ANALYZING SUSTAINABLE INDEXES OF UNEMPLOYMENT PROBLEMS AND PREDICTING UNEMPLOYMENT RATE AN EMPIRICAL CASE ANALYSIS IN VIETNAM

LITERATURE REVIEWAppendix 1 Featured Studies related to Unemployment 1 Hossain and Afrin 2018 Klang Valley, Malaysia Regression There is a strong relationship between graduate attribu

GRADUATION THESIS



INTRODUCTION

0 50000 100000 150000 200000 250000 300000 350000

1-Dec 20-Jan 10-Mar 29-Apr 18-Jun 7-Aug 26-Sep 15-Nov 4-Jan

Number of Covid 19 infected cases

Americas Europe South-East-Asia Western Pacific Eastern Mediterranean Africa

1-Dec 20-Jan 10-Mar 29-Apr 18-Jun 7-Aug 26-Sep 15-Nov 4-Jan

Number of deaths by Covid 19

South East Asia Eastern Mediterranean

INTRODUCTION

750,967 Americas452,065 Europe 169,070 South-East Asia 107,866 Eastern Mediterranean

17,776 Western Pacific

Maintain a positive two-digit GDP growth

RESEARCH SCOPE

Red River Delta

Highlands Region

Northen Midlands and Mountains

North Central Region

LITERATURE REVIEW

Appendix 1 Featured Studies related to Unemployment

1 Hossain and Afrin (2018) Klang Valley,

Malaysia Regression There is a strong relationship between graduate attributes, employability skills and job mismatch

2 Yuksel and Adah (2017) Turkey MARS method Higher inflation rate negatively affects unemployment rate

Interest rate has a positive influence on the unemployment rate

3 Ogbeide et al (2016) Nigeria Regression FDI, economic growth and exchange rate affect unemployment

4 Bayrak and Tatli (2016) Turkey ARDL Higher education level and producer price index decrease

unemployment rate

Economic growth rate effect YUR negatively but insignificantly in the long term

5 Andrew E Clark, Anthony Lepinteurb

(2019) France Regression Growing up in a favorable context (high family income, educated and engaged parents) significantly reduces the unemployment

experience

6 Robert E Hall (2017) The USA DMP model High discount rates imply high unemployment

7 Erna A R Puspadjuita 1 (2017) Indonesia Descriptive and

multiple linear regression

Urbanization, labor absorption elasticity and the provincial minimum wage have negative effect on unemployment rate Industrialization rate has a positive effect on unemployment.

8 JB Morgan, A Mourougane (2001) Europe Cobb-Douglas

method The replacement ratio positively associates with structural unemployment

Measures of mismatch and trade union density were positively associated with structual unemployment

9 LX Chen, YB Chew, RLH Lim, WY Tan,

KY Twe (2017) China ARDL approach GDP growth, Population are significant to unemployment rate

People of working age

People of the age with rights specified in the constitution

People outside the workforce

People who are attending school, housewives, people unable to work due to illness or diseases, and those who do not want to find work for different reasons.

Workforce

The workforce is a part of the working-age population that is engaged in labor and who are unemployed but looking for jobs.

self-People experiencing underemployment

DEFINITIONS

Those who are employed but have an actual working time of fewer than 35 hours, are willing

to work overtime.

Underemployment rate

The percentage of underemployed workers out of the total number of employed workers.

Unemployment

DEFINITIONS

A state when workers want to find jobs but cannot find them

METHODOLOGY

Proposed method: GDEMATEL

The concept of “deep and wide”

Why sustainability indexes ?

Output

Grey variables

Professor Deng Julong

(1933-2013)

Step 1: Normalizing the grey values

Step 4: Computing the sum of rows (Di) and the sum of columns (Ri), respectively:

D= [ σ𝒋=𝟏𝒏 𝒎𝒊𝒋]n x 1

R= [ σ𝒊=𝟏𝒏 𝒎𝒊𝒋]1 x n

Step 5: Creating the value of (Ri+Di), (Ri−Di) The influencing factors can then be shown in

the causal relationship diagram

Step 6: In the GDEMATEL method, structural relationships occur between the analyzed

elements, and it is a premise for the use of GDEMATEL in the weighting of criteria We

determine criteria weights using the results of GDEMATEL with Eq (15), (16) in this study

𝑊𝑖 = [(𝑅𝑖 + 𝐷𝑖)2 + (𝑅𝑖 − 𝐷𝑖)2]1/2

𝑊𝑖𝑛𝑜𝑟= 𝑊𝑖/σ𝑖=1𝑛 𝑊𝑖Where 𝑊𝑖𝑛𝑜𝑟 are normalized weights of criteria

GDEMATEL

METHODOLOGY

Grey Forecasting Models

Classical Grey Forecasting

Model – GM(1,1) Grey Verhulst Forecasting

Model – GVM

To establish GM (1,1) and calculate coefficients, first-degree grey differential equality is

applied by using 𝑥(0) and 𝑥(1) series

−𝑧11 (𝑛)

1 1

𝑌𝑁 =

𝑥 0 (2)

𝑥 0 (3)

𝑥 0 (𝑛)

To measure the efficiency of GM (1,1), mean absolute percentage error (MAPE) values are

calculated by comparing the recognized and forecasted values

𝑒 𝑘 = 𝑥(0)𝑘− ො𝑥(0)𝑘

𝑌𝑁 =

𝑥 1 (2)

𝑥 1 (3)

𝑥 1 (𝑛)

Criteria Crisp Di+Ri Crisp Di-Ri Wi W nor Rankings

RESULTS FROM GDEMATEL

Top 10 most significant variables

RESULTS FROM GDEMATEL

ERROR CHECKS FOR

Actual 0.0279 0.032 0.0374 0.0363 0.0374 0.0386

2.25% Forecasted 0.027

9 0.04 0.042 0.043 0.045 0.047

Classical Grey

Forecasting

Model

ERROR CHECK FOR FORECASTING MODELS

1

0.0200 8

0.0200 6

4

COMPARISION OF FORECASTING

MODELS’ RESULTS

Red River Delta 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%

COMPARISION OF FORECASTING

MODELS’ RESULTS

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 3.71% 4.14% 4.28% 4.03% 3.95% 4.09% 2.69% 1.69% 0.98% 0.55% 0.30% 0.16%

3.71%

4.14% 4.28% 4.03%

3.95% 4.09% 3.97% 3.93% 3.89% 3.85% 3.81%

3.77% 3.71%

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 3.71% 4.14% 4.28% 4.03% 3.95% 4.09% 2.69% 1.69% 0.98% 0.55% 0.30% 0.16%

3.71%

4.14% 4.28% 4.03%

3.95% 4.09% 3.97% 3.93% 3.89% 3.85% 3.81%

3.77% 3.71%

COMPARISION OF FORECASTING

MODELS’ RESULTS

Mekong River Delta

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%

contributing factors are:

Economic growth, Foreign direct

investment, Real GDP per capita

Industrialization, Education level,

Trade Openness, Capacity Utilization

rate



Urbanization, Employability Skills,

Education system expansion

GM (1,1) is the optimal model for

forecasting future Unemployment

rate



Grey Verhulst is not suitable for

predicting Unemployment rate

Value efficiencies

in forecast

>50 Weak and inaccurate forecasting

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



the barrier in communicating in

foreign languages must be eliminated

Invest more capital in education

sustaining stable economic growth

while maintaining a welfare system

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