Econometrics research report factors affecting the unemployment rate in listed nations

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS -o0o - ECONOMETRICS RESEARCH REPORT FACTORS AFFECTING THE UNEMPLOYMENT RATE IN LISTED NATIONS Team: 12 Nguyễn Thị Phương Thảo 2212150160 20% Trần Thái Hưng 2212150087 20% Đoàn Nguyễn Dũng 2212150045 Thái Duy Hoàng Minh 2212150117 20% Hoàng Tấn Dũng 2212150046 20% Class: KTEE309(HK1-2324)2.1 Instructor: PhD Đinh Thị Thanh Bình Hanoi, December 2023 20% TABLE OF CONTENTS ABSTRACT INTRODUCTION CHAPTER I: OVERVIEW OF THE TOPIC Literature review Dependent variable: Youth unemployment rate Independent variables 3.1 Labor productivity 3.2 Population 3.3 Foreign Direct Investment 10 3.4 Inflation rate 10 3.5 Tertiary enrollment rate 11 Research hypothesis 12 CHAPTER II: MODEL SPECIFICATION AND DATA 13 Methodology .13 1.1 Method used to derive the model 13 1.2 Method used to collect and analyze the data 13 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 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 Estimated result 19 Hypothesis testing 20 2.1 Statistical significance 20 2.2 Overall significance .25 Recommendation .25 CONCLUSION 27 Conclusion 27 Contributions of the research 27 Limitation of the research 27 ACKNOWLEDGEMENT 28 REFERENCES 29 APPENDIX 31 ABSTRACT Youth unemployment is a pressing issue for industrializing nations, with particular impact on ASEAN countries The competitive job market poses a significant challenge for individuals aged 15 to 24 This research endeavors to identify the root causes of youth unemployment 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 youth unemployment rate, thereby contributing to ASEAN's pursuit of sustainable development goals Furthermore, this thesis has compiled secondary data from diverse sources Five pivotal factors affecting youth unemployment in Southern Asia have been identified: Labor productivity rate, Population growth, Foreign Direct Investment, Tertiary enrollment rate, and Inflation (Consumer Price Index) Rate Through the amalgamation of data from ten countries sourced from entities such as the World Bank, International Labour Organization, ASEANstats and other websites, detailed and precise indicators have been gathered to elucidate the explanatory variables Subsequently, the Ordinary Least Squares method (OLS) was applied, employing STATA software for the regression model, yielding the following outcomes: Labor productivity and population growth exhibit a positive correlation with youth unemployment rate, whereas FDI, Tertiary enrollment rate, and inflation exert a negative impact on the youth unemployment rate This thesis recommends that ASEAN authorities take measures to create a more favorable job market for young individuals, offering ample opportunities for them to showcase their capabilities in the workplace Additionally, individual governments should collaborate closely with educational and training institutions in the private sector, affording young people the chance to acquire knowledge, experience, and skills necessary for selfsufficiency 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 the International Labour Organization (ILO), the unemployed are defined as those people who have not worked more than one hour during the short reference period but who are available for and actively seeking work The "Global Employment Trends 2013" report from the International Labour Organization (ILO), published in May 2013 in Geneva, Switzerland, highlights an unprecedented global youth unemployment crisis Globally, young people are facing 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 In Southeast Asia and the Pacific, this disparity is even more alarming, standing at 4.6 times higher than older workers – the highest level ever recorded worldwide This has resulted in the emergence of "a generation pushed to the sidelines," including individuals with basic education and crucial soft skills required for knowledge-based jobs but with limited prospects for securing stable and sustainable employment opportunities Numerous earlier research has supported the damaging impact of unemployment on people's well-being and the economy's financial and non-financial aspects Individually speaking, when a person remains unemployed for a long period of time, they tend to live a destructive life, which eventually would cause them mentally and physically inactive and unsound leading to disruption in social disharmony From an economic perspective, rising unemployment signifies the underutilization of the social labor force in productive business activities, representing a fundamental squandering of human resources essential for socioeconomic advancement As a consequence, the national economy may decline due to the actual national income falling short of its potential, primarily driven by a lack of investment capital resulting from reduced government budget revenues due to tax shortfalls and the need to support unemployed workers Unemployment is the most considerable factors pushing the economy to the edge of inflation In the interest of the nation's economic growth, it is imperative for the government to carefully manage and maintain the unemployment 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, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam, spanning the period from 2007 to 2020 We focused on these significant factors including labor productivity rate (LPR), population (POP), Foreign Direct Investment (FDI), inflation (Consumer Price Index) rate (INF), and tertiary enrollment rate (TER) Through the application of econometric models, we have conducted an in-depth analysis of data from these regions and have formulated a set of solutions aimed at mitigating youth unemployment During the process, we have used the sources of data from World Bank, International Labor Organization and ASEANSTATS to find more information about all variables that are mentioned in our research We also applied the STATA software into running the regression model, therefore getting precise results Our research will contain three main contents:  Overview of the topic  Model specification and Data  Estimation, Hypothesis testing and Recommendation CHAPTER I: OVERVIEW OF THE TOPIC Literature review Economic development in a country cannot only be measured by the level of income growth However, quality development is how the income can be distributed equally to each population and can find out who benefits from thedevelopment (Todaro, 1998) There are many indicators to describe a country's economic development, one of which is the unemployment rate Unemployment is a global problem that occurs not only in developing countries but also in developed countries In the current context, the growing youth unemployment rate as well as the significantly 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 the factors that have an effect on youth unemployment rate, therefore presenting some feasible solutions to solve this problem: Through the research “Factors influencing unemployment rate: A comparison among five ASEAN countries”, Amyir Aljileedi Mustafa Rayhan, Heri Yanto revealed that unemployment may result from labor market imbalances This indicates that the quantity of available 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 is documentation with secondary data from 2000 to 2018 Regression analysis is used in this study to evaluate the hypothesis The findings of the analysis look at the factors that influence the unemployment rate and present a comparison among several ASEAN nations Wage, inflation, economic growth, and education have a substantial influence on unemployment in these nations The findings revealed that the most major factor causing unemployment 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 link between macroeconomic conditions and the Chinese unemployment rate from 1982 to 2014 World Development Indicators provided the data Inflation, GDP growth, population, and foreign direct investment are among the topics discussed Before employing the Autoregressive Distributed Lag (ARDL) technique, methodologies such as the Unit Root Test and the Augmented Dickey Fuller (ADF) Test are used ARDL is used to investigate the long-run relationship between the unemployment rate and other factors This method can only be used when the Unit Root Test and the ADF Test have been passed As a consequence, GDP growth and population are relevant to unemployment rate, indicating a long-run link, but inflation and foreign direct investment are negligible to unemployment rate Possible explanations for such findings include data restrictions and the exclusion of key variables Thus, some recommendations are offered to future researchers or policymakers to raise the sample size and apply panel data analysis so that additional judgment on the validity of study may be made for other nations other than China In the reseach “Determinants of Unemployment in Selected Developing Countries: A Panel Data Analysis”, after collecting data of ten selected developing countries for the period of 2000 to 2019 from the World Bank and applying the Generalized Method of Moments (GMM) model, Ayesha Siddiqa concluded that GDP, inflation, remittances, exchange rate, and expenditure on education has a negative impact on unemployment while population and external debt has a positive impact on unemployment Hence, if developing economies want to reduce unemployment, they should increase their GDP, remove deficit in the balance of payment, control the inflation rate, gain the foreign remittance, control the population expansion, decrease their imports and increase their exports and increase the expenditure 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 research took place all over the world, in some developing economies or a specific country while little reseach is conducted in ASEAN countries, most of which did not select all countries in this area In addition, little research focus on the unemployment rate of the youth in all ASEAN countries Therefore, our research team want to find more insight about the factors that 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 Dependent variable: Youth unemployment rate Unemployment can be defined as not finding a job while searching for a job actively with enthusiasm for a wage (Ünsal, 2000: 14) Youth unemployment has been on the rise in many countries in the world despite the efforts that have been made by different governments in order to improve the economic wellbeing of the youth, persons aged 15–24 years (United Nations, 2008) The youth unemployment rate is known to be 2-4 times higher 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 youth unemployment Productivity is one of the most important drivers of economic development, social progress and higher living standards (Prokopenko, 2001, p.7) According to the research of Riza Bayrak and Halim Tatli (2018), in the short run, an increase in labor productivity may lead to a decrease in labor demand Population growth was included in the analysis as the third variable affecting youth unemployment rate The addition of population means increasing unemployment, regardless of urbanization rate in developing countries Generally summarized, unemployment is positively affected by total population growth (Laku and Deda; 2013) Foreign direct investment was included in the analysis as the fourth variable that has negative correlation with youth unemployment rate Foreign investments will increase the state's tax revenues, which in turn will lead to increased government spending and local investments, the creation of new job opportunities, the stability of seasonal employment, and the creation of labor-intensive projects that are characterized by the use of modern technology and thus the creation and diversification of new job opportunities (Mustafa Alalawneh, Azizun Nessa; 2020) The Phillips Curve shows that there is a negative relationship between inflation and unemployment Thus, a change in unemployment within an economy has a predictable effect 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 and unemployment 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 the Phillips Curve In the literature, according to the study of Green et al (2000), one of the most important factors affecting youth unemployment is noted as the level of education In addition, in the study of Sayın (2012) which was conducted on youth unemployment, youth unemployment is affected by growth and higher education schooling rates at most Independent variables 3.1 Labor productivity Igbokwe-Ibeto (2012) claims that productivity is the sum of output and input, which represents the relationship between the input of labor and output units However, the output can be measured in terms of a variety of inputs, including hours worked, the sum of labor and 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 been conducted in regard to the relationship between productivity growth and unemployment As a result, the connection between productivity and unemployment in the labor markets has also 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, GDP growth will eventually climb and surpass input growth Thus, productivity is a key factor in the 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 new employment prospects (Uzay, 2005, p.61) Numerous researchers have investigated into how labor productivity affects employment (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 productivity was 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 on unemployment Population increase has a beneficial effect on the unemployment rate and has contributed to unemployment From 1976 to 2012, Maqbool et al (2013) conduct research on the culprits of unemployment in Pakistan They discovered that in Pakistan, population had a positive connection with unemployment There was also a strong short-run and long-run related influence on both unemployment and population As stated by Asif (2013), his research delves into the macroeconomic factors influencing unemployment across three nations: China, India, and Pakistan The data spans from 1980 to 2009 The findings underscore a substantial correlation between population size and unemployment rates in these countries Mahmood, Akhtar, Amin, and Idrees (2011) conducted an examination of determinants impacting unemployment in the education sector of Pakistan's Peshawar Division They collected data from 442 residents possessing either a first-degree qualification or professional/technical training, regardless of their employment status The results 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 association between unemployment rates and population This phenomenon arises from a scenario where job demand surpasses job availability, predominantly driven by rapid population expansion 𝛼 = 10% P-value = 0.04% < 10% => Reject H0, accept H1 at 𝛼 = 10% P-value = 0.04% < 5% => Reject H0, accept H1 at 𝛼 = 5% 𝛼 = 5% 𝛼 = 1% Conclusion: P-value = 0.04% < 1% => Reject H0, accept H1 at 𝛼 = 1% + Population has statistically significant effect on youth unemployment rate at ̂ significance level of 10%, 5% and 1% And the effect is positive (because 𝛽𝑃𝑂𝑃 > 0) + Given the sample we have, when POP increases by million people, the average youth unemployment rate increases by 0.0552661%, holding other factors fixed - Foreigne Direct Investment (FDI) 𝐻0: 𝛽3 = {𝐻1: 𝛽3 ≠ Method 1: Critical value (t-test) 𝑡 = 𝑠 𝛽̂ − 𝛽∗ 3 = 𝑠𝑒(𝛽̂ ) −0.0001668 − 0.0000275 = −6.07 𝛼 = 10% 𝑡𝛼2,𝑛−𝑘−1 = 𝑡0.05,104 = 1.66  |𝑡𝑠| = 6.07 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 10% 𝛼 = 5% 𝛼 = 1% 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.025,104 = 1.984  |𝑡𝑠| = 6.07 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 5% 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.005,104 = 2.626  |𝑡𝑠| = 6.07 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 1% Method 2: Confidence interval 𝑠𝑒(𝛽̂ ); + 𝑡𝛼 𝑠𝑒(𝛽̂ )) 3 ,𝑛−𝑘−1 ,𝑛−𝑘−1 2 𝛽̂ 𝛼 = 10% 𝛽3 ∈ (−0.0001668 − 1.66 ∗ 0.0000275; −0.0001668 + 1.66 ∗ 0.0000275 𝛽3 ∈ (−0.000212; −0.000121) 𝛽3 ∈ (𝛽3 − 𝑡𝛼 𝛼 = 5% 𝛼 = 1% 𝛽∗ = => 𝛽∗ ∉ (−0.000212; −0.000121) 3  Reject H0, accept H1 at 𝛼 = 10% 𝛽3 ∈ (−0.0001668 − 1.984 ∗ 0.0000275; −0.0001668 + 1.984 ∗ 0.0000275) 𝛽3 ∈ (−0.000221; −0.000112) 𝛽∗ = => 𝛽∗ ∉ (−0.000221; −0.000112) 3  Reject H0, accept H1 at 𝛼 = 5% 𝛽3 ∈ (−0.0001668 − 2.626 ∗ 0.0000275; −0.0001668 + 2.626 ∗ 0.0000275) 𝛽3 ∈ (−0.000239; −0.000095) 𝛽∗ ∗ 23  Method 3: P-value Reject H0, accept H1 at 𝛼 = 1% 𝑡𝑠 = - 6.07 < P-value = 2𝜑(𝑡𝑠) = 2𝜑(−6.07)) = 2*0.0002 = 0.0004 =0.04% 𝛼 = 10% P-value = 0.04% < 10% => Reject H0, accept H1 at 𝛼 = 10% 𝛼 = 5% 𝛼 = 1% Conclusion: P-value = 0.04% < 5% => Reject H0, accept H1 at 𝛼 = 5% P-value = 0.04% < 1% => Reject H0, accept H1 at 𝛼 = 1% + Foreigne Direct Investment has statistically significant effect on youth unemployment rate at significance level of 10%, 5% and 1% And the effect is negative 𝐹𝐷𝐼 ̂ (because 𝛽 < 0) + Given the sample we have, when FDI increases by million USD, the average youth unemployment rate decreases by 0.0001668%, holding other factors fixed - Inflation rate (INF) Method 1: Critical value (t-test) 𝑡 = 𝛽̂ − 𝛽∗ 𝐻0: 𝛽4 = {𝐻1: 𝛽4 ≠ = −0.1745881 − = −2.42 0.0721035 𝑠𝑒(𝛽4̂ ) 𝛼 = 10% 𝑡𝛼2,𝑛−𝑘−1 = 𝑡0.05,104 = 1.66  |𝑡𝑠| = 2.42 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 10% 𝑠 𝛼 = 5% 𝛼 = 1% 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.025,104 = 1.984  |𝑡𝑠| = 2.42 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 5% 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.005,104 = 2.626  |𝑡𝑠| = 2.42 < 𝑡𝛼,𝑛−𝑘−1=> Cannot reject H0 at 𝛼 = 1% Method 2: Confidence interval 𝑠𝑒(𝛽̂ ); + 𝑡𝛼 𝑠𝑒(𝛽̂ )) 4 ,𝑛−𝑘−1 ,𝑛−𝑘−1 2 𝛽̂ 𝛼 = 10% 𝛽4 ∈ (−0.1746 − 1.66 ∗ 0.0721; −0.1746 + 1.66 ∗ 0.0721 𝛽4 ∈ (−0.2943; −0.0549) 𝛽4 ∈ (𝛽4̂ − 𝑡𝛼 𝛼 = 5% 𝛼 = 1% 𝛽∗ = => 𝛽∗ ∉ (−0.2943; −0.0549) 4  Reject H0, accept H1 at 𝛼 = 10% 𝛽3 ∈ (−0.1746 − 1.984 ∗ 0.0721; −0.1746 + 1.984 ∗ 0.0721) 𝛽3 ∈ (−0.3176; −0.0316) 𝛽∗ = => 𝛽∗ ∉ (−0.3176; −0.0316) 3  Reject H0, accept H1 at 𝛼 = 5% 24 𝛽3 ∈ (−0.1746 − 2.626 ∗ 0.0721; −0.1746 + 2.626 ∗ 0.0721) 𝛽3 ∈ (−0.3639; 0.0147) 𝛽∗ = => 𝛽∗ ∈ (−0.000239; −0.000095) 3  Cannot reject H0 at 𝛼 = 1% Method 3: P-value 𝑡𝑠 = - 2.42 < P-value = 2𝜑(𝑡𝑠) = 2𝜑(−2.42)) = 2*0.0078 = 0.0156 =1.56% 𝛼 = 10% 𝛼 = 5% 𝛼 = 1% Conclusion: P-value = 1.56% < 10% => Reject H0, accept H1 at 𝛼 = 10% P-value = 1.56% < 5% => Reject H0, accept H1 at 𝛼 = 5% P-value = 1.56% > 1% => Cannot reject H0 at 𝛼 = 1% + Inflation rate has statistically significant effect on youth unemployment rate at ̂ < 0) significance level of 10% and 5% And the effect is negative (because 𝛽𝐼𝑁𝐹 + Given the sample we have, when INF increases by 1%, the average youth unemployment rate decreases by 0.1745881%, holding other factors fixed - Tertiary enrollment rate (TER) Method 1: Critical value (t-test) 𝛼 = 10% 𝛼 = 5% 𝛼 = 1% 𝑡 = 𝑠 𝛽̂ − 𝛽∗ 𝐻0: 𝛽5 = {𝐻1: 𝛽5 ≠ = 𝑠𝑒(𝛽̂ ) −0.0689213 − 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.05,104 = 1.66  0.0288844 = −2.39 |𝑡𝑠| = 2.39 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 10% 𝑡𝛼,𝑛−𝑘−1 = 𝑡0.025,104 = 1.984 𝑡  |𝑡𝑠| = 2.39 > 𝑡𝛼,𝑛−𝑘−1=> Reject H0, accept H1 at 𝛼 = 5% 𝛼 2,𝑛−𝑘−1  = 𝑡0.005,104 = 2.626 |𝑡𝑠| = 2.39 < 𝑡𝛼,𝑛−𝑘−1=> Cannot reject H0 at 𝛼 = 1% Method 2: Confidence interval 𝑠𝑒(𝛽̂ ); + 𝑡𝛼 𝑠𝑒(𝛽̂ )) 5 ,𝑛−𝑘−1 ,𝑛−𝑘−1 2 𝛽̂ 𝛼 = 10% 𝛽5 ∈ (−0.0689 − 1.66 ∗ 0.0289; −0.0689 + 1.66 ∗ 0.0289) 𝛽5 ∈ (−0.1169; −0.0209) 𝛽5 ∈ (𝛽5 − 𝑡𝛼 𝛼 = 5% 𝛽∗ = => 𝛽∗ ∉ (−0.1169; −0.0209)  Reject H0, accept H1 at 𝛼 = 10% 𝛽3 ∈ (−0.0689 − 1.984 ∗ 0.0289; −0.0689 + 1.984 ∗ 0.0289) 𝛽3 ∈ (−0.1262; −0.0116) 𝛽∗ = => 𝛽∗ ∉ (−0.1262; −0.0116) 3 25  Reject H0, accept H1 at 𝛼 = 5% 𝛼 = 1% 𝛽3 ∈ (−0.1746 − 2.626 ∗ 0.0721; −0.1746 + 2.626 ∗ 0.0721) 𝛽3 ∈ (−0.1448; 0.007) 𝛽∗ = => 𝛽∗ ∈ (−0.1448; 0.007) 3  Cannot reject H0 at 𝛼 = 1% Method 3: P-value 𝑡𝑠 = - 2.39 < P-value = 2𝜑(𝑡𝑠) = 2𝜑(−2.39)) = 2*0.0084 = 0.0168 =1.68% 𝛼 = 10% P-value = 1.68% < 10% => Reject H0, accept H1 at 𝛼 = 10% 𝛼 = 5% 𝛼 = 1% Conclusion: P-value = 1.68% < 5% => Reject H0, accept H1 at 𝛼 = 5% P-value = 1.68% > 1% => Cannot reject H0 at 𝛼 = 1% + Tertiary enrollment rate has statistically significant effect on youth unemployment rate at significance level of 10% and 5% And the effect is negative (because 𝛽̂𝑇𝐸𝑅 < 0) + Given the sample we have, when TER increases by 1%, the average youth unemployment rate decreases by 0.0689213%, holding other factors fixed 2.2 Overall significance 𝛼 = 10% 𝑅2/𝑘 { 𝐻0: 𝑅2 = 𝐻1: 𝑅2 > 𝐹𝑠 = (1 − 𝑅2)/(𝑛 − 𝑘 − 1) 𝛼 = 5% 𝛼 = 1% Conclusion: 𝐹𝛼(𝑘,𝑛−𝑘−1) = 𝐹0.1(5,104)  = 0.8109/5 (1 − 0.8109)/104 = 1.91 = 89.1947 Reject H0, accept H1 at 𝛼 = 10% 𝐹𝛼(𝑘,𝑛−𝑘−1) = 𝐹0.05(5,104) = 2.31 𝐹𝑠 = 89.1947 > 𝐹𝛼(𝑘,𝑛−𝑘−1) = 2.31  Reject H0, accept H1 at 𝛼 = 5% 𝐹𝛼(𝑘,𝑛−𝑘−1) = 𝐹0.01(5,104) = 3.21 𝐹𝑠 = 89.1947 > 𝐹𝛼(𝑘,𝑛−𝑘−1) = 3.21  Reject H0, accept H1 at 𝛼 = 1% The model is statistically significant at significance level of 10%, 5% and 1% Recommendation - Improving education and training quality: Providing chances for people to improve their abilities, skills, expertise as well as education and training at an accessible cost, helps to increase a corporation's and an economy's productivity People with better skills and knowlegde will enter the labor work market easier and cannot be expelled from the market, especially in the current situation with the ever-accelerating advancement of technology, 26 27 artificial intelligence (AI) and the development of the Internet all over the world Moreover, in order to increase tertiary enrollment, the government should allocate a larger budget on upgrading universities’ infrastructure and facilities to create optimal conditions for studying - Increasing physical capital investment: Increasing government and private sector investment in capital goods, particularly infrastructure, may boost productivity while cutting company costs Additionally, more physical capital investment will demand more labor; hence, decreasing the unemployment rate - Making technological progress: Developing new technologies, including hard technology such as computerization or robots and soft technologies such as new business models or pro-free market government policy reforms, can boost worker productivity - Controlling population growth: As a country's population grows, so does its youth unemployment rate Therefore, one of the best ways to reduce youth unemployment rate is controlling population growth by combining political education with effective economic measures The recommendations are: Coordinate employment, salaries, bonuses, health care, age and condition of retirement, preschool care and education with family planning programs; Educate people about family planning and incorporate population growth and family planning into political and economics plans; Establish a permanent population committee to plan, develop, and implement population policies - Controlling inflation rate: Although inflation rate has a negative impact on youth unemployment rate, we still have to control it In times of inflation, businesses tend to attempt maximizing prices to uphold profitability and cope with escalating expenses Introducing price controls is one strategy to counteract this 'profit-driven' inflation, whereby the government establishes limits on price hikes If inflation stems from wage increases, such as potent unions negotiating for higher real wages, restraining wage growth can serve to moderate inflation A decrease in wage growth translates to lower costs for businesses and a reduction in excess demand within the economy In addition, monetarism aims to curb inflation by managing the money supply  Attracting more FDI: The more Foreign Direct Investment a country can attract, the lower youth unemployment rate is There are some crucial actions that the government should to improve the FDI in their nations: Establish a national plan for skill development to boost the proportion of skilled labor in the workforce; Modernize investment promotion activities and concentrate on priority sectors; Examine and modify the current investment incentive programs to guarantee high-quality FDI; Open service sectors including educations, logistics, and financial services to increase competitiveness and growth; Promote and facilitate investments abroad; Seize opportunities to lessen Industry 4.0’s harmful effects; Create a new FDI management agency for effective implementation of the policies and strategies 28 CONCLUSION Conclusion After applying STATA software to run an econometric model using the OLS method for linear regression with the data collected from World Bank, International Labor Organization, ASEANSTATS and other websites about the impact of labor productivity (LPR), population (POP), Foreign Direct Investment (FDI), inflation rate (INF) and tertiary enrollment rate (TER) on youth unemployment rate (YUR) in 10 ASEAN countries during a period of 14 years, from 2007 to 2020, we have gained an in-depth comprehension of econometrics with regard to the theoretical foundation and application to understand and suggest solutions to real-world circumstances in economic and business field Our research team comes to conclusion that labor productivity (LPR) and population (POP) have a positive effect on the youth unemployment rate (YUR) whereas the three remaining independent variables (Foreign Direct Investment (FDI), inflation rate (INF) and tertiary enrollment rate (TER)) have a negative one In addition, all independent variables have statistically significant impact on YUR and the model is statistically significant Contributions of the research In terms of academic significance, the research defined which factors having an impact on youth unemployment rate of ASEAN countries from 2007 to 2020 Then, we go in-depth on the effect on youth unemployment rate of factors by collecting data and use STATA to find the regression model This helps us gain insightful knowledge of the research topic Regarding practical significance, this research will make valuable contributions to policymakers in all ASEAN countries to find solutions to address youth unemployment They should invest more in improving the education and training quality, make technological advance, control population growth and inflation rate, attract more FDI From the perspective of the youth, this research will help them to be aware of unemployment, therefore, they will improve their knowledge and skills to find a well-paid job more easily and not to be expelled from the labor market Limitation of the research This research has to be seen in lights of some limitations First, since data is collected from 2007 to 2020, the research might be affected by the Covid pandemic Second, there is missing data in terms of tertiary enrollment rate in some ASEAN countries during the period Thus, it has an impact on the estimation result Third, due to our limited knowlegde, we only use OLS method instead of comparing different methods to find the best suitable for the research Fourth, the analysis of collected data should be conducted through more professional software for data analysis Finally, there may be some other factors impacting youth unemployment rate in ASEAN countries between 2007 and 2020 not presented here 29 ACKNOWLEDGEMENT Prosperous conclusion of any research projects requires support from various personnel and we were fortunate to have that support, direction and supervision in every aspect from our professors and Foreign Trade University First of all, we would like to thank all the teachers in Foreign Trade University, who taught us carefully about academic insights as well as social knowledge Specicially, we want to express our profound gratitude to PhD Dinh Thi Thanh Binh, our lecturer of Econometrics 1, who guided directly and spent a lot of the time to help us, with her kind help and enthusiasm, we have been completed our research proposal for the topic “Factors affecting youth unemployment rate of ASEAN countries from 2007 to 2020” We greatly appreciate the encouragement and advice from our upperclassmen at Foreign Trade University and our family from our first ideas to the final draft of this research The expertise and kindness of all people we have mentioned above have improved the research in innumerable ways and saved us from many errors However, due to our limited ability, this research inevitably remains some mistakes that need to be corrected in the future We are looking forward to receiving your feedbacks for the better research Sincerely, 30 REFERENCES Amyir Aljileedi Mustafa Rayhan, Heri Yanto, 2020 Factors influencing unemployment rate: A comparison among five ASEAN countries DOI: https://doi.org/10.15294/JEEC.V9I1.38358 Anyanwu, JC, 2013 Characteristics and macroeconomic determinants of youth employment in Africa African Development 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Economic vs non‐ economic determinants International labour review, 157(3), 379-408 DOI: https://doi.org/10.1111/ilr.12113 24 Yelwa, M., David, O O., & Awe, E O (2015) Analysis of the relationship between inflation, unemployment and economic growth in Nigeria: 1987-2012 Applied economics and finance, 2(3), 102-109 DOI: http://dx.doi.org/10.11114/aef.v2i3.943 32 APPENDIX 33 YEAR 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2013 2013 2013 2013 2013 COUNTRY Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia YUR LPR POP 18.12 65.91 2.09 8.19 24.71 2.34 2.63 3.76 10.83 1.76 1.55 19.17 10.32 6.8 8.86 56.22 4.88 9.99 5.53 4.41 18.12 63.28 2.09 8.55 24.71 2.44 2.63 3.95 10.83 1.94 1.55 19.96 10.32 6.96 8.86 53.95 4.88 10.08 5.53 4.58 20.90 60.96 0.81 2.43 19.18 8.77 1.88 4.14 11.53 19.07 1.60 2.12 10.48 6.86 12.78 52.72 5.76 9.98 4.49 4.76 21.14 61.51 0.97 2.41 17.75 8.98 1.60 4.39 11.31 19.96 1.60 2.31 10.05 7.17 9.87 56.69 2.61 10.76 3.47 4.67 22.10 62.85 0.60 2.49 18.48 9.40 2.18 4.66 9.88 20.23 1.62 2.46 9.75 7.18 9.22 58.52 2.97 10.24 3.27 5.15 22.82 62.95 0.68 2.57 15.74 9.78 2.75 4.96 10.46 20.63 1.62 2.59 9.60 7.56 8.94 59.44 2.78 10.94 3.70 5.49 23.55 61.23 0.77 2.67 16.07 10.66 3.34 5.28 10.30 20.90 34 FDI 0.3787 13.7148 234.8583 6.0413 27.0926 48.4456 89.5614 4.5886 66.8268 84.7623 0.3846 13.9439 237.9365 6.1359 27.6643 48.7295 91.2523 4.8394 67.3282 85.5972 0.3903 14.1557 240.9813 6.2299 28.2172 49.0158 92.9470 4.9876 67.8137 86.4829 0.3961 14.3635 244.0162 6.3234 28.7177 49.3910 94.6367 5.0767 68.2705 87.4110 0.4015 14.5739 247.0997 6.4163 29.1841 49.7945 96.3379 5.1837 68.7128 88.3491 0.4066 14.7866 250.2227 6.5088 29.6602 50.2182 98.0323 5.3124 69.1570 89.3013 0.4117 14.9997 253.2759 6.6007 30.1348 INF 257.6357 867.2885 6928.4800 323.5200 9071.3698 709.9220 2918.7248 47337.9478 8633.9034 6700.0000 222.1845 815.1802 9318.4536 227.7700 7572.5124 863.8804 1340.0276 13598.2985 8561.5577 9579.0000 325.5868 928.3936 4877.3692 318.5982 114.6644 1078.9722 2064.6207 23436.0641 6411.4585 7600.0000 625.4000 13770.2000 782.6000 332.6000 2248.8000 9155.9000 1298.0000 57460.6000 14746.7000 8000.0000 1208.3000 19241.6000 891.7000 466.8000 2058.2000 12000.9000 1815.9000 39886.6000 2473.7000 7519.0000 864.8000 19137.9000 1557.1000 294.4000 1354.2000 9400.0000 2797.0000 60101.9000 12899.0000 8368.0000 725.5000 18443.8000 1274.9000 426.7000 2620.9000 TER 0.97 8.71 6.41 4.66 2.03 35.02 2.90 2.10 2.24 8.34 2.08 24.10 10.23 7.63 5.44 26.80 8.26 6.63 5.47 23.12 1.04 -1.24 4.39 0.14 0.58 1.47 4.22 0.60 -0.85 6.72 0.36 4.00 5.13 5.98 1.62 7.72 3.79 2.82 3.25 9.21 0.14 5.48 5.36 7.57 3.17 5.02 4.72 5.25 3.81 18.68 0.11 2.93 4.28 4.26 1.66 1.47 3.03 4.58 3.01 9.09 0.39 2.94 6.41 6.37 2.11 15.01 7.31 17.79 11.50 30.01 10.61 49.03 18.48 15.74 9.12 20.67 13.31 33.44 29.16 48.67 19.06 16.77 11.77 22.99 16.39 35.49 28.49 49.40 20.23 15.46 13.96 24.08 16.65 37.03 29.56 50.37 22.82 17.40 14.89 26.30 17.81 36.15 14.18 30.80 52.26 24.95 22.43 30.43 17.67 37.61 13.53 31.21 50.68 25.19 24.34 31.06 19.02 39.07 2013 2013 2013 2013 2013 Myanmar Philippines Singapore Thailand Vietnam 1.60 9.40 9.35 1.25 4.63 2.76 7.82 61.59 12.03 5.80 50.6483 99.7001 5.3992 69.5786 90.2677 35 12107.1000 3859.8000 56670.9000 15936.0000 8900.0000 5.64 2.58 2.36 2.18 6.59 33.52 49.85 25.19 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2020 2020 2020 Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam Brunei Darussalam Cambodia Indonesia 24.40 1.07 16.04 3.94 9.97 1.59 9.66 8.77 3.15 4.76 26.26 0.77 17.27 4.56 11.26 1.60 8.69 9.36 3.15 6.35 27.81 1.16 16.02 5.17 11.10 2.53 7.77 9.14 3.80 6.79 29.65 0.38 14.57 5.79 11.61 3.60 7.48 9.69 4.47 7.02 31.45 0.41 16.41 5.85 11.74 1.85 6.70 8.68 4.14 4.51 21.51 0.43 13.59 5.93 11.27 1.32 6.78 7.68 4.34 5.50 27.13 0.79 14.77 59.46 2.75 10.62 5.60 21.42 2.96 8.25 63.60 12.30 6.19 59.05 2.90 10.78 5.92 22.44 3.03 8.60 63.99 12.90 6.55 57.39 2.98 11.18 6.24 23.09 3.47 8.90 65.34 13.54 6.89 58.28 3.09 11.46 6.58 23.95 3.82 9.64 67.54 14.21 7.58 54.25 3.21 12.05 6.86 24.75 4.20 9.87 70.19 14.69 7.76 55.81 3.33 12.29 7.10 25.20 4.55 10.17 69.10 15.16 8.64 58.37 3.39 12.76 0.4167 15.2108 256.2298 6.6915 30.6065 51.0724 101.3252 5.4697 69.9609 91.2355 0.4214 15.4175 259.0920 6.7874 31.0688 51.4839 103.0314 5.5350 70.2944 92.1914 0.4260 15.6246 261.8502 6.8914 31.5264 51.8923 104.8753 5.6073 70.6070 93.1265 0.4303 15.8307 264.4989 6.9979 31.9758 52.2883 106.7385 5.6123 70.8982 94.0330 0.4343 16.0252 267.0668 7.1050 32.3993 52.6660 108.5688 5.6387 71.1278 94.9143 0.4380 16.2077 269.5829 7.2121 32.8040 53.0402 110.3808 5.7036 71.3078 95.7767 0.4417 16.3969 271.8580 36 568.2000 21810.4000 1726.5000 913.2000 946.2000 10875.3000 5814.6000 73284.5000 4975.5000 9200.1000 171.3000 16642.1000 1701.0000 1079.2000 2824.5000 10180.0000 5639.2000 59702.3000 8927.7000 11800.0000 -150.4000 3920.7000 2475.9000 1075.7000 2989.5000 11290.3000 8279.5000 67504.5000 3486.3000 12600.0000 460.1000 20579.2000 2788.1000 1695.4000 4002.4000 9295.8000 10256.4000 85383.1000 8285.2000 14100.0000 517.3000 20563.5000 3212.6000 1358.0000 1609.8000 7611.3000 9948.6000 73546.7000 13751.8000 15500.0000 374.6000 23883.3000 3663.0000 755.5000 1729.9000 7859.7000 8671.4000 97480.4000 5518.7000 16120.0000 577.4000 18591.0000 3624.6000 -0.21 3.86 6.39 4.13 3.14 4.95 3.60 1.03 1.90 4.08 -0.49 1.22 6.36 1.28 2.10 9.45 0.67 -0.52 -0.90 0.63 -0.28 3.02 3.53 1.60 2.09 6.93 1.25 -0.53 0.19 2.67 -1.26 2.91 3.81 0.83 3.87 4.57 2.85 0.58 0.67 3.52 1.03 2.46 3.20 2.04 0.88 6.87 5.31 0.44 1.06 3.54 -0.39 1.94 3.03 3.32 0.66 8.83 2.39 0.57 0.71 2.80 1.94 2.94 1.92 29.29 30.90 18.37 39.51 35.63 50.18 30.72 37.56 13.14 33.25 18.17 45.59 37.80 29.07 34.87 35.44 17.26 46.76 40.42 83.94 49.29 28.54 35.07 11.76 36.44 15.74 43.72 35.48 84.79 47.25 31.20 12.18 36.31 14.97 45.13 18.82 29.55 88.89 45.95 30.77 11.85 14.45 43.06 31.62 91.09 44.85 28.64 31.99 12.89 2020 2020 2020 2020 2020 2020 2020 Lao PDR Malaysia Myanmar Philippines Singapore Thailand Vietnam 6.68 12.61 4.96 7.05 10.67 5.31 6.42 7.28 26.54 5.14 11.00 70.58 14.91 8.98 7.3194 33.2000 53.4232 112.1910 5.6858 71.4757 96.6487 37 967.7000 2205.6000 3185.3000 6822.1000 72931.6000 -4951.0000 15800.0000 5.10 -1.14 5.70 2.39 -0.18 -0.85 3.22 13.48 42.57 33.37 93.13 42.64