The Factors Affecting the Organization and Management of Economic Zones in Vietnam Download PDF

Journal Name : SunText Review of Economics & Business

DOI : 10.51737/2766-4775.2020.007

Article Type : Research Article

Authors : Tran Ngoc Son

Keywords : Economic zone; People; Policies; Project; Position; Potential; Cronbach’s alpha; EFA; KMO; Regression

Abstract

In Vietnam, the Economic zone is a driving force for growth in the emerging economy. An economic zone is included as an area with a separate economic space with an investment and business environment that is particularly favourable to investors, has defined geographical boundaries, is established under the conditions, and order and procedures specified. Besides, the organization and management are decisive issues for this new model. However, at present, to identify the factors affecting the organization and management of the model of economic zones in Vietnam has not been studied properly by domestic and foreign authors. New studies in this paper using SPSS data analysis method will describe the relationships between the independent variables, the organizational model and management of economic zones in Vietnam and 05 dependent variables: People, Policies, Project, Position, Potent ion use multiple linear regression models to help research, organization and management of economic zones in Vietnam better.


Introduction

Current situation of economic zones in Vietnam

In the Decree No. 82/2018/ND dated May 22, 2018 of the Prime Minister of Vietnam, stipulates that: An economic zone (EZ) is an area with defined geographical boundaries, including many functional areas, established to realizing the objectives of attracting investment, developing socio-economic, and protecting national defence and security. The EZ in this study is a coastal EZ. Coastal EZ means an EZ formed in the coastal area and in the vicinity of the coastal area. On the basis of the provisions of Decree No. 29/2008/ND-CP, a coastal EZ is an area with a separate economic space with a business investment environment especially favourable for investors. Defined geographical boundary attached to a deep-water seaport (or airport). Coastal EZ are organized into functional areas including: non-tariff zones, tax-suspension zones, export processing zones, industrial zones, entertainment zones, tourist resorts, urban areas, residential areas, administrative areas. Key and other functional areas suitable to the characteristics of each EZ:

·  The Government performs the unified state management of coastal EZ nationwide on the basis of assigning specific tasks and powers of each ministry, branch, provincial People's Committee and EZ Management Board health.

·   To direct the formulation and implementation of development planning’s and plans and to promulgate policies and legal documents on EZ.

·  Corporate income tax incentives: According to Decree No. 218/2013/ND-CP, projects investing in a coastal EZ are entitled to 10% corporate income tax rate for 15 years, tax exemption for 04 years and 50% reduction in the next 9 years.

·  Import tax incentives and personal income tax incentives: according to Decree No. 29/2008/ND-CP, Vietnamese people and foreigners working in coastal EZ are reduced by 50%. Personal income tax for people with taxable income.

·    The land use term of an investment project in a coastal EZs is 70 years.

About the establishment planning: since the first coastal EZ, Chu Lai Open Economic Zone, was established in 2003, up to 2020, in Vietnam, 18 EZs have been established and 16 EZs in operation, including: 2 EZs in the Red River Delta region: Van Don (Quang Ninh province) and Dinh Vu - Cat Hai (Hai Phong city); Economic zones in the Central Coast region are Nghi Son (Thanh Hoa province), Southeast Nghe An (Nghe An province), Vung Ang (Ha Tinh province), Hon La (Quang Binh province), Chan May-Lang Co ( Thua Thien Hue province), Chu Lai (Quang Nam province), Dung Quat (Quang Ngai province), Nhon Hoi (Binh Dinh province), Van Phong (Khanh Hoa province), Nam Phu Yen (Phu Yen province) and Southeast Quang Tri (Quang Tri province); 03 EZs in the South are Phu Quoc Island EZ and Nam An Thoi Island Cluster (Kien Giang Province), Dinh An (Tra Vinh Province), Nam Can (Ca Mau) Total land and sea surface area of 16 EZs are nearly 815 thousand hectares.

Factors influencing the organization and management of EZ in Vietnam

Over the past 20 years in Vietnam, the development of coastal EZ covering 18/28 coastal provinces and cities is mainly. The organization and management of EZ have not yet relied on the scientific basis of theories of industrial territory. Awareness of factors affecting the organization and management of EZ is inadequate. Since then, there is no most common model to organize and manage. The factors that this article will mention is that considering the relationship as to form EZ (dependent variables), it is necessary to have factors (independent variables) such as People, Policies, Project, Position, Potential. This relationship is shown through survey and data analysis method using SPSS 26 Premium tool will clarify. The author of the paper has identified factors through theories of Industrial Territories and the Vietnamese government's views on the EZ and through the experiences of countries around the world such as China, South Korea, and Singapore, Indonesia, United Arab Emirates (Figure 1).

Figure 1: The factors affecting the organization and management of economic zones in Vietnam.


Materials and Methods

The research applies and combines many methods, in which the final decision method is to analyse the discovery factor EFA, analyse the multiple regression model to assess the influence of the factors on the organization. And management of economic zones in Vietnam. To use the above methods, the author used SPSS 26 Premium tool to analyze the data. There are many conventions on sample size, such as that the sample size must be in accordance with the formula: n ? 8m + 50 (n is the sample size, m is the number of toxic variables model). Suggested that when using regression analysis, the sample size needs at least 200 observations. Meanwhile, Hair, Anderson, assumed that the minimum sample size should be 50, preferably 100, and the observed / observed ratio is 5/1, meaning that for each variable Observation requires a minimum of 5 observations. Accordingly, this study has a research model with 28 questions, so the minimum sample size is 28 x 5 = 140. To achieve a minimum of 140 observations, the author sent 250 questionnaires to the representative. 216 survey forms have been received, of which 06 questionnaires were rejected due to their invalid validity. Therefore, the number of remaining observations for analysis is 210 votes [1-3].

Results

Descriptive statistics of factors in the research model

With data of 210 observations. The average value of the observed variables (belonging to groups of independent variables) ranged from 2.65 to 4.18, this reflects that the customers all think that the factors included in the study are evaluated concentrated in the "3: Normal" and "4: Agree" levels (Table 1).

Test the scale Cronbach’s alpha

Cronbach's Alpha coefficients are a statistical test used to examine the rigor and correlation of observed variables. This relates to two aspects: the correlation between the variables themselves and the correlation of the scores of each variable with the scores of all variables for each respondent. This method allows the analyst to eliminate the inconsistent variables and limit the trash in the research model because otherwise we cannot know the exact variation and error of the variables. Accordingly, only variables with Corrected Item - Total Correlation coefficient greater than 0.3 and Cronbach's Alpha coefficient of 0.6 or more are considered acceptable and suitable for analysis in the next steps [4].


People (PEO)

Cronbach's Alpha scale test (1st time)

Cronbach's Alpha

Number of observations

710

4


 

Observed variables

Average scale if variable type

Scale variance if variable type

Total variable correlation

Cronbach's Alpha if variable type

PEO1

11.24

7,656

0.600

0.587

PEO2

11.11

7,528

0.583

0.594

PEO3

11.22

7,438

0.548

0.614

PEO4

11.87

8,863

0.290

0.772

  • We see:


    • Cronbach's Alpha is greater than 0.6.

    • The observed variable PEO4 has the total variable correlation coefficient is less than 0.3, so we eliminate this variable.

    • The variables related damage rest with coefficients relatively important variables total are greater than 0.3.

    Test Cronbach's Alpha scale (2nd time)

    Cronbach's Alpha

    Number of observations

    772

    3


     

    Observed variables

    Average scale if variable type

    Scale variance if variable type

     

    Total variable correlation

    Cronbach's Alpha if variable type

    PEO1

    7.96

    4,491

    0.632

    0.667

    PEO2

    7.83

    4,484

    0.586

    0.715

    PEO3

    7.94

    4.188

    0.604

    0.698

    We see:

    Cronbach's Alpha is greater than 0.6.

    POL5 observed variable has the total variable correlation coefficient is less than 0.3, so we remove this variable.

    The variables related damage rest with coefficients relatively important variables total are greater than 0.3


Policies (POL)

Check Cronbach's Alpha scale (1st  time)


Cronbach's Alpha

Number of observations

801

5


 

Observed variables

Average scale if variable type

Scale variance if variable type

Total variable correlation

Cronbach's Alpha if variable type

POL1

14.90

15,660

0.636

0.746

POL2

14.97

14,985

0.678

0.731

POL3

14.90

16,684

0.564

0.769

POL4

14.81

15,045

0.791

0.699

POL5

16.32

19,005

0.296

0.847

We see:


Cronbach's Alpha is greater than 0.6.

POL5 observed variable has the total variable correlation coefficient is less than 0.3, so we remove this variable.

The variables related damage rest with coefficients relatively important variables total are greater than 0.3

      Test Cronbach's Alpha scale (2nd time)

      Cronbach's Alpha

      Number of observations

      .847

      4


       

      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      POL1

      12.25

      11,728

      0.595

      0.845

      POL2

      12.32

      10,515

      0.726

      0.788

      POL3

      12.25

      11,852

      0.625

      0.831

      POL4

      12.16

      10,841

      0.808

      0.756


      We see:

      Cronbach's Alpha is greater than 0.6

      The variables relating police are there coefficient relatively important variables total are greater than 0.3.

      Because of this, the variation observed is put in to distribution volume in steps to follow.


      Position (POS)

      Cronbach's Alpha

      Number of observations

      .859

      5


       

      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      POS1

      16.47

      13,131

      0.741

      0.813

      POS2

      16.61

      13,033

      0.605

      0.851

      POS3

      16.44

      13,740

      0.617

      0.844

      POS4

      16.49

      13,524

      0.673

      0.830

      POS5

      16.45

      13,053

      0.756

      0.809

      1. We see:

        Cronbach's Alpha is greater than 0.6.

        The variables relating police are there coefficient relatively important variables total are greater than 0.3.

              Because of this, the variation observed is put in to distribution volume in steps to follow

      Project (PRO)

      Cronbach's Alpha

      Number of observations

      .897

      5


       

      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      PRO1

      15.79

      15,825

      0.813

      0.859

      PRO2

      15.75

      16.218

      0.752

      0.872

      PRO3

      15.85

      17,074

      0.645

      0.895

      PRO4

      15.90

      15,468

      0.719

      0.882

      PRO5

      15.80

      15,788

      0.808

      0.860

      We see:

      Cronbach's Alpha is greater than 0.6.

      The variables relating police are there coefficient relatively important variables total are greater than 0.3.

      Because of this, the variation observed is put in to distribution volume in steps to follow.

      Potential (POT)

      Cronbach's Alpha

      Number of observations

      0.747

      6


       

      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      POT1

      17.89

      23.208

      0.361

      0.744

      POT2

      18.27

      22,744

      0.385

      0.738

      POT3

      17.84

      20,854

      0.557

      0.691

      POT4

      17.67

      22,185

      0.440

      0.724

      POT5

      17.85

      20,117

      0.623

      0.671

      POT6

      17.81

      20,917

      0.557

      0.691

      We see:

       Cronbach's Alpha is greater than 0.6

      The variables relating police are there coefficient relatively important variables total are greater than 0.3

      Because of this, the variation observed is put in to distribution volume in steps to follow.

      MODEL of organization and management of economic zones in Vietnam (OZ)

      Cronbach's Alpha

      Number of observations

      0.626

      3


       

      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      OZ1

      8.37

      1,066

      0.444

      0.525

      OZ2

      7.83

      1,368

      0.431

      0.538

      OZ3

      8.39

      1,272

      0.443

      0.518



      We see:

      ·         Cronbach's Alpha is greater than 0.6.

      ·         The variables related damage rest with coefficients relatively important variables total are greater than 0.3.

      ·         Because of this, the variation observed is put in to distribution volume in steps to follow.

      Explore factor analysis EFA

      ·         After analysing the reliability of the scale, the next step to determine the set of variables needed for the research problem, we continue to use the exploratory factor analysis method to consider the convergence level. Of the observed variables for each component and the distinguishing value between the factors. After factor analysis, only groups of factors that satisfy the conditions can participate in the regression part of the next analysis.

      The important statistical parameters in factor analysis include

      ·         KMO (Kaiser - Meyer - Olkin measure of sampling adequacy) index is an indicator used to consider the adequacy of factor analysis. The KMO index must be large enough (> 0.5), then factor analysis is appropriate, and if it is less than 0.5, factor analysis is likely not suitable for the data.

      ·         Eigenvalue index represents the amount of variation explained by the factor. Only factors with Eigenvalue greater than 1 are retained in the analytical model, factors with Eigenvalue less than 1 will be excluded from the model. Variance Explained Criteria: the total extracted variance must be greater than 50%. Factor loadings is a single correlation coefficient between variables and factors. The larger this coefficient indicates the more closely related the variables and factors are related. With a sample of about 200, the accepted factor load factor is greater than 0.55, variables with factor load coefficients less than 0.55 will be excluded from the model. Bartlett test to test the correlation between observed variables and the population, the analysis only has significance when sig. have a value less than 5% [1].

       

       

       

       

       

       

       

       

       

       

       

      Result

       

       

       

       

       

       

       

       

       

       

      Compare

       

      Observed variables

      Factor

       

      1

      2

      3

      4

      5

      PRO1

      .887

       

       

       

       

      PRO5

      .886

       

       

       

       

      PRO2

      .828

       

       

       

       

      PRO4

      .802

       

       

       

       

      PRO3

      .748

       

       

       

       

      POS5

       

      .856

       

       

       

      POS1

       

      .849

       

       

       

      POS4

       

      .802

       

       

       

      POS3

       

      .746

       

       

       

      POS2

       

      .731

       

       

       

      POL4

       

       

      .905

       

       

      POL2

       

       

      .851

       

       

      POL3

       

       

      .779

       

       

      POL1

       

       

      .765

       

       

      POT5

       

       

       

      .782

       

      POT3

       

       

       

      .748

       

      POT6

       

       

       

      .693

       

      POT4

       

       

       

      .588

       

      POT2

       

       

       

      .573

       

      POT1

       

       

       

       

       

      PEO1

       

       

       

       

      .831

      PEO2

       

       

       

       

      .821

      PEO3

       

       

       

       

      .813

      Factors to evaluate

       

       

       

       

      KMO coefficient

       

      0 .755

      0.5

      <0 .755 <1

       

       

      Sig value. in the Bartlett test

       

      0.000

      0.000

      < 5%

       

       

      Citation variance

       

      63,135 %

      63.135 %> 50%

       

      Eigenvalue value

      1,943

      1.943 > 1

       

      The results of the discovery factor analysis have extracted 05 components. Statistical indicators ensure the conformity. However, b ien related damage can POT1 coefficient load factor (factor loadings) is less than 0.55, so we type this observed variable to analyse the discovery factor EFA (2nd time).

      Results of factor analysis to explore independent variables (the second time)

       

       

       

       

       

       

       

       

       

      Compare

       

       

      Observed

      variables

      Factor

       

       

       

      1

      2

      3

      4

      5

       

       

      PRO1

      .888

       

       

       

       

       

       

      PRO5

      .887

       

       

       

       

       

       

      PRO2

      .829

       

       

       

       

       

       

      PRO4

      .805

       

       

       

       

       

       

      PRO3

      .747

       

       

       

       

      Result

       

      POS5

       

      .858

       

       

       

       

       

       

      POS1

       

      .850

       

       

       

       

       

      POS4

       

      801

       

       

       

       

       

       

       

       

       

       

       

       

       

      POS3

       

      .745

       

       

       

       

       

      POS2

       

      .730

       

       

       

       

       

      POL4

       

       

      .906

       

       

       

       

       

      POL2

       

       

      .851

       

       

       

       

       

       

       

      POL3

       

       

      .779

       

       

       

       

       

      POL1

       

       

      .765

       

       

       

      POT5

       

       

       

      .802

       

       

      POT3

       

       

       

      .779

       

       

      POT6

       

       

       

      .715

       

       

      POT2

       

       

       

      .594

       

       

      POT4

       

       

       

      .553

       

       

      PEO1

       

       

       

       

      .830

       

      PEO2

       

       

       

       

      .820

       

      PEO3

       

       

       

       

      .814

       

      Factors to evaluate

       

      KMO coefficient

      0 .756

      0.5 <0 .756 <1

      Sig value. in the Bartlett test

      0.000

      0.000 < 5%

      Citation variance

      65,115 %

      65.115 %> 50%

      Eigenvalue value

      1,881

      1.881 > 1

      The results of the discovery factor analysis extracted 05 components. The statistical indicators to ensure conformity, c evil observers variable coefficient load factor (factor loadings) is greater than 0. 55. Do it, exploring factor analysis is said to be compatible with the data collected.


      The results of factor analysis to discover the dependent variable EFA

       

      Factors to evaluate

       

      Result

       

      Compare

      KMO coefficient

      0 .650

      0.5 <0 .650 <1

      Sig value. in the Bartlett test

      0.000

      0.000 < 5%

      Citation variance

      57.521 %

      57.521 %> 50%

      Eigenvalue value

      1,726

      1.726 > 1


       

      Factor

      first

      OZ1

      0.763

      OZ3

      0.761

      OZ2

      0.751

      The results of the discovery factor analysis extracted 01 component. The statistical indicators to ensure conformity, c evil observers variable coefficient load factor (factor loadings) is greater than 0.55. Do it, exploring factor analysis is said to be compatible with the data collected.



      Potential (POT)

      Cronbach's Alpha

      Number of observations

      0.744

      5


      Observed variables

      Average scale if variable type

      Scale variance if variable type

      Total variable correlation

      Cronbach's Alpha if variable type

      POT2

      14.70

      16,921

      0.387

      0.743

      POT3

      14.26

      15,170

      0.576

      0.673

      POT4

      14.09

      16,963

      0.390

      0.742

      POT5

      14.28

      14,670

      0.630

      0.652

      POT6

      14.24

      15,292

      0.569

      0.676

      We see:

      Cronbach's Alpha is greater than 0.6.

      The variables relating police are there coefficient relatively important variables total are greater than 0.3.

      Because of this, the variation observed is put in to distribution volume in steps to follow.


      Regression Analysis

      Regression analysis will determine the relationship between the dependent variable and the independent variables. Regression analysis model will describe the form of the relationship and thereby help us predict the level of the dependent variable when knowing in advance the values of the independent variables. When running the regression, it is necessary to pay attention to the following parameters [6-9]:

      Beta coefficients: Standardized regression coefficients allow direct comparison between coefficients based on their explanatory relationship with the dependent variable.

      Coefficient R2: Evaluating the volatility of the dependent variable explained by the predictor or independent variable. This factor can vary from 0 to 1.

      ANOVA test: To check the suitability of the model with the original data set. If the significance level of the test is <0.05, we can conclude that the regression model is consistent with the data set.

      Based on the adjusted model after the discovery factor analysis, we have a multiple linear regression model as follows:

       

      Regression coefficient is not standardized

      Standardized regression coefficients

       

       

      t

       

       

      Sig.

       

      Multi-collinear statistics

      B

      Standard error

      Beta

      Tolerance coefficient

      VIF

       

      Constant

      -.008

      .174

       

      -.046

      0.964

       

       

       

      PRO

      .149

      .019

      .288

      7,732

      .000

      .902

      1,109

       

      POS

      .307

      .021

      .540

      14,874

      .000

      .953

      1,049

       

      POL

      .243

      .017

      .520

      14,498

      .000

      .976

      1,025

       

      POT

      .093

      .020

      .175

      4,683

      .000

      .898

      1,114

       

      PEO

      .234

      .018

      .455

      12,747

      .000

      .984

      1,016

      Based on the table above we see:

      Testing the suitability of the model

      ·         Testing the multicollinearity phenomenon: The variance magnification factor (VIF) of all independent variables is less than 10, so the multicollinearity phenomenon in the model is assessed as not serious [6, 11-15].

      ·         The results of ANOVA test with significance level sig = 0.000 showed that the built multiple linear regression model was consistent with the data set and used [16-18].

      Evaluate the level of explanation by the independent variables in the model

      R2 coefficient (R Square) = 0 .744, which means that 74.4 % variation in financial results will be explained by the factors that are independent variables that were selected in the model. Research model results show that all independent variables have statistically significant effects (due to Sig. <5%).

      So, the normalized regression equation (see column Beta):

      The degree of impact of the independent variables on the dependent variable in the order of strong to weak is as follows (based on Beta coefficient) [19-21]:

      POS (Beta = 0.540) -> POL (Beta = 0.520) -> PEO (Beta = 0.455) -> PRO (Beta = 0.288) -> POT (Beta = 0.175)


      Discussion

      Based on the standardized regression coefficients of the statistically significant variables (column Beta), the larger the regression coefficient, the stronger the impact on the dependent variable will be. From the regression equation, we see that to organize and manage economic zones in Vietnam, the Position factor is the most important factor. It means that in order to form an economic zone in Vietnam, we first consider which Position is of primary concern, near seaports, airports, and convenient transportation systems. Benefit or not. Next, consider the Policies of the Government and localities that attract investment in the Economic Zone or not. There are People operating well. Are national and international key Projects being attracted. And finally there is the promotion of Potential advantages on the spot or not. These are the criteria for organizing and managing economic zones in Vietnam [23,24].


      Conclusion

      In summary, through research on the basis of survey and analysis of data using SPSS tools and the results give us a standardized regression equation can confirm that in Vietnam or in the world countries are trending forming a concentrated industrial space to create the spread of an affected neighbourhood for mutual development. A concentrated industrial space that in Vietnam is often called an economic zone or other countries called a special economic zone formed for development management is affected by factors in order such as: Position 1, Policies 2, People 3, Project 4, and Potential 5 that managers need to pay attention to organize and manage. How these five factors have a causal relationship will be studied in the author's later paper.


      Acknowledgements

      During the research process, the author would like to thank enterprises of 16 economic zones in Vietnam, Economic Zone Policy Makers in Vietnam, Managers of Economic Zones in Vietnam, Experts specializing in research on the economic zone in Vietnam have facilitated the survey by questionnaires, in-depth interviews and provided data in primary and secondary formats with sufficient evidence for research.


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