GRADUATION THESIS STRUCTURE OF THE PRESENTATION INTRODUCTION LITERATURE REVIEW METHODOLOGY RESEARCH RESULTS AND DISCUSSIONS CONCLUSIONS AND RECOMMENDATIONS INTRODUCTION 350000 Number of Covid 19 infected cases 300000 Americas 28,355,791 250000 Europe South-East Asia 20,154,730 11,114,545 200000 150000 100000 Eastern Mediterranean 4,319,467 Africa 1,556,168 Western Pacific 920,613 50000 1-Dec 20-Jan 10-Mar 29-Apr 18-Jun 7-Aug 26-Sep 15-Nov Americas Europe South-East-Asia Western Pacific Eastern Mediterranean Africa 4-Jan 7000 INTRODUCTION Number of deaths by Covid 19 750,967 Americas 452,065 Europe 169,070 South-East Asia 6000 5000 4000 3000 2000 107,866 1000 1-Dec 20-Jan 10-Mar 29-Apr 18-Jun 7-Aug 26-Sep 15-Nov Americas Europe South East Asia Eastern Mediterranean Africa Western Pacific 4-Jan Eastern Mediterranean 34,660 Africa 17,776 Western Pacific INTRODUCTION The number of workers starting to work remotely in Europe Number of workers starting to work remotely in Europe 18.7% Romania Croatia Bulgaria Slovakia Latvia Malta Czechia EU27 France Italy Sweden Denmark 26.3% 28% 28.4% 29% 30.2% 31% 31.3% 31.8% 34% 35.5% 36% 36.8% 37% 37.3% 37.2% 38% 38.8% 41% 41.7% 42% 43% 46% Netherland Finland 0% 10% 20% 30% 40% 50% 37% of workers in the EU started working remotely due to the outbreak Nordic countries have the highest proportion of remote workers 53% 54% 56% 59% 60% The figures for other nations in EU fall between 26% to 38% INTRODUCTION A significant increase from 2019 Global Unemployment rate Percent 20.0% 18.0% 17.5% 16.0% 14.0% 12.0% The highest rate over 12 years since 2008 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 INTRODUCTION Vietnam’s forecasted GDP growth for 2020 A sharp decline from the previous year GDP growth dropped by almost percentage points 9.00% 8.00% q4-19 q4-17 q4-16 Maintain a positive two-digit GDP growth 7.00% 6.00% 5.00% q4-18 4.00% 3.00% 2.00% 1.00% 0.00% q1-17 q2-20 A drop by percentage points RESEARCH QUESTIONS Question What are the factors affecting the unemployment rate under Covid 19 impacts in Vietnam? Question What is the trend for the unemployment rate in Vietnam in the next years? RESEARCH SCOPE Northen Midlands and Mountains Red River Delta North Central Region Highlands Region Mekong River Delta Southeast Region LITERATURE REVIEW No Authors Hossain and Afrin (2018) Appendix Featured Studies related to Unemployment Scope Method Results Klang Valley, Regression There is a strong relationship between graduate attributes, Malaysia employability skills and job mismatch Yuksel and Adah (2017) Turkey MARS method Higher inflation rate negatively affects unemployment rate Interest rate has a positive influence on the unemployment rate Ogbeide et al (2016) Nigeria Regression FDI, economic growth and exchange rate affect unemployment Bayrak and Tatli (2016) Turkey ARDL Andrew E Clark, Anthony Lepinteurb (2019) France Regression Higher education level and producer price index decrease unemployment rate Economic growth rate effect YUR negatively but insignificantly in the long term Growing up in a favorable context (high family income, educated and engaged parents) significantly reduces the unemployment experience Robert E Hall (2017) Erna A R Puspadjuita (2017) The USA Indonesia DMP model Descriptive and multiple linear regression High discount rates imply high unemployment Urbanization, labor absorption elasticity and the provincial minimum wage have negative effect on unemployment rate Industrialization rate has a positive effect on unemployment JB Morgan, A Mourougane (2001) Europe Cobb-Douglas method LX Chen, YB Chew, RLH Lim, WY Tan, KY Twe (2017) China ARDL approach The replacement ratio positively associates with structural unemployment Measures of mismatch and trade union density were positively associated with structual unemployment GDP growth, Population are significant to unemployment rate METHODOLOGY Grey Verhulst 𝑥 (0) = (𝑥 𝑥 (1) = (𝑥 𝑥 𝑧 (1) = (𝑧 𝑧 1 1 ,𝑧 (𝑘) = 𝑥 𝑑𝑥 (1) 𝑑𝑡 𝑥 ,𝑥 𝑘 𝑘 = 𝛴𝑖=1 𝑥 1 ,𝑥 1 ,𝑥 ,𝑥 ,…,𝑥 ,…,𝑥 𝑖 , 𝑘 = 1,2, … , 𝑛 ,𝑧 𝑘 +𝑥 1 ,…,𝑧 𝑘 = 𝑏(𝑧 (𝑛)) (𝑛)) (26) (𝑛)) (𝑘 − 1) , 𝑘 = 2,3, … , 𝑛 + 𝑎𝑥 (1) = 𝑏(𝑥 (1) )2 𝑘 + 𝑎𝑧 (27) (28) (29) (𝑘))(2) (30) METHODOLOGY Grey Verhulst 𝑎ො = 𝑎 = (𝐵𝑇 𝐵)−1 𝐵𝑇 𝑌𝑁 𝑏 −𝑧11 (2) (𝑧 (2))2 −𝑧11 (3) 𝐵= −𝑧1 (𝑛) (𝑧 (3))2 (𝑛))2 (𝑧 𝑥 𝑥 (2) (3) 𝑌𝑁 = 𝑥 (31) (𝑛) As a result of the solution of 𝑥 (1) (t) for k time, GVM is obtained 𝑥ො 𝑘+1 = (32) 𝑎𝑥 (1) 𝑏𝑥 +(𝑎−𝑏𝑥 (1)𝑒 𝑎𝑘 The mean absolute percentage error (MAPE) obtained as a result of GVM is calculated by the formula below ∆𝑘 = 𝜀 (𝑘) 𝑥 (1) 𝑘 , 𝜀 (1) 𝑘 = 𝑥 (1) 𝑘 − 𝑥ො (𝑘) (33) RESEARCH RESULTS AND DISCUSSIONS Research results GM (1,1) Grey Verhulst A comparision of two forecasting models RESULTS FROM GDEMATEL Criteria Crisp Di+Ri Crisp Di-Ri Wi W nor Rankings Top 10 most significant variables M1 M2 M3 M4 3.5148 2.8971 2.5691 2.0577 0.2881 -0.3509 -0.6079 -0.3803 3.527 2.918 2.64 2.093 M5 M6 M7 M8 M9 2.5521 3.1811 2.238 1.9241 2.965 0.2145 0.2363 -0.0699 -0.1451 0.3394 2.561 3.19 2.239 1.93 2.984 M10 M11 M12 M13 M14 M15 M16 M17 M18 M19 M20 1.7734 2.1923 2.8684 2.3323 1.3938 1.4979 2.066 1.542 2.5009 2.3464 1.6917 -0.3396 0.6276 -0.0213 -0.4371 -0.2279 -0.1005 0.6558 -0.344 0.8629 -0.0282 -0.172 1.806 2.28 2.869 2.373 1.412 1.501 2.168 1.58 2.646 2.347 1.7 0.075 0.062 0.056 0.045 0.055 0.068 0.048 0.041 0.064 0.039 0.049 0.061 0.051 0.03 0.032 0.046 0.034 0.057 0.05 0.036 14 12 15 16 11 20 19 13 18 10 17 Economic growth Industrialization FDI Real GDP per capita Education level Trade Opennes Capacity Utilization Urbanization Employability skills 10 Education system expansion RESULTS FROM GDEMATEL ERROR CHECKS FOR FORECASTING MODELS No Classical Grey Forecasting Model Regions RRD NMM NCR HLR SER Unemployment rate – Urban areas Years 2014 2015 2016 2017 2018 2019 Actual Forecasted Actual Forecasted Actual Forecasted Actual Forecasted Actual 0.049 0.049 0.024 0.024 0.037 0.037 0.019 0.019 0.03 0.03 0.032 0.036 0.032 0.021 0.043 0.039 0.022 0.017 0.026 0.028 0.037 0.032 0.032 0.027 0.02 0.04 0.039 0.02 0.016 0.028 0.028 0.036 0.03 0.028 0.021 0.019 0.04 0.038 0.015 0.015 0.029 0.028 0.037 0.025 0.025 0.029 0.017 0.041 0.038 0.025 0.014 0.029 Forecasted 0.043 0.041 0.033 0.023 0.041 0.04 0.028 0.018 0.031 0.028 0.042 0.043 0.045 0.047 Actual MAPE MRD Forecasted 0.027 0.027 0.032 0.04 0.028 0.038 3.72% 9.06% 1.76% 14.94 % 3.56% 2.25% ERROR CHECK FOR FORECASTING MODELS No Grey Verhulst Forecasting Model Regions RRD NMM NCR Unemployment rate – Urban areas Years Actual Forecasted Actual Forecasted Actual 2014 0.018 0.018 0.005 0.005 0.017 2015 2016 2017 2018 0.022 0.017 0.016 0.014 0.012 0.011 0.01 0.009 0.009 0.008 0.007 0.009 0.009 0.01 0.01 0.011 0.019 0.022 0.02 0.022 0.0201 0.0200 0.0200 0.0200 Forecasted 0.017 Actual 0.0093 0.0076 0.0088 0.0069 0.0088 Forecasted 0.009 0.0095 0.0099 0.0104 0.0108 Actual 0.03 0.0308 0.0262 0.0282 0.0293 Forecasted 0.03 0.0186 0.0178 0.0169 0.0161 Actual 0.0183 0.0264 0.0262 0.0261 0.0237 Forecasted 0.0183 0.0247 0.0244 0.0241 0.0238 HLR SER MRD MAP E 2019 0.015 4.53% 0.008 0.01 6.99% 0.011 0.019 5.74% 0.02 0.009 5.94% 0.011 0.028 5.57% 0.015 0.026 0.023 2.21% Comparision of proposed methods 5.00% Unemployment rate COMPARISION OF FORECASTING MODELS’ RESULTS 6.00% 4.32% 4.30% 4.00% 3.24% 3.21% 3.00% 2.53% 3.00% 2.00% 2.24% 1.99% 1.44% 0.84% 1.00% 0.00% Red River Delta 4.86% 1.77% 0.47% 1.57% 1.39% 1.23% 0.26% 0.14% 0.07% 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 GM(1,1) 4.86% 4.30% 3.24% 3.21% 3.00% 2.53% 2.24% 1.99% 1.77% 1.57% 1.39% 1.23% GVM 4.86% 4.32% 3.24% 3.21% 3.00% 2.53% 1.44% 0.84% 0.47% 0.26% 0.14% 0.07% GM(1,1) GVM Comparisions of proposed methods Unemployment rate 2.50% 2.00% 2.20% 1.84% 1.73% 1.64% 1.44% 1.50% 1.46% 1.20% 1.00% 1.00% 0.79% 0.90% 0.80% 0.70% 0.45% 0.50% 0.00% 1.10% 0.24% 0.13% 0.06% 0.03% 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 GM(1,1) 1.84% 2.20% 1.73% 1.64% 1.44% 1.46% 1.20% 1.10% 1.00% 0.90% 0.80% 0.70% GVM 1.84% 2.20% 1.73% 1.64% 1.44% 1.46% 0.79% 0.45% 0.24% 0.13% 0.06% 0.03% GM(1,1) GVM Comparisions of proposed methods 4.00% 4.28% 3.71% 4.03% 3.95% 4.09% 3.97% 3.93% 3.89% 3.85% 3.81% 3.77% 3.50% Unemployment rate COMPARISION OF FORECASTING MODELS’ RESULTS 4.14% 4.50% 2.69% 3.00% 2.50% 1.69% 2.00% 1.50% 0.98% 1.00% 0.55% 0.50% 0.00% 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 3.71% 4.14% 4.28% 4.03% 3.95% 4.09% 3.97% 3.93% 3.89% 3.85% 3.81% 3.77% GVM 3.71% 4.14% 4.28% 4.03% 3.95% 4.09% 2.69% 1.69% 0.98% 0.55% 0.30% 0.16% 3.89% 3.85% 3.81% 3.77% 4.50% 4.00% Unemployment rate 0.16% GM(1,1) GM(1,1) North Central Region 0.30% 4.14% 4.28% 3.71% GVM Comparisions of proposed methods 4.03% 3.95% 4.09% 3.97% 3.93% 3.50% 2.69% 3.00% 2.50% 1.69% 2.00% 1.50% 0.98% 1.00% 0.55% 0.50% 0.00% 0.30% 0.16% 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 GM(1,1) 3.71% 4.14% 4.28% 4.03% 3.95% 4.09% 3.97% 3.93% 3.89% 3.85% 3.81% 3.77% GVM 4.14% 4.28% 4.03% 3.95% 4.09% 2.69% GM(1,1) GVM 1.69% 0.98% 0.55% 0.30% 0.16% 3.71% Unemployment rate Comparisions of proposed methods 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% 4.50% 4.70% 4.80% 4.30% 4.20% 3.74% 3.63% 3.74% 3.86% 4.00% 3.20% 2.79% 2.74% 1.78% 1.06% 0.59% 0.32% 0.17% COMPARISION OF FORECAS MODELS’ RESULTS 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 GM(1,1) 2.79% 3.20% 3.74% 3.63% 3.74% 3.86% 4.00% 4.20% 4.30% 4.50% 4.70% 4.80% GVM 2.79% 3.20% 3.74% 3.63% 3.74% 3.86% 2.74% 1.78% 1.06% 0.59% 0.32% 0.17% GM(1,1) GVM Comparisions of proposed methods Unemployment rate 3.00% 2.64% 2.62% 2.61% 2.50% 2.00% 2.60% 2.37% 1.83% 2.47% 2.44% 2.41% 2.34% 2.31% 1.71% 1.50% 1.04% 1.00% 0.59% 0.31% 0.50% 0.00% 2.38% 0.16% 0.08% 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 GM(1,1) 1.83% 2.64% 2.62% 2.61% 2.37% 2.60% 2.47% 2.44% 2.41% 2.38% 2.34% 2.31% GVM 1.83% 2.64% 2.62% 2.61% 2.37% 2.60% 1.71% 1.04% 0.59% 0.31% 0.16% 0.08% GM(1,1) GVM Mekong River Delta CONCLUSIONS The unemployment contributing factors are: rate’s Economic growth, Foreign direct investment, Real GDP per capita Industrialization, Education level, Trade Openness, Capacity Utilization rate Urbanization, Employability Education system expansion Skills, CONCLUSIONS GM (1,1) is the optimal model for forecasting future Unemployment rate Grey Verhulst is not suitable for predicting Unemployment rate CONCLUSIONS Value efficiencies in forecast MAPE (%) Forecast power 50 Weak and inaccurate forecasting RECOMMENDATIONS It is of great necessity for the Vietnamese government to create new labor markets A nightlife economy operating under the government’s control Vietnamese authorities should also allocate finances for the development of logistics, ports, aviation, banking, and tourism industry RECOMMENDATIONS the barrier in communicating in foreign languages must be eliminated Invest more capital in education sustaining stable economic growth while maintaining a welfare system ... development of logistics, ports, aviation, banking, and tourism industry RECOMMENDATIONS the barrier in communicating in foreign languages must be eliminated Invest more capital in education sustaining... affect unemployment Bayrak and Tatli (2016) Turkey ARDL Andrew E Clark, Anthony Lepinteurb (2019) France Regression Higher education level and producer price index decrease unemployment rate Economic... inflation rate negatively affects unemployment rate Interest rate has a positive influence on the unemployment rate Ogbeide et al (2016) Nigeria Regression FDI, economic growth and exchange rate