1. Introduction
The process of urbanization is an opportunity for the Government to organize and re-plan the operation of cities and residents. Accordingly, areas with potential for economic development, cultural development and social development will be planned according to a modern system, areas lack of economic conditions or low population density will be adjusted with suitable industries and project to increase development opportunities in the future. Urbanization in a positive direction will promote economic growth and social development of urban and peri-urban areas. According to The World Bank, there are many examples of developing countries focusing on building and completing institutions and policies to promote urbanization and sustainable urban development that have created impressive economic development based on urbanization such as China and Taiwan in 1950, Korea in 1970, Southeast Asian countries such as Singapore in 1970, Thailand in 1960. Since then, many scientific works have been published, many seminars related to the factors affecting urbanization in a country such as Anett Hofmann and Guanghua Wan (2013); research papers on the process and development of urbanization such as Henderson (2003), Moomaw and Shatter (1996); etc. In general, these studies refer to the main factors that can affect urbanization rate such as Gross domestic product (GDP), Gross national income (GNI), import-export rate, population density, and Foreign direct investment (FDI). Those are all core values within an economy that have been proven through real-life studies to affect the rate of urbanization. However, along with the development of science and technology and human resources nowadays along with The Fourth Industrial Revolution (4IR), the rate of urbanization in each country depends on both macro factors and micro factors. These factors are intertwined and complement each other in the process of urban development today.
Since the reform and opening up, Vietnam's urbanization level has increased rapidly. Especially in recent years, the development is even faster and the urbanization average anual growth rate has represented an increase of 2.8% (Vietnam Statistical Yearbook 2020), making it one of the fastest urbanizing countries in East Asia. By 2021, the statistics of the General Statistics Office have shown that the national urbanization rate has reached about 38.1%, has now officially entered the period of strong development and is in the process of transition, vertical developement from increasing urbanization speed to improving urbanization quality.
Along with the importance of urbanization in the Industrial Revolution 4.0, required studies of the factors affecting the urbanization process in Vietnam. However, current studies have only mentioned the issues of urbanization in Vietnam, research on sustainable urban development and policies to promote the increase of urban proportions. For example Tran Thi Lan Anh (2013) mention the reality of urbanization in Vietnam, the current challenges currently; Dao Hoang Tuan (2009) in the book talks about some theoretical and practical issues for a sustainable development of urban areas. In addition, there are a number of research papers focusing on specific regions such as the research paper of Prof. Dr. Hoang Ba Thinh (2014) in the article "Urbanization and urbanization management in sustainable development in the Central Highlands", Nguyen Quang Giai (2017) in the research: "Binh Duong’s urbanization process and the selection of a sustainable urban development model"; etc. In general, these studies has been very successful in presenting the current status and evaluate the impact of urbanization on the surrounding environment and development policies. However, there has not been a scientific study about factors that play the decisive role in the urbanization process in Vietnam. Meanwhile, the Industrial Revolution 4.0 is taking place more and more vigorously, and Vietnam is increasingly infiltrating the process of international economic integration, which requires the Government to have sharper policies to develop the economy towards the strategy of 2030, with a vision to 2045. With the aim of achieving that goal, understanding the core causes affecting the process and rate of urbanization in Vietnam is of top importance.
The measurement and study of the factors that robustly affect the urbanization rate in Vietnam is of great significance in finding basic solutions to limit the negative change towards building and developing an economy, helping the Government and policymakers make better decisions, meeting the requirements of the new industrial revolution, and improving academic understanding of the urbanization and the origin of the increase in the rate of urbanization. Upon that, our group of students chose the issue "Factors play a decisive role in the process of urbanization in Vietnam." as the subject of this scientific research.
2. Theoretical basis
2.1. Related concepts
From the perspective of social and economic management, an urban area is defined as an area with a high density of people living and mainly operating in the field of non-agricultural economy, it is the economic, cultural and political center of an area having a role in promoting the social and economic development of a country or a territory or a locality, including inner cities and suburbs of a city; inner and outer towns of the town; towns, with a population of 4,000 people or more, an average population density of 2,000 people/km2 or more, of which over 65% are employed in the non-agricultural sector.
2.2. Factors affect economic growth
Krugman's research (1991) set the foundation on factors and conditions for the population to be concentrated in one area instead of spreading over several areas. However, both endogenous models of city size and core and periphery models provide little insight into the determinants of a country's total urban population. Nevertheless, the first idea of urbanization has gradually formed from the factors of production conditions, population concentration has been expanded to factors of economic growth, science and technology.
The table below shows some previous research models related to urbanization rate:
Table 1. Summary of experimental research models
STT
|
Author/ year
|
Variable affecting urbanization and measurement methods
|
Research model
|
11
|
Pandey (1977)
|
- Population density= Population / Area (person/km2)
- Income per worker = Total income of all employees/ Number of employees
- Literacy rate = Literacy population/ Population*100%
- Population growth = (Total population in current year - total population in previous year)/ Total population in previous year*100%
|
OLS
|
22
|
Moomaw and Shatter (1996)
|
- GDP per capita = GDP/ population
- Urban concentration (the proportion of urban population in cities is greater than 100,000).
- Urban priority (proportion of population in largest cities).
|
OLS
|
33
|
Gene Hsin Chang and Josef C.Brada (2006)
|
- Rate of industrialization= Industrial production rate/gross domestic product of a country
- Foreign Direct Investment (FDI)
|
OLS
|
44
|
Anett Hofmann and Guanghua Wan (2013)
|
- Economic growth= (current year GDP - previous year's GDP)/ previous year's GDP*100%
- Education is calculated based on three main indicators: the proportion of children attending school, the proportion of the population over the age of 25 with the highest degrees, and GDP per capita.
- Rate of industrialization = Ratio of industrial production/gross domestic product of a country.
|
OLS
|
55
|
Michel Dimou and Alexandra Schaffar (2014)
|
- Gross Domestic Product (GDP)
- Export rate = (Export value/ Production value)*100%
|
SAR, SEM
|
66
|
Yingchun Yang, Jianghua Liu, Yutao Zhang (2017)
|
- Economic growth = (current year's GDP - previous year's GDP)/ previous year's GDP*100%
- GDP per capita = GDP/ population
- Urbanization policy is calculated by two factors:
- Urban land use ratio: This ratio represents the urban land area compared to the total area of an area.
- Urban management index: This index shows the level of effectiveness in managing urban-related issues such as security, traffic, environmental sanitation, land resource management, planning Urban Development.
|
FEM, OLS, REM
|
67
|
Yuan Zhang and Guanghua Wan (2017)
|
- GDP per capita = GDP/ population
- Total population
- Trade openness = Total export value/ Total production value * 100%
|
OLS
|
Source: Synthesis of the research team
3. Method
To study the factors affecting urbanization, we analyze according to panel data and based on the premise model of Moomaw and Shatter (1996). Moomaw and Shatter (1996) modeled and considered a range of determinants (not limited to GDP per capita, rate of industrialization, production and export orientation, education) and political aspects). In addition, Moomaw and Shatter further researched the comparison of the relationship of the factors telling about the rate of urbanization and city concentration. The authors have consulted the above model and also learned and proposed a few other important variables.
3.1. Secondary data collection method
Collecting secondary data at relevant agencies such as the General Statistics Office, People's Committee,...
- Vietnam Statistical Yearbook 2010-2021
- Vietnam import and export report 2016-2021
- Statistical report of 63 provinces and cities in Vietnam
- Summary of socio-economic criteria for the period 2016-2021
3.2. Methods of synthesizing and researching in-depth documents
- Collect documents, search for information in related books, search on the internet all documents related to the problem to be researched.
- Refer to the results of relevant previous scientific studies, analyze the data and pre-requisite documents on the topic of urbanization in Vietnam in the period 2010-2021, articles and research articles on urbanization, legal documents, urban and economic policies, etc.
- A system of available discrete data documents on the natural and social characteristics of Vietnam. Current status of urbanization, influencing factors and other documents related to the topics covered.
3.3. Quantitative research
The study uses panel data regression model, applies Pooled OLS, FEM, REM, FGLS techniques to evaluate the aggregate effects of independent variables on the dependent variable. The study sample includes 63 provinces, with a total of 756 per year-over-year observations for 12-year table data for the period 2010-2021. Based on the above theoretical basis and on the basis of a number of criteria considered urban, the model of estimating and testing the factors affecting the urbanization rate has the following specific form:
LnTyleDTHit = β0 + β1*DNHDit + β2*DTDVit + β3*MDDSit + β4*SoTPit + β5*GRDPit+ β6*LnDTDLit + β7*LnVonit + β8*LnChisosxCNit + β9*LnTyleLDquadaotaoit + β10*LnPCIit+ eit
Where subscript i denotes 63 provinces of Viet Nam; t denotes year, detailed measurement of variables is shown below:
Table 2. Interpret the variables included in the model
Variable
|
Description
|
Unit
|
Dependent variable
|
TyleDTH
|
Urbanization rate of provinces in Vietnam (63 provinces) in the period 2010-2021.
|
%
|
Independent variables
|
DNHD
|
Number of enterprises operating in provinces in Vietnam (63 provinces) in the period 2010-2021.
|
number of business
|
DTDV
|
Revenue of the service industry in the provinces in Vietnam (63 provinces) in the period 2010-2021.
|
billion Vietnam dong
|
MDDS
|
Population density of provinces in Vietnam (63 provinces) in the period 2010-2021.
|
person/km2
|
SoTP
|
Number of cities under provinces in Vietnam (63 provinces) in the period 2010-2021.
|
number of city
|
GRDP
|
Gross regional product in Vietnam at constant prices in 2010 (63 provinces) in the period
2010-2021.
|
billion Vietnam dong
|
DTDL
|
Revenue from the tourism industry in the province in Vietnam (63 provinces) in the period 2010-2021.
|
billion Vietnam dong
|
Von
|
Realized investment capital of provinces in Vietnam (63 provinces) in the period 2010-2021.
|
billion Vietnam dong
|
Chisosxcn
|
Industrial production index of provinces in Vietnam (63 provinces) in the period 2010-2021.
|
%
|
TyleLDquadaotao
|
Percentage of trained workers aged 15 and over in provinces in Vietnam (63 provinces) in the period 2010-2021.
|
1000 %
|
PCI
|
Provincial Competitiveness Index of Provinces in Vietnam (63 Provinces) for the period 2010-2021.
|
%
|
Source: Synthesis of the research team
4. Results
The following regression results from Stata software have given the results of estimating factors affecting the urbanization rate of provinces and cities in Vietnam.
4.1. Descriptive statistics
Table 3. Descriptive statistics for the variables used in the study
Variable
|
Observation
|
Mean
|
Standard deviation
|
Minimum value
|
Maximum value
|
TyleDTH
|
756
|
34.14016
|
15.05494
|
9.66
|
87.62
|
GRDP
|
756
|
274,861.73
|
49,237.37
|
19,676.38
|
991,424.1
|
MDDS
|
756
|
481.6311
|
333.1845
|
42
|
4476
|
DNHD
|
756
|
8,558.378
|
27045.52
|
399
|
268465
|
DTDV
|
756
|
204,137.1
|
125,667.1
|
63,195.8
|
709,963.2
|
DTDL
|
756
|
1,452.486
|
1,027.713
|
0
|
107,487.018
|
SoTP
|
756
|
1.087366
|
0.6196874
|
0
|
4
|
Chisosxcn
|
756
|
123.5762
|
87.14356
|
70.96
|
345.8185
|
TyleLDquadaotao
|
756
|
170.5431
|
134.9974
|
100.47886
|
845.3292
|
PCI
|
756
|
55.82017
|
48.78101
|
42,12
|
75.09
|
Von
|
756
|
4.41e+07
|
3.47e+09
|
6.98e+05
|
2.17e+10
|
Source: Results of the research team estimated from Stata
The urbanization rate (TyleDTH) has an average value of 34.14016%, the lowest is Bac Giang province in 2010 is 9.66% and the highest is Da Nang City in 2017 is 87.62%. Gross regional domestic product (GRDP) has an average value of 274,861.73 billion VND, the lowest is Lai Chau province (19,676.38 VND billion, in 2010) and the highest is Ho Chi Minh City (991,424.1 VND billion in 2020). The population density has an average value of 481.6311 (people/ km2), the lowest is Dien Bien province (42 people/ km2, in 2010), the highest is Ho Chi Minh City (4476 people/ km2 in 2020). The number of operating enterprises (DNHD) with an average value of 8,558,378 enterprises is the lowest in Bac Kan province (399 enterprises, in 2010), the highest is Ho Chi Minh City. HCM (268465 enterprises in 2021). Service revenue (DTDV) has an average value of 204,137.1VND (billion), the lowest is Son La province (63195 billion VND in 2010), the highest is Ho Chi Minh City (709,963.2 billion VND in 2021). Tourism revenue has an average value of VND 1.08,486 (billion), the lowest is Bac Kan, Dien Bien, Son La, Lai Chau provinces (0 billion VND, in 2010), the highest is Ho Chi Minh City (107487,018 billion VND in 2019). The number of cities with an average value of 1.0873866 (cities) is lowest in Binh Phuoc province (0 cities in 2010-2018), the highest is Quang Ninh (4 cities in 2012-2021). Industrial production index (Chisosxcn) has an average value of 123.5762 % the lowest is Bac Kan province (70.96%, in 2010) the highest is Thai Nguyen (345.8185%, in 2018). The proportion of trained workers with an average value of 170.5431 (1000%), the lowest is Dong Thap province (100.47886%, in 2010), the highest is Hanoi (845.3292% in 2021). The provincial competitiveness index (PCI) has an average value of 55.82017%, the lowest is Dien Bien province (42.12% in 2012), the highest is Quang Ninh (75.09% in 2020). Capital investment (Von) has an average value of 4.41e+07 (billion VND), the lowest is Kon Tum province (6.98e+05 billion VND in 2010) and the highest is Ho Chi Minh City (2.17e + 10 billion VND in 2020).
4.2. Correlation analysis and linear multi-additive testing
Table 4. Correlation matrix
|
GRDP
|
DNHD
|
DTDV
|
MDDS
|
SoTP
|
Ln
(Chisosxcn)
|
ln(
DTDL)
|
Ln
(TyleLD
quadaotao)
|
Ln
(Von)
|
Ln
(PCI)
|
GRDP
|
1
|
|
|
|
|
|
|
|
|
|
DNHD
|
0.115
|
1
|
|
|
|
|
|
|
|
|
DTDV
|
0.908
|
0.125
|
1
|
|
|
|
|
|
|
|
MDDS
|
0.765
|
0.106
|
0.829
|
1
|
|
|
|
|
|
|
SoTP
|
0.087
|
-0.121
|
0.041
|
0.061
|
1
|
|
|
|
|
|
Ln
(Chisosxcn)
|
0.777
|
0.177
|
0.832
|
0.713
|
0062
|
1
|
|
|
|
|
ln(DTDL)
|
0.800
|
0.126
|
0.871
|
0.732
|
0.041
|
0.760
|
1
|
|
|
|
ln(TyleLD
quadaotao)
|
0.734
|
0.155
|
0.792
|
0.673
|
0.070
|
0.658
|
0.742
|
1
|
|
|
ln(Von)
|
0.765
|
0.169
|
0.832
|
0.689
|
0.036
|
0.745
|
0.754
|
0.681
|
1
|
|
ln(PCI)
|
0.807
|
0.100
|
0.874
|
0.711
|
0.036
|
0.740
|
0.761
|
0.701
|
0.721
|
1
|
Source: Results of the research team estimated from Stata
Table 4.2 shows the correlation matrix between variables in the model. The magnitude of the correlation coefficients between the independent pairs of variables is less than 0.8 except for 8 pairs of variables with very high correlation coefficients, greater than 0.8, which can occur multicollinearity in the model. The results of multicollinearity test by Variance Inflation Factor-VIF according to Table 4.3 show that the VIF coefficients except for the DTDV variable with VIF=11 > 10 (or 1/VIF = 0.090935 < 0.1) occur severe multicollinearity; the rest of the variables all have a VIF coefficient between 1 to 10 (or 1/VIF > 0.1), means there is a slight multicollinearity phenomenon, which can be accepted
Table 5. Multicollinearity test results
Variable
|
VIF
|
1/VIF
|
DTDV
|
11.00
|
0.090935
|
GRDP
|
5.90
|
0.169396
|
lnDTDL
|
3.26
|
0.306748
|
lnPCI
|
3.23
|
0.309871
|
MDDS
|
3.14
|
0.318306
|
lnChisosxCN
|
2.74
|
0.364380
|
lnVon
|
2.55
|
0.391779
|
lnTyleLDquadaotao
|
1.98
|
0.505354
|
SoTP
|
1.06
|
0.943059
|
DNHD
|
1.05
|
0.949870
|
MeanVIF
|
3.59
|
|
Source: Results of the research team estimated from Stata
4.3. Experimental results
Table 6. Regression results using OLS, FEM, REM, FGLS regression methods
Model
|
OLS
|
FEM
|
REM
|
FGLS
|
DNHD
|
-6.65e-07***
(1.59e-07)
|
4.65e-07
(3.20e-07)
|
-2.73e-07
(2.63e-07)
|
-6.22e-07***
(1.34e-07)
|
DTDV
|
1.57e-06***
(1.17e-07)
|
-
|
-
|
-
|
MDDS
|
0.0000669***
(0.0000233)
|
0.0000767***
(0.0000163)
|
0.0001798***
(0.0000213)
|
0.0002004***
(0.0000225)
|
SoTP
|
0.0154957**
(0.0070279)
|
0.0854864***
(0.0111484)
|
0.0124964
(0.0106555)
|
0.0040776
(0.0088219)
|
GRDP
|
6.00e-07***
(2.14e-07)
|
1.10e-06***
(1.48e-07)
|
2.41e-06***
(1.80e-07)
|
2.73e-06***
(1.91e-07)
|
lnDTDL
|
0.1389863***
(0.0128009)
|
0.0734691***
(0.0095929)
|
0.1438445***
(0.0126312)
|
0.1522754***
(0.0122785)
|
lnVon
|
0.0836695***
(0.0103456)
|
0.0362608***
(0.0078119)
|
0.0903194***
(0.0103138)
|
0.1020028***
(0.0099589)
|
lnChisosxCN
|
0.0825055***
(0.0116951)
|
0.0469066***
(0,0084082)
|
0.0850371***
(0.0114094)
|
0.0962152***
(0.0112681)
|
lnTyleLDquadaotao
|
0.0578095***
(0.0081788)
|
0.0345617***
(0.0060275)
|
0.0665453***
(0.0080737)
|
0.075915***
(0.008047)
|
lnPCI
|
0.1357208***
(0.0130391)
|
0.0628573***
(0.0098825)
|
0.1403832***
(0.0129106)
|
0.1489671***
(0.0125035)
|
_cons
|
-1.22063***
(0.2136956)
|
1.034819***
(0.1800477)
|
-1.343873***
(0.2136774)
|
-1.80675***
(0.2041262)
|
Coefficient of determination (R2)
|
0.9473
|
0.8817
|
0.9361
|
|
Adjusted R square (Adj R2)
|
0.9466
|
0.7744
|
0.8746
|
|
Test value
|
F=1317.86
Prob>F = 0.0000
|
F=67.32
Prob>F = 0.0000
|
Wald=3906.96
Prob>chi2 = 0.0000
|
Wald=7794.97
Prob>chi2 = 0.0000
|
Test Breusch-Pagan (xttest3)
|
P-value = 0.000<0.05
|
|
Test Hausman
|
P-value = 0.000<0.05
|
|
***p<0.01 , **p<0.05 , *p<0.1 ; Standard Error is shown in brackets
|
Source: Results of the research team estimated from Stata
Due to different methods, the estimation results of each model are also different. To select the suitable model, the study conducts the Breusch-Pagan Test (xttest3) to choose between OLS and FEM/ REM and the Hausman Test to choose between FEM and REM. The Hausman test gives results of Prob > chi2 <0.05, so it rejects the H0 hypothesis, therefore, the FEM model is suitable and reliable. We went on using the FEM model to assess the impact of independent variables on the dependent variable. To test whether the model has a serial correlation phenomenon, the author used the Wooldridge test. The test results show that the model has the phenomenon of autocorrelation. To fix the above phenomenon, we used the FGLS model to increase the stability of the research model. The model estimation results in table 4.4 show that many variables are marked as expected.
Performed investment (LnVon): having a positive mark as originally expected, with a significant level of 1%, indicates that if investment rate increases by 1%, the urbanization rate will increase to 0.102%. This is easy to explain because when investment increases, it often leads to the establishment of new businesses, which can create new job opportunities. These job opportunities can attract people from rural to urban areas in search of better job opportunities, leading to an increase in population density in urban areas and leading to an increase in urbanization rates.
Tourism revenue (lnDTDL): has positive effect on urbanization rate, as expected, with the Cronbach's Alpha of 1%, when tourism revenue increases by 1%, it will cause the urbanization rate to increase by 0.152%. The rapid increase in tourism revenue contributes to the increasement of the contribution of the service sector to the structure of the economy. Development of the service industry is an important driving force for the implementation of industrialization and modernization goals. Along with the development of tourist cities, all local residents can be involved in tourism business and service activities, thereby contributing to improving the living standard of indigenous people, as well as changing the economic appearance of the whole region. Therefore, tourist urban development is identified as a central issue in the tourism and economic development strategies of many countries around the world.
Industrial production index (LnChisosxCN): bears a positive sign similar to the initial expectation, with the Cronbach's Alpha of 1%, when the index of industrial production increases by 1%, the urbanization rate will increase to 0.096%. There is plenty of evidence that when the index of industrial production increases, it leads to an increase in urban rates: industrialization can create new jobs in the manufacturing sector, jobs in the industrial sector can pay more than other agricultural or rural jobs, etc. Wage differences encourages people to move to urban areas. Moreover, industrialization can stimulate the development of infrastructure, such as roads, bridges and public transport, which can make urban areas more accessible and attractive to citizens.
Proportion of workers over 15 years old who have been trained (TyleLĐquadaotao): have a positive coefficient consistent with the author's initial expectations, with the Cronbach's Alpha of 1%, an increase of 1% in the proportion of people with a college degree increases the urbanization rate by 0.076%. The skilled labor force inprove the competing capability of the economy, especially for workers operating in the non-agricultural sector, an important indicator to achieve the conditions to upgrade to urban status. Thus, skilled labor is an important factor to achieve economic growth, and economic growth creates favorable conditions for urban infrastructure adjustment, which helps increase the urbanization rate.
Provincial Competitiveness Index (LnPCI): PCI bearing a positive effect in line with the author's initial expectations and previous studies. With the Cronbach's Alpha of 1%, when the provincial competitiveness index increases by 1%, the urbanization rate will increase by 0.149%.. A high PCI may also indicate a higher standard of living, which may be a factor that attracts people to move to urban areas in search of better housing, education, and healthcare facilities.
Gross regional domestic product (GRDP): has a positive coefficient consistent with the group's initial marking expectations, with the Cronbach's Alpha of 1%, indicates that when the gross product in the area increases by 1%, it will make the urbanization rate increase by 2.73e-04%. This demonstrates that economic growth is an important factor in increasing the rate of urbanization in Vietnam. It also emphasized the importance of economic development in promoting urbanization in developing countries.
Population density of provinces in Vietnam (MDDS): with a positive sign in line with the initial expectation, with the Cronbach's Alpha of 1%, the model indicates that while other factors remain constant, when the population density increases by 1% then the urbanization rate increases to 0.02%. High population density is one of the main factors driving urbanization in Vietnam. Big cities, the center of the metropolitan area attract a lot of migrants from other parts of the country, leading to urban population growth and thereby helping to increase the urbanization rate.
City Number (SoTP): The regression model shows that p-value=0.644>0.1. From the model, it is not proven that the number of cities affects the urbanization rate in Vietnam. This is contrary to the original prediction of the author. Some of the reasons are that some cities do not have an increase in urbanization rate because the province/city does not have equivalent economic development. A weak economy often leads to no investment, no jobs, and low income, which can lead to hindering local urbanization.
Number of enterprises operated in each region in Vietnam in the period 2010-2021 (DNHD): Regression model shows that with the Cronbach's Alpha of 1%, when business activity increases by 1%, the urbanization rate will decrease by 6.22e-05%. This is contrary to the original hypothesis of the author. One of the reasons for the above results is because a higher degree of urbanization can lead to a higher cost of living, increased production costs, property taxes and higher labor costs. This leads businesses to move to neighboring suburbs to build factories, leading to a decrease in population, employment and goods and services in the city area and reduce the rate of urbanization in the city.
5. Discussion and conclusions
5.1. Policy implications
5.1.1. Increase service revenues
Identifying tourism as the strongest positive impact factor with the expectation of contributing significantly to the urbanization rate, and also a key economic sector of our country in the coming time, so we need to focus on developing tourism in a sustainable way, developing tourism must based on synchronous and community-based projects and plans, preserving traditional values but still promoting the strengths of "hidden beauty" of Vietnam's tourism.
5.1.2. Increase the proportion of trained workers over 15 years old
Based on the above regression results, the quality of labor positively affects the urbanization rate with the expectation of improving labor productivity to contribute to economic growth, we realize that focusing on training to improve the quality of human resources is very necessary.
5.1.3. Increase investment in Vietnamese provinces/ cities
The research model shows that investment has a positive influence on the urbanization rate in Vietnam. This finding contributes to supporting the pursuit of policies and measures to promote the progress of disbursement and attraction of investment, especially Foreign Direct Investment (FDI).
5.1.4. Improve the Provincial Competitiveness Index - PCI
The above research model shows that the PCI provincial competitiveness index has a positive influence on the urbanization rate in Vietnam. Improving the Provincial Competitiveness Index (PCI) in Viet Nam requires a holistic approach that addresses the various factors affecting competing capability.
5.1.5. Improve industrial production index
The above research model shows that industrial production index has a positive influence on the urbanization rate in Vietnam. This finding contributes to the pursuit of policies and measures to boost the index of industrial production in regions in Vietnam.
5.1.6. Increase population density
Population density is a variable that has a positive impact on the urbanization rate as analyzed above, so having solutions to continue maintaining the golden population structure period, thereby increasing job opportunities, diversification of professions in rural areas; improve the quality of vocational training and training according to labor market needs.
5.1.7. Increase in gross regional domestic product - GRDP
Based on the above regression results, the gross product in each province positively affects the urbanization rate with the expectation of increasing the income and living standards of residents in the region, we realize that focusing on economic development for each locality is very necessary and needs to be focused.
5.1.8. Improve the work of performed business
DNHD is currently a variable that has a negative impact on the rate of urbanization because in fact, although this variable increases in quantity, its quality is not guaranteed and has not been appropriately improved, so it becomes a negative impact factor. In order to overcome the situation of enterprises operating at a loss, which is a waste of resources, it is necessary to take measures to reduce the number of "shell corporation" while improving the quality to help reduce losses and generate profits to the end of the year from both business and government intervention.
5.1.9. Policies of the Party and the State
In the coming time, it is necessary to continue to improve institutions and policies to facilitate the process of urbanization, construction, management and improve the quality of urban to meet the requirements of sustainable urban development construction and management. Focus on building and developing a sustainable and synchronous national urban system in terms of network. Promote housing development, synchronous, modern, linked and adaptable urban infrastructure system to climate change. Building and perfecting the urban government model; improve the effectiveness and efficiency of urban management and the quality of urban life, ensure social security and welfare, security, safety and urban order. Economic development of urban areas; innovate mechanisms, financial policies and investment in urban development.
5.2. Conclusion
This research article evaluates the impact of factors affecting the urbanization rate in Vietnam. The econometric model is built based on data collected from 63 provinces and cities of Vietnam with secondary data for the period 2010-2021. After running the model with 756 observations, the study has shown the influence of these factors on the urbanization rate of Vietnam.
The study has provided empirical evidence on the factors affecting the rate of urbanization in Vietnam, thereby contributing to affirming the pursuit of strategies and policies associated with the reality of the provinces in particular and Vietnam in general.
Dr. Nguyen Thi Thanh Huyen, huyennt@neu.edu.vn
Le Thu Trang, lethutrang2082@gmail.com
Nguyen Thi Thanh Thuy, thanhthuy.kcer@gmail.com
Vu Phuong Anh, vuphuonganh1409@gmail.com
Ngo Duy Chuong, ngoduychuong2711@gmail.com
Faculty of Environmental, Climate Change and Urban Studies,
National Economics University, Hanoi, Vietnam
(International Conference ICSEED2023)
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