governments not to separate climate and environmental funds from foreign aid, arguing that the two had to go hand-in-hand in order to produce long-term benefits.

All levels of education are important stepping-stones to development, from the fundamentals of kindergarten, to the advanced quantum physics courses at the university Each class ought to be taught with the overarching goals of quality of life and economic improvement in mind Education stops terrorist groups from gaining strength and trains doctors and scientists to research and cure diseases.

It is one of the primary movers that help impoverished nations to help themselves

Education is most valuable to a developing country’s most vulnerable groups. The most common demographic among all of these populations—farmers, small- scale producers, victims of epidemics and terrorist groups—are women Children of both genders are vulnerable as well, but the impoverished boys who do not die prematurely or join the terrorists are more likely to have enough social mobility to get educated and leave than girls In the least educated African countries— Somalia, Niger, Liberia, Mali—over 70 percent of girls between seven and

By empowering women and equalizing academic opportunity, countries can increase incomes by average They can do this by investing in schools closer to rural areas so that the children of farmers do not have to walk hours each day to get to and from school, straining their parents’ time and resources in the process. That way, neither parents nor children would feel pressure to force a decision between farm work and schoolwork and the poorest populations could begin to make progress.

Americans have seen firsthand what happens when big businesses and lobbyists become too deeply involved with politicians When it happens in third-world countries, their poorest, most disadvantaged citizens are the ones who suffer. This often leads to violent uprisings with scads of victims on both sides There’s a reason why college majors such as international relations and politics are practically universal Aligning with people who have considerable political power and pathetically few scruples seldom benefits the poorer country For that reason it is imperative that the educated learn to choose their political allies carefully in order to make the greatest leaps in ecological, economic and humanitarian development.

5 Reform thesystems of food and aid distribution

So many millions of people still suffer from world hunger each day Their problem springs less from stinginess among foreign taxpayers, but from inefficient systems of distribution Here again, the rally call ought to be to support Africans rather than the inexperienced, inadvertently patronizing, members of the aid business Instead of pouring money into resources, shipping and energy costs, developed countries ought to invest in local African businesses so that the people can more effectively improve their own circumstances without having to resort to the whims of potentially corrupt and incompetent leaders.

Economic well-being can be impacted by a wide variety of factors and this model only encapsulated the ones we deemed important for the scope of this research paper To truly determine the scope and impact of some social and economical factors on economic well-being, it is important to narrow the scope of the model.Examining a full economy and economic structure required many variables to be accounted for and reducing the field perhaps by sector or some other form may allow a more significant and nuanced relationship between trade and economic growth to be uncovered.

1 OECD (2013), “Economic well-being”, in OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth, OECD Publishing, Paris DOI: https://doi.org/10.1787/9789264194830-5-en

2 OECD (2013), “GDP per capita”, in National Accounts at a Glance 2013, OECD Publishing, Paris DOI: https://doi.org/10.1787/na_glance-2013-5- en

3 Ndedi, A., 2017 Analyzing Factors Affecting Economic Growth within

CEMAC Countries [ebook] Available at:

4 Sen, K., 2021 Why should I care about economic growth? [Blog] Available at: [Accessed 15 December 2021].

5 2015 The main determinants affecting economic growth [ebook]

[Accessed 15 December 2021].

6 M Kreishan, F., 2011 Economic Growth and Unemployment: An Empirical Analysis [ebook] Available at:

[Accessed 15 December

7 Econstor, 2021 The impact of social factors on economic growth:

Empirical evidence for Romania and European Union countries [online] Available at:

8 Were, M., 2015 Differential effects of trade on economic growth and investment: A cross-country empirical investigation [ebook] Available at:

9 Yong, L., 2021 Why industrial development matters now more than ever before [Blog] Available at: [Accessed 15 December 2021].

10.Paprotny, D., 2020 Convergence Between Developed and Developing Countries: A Centennial Perspective [ebook] Available at:

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

Tiêu đề	Analyzing Social and Economic Factors Affecting Global Economic Well-being
Tác giả	Tố ng Khang An, Nguyễn Ngọ c Anh, Nguyễn Hả i Đă ng, Nguyễn Hữ u Hoà ng, Lạ i Quang Huy
Người hướng dẫn	PTS. Dinh Thi Thanh Binh
Trường học	FOREIGN TRADE UNIVERSITY
Chuyên ngành	ECONOMETRICS
Thể loại	Study Report
Năm xuất bản	2021
Thành phố	Ha Noi

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Số trang	32
Dung lượng	366,65 KB