Spatial analysis of income sources at province level in Vietnam.

The trained labor force, the immigration rate and the working population aged 15 and over in the economy by province in the neighboring provinces have comparative effect on the total i[r]

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

NGUYEN THU HANG

SPATIAL ANALYSIS OF INCOME SOURCES AT PROVINCE LEVEL IN VIETNAM

MAJOR: MASTER’S PROGRAM OF PUBLIC POLICY CODE: ………

RESEARCH SUPERVISOR: Prof MORITO TSUTSUMI

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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION

1.1 Background of the study

1.2 Rationale of the study

1.3 Objectives of the study

1.4 Research questions

1.5 Significance of the study

1.6 Design of the study

CHAPTER 2: LITERATURE REVIEW

2.1 Spatial analysis

2.2 Income as an aspect of livelihoods

2.3 Background of ethnicity and income structure in Vietnam

2.3.1 Ethnic geographical distribution in Vietnam

2.3.2 Poverty distribution by ethnicity in Vietnam

2.3.3 Changes in Vietnam‟s income structure in Vietnam 10

2.4 Previous studies 11

CHAPTER 3: METHOD AND METHODOLOGY 14

3.1 Method and methodology 14

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CHAPTER 4: FINDINGS AND DISCUSSIONS 19

4.1 Area of Study 19

4.1.1 An overview 19

4.1.2 Economic growth 21

4.1.3 Production of agriculture, forestry and fishery 22

4.1.3 Industry 23

4.1.4 Service activities 24

4.1.5 Development investment 24

4.2 Descriptive statistics 25

4.3 Changes in income sources in Vietnam 2008-2016 77

4.4 Discussions 82

CHAPTER 5: CONCLUSION AND RECOMMENATIONS 85

5.1 Conclusion 85

5.3 Limitations 87

5.4 Suggestions for the further studies 88

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LIST OF TABLES

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Table 23: A comparison of Spatial regression models and OLS regression model Year: 2016 Dependent variable: Income from NonAgric (million VND) 72 Table 24: A comparison of Spatial regression models and OLS regression model Year: 2016 Dependent variable: Income from wages (million VND) 73 Table 25: A comparison of Spatial regression models and OLS regression model Year: 2016 Dependent variable: Income from other sources (million VND) 75

LIST OF FIGURES

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ABBREVIATION SDM Spatial Durbin model

SLM Spatial Lag Model

SEM Spatial Error Model

OLS Ordinary Least Square

GIS Geographic Information system

GDP Gross Domestic Product

CPI Consumer Price Index

ASEAN Association of South-East Asian Nations

WTO World Trade Organization

HDI human development index

UNDP United Nations Development Programme

Agric Income from Agricultural, Forestry and Fishery activities

NonAgric Income from Non- Agricultural, Non-Forestry, Non-Fishery activities Other Income from other sources

Wages Income from Wages

Total The total income

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ACKNOWLEDGEMENT

In order to complete my thesis, I have received many advices and guidance from my supervisor - Professor Morito Tsutsumi as well as my friend Rim Er-rbib Thank to professor Morito Tsitsumi, I can acquire more knowledge and more skills Before coming back to VietNam, my supervisor gave me a valuable book that helps me a lot to complete this thesis With all my respect and gratitude, I would like to express my sincere appreciation to:

My supervisor, Professor Morito Tsutsumi for his inspiring guidance and great support throughout my thesis procedure His insightful advices and scientific knowledge has inspired me and helped me in improving research and preparation for my Master thesis He also supported me a lot to get the data of FDI licensed projects which seemed really hard to acquire Without his great support, I cannot finish my thesis

My academic tutor, Ms Rim Er-rbib, for her useful support and encouragement, who is always willing to help and gave me so many useful and constructive instructions especially for how to use GIS software

University of Tsukuba and Vietnam Japan University for giving me such a excellent environment with so many amazing people

Finally, I would like to thank my family for being a wonderful moral support that gives me so much motivation and enthusiasm to overcome the challenges and difficulties in writing this thesis

Student,

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CHAPTER 1: INTRODUCTION

1.1Background of the study

Vietnam has been through a rapid economic growth in the last three decades The characteristics of this rapid growth are the decline of the number living in poverty and the rising average income Since the 1990s, there has been nearly 30 million people overcoming the poverty line More specifically, the GDP per capita from 1990 to 2015 has increased from $100 to $2,300, respectively (Oxfam, 2017) In the last 30 years, the average of the economic growth has increased from 5-6 percent to 6.4 percent The rapid growth especially the increasing economic has several impacts on the Vietnamese On the one hand, it improves people‟s living standards However, it also causes the economic inequality as well as the uneven opportunity among people Which means the equal distribution of income of the people has an important role in a society with high equality So now the challenge is that in the situation of the rapid economic growth how does Vietnam make solutions so that the distribution of income across Vietnam becomes much more equal

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According to Oxfam (2017), in one day, the Vietnamese richest man earn more than the poorest earns in 10 years This man possessed assets worth $2.3bn which could be used to help 13 million poor people to get out of poverty According to the World Bank (2013), from 1992 to 2012, the Gini index has risen from 35.7 to 38.7, showing that the income inequality rose However, this kind of data may underestimate the serious impacts that inequality can have on Vietnam For example, the expenditures or the income of rich individuals may be under-reported in the household surveys, so the empirical measures of inequality may be biased

Since 2004, among the first four quintiles (the bottom 80 percent) there is a small difference in the income distribution However, in comparison between those quintiles with the richest quintiles (the top 20 percent), the income distribution has been widening significantly In other words, the benefit of growth has been distributed unequally in recent years This is consistent with the report conducted by Oxfam in 2016 The survey did depth-interview with 600 respondents from three provinces (Lao Cai, Nghe An, Dak Nong) The results showed that the income of the 20 percent of the richest households is 21 times higher than that of the 20 percent of the poorest households.There is one point suggesting that income at the province level is serious and has been increasing over time, especially in the remote areas where agriculture is the main source of income (Lam et al., 2016) Therefore, it is necessary to look into the income sources at province level to justify the income disparity

1.2Rationale of the study

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the poor live According to VHLSS (2012), the South East has the highest monthly income per capita in the country (VND3,016,000 or $150), which is more than three times the average monthly income found in the North West region (VND999,000 or less than $50)

Using VHLSS data (2004–2014), the findings by McCaig &Brandt (2015) show that households in the South East (the richest region in Vietnam) have the highest income mobility of any region Compared with households in the Red River Delta (the reference group), households in the North East, South Central Coast, and Central Highlands are less likely to move up from the lowest quintile Households in the South East are more likely to move up from the lower 40 percent With downward mobility, households in the North Central Coast and Central Highlands are more likely to move down from the high-income quintiles

Such regional variation is also the product of ethnic factors in Vietnam (McCaig &Brandt, 2015) Vietnam is an ethnically diverse country: there are 54 ethnic groups, in which the Kinh majority accounts for 85 percent of the population Kinh tend to live in delta areas, and have higher living standards than other ethnic minorities Hoa (Chinese) are also a rich group, and also live in delta areas Thus, Hoa are often grouped together with Kinh in studies on household welfare, although they may face ethnic discrimination in other areas

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quintile, while for Kinh and Hoa, this figure was 49 percent In addition, ethnic minorities are more likely to move down but less likely tomove up, compared with Kinh and Hoa It is revealed that both the absolute and relative income gap between Kinh/Hoa and other ethnic groups has increased over time The ratio of per capita income of Kinh/Hoa to that of other ethnic groups increased from 2.1 in 2004 to 2.3 in 2014

The income disparity sourced from the ethnic and regional differences has led to the income inequality at the provincial level Therefore, it is meaningful to analyze the income sources at province level from 2008 – 2016 and how various factors affect them by using spatial analysis

1.3Objectives of the study

The overarching aims governing this current study is to obtain the insights into the current income distribution and to reduce the income disparity in Vietnam at the provincial level Therefore, the thesis‟s objective is to promoting income diversification by examining what economic and demographic variables affect the income sources among provinces in Viet Nam using spatial approach

1.4Research questions

The following research questions are derived in this current study:

(1)Is there the presence of spatial autocorrelation of income sources among provinces in Viet Nam?

(2)How economic and demographic factors affect the income sources in Viet Nam? 1.5Significance of the study

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Secondly, this study provides an analysis of long-term changes in income sources in Vietnam in the last ten years from 2006 to 2016 This research expects to provide the income inequality decomposition by income sources, based on the Vietnam Household Living Standard Surveys (VHLSS) carried out every two years, to reduce the errors that resulted from data aggregation process Lastly, the recommendations generated in this current study expect to make contributions to policy development to diversify the sources of income, contributing to minimize the income inequality in Vietnam

1.6Design of the study

There are five chapter included in this current study, including:

Chapter – Introduction – presents the background and rationales of the current study The research aims and objectives, research questions and design of the study are also generated in this chapter

Chapter – Literature review – critically explores the theoretical fundamentals concerning the spatial analysis and income inequality and sources This chapter also looks in the previous literatures to identify the literature gaps

Chapter – Methodology - presents research methodology The research method, data collection measures and how such models as Spatial Durbin Model (SDM, Spatial lag model (SLM), Spatial error model (SEM) are used for data analysis This chapter also discusses the validity and reliability of the research instruments

Chapter – Findings and discussions – shows the results of data analysis and discusses the income sources of Vietnam with the provincial levels

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CHAPTER 2: LITERATURE REVIEW

2.1Spatial analysis

Spatial analysis or spatial statistics includes any of the formal techniques which study entities using their topological, geometric, or geographic properties Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data

Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research The most fundamental of these is the problem of defining the spatial location of the entities being studied

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Spatial heterogeneity means in turn that economic behavior is not stable across space and may generate characteristic spatial patterns of economic development under the form of spatial regimes: a cluster of forward States (rich regions, the core) being distinguished from a cluster of backward States (poor regions, the periphery) The methodology of exploratory spatial data analysis (ESDA) is applied to find the evidence of spatial autocorrelation and spatial heterogeneity The estimation of global spatial autocorrelation (Moran‟s I) and local spatial autocorrelation (LISA) will indicate how economic activities are located in India during the reform period 1993– 2004 Moreover, local spatial statistics confirms the existence of spatial heterogeneity and, consequently, raises an agenda behind the differential growth profile of forward States and backward States

2.2Income as an aspect of livelihoods

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dimension of poverty (Ahebwa, 2012; Brocklesby and Fisher, 2003; de Haan and Zoomers, 2005; Kaag et al., 2008)

In Vietnam, the majority of rural dwellers secure their livelihoods primarily through small-scale subsistence agriculture (Nguyen, 2004; Lam et al., 2016) Most Vietnamese households are dependent on small-scale agriculture activities for their earnings (GSO, 2016) Vietnamese households use their goods from their agricultural surpluses to sell in the local markets for cash generation Some other affluent households invest in large scale crops or livestock farming for commercial purposes Livestock is also used as savings for covering difficult periods or extraordinary expenditures for celebrations or holidays, such as the payment of dowries However, it is indicated by the previous studies in the sources of income in Vietnam that it is critical to expand the horizons of sources of income with the focus of non-agricultural sector (Nguyen, 2004; Hartter, 2007; Mackenzie, 2011; Lam et al., 2016) Drawing on the country‟s abundant resources, the Vietnamese government is stimulating the development of non-agriculture sectors to diversify the sources of incomes

2.3Background of ethnicity and income structure in Vietnam

2.3.1 Ethnic geographical distribution in Vietnam

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comparison, the minority groups are primarily located in the East and West Northern mountains, in the Central Highlands, and in the North Central Coast

2.3.2 Poverty distribution by ethnicity in Vietnam

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2.3.3 Changes in Vietnam’s income structure in Vietnam

Income structure in Vietnam has changed over time The proportion of income from agriculture has declined, while wage income has contributed to an increasing share of total household income in 2000s as well as in the previous decade In rural areas, crop income and agricultural side-line income remained two main sources of household income, but together they contributed one third of total household income for top ten percentile income households However, income from cultivation declined sharply by half compared with its level a decade ago (Benjamin et al., 2017; McCaig, Benjamin, & Brandt, 2009) The proportion of income from wages in rural areas increased faster than in urban areas

The share of wage income of the bottom-income household group increased faster than that of the top-income households In the meantime, in urban areas, changes in income structure have not been as fast as in rural areas in 2000s However, wages had already become the main income source of urban households since the 1990s The share of agricultural side-line income in total household income has remained stable at a small share in urban areas during the 2000s The top income quartile households experienced a faster increase in income than the other quartiles The income share from remittances and other income sources in 2000s has moderately decreased compared to the 1990s There was also a shift in the employment structure among ethnic minorities toward wages in nonfarm employment and nonfarm self-employment in the early 2000s (Pham & Bui, 2010) However, the ethnic minorities still received a smaller amount of their income from non-agricultural wages and nonfarm businesses In the meantime, the ethnic majority received a higher portion of their income from wages (Cuong, 2012; Dang, 2012; Kozel, 2014)

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terms of employment, in 2006 agriculture accounted for 30% of ethnic majority employment, but made up 55% of ethnic minority employment (Kozel, 2014) There was a significant rise in income share from wages, while the level of income from the agricultural sector has declined However, the change toward wage-earning employment of ethnic minorities was slower than those of the ethnic majority There are several studies on income inequality between ethnicities in Vietnam (Benjamin et al, 2017; Kozel, 2014; Cuong, 2012; Baulch, Pham, and Reilly, 2012; Baulch, 2011; Epprecht et al 2011; World Bank, 2009; Van de Walle and Gunewardena, 2001) However, most of them focused on various characteristics to explain the widening income or income inequality gap Although ethnic minorities have made significant progress in improving living standards, health and education in recent years, this group still lag behind the ethnic majority in terms of household per capita expenditure and income The absolute gap between the ethnic majority and ethnic minorities widened dramatically in the 2000s (Benjamin et al., 2017) The main causes of the disparity between the ethnic groups are differences in educational attainment, residential area, accessibility to public services and household assets (Cuong, 2012; Dang, 2012; Tuyen, 2016; van de Walle & Gunewardena, 2001; World Bank, 2009) Furthermore, Benjamin et al (2017) and Cuong (2012) find that the main contributors to the widening income gap are the ethnic minority‟s lower wages and lower non-farm business income In addition, the income structure of the ethnic majority people has shifted from the agricultural sector to non-agricultural sectors more quickly than that of the ethnic minority This income source disparity is also the drivers of the larger income gap between ethnic minority groups (Cuong, 2012)

2.4Previous studies

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factors influencing the increasing inequality worldwide It is realized that the issue of income equality under the effects of sources of income has become alarming in not only such developed countries as the US, European countries, Japan and Korea, etc but in developing and poor nations as well (Dabla-Norris et al., 2015; Furrer, 2016)

The findings by Milanovic (2013) have revealed that the effects generated by the trend of globalization provide benefits for those with middle and high income levels rather than those with the low level By using the data obtained between 1988 and 2008, he concluded that while those who have the top 1% income experienced a 70% increase in their income over the given period, their poor counterparts hardly enjoyed any increase in their income Oxfam (2017) emphasized that the top 1% rich people are those who own the majority of global wealth These findings are also supported by the researches concerning the expanding income inequality in such others countries in BRICS by Berg (2015) and Haldane et al (2015) Additionally, these researches identified that among the most powerful factors influencing the income inequality, the source of income have significant impacts on income inequality

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policymakers and scholars look into income sources as a significant and meaningful factor to the income inequality in Vietnam

In Vietnam, the income gap is regarded as one of the most challenging barriers to the attempts to obtain the sustainable development of the Government Despite the reduction of the poverty rate to less than 10%, the income inequality has still lowered the progress as the whole (Kozel, 2014) It is planned by the Government that the development policies will target to earn a 2% decrease per year in the poverty rate (Gibson, 2016) Dealing with the inequality requires the investigation into sources of income in Vietnam

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CHAPTER 3: METHOD AND METHODOLOGY

3.1. Method and methodology

The major difference between spatial econometrics and standard econometrics is that spatial econometrics requires diffrent sets of information It relates to the observed values of the variables and it also relates to the particular location where the variables are observed This means spatial regression takes into account the spatial correlation This study uses Moran I‟s test to test the presence of spatial autocorrelation of the income sources among provinces If this index is significant at 5% then applying spatial model is necessary The Moran I‟s test takes the form like this:

𝑛 𝑛𝑖=1 𝑛𝑗=1 𝑤𝑖𝑗 𝑋𝑖 − 𝑋 𝑋𝑗 − 𝑋 𝑛𝑖=1 𝑛𝐽=1𝑤𝑖𝑗 𝑛𝑖=1 𝑋𝑖 − 𝑋 With the hypothesis:

Ho: no spatial correlation among provinces H1: there is spatial correlation among provinces Where:

Xi : Observed variable at the province i Xj : Observed variable at the province j 𝑋 : Average variable of X

n: observations

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In this thesis, spatial weight matrix (Spatial contiguity weights) indicating whether spatial units share a boundary or not is used to summarize the spatial relation among 63 spatial units (provinces) The spatial weight matrix contains 63 columns and 63 rows associated with 63 provinces in Viet Nam:

n𝑤𝑛 =

𝑤11 ⋯ 𝑤𝑛1

⋮ 𝑤𝑖𝑗 ⋮

𝑤1𝑛 ⋯ 𝑤𝑛𝑛 (1)

and has the standard form as following: Wij=

1, 𝑏𝑛𝑑(𝑖)𝑏𝑛𝑑(𝑗)

0, 𝑏𝑛𝑑(𝑖)𝑏𝑛𝑑(𝑗) = (2) Where:

i, j: provinces taken into consideration bnd: boundary

The weight matrix receives the value as when these two provinces share the border and as otherwise

Besides the OLS, this paper also runs three other spatial models which are Spatial Durbin Model, Spatial lag model and spatial error model and then compares between them to choose the best model for analyzing

The Spatial Durbin model takes the form:

Y = 𝝆WY+ X𝜷(1) +WX𝜷(2) + 𝜺 (3) Where:

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W: the weight matrix exogenously given WY: the spatially lagged variable of Y WX: the spatially lagged variable of X The spatial lag model takes the form:

Y = 𝝆WY + 𝜷X +u (4) Where:

U: stochastic disturbances

β, ρ: parameters to be estimated

W: the weight matrix exogenously given WY: the spatially lagged variable of Y The spatial error model takes the form:

Y = X + u (5) U = Wu + 

 ~ N (0, 2 In)

Where:

X: a matrix of non-stochastic regressors

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Finally this paper uses the software QGIS for drawing maps to see the changing rate of income sources across provinces as well as the main regions

3.2. Data collection

Dependent data: Per capita monthly income at current prices by income sources and by provinces (deflated by CPI with year 2008 as base year)

Per capita monthly income is calculated by dividing the household‟s total income by the number of family members then dividing by 12 months This income includes: income from wages; income from agriculture, forestry and fishery (after tax); income from non-agricultural, forestry and fishery (after tax); and the last is other income sources such as donations, gratuities, savings interest, etc Items which are not taken into account in the income include savings, debt collection, asset sale, debt financing, transfers, capital from joint ventures, associates in production and business

Explanatory variable data

- Immigration and migration rate: a Immigration rate:

The number of people from another territorial unit (original place) immigrating to a territorial unit during the study period (usually one year) on average per 1000 inhabitants of that territorial unit

IMR ‰ = 𝐼

𝑃𝑡𝑏 ×1000 Where: IMR: immigration rate (‰)

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b Migration rate:

The number of people from a territorial unit migrating to other territorial unit during the study period (usually one year) on average per 1000 inhabitants of that territorial unit

OMR ‰ = 𝑂

𝑃𝑡𝑏 ×1000 Where: OMR: migration rate (‰)

O: The number of migrants

Ptb : average population (calculated until midyear)

- Percentage of workers aged 15 and over who are working in a trained economy by province

- The percentage of working population aged 15 and over working in the total population by province

- Sex ratio of population by provinces: reflects the number of males over 100 females - FDI Projects licensed

- Registered capital for FDI projects (mill.USD)

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CHAPTER 4: FINDINGS AND DISCUSSIONS

4.1Area of Study

4.1.1 An overview

According to GSO (2016), there are total 64 provinces in Vietnam with Hanoi and Ho Chi Minh as the socio-economic centers Currently, the 64 provinces in Vietnam are grouped into eight regions depending their geographic features, including:

(1)Northwest

The region of Northwest in Vietnam, consists of six provinces including Hoa Binh Lao Cai, Lai Chau, Yen Bai, Son La and Dien Bien which are located in the mountainous northwestern areas of Vietnam With the population of 4.5 million people, these Northwest provinces are recorded as the poorest provinces in Vietnam with the income majorly sourced from agricultural activities

(2)Northeast

The Northeast Vietnam consists of such provinces as Bac Giang, Bac Kan, Cao Bang, Ha Giang, Lang Son, Phu Tho, Quang Ninh, Thai Nguyen, and Tuyen Quang which are located in the north of the Red River Delta With the population of more than 8.5 million people and economic and geographic advantages, this region is regarded as one of the regions taking the most important role in the development of Vietnam Owing to the abundance of natural resources, these Northeast provinces have developed such industries as mining or mineral processing industries Additionally, the favorable geographic conditions also support the development of agriculture and forestry sectors (3)Red River Delta

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Nam Đinh, Ninh Binh, Thai Binh, Vinh Phuc with the population of more than 19 million people This region is featured with many advantages for economic development including:

(i) Geographic advantages: the location of Hanoi as the economic, cultural and political center of Vietnam; and centrally controlled municipal provinces with dynamic economy including Ha Noi, Hai Phong, Hai Duong, Hung Yen, Bac Ninh and Vinh Phuc;

(ii) Transportation advantages: a wide ranges of transportation infrastructure with good conditions and known as the gate of the whole country;

(iii) Natural resources: diversified ecology with abundant sources of natural resources With these advantages, this region is recorded as the region with the second highest income region in Vietnam

(4)North Central Coast

North Central Coast is one of key economic regions in Vietnam which is adjacent to Red River Delta in the North of the country Such provinces in this region include Ha Tinh, Nghe An, Quang Binh, Quang Trị, Thanh Hoa, and Thua Thien Hue The income of these provinces depends on the mining and building material industry, livestock, perennial industrial crops and rice intensification

(5)South Central Coast

South Central Coast consists of provinces which are located in the coastal central part of Vietnam, including Binh Dinh, Binh Thuan, Da Nang, Khanh Hoa, Ninh Thuan, Phu Yen, Quang Nam, and Quang Ngai These provinces are features as agriculture reliance provinces with the majority of income sourced from agriculture, forestry and fishery In recent years, under the economic innovation, there is a significant change in the income source of this region with the increasing portion of income from tourism

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Central Highlands, Tay Nguyen, consists of five mountainous inland provinces which are located at the South-central Vietnam, including Đac Lak, Đak Nong, Gia Lai, Kon Tum, and Lam Dong With the specific geographic and social features, this region is regarded as one of the most disadvantageous regions in Vietnam The major sources of income in these provinces consist of agriculture and forestry

(7)Southeast

There are six provinces in the Southeast in Vietnam, including Ba Ria–Vung Tau, Binh Duong, Binh Phuoc, Dong Nai, Ho Chi Minh City, and Tay Ninh This region is ranked at the first place with income level in Vietnam This region has led the country with exports, foreign direct investment, GDP, and other socio-economic sectors for years Such provinces as Dong Nai, Binh Duong and Ho Chi Minh City are provinces attracting a large amount of FDI, contributing to the development of industry and services of the region There are many manufacturing industrial zones in this area located in Binh Duong, Dong Nai and Ho Chi Minh City The income of this area is sourced from industry

(8)Mekong River Delta

Mekong River Delta which is located at the Southwest of Vietnam, contains 13 provinces, including An Giang, Ben Tre, Bac Lieu, Ca Mau, Can Tho, Dong Thap, Hau Giang, Kien Giang, Long An, Soc Trang, Tien Giang, Tra Vinh, and Vinh Long The strengths of this region consist of rice farming, fruit planting, and tourism

Besides the geographic characteristics, the provinces in each region are also featured by the targeted economic sectors which are regarded as the major sources of incomes

4.1.2 Economic growth

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the given period 2008-2016 is recorded at 33.58% which rose to 43.29% during the past three years 2016-2018 The labor productivity of Vietnam has also witnessed a significant increase from only US$1623 per worker in 2008 to US$3827 in 2016, reaching the peak of US$ 4512 per worker as the end of 2018 (GSO, 2018) The reasons attributed to the stable increase of labor productivity in Vietnam since 2008 include the added labor force and rising employment rate

Particularly, the efficiency of national economies is significantly boosted by the additional new productive capacities It is reported that there is a fall in the records of average incremental capital output ratio (ICOR) since 2008, decreasing from 6.2 during the given period 2008-2016 to 6.17 during the last three years

4.1.3 Production of agriculture, forestry and fishery

According to a report by the Ministry of Agriculture and Rural Development (MARD, 2018), despite the great transformation in the economy structure with the shift from agriculture to industry and service the agricultural sector has still maintained their important role to the national economic growth During 2008-2016, the report by GSO (2018) reveals that GDP of the sector of agriculture, forestry and fishery experienced an average increase of 3.63% which expanded by 3.86% during 2016-2018 The trade of agriculture, forestry and fishery also witnessed an average surplus of US$8.72 billion during the given period 2008-2016, with the average exports of US$40.02 billion (Oxfam, 2018) The top goods of exports in this sector include wood, shrimp, fruits and vegetables, coffee and cashew nuts with the exports value from more than US$3.5 billion to US$8.8 billion

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agricultural production The report by MARD (2016) reveals that over the period of 2008-2016 the crop productivity enjoyed a five-time increase

With reference to livestock production, owing to the new exports of some livestock products this sector also experienced the highest exports ever during the period of 2008-2016, representing an increase of 3.98% per year (MARD, 2018)

The sector of fishery in Vietnam also continued to remain the key role in the whole economy with a continuous increase output during 2008-2016 Respectively in 2016, the total sector output reached 7.32 million tones, the highest records during 2008-2016 (MARD, 2018) Shrimp and pangasius are the two products which reaped the highest export value and growth rate during 2008-2016 (averagely at 7.38% and 11.26% respectively)

Lastly, in the shedding light of the Voluntary Partnership Agreement on Forest Law Enforcement, Governance and Trade (VPA/FLEGT) by the Vietnam Government and the European Union (EU), the production of forestry witnessed an average expansion of more than 6% while exports of forestry expanded by 10.3%

4.1.3 Industry

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treatment also experienced an average growth rate of 12.3% and 7.6% respectively during 2008-2016 (World Bank, 2016) Contrastively, the sector of mining and quarrying witnessed a downward trend with a 2.2% fall under the effects of decreasing exploitation of crude oil (Economic, 2018)

4.1.4 Service activities

Besides the industry sector, the service categories also experienced an amazing growth rate since 2008 The booming expansion of service sector has improved the contribution of services to the economic growth of the whole country Among the services, the retailing sector is recorded as with the highest value As the end of 2016 the total income generated by this sector reached the peak with more than VND4000 trillion during the given period, representing a 13.1% increase per year (Vietnam Briefing, 2018)

Regarding the categorized economic activity, during 2008-2016 there is an average increase of 12.3% in the retail sales of consumer goods, representing more than VND3200 trillion, while the sector of accommodation and catering services was calculated with a sales amount of nearly VND600 trillion, accounting for a 9.3% increase Tourism is also recognized as one of the service sectors enjoying the fastest growth during 2008-2016 with a 16.3% growth rate per year Lastly, the total values of other services amount to nearly 12% contribution to the total income of the sector service with a 9.3% increase

4.1.5 Development investment

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of investment projects under the management of local authorities, the total investments amounted VND 273.2 trillion, accounting for a growth of 16.3% (Oxfam, 2016)

Concerning the FDI sector, 2016 is remarked as the year of FDI projects with a sharp increase in the number of registered FDI projects and amount of investment The report by GSO (2016) revealed that respectively in 2016 there are more than thousand of FDI investment projected which were licensed in 2016 with the capital of US$18,012 million, representing a 19.36% in the number of projects and 17.3% in the amount of investment (Vietnam Briefing, 2018) The FDI investment is considered as one of the most important and meaningful sources of capital, contributing to the economic development of Vietnam during 2008-2016 The capital from FDI was mainly distributed in the manufacturing sector with the penetration of various multinational giants Production of electronics and consumer goods are those sectors attracting the largest amount of FDI capital The FDI inflows during 2008-2016 has significantly boosted the income generated by wages in Vietnam with the employment of local residents from the factories

4.2Descriptive statistics

This section represents the descriptive data which are analyzed with the use of different models such as OLS, SDM, SEM and SLM

Table – 25 shows a comparison of Spatial regression models and OLS regression model concerning income in total and breakdowns in different sources such as agriculture, non-agriculture, wages, and other sources during 2008-2016

Where:

X1: Percentage of workers aged 15 and over who are working in a trained economy by province

X5: Number of farms

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X2: Immigration rate X3: Migration rate

X4:The percentage of working population aged 15 and over working in the total population by province

X7: the number of FDI projects licensed

X8: Registered capital (mil USD) Lag X1, X2, X3, X4, X5, X6, X7, X8: Spatially lagged variable of the observed variables

The results show that twenty four out of twenty-five have the Moran I‟s test is significant at 5% which means there is a necessity to use the spatial models to analyze the income sources among provinces And most of the regressions, SDM show the much more effectiveness than other models

Moran I test: P value = 2.910621e-05 <0.05

Coefficients OLS SDM SEM SLM

Intercept -2.068E+02 -1.3153E+03 1.0050E+03 -8.6682E+02 X1(Trained) 2.762E+01*** 3.5247E+01*** 3.3589E+01*** 2.7866E+01

X2 (Immigration) 2.136E+01*** 1.6134E+01*** 1.5384E+01*** 2.2265E+01

X3 (Migration) 3.176E+01** 1.7859E+01** 1.514E+01** 1.8165E+01

X4 (Working) 1.769E+00 -3.0561E+00 -8.5310E+00 4.5249E+00 X5 (Farms) 4.277E-02*** 7.7418E-03 -1.9263E-03 2.7433E-02

X6 (sex ratio) 2.530E+00 6.4659E-01 -2.6216E+00 3.8791E+00 X7 (Projects) -8.063E-02 -8.8995E-01*** -9.7628E-01*** -7.2484E-01

X8 (Registered

(35)

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 1: A comparison of Spatial regression models and OLS regression model (Year 2008, dependent variable: total income (Mill VND))

In the table 1, Moran I‟s test is significant at 5% so it is necessary to use spatial models and the AIC of SDM is the smallest so SDM is used for analyzing in this case

SDM: Y = 𝜌WY + X𝛽(1) +WX𝛽(2) + 𝜀 In the observed province:

Source: Developed by the researcher

lag x1 -1.8657E+01**

lag x2 5.2421E-03

lag x3 6.5552E+00

lag x4 1.5363E+01*

lag x5 3.5E-02***

lag x6 4.7212E+00

lag x7 1.3224E+00**

lag x8 -1.9076E-03

Rho 0.5185*** 0.57549***

Lambda 0.88264***

(36)

The percentage of workers aged 15 and over in the trained economy, the immigration rate, the migration rate had positive impact on the total income

However the FDI Projects licensed had negative effect on the total income This could be explained by the economic crisis, the enterprises were affected, people‟s job transformed from agricultural to non – agricultural activities but it took quite a long time for them to equip necessary skills The authorities‟ acting to attract FDI projects affecting the domestic companies

In the neighboring provinces:

The total income had negative effect on that of the observed province.

The percentage of their trained labor force had competitive impact on the household‟s total income of the observed province The more percentage at the neighboring provinces, the less income in the observed unit Because they can attract and make a good deal with the companies

(37)

Moran I test: Pvalue = 4.372347e-10 <0.05

Intercept -6.97E+02* -1.5875e+03** -6.4899e+02* -5.9937e+02*

X1(Trained) -1.03E+01*** -8.2703e+00*** -1.0371e+01*** -8.7310e+00***

X2

(Immigration) 3.24E+00* 2.8755e+00* 3.3047e+00** 2.6432e+00* X3 (Migration) 2.04E+00 1.7810e+00 1.8327E+00 1.8057E+00

X4 (Working) 1.45E+00 2.1826E+00 1.2028E+00 1.1398E+00 X5 (Farms) 1.65E-02*** 1.2559e-02*** 1.5329e-02*** 716.9538***

X6 (sex ratio) 9.71E+00*** 1.0031e+01*** 9.3933e+00*** 8.0144e+00***

X7 (Projects) -7.88E-02 -1.0182E-01 -8.4916E-02 -8.8284E-02 X8 (Registered

capital) -6.11E-03* -6.0107e-03*

-5.5163e-03*

-5.3487e-03*

lag x1 7.0129E-01

lag x2 -7.7203E-01

lag x3 9.6291e+00

lag x4 3.4064E+00

lag x5 1.5439e-02**

lag x6 5.7059E+00

lag x7 -2.8746E-02

lag x8 -1.0914e-02 *

(38)

Lambda 0.12281

AIC 676.4237 673.26 678.27 671.28

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Source: developed by the researcher

Table 2: A comparison of Spatial regression models and OLS regression model Year: 2008 Dependent variable: Income from Agric (million VND)

In this model, the Moran I test is significant at 5% The AIC of SLM is the smallest so SLM is used for analyzing

SLM: Y = 𝝆WY + 𝜷X +u

Rho is significant at 5% which means the income from Agric was affected positively by that at the neighboring provinces This indicates quite perfect market in the Agric sector which is a good sign

At the observed province:

(39)

that some of the immigrants were not in the trained labor force and the males contributed more on increasing income from Agric

Coefficients OLS SDM SEM SLM

Intercept 4.05E+02 -6.0639E+02 1.0968e+03* 257.1935639

X1(Trained) 8.64E+00*** 1.0649e+01*** 9.1188e+00*** 8.3053705***

X2 (Immigration) 5.55E+00** 3.6284e+00 * 3.4897e+00* 5.6237051***

X3 (Migration) 1.31E+01** 9.3060e+00** 9.5909e+00** 9.6050720**

X4 (Working) -1.26E+00 -3.5688E+00 -5.2158E+00 0.2394009

X5 (Farms) 1.02E-02* 1.9152E-03 -1.8818E-03 0.0059100

X6 (sex ratio) -3.98E+00 -5.8360e+00 -8.2582e+00* -3.8364300

X7 (Projects) 3.86E-03 -2.1967E-01 -2.6811e-01* -0.1738215

X8 (Registered

capital) 5.30E-03 4.9135E-03 4.8574E-03 0.0053402

lag x1 -7.1378E-01

lag x2 4.0635E+00

lag x3 1.0544E+00

lag x4 6.1940E+00

lag x5 1.8561e-02***

lag x6 9.2653e+00

lag x7 5.5782e-01*

(40)

Coefficients OLS SDM SEM SLM

Rho 0.21695 0.48361***

Lambda 0.67603***

AIC 722.8604 706.56 712.84 712.84

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 3: A comparison of Spatial regression models and OLS regression model Year: 2008 Dependent variable: Income from Nonagric (million VND)

In the table 3, the Moran I test is significant at 5% and the AIC of SDM is the smallest, so this model is used for analyzing

SDM: Y = 𝜌WY + X𝛽(1) +WX𝛽(2) + 𝜀 At the observed province:

The more number of trained labor force, the immigration rate and the migration rate the more income from this source The skilled labor mostly worked in the Nonagric sector which lies at the higher position than the previous one in the value chain reflecting higher income Comparing the two parameters of the variable immigration between Nonagric and Agric sector, we can see that most of the immigrants to provinces were white-collars The migrants seemed to be unskilled workers to their original provinces but better workers to provinces where they moved to, this reflects the different level gaps among provinces

(41)

The more number of farms and the number of licensed FDI projects the more income from this Nonagric at the observed province This indicates that the Agric sector was the foundation of the Nonagric sector and the licensed FDI mostly focused on the Nonagric activities

Moran I test: P value = 0.0002394576 <0.05

Intercept -4.815E+02 -5.4458E+02 -1.9322E+02 -6.5680E+02 X1(Trained) 1.962E+01*** 2.2016e+01*** 2.1521e+01*** 1.8507e+01 ***

X2

(Immigration) 9.120e+00*** 6.6688e+00** 6.5751e+00*** 9.4630e+00 *** X3 (Migration) 3.6290E+00 4.3674E+00 3.6290E+00 6.9875E+00

X4 (Working) -7.1153E-01 1.4374E+00 -7.1153E-01 1.8144E+00 X5 (Farms) -2.2672E-03 -3.3965E-05 -2.2672E-03 9.9640e-03*

X6 (sex ratio) 1.9868E+00 3.2987E+00 1.9868E+00 3.9910E+00 X7 (Projects) -2.1804E-01 -1.9645E-01 -2.1804E-01 -6.3111E-02 X8 (Registered

capital) -2.9033E-03 9.3468E-04 -2.9033E-03 -5.5771E-04

lag x1 -1.0638e+01*

lag x2 1.4922E+00

lag x3 5.0559E+00

lag x4 2.9238E+00

lag x5 1.3369e-02*

(42)

Moran I test: P value = 0.0002394576 <0.05

lag x7 5.7806e-01 (.)

lag x8 5.0756E-03

Rho 0.41388** 0.38781***

Lambda 0.7524***

AIC 752.1072 730.82 734.1 743.23

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 4: A comparison of Spatial regression models and OLS regression model ,Year: 2008 Dependent variable: Income from Wages (million VND)

In the table 4, the Moran I test is significant at 5% and the AIC of SDM is the smallest, so this model is used for analyzing

SDM: Y = 𝜌WY + X𝛽(1) +WX𝛽(2) + 𝜀

Rho is significant at 5% and has positive value which means the income from wages of the examining province was influenced in the same way by that of the neighboring provinces If considering the wage as a good then the non-farm business was quite a perfect market which is a good signal for the country

(43)

At the neighboring provinces, the more trained labor force in the neighboring provinces the less income from wages in the observed province This situation could be explained clearly by the fact that provinces that had many skilled workers attracted more investments The other factor also reflects this situation is the number of farms The more number of farms the more income from wages in the observed province

Moran I test: P value = 0.05235805 >0.05

Intercept 5.67E+02

X1(Trained) 9.67E+00***

X2

(Immigration) 3.44E+00** X3 (Migration) 5.56E+00*

X4 (Working) 3.42E-01 X5 (Farms) 4.59E-03

X6 (sex ratio) -6.37E+00**

X7 (Projects) -1.26E-01 X8 (Registered

capital) 3.51E-03

AIC 659.6337

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

(44)

The Moran I test is not significant at 5% so spatial models are not used OLS is used The income from other sources was affected positively by the trained labor force, the immigration rate, migration rate and affected negatively by the sex ratio This indicates that the more education they had, the more income from other sources they got

Intercept 3.26E+03** 2.7198e+03* 1.6747e+03* 1.8632e+03

X1(Trained) 2.57E+01*** 4.0121e+01*** 4.1232e+01*** 2.9195e+01***

X2

(Immigration)

1.92E+01*** 1.6324e+01*** 1.6183e+01*** 1.7819e+01***

X3 (Migration) 1.91E+00 -5.1055E+00 -1.7782E+00 5.737E-01

X4 (Working) -1.86E+01* -1.1689e+01 -8.3231E+00 -1.2910e+01*

X5 (Farms) 3.85E-02*** 1.5458e-02* 9.2994E-03 2.9248e-02***

X6 (sex ratio) -1.62E+01 -6.5869E+00 -6.8003E+00 -1.1186E+01

X7 (Projects) -7.07E-02 -9.1477e-01** -1.3022E+00 -7.3092E-01

X8 (Registered capital)

3.36E-02 5.0003e-02* 8.1177e-02*** 2.0245E-02

lag x1 -3.4288e+01***

lag x2 -6.8089E+00

lag x3 -4.0014E+00

lag x4 -3.2590E+00

(45)

lag x6 -8.8032E+00

lag x7 2.7635e+00***

lag x8 -1.5747e-01**

Rho 0.60029*** 0.50172***

Lambda 0.86408***

AIC 872.0481 822.58 829.46 852.43

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 6: A comparison of Spatial regression models and OLS regression model Year: 2010 Dependent variable: Total income (million VND)

The Moran I test is significant at 5% and the AIC of SDM is the smallest so SDM is used for analyzing

Rho is significant at 5% and has the positive value which means the total income of the observed province was affected positively by that of the neighboring provinces

(46)

The FDI projects licensed had negative effect on the total income I think that it is because the number of FDI projects licensed in this year declined in comparison with that of the year 2008 (GSO, 2010) and with the economic crisis so the FDI enterprises in Viet Nam saw the country as a less attractive environment As a result, they withdrew their money out of Viet Nam

At the neighboring provinces:

The trained labor force and the registered capital had indirectly negative effect on the total income of the observed province which means the more trained labor force and registered capital in the neighboring provinces the less total income in the examining province This situation is explained above in the interpretation of the Table

The FDI projects licensed has indirectly positive effect on the total income of the observed province The reason can be illustrated in the same way above

Intercept -1.14E+3* -1.83E+3*** -7.03E+2 -8.96E+2*

X1(Trained) -1.23E+1*** -1.01E+1*** -1.15E+1*** -9.41E+0***

X2

(Immigration)

5.27E+0*** 4.50E+00 4.37E+0*** 4.27E+0***

X3 (Migration) 3.13E+00 6.56E-01 2.94E+00 1.70E+00

X4 (Working) 3.00E+00 1.07E+00 1.49E+00 4.11E+00

X5 (Farms) 7.92E-3* 3.91E-3** 5.95E-3 5.32E-3

X6 (sex ratio) 1.37E+1*** 9.00E+00 1.02E+1** 9.13E+0**

X7 (Projects) 4.05E-02 -1.55E-1*** -8.68E-02 -7.17E-02

-2.94E-2 -2.81E-2*** -2.18E-02 -2.13E-02

(47)

lag x2 4.79E+00

lag x3 3.64E+00

lag x4 6.03E+00

lag x5 6.00E-3**

lag x6 7.75E+00

lag x7 1.59E-01

lag x8 -3.88E-2***

Rho 0.24084 0.44122***

Lambda 0.38273

AIC 744.3611 740.23 743.67 735.06

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

The Moran I test is significant at 5% and the AIC of SLM is the smallest so SLM is used for analyzing

SLM: Y = 𝜌WY+ 𝛽X+u

(48)

Agric in neighboring provinces the more of this income source in the observed province

In the observed province, the trained labor force, the immigration rate and the sex ratio affected positively the income from this source This implies the fact that the skilled labor force had gradually moved from non-farm business to farming business which is a really good signal And because it is just the very first time of this trend, it is clear to understand why SDM is not applied in this case, instead the stochastic variable u is used

Moran’s I test: Pvalue = 0.0001089363 <0.05

Intercept 1.55E+3** 1.72E+3* 1.02E+3* 9.94E+2*

X1(Trained) 6.96E+0* 9.63E+0*** 9.35E+0*** 8.08E+0***

X2

(Immigration)

5.85E+0*** 5.36E+0*** 5.65E+0*** 5.55E+0***

X3 (Migration) 5.42E-01 -1.19E+00 1.07E+00 8.45E-02

X4 (Working) -8.97E+0* -6.71E+00 -6.35E+00 -5.72E+0

X5 (Farms) 1.15E-2* 6.63E-3 4.73E-03 8.27E-3*

X6 (sex ratio) -9.45E+0* -5.08E+00 -5.69E+00 -6.94E+0

X7 (Projects) -2.29E-04 -1.52E-01 -2.8E-01 -1.63E-01

6.22E-03 4.72E-03 1.46E-02 9.88E-04

lag x1 -6.26E+00

(49)

Moran’s I test: Pvalue = 0.0001089363 <0.05

lag x3 -6.05E+00

lag x4 -4.15E+00

lag x5 6.83E-03

lag x6 -4.88E+00

lag x7 9.49E-1*

lag x8 -3.65E-02

Rho 0.43204* 0.47405***

Lambda 0.61029***

AIC 767.6474 762.37 758.76 758.09

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 8: A comparison of Spatial regression models and OLS regression model Year: 2010 Dependent variable: Income from NonAgric (million VND)

(50)

In the observed province, the trained labor force, the immigration rate and the number of farms affected positively the income from this source Unlike in 2008, in 2010, the number of farms affected this income source which proved that farming as well as Agric sector had played an important role in the NonAgric sector I believe that it was due to the trending of skilled workers had moved to work in Agric sector

Moran’s I test: Pvalue = 0.001514322<0.05

Intercept 1.99E+3** 2.80E+3** 1.02E+3 1.64E+3**

X1(Trained) 2.64E+1*** 3.25E+1*** 3.25E+1*** 2.59E+1***

X2

(Immigration) 8.01E+0*** 7.01E+0*** 7.36E+0*** 7.94E+0*** X3 (Migration) -3.08E+00 -5.47E+00 -2.78E+00 -2.56E+00

X4 (Working) -9.94E+0* -5.79E+00 -5.98E+00 -9.66E+0*

X5 (Farms) 1.41E-2* 7.29E-03 5.83E-03 1.35E-2**

X6 (sex ratio) -1.37E+1* -7.27E+0 -6.79E+00 -1.14E+1*

X7 (Projects) -7.91E-02 -4.07E-1 -5.65E-1* -2.12E-01

X8 (Registered

capital) 5.14E-2* 5.38E-2** 6.32E-2*** 4.34E-2

lag x1 -2.43E+1***

(51)

Moran’s I test: Pvalue = 0.001514322<0.05

lag x3 -5.38E+00

lag x4 -5.64E+00

lag x5 3.89E-03

lag x6 -1.23E+01

lag x7 1.47E+0**

lag x8 -7.90E-2*

Rho 0.39784** 0.2284*

Lambda 0.67209***

AIC 794.8775 778.68 780.43 792.18

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 9: A comparison of Spatial regression models and OLS regression model , Year: 2010 Dependent variable: Income from Wages (million VND)

The Moran I test is significant at 5% and the AIC of SDM is the smallest, so this model is used for analyzing

(52)

Rho is significant at 5% and receives positive value which means the income from wages of the examining province was influenced positively by that of the neighboring provinces

The more number of trained labor force, the immigration rate and the registered capital the more income from this source

The more trained labor force as well as the registered capital in the neighboring provinces the less income from wages in the observed province Provinces which attracted more white-collars and more investments would result in less income from wages in the observed province

The more number of FDI projects licensed in the neighboring provinces the more income from wages in the observed province I think that the information channels of the enterprises at the observed province allowed them to know that there would be more FDI companies in the neighboring counterparts which forced them to expand their business scale Thus, the income from wages of the workers in the observed unit increased

Moran’s I test: Pvalue = 3.034922e-05 <0.05

Intercept 8.59E+2*** 1.04E+3** 4.19E+2* 5.82E+2**

X1(Trained) 4.56E+0*** 6.26E+0*** 6.64E+0*** 4.72E+0***

X2

(53)

Moran’s I test: Pvalue = 3.034922e-05 <0.05

X4 (Working) -2.70E+00 -1.76E+00 -1.42E+00 -2.36E+0

X5 (Farms) 5.05E-3* 1.11E-03 1.51E-04 3.50E-3*

X6 (sex ratio) -6.71E+0*** -3.11E+0* -3.03E+0* -4.63E+0**

X7 (Projects) -3.21E-02 -1.04E-01 -1.86E-1* -9.36E-02

X8 (Registered

capital) 5.34E-03 1.26E-2* 1.34E-2* 5.95E-03

lag x1 -5.31E+0**

lag x2 -1.44E+00

lag x3 4.14E-01

lag x4 -6.22E-01

lag x5 3.27E-03

lag x6 -5.39E+0

lag x7 5.30E-1***

lag x8 -2.04E-2

Rho 0.3514* 0.44513**

Lambda 0.73255***

AIC 663.3314 640.06 647.28 655.02

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

(54)

Rho is significant at 5% and receives positive value which means the income from other sources of the examining province was influenced positively by that of the neighboring provinces

The more number of trained labor force and the registered capital the more income from this source The more number of man over 100 females the less income from other sources By analysis from 2008, I think that the white- collars and the number of females played an important role in increasing this income

The more trained labor force in the neighboring provinces the less income from other sources in the observed province This variable had a comparative effect on this income source among observed province and its counterparts

(55)

Intercept 7.86E+3*** 5.30E+3* 5.04E+3*** 4.23E+3***

X1(Trained) 1.90E+1* 4.10E+1*** 4.45E+1*** 2.87E+1***

X2

(Immigration)

2.41E+1*** 1.56E+1*** 1.72E+1*** 1.74E+1***

X3 (Migration) -3.25E+00 -2.11E+1* -2.21E+1* -3.4E-01

X4 (Working) -4.70E+1*** -1.31E+01 -9.51E+00 -2.98E+1***

X5 (Farms) 2.51E-1** 1.24E-1** 8.46E-2* 1.49E-1**

X6 (sex ratio) -3.96E+1** -3.35E+1*** -3.42E+1*** -2.39E+1**

X7 (Projects) 2.10E+0* 7.00E-01 6.18E-01 1.55E+0**

5.55E-02 3.72E-02 6.09E-03 -1.42E-02

lag x1 -4.15E+1***

lag x2 -1.04E+01

lag x3 5.52E+00

lag x4 -6.63E+00

lag x5 9.88E-02

lag x6 -3.32E+00

lag x7 -6.83E-02

lag x8 1.21E-01

Rho 0.68082*** 0.58231***

(56)

AIC 917.438 868 872.39 876.33

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 11: A comparison of Spatial regression models and OLS regression model Year: 2012 Dependent variable: Total Income (million VND)

Rho is significant at 5% and receives positive value which means the total income of the examining province was influenced positively by that of the neighboring provinces This is a good signal

The more number of trained labor force, the immigration rate and the number of farms the more income from this source This shows the importance of the skilled workers and the Agric sector to the total income by province

The more number of man over 100 females and the migration rate the less total income I believe that in Viet Nam, the females had proved to be more efficient than men in making a living

(57)

Intercept -1.10E+3 -2.99E+3** -1.15E+3* -9.91E+2

X1(Trained) -1.29E+1*** -3.88E+00 -1.32E+1*** -9.40E+0***

X2

(Immigration)

7.77E+0*** 6.99E+0*** 8.61E+0*** 6.59E+0***

X3 (Migration) 9.00E+00 1.22E+00 7.85E+00 6.77E+00

X4 (Working) 8.24E+0 1.97E+1*** 8.68E+0* 1.02E+1**

X5 (Farms) 1.66E-1*** 1.58E-1*** 1.79E-1*** 1.47E-1***

X6 (sex ratio) 1.09E+1* 5.28E+00 1.12E+1* 6.89E+00

X7 (Projects) -3.14E-01 -6.99E-1** -2.8E-01 -6.01E-1*

6.61E-03 -1.16E-02 -5.31E-03 2.24E-02

lag x1 -6.15E+00

lag x2 1.43E+1***

lag x3 7.00E+00

lag x4 7.14E-01

lag x5 2.07E-1***

lag x6 1.70E+1

lag x7 8.11E-02

lag x8 -1.36E-1*

Rho -0.23311 0.34356**

(58)

AIC 785.9792 766.61 787.74 779.34

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 12: A comparison of Spatial regression models and OLS regression model : 2012 Dependent variable: Income from Agric (million VND)

Rho is not significant at 5% which means the income from Agric of the examining province was not influenced by that of the neighboring provinces There is no spatial correlation of the income from Agric in this year

The more immigration rate, percentage of working population aged 15 and over working in the total population by province and the number of farms the more income from this source Skilled workers were not necessary in these factors, the experience as well as the strength mattered in increasing the income from Agric

The more FDI projects licensed the less income from Agric At the neighboring provinces:

(59)

neighboring provinces develop then the income from Agric at the observed unit increased

The more registered capital the less income from this source in the observed province

Intercept 3.40E+3*** 4.20E+3* 2.97E+3*** 2.57E+3***

X1(Trained) 5.41E+00 7.66E+0 8.23E+0* 6.77E+0*

X2

(Immigration)

6.69E+0* 5.24E+0* 6.63E+0** 5.55E+0*

X3 (Migration) -4.02E+00 -8.13E+00 -6.93E+00 -4.46E+00

X4 (Working) -2.29E+1*** -1.82E+1* -1.96E+1** -1.83E+1***

X5 (Farms) 2.50E-02 8.97E-03 8.07E-03 1.08E-02

X6 (sex ratio) -1.81E+1** -1.44E+1** -1.58E+1** -1.40E+1**

X7 (Projects) 1.61E-01 -1.15E-01 -5.25E-02 9.71E-02

3.09E-02 3.04E-02 1.82E-02 2.42E-02

lag x1 -9.47E+0

lag x2 -3.14E+00

lag x3 -7.57E-01

lag x4 1.56E+00

lag x5 4E-02

lag x6 -1.54E+01

lag x7 1.41E-01

(60)

Rho 0.36261* 0.40383**

Lambda 0.47593*

AIC 807.7027 809.08 803.31 799.8

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 13: A comparison of Spatial regression models and OLS regression model Year: 2012 Dependent variable: Income from Nonagric (million VND)

Rho is significant at 5% and receives positive value which means the income from NonAgric was influenced positively by that of the neighboring provinces

In the observed province:

The trained labor force and the immigration rate affected positively the income from this source Because basically the Nonagric activities requires skilled workers and most of the immigrants are well trained As a result, the income from this source would increase

(61)

Intercept 4.09E+3*** 3.19E+3* 2.71E+3*** 2.53E+3***

X1(Trained) 2.29E+1*** 2.95E+1*** 3.26E+1*** 2.33E+1***

X2

(Immigration)

9.48E+0** 5.20E+0* 7.50E+0** 7.08E+0**

X3 (Migration) -1.14E+01 -1.24E+1 -1.33E+1 -3.51E+00

X4 (Working) -2.12E+1** -7.42E+00 -4.76E+00 -1.78E+1***

X5 (Farms) 4.95E-02 -2.24E-03 -8.96E-03 1.94E-02

X6 (sex ratio) -2.52E+1** -1.84E+1*** -2.16E+1*** -1.50E+1*

X7 (Projects) 1.81E+0*** 1.36E+0*** 1.11E+0*** 1.81E+0***

2.42E-02 -6.18E-03 -4.43E-02 -3.40E-02

lag x1 -2.59E+1***

lag x2 -6.32E+00

lag x3 -4.91E+00

lag x4 -1.25E+01

lag x5 4.89E-02

lag x6 1.20E+00

lag x7 1.4E-01

lag x8 1.51E-1

(62)

Lambda 0.81087***

AIC 841.603 810.7 817.39 815.93

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 14: A comparison of Spatial regression models and OLS regression model Year: 2012 Dependent variable: Income from Wages (million VND)

Rho is significant at 5% and receives positive value which means the income from Wages of the examining province was influenced positively by that of the counterparts At the observed province:

The more number of trained labor force, the immigration rate and the FDI projects licensed the more income from this source This situation happened in the same way as in 2010

The more number of men over 100 females the less income from wages This maybe because of the migration of more males than females to other countries or the majority of men working in the self-employed sector

(63)

Intercept 1.47E+3*** 1.24E+3 1.02E+3*** 7.81E+2**

X1(Trained) 3.60E+0 6.40E+0*** 6.76E+0*** 4.91E+0***

X2

(Immigration)

1.35E-01 3.51E-01 1.20E+00 9.98E-02

X3 (Migration) 3.21E+00 -4.99E+0 -6.37E+0* 4.88E-01

X4 (Working) -1.11E+1*** -6.22E+0 -5.94E+0* -7.91E+0***

X5 (Farms) 1.04E-02 2.55E-04 -4.31E-03 -5.47E-04

X6 (sex ratio) -7.20E+0* -5.45E+0* -5.44E+0* -3.24E+00

X7 (Projects) 4.41E-1* 3.55E-1* 3.37E-1** 4.72E-1**

-6.24E-03 -1.12E-02 -1.97E-02 -2.06E-02

lag x1 -6.01E+0*

lag x2 -3.17E+00

lag x3 1.06E+1*

lag x4 2.10E+00

lag x5 1.63E-02

lag x6 -4.24E+00

lag x7 -9.77E-02

lag x8 2.14E-02

(64)

Lambda 0.7789***

AIC 733.7379 701.84 698.93 708.58

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 15: A comparison of Spatial regression models and OLS regression model Year: 2012 Dependent variable: Income from other sources (million VND)

The Moran I test is significant at 5% and the AIC of SEM is the smallest, so this model is used for analyzing

SEM: Y = X + u

Lambda is significant at 5% and receives positive value which means the income from other sources of the examining province was influenced positively by that of the neighboring provinces

The more trained labor force and the FDI projects licensed the more income from this source This trend had not been changed for the last years

(65)

Intercept 1.11E+4*** 2.26E+4*** 5.64E+3*** 5.53E+3**

X1(Trained) 1.92E+1* 2.96E+1*** 3.62E+1*** 2.89E+1***

X2

(Immigration)

1.59E+1* 8.23E+0* 1.06E+1* 7.03E+00

X3 (Migration) 1.61E+00 -1.30E+01 4.22E-01 9.60E+00

X4 (Working) -8.42E+1*** -4.84E+1*** -4.58E+1** -5.19E+1***

X5 (Farms) 1.47E-01 1.06E-1 5.84E-02 5.14E-02

X6 (sex ratio) -4.56E+1* -4.30E+1** -1.43E+01 -2.12E+01

X7 (Projects) 1.72E+00 5.96E-01 1.59E+0* 1.79E+0*

1.84E-1 2.08E-1** 1.07E-01 1.13E-01

lag x1 -3.94E+1***

lag x2 -4.73E+00

lag x3 -2.55E+01

lag x4 -3.55E+01

lag x5 1.73E-01

lag x6 -1.19E+2***

lag x7 -4.16E+0**

lag x8 3.26E-1

Rho 0.37172** 0.51823***

(66)

AIC 943.6768 907.16 924.48 915.04

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 16: A comparison of Spatial regression models and OLS regression model ,Year: 2014 Dependent variable: Total Income (million VND)

Rho is significant at 5% and receives positive value which means the total income of the examining province was influenced positively by that of the neighboring provinces At the observed province:

The more trained labor force, the immigration rate and the registered capital the more total income

The more males over 100 females and the percentage of working population aged 15 and over in the economy the less income from wages

(67)

Intercept -5.83E+02 -3.57E+02 -3.56E+02 -5.99E+02

X1(Trained) -1.65E+1*** -6.82E+0* -9.06E+0** -9.38E+0**

X2

(Immigration)

4.64E+0* 2.81E+0 2.25E+00 3.70E+0*

X3 (Migration) 5.09E+00 -1.01E+1 5.29E+00 -1.17E-01

X4 (Working) -2.10E+00 1.07E+1 2.41E+00 3.14E+00

X5 (Farms) 1.32E-1*** 1.43E-1*** 7.53E-2** 1.18E-1***

X6 (sex ratio) 1.39E+1 3.97E+00 8.13E+00 7.63E+00

X7 (Projects) -1.41E-01 -7.23E-1** -2.21E-01 -2.64E-01

-3.88E-02 -3.43E-02 -7.23E-2* -5.43E-2

lag x1 -1.76E+1**

lag x2 3.40E+00

lag x3 2.36E+00

lag x4 -5.22E+00

lag x5 2.39E-1***

lag x6 3.76E+00

lag x7 -1.05E+00

lag x8 1.29E-1*

(68)

Lambda 0.62302**

AIC 822.1876 790.37 813.92 804.42

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Rho is not significant at 5% which means the income from Agric of the examining province was not influenced by that of the neighboring provinces

The more trained labor force and the FDI projects licensed the less income from this source This situation reflects the transformation from reliance from agriculture to non-agricultural activities

The more farms the more income from Agricultural, Forestry and Fishery activities At the neighboring provinces:

(69)

The farms and the registered capital in the neighboring provinces the more income from Agric This reflects the spillover effect

Intercept 4.16E+3*** 9.10E+3*** 4.01E+3*** 2.90E+3**

X1(Trained) 6.69E+00 6.94E+00 6.99E+0 8.22E+0*

X2

(Immigration)

7.60E+0** 3.68E+00 7.66E+0** 6.09E+0*

X3 (Migration) -2.28E+00 4.65E+00 -1.95E+00 -6.74E-01

X4 (Working) -3.86E+1*** -2.91E+1*** -3.79E+1*** -3.05E+1***

X5 (Farms) -4.42E-02 -6.55E-2 -4.50E-02 -6.33E-02

X6 (sex ratio) -1.54E+01 -1.16E+01 -1.43E+1 -9.48E+00

X7 (Projects) -2.72E-01 -3.98E-01 -2.95E-01 -3.29E-01

1.08E-1* 1.07E-1* 1.06E-1* 1.04E-1*

lag x1 -6.32E+00

lag x2 -3.36E+00

lag x3 -1.85E+01

lag x4 -2.93E+1*

lag x5 -7.44E-03

lag x6 -3.97E+1*

(70)

lag x8 7.95E-02

Rho -0.016589 0.36453**

Lambda 0.059274

AIC 848.9699 841.53 850.92 843.83

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Rho is not significant at 5% which means the income from NonAgric of the examining province was not influenced by that of the neighboring counterparts

The percentage of working population aged 15 and over in the total population by province has positive effect on this income source

The more registered capital the less income from NonAgric activities At the neighboring provinces:

(71)

Intercept 5.19E+3*** 1.17E+4*** 3.42E+3*** 3.51E+3**

X1(Trained) 2.71E+1*** 2.45E+1*** 2.86E+1*** 2.68E+1***

X2

(Immigration)

5.74E+0 3.57E+00 3.73E+00 2.34E+00

X3 (Migration) -7.07E+00 -6.99E+00 -3.32E+00 6.65E-01

X4 (Working) -3.04E+1*** -2.49E+1** -2.14E+1** -2.38E+1***

X5 (Farms) 5.56E-02 3.50E-02 4.84E-02 3E-02

X6 (sex ratio) -2.99E+1** -2.78E+1*** -1.77E+1* -2.02E+1*

X7 (Projects) 1.62E+0** 1.14E+0* 1.81E+0*** 1.76E+0***

1.07E-1 1.38E-1** 7.59E-2 7.83E-2

lag x1 -1.55E+1

lag x2 2.15E+00

lag x3 -1.47E+01

lag x4 -1.53E+01

lag x5 -3.62E-02

lag x6 -6.01E+1**

lag x7 -3.31E+0**

(72)

Rho 0.30009 0.31096***

Lambda 0.56019**

AIC 864.46 852.67 857.35 853.85

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 19: A comparison of Spatial regression models and OLS regression model Year: 2014 Dependent variable: Income from wages (million VND)

Rho is not significant at 5% which means the income from wages of the examining province was not influenced by that of the neighboring provinces

The more trained labor force, the FDI projects licensed and the registered capital the more income from wages

The more number of men over 100 females and the percentage of working population aged 15 and over in the economy the less income from wages Because the majority of the workers having wages are in trained economy

(73)

The sex ratio and the FDI projects licensed in the neighboring provinces have comparative effect on the income from wages of the observed counterparts The more of them the less income from wages the observed province got

The registered capital had positive effect on the income from wages in the observed province

Intercept 2.36E+3*** 5.23E+3*** 1.38E+3** 1.45E+3**

X1(Trained) 1.93E+00 2.38E+00 2.99E+00 2.99E+0

X2

(Immigration)

-2.13E+00 -1.50E+00 -6.5E-01 -2.22E+0

X3 (Migration) 5.87E+00 -2.91E+00 6.92E-01 3.93E+00

X4 (Working) -1.31E+1*** -8.97E+0* -9.27E+0* -8.80E+0**

X5 (Farms) 3.90E-03 1.42E-02 -1.91E-03 -7.49E-03

X6 (sex ratio) -1.42E+1** -1.30E+1*** -6.35E+0 -8.81E+0*

X7 (Projects) 5.12E-1* 4.46E-1* 5.64E-1** 5.45E-1**

8.20E-03 2.01E-02 1.39E-02 5.44E-03

lag x1 -3.18E+00

lag x2 -4.32E+0

lag x3 1.79E+00

lag x4 -2.48E+00

lag x5 4.76E-02

(74)

lag x7 -8.93E-1*

lag x8 -2.51E-03

Rho 0.34801* 0.47572***

Lambda 0.63157***

AIC 764.064 746.71 751.61 749.4

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 20: A comparison of Spatial regression models and OLS regression model Year: 2014 Dependent variable: Income from other sources (million VND)

Rho is significant at 5% and receives positive value which means the income from other sources of the examining province is influenced positively by that of the neighboring provinces

The more FDI projects licensed the more income from other sources

The more number of man over 100 females and the percentage of working population aged 15 and over in the economy the less income from other sources

(75)

The sex ratio and the FDI projects licensed in the neighboring provinces have comparative effect on the income from other sources of the observed province The more of them the less income from other sources the observed province gets

Intercept 7.87E+3** 9.70E+3*** 2.18E+03 2.90E+03

X1(Trained) 1.44E+01 5.39E+1*** 5.32E+1*** 3.30E+1***

X2

(Immigration)

6.57E+1*** 4.15E+1*** 5.93E+1*** 4.52E+1***

X3 (Migration) -2.43E+01 9.44E+00 1.50E+00 -1.26E+00

X4 (Working) -8.18E+1*** -1.47E+01 -2.37E+01 -4.57E+1***

X5 (Farms) 9.38E-02 1.10E-1 3.37E-02 4.77E-02

X6 (sex ratio) -9.42E+00 -2.62E-01 7.43E+00 1.28E+00

X7 (Projects) -3.49E-01 8.20E-02 -2.42E-01 -4.35E-02

3.91E-1* 1.21E-01 1.21E-01 2.04E-1

lag x1 -5.71E+1***

lag x2 -5.37E+1**

lag x3 -1.62E+01

lag x4 -4.72E+1*

lag x5 9.03E-02

lag x6 -5.08E+1

lag x7 -7.25E-02

(76)

Rho 0.56137*** 0.55968***

Lambda 0.8507***

AIC 964.0562 909.94 927.31 928

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Table 21: A comparison of Spatial regression models and OLS regression model Year: 2016 Dependent variable: Total Income (million VND)

Rho is significant at 5% and receives positive value which means the total income of the observed province is influenced positively by that of the neighboring provinces At the observed province:

The more number of trained labor force and the immigration rate the more total income

(77)

Intercept 3.63E+00 -1.10E+03 5.95E+01 -2.42E+02

X1(Trained) -2.18E+1*** -7.78E+0* -8.09E+0* -1.25E+1***

X2

(Immigration)

-2.16E+00 -9.80E+0* -8.98E+0 -3.02E+00

X3 (Migration) -4.07E+00 3.02E+00 -3.85E-02 -2.98E+00

X4 (Working) -6.75E+00 7.09E+00 3.43E+00 -7.08E-01

X5 (Farms) 1.29E-1** 1.15E-1*** 7.50E-2* 1.05E-1**

X6 (sex ratio) 1.35E+1 6.06E+00 4.67E+00 7.39E+00

X7 (Projects) 6.29E-02 -3.92E-01 -1.72E-01 -1.65E-01

-4.61E-02 -2.75E-02 -4.41E-02 -3.45E-02

lag x1 -1.55E+1*

lag x2 1.67E+1

lag x3 7.75E+00

lag x4 -1.97E+00

lag x5 2.13E-1**

lag x6 8.05E+00

lag x7 5.77E-02

(78)

Rho 0.32401* 0.59145***

Lambda 0.69595***

AIC 840.9333 817.11 830.03 821.85

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

Rho is significant at 5% and receives positive value which means the income from Agric of the examining province is influenced positively by that of the neighboring provinces

The more number of trained labor force, the immigration rate the less income from Agricultural, Forestry and Fishery activities

The more number of farms the more income from Agricultural, Forestry and Fishery activities

(79)

The trained labor force in the neighboring provinces has comparative effect on the income from Agric in the observed province

The more farms in the neighboring provinces the more income from Agric in the examining province

Intercept 2.97E+3* 5.05E+3* 1.96E+3 1.68E+03

X1(Trained) 4.66E+00 1.69E+1** 1.01E+1 9.12E+0

X2

(Immigration)

3.27E+1*** 1.85E+1** 3.36E+1*** 2.77E+1***

X3 (Migration) -9.42E+00 3.95E+00 -6.48E+00 -6.32E+00

X4 (Working) -3.83E+1*** -1.37E+01 -2.91E+1** -2.72E+1**

X5 (Farms) -7.68E-02 -5.84E-02 -1.13E-1* -9.62E-2

X6 (sex ratio) -2.22E+00 -8.71E-01 1.82E+00 8.7E-01

X7 (Projects) -2.36E-01 3.07E-02 -3.75E-01 -2.29E-01

1.14E-01 2.39E-02 8.42E-02 8.35E-02

lag x1 -1.43E+1

lag x2 -2.63E+1

lag x3 5.88E+00

lag x4 -2.61E+01

lag x5 1.84E-1

lag x6 -2.37E+01

(80)

lag x8 5.96E-02

Rho 0.19653 0.4253**

Lambda 0.45626*

AIC 883.4102 870.8 881.31 875.95

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

The Moran I test is significant at 5% and the AIC of SDM is the smallest, so this model is used for analyzing SDM: Y = 𝜌WY + X𝛽(1) +WX𝛽(2) + 𝜀

Rho is not significant at 5% which means the income from NonAgric activities of the examining province is not influenced by that of the neighboring provinces

At the observed province, the more number of trained labor force and the immigration rate e more income from NonAgric activities

(81)

Intercept 2.74E+3* 4.20E+3* 1.30E+03 1.56E+3

X1(Trained) 2.98E+1*** 3.66E+1*** 3.47E+1*** 3.06E+1***

X2

(Immigration)

3.64E+1*** 2.78E+1*** 3.70E+1*** 2.83E+1***

X3 (Migration) -1.39E+01 3.55E+00 -4.06E-01 -3.12E+00

X4 (Working) -2.24E+1* -5.82E+00 -1.36E+01 -1.70E+1*

X5 (Farms) 6.54E-02 7.87E-2* 7.83E-2 6.04E-02

X6 (sex ratio) -9.77E+00 -8.21E-01 -1.52E+00 -5.37E+00

X7 (Projects) 1.14E-01 4.59E-01 3.89E-01 3.45E-01

2.22E-1** 1.02E-1 8.77E-02 1.44E-1*

lag x1 -2.03E+1*

lag x2 -1.96E+01

lag x3 -2.71E+01

lag x4 -2.80E+1*

lag x5 -1.85E-1*

lag x6 -1.70E+01

lag x7 -1.15E+00

lag x8 3.76E-1**

Rho 0.33945* 0.34505***

Lambda 0.64102***

AIC 871.0025 848.4 861.81 851.53

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

(82)

Rho is significant at 5% and receives positive value which means the income from wages of the examining province is influenced positively by that of the neighboring provinces

The more number of trained labor force, the immigration rate and the number of farms the more income from wages

The trained labor force, the working population ages 15 and over in the economy by province and the number of farms in the neighboring provinces have comparative effect on the income from wages of the observed province The more of them the less income from wages the examining province gets

The registered capital in the neighboring provinces has positive effect on the income from wages

Intercept 2.16E+3*** 3.22E+3*** 8.55E+2* 1.09E+3**

X1(Trained) 1.77E+00 5.38E+0** 5.48E+0** 4.18E+0*

X2

(Immigration)

-1.27E+00 1.66E+00 2.59E+00 -1.31E+00

(83)

X4 (Working) -1.44E+1*** -5.28E+00 -6.31E+0 -7.87E+0**

X5 (Farms) -2.38E-02 -1.34E-03 -3.71E-03 -1.45E-02

X6 (sex ratio) -1.10E+1* -5.84E+0 -3.30E+00 -5.99E+0

X7 (Projects) -2.9E-01 -8.69E-02 -1.23E-01 -1.61E-01

1.02E-1** 5.84E-2* 6.67E-2** 7.17E-2**

lag x1 -6.59E+0*

lag x2 -6.95E+00

lag x3 -8.39E+00

lag x4 -8.53E+00

lag x5 -4.16E-02

lag x6 -1.64E+1*

lag x7 -2.2E-01

lag x8 5.53E-03

Rho 0.54334*** 0.52392***

Lambda 0.69209***

AIC 766.6379 743.84 742.43 747.02

Signif codes: „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟

(84)

The Moran I test is significant at 5% and the AIC of SEM is the smallest, so this model is used for analyzing

SEM: Y = X + u

Lambda is significant at 5% and receives positive value which means the income from other sources of the examining province was influenced positively by that of the neighboring provinces

(85)

4.3Changes in income sources in Vietnam 2008-2016

Figure demonstrates the total income of Vietnam from 2008 to 2016

Figure 1: The changing rate of total income between 2008 and 2016

Figure reveals that the provinces in Red River Delta and Southeast regions are those with the highest levels of income while the Northwest and North Central Coasts provinces are recorded with the lowest levels of income This income pattern is similar from 2008-2016

(86)

Figure 2: The changing rate of income from wages between 2008 and 2016 Provinces with darker color in Red River Delta and Northeast regions represent the larger changing rate of income from wages These provinces have the same common things such as being attractive to FDI, having good policies for development and the rate of urbanization is fast

(87)

Figure 3: The changing rate of income from Agriculture, Forestry, Fishery between 2008 and 2016

(88)

Figure shows the changing rate of income from Agriculture, Forestry, Non-fishery between 2008 and 2016

Figure 4: The changing rate of income from Agriculture, Forestry, Non-fishery between 2008 and 2016

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The common thing among these provinces is that they are concentrated quite near the two largest plains, and thus, they can focus on the activities which not directly create products from agriculture, fishery and forestry

Lastly, Figure demonstrates the changing rate of income from other sources between 2008 and 2016

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4.4Discussions

There was a transition in income sources at the provincial level during the given period in this current study Firstly, it is proven that there is in comparison to income sourced from non-agricultural activities, income from agricultural activities tend to significantly shrank during 2008-2016 With reference to the four income categories including agriculture, non-agriculture, wages and others, the income generated from wages is ranked with the highest proportion which is followed by non-agriculture, agriculture and other sectors It is calculated that the contribution of wages to the total income in Vietnam has drastically increased from less than 31% in 2008 to 46% in 2016 Despite the rapid and significant increase in the proportion of income sourced from wages, this portion is relatively smaller than that of those countries in the same group The report by ILO (2016) on the global wages shows that in developing countries more than 60% of the national income is attributed to wages and salary Owing to FID investments into such provinces as Hanoi, Bac Ninh, Quang Ninh, Vinh Phuc, Dong Nai, Binh Duong, and Ho Chi Minh City, many people are employed by manufacturing firms in the industrial zones in these provinces The employment from these industrial zones has boosted the income amount generated from wages and salary Other provinces also enjoyed a small increase in the income sourced from wages and salaries

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agricultural reliance country It is also realized that such centrally controlled municipal provinces as Hanoi, Ho Chi Minh City, Quang Ninh, Hai Phong, etc present an upward trend in the income amount sourced from non-agriculture sectors such as manufacturing or services while such provinces as Da Nang, Khanh Hoa, Quang Ninh, Kien Giang, Phu Yen, etc have shifted their income structure from agriculture reliance to tourism and service reliance

Changes in income structure vary across provinces There is a significant fall in the amount of income generated from agriculture activities in such high-income provinces as Red Delta River and Southeast provinces in Vietnam Contrastively, the poor provinces still present a heavy reliance on agricultural activities for income generation In other words, the incomes sourced from wages and salaries, non-agriculture activities and other sectors account for the smaller portions in the income structure In the provincial level, while such provinces in Red River Delta as Ninh Binh, Thai Binh, Hai Duong have decreased their income dependence on agriculture sector (from 43.6% in 2008 to 18.9% in 2016) other provinces as An Giang, Ca Mau, Dong Thap, Long An, Soc Trang, Tien Giang, Tra Vinh, and Vinh Long still maintain the significant contribution of agriculture to the total provincial income

In summary, there is a rapid and constant increase in the contribution of incomes generated by wages in all provinces in Vietnam Despite the faster growth rate in the incomes by wages in poorer provinces of Vietnam, the contribution of wages to the total income in these provinces is still much lower than that of rich provinces Additionally, although the declining contribution to the provincial and national income, agriculture has still maintained their important role in income generation of all provinces in Vietnam

The analysis indicates the following findings:

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immigration, the farms And the trained labor force has competitive effect among provinces due to the negative value of parameter of the variable spatially lagged percentage of workers aged 15 and over who are working in a trained economy by provinces The more man over 100 females, working labor force, immigration rate of the neighboring provinces, the less total income in the observed province From 2008 – 2010, the more FDI projects licensed in neighboring provinces the more total income in the observed province

ii Income from wages of one province has positive spatial correlation by that of the neighboring provinces The trained labor force and the immigration rate play an important role and have competitive effect among provinces The number of farms of the neighboring provinces have positive effect on the observed unit From 2008 to 2010 the FDI projects has positive effects among provinces but later in 2014 it has competitive effect (when the market is more stable than the crisis time)

iii Income from Agriculture, Forestry, Fishery activities: in the crisis period 2008 to 2010, this variable is affected by that of neighboring province (when people focused more on agriculture) The trained labor force plays negative role for this variable but the variables number of farms, sex ration have direct effect From the year 2012, there is spatial correlation of farms among provinces (sharing knowledge, creating jobs serving farming, etc) The more immigration rate in the neighboring provinces the more income from Agricultura (because there will be more land for farming)

iv Income from Non-Agriculture, Non-Forestry, Non-Fishery activities is basically affected by that of neighboring provinces in the year 2010 – 2012 The trained and the immigration rate still play an important role and have competitive effect The number of man as well as the number of farms have directly negative effect

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CHAPTER 5: CONCLUSION AND RECOMMENATIONS

5.1Conclusion

The investigation into the income in Vietnam during 2008-2016 indicates that in this period Vietnam has experienced a significant improvement in the average level of income per capital, contributing to the progress of reduction poverty of the country Despite the improvement, there is an inherent problem raising the concerns of the policymakers due to their impacts which is the income disparity The research by Kozel (2014) indicates that there is hardly any improvement in income inequality in Vietnam for the ten-year period This income disparity mainly results from the less diversity of income sources and the distribution of income across the provinces in Vietnam Consequently, the challenge to diversify the sources of income been becoming an important issue for researchers and policymakers

This study uses the spatial analysis method to investigate the current situation of income sources among provinces in Vietnam; and to explore the influencing factors as economic and demographic variables affect the income sources among provinces in Viet Nam during the given period of 2008-2016

The analysis indicates the following findings:

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(ii) Among all the four sources of income, the sector of non-agriculture is ranked as the second largest source of income for rich provinces in Vietnam Contrastively, this source only represents about 14% of the total income generated in poorer provinces The portion of income generated from agricultural cultivation is much higher than incomes from other sources in the poorer provinces In such rich provinces, the portion of income generated from agricultural activities has experienced a stable and constant decrease during the given period

(iii) The expanding income generated by wages and salary has increasingly expanded the income disparity in Vietnam during 2008- 2016 However, there is an increasingly even distribution in wages generated from wages because of the increasing amount of wages in the households with the low income levels

(iv) Incomes by wages and non-agriculture sectors are the major and powerful drivers to the changes in the total in the provinces in Vietnam Although the share of agricultural activities to the total income of both groups of provinces has decreased, however, these sources of income are also the important contribution to the income disparity in Vietnamese provinces While in the rich provinces incomes from wages account for the larger portion, it is contrastive with the poorer counterparts with the large contribution of agricultural activities

5.2.Recommendations

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support that the income sourced from the improvement in agricultural productivity can significantly improve the income levels of households in poor provinces in Vietnam It is also recommended that in order to facilitate the shift in the income structure of Vietnam from agriculture reliance to non-agriculture reliance economy, the Vietnamese Government should develop the policies to enhance the industries and service sectors in Vietnam These policies can improve the income sources from wages and other sources in both rich and poor provinces in Vietnam

The Government should issue social protection policies to the citizens especially to the working people In order to this, the Government should

(i) Provide information and encourage workers to notice on any measures taken or envisaged related to wages among the working people as well as the officials so that they can raise their awareness of the 2012 Labor Code‟s provisions;

(ii) Apply necessary solutions to enforce Article 91 of the Labor Code 2012 so that the minimum wage as well as the minimum living standards of workers and their families are assured;

(iii) Make sure all companies to pay increased wages in line with government legislation, to encourage worker representation in decision making, and to build freedom in company policy;

(iv) Commit to promote accountability of trade unions and Corporate Social Responsibility (CSR)

5.3.Limitations

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meaningful because in this current research income from these sources presents a high portion of the national and provincial income The measurement errors can potentially devalue the effects of other sources in this study Moreover, the given period of study is only conducted with the data during 2008-2016 due to the limited access to the data in the most recent year 2017 and 2018 It can devalue the significance and contributions of this current study

5.4.Suggestions for the further studies

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