An asset based geographic targeting evidence from rural vietnam

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HOCHIMINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM – NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AN ASSET-BASED GEOGRAPHIC TARGETING: EVIDENCE
FROM RURAL VIETNAM
A thesis submitted in partial fulfillment of the requirement for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
PHAM THI NGOC AI

Academic Supervisor:
Dr. PHAM KHANH NAM

HO CHI MINH CITY, MAY 2014


ACKNOWLEDGEMENT
The thesis would not have been finished without the kind assistance and fruitful
guidance of many people who have the contributions of different aspects for

accomplishing the thesis.
First of all, I am specially grateful to Dr. Pham Khanh Nam who encourages me at
the beginning of title and help my deep understanding on literature theory as well as
thesis writing.
In addition, I would like to express the sincere gratitude to Dr. Truong Dang Thuy
for sharing his knowledge for the technique of the model and some valuable advices
for the methodology.
I would like to give special thank for my boss and colleagues who create conditions
and assist working in order that I have more time for the research.
Finally, my most gratitude is for my family, especially my parents and husband who
have been always side by side with me during learning this program and researching
process.


ABSTRACT
The purpose of this paper is to find out which asset is the most suitable for a
particular region through calculating marginal return to a range of assets and then
creating a serial of maps. The data are taken from Vietnam Living Standard Survey
in 2006. The Weighted Least Squares is used for running the regression and
combining with technique bootstrap and stepwise iterative deletion with the
threshold of 5%. All targetable assets are focused on calculating marginal benefit. It
gives the reasonable findings that have very heterogeneous average marginal benefit
across areas. The results give suggestion for choosing which assets are suitable for a
particular region, thus it makes increases their efficacy. However, the governors and
donors should consider the existence of trade-off equity and efficacy.


TABLE OF CONTENTS

CHAPTER I: INTRODUCTION ............................................................................... 1

1.1

Problem statement ........................................................................................... 1

1.2

Research objective .......................................................................................... 2

1.3

Research questions .......................................................................................... 3

1.4

Research contributions .................................................................................... 3

1.5

Organization of the paper ................................................................................ 3

CHAPTER II: LITERATURE REVIEW ................................................................... 5
2.1

Geographic targeting theory ............................................................................ 5

2.2

Household welfare function ............................................................................ 6

2.3

The small estimation method........................................................................... 7

2.4

Transfer in-kind .............................................................................................. 8

2.5

The linkage between household welfare and return to assets ........................... 9

2.6

Review of empirical studies .......................................................................... 12

CHAPTER III: OVERVIEW OF HOUSEHOLD WELFARE IN VIETNAM AND
METHODOLOGY ....................................................................................... 15
3.1

Overview of household welfare in Vietnam .................................................. 15

3.2

Econometric models ...................................................................................... 19

3.3

Data .............................................................................................................. 22

3.3.1 Independent variables ................................................................................... 23

3.3.2 Dependent variable ....................................................................................... 28


CHAPTER IV: EMPIRICAL RESULTS ................................................................. 29
4.1

Descriptive statistics ..................................................................................... 29

4.2

Econometric results ....................................................................................... 33

4.2.1 Statistics and value of marginal return of assets at national level ................... 34
4.2.2 Analysis for average of mean marginal return of assets at provincial level .... 36
4.2.3 Kinds of maps for Vietnam ........................................................................... 42
CHAPTER V:CONCLUSION, POLICY IMPLICATION, LIMITATION AND
FURTHER RESEARCH ............................................................................... 49
5.1

Conclusion .................................................................................................... 49

5.2

Policy implication ......................................................................................... 50

5.3

Limitation of this study ................................................................................. 51

5.4

Direction for Further research ....................................................................... 52

REFERENCES ........................................................................................................ 53


LIST OF CHARTS
Graph 3.1:The quintiles of income in urban and rural of Vietnam ......................... 16
Graph 3.2: The quintiles of expenditure in urban and rural of Vietnam ................ 16
Graph 3.3: The Quintiles of income in the eight regions of Vietnam...................... 17
Graph 3.4: The quintiles of expenditure in the eight regions of Vietnam ............... 18
Graph 3.5: Poverty rate at different level of region in Vietnam (Unit: %) .............. 18
Graph 4.1: Proportion of literate for each region .................................................... 29
Graph 4.2: Distribution of educational level for each region .................................. 30
Graph 4.3: Distribution of expenditure for each educational level and each region 30
Graph 4.4: Distribution of ethnic minorities across regions ................................... 31
Graph 4.5: Expenditure of some ethnics ............................................................... 32
Graph 4.6: Distribution of livestock for each region .............................................. 32
Graph 4.7: Distribution of other assets across regions ............................................ 33


LIST OF FIGURES
Figure 4.1: Maps of AMB that is significantly greater than zero ............................ 43
Figure 4.2: Maps of proportion of positive AMB ................................................... 44
Figure 4.3: Maps of maximum significant AMB.................................................... 45
Figure 4.4: Maps of maximum proportion of positive ............................................ 46
Figure 4.5: Example of choosing cattle transferred to provinces which meet three
conditions: the magnitude of AMB at 0.035, 95% households have positive AMB
and poverty rate 30% ............................................................................................ 48

LIST OF TABLES
Table 3.1: Asset variables ...................................................................................... 26
Table 3.2: Control variables................................................................................. 270
Table 4.1: Average standard deviation of mean marginal return for national,
regional and provincial level .................................................................................. 34
Table 4.2: Mean of AMB and proportion of provinces with positive AMB at
national level ......................................................................................................... 36
Table 4.3: Values of AMB that are significantly greater than zero ........................ 38
Table 4.4: Data for proportion (%) of positive AMB of households in provinces .. 40
Table 4.5: Correlating between asset holdings and poverty with significant and
proportion of positive AMB .................................................................................. 47



CHAPTER I: INTRODUCTION
1.1.

Problem statement

Alleviating poverty is always the major targeting interested in by policy-makers in the
developing countries. There are many transfer programs made around the world from
years to years. However, increasing transfer program efficacy under the condition of
scarce resources is the extremely important issue which governors and donors consider.
One of the methods used widely and popularly for researching and practicing is
geographic targeting. As it gives a visual and useful tool and performance with low
cost and easy administration. Thus, the important rule of the geographic targeting for
poverty reduction is emphasized by Baker and Grosh(1994), Bigman and Foback
(2000). Moreover, many papers have proven that the efficacy of transfer programs is
higher when geographic units are smaller (Elbers et al. 2007, Minot 2000, Bigman and

Foback 2000).
Poverty map is a tool of geographic targeting. It displays poverty indicator across
geography and answer the question where the poor people reside and who the poor
people are (Elbers et al. 2003, Minot and Baulch 2005) as well as why the area has
high incidence poverty which is driven by natural resources (Szonyi et al. 2010).
However, the greatly advanced step of geographic targeting is targeting map with
asset-based approach. It answers the extremely important question that governors and
donors should use in-kind transfer for a particular region to bring the highest benefit
for the poor and might create motivation for them out of poverty. Besides, it gives a
visual and practical tool for policy-makers and donors to manage their transfer
programs with budget limitation. Benefit of transferring whether in-kind or cash is
better, always consider by the researchers, donors and governors. However, in many
places in the world, they prefer transferring in-kind and in some cases, transfer in-kind
is better (Hoffmann, Barrett and Just, 2009).
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Commented [PKN1]: I’ve made a new paragraph here.


For Vietnamese case, poverty alleviation is one of the most national goals. After many
programs have been applied, the poverty reduction has been gained some remarkable
achievement. The poverty rate felt from 58% in 1993 to 14,23% in 2010 (GSO). The
efficacy of these programs stems from the approximately exact determination who is
poor or where the region is poor so that the transfer can go right address (Minot 2000,
Minot and Baulch 2005, Cuong et al. 2010, 2011). These researchers have generated
poverty and inequality map showing poverty and inequality at disaggregated level.
The policy-makers always wonder which transfer program will bring the best benefit
for a particular region and how to measure it. Thus, determining scope and magnitude
of marginal benefit to assets at various regional level is very necessary. They may help
the governors to identify which asset brings the most benefit for a particular region.

Based on that it makes the transfer programs increase efficacy. This is also the aim of
my paper. The way that I study, is to determine the expected marginal return of a given
asset to vary households across geography. Then, we can identify expected marginal
benefit of a given asset for a particular area and map the results. It gives a visual map
for rapid detection of which assets are the best for that region. This thing has extremely
necessary meaning for poverty alleviation intervention.
1.2.

Research objective

The goal of this paper is to create a map which shows the expected average marginal
benefit to assets of household across space. Thus, the research has three particular
objectives as follows:
1. To determine the mean marginal returns to each asset at household level.
2. To determine the average expected marginal benefit of assets for a specific region.
3. To create a visual map that illustrates marginal returns to household’s assets in
Vietnam.

2

Commented [PKN2]: Here you should add a paragraph
explaining why estimating marginal benefit of an asset could help
transfer programs and therefore help poverty alleviation.


1.3.

Research questions

In this paper, we are going to answer following specific research questions:

(1)

How much is marginal gross benefit of an asset in the studied area?

(2)

How does a map of marginal return to asset in Vietnam look like?

1.4.

Research contributions

This research estimates average marginal return for varying assets across household
and across geographic levels. Based on that, we know the magnitude and scope of
marginal benefit of asset for a particular region. Combing with the map software and
other poverty indicators, it is a powerfully visual and practical tool for the policymakers or donors to consult about the in-kind transfer scheme. Moreover, the large
advantage of this method is the cost for implementing and administering lowly and
easy for performance though it still exists some shortcomings unavoidably.
1.5.

Organization of the paper

The study consists of the five chapters which show theoretical and empirical study to
estimate average marginal return to a range of asset across space. The first chapter
gives the overview of the study. It shows the problem statement that explained
importance of research this matter, Based on that, we give the research objectives and
research questions for finding out the result. The research contribution and
organization of the paper also present at the first chapter. At the second part, it consists
of the main theories related to the linkage between household welfare and asset return,
household welfare function, the small estimation method, geographic targeting and

transfer –in. Overview of household welfare in Vietnam, data and methodology are
presented in Chapter 4 in which thedata from Vietnam Household Living Standard
Surveys (VHLSS) and the Rural Agriculture and Fishery Cencus (RAFC) in 2006 is
3


used and the model regression is the Weighted Least Square combined bootstrap
technique and stepwise repeated deletion with threshold at 5% to calculate marginal
return of asset. At the final chapter, it discusses the main results and gives policy
implication and limitation of study as well as direction for further research.

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CHAPTER II: LITERATURE REVIEW

2.1

Geographic targeting theory

For poverty reduction policy, determining the people who are eligible to receive the
transfer is very important because of the budget constraints and avoiding waste of
resources. If no targeting, the program is not effective as many people need the help
but not receiving and vice versa. According to Coady et al. (1993), “Targeting is a
means of increasing program efficiency by increasing the benefit that the poor can get
within a fixed program budget.” There are some kinds of targeting method including
community targeting, categorical targeting, self-selection targeting, indicator targeting.
While geographic targeting is applied broadly and commonly as it is applied easily and
less cost for administering and monitoring. As well, it overcomes the limitation on
information of individuals and households in developing countries (Bigman and

Foback 2000). Geographic targeting is a part of allocating budget to geographic region.
It is a method to refer all individuals in a particular region in which they are
determined to be eligible for receiving benefit. So, this method always is suitable for
public works and social funds. The popular approach of geographic targeting is
“poverty map” where the poverty indictor as well as variables used for calculating,
reflect the basic needs or capabilities (Skoufas and others 2001).
The efficacy of transfer scheme increases and “leakage” of program reduces
significantly when unit of areas is smaller (Baker and Grosh 1994 and Bigman and
Foback 2000). Thus, to increase possibility of application of geographic targeting, the
researchers usually apply the small estimation method for calculating (Elber 2002,
2003, Minot and Baulch 2005, Cuong et al. 2010, 2011 and Land et al. 2013). This
method is to combine the household survey data and the population census data for
determining the poverty indicators in small areas. The reason of combining is that the
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household survey usually contains the information on expenditure at regional level. For
census data, it contains information at provincial, district as well as commune level, but
not expenditure information. Based on the common variables of two sets of data and
the model of household welfare, we predict information income or expenditure for each
household in the census. Thus, poverty rate is calculated for each small area.
The important disadvantage of this method, the poor does not live in areas which is
determined eligibility, will not receive benefit (under-coverage) and the person is not
poor but still receive benefit due to live in poor area (leverage) (Bigman and Foback
2000). The first method for overcoming this problem is combining geographic
targeting and other targeting tool. In a study for Mexico, Coady (2006) uses four
targeting tools. The second way is the areas which can be divided more as smaller
geographic unit increase homogeneity of areas which increase efficacy of targeting

method (Baker and Grosh 1994).
Geographic targeting with asset-based approach is established instructing geographic
targeting of in-kind transfer. It is a little possibility that one kind of in-kind transfer is
the righest for all the places as the poverty incidence and its causes are influenced by
significant geographic heterogeneity (Kam et al. 2005). Benefit of assets is certainly
impacted by space. Thus, governors should make specific intervention for particular
geography. It will bring substantial gain for poverty reduction policies. Our research
combines the data of marginal benefit to a range of assets determined in framework to
comparing marginal benefit of each other assets in a determined area. Then, the next
steps make the link to poverty indicators across space. It creates a powerful tool for inkind transfer intervention.

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2.2

Household welfare function

According to Anelson (1993),“Household welfare as the material standard of living of
each individual in the house-hold.” Each individual who is mentioned, is commonly
adult (or parent). Income and expenditure are two indicators which traditionally are
used as proxies for household welfare (Coady et al. 2004). To calculate household
welfare, Lang et al. (2013) and Naschold & Barrett (2011) have used the function of
asset holdings and place- particular return of assets. The assets which are chosen, their
stock influences significantly on the household welfare. The general function is as
follows:
LnY ic= Aic Ric + +δ’ Xic +εic
Where Y: the logarithm of household welfare i at location c
Xic: variables related to household characteristics like housing conditions, some
durable assets and other services.

Aic: assets holding
Ric: the function of return of asset.
The function of asset returns is considered as a function of expected return of asset. Its
value depends on the inventory of each other assets (Adato et al. 2006, Lang et al.
2013). For instance, pig’s return changes according to education of household head,
average of pig existed at that area, rainfall or/and having a market or not. For locationparticular average has only the link with the same indicator at household level.
The function has the error term composed of a location component and a housespecific component:
εic =βc + δic
2.3

The small estimation method

The small estimation areas method is pioneered by Elbers et al(2002, 2003). This
method combinesthe two sets of household survey and population census to estimate
7


poverty index for small areas which increase the degree of efficacy for the poverty
alleviating intervention.
The authorsset upa function of household welfare (Ych) with the explanatory variables
(Xch). The regression model has the error term with two components such as a
household-specific (Cch) and cluster-specific (Bc). The general function is as follows:
Ln (ych) = aXch + Bc + Cch
Where Ych is under the form of logarithmic expenditure per capita.
In the next step, combining all the parameters calculated in the first stage and the value
of explanatory variables of household in small area to calculate the value of
expenditure’s household which is not existed in the household survey. From that, we
can compute poverty index and map this index to find out the poverty of level the small
region. Besides, to estimate house welfare is to have significant statistic, the chosen
explanatory variables which influence mainly on income-earning capacity of the

household is important.
One thing, we have to pay attention when the small estimation areas method is used, is
to satisfy two assumption according to Tarozzi and Deaton (2007). The first
assumption is called “measurement of predictors”. It assumes that the variables of the
expenditure function which are taken from the survey and census data are the same.
The second assumption is called “area homogeneity”. It means that the conditional
distribution of expenditure for both large and small area is the same. The purpose of
this assumption is that expenditure function is regressed from the data of household
survey is validity for expenditure function the census. Then, we can estimate
expenditure for small areas (communes or district) from expenditure equation of large
areas such as region.

8


2.4

Transfer in-kind

The transfer in kind or money always is thing which the governors or donors wonder.
In fact, in-kind transfer is performed commonly around the world. There are some
cases proven that transfer in kind brings more effectively than monetary like the caseof
Hoffmann, Barret, and Just (2009)’s transfer for insecticide treated bednets. The result
is shown that the greater use of these beds is more than if equivalent to cash. Lang,
Barrett and Naschold (2013) give some reasons why the in kind transfer is better
monetary transfer in some cases. Firstly, to buy assets becomes difficult, especially at
remote or/and diseased clusters, due to imperfect market. Secondly, according to
endowment effect theory, the asset transfer to targeting household is more valuable
than giving them cash to buy it from other people. Thirdly, there are some assets like
road, school, market are rarely provided from private sector, especially at remote and

poverty areas.

2.5

The linkage between household welfare and return to assets

According to Lang et al. (2013), return to asset can be considered as expected return
which we can calculate and are allowed to accept. Thus, in this study, we examine the
theory of link between household welfare and expected return of asset for each
household. Expected return to asset depends on other assets, for example expected
return of a head pig depends on average of pig’s holding, temperature, household
head’s education, existence to market or not. To identify the relationship between
household welfare and return to assets, we focus on the relationship between household
welfare and asset themselves. Thus, assets which are chosen, have a significant impact
on household welfare as well as show nature, extent, and persistence of poverty and
vulnerability (Adato, Carter & May 2006, Ellis &Freeman 2004; Moser 1998) and
when they are transferred to the poor, they can help the poor to escape poverty trap
(Carter and Barrett 2006).
9


Many studies prove that education is an important indicator to escape poverty. The
findings show that household welfare is divided into different levels by different level
of household head education. Household welfare is improved if the literate of adult
increases, then the ability of using clean water, toilet equipment and electricity is
better. (Dollar et al. 1998).
Kam et al (2005) finds that educational attainment has strong relationship with poverty
incidence in Bangladesh. As labor productivity and chance for taking part in economic
activities are improved through education. He suggests that enhancing infrastructure is
still a necessary intervention for the region with high poverty incidence. The purpose

of enhancing accessibility is to supply chances for improving and including different
type of agricultural production and applying different technology. He acknowledges
that relationship between poverty pocket and unfavorable areas is remarkable.
However, there is not the significant link between climatic variables with poverty
indices. In contrast, land-related factors such as land ownership, type of land…affect
strongly poor households. The correlation between livestock and poverty indices at
some areas proves that livestock play important role as insurance for the poor in
determined region of Bangladesh.
According to Ellis and Freeman (2006), the household welfare significantly changes
across land holding. Little or no land is owned by the household with low welfare.
Similar result for livestock ownership, the variation of income level changes across
livestock holding and the country which have steepest trend, is Tanzania and Uganda.
Livestock is considered as substitutable asset as they can be sold for investing and as
food for eating or establishing herd or creating non-farm income. In addition of two
above assets, education attainment, labor of household and tools and technique of
ownership are the very important assets of rural households.

10


To the studyof Okwi et al. (2007), he considers other elements which impact on
household welfare. He suggests that income distribution is broadly various across
geography due to variation in climate, geographic characteristics, infrastructure, public
equipment, building and services, natural resources and element of politics and history.
Population density is negative impact significantly on poverty rate. Where population
density is high, poverty rate at there is low. This thing is explained by impacting on
intensity of rural labor of production such as choosing technology, commodities, and
land control for production process. Moreover, people tend to move the place where
they can improve their welfare. Infrastructure like road school and clinic, access to
market and distance to urban areas is significantly correlated with household welfare.

These indicators are better, it leads to be lower transaction cost, more easily access to
market and increasing livelihoods choice and at end, the household welfare may
increase. The important indicators which related strongly household welfare is
geographical characteristics and availability of natural resources. These things coincide
with the findings of Gallup and Sachs (1999) which the level of income and growth are
impacted significantly by geographic characteristics and natural resources of the region
through diseases, expense of transport and quantity and quality of agricultural product.
One of the important factors which is considered almost both international and
Vietnamese researches about household welfare is ethnic (Easterly and Levine, 1997,
Cuong et al. 2010,2011, Minot and Baulch et al. 2005, World Bank 2004, Lang et al.
2013). Ethnic diversity which is measured by matching randomly two people with
different ethnic group, is negatively impacted on welfare as it has the strong correlation
with low infrastructure, low education, rent-seeking policies, and other elements
related to slow economic growth (Easterly and Levine, 1997).
In Vietnam, Vietnam Development Report (2004) of World Bank shows that element
of ethnic minorities plays very important role in poverty reduction. There are
approximately 50% ethnic minorities under poverty line. Although, other
11


characteristics are the same, their expenditure is still lower than about 14 time
compared to Kinh/Hoa ethnic. There is the huge gap between Kinh/Hoa and ethnic
minorities due to weak infrastructure and difficult accessibility, difficult to
communication and approach technology due to difference language, and poor belief
on education. This main reason explains why education, asset and other basic condition
of ethnic minorities is lower than Kinh/Hoa ethnic. However, the impact of the
population density is in contrast to the finding of Okwi et al (2007), rate of low income
incurs the most in the two largest deltas where population density is higher than high
land.
2.6

Review of empirical studies

Lang et al. (2013) has built asset-based geographic targeting which shows expected
marginal benefit of a range to assets across parishes. They use the small estimation
method to combine two sets of data: household survey and population census in
Uganda in 2002 and use household welfare as the function of asset holding and regionspecified asset’s return. The function of return of asset is allowed as expected return of
asset which influences the stock of other kinds of assets. The model regression is
Weighted Least Squares with elements of population expansion. The process of
running regression is combined with bootstrap technique 200 times and stepwise
iterative deletion to remove the unimportant variables out of the model. For each
iteration of 200 times, they impute the estimated coefficient into the census data. Then,
all the targetable assets are calculated derivatives. They combine all the iterations and
can calculate average expected marginal return of asset for each household.
In this papers, they divide assets into four categories: private and targetable asset:
education of household head, chicken, cattle and pig, land ownership, motorbike,
water, proportion of household literate; private non-targetable education of household
head ; public targetable: road access and microfinance access; public non-targetable
12


assets: rainfall, sunshine, temperature, rainfall in dries month, existence to market,
population density, ethnic diversity.
Their findings show that expected marginal benefit (AMB) of motorbike fold eight
time compared to bicycles, AMB of live stock is low and AMB of road access is
negative. This thing is suitable with recent research of Uganda. The results seem to be
reasonable and variation across geography. They establish targeting map where not
only shows who or where the poor they are, but also which transfer program will bring
largest expected marginal benefit for that region.
Other approach of geographic targeting is the map of natural resource-based poverty in

rural Syria studied by Szonyi et al. (2010). He combines poverty map and parameters
of environment to give the map in which can show systematically and analytically view
of poverty and human well-being. The data from the census population of rural Syria in
2004 and other geographic variables is used and combining with spatial analytical
techniques such as interpolation, simulation and modeling. The result shows that the
areas with higher income in which have higher rainfall and irrigation. However,
households with lower welfare exists in that region, there are high population density
or presence of element of geography and topography. The method gives a picture in
which we can detect high poverty incidence driven by natural resources rapidly with
low cost of undertaking and monitoring.
For Vietnamese case, Minot and Baulch (2005) have used geographic targeting with
small estimation method to determine spatial distribution of poverty. He found some
good indicators for the function of household welfare including household size,
household head’s education, housing condition, electrification, access to water,
sanitation equipment, television ownership, radio ownership are the good indicators
for the function of household expenditure. For instance, the expenditure household will
reduce 7%-8% if household size increases one person; more higher level of education
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Commented [PKN4]: Never write any specific models in a
review of empirical studies


for household head is in consistence with significantly higher household consumption;
the different expenditure between having permanent and temporary house, semipermanent and temporary housing, respectively 23%, 14-15%; television ownership is
positive and significant highly at 1% level; radio ownership is similar with television
ownership, but at lower significant level. At end, the poverty map which is built by
poverty indicators shows that incidence of poverty is highest at mountainous areas like
Northern Upland, but these areas are not high poverty density. The lowest one is at
delta areas like South-East and cities, however two deltas in which have highest

poverty density.
For another research using geographic targeting method, Cuong et al. (2010) also
establishes poverty and inequality map in Vietnam. They use the small estimation
method by combining the Vietnam Living Standard Survey and Rural Agriculture and
Fishery Census in 2006. The poverty and inequality indicators are calculated based on
the household welfare function. This function shows the relationship between
logarithm of household consumption or income per capita and predictable variables of
household characteristics like durables assets, condition on housing, water, electricity
and toilet, commune variables like road, school, market and geographic variables like
sunshine, temperature and rainfall. The chosen model regression is the forward
stepwise regression for each of eight regions. The explanatory power of the model has
the range from 0.43 to 0.7. The findings show that poverty indicator calculated by
expenditure model and income model is similar almost, the rest is different for areas
which have higher number of poverty. However, poverty indicators determined from
expenditure model is more strongly correlated with MOLISA poverty rate than income
model. The household expenditure reflects the household welfare better than household
income. The level of poverty strongly correlates with ethnic minority. It means that
return of endowments in household ethnic minorities are much lower than household
Kinh/Hoa. All durables assets and education for each region have different influence
14


on household welfare but always positively. Besides, the housing condition is strongly
correlated with household welfare. Household will have more expenditure if housing
condition is better.

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CHAPTER III: OVERVIEW OF HOUSEHOLD WELFARE IN

VIETNAM AND METHODOLOGY

3.1 Overview of household welfare in Vietnam
Vietnam is a country with 84 million people approximately, average income per capita
is about 636.000 dong (GOS 2007) and GDP growth about 7% per year in 2006. It is
considered as a rural country because 72% population resides in rural areas. The whole
country has 64 provinces and divides into eight regions including Red River Delta,
North East, North West, North Central Coast, Central Highland, South Central Coast,
South East and Mekong River Delta. The regions in which converge the most urban
residents are the Southeast and the second is Red River Delta, which contains Hanoi
Capital. Other regions such as North West and Central Highlands have the least
urbanized.
To understand the various level of household welfare which belongs to different
population groups at heterogeneous level of region, we divide population into 5
quintiles.

In each quintile, there is 20% population equally in term of welfare

elements. Through Graph 3.1, Graph 3.2 and Graph 3.5, we can recognize that each
quintile of income and expenditure always in rural areas is much lower than in urban
one, especially the various level of quintiles increases for part of population with high
incomes. For example, the income and expenditure of Quintile 1 which belongs to 20%
population with lowest income, is respectively 15.640.000 dong, 13.263.000 dong in
urban areas and 8.425.000 dong, 7.624.000 dong in rural areas. For Quintile 5 which
belongs to 20% population with highest income, is respectively 111.450.000 dong,
90.486.000 dong in urban one and 63.368.000 dong, 49.729.000 dong in rural one.
Welfare in urban regions has twice in rural one approximately. Poverty rate of urban
16