Spatial analysis of income sources at province level in vietnam

54 Table 15: A comparison of Spatial regression models and OLS regression model Year: 2012 Dependent variable: Income from other sources million VND .... 56 Table 16: A comparison of Spa

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

Hanoi, 2019

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1

1.1 Background of the study 1

1.2 Rationale of the study 2

1.3 Objectives of the study 4

1.4 Research questions 4

1.5 Significance of the study 4

1.6 Design of the study 5

CHAPTER 2: LITERATURE REVIEW 6

2.1 Spatial analysis 6

2.2 Income as an aspect of livelihoods 7

2.3 Background of ethnicity and income structure in Vietnam 8

2.3.1 Ethnic geographical distribution in Vietnam 8

2.3.2 Poverty distribution by ethnicity in Vietnam 9

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

3.2 Data collection 17

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

REFERENCES 89

Table 12: A comparison of Spatial regression models and OLS regression model :

2012 Dependent variable: Income from Agric (million VND) 50 Table 13: A comparison of Spatial regression models and OLS regression model Year:

2012 Dependent variable: Income from Nonagric (million VND) 52 Table 14: A comparison of Spatial regression models and OLS regression model Year:

2012 Dependent variable: Income from Wages (million VND) 54 Table 15: A comparison of Spatial regression models and OLS regression model Year:

2012 Dependent variable: Income from other sources (million VND) 56 Table 16: A comparison of Spatial regression models and OLS regression model Year:

2014 Dependent variable: Total Income (million VND) 58 Table 17: A comparison of Spatial regression models and OLS regression model Year:

2014 Dependent variable: Income from agric (million VND) 60 Table 18: A comparison of Spatial regression models and OLS regression model Year:

2014 Dependent variable: Income from Nonagric (million VND) 62 Table 19: A comparison of Spatial regression models and OLS regression model Year:

2014 Dependent variable: Income from wages (million VND) 64 Table 20: A comparison of Spatial regression models and OLS regression model Year:

2014 Dependent variable: Income from other sources (million VND) 66 Table 21: A comparison of Spatial regression models and OLS regression model Year:

2016 Dependent variable: Total Income (million VND) 68 Table 22: A comparison of Spatial regression models and OLS regression model Year:

2016 Dependent variable: Income from Agric (million VND) 70

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

Figure 1: The changing rate of total income between 2008 and 2016 77 Figure 2: The changing rate of income from wages between 2008 and 2016 78 Figure 3: The changing rate of income from Agriculture, Forestry, Fishery between

2008 and 2016 79 Figure 4: The changing rate of income from Non-Agriculture, Non-Forestry, Non-fishery between 2008 and 2016 80 Figure 5: The changing rate of income from other sources between 2008 and 2016 81

ABBREVIATION

SDM Spatial Durbin model

GIS Geographic Information system

ASEAN Association of South-East Asian Nations

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

Total The total income

VHLSS Vietnam Household living standards survey

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

CHAPTER 1: INTRODUCTION

1.1 Background 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

The rapid economic growth and the good policies in the last 30 years have significant contribute to poverty reduction However, the gap between the rich and the poor has been expanding seriously This gap has been causing many social problems and need to

be solved as soon as possible So the Government need to issue new policies that ensure the poverty and the inequality will be controlled According to Saumik et al (2016), while Vietnam experiencing the economic structural transformation as well as the poverty reduction, the growth is more beneficial for the rich than the poor This is realized as the returns to manufacturing and to agriculture increasing only for the top

10th - 20th percentiles In general, the economic inequality has been rising dramatically

in the last twenty years

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

1.2 Rationale of the study

It is revealed by the evidences in the research by Nguyen (2016) that reductions in poverty and dividends from growth have been spread unevenly across Vietnam, increasing income inequality between regions and to some extent within regions By region, the Red River Delta and the South East are considerably overrepresented in middle income groups, whereas the Mekong River Delta is overrepresented in the near-poor group The North West and Central Highlands are the two regions where most of

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

Income poverty is disproportionately higher among ethnic minority groups Members

of ethnic minority groups make up less than 15 percent of the country‟s population but account for 70 percent of the extreme poor According to the 2014 survey conducted by the Ministry of Labor, Invalids and Social Affairs, the incidence of poverty among ethnic minorities was as high as 46.6 percent, compared to 9.9 percent for the Kinh and Hoa groups The gap in income mobility among ethnic groups is also large, and there are signs that this gap has been increasing over time Between 2010 and 2014, around

19 percent of ethnic minorities in the bottom quintile moved to a higher income

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

1.3 Objectives 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

1.4 Research 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 do economic and demographic factors affect the income sources in Viet Nam?

1.5 Significance of the study

This study has several contributions to the literature Firstly, it is the first to identify the composition of sources of income in Vietnam and the contribution of various income sources to total income inequality with reference to the use of spatial analysis

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

1.6 Design of the study

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

Chapter 1 – 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 2 – 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 3 – 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 4 – Findings and discussions – shows the results of data analysis and discusses the income sources of Vietnam with the provincial levels

Chapter 5 – Conclusion and recommendations – summarizes the whole study and research findings In addition, limitation of the thesis and suggestions for further research are also given out

CHAPTER 2: LITERATURE REVIEW

2.1 Spatial 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

More specifically, spatial autocorrelation can be defined as the coincidence of value similarity with location similarity (Anselin, 2003) There is positive spatial autocorrelation when high or low values of a random variable tend to cluster in space and there is negative spatial autocorrelation when geographical areas tend to be surrounded by neighbors with very dissimilar values There are at least three possible explanations One reason is that there is a simple spatial correlation relationship, showing what is causing an observation in one location also causes similar observations in nearby locations Another possibility is spatial causality, meaning that something at a given location directly influences it in nearby locations A third explanation is spatial interaction: the movement of people, goods or information creates apparent relationships between locations

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

2.2 Income as an aspect of livelihoods

Although income from agricultural activities is the base of livelihood strategies for rural households in developing countries (Ashley, 2000; Dolan, 2004; Ellis, 1999; Sandbrook, 2006), empirical evidence suggests that households regularly engage in nonagricultural activities as a source of income (Ashley, 2000; Ellis, 1998, 1999; Hartter, 2007; Kaag et al., 2008; Lepper and Schroenn Goebel, 2010; Smith et al., 2001) It has been found that high levels of non-agricultural income are often associated with higher levels of agricultural productivity and higher overall household income (Dolan, 2004; Ellis, 1999; Ellis and Bahiigwa, 2003; Evans and Ngau, 1991) According to Ellis (1999: 2) livelihoods are defined as „the activities, the assets and the access that jointly determine the living gained by an individual or household‟ The concept of livelihoods was originally coined in the 1940s to describe people‟s strategies of making a living (Kaag et al., 2008) However, with the macroeconomic development literature of the 1980s and the pioneering literature of Robert Chambers, the livelihood approach transformed into its current meaning by including the complex

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

2.3 Background of ethnicity and income structure in Vietnam

2.3.1 Ethnic geographical distribution in Vietnam

Vietnam has 54 officially recognized ethnic groups, with more than 85% of the population made up of Kinh people The rest of the population, 15%, is distributed among 53 ethnic minorities Most of these ethnic groups, however, have a few thousand people each According to the General Statstisitics Office Vietnam (GSO, 2015), of the ethnic minority group, the most numerous are the Tay (1.9%), Thai (1.8%), Muong (1.5%), Kho Me (1.5%), H‟Mong (1.2%) and Nung (1.1%) Most ethnic minority groups reside in mountainous areas, while the Kinh and Chinese are found in the lowland areas in Red River delta, Central Coast and Mekong Delta By

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

Since the economic reform introduced in 1986, known as Doi Moi, both majority and minority ethnic groups have experienced an improvement in living standards, which has been reflected in increasing average expenditure per capita, falling fertility rate and household size, and declining in the level of malnutrition (Epprecht, Müller, & Minot, 2011) However, Vietnam‟s ethnic minority groups lagged behind the Kinh ethnic majority Initially, early in the last decade, the ethnic minority groups achieved a significant success in poverty reduction, e.g poverty rates fell from 75.2% in 1998 to 50.3% in 2008 Nevertheless, ethnic minorities have increasingly accounted for most of the poor in Vietnam Although they contributed only 15% of Vietnam‟s total population, ethnic minorities accounted for about half of the poor and 68% of the extremely poor (Kozel, 2014) Poverty rates among ethnic minorities average between four and seven times higher than that of the Kinh people The malnutrition rate of children from ethnic minority households is also considerably higher than among children from ethnic majority households Vietnam‟s poverty map shows that the majority of the poor live in the upland regions, whereas the better off households are found in Vietnam‟s urban centres along the coast There existed an increasing disparity between the ethnic majority and ethnic minorities among income percentiles in Vietnam from 1998 to 2010 In 1993, the ethnic minority was 1.6 times poorer than the ethnic majority This gap increased to 2.4 times in 1998, 4.5 times in 2004 and 5.1 times in 2010 The proportion of the poor from Vietnam‟s ethnic minorities in 2010 was considerably higher than in 1998

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)

The main income source for the ethnic majority was from wage employment, whereas for the ethnic minority, the main source was crop income Poorer ethnic minority households had a larger proportion of their total income from crops (Cuong, 2012) In

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)

2.4 Previous studies

Concerns about increasing income inequality with reference to the sources of income have become the areas of focus in many researches which investigating the income issues in the world and Vietnam Dabla-Norris et al (2015) have investigated the

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

For the past decades, owing to the stable and skyrocketing economic development Vietnam has experienced the significant increase in the amount of average income per capita, contributing to the poverty reduction However, the studies by McCaig et al (2009) and Kozel (2014) indicates that despite the economic development and income increase, the income gap in Vietnam has been continuously widened These scholars provide the evidences with different measures to indicate that there is a rise in the absolute income gap between the top and bottom income groups in Vietnam Oxfam (2017) also reported that the daily income of the top 1% is at least ten times more than the annual income of the top 5% bottom in Vietnam The difference between the income level of rural and urban households has also witnessed the same pattern Consequently, the income disparity has remained as one of the most problematic issues

in Vietnam Kozel (2014) also attempts to prove that the increasing income gap across the country is significantly attributed to the sources of income which are different

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

According to Abdulai, A., & CroleRees, A (2001): “the income of agricultural households is affected by various factors such as land, the level of education, the number of labors” The research titled Effect of Resources on Incomes of Agricultural Households in Thanh Hoa Province: A Case Study at Tho Xuan and Ha Trung Districts

by Chu Thi Kim Loan & Nguyen Van Huong (2015) also points out that sources like the scale of production (lands, farms), the The research by Nguyen & Tran (2018) concerning the effects of various income sources on income inequality also points out

that They also revealed that among the sources of income wages and non-agriculture

incomes are the most influencing drivers of income gap in Vietnam Their counterpart from agricultural activities were also relatively evenly distributed The research findings also imply significant changes in the structure of incomes in Vietnam with a shift from agriculture reliance to non-agriculture reliance economy Therefore, it can be concluded that the income sources have significant impacts on the income equality However, in all the studies concerning this issue there has been no significant research concerning the spatial analysis of income sources in Viet Nam at province level especially after Viet Nam‟s signing WTO in 2006 This study will focus on the income sources based on different economic and demographic variables at the province level and will explain the influence of those variables on the income sources using these variables

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

Wij : spatial weight matrix between two provinces

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:

The Spatial Durbin model takes the form:

Y = 𝝆WY+ X𝜷(1) +WX𝜷(2) + 𝜺 (3) Where:

X: a matrix of non-stochastic regressors

β(1), β(2), ρ: parameters to be estimated

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 + 𝜷 X +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:

X: a matrix of non-stochastic regressors

U: stochastic disturbances, β: parameters to be estimated

Wu: the weight matrix of stochastic disturbances

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 (‰)

I: The number of immigrants

Ptb : average population

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)

All the explanatory variables are collected from the Population and Household investigation conducted every 10 years, and from Population change survey and Labor force survey conducted every year

CHAPTER 4: FINDINGS AND DISCUSSIONS

4.1 Area 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

Red River Delta consists 11 provinces located in the South of the Northern area of the country, including Bac Ninh, Ha Nam, Ha Noi, Hai Duong, Hai Phong, Hung Yen,

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 6 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 8 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

(6) Central Highlands

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

It is reported by GSO (2018) that the Total Factor Productivity (TFP) accounted for 43.50% of the national GDP growth This contribution has experienced an increasing trend since 2008 The average contribution of TFP to the GDP growth of Vietnam in

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

In term of the production structure, under the effects of market economy, there are significantly effective and positive changes in the sector of agriculture, forestry and fishery The development of new effective production models with the incorporation of innovative technologies has not only boosted the productivity but also quality of

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

Due to the increasing globalization, Vietnam has become one of the most crowded manufacturing hubs in the Asian region and worldwide, leading to the impressive growth of industry It is reported by GSO (2016) that the index of industrial production (IIP) for the whole country expanded by 11.32% per year during the given period 2008-2016 This growth tended to decrease in the recent years 2016-2018, decreasing

to 8.2% in 2018 Of the industries, the sector of manufacturing continued to play a key role promoting the general growth of the whole industry with the increase of 12.3% per year during 2008-2016 This rate increased to 14.7% in 2018 The manufacturing sector is attributed to nearly 10% in the total growth of industry during the given period The growth rate of such sectors as electricity and water supply and waste

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

The economic development of Vietnam during the period 2008-2016 facilitated the climb of investment Firstly, it is reported that the investment from the State budget showed an increasing pattern during the studied period, reaching the peak of VND310 trillion in 2016 (an equivalent to a yearly 12.3% increase) Particularly, in comparison

to 2008 the investment amount from the State in 2016 increased nearly 102% In terms

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 3 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

Where:

X1: Percentage of workers aged 15

and over who are working in a

trained economy by province

X5: Number of farms X6: Sex ratio of population by

provinces

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

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

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

X7 (Projects) -8.063E-02 -8.8995E-01*** -9.7628E-01*** -7.2484E-01

X8 (Registered

Signif codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 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

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

The percentage of working population aged 15 and over in the economy, the number of farms and the FDI licensed projects had negative effects on the total income

of the observed unit This means the more untrained workers, especially working in agricultural sector, the more income people at the province observed got

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

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***

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

Signif codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 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 + 𝜷 X +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:

The more labor force in the trained economy and the more registered capital the less income from this source This could be explained that the trained labor force focused mainly on the non-farm business and the FDI projects were for manufacturing, services sector This is an unbalancing situation that can be solved by appropriate policies The more rate of immigration, number of farms and number of men over 100 females the more income from this source Based on the interpretation above we can conclude

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

Moran I test: Pvalue = 9.016123e-05 <0.05

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

Moran I test: Pvalue = 9.016123e-05 <0.05

Signif codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 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

At the neighboring provinces: