Econometrics research report factors affecting the unemployment rate in listed nations

Fivepivotal factors affecting youth unemployment in Southern Asia have been identified: Laborproductivity rate, Population growth, Foreign Direct Investment, Tertiary enrollment rate,and

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

-o0o -ECONOMETRICS

RESEARCH REPORT FACTORS AFFECTING THE UNEMPLOYMENT RATE

TABLE OF CONTENTS

ABSTRACT 3

INTRODUCTION 4

CHAPTER I: OVERVIEW OF THE TOPIC 6

1 Literature review 6

2 Dependent variable: Youth unemployment rate 7

3 Independent variables 8

3.1 Labor productivity 8

3.2 Population 9

3.3 Foreign Direct Investment 10

3.4 Inflation rate 10

3.5 Tertiary enrollment rate 11

4 Research hypothesis 12

CHAPTER II: MODEL SPECIFICATION AND DATA 13

1 Methodology 13

1.1 Method used to derive the model 13

1.2 Method used to collect and analyze the data 13

2 Theoretical model specification 13

2.1 Econometric model 13

2.2 Model specification 14

2.3 The theoretical relationship between dependent variable and independent variables with supporting researches 15

3 Data description 16

3.1 Data sources 16

3.2 Descriptive statistics 16

3.3 Correlation matrix between variables 18

CHAPTER III: ESTIMATION, HYPOTHESIS TESTING AND RECOMMENDATIONS 19

1 Estimated result 19

2 Hypothesis testing 20

2.1 Statistical significance 20

2.2 Overall significance 25

3 Recommendation 25

CONCLUSION 27

1 Conclusion 27

2 Contributions of the research 27

3 Limitation of the research 27

ACKNOWLEDGEMENT 28

REFERENCES 29

APPENDIX 31

ABSTRACT

Youth unemployment is a pressing issue for industrializing nations, with particularimpact on ASEAN countries The competitive job market poses a significant challenge forindividuals aged 15 to 24 This research endeavors to identify the root causes of youthunemployment and its implications for young people in ASEAN spanning from 2007 to

2020 The study aims to offer valuable insights into the factors influencing the youthunemployment rate, thereby contributing to ASEAN's pursuit of sustainable developmentgoals

Furthermore, this thesis has compiled secondary data from diverse sources Fivepivotal factors affecting youth unemployment in Southern Asia have been identified: Laborproductivity rate, Population growth, Foreign Direct Investment, Tertiary enrollment rate,and Inflation (Consumer Price Index) Rate Through the amalgamation of data from tencountries sourced from entities such as the World Bank, International Labour Organization,ASEANstats and other websites, detailed and precise indicators have been gathered toelucidate the explanatory variables Subsequently, the Ordinary Least Squares method(OLS) was applied, employing STATA software for the regression model, yielding thefollowing outcomes: Labor productivity and population growth exhibit a positive correlationwith youth unemployment rate, whereas FDI, Tertiary enrollment rate, and inflation exert anegative impact on the youth unemployment rate

This thesis recommends that ASEAN authorities take measures to create a morefavorable job market for young individuals, offering ample opportunities for them toshowcase their capabilities in the workplace Additionally, individual governments shouldcollaborate closely with educational and training institutions in the private sector, affordingyoung people the chance to acquire knowledge, experience, and skills necessary for self-sufficiency

INTRODUCTION

The Great Recession broadly affected labor markets around the world, but individuals

in vulnerable positions were strongly hit—including the young generation According to theInternational Labour Organization (ILO), the unemployed are defined as those people whohave not worked more than one hour during the short reference period but who are availablefor and actively seeking work The "Global Employment Trends 2013" report from theInternational Labour Organization (ILO), published in May 2013 in Geneva, Switzerland,highlights an unprecedented global youth unemployment crisis Globally, young people arefacing an unemployment rate that is more than three times higher than that of older workers,with four out of every ten unemployed individuals belonging to the youth category InSoutheast Asia and the Pacific, this disparity is even more alarming, standing at 4.6 timeshigher than older workers – the highest level ever recorded worldwide This has resulted inthe emergence of "a generation pushed to the sidelines," including individuals with basiceducation and crucial soft skills required for knowledge-based jobs but with limitedprospects for securing stable and sustainable employment opportunities

Numerous earlier research has supported the damaging impact of unemployment onpeople's well-being and the economy's financial and non-financial aspects Individuallyspeaking, when a person remains unemployed for a long period of time, they tend to live adestructive life, which eventually would cause them mentally and physically inactive andunsound leading to disruption in social disharmony From an economic perspective, risingunemployment signifies the underutilization of the social labor force in productive businessactivities, representing a fundamental squandering of human resources essential for socio-economic advancement As a consequence, the national economy may decline due to theactual national income falling short of its potential, primarily driven by a lack of investmentcapital resulting from reduced government budget revenues due to tax shortfalls and theneed to support unemployed workers Unemployment is the most considerable factorspushing the economy to the edge of inflation In the interest of the nation's economicgrowth, it is imperative for the government to carefully manage and maintain theunemployment rate at an acceptable level

Our research team has choosen the topic “Factors affecting youth unemployment

rate of ASEAN countries from 2007 to 2020” to investigate numerous factors influencing

youth unemployment rates in 10 ASEAN countries, including Cambodia, Indonesia, LaoPDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, spanning theperiod from 2007 to 2020 We focused on these significant factors including laborproductivity rate (LPR), population (POP), Foreign Direct Investment (FDI), inflation(Consumer Price Index) rate (INF), and tertiary enrollment rate (TER) Through theapplication of econometric

Our research will contain three main contents:

 Overview of the topic

 Model specification and Data

 Estimation, Hypothesis testing and Recommendation

In the current context, the growing youth unemployment rate as well as thesignificantly high number of young workers living in poverty have made youth employment

a global priority In Vietnam and abroad, there have been some papers focusing on thefactors that have an effect on youth unemployment rate, therefore presenting some feasiblesolutions to solve this problem:

Through the research “Factors influencing unemployment rate: A comparison amongfive ASEAN countries”, Amyir Aljileedi Mustafa Rayhan, Heri Yanto revealed thatunemployment may result from labor market imbalances This indicates that the quantity ofavailable labor forces exceeds the number of labor forces requested The purpose of this study

is to investigate the factors that influence the unemployment rate in various ASEAN countries.The research utilized quantitative data This study's data gathering approach isdocumentation with secondary data from 2000 to 2018 Regression analysis is used in thisstudy to evaluate the hypothesis The findings of the analysis look at the factors thatinfluence the unemployment rate and present a comparison among several ASEAN nations.Wage, inflation, economic growth, and education have a substantial influence onunemployment in these nations The findings revealed that the most major factor causingunemployment in a country is inflation Other variables, such as wage, economic growth,and education, have a smaller effect

The study “Macroeconomic factors affecting unemployment rate in China” OF Chen

Li Xuen, Chew Yun Bee, Rick Lim Li Hshien, Tan Wan Yen examines the long-run linkbetween macroeconomic conditions and the Chinese unemployment rate from 1982 to 2014.World Development Indicators provided the data Inflation, GDP growth, population, andforeign direct investment are among the topics discussed Before employing theAutoregressive Distributed Lag (ARDL) technique, methodologies such as the Unit RootTest and the Augmented Dickey Fuller (ADF) Test are used ARDL is used to investigatethe long-run relationship between the unemployment rate and other factors This method canonly be used when the Unit Root Test and the ADF Test have been passed As aconsequence, GDP growth and population are relevant to unemployment rate, indicating along-run link, but inflation

and foreign direct investment are negligible to unemployment rate Possible explanations forsuch findings include data restrictions and the exclusion of key variables Thus, somerecommendations are offered to future researchers or policymakers to raise the sample sizeand apply panel data analysis so that additional judgment on the validity of study may bemade for other nations other than China

In the reseach “Determinants of Unemployment in Selected Developing Countries: APanel Data Analysis”, after collecting data of ten selected developing countries for theperiod of 2000 to 2019 from the World Bank and applying the Generalized Method ofMoments (GMM) model, Ayesha Siddiqa concluded that GDP, inflation, remittances,exchange rate, and expenditure on education has a negative impact on unemployment whilepopulation and external debt has a positive impact on unemployment Hence, if developingeconomies want to reduce unemployment, they should increase their GDP, remove deficit inthe balance of payment, control the inflation rate, gain the foreign remittance, control thepopulation expansion, decrease their imports and increase their exports and increase theexpenditure on education

All research we have mentioned above affirmed that there are many factors that have

an impact on unemployment However, they have some limitations related to the context,time period and other factors which are not included in these research Most of the researchtook place all over the world, in some developing economies or a specific country whilelittle reseach is conducted in ASEAN countries, most of which did not select all countries inthis area In addition, little research focus on the unemployment rate of the youth in allASEAN countries Therefore, our research team want to find more insight about the factorsthat having an effect on youth unemployment rate in 10 ASEAN countries during the period

of 14 years, from 2007 The answer will be presented in this research

2 Dependent variable: Youth unemployment rate

Unemployment can be defined as not finding a job while searching for a job activelywith enthusiasm for a wage (Ünsal, 2000: 14) Youth unemployment has been on the rise inmany countries in the world despite the efforts that have been made by differentgovernments in order to improve the economic wellbeing of the youth, persons aged 15–24years (United Nations, 2008) The youth unemployment rate is known to be 2-4 timeshigher than the adult unemployment rate (Torun and Arıca, 2011: 170; Sayın, 2012: 35).And this has a variety of determinants

Labor productivity was included in the analysis as the first variable affecting youthunemployment Productivity is one of the most important drivers of economic development,social progress and higher living standards (Prokopenko, 2001, p.7) According to the

The Phillips Curve shows that there is a negative relationship between inflation andunemployment Thus, a change in unemployment within an economy has a predictableeffect on price inflation The inverse relationship between unemployment and inflation can

be depicted as a downward sloping, concave curve, with inflation on the Y-axis andunemployment on the X-axis Increasing inflation decreases unemployment and vice versa(Friedman, 1977, p 455) Several researchers (Kabaklarli et al., 2011; Maqbool et al., 2013,Arslan and Zaman, 2014) have examined the effects of inflation on employment using thePhillips Curve

In the literature, according to the study of Green et al (2000), one of the mostimportant factors affecting youth unemployment is noted as the level of education Inaddition, in the study of Sayın (2012) which was conducted on youth unemployment, youthunemployment is affected by growth and higher education schooling rates at most

3 Independent variables

3.1 Labor productivity

Igbokwe-Ibeto (2012) claims that productivity is the sum of output and input, whichrepresents the relationship between the input of labor and output units However, the outputcan be measured in terms of a variety of inputs, including hours worked, the sum of laborand capital inputs, or anything in between (Igbokwe-Ibeto, 2012)

As stated by Blanchflower and Oswald (1994), Blanchard and Katz (1999), and Bell

et al (2002), there has been an increase in the amount of empirical research that has beenconducted in regard to the relationship between productivity growth and unemployment As

a result, the connection between productivity and unemployment in the labor markets hasalso drawn a lot of attention in the economic literature

According to Ulgener (1991), production factors have an impact on economic growth

in terms of quantity as well as effectiveness and productivity If productivity rises, GDPgrowth will eventually climb and surpass input growth Thus, productivity is a key factor inthe advancement of society, economic growth, and greater standards of life (Prokopenko,

2001, p 7) A rise in labor productivity can cause a temporary drop in demand for labor.However, in the long run, boosting productivity will support the creation of newemployment prospects (Uzay, 2005, p.61)

Numerous researchers have investigated into how labor productivity affectsemployment (e.g., Linzert, 2001; Tripier, 2002; Saygili et al., 2001; Lentz and Mortensen,2004; Pissarides and Vallanti, 2004, Pazarlioglu and Cevik, 2007, Ladu, 2005; Hall et al.,2008; Bocean et al., 2008; Korkmaz, 2010; Kabaklarl et al Therefore, labor productivitywas considered the first factor determining unemployment in the research

3.2 Population

Population growth defined as the average annual percentage change in population size,which counts all residents regardless of citizenship or legal status, in a given time period.According to Arslan and Zaman (2014), population expansion has a significant impact onunemployment Population increase has a beneficial effect on the unemployment rate andhas contributed to unemployment

From 1976 to 2012, Maqbool et al (2013) conduct research on the culprits ofunemployment in Pakistan They discovered that in Pakistan, population had a positiveconnection with unemployment There was also a strong short-run and long-run relatedinfluence on both unemployment and population

As stated by Asif (2013), his research delves into the macroeconomic factorsinfluencing unemployment across three nations: China, India, and Pakistan The data spansfrom 1980 to 2009 The findings underscore a substantial correlation between populationsize and unemployment rates in these countries

Mahmood, Akhtar, Amin, and Idrees (2011) conducted an examination ofdeterminants impacting unemployment in the education sector of Pakistan's PeshawarDivision They collected data from 442 residents possessing either a first-degreequalification or professional/technical training, regardless of their employment status Theresults indicate a positive relationship between population growth rate and unemployment,particularly among the educated population

Bakare (2011) focused on discerning the roots of urban unemployment in Nigeria over

a thirty-year period spanning from 1978 to 2008 The study highlights a positive associationbetween unemployment rates and population This phenomenon arises from a scenariowhere job demand surpasses job availability, predominantly driven by rapid populationexpansion

3.3 Foreign Direct Investment

The important issue of youth unemployment rate is invariably linked to massiveinflows of foreign direct investment (FDI) into a nation According to the InternationalMonetary Fund (IMF), FDI refers to an investment made to acquire lasting or long-terminterest in enterprises operating outside of the economy of the investor It is also defined as

a transfer of a package of resources across the countries and that includes capital, technology,management and marketing expertise (Odozo, 1995) To simplify words, FDI refers tocapital inflows from abroad that are invested in or to enhance the production capacity of theeconomy It serves as an impetus for accelerating economic development for a country(Yusop & Keong, 2002) Its contribution to economic growth is both direct and indirect(Zebregs, 2002)

Lin and Wang (2004) studied the relationship of FDI and unemployment in the G-7countries Generalized least squares (GLS) had been used in estimating the regression in asystem of separate equations for the individual countries The study showed that FDI isfound to have a negative correlation with the unemployment rate in all of the G-7 countries.Mpanju (2012) made a study about the effect of the inflows of FDI on employmentgeneration creation in the country of Tanzania from 1990 until 2008 There is a strongpositive correlation between the variables, indicating that FDI has a considerable impact onthe pattern of opportunities for employees in this study A study also had been made bySchemerer (2012) that is about a simple multi-industry trade model with some searchfrictions in the labour market Unemployment and FDI has been tested using Economic Co-operation and Development countries on unemployment rate, labour market institutions andFDI macroeconomic data The study's findings reveal that the model in net percentage FDI

is associated with a lower rate of aggregate unemployment According to Mohd ShahidanShaari, Nor Ermawati Hussain and Mohd Suberi Ab Halim (2012), they alsostated that FDI helped in reducing the unemployment rate, that is a one percent increase inFDI will cause a decrease of 0.0009 percent in unemployment Chaudhuri and Banerjee(2010) analysed the three sectors in general equilibrium model with the simultaneousunemployment for the both unskilled and skilled labour to identify the effect of FDI onagricultural land and also the developing economy

It is also evident from the above literature that FDI offers additional job opportunitiesfor unemployed youth In light of the preceding literature, the present study attempts tofigure out the perception of FDI and other indicators on the youth unemployment rate insome Asian countries within 15 years

3.4 Inflation rate

Over the past few decades, professional perspectives on the relationship betweeninflation and unemployment have gone through two stages and are now entering a third The

first involved accepting a predictable trade-off (a steady Phillips curve) The secondinvolved the inclusion of inflation expectations as a variable that shiftes the short-runPhillips curve and the natural rate of unemplyment as a factor that controlled the location of

a vertical long- run Phillips curve The third comes from the empirical reality that thereappears to be an inverse correlation between inflation and unemployment

The Phillips Curve illustrates how unemployment and inflation are inversely related Achange in unemployment within an economy will therefore unavoidably have an effect onprice inflation The inverse link between unemployment and inflation can be shown as adownward-sloping, concave curve with unemplyment on the horizontal axis and inflation onthe vertical axis Inflation increases as unemployment declines, and vice versa (Friedman,

1977, p 455) Several scholars have utilized the Phillips Curve to examine how inflationaffects employment (Kabaklarli et al., 2011; Maqbool et al., 2013, Arslan and Zaman,2014) Similar to this, Bayrak & Tatli (2018) demonstrated that, within the context ofOECD countries, inflation has a negative impact on youth unemployment

The relationship between inflation and unemployment rate has been the subject ofnumerous studies (Singh, 2018; Yelwa, David & Awe, 2015; Ni, Yusof, Misiran & Supadi,2021; etc.), and the findings are generally similar Few of them, nevertheless, have looked atthe youth unemployment rate (YUR) As a result, this study will evaluate this variable usingYUR in the ASEAN nations

3.5 Tertiary enrollment rate

The possibility of a person pursuing a particular career will rely on their level ofpersonal competence, claims Behrman (1999) In addition, skills are concerned withdetermining the standard of employment, particularly through their impact on pay Similar

to how those with low human capital and few skills—i.e., education and level of quality thatcannot meet market requirements—are more likely to experience long-term unemploymentand possibly social marginalization than young people with higher levels of human capitaland superior abilities (Icli, 2001; Muller, 2005; OECD, 2005)

Additionally, according to Altman (2014), young people with higher levels ofeducation will have a better chance of becoming global citizens who can easily obtainformal jobs both locally and internationally and use their skills across borders

According to Andre (1980), children with low educational attainment face a number ofsocial disadvantages Early pregnancies are common, especially among young womenwithout formal schooling Children of young women who opted not to marry or whobecame single mothers may have important outcomes because they may be growing swiftlyduring trying times, especially if the mother lacks the financial means to raise her children

or has no marketable skills

George Ogola looked into the various types of unemployment in Toronto as well as theimpact of a few significant factors on job development in October 1994 He blamed issuessuch as discriminatory hiring procedures for school dropouts and a desire to work in thecurrent sector despite the absence of employment chances in the main for the unemploymentproblem

In Vietnam, Phan Thuc Uyen Nhung & Nguyen Thi Hieu Han (2018) came to theconclusion that a lack of workable skills, a low level of education, stringent jobrequirements, an excess of labor, and a lack of jobs Young unemployment is most impacted

by two characteristics: a lack of experience and working skills

On the other hand, Callaway (1971) also argued that the growth in youngunemployment has been significantly influenced by the fast development of formalschooling in many countries Along with a general rise in trust that the educational systemcan contribute to economic growth, this enormous expansion also took place

4 Research hypothesis

Based on the previous theories and research related to youth unemployment rate, all thevariables, which are labor productivity (LPR), population (POP), Foreign Direct Investment(FDI), inflation rate (INF) and tertiary enrollment rate (TER) are believed to have an impact

on the youth unemployment rate (YUR) To be more specific, LPR and POP have a positiveimpact on YUR while the opposite was true for the remaining variables (FDI, INF, TER).Therefore, our research hypotheses are listed below:

● H1: Labor productivity (LPR) has a positive impact on the youth unemployment rate(YUR)

● H2: Population (POP) has a positive impact on the youth unemployment rate (YUR)

● H3: Foreign Direct Investment (FDI) had a negative impact on the youth unemployment rate (YUR)

● H4: Inflation rate (INF) has a negative effect on the youth unemployment rate (YUR)

● H5: Tertiary enrollment rate (TER) has a negative effect on the youth unemploymentrate (YUR)

CHAPTER II: MODEL SPECIFICATION AND DATA

1 Methodology

1.1 Method used to derive the model

Regression analysis is a method of inference, it allows us to make inferences about thewhole population out of a representative sample In regression analysis, the sample statistics(or the regression coefficients) are used to estimate the population parameters It is ourmission to find the best possible estimates for the population model Ordinary Least Squares(OLS) method is the easiest and the most popular way to estimate the parameters of a linearregression model OLS estimators minimize the sum of all squared residuals The OLSestimators is consistent when the model satisfies 7 basic assumptions of the classical linearregression model Then, according to the Gauss - Markov theorem, OLS is BLUE, which isstated for Best Linear Unbiased Estimator For the above reasons, we decided to use theOrdinary Least Squares method to derive the model for our research

1.2 Method used to collect and analyze the data

The objective of our research is to clarify which factors among the independentvariables truly have a significant impact on the dependent variable, which means that wehave to calculate the differences in the effect of each factor on the youth unemployment ratespecifically Thus, we conclude that there are five factors causing tremendous changes.After determining the independent variables, we collect and analyze the data on reliablewebsites and sources to examine the dependency of those variables

2 Theoretical model specification

2.1 Econometric model

Based on relevant economics theories and previous research, our group determinedthat youth unemployment rate depends on 5 main factors, which are labor productivity,population, Foreign Direct Investment, inflation rate and tertiary enrollment rate Therefore,

we build a multiple linear regression model:

Population regression model:

𝑇𝐸𝑅𝑖 : tertiary enrollment rate (% gross)

Our research team decided to use labor productivity as one factor that has an impact onyouth unemployment rate.

- Population (POP)

Population is the number of people living in a particurlar area In our research, wecollected the data of population of 10 ASEAN countries during the period of 14 years

- Foreign Direct Investment (FDI)

Foreign direct investment (FDI) is defined as investment in an enterprise operating in aforeign economy, where the purpose is to have an “effective voice” in the management ofthe enterprise FDI can be either inward or outward:

 Inward FDI measures investments made in a country from another country

 Outward FDI measures investments made by domestic companies in a foreigneconomy

In our research, we collected the flows of inward FDI to all ASEAN countries duringthe period

- Inflation rate (INF)

Inflation is the gradual increase in the price level as well as the depreciation of

currency in the exchange rate system, which indicates the health of an economy

We can compute the inflation rate from the Consumer Price Index (CPI) Published bythe Bureau of Labor Statistics (BLS), CPI is a measure of the average price level of a typicalbasket that a household purchases Inflation rate can be measured through CPI:

𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑏𝑎𝑠𝑘𝑒𝑡 𝑜𝑓 𝑔𝑜𝑜𝑑𝑠 𝑎𝑛𝑑 𝑠𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑖𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑦𝑒𝑎𝑟

𝐶𝑃𝐼𝑛 − 𝐶𝑃𝐼𝑛−1

100𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑛 =

- Tertiary enrollment rate

(TER)

𝐶𝑃𝐼𝑛−1 100 (𝑛: 𝑦𝑒𝑎𝑟)

Tertiary enrollment rate can be expressed as net or gross enrollment rate, which is theratio of the number of enrollments in tertiary education regard or regardless of age to thepopulation of the official age group corresponds to the same level of education (tertiary)Our group do research using the gross enrollment rate, which is calculated inpercentage (%) Gross enrollment ratio for tertiary school is calculated by dividing thenumber of students enrolled in tertiary education regardless of age by the population of theage group which officially corresponds to tertiary education, and multiplying by 100

- Youth unemployment rate (YUR)

Unemployment occurs when workers who want to work are unable to find jobs To bemore specific, the unemployed are those who are in the working age, have the ability andobligation to work, are willing to find a job, but have not found one

The youth unemployment rate is expressed as the percentage (%) between the number

of unemployed 15-to-24-year-old people and the youth labor force (including both theemployed and unemployed) according to OECD

2.3 The theoretical relationship between dependent variable and independent variables with supporting researches

Increases in labor productivity can lead to higher unemployment rates in the short termdue to the displacement of workers by machines or automation This occurs becausemachines are often able to produce goods and services more efficiently and quickly than

human labor which can lead to firms scaling back their workforce Consequently, labor productivity may be positively associated with youth unemployment rate.

As the population increases, the number of people who are seeking employment alsoincreases This means that there will be a larger pool of job seekers competing for a limitednumber of available jobs As a result, the unemployment rate may increase because not all

of the job seekers may be able to find suitable employment Hence, there may be a positive relationship between population and youth unemployment rate.

FDI increases employment opportunities as it creates new businesses, expands existingones and generates more jobs in the economy More FDI implies that there is a higherdemand for labor in the host country as foreign companies bring in new techniques, skillsneeded in

operating their businesses That generates employment opportunities, which in turn helps

reduce unemployment As a result, the Foreign Direct Investment seems to have a negative relationship with the youth unemployment rate.

Economic theory suggests that a rise in the rate of inflation will lead to a decrease inthe unemployment rate According to the Phillips Curve, lower unemployment rate meanspeople consume and spend more, putting more pressure on prices which then results in

higher inflation Thus, the inflation rate seems to have a negative relationship with the youth unemployment rate.

It is often said that young people with higher education levels are more likely to have ajob and often receive a higher income since people in higher education level may gain moreskills and knowledge which easily meet the job requirements It can be inferred that highereducation levels, namely tertiary education, can lower the youth unemployment rate

Therefore, we can assume that there is a negative relationship between tertiary enrollment rate and youth unemployment rate.

3 Data description

3.1 Data sources

YUR Youth unemployment rate https://data.worldbank.org/indicator/SL.UEM.152

4.ZS?end=2022&name_desc=false&start=1991&view=chart

LPR Labor productivity https://www.ilo.org/shinyapps/bulkexplorer32/?la

From the year 2007 to 2020, ASEAN has 10 country members Our group found data

of all 10 countries over 14 years To be more specific, we collected 140 observations foreach variable Nevertheless, because of the missing data in tertiary enrollment proportion insome countries over the period, we only collected 110 observations for this variable,which still

accounted for 78.6% of the total Therefore, we think that our sample data can represent thepopulation as a whole The statistis description of our sample data is presented below:

- Youth unemployment rate (YUR)

The youth unemployment rate (YUR) in our research is the proportion betweenunemployed people and the labor force population in the 15-to-24-year-old age group in 10ASEAN countries during a period of 14 years

The mean of youth unemployment rate is 8.663486% and the standard deviation is7.210641 The maximum and minimum figure for this variable is 31.45% (BruneiDarussalam in 2018) and 0.38% (Cambodia in 2017) in the given order

- Labor productivity (LPR)

The mean of labor productivity is 19.27386 with the standard deviation of 21.78024

In addition, the maximum value is the labor productivity of Singapore in 2020, at 70.58while the minimum figure is 1.76, which belongs to Malaysia in 2007

- Population (POP)

The mean of population of 10 ASEAN countries during the reseach period is 62.37783million people while the standard deviation is 73.00337 Moreover, the maximum andminimum value belong to Indonesia in 2020 (at 271.858 million people) and BruneiDarussalam in 2007 (at 0.378748 million people) correspondingly

- Foreign Direct Investment (FDI)

The mean of FDI invested in ASEAN countries is 11208.98 million USD with thestandard deviation is 18376.91 Furthermore, the maximum and minimum figure for FDI is97480.4 million USD (Singapore in 2019) and -4951 million USD (Thailand in 2020)

- Inflation rate (INF)

The mean of inflation rate is 3.946498 and the standard deviation is 5.010654 Inaddition, the maximum value is the Myanmar’s inflation rate in 2007, at 35.0246, at whilethe minimum figure is -1.260506, which belongs to Brunei Darussalam in 2017

- Tertiary enrollment rate (TER)

With 110 observations, the mean of tertiary enrollment rate in ASEAN countriesbetween 2007 and 2020 is 31.19357% with the standard deviation of 17.29615.Furthermore,

the maximum figure belongs to Singapore in 2020 (at 93.13477%) whereas the minimum is7.31446% (Cambodia in 2007)

3.3 Correlation matrix between variables

To figure out the correlation between variables, we applied the “corr” command

before running the regression model

Looking at the table in more detail, we can see that:

- The relationship between dependent variable (YUR) and 5 independent variables:

 The correlation between LPR and YUR is quite high, about 0.6304, which describesthe positive relationship between 2 variables When the labor productivity increases, in shorttime, the youth unemployment rate will rise

 The population (POP) also has a positive correlation coefficient with the youthunemployment rate, which stands at 0.2004 To be more specific, the growth in populationwill lead to the increase in youth unemployment rate

 In contrast, foreign direct investment (FDI) has a negative relationship with YUR asthe coefficient is -0.1619 This is explained by the fact that, when there is an increase in FDIthat an ASEAN country receives, more jobs will be created, which brings about a reduction

in youth unemployment rate

 This is also the case for inflation rate (INF) with the coefficient of -0.2443 Thus, itdemonstrates the inverse relationship between inflation and unemployment, which thePhillips curve stated

 The last independent variable, tertiary enrollment rate (TER), has a positiverelationship with YUR as the coefficient is 0.0866

- The relationship among 5 independent variables:

 The table indicates that there is no perfect multicollinearity among our regressorsbecause no correlation coefficient equals to 0 or 1 Therefore, our sample data satifies theassumption that there is no perfect multicollinearity among independent variables of theGauss-Markov Classical Linear Regression Model assumptions