The Spatial Distribution of Poverty in Vietnam and the Potential for Targeting

Minot and Baulch combine household survey and census concentrated in 10 provinces in the Northern Uplands, 2 data to construct a provincial poverty map of Vietnam provinces in the Central Highlands, and 2 provinces in and evaluate the accuracy of geographically targeted the Central Coast. antipoverty programs. First, they estimate per capita The authors use Receiver Operating Characteristics expenditure as a function of selected household and curves to evaluate the effectiveness of geographic geographic characteristics using the 1998 Vietnam Living targeting. The results show that the existing poor Standards Survey. Next, they combine the results with communes system excludes large numbers of poor data on the same household characteristics from the people, but there is potential for sharpening poverty 1999 census to estimate the incidence of poverty in each targeting using a snmall number of easytomeasure province. The results show that rural poverty is household characteristics.

(Is a>

The Spatial Distribution of Poverty

in Vietnam and the Potential for Targeting

Nicholas Minot Bob Baulch

The World Bank

Macroeconomics and Growth

and

[iOLICY RESEARCH WORKING PAPER 2829

Summary findings

This paper is a joint product of Macroeconomics and Growth, Development Research Group, and the International Food

Policy Research Institute Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington,

DC 20433 Please contact Rina Bonfield, room MC3-354, telephone 202-473-1248, fax 202-522-3518, email address

authors may be contacted at n.minot@cgiar.org or b.baulch@lds.ac.uk April 2002 (43 pages)

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about

development issues An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished The

papers carry the names of the authors and should be cited accordingly The findings, interpretations, and conclusions expressed in this

The Spatial Distribution of Poverty in Vietnam

and the Potential for Targeting

Nicholas Minot and Bob Baulch

April 2002

Contact information: Nicholas Minot is a Research Fellow at the International Food Policy

Research Institute (IFPRI), 2033 K Street N.W., Washington, D.C 20006 U.S.A., email:

n.minotgcgiar.org Bob Baulch is a Fellow at the Institute of Development Studies, University of Sussex and formerly Quantitative Poverty Specialist at the World Bank, Vietnam, email:

b.baulch@ids.ac.uk Senior authorship is not assigned.

Acknowledgements: We thank Phan Xuan Cam and Nguyen Van Minh for their help

understanding the Vietnam Census data and Peter Lanjouw for helpful methodological

discussions Paul Glewwe and participants at workshops in Hanoi produced valuable

comments on earlier versions this paper The financial assistance of the DFID Poverty

Analysis and Policy support Trust Fund and World Bank Development Economics research Group is acknowledged.

Table Of Contents

1 Introduction 2

1.1 B ackground 2

1.2 Objectives 3

1.3 Organization of paper 4

2 Data and Methods 5

2.1 Data 5

2.2 Estimating poverty with a household survey 7

2.3 Applying regression results to the census data 8

3 Factors Associated with Poverty in Vietnam 11

3.1 Household size and composition 13 3.2 Education 15 3.3 Occupation 15 3.4 Housing and basic services 17 3.5 Consumer durables 18 3.6 Region 18 4 Poverty Maps of Vietnam 19

4.1 Regional poverty estimates 19

4.2 Provincial poverty estimates 22

5 The Potential of Geographic and Additional Targeting Variables 30

6 Summary and Conclusions 35

References 38

Annex 1 Descriptive statistics for variables used in regression analysis 40

Annex 2 Determinants of per capita expenditure of each stratum 41

Annex 3 Tests of significance of groups of explanatory variables in stratum-level regression models 42

Annex 4: Poverty headcounts estimated with stratum-level regression 43

List Of Tables

Table 1 Household characteristics common to the Census and the VLSS 6

Table 2 Determinants of per capita expenditure for rural and urban areas 14

Table 3 Tests of significance of groups of explanatory variables in urban-rural 16

Table 4 Comparison of original and Census-based poverty headcounts 20

Table 5 Differences in regional poverty headcounts and their statistical significance 21

Table 6 Provincial poverty headcounts estimated with urban-rural regression model 25

Table 7 Accuracy of different variables in targeting poor households 34

List Of Figures Figure 1 Incidence of poverty by province 23

Figure 2 Incidence of rural poverty by province 26

Figure 3 Provincial Poverty Headcounts estimated using Urban-Rural and Stratum-Level Regression Models 29

Figure 4 Receiver Operating Characteristic Curves for Selected Targeting Variables 32

1 Introduction

In most countries, poverty is spatially concentrated Extreme poverty in inaccessible areas withunfavorable terrain often coexists with relative affluence in more favorable locations close tomajor cities and markets Information on the spatial distribution of poverty is of interest topolicymakers and researchers for a number of reasons First, it can be used to quantify suspectedregional disparities in living standards and identify which areas are falling behind in the process

of economic development Second, it facilitates the targeting of programs whose purpose is, atleast in part, to alleviate poverty such as education, health, credit, and food aid Third, it mayshed light on the geographic factors associated with poverty, such as mountainous terrain ordistance from major cities

Traditionally, information on poverty has come from household income and expenditure surveys.These surveys generally have sample sizes of 2000 to 8000 households, which only allow

estimates of poverty for 3 to 12 regions within a country Previous research has, however,

shown that geographic targeting is most effective when the geographic units are quite small, such

as a village or district (Baker and Grosh, 1994; Bigman and Fofack, 2000) The only householdinformation usually available at this level of disaggregation is census data, but census

questionnaires are generally limited to household characteristics and rarely include questions onincome or expenditure

In recent years, new techniques have been developed that combine household and census data toestimate poverty for more disaggregated geographic units Although various approaches havebeen used, they all involve two steps First, household survey data is used to estimate poverty orexpenditure as a function of household characteristics such as household composition, education,occupation, housing characteristics, and asset ownership Second, census data on those samehousehold characteristics are inserted into the equation to generate estimates of poverty for smallgeographic areas

For examnple, Minot (1998 and 2000) used the 1992-93 Vietnam Living Standards Survey and aprobit model to estimate the likelihood of poverty for rural households as a function of a series ofhousehold and farm characteristics District-level means of these same characteristics were thenobtained from the 1994 Agricultural Census and inserted into this equation, generating estimates

of rural poverty for each of the 543 districts in the country

Hentschel et al (2000) developed a similar method using survey and census data from Ecuador.

Using log-linear regression models and household-level data from a census, they demonstratethat their estimator generates unbiased estimates of the poverty headcount and show how tocalculate the standard error of the poverty headcount.1 This approach has been applied in anumber of other countries including Panama and South Africa (see World Bank, 2000; StatisticsSouth Africa and the World Bank, 2000)

The earlier Vietnam study has several limitations First, since it relied on the Agricultural

Census, it generated poverty estimates only for the rural areas Second, the use of a probit

regression and district-level means, although intuitively plausible, does not necessarily generateconsistent estimates of district-level poverty2 Third, in the absence of household-level censusdata, it was not possible to estimate the standard errors of the estimates to evaluate their

it builds on an earlier report describing the characteristics of poor households in Vietnam

(Poverty Working Group, 1999)

Second, it examines the spatial distribution of poverty in Vietnam using the 1998 VLSS and a 3percent sample of the 1999 Population and Housing Census This analysis represents an

improvement on the earlier Vietnam study in several respects: a) the data are more recent, animportant consideration in a rapidly growing country such as Vietnam, b) the analysis coversboth urban and rural areas, providing a broader view of poverty in Vietnam, and c) we calculatethe standard error of the poverty headcount The standard errors are based on the methods

suggested by Hentschel et al (2000), with extensions to incorporate the sampling error

associated with the fact that we are using a 3% sample of the Population Census rather than thefull Census

Third, this study examines the efficacy of Vietnam's existing geographically targeted poverty programs and investigates the potential for improving the targeting of the poor by usingthe type of additional household level variables that could be collected in a "quick-and-dirty"enumeration of households

Section 2 describes the data and methods used to generate poverty maps for Vietnam fromhousehold survey data and census data Section 3 describes the results of the regression analysis.Although these are an input in the poverty mapping procedure, they also yield insights on thefactors associated with poverty and how they vary between urban and rural areas Section 4presents the provincial estimates of urban and rural poverty in Vietnam, along with the standarderrors of these estimates Section 5 examines the efficacy of Vietnam's poor and disadvantagedcommunes program and investigates whether use of additional household variables might

improve poverty targeting Finally, Section 6 summarizes the results, discusses some of theirpolicy implications, and suggests areas for future research

2 Data and Methods

The 1999 Census was carried out by the GSO and refers to the situation as of April 1, 1999 Itwas conducted with the financial and technical support of the United Nations Family PlanningAssociation and the United Nations Development Program As the full results of the Census havenot yet been released, this analysis is based on a 3 percent sample of the Census The 3 percentsample was selected by GSO using a stratified random sample of 5287 enumeration units and534,139 households The 3 percent sample of the Census was designed to be representative atthe provincial level

There are a number of variables which are common to both the VLSS and the Census, and whichallow household level expenditures to be predicted and disaggregated poverty estimates

produced Table 1 summarizes the 17 variables that were selected for inclusion in our povertymapping exercise

Table 1 Household characteristics common to the Census and the VLSS

Question number

primary school, lower secondary school, upper secondary school

technical or vocation training, college diploma or university degree)

primary school, lower secondary school, upper secondary school

technical or vocation training, college diploma or university degree)

professional or technical worker, clerk or service worker, agriculture

non-farm enterprises, unskilled worker, not-working)

rivers and lakes)

Source: Questionnaires for 1998 VLSS and 1999 Population and Housing Census

To estimate the poverty headcount, we predict expenditures using these common variables andthen apply the food and overall poverty lines developed by the GSO and the World Bank for usewith the VLSS surveys (Poverty Working Group, 1999) The lower of these two lines, the foodpoverty line, corresponds to the expenditure (including the value of home production and

adjusted regional and seasonal price differences) required to purchase 2100 kilocalories perperson per day The upper overall poverty line also incorporates a modest allowance for non-food expenditures.3

The Ministry of Labor, Invalids, and Social Assistance (MOLISA) estimates provincial povertyrates based on a system of administrative reporting that uses different welfare indicators (riceequivalent income), different poverty lines, and a different unit of analysis (households)

Nonetheless, the results are fairly similar to those obtained in this study

As mentioned above, the first step in implementing this approach is to estimate poverty or

household welfare as a function of household characteristics In this study, we use per capitaconsumption expenditure as the measure of household welfare The explanatory variables must

be useful in "predicting" household welfare and they must exist in both the household survey andthe census Economic theory provides no guidance on the functional form, but often a log-linearfunction is used:

ln(y1) = X' i3 + E()

where y1is the per capita consumption expenditure of household i, X's is a kxl vector of

household characteristics of household i, D is a kxl vector of coefficients, and Ej is a random

3 In 1998, the food poverty line was VND 1286,833 and the overall poverty line was VND1,789,871 per person per year See Annex 2 of Poverty Working Group (1999) for further details concerning the estimation of these poverty lines.

disturbance term distributed as N(O,cy) Because our main interest is predicting the value of ln(y)rather than assessing the impact of each explanatory variable, we are not concerned about thepossible endogeneity of some of the explanatory variables.

Hentschel et al (2000) show that the probability that household i with characteristics Xi is poor

can be expressed as:

where Pi is a variable taking a value of 1 if the household is poor and 0 otherwise, z is the

poverty line expressed in terms of consumption expenditure per capita, and 1D is the cumulativestandard normal function

In the second step, the estimated regression coefficients from the first step are combined withcensus data on the same household characteristics to predict the probability that each household

in the Census is poor This is accomplished by inserting the household characteristics for

household i from the census, Xic, into equation 2:

For a given area (such as a district or province), Hentschel et al (2000) show that the proportion

of the population living in households that are below the poverty line is estimated as the mean ofthe probabilities that individual households are poor:

where mi is the size of household i, M is the total population of the area in question, N is thenumber of households, and X is an N x k matrix of household characteristics The advantage ofusing the Census data, of course, is that the large number of households allows estimation ofpoverty headcounts for geographic units much smaller than would be possible with the VLSSdata.

Provided that a) the error term is homoskedastic, b) there is no spatial auto-correlation, and c) thefull Census data are used, the variance of the estimated poverty headcount can be calculated asfollows:

Var(P*) var(3) amrP(1Pi*)p*(;P*)2 2v4

where n is the sample size in the regression model Thus, n, k, and a 2are from the regression

analysis, while mi, M, and N are obtained from the census data The partial derivatives of P*

with respect to the estimated parameters can be calculated as follows:

2

a , as well the effect of this variation on P* The third term in equation 5 measures the

"idiosyncratic error" which is related to the fact that, even if P and a are measured exactly,

household-specific factors will cause the actual expenditure to differ from predicted expenditure.

These equations are described in more detail in Hentschel et al (2000) and Elbers et al (2001).

As noted above, equation 5 is valid only if the full Census data are available for the second stage

of the mapping procedure When we are using a sample survey or a sample of the Census data inthe second stage, this expression must be modified as follows:

apa* aP *'\2 2&4 N,m 2Pi*(I -Pi)

a 8/)a aft) -k1I1=1

where V, represents the variance associated with the sampling error in the Census, taking intoaccount the design of the sample In this study, we rely on the software package Stata to

calculate the variance associated with the sampling error, taking into account the design of thesurvey4

In order to compare poverty headcounts in different regions or provinces, it is convenient tocalculate the variance of the difference between two estimates of poverty Hentschel et al (2000,footnote 17) provide an expression for the case when full Census data are used Here we extendthe expression to include the variance associated with sampling error:

+Vi(PI)+Vi(P2)+V 3 (PI)+V 3 (P 2 )-2cov,(PI,P 2 )

where Vi(Pr) is the idiosyncratic variance of the poverty estimate for region r (the third term in

equation 5), Vs(Pr) is the sampling variance of the poverty estimate for region r, and cov5(PI,P2)

is the covariance in the poverty estimates for regions 1 and 2 associated with sampling error

4 This is accomplished with the "svymean" comrnand Stata calculates a linear approximation (a first-order

Taylor expansion) of the sampling error variance based on information on the strata, the primary sampling unit, and the weighting factors See Stata Corporation, 200 lb for more information.

Two qualifications need to be made regarding the implementation of this method in the case ofVietnam Researchers at the World Bank have recently been addressing the issue of spatialautocorrelation in the first-stage regressions (equation 1) Analytical solutions for the variance

of the headcount are not possible in this case, and it becomes necessary to use complex

simulation methods to calculate the estimators and their standard errors (Elbers et al, 2001).

Although preliminary analysis indicates the presence of some spatial autocorrelation, we werenot able to eliminate it by including community-level variables in the regression analysis Thissuggests that there may be some inefficiency in the results of the first-stage regression analysis,though the magnitude of these effects is difficult to assess

In addition, the estimate of the variance associated with sampling error produced by Stata is only

an approximation Exploratory analysis reveals that the sampling error is relatively small

compared to the model error, suggesting that this approximation does not influence the resultssubstantively

3 Factors Associated with Poverty in Vietnam

As described in Section 2.2, the first step in constructing a poverty map is to estimate

econometrically per capita consumption expenditure as a function of variables that are common

to the Census and the VLSS These household characteristics include household size and

composition, ethnicity, education of the head of household and his/her spouse, occupation of thehead of household, housing size and type, access to basic services, and ownership of selectedconsumer durables Table 1 lists the variables and Annex 1 provides descriptive statistics foreach of them

It is reasonable to expect that the factors which "predict" expenditure in rural areas may bedifferent than those predicting expenditure in urban areas Indeed, a Chow test strongly rejectsthe hypothesis that the coefficients for the urban sub-sample are the same as those for the rural

sub-sample (F=6.16, p< 001) This implies that we should carry out separate analyses on ruraland urban samples.

The next level of disaggregation is the stratum used in the VLSS sample The VLSS was

designed to be representative for each of ten strata, comprising three urban strata and seven ruralstrata For this analysis, it was necessary to collapse the three urban strata (Hanoi/Ho Chi MinhCity, other cities, and towns) into two (Hanoi/Mo Chi Minh City and other urban areas) becausethe Census data do not allow us to distinguish between "other cities" and towns Within urbanareas, a Chow test suggests that Hanoi and Ho Chi Minh City differ significantly from otherurban areas (F=2.20, p<.001) In addition, the seven rural regions differ significantly from eachother (F=12.61, p<.001) In other ways, however, the stratum-level regressions are not verysatisfactory Because of the small sample size in each stratum (ranging from 368 to 1111

households), many of the coefficients are not statistically significant at conventional levels orhave counter-intuitive signs Furthermore, the goodness-of-fit of most of the stratum regressions

is below 0.5, compared to 0.54 and 0.55 for the rural and urban regressions One result of this isthat the standard errors of the poverty estimates from the stratum-level regressions are higherthan those obtained from the urban-rural regressions (see Section 4.1)

In this paper, we will present the results of both the urban-rural regressions (see Tables 2 and 3)and the stratum-level regressions (see Annexes 2 and 3), as well as the poverty estimates derivedfrom each (Tables 4-6 and Annex 4) However, we will give greater prominence to the resultsfrom the urban-rural regression analysis As will be shown later, the two methods yield similarpoverty headcounts and rankings, particularly for the poorest provinces In the six sub-sectionsthat follow, we summarize the results of the regression analysis to "predict" per capita

expenditures

3.1 Household size and composition

Large households are strongly associated with lower per capita expenditure in both urban andrural areas, as shown in Table 2 The negative sign of the coefficient on household size impliesthat, other factors being equal, each additional household member is associated with a 7-8

percent reduction in per capita expenditure5 The stratum-level regressions show similar results(see Annex 2)

In rural areas, a household with a large number of elderly members, of children, and of females

is likely to have low per capita expenditure In urban areas, however, only the number of

children is statistically significant (see Table 2) Household composition appears to matter less

in urban areas than rural ones It may be that the number of children, women, and elderly peoplehave less effect on household welfare in urban areas because income-earning capacity in thecities and town is less dependent on physical strength

Ethnicity6 is a predictor of per capita expenditure, but a surprisingly weak one In rural

areas, the coefficient on ethnicity was significant only at the 10 percent level while in urbanareas, it was not statistically significant (see Table 2) The urban coefficient is not

surprising given the very small sample of ethnic minority households in urban areas (just

19 households) The weakly significant, although appropriately signed, coefficient for

rural areas is more surprising given the strong correlation between poverty and ethnicity inVietnam Other research (Van de Walle and Gundewardana, 2000, Baulch et al.,

forthcoming) suggests that ethnic minorities have both lower levels of endowments and

lower returns to those endowments Our results are consistent with these findings,

5 A coefficient of -0.772 implies that a one-unit increase in the explanatory variable is associated with 7.4

that larger households are worse off than smaller ones, however, for two reasons First, there may be economies of scale in household size, so that larger households do not "need" the same per capita expenditures as smaller

households to reach an equivalent level of welfare Second, our measure of welfare does not take into account household composition, so if larger households have more children than smaller households they might still have

equivalent levels of expenditure per adult equivalent.

households along with the Kin/h (ethnic Vietnamese).

Table 2 Determinants of per capita expenditure for rural and urban areas

Note: The dependent variable is log of per capita expenditure.

* coefficient is significant at the 10% level, ** at the 5% level, and *** at the 1% level.

showing that after controlling for differences in endowments (education, housing characteristics,and ownership of consumer durables), differences in per capita expenditure between ethnicminority households and others remain, but are much smaller.

In both urban and rural areas, the level of schooling of the head of household is a good predictor

of a household's per capita expenditure.7 The five dummy variables that represent the education

of the head are jointly significant at the 1 percent level in both rural and urban areas (see Table3) In rural areas, heads of household who complete primary school earn 6 percent more thanthose not completing primary school In urban areas, households whose head has completedprimary or lower secondary school do not seem to be better off than those whose head has notcompleted primary school, but higher levels of education are associated with significantly higherearnings (see Table 2)

In general, the educational level of the spouse is less significant than that of the household head

as a predictor of per capita expenditure.8 In the rural areas, only the highest two levels of

education of the spouse (advanced technical training and post-secondary education) show anysignificant effect relative to the base level (not completing primary school) The education of thespouse is a better predictor in urban areas than in rural areas (see Table 2)

8 Education of the spouse may have other benefits, such as improved health or nutrition, that are not captured

by the measure of welfare used in this analysis, per capita expenditure Note that 11.4 per cent of spouses in the

9 Although information on the employer of households heads is available in both the Census and the VLSS, the categories they use to describe different categories of employers differ substantially and cannot be reconciled.

Table 3 Tests of significance of groups of explanatory variables in urban-rural

regressions

Main source of water 2 55 17.17 0.0000 ***

Source: Regression analysis of per capita expenditure using 1998 VLSS

Note: The dependent variable is log of per capita expenditure.

* coefficient is significant at the 10% level, ** at the 5% level, and *** at the 1% level.

occupational categories (political leaders/managers, professionals/technicians, and clerks/service workers) are significantly better off than households in which the head is not working On the other hand, there is no statistically significant difference between the expenditure of farm

households and households with non-working heads (see Table 2) This somewhat intuitive finding probably reflects the fact that non-working heads include retirees as well as a disproportionate number of urban workers who can "afford" to look for work.

counter-In urban areas, households whose head is a leader/manager are significantly better off than those with non-working heads, while those whose head is an unskilled worker are significantly worse

For this reason, a set of dummies for employer of the household head were not included in the predictive

regressions.

off (see Table 2) This suggests that in urban areas, a non-working head of household is not areliable indicator that the household is poor.

Various housing characteristics are good predictors of expenditures Living in a house or otherdwelling made of permanent rather than temporary materials is associated with 19 percent (24percent) higher per capita expenditure in rural (urban) areas.10 Similarly, having a house ofsemi-permanent rather than temporary materials implies a significantly higher level of per capitaexpenditure The living area of houses is also a useful predictor of household well being

Houses in Vietnam have an average living area of about 45 square meters, and each 10 percentincrease in area is associated with a 12-30 percent increase in per capita expenditure, depending

on the area of residence (urban or rural) and the type of house (permanent or semi-permanent)1 1

12

Electrification is a statistically significant predictor of household welfare in rural areas, where

71 percent of the household have access to electricity By contrast, in urban areas, where 98percent of the households are already electrified, electricity is not a significant predictor ofexpenditures (see Table 2)

The main source of water is also useful in distinguishing poor households In rural areas,

households with access to well water have higher level of per capita expenditures than

households using river or lake water (the omitted category) Access to tap water is not a

statistically significant predictor of expenditures in rural areas, presumably because just 2

percent of the rural households fall into this category By contrast, in urban areas more than halfthe sample households (58 percent) have access to tap water, and this variable is a good predictor

of urban per capita expenditures

term htyplal (Ahouse_lxln(area)), the marginal effect is calculated as 3

lhouse 1 + Jhtyplal X In (area) We evaluate the marginal effect at the mean values of In(area), which are 3.72 in rural areas and 3.66 in urban areas.

11 The Census did not collect information on the area of houses made of temporary materials, so we cannot

use housing area to help predict expenditures for these houses.

lighting for the house.

Finally, sanitation facilities can be used to separate poor from non-poor households In ruralareas, flush toilets and latrines are statistically significant indicators of higher per capita

expenditure at the 5 percent level In urban areas, having a flush toilet is a significant predictor

of expenditures at the 5 percent level but having a latrine is not (see Tables 2)

Television ownership is one of the strongest predictors of per capita expenditures, being astatistically significant predictor in both urban and rural areas Radio ownership is almost asgood a predictor, being statistically significant at the 1 percent level in both urban and ruralareas As expected, the coefficient for radio ownership is smaller than that of television

ownership (see Table 2) In Section 5 below, we examine to what extent the addition of

variables reflecting ownership of consumer durables or housing characteristics can improve thegeographic targeting of the poor

Regional dummy variables were included in the urban and rural regression models, with, theNorthern Uplands, as the base region Even after controlling for other household characteristics,rural households in the four southern regions are shown to be better off than those in the

Northern Uplands The coefficient in the Southeast is the largest, implying that households inthis region have expenditure levels 72 percent higher than similar households in the NorthernUplands A similar pattern holds for urban households (see Table 2) The regional dummy

variables are jointly significant at the 1 percent level in both urban and rural areas (see Table 3).

4 Poverty Maps of Vietnam

As discussed in Section 2.3, the second stage in constructing a poverty map is to combine theregression coefficients estimated from the VLSS in the first stage and the Census data on thesame household characteristics This gives us predicted expenditures for each household in theCensus which are then used to estimate the incidence of poverty (the poverty headcount) forindividual regions and provinces, as well as the standard errors associated with these estimates

We present the estimates of the incidence of poverty first at the regional level and then at theprovincial level

Regional poverty headcounts and their standard errors, as estimated directly from the 1998Vietnam Living Standards Survey, are shown in the first two columns of Table 4 For the

country as a whole, the incidence of poverty is 37.4 percent with a 95 percent confidence interval

of ± 3.2 percentage points The regional poverty headcounts range from 0.9 percent in urbanHanoi and Ho Chi Minh City to 65.2 percent in the rural Northern Uplands The standard errorssuggest that the degree of precision in the estimates of regional poverty using the VLSS is

relatively low: four of the nine regions have confidence limits of ± 10 percentage points or more.

By combining the urban-rural regression models and the Census data (as described in Section 2),

we get an alternative set of estimates of regional headcount poverty rates and standard errors,shown in the second pair of columns in Table 4 Seven of the nine regional estimates are within

3 percentage points of the corresponding estimate from the VLSS However, the Census-basedpoverty estimates tend to be less extreme: they are higher than

Table 4 Comparison of original and Census-based poverty headcounts

Source: Data from 1998 VLSS and 3% sample of 1999 Population and Housing Census

Note: Poverty headcounts are expressed as fractions rather than percentages.

the VLSS estimates where the incidence of poverty is low (such as in the rural Southeast and inurban areas) and lower where the incidence is high (such as in the rural Northern Uplands) Inevery region except one (Hanoi and Ho Chi Minh City), the standard errors of the Census basedestimates are substantially smaller than those of the VLSS estimates Apparently, the gains inaccuracy from using a larger sample exceed the losses due to estimating expenditure based onhousehold characteristics

According to the urban-rural regression results in Table 4, the rural Northern Uplands is thepoorest region In fact, it is significantly poorer than the other eight regions at the I percentconfidence level (see Table 5) The rural Central Highlands and the rural North Central Coastare the next poorest regions, although there is no statistically significant difference between thetwo Then follows the rural South Central Coast, the rural Mekong Delta, and the rural Red