Econometrics report analyzing the dependency of economic growth on the social factors in european union from 2011 to 2021

ABSTRACT 1 1. INTRODUCTION 2 2. LITERATURE REVIEW 4 2.1. Social factors and social factor indicators 4 2.2. Theoretical perspective on the relationship between Social Factors and Economic growth 5 2.3. Empirical Studies on the Relationship between Social Factors and Economic Growth 6 2.4. Research gap 7 2.5. Research hypothesis 7 3. MODEL SPECIFICATION AND DATA 9 3.1. Methodology 9 3.1.1. Method used to collect data 9 3.1.2. Method used to analyze data 9 3.2. Empirical Model 9 3.2.1. Model Specification 9 3.2.2. Variables Description 10 3.3. Data 13 4. STATISTIC DESCRIPTION OF VARIABLES 14 4.1. Description Statistics and Interpretation for each variable 14 4.2. Correlation matrix between variables 15 5. QUANTITATIVE ANALYSIS 18 5.1. Model selection Testing 18 5.2. Model defect identification 19 5.2.1. Multicollinearity Test 19 5.2.2. Heteroskedasticity Test 20 5.2.3. Test the autocorrelation of model 21 5.2.4. Test for crosssection correlation 21 5.3. Remedy model defect 22 6. CONCLUSION 25 REFERENCE 27 APPENDIX 31

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

-ECONOMETRICS 2

MIDTERM ASSIGNMENT

TOPIC: ANALYZING THE DEPENDENCY OF

ECONOMIC GROWTH ON THE SOCIAL FACTORS IN

EUROPEAN UNION FROM 2011 TO 2021

Class: KTEE318 Lecturer: Dr Dinh Thanh Binh GROUP:

Hanoi, June 2023

1 INTRODUCTION 2

2 LITERATURE REVIEW 4

2.1 Social factors and social factor indicators 4

2.2 Theoretical perspective on the relationship between Social Factors and Economic growth 5

2.3 Empirical Studies on the Relationship between Social Factors and Economic Growth 6

2.4 Research gap 7

2.5 Research hypothesis 7

3 MODEL SPECIFICATION AND DATA 9

3.1 Methodology 9

3.1.1 Method used to collect data 9

3.1.2 Method used to analyze data 9

3.2 Empirical Model 9

3.2.1 Model Specification 9

3.2.2 Variables Description 10

3.3 Data 13

4 STATISTIC DESCRIPTION OF VARIABLES 14

4.1 Description Statistics and Interpretation for each variable 14

4.2 Correlation matrix between variables 15

5 QUANTITATIVE ANALYSIS 18

5.1 Model selection Testing 18

5.2 Model defect identification 19

5.2.1 Multicollinearity Test 19

5.2.2 Heteroskedasticity Test 20

5.2.3 Test the autocorrelation of model 21

5.2.4 Test for cross-section correlation 21

5.3 Remedy model defect 22

6 CONCLUSION 25 REFERENCE 27 APPENDIX 31

This research aims to analyze the dependency of economic growth on social factorswithin the European Union from 2011 to 2021 using quantitative methods The studyutilizes data from reputable sources such as the World Bank and Eurostat The socialfactors examined include the poverty rate, life expectancy, expected years ofschooling, and unemployment rate Through econometric analysis, the study identifiesthe relationships between these social factors and economic growth, providingvaluable insights for policymakers The findings emphasize the significance ofaddressing poverty, enhancing human development, promoting education, reducingincome inequality, improving healthcare, and addressing unemployment to fostersustainable and inclusive economic growth The study acknowledges limitations such

as potential data biases and the narrow focus on the European Union during a specifictime period Recommendations for future research include incorporating qualitativemethods, exploring additional variables, and extending the analysis to other regionsand timeframes By implementing evidence-based policies derived from this research,policymakers can contribute to the advancement and prosperity of the EuropeanUnion as well as ROW

1 INTRODUCTION

Social factors and economic development are intricately linked, with social factorsplaying a pivotal role as important backbones of economic growth and development.According to Méndez-Picazo et al.,2021, social factors can stimulate economicactivities that can lead to sustainable development Social factors serve as crucialfoundations that influence individuals' decisions regarding employment, housing,education, health, and personal growth, which hold significant importance in theirlives However, certain social factors are found to exert adverse effects on economicgrowth These encompass indicators such as poverty rates, hunger, crime rates, andenvironmental degradation, which can impede the progress and prosperity of asociety According to Popa (2012), poverty and unemployment rates provide anegative relationship between economic growth based on the study conducted inRomania and among European countries Over time, the concept of economicdevelopment and its factors has changed drastically Traditionally, economicdevelopment centered on the enhancement of welfare, as indicated by the growth rateand per capita GDP However, the scope of economic development has broadened toencompass social dimensions such as poverty alleviation, improved education andhealthcare, and more equitable income distribution The long-term perspective oneconomic development also emphasizes sustainability, ensuring that presentadvancements do not compromise the ability of future generations to meet their ownneeds Popa (2012) argued that it is imperative to recognize the significance of varioussocial factors and their economic implications Méndez-Picazo et al (2021) alsosuggested that it is necessary to introduce additional variables that capture therelevance of social factors Realizing the significance of analyzing the relationshipbetween economic growth and various social factors in the EU, this research focuses

on “Analyzing the dependency of economic growth on the social factors in the European Union from 2011 to 2021” The purpose of this research is to investigate

the impact of various social factors on economic growth of 27 countries of the EU inthe period of 2011-2021, shedding light on the intricate interplay between thesedomains and providing insights into potential drivers and barriers to growth within theregion In this research, 4 indicators including poverty rate, expected years of

schooling, life expectancy, and unemployment rate are used to represent the socialfactors By focusing on this specific time frame, this research aims to capture theeffects of social factors on economic growth and identify any temporal patterns orshifts in their relationship over the past decade We collect secondary data sourcesfrom OECD, Eurostat, UNDP and employ panel regression analysis to answer theresearch question “Do indicators for social factors have a positive or negative effect

on economic growth in the EU from 2011 to 2021?”

This research has the following structure.: The first section is the introductionfollowed by section 2, which will cover the literature review, overview of theoriesabout social factors, economic growth, and previous research Section 3 will providethe methodology and model specification Statistical description of variables will bediscussed in section 4 Based on quantitative analyses about model selection testing aswell as model defect identification and remedies in section 5, we can propose somerecommendations toward the topic in the final section

During the process of doing this thesis, we can figure out many conclusions regardingthe effects of chosen variables However, limitations are inevitable Thus, we hope toreceive your comments and feedback on our work so that we can better our thesis.We’d like to thank Mrs Dinh Thanh Binh for your dedication and careful guidancefor us throughout the course

2 LITERATURE REVIEW

2.1 Social factors and social factor indicators

Social factors refer to the various aspects of society that influence individuals' being, behavior, and interactions These factors play a crucial role in shapingeconomic, political, and cultural landscapes, and have major impact on developmentoutcomes, including economic growth (Harrison & Huntington, 2001) The citizens of

well-a country cwell-an become vwell-aluwell-able humwell-an cwell-apitwell-al resources thwell-at contribute to economicgrowth and development when they receive social benefits that motivate them to bemore productive (Langelett, G., 2002) Social factors, which encompass non-economic variables affecting citizens' living standards, are influenced by complexinterplay between genetic and environmental factors (McGowan et al., 2014) Manycountries allocate budgetary resources for these social factors, including institutionsfocusing on population, health, education, labor, development, environment, nationalsecurity, and other social issues These investments are important components ofpublic finance decisions, ensuring the social well-being of citizens and fosteringeffective human capital that embodies physical capacities and human values Failure

to invest in social development results in missed opportunities for citizens to improvetheir living standards and social conditions, and may even lead to economic decline,social unrest, and negative cultural outcomes such as gambling, drug abuse,alcoholism, and lack of skills due to inadequate training and education

Social factors significantly shape citizens' choices regarding employment, housing,education, health, and personal growth For instance, a healthy population leads toincreased productivity and reduced household spending on healthcare An educatedpopulation has greater employment opportunities and income potential Reducingunemployment stimulates the market and increases the demand and supply of goodsand services (Méndez-Picazo et al., 2021) When citizens are secure and healthy, theyactively participate in the provision of goods and services, fostering development,entrepreneurship, and market opportunities Overall, citizens contribute to nationaltaxes when they receive various social benefits, providing the government with moreresources to enhance social and developmental projects Consequently, a positive

relationship between social factors and economic development is expected Incontrast, some social factors have a negative impact on economic growth.

Popa and Ana - Maria (2012) conducted a study in Romania and other EuropeanUnion states to explore the relationship between economic growth and social

determinants The findings revealed that certain social factors, such as life expectancy

and years of schooling, showed a positive correlation with economic growth On the

other hand, variables like the unemployment rate and poverty risk were found to have

a negative correlation with economic growth

2.2 Theoretical perspective on the relationship between Social Factors and Economic growth

Economic growth refers to an increase in the production of goods and services overtime, typically measured using indicators such as gross national product (GNP) or

gross domestic product (GDP) In this study, real GDP per capita will serve as a

variable to represent economic growth, which provides a measure of the value ofoutput per person, serving as an indirect indicator of per capita income (World Bank,2010)

Human Capital Theory (HCT) provides insights into the relationship between

education, human capital development, and economic growth According to TheodoreSchultz (1961), education plays a crucial role in the development of human capital.Through education, individuals acquire knowledge, skills, and abilities that enhancetheir productivity and contribute to economic growth Gary Becker (1994) furtheremphasized the impact of education on economic growth He argued that investments

in education lead to higher levels of human capital, which in turn drive productivity,innovation, and economic development

Based on HCT, human capital, which encompasses personal attributes such as habits,knowledge, and social and personality attributes, plays a vital role in producingeconomic value (Breton, 2014) It asserts that humans are essential componentsalongside physical capital, as their intellectual capacity enables countries and firms toachieve goals, foster growth, and promote innovation Furthermore, this study expandsthe scope by integrating national social factors and positing that if every country's

citizens can benefit from other social factors, it can lead to economic growth.Although social development requires substantial public expenditure in the short-term,investing in human capital yields long-term economic growth and development(Alawamleh et al., 2019) For example, improved education and health lead to longerlife expectancy, resulting in higher productivity and lower depreciation of humancapital assets Additionally, individuals with enhanced human capital can generateincome, meet their basic needs, and reduce extreme poverty and hunger

According to neoclassical growth theory, an increase in capital and labor inputs cancontribute to economic growth, as both factors are involved in the production process(Solow, 1956) Furthermore, capital and labor are complementary factors, and theircombined contribution enhances overall productivity (Jorgenson & Griliches, 1967).However, the impact of capital and labor on economic growth depends on the quality

of these inputs and the efficiency of the production process (Mankiw, Romer, & Weil,1992)

Overall, human capital development positively influences economic growth byfostering new ideas, technological innovations, expertise, and an improved labor force(Pradhan & Abraham, 2002) By considering these perspectives, policymakers andresearchers can gain a deeper understanding of the mechanisms through which socialfactors influence economic growth

2.3 Empirical Studies on the Relationship between Social Factors and Economic Growth

Research “The impact of social factors on economic growth: Empirical evidence forRomania and European Union countries” (2012) of Popa and Ana - Maria, examinedthe changes in the social and economic environment worldwide and establishes a clearconnection between human development and economic well-being The study utilized

an econometric analysis performed using panel data consisting in annual dataextracted from 2005-2009 for EU countries The model had a dependent variable - realGDP per capita (indicator for economic growth) and 4 independent variablesrepresenting social factors: expected years of schooling, life expectancy, population atrisk of poverty and unemployment rate The findings of the research demonstrate a

positive correlation between certain social factors (independent variables: expectedyears of schooling and life expectancy) and economic growth (dependent variable).Conversely, there is a negative correlation between other social factors (such as thepopulation at risk of poverty and the unemployment rate) and economic growth.

Gerry O Gatawa conducted research “The Effect of Social Factors to EconomicGrowth” (2022) and stated that social factors play a crucial role in driving economicgrowth and can lead to tangible benefits that promote sustainable development Thestudy analyzed the impact of social factors on economic growth by conducting a timeseries analysis involving 58 countries and 290 data points spanning the period from

2014 to 2018 Through correlation and regression analyses, the research revealssignificant relationships between social factors, including population, health,education, development, labor force, environment, military, and geography, andvarious indicators of economic growth such as GDP, GDP growth, GDP-per-capita,GNI, GNI-per-capita, manufacturing, and tourism The findings highlight thesignificant influence of social factors on economic growth outcomes

2.4 Research gap

The main problem of the related research is the 4 -year limit of the database of thesocial factor indicators This could be insufficient for macroeconomic factors toprovide a precise evaluation for the real GDP per capita in the researched nations.Another problem related to the theories is that the independent variables and controlvariables in the model are from different papers, which means that this study willcover more broadly the economic growth Therefore, the result of the new regressionmodel could be ensured in terms of accuracy rate and might differ from the result ofthese mentioned papers

2.5 Research hypothesis

Based on the point of view of other scholars on economic growth, all the variablesrepresenting social factors such as expected years of schooling, life expectancy,poverty rate and unemployment rate are believed to have an impact on economicgrowth To be more specific, expected years of schooling and life expectancy areexpected to have a positive impact on economic growth, which has been represented

by the Real GDP per capita variable While the poverty rate and unemployment ratehave a negative influence on economic growth.

H1: Poverty rate has a negative effect on real GDP per capita.

H2: Expected years of schooling has a positive effect on real GDP per capita.

H3: Life expectancy has a positive effect on real GDP per capita.

H4: Unemployment rate has a negative effect on real GDP per capita

3 MODEL SPECIFICATION AND DATA

3.1 Methodology

3.1.1 Method used to collect data

For the research paper on analyzing the dependency of economic growth on socialfactors in the European Union from 2011 to 2021, a mixed-method approach wasemployed to collect data The primary data sources were OECD, EuroStat, and GlobalLab, which provided comprehensive datasets covering various socio-economicindicators The four independent variables considered in this study were poverty rate(POV), expected years of schooling, income, life expectancy, and unemployment rate.Quantitative data analysis techniques, such as descriptive statistics, regressionanalysis, and correlation analysis, were applied to examine the relationships betweeneconomic growth and these social factors The combination of these data sources andanalytical methods enabled a comprehensive assessment of the interdependenciesbetween economic growth and the selected social factors within the European Unionover the specified period

3.1.2 Method used to analyze data

Descriptive statistics and regression analysis were employed to examine therelationships between economic growth and these social factors By leveraging therich datasets from these sources and employing strict statistical analysis, the studyaims to provide comprehensive insights into the interdependencies between economicgrowth and the selected social factors within the European Union during the specifiedtime frame Diagnostic tests of heteroskedasticity and autocorrelation are performed toevaluate the reliability of the model

(POV), expected years of schooling (EYS), life expectancy (LE), and unemploymentrate (UR) Therefore, the theoretical model of this study is as follows:

logGDPpc it = 𝛽0 + 𝛽1.POV it + 𝛽2.EYS it + 𝛽3.LE it + 𝛽4.UR it + u it

In which:

logGDPpc i: dependent variable, represent the economic growth

Social factor indicators: POV i , EYS i , LE i , UR i

i, t: country and time indices respectively

u it : the residuals

3.2.2 Variables Description

In contemporary economic research, understanding the intricate relationship betweeneconomic growth and social factors has gained significant attention In our study, fourmacroeconomic factors were used to describe economic growth, and their impact onthe 27-Europe countries’ economies was analyzed In our model, the four indicatorswill be the independent factors of the regression model

Table 1: Definition of variables used in the model

Dependent variable

GDPpc i

Real GDPper capita

Measurement of thetotal economicoutput of a countrydivided by thenumber of peopleand adjusted forinflation

Euro percapita

Elistia et al.(2018);

Picazo et al.(2021)

Méndez-Independent variables

POV i Poverty rate

Percentage ofpopulation below theincome thresholddefining poverty

Barro, R J.(1991);

Banerjee &Duflo (2011)

EYS i

Expectedyears ofschooling

Average years offormal education anindividual is

complete in theirlifetime

Hanushek &Woessmann(2012); Barro

& Lee (2013)

LE i

Lifeexpectancy

The average number

of years a person isexpected to live

Pritchett &Summers(1996);

Bloom et al.(2001);

Cutler et al.(2006)

UR i

ment rate

Unemploy-The proportion ofindividuals who arewithout employment

Blanchard &Summers(1986); Lin

& Huang(2009)

Source: The authors (2023)

Dependent variable

Real GDP per capita (GDPpc): Méndez-Picazo et al (2021) conducted a study and

discovered that social factors significantly impact sustainable development

Consequently, they recommend that public policies should prioritize the enhancement

of human capital, employment opportunities, and the promotion of free markets, whilesimultaneously addressing negative social factors Furthermore, economic growthplays a crucial role in the overall development of a nation The rate of growth istypically measured through the Gross Domestic Product (GDP) per Capita, ashighlighted by Elistia et al (2018) Based on those researches, the Real GDP percapita (GDPpc) will serve as the basis for examining the relationship between SocialFactors and Economic Growth

Independent variables

Poverty rate (POV): Several theoretical frameworks provide insights into the

negative relationship between poverty rate and economic growth The most prominent

is the poverty trap theory, which suggests that high levels of poverty can perpetuate acycle of low productivity, limited human capital investment, and reduced economicgrowth (Banerjee & Duflo, 2011) Barro, R J (1991) conducted a study thatexamined the determinants of economic growth across countries, including the impact

of poverty rate It employed econometric techniques to analyze data and found thathigh poverty rates are associated with lower economic growth

Expected years of schooling (EYS): Expected years of schooling reflect the access to

education, which play a vital role in human capital development and long-termeconomic growth (Barro & Lee, 2013) A research by Hanushek & Woessmann(2012) used Cross-country growth regressions to generate a close relationship betweeneducational achievement and GDP growth The result showed that higher levels ofeducation have been associated with higher labor productivity, technologicaladvancements, and economic competitiveness

Life expectancy (LE): Healthier populations tend to be more productive and

contribute to economic development Longer life expectancy is linked to a reduction

in mortality rates, improved labor force participation, and increased human capitalaccumulation (Bloom et al., 2004; Cutler et al., 2006) Additionally, investments inhealthcare systems and improved public health measures can lead to a moreproductive workforce and higher economic output (Pritchett & Summers, 1996)

Unemployment rate (UR): The labor market theory suggests that high levels of

unemployment can lead to reduced labor productivity and hinder economic growth(Blanchard & Summers, 1986) Ozekicioglu, H (2019) employed a panel dataanalysis to investigate the relationship between unemployment and economic growthacross 23 OECD countries Their results supported the negative impact ofunemployment on economic growth, highlighting the importance of reducingunemployment for sustained economic development

capita (GDPpc), Poverty rate (POV i ), Expected years of schooling (EYS i), Life

expectancy (LE i ), and Unemployment rate (UR i ), originates from World Bank andEUROSTAT

Table 2: Data source

4 STATISTIC DESCRIPTION OF VARIABLES

4.1 Description Statistics and Interpretation for each variable

To estimate the model determined the dependent relationship between the indicators

of the social factors and economic growth in the EU-27, we decided to choose theresearch sample in the 11-year period from 2011 to 2021 with a total of 297observations

A statistical description of the indicators used in the regression model - i.e., minimum,maximum, mean, and standard deviation

Table 3: Descriptive statistics

Source: The authors (2023)

With 297 observations, from 2011 to 2021, we can see that the EU now is morediligent on developing social factors, thereby promoting outstanding economicdevelopment:

- The average value of lnGDPpc is 9.9223, the highest value is 11.3475, thelowest is 1.8579 The huge difference between these data illustrates a diversedistribution of different levels in the data sample

- About the variable POV, the poverty rate in European countries is maintained

at a low level with an average of 0.1659% with the highest rate at 0.254% and astandard deviation of 0.0382%, which indicates uneven development across thecountries studied

- The subsequent variable to consider is EYS, which assesses the duration ofschooling in different countries, and favorable educational advancements inEurope where, on average, each child receives 16,334 years of education Thestandard deviation of 1.2761, with the highest rate of 18 years and the lowest at13.79, signifies the varying levels of educational interest observed across the

27 countries

- The issue of limited job opportunities for individuals in European countries isalso highlighted by the unemployment rate With the lowest rate recorded at0.02 and the highest reaching 0.278, a notable disparity is evident betweencountries in terms of unemployment levels

Furthermore, the social indicators of life expectancy (LE) exhibit a notably elevatedmean value and standard deviation, indicating variables used by significant dispersion.This signifies a significant rise in the average life expectancy across European nations

in recent decades, preeminently attributed to advancements in scientific knowledgeand an advanced medical landscape However, due to disparities in the development

of medical facilities and healthcare standards among the countries under study, thefindings reveal significant variations among nations

In general, the data sample shows a wide range of levels distributed in different waysand the variables are normally distributed; therefore, this data sample can representthe population

4.2 Correlation matrix between variables

The table below clearly illustrates the correlation, which is the degree of linearassociation between the dependent variable and the independent variables

Table 4: Correlation matrix between variables

logGDP 1.0000

EYS 0.3364 -0.2545 1.0000

Source: The authors (2023)

As can be seen from the table, the correlation between the dependent variable andindependent variables completely meets the expectation The correlation coefficientbetween Real GDP per capita and LE and EYS is among the highest (0.6233 and0.3364 respectively) and positive, which indicates a strong relationship between thesevariables as expected However, the correlation value between GDP and POV is -0.3842, and -0.1187 for the UR variable showing a weak and inverse relationship.These results are consistent with the author's hypothesis

Regarding the correlation among independent variables, it fluctuates from -0.3486 to0.4956 The correlation between life expectancy and expected years of schooling(r(LE, EYS) = 0.4956) is the strongest while the correlation between life expectancyand the poverty rate is the weakest (r(LE, POV) = -0.3486) Furthermore, thecorrelation between expected years of schooling and poverty rate as well as thecorrelation between life expectancy and poverty rate, is negative

The negative correlation between the expected number of years of schooling and thepoverty rate has been widely discussed in literature Several studies have found thathigher levels of education are associated with lower poverty rates For example,Safarova, N (2021) conducted a study across multiple countries and found that anincrease in education attainment is significantly associated with a reduction in poverty.Similarly, the negative correlation between life expectancy and the poverty rate hasalso been well-documented Numerous studies have shown that poverty is linked topoorer health outcomes and lower life expectancy For instance, Sen (2000) arguedthat poverty limits access to healthcare and adequate nutrition, leading to increasedmortality rates and lower life expectancy

These findings highlight the importance of addressing poverty in order to improveeducational outcomes and enhance overall well-being Investing in education andhealthcare can contribute to reducing poverty rates and improving life expectancy,thereby fostering socio-economic development.

In general, the degree of correlation between the independent variables is not high, allless than 80%, so we can predict that the model does not suffer from multicollinearity

5 QUANTITATIVE ANALYSIS

5.1 Model selection Testing

We analyze the regression model with panel data:

logGDPpc it = 𝛽0 + 𝛽1.POV it + 𝛽2.EYS it + 𝛽3.LE it + 𝛽4.UR it + u i t

and get the estimation and testing results which are described in Table 4 as follow:

Table 5: Estimation and testing results

Lagrange multiplier test chibar2(01) = 69.02

Prob > chibar2 = 0.0000

Hausman test

chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 4.63

Prob>chi2 = 0.3277

Standard errors in parentheses ***p<0.01, **p<0.05,*p<0.1

Source: The authors (2023)

The test to determine the appropriate model in three pooled regression models(POLS), fixed effects model (FE) and random effects model (RE) was based on theresults of Breusch and Pagan Lagrangian multiplier test for random effects andHausman test are shown in Table 4 The authors conducted a Lagrange test, the resultsp-value = 0.0000 < α = 0.05 So, the POLS model is not suitable After that, theauthors continued to use the Hausman test to choose between RE and FE models, p-value = 0.3277 > α = 0.05 and RE model was selected

5.2 Model defect identification

5.2.1 Multicollinearity Test

Set up hypothesis H0, H1

H0: The model does not exist multicollinearity

H1: The model exists multicollinearity

In STATA, by using vif, we have the result as follow:

Table 6: Multicollinearity Test

Mean VIF 1.40

Source: The authors (2023)

From the above result, mean VIF = 1.40 < 10 → Do not reject H0

Conclusion: The model does not exist multicollinearity at the significance level of

1%, 5% and 10%

5.2.2 Heteroskedasticity Test

Set up hypothesis H0, H1

H0: The model has homoskedasticity

H1: The model has heteroskedastic

We use Breush-Pagan test for heteroscedastic, the result as follow:

Table 7: Heteroskedastic test