Poverty projection using a small area estimation method: Evidence from Vietnam

Journal of Comparative Economics 39 (2011) 368–382

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Journal of Comparative Economics

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Poverty projection using a small area estimation method: Evidencefrom Vietnam

Nguyen Viet Cuong ⇑Indochina Research; Consulting, Suite 1701, C’land Tower, Xa Dan, Dong Da, Hanoi, Vietnam

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 July 2009Revised 21 April 2011Available online 6 May 2011

JEL classification:I31I32C53

Keywords:Poverty measurementPoverty projectionPoverty mappingVietnam

0147-5967/$ - see front matter � 2011 Associationdoi:10.1016/j.jce.2011.04.004

⇑ Fax: +84 4 38693369.E-mail address: [email protected]

Cuong, Nguyen Viet—Poverty projection using a small area estimation method: Evidencefrom Vietnam

For poverty monitoring and evaluation, one needs poverty estimates at the different disag-gregation levels. The prediction of poverty trend is also of interest for policy makers as wellas researchers. This paper presents a method – that is based on a small area estimationmethod of Elbers et al. (2003) – to project a map of disaggregated poverty measures inthe future. This method is applied to project a poverty map in rural Vietnam for the year2008 using the 2006 Rural, Agricultural and Fishery Census and the 2004 and 2006Vietnam Household Living Standard Surveys. Journal of Comparative Economics 39 (3)(2011) 368–382. Indochina Research; Consulting, Suite 1701, C’land Tower, Xa Dan, DongDa, Hanoi, Vietnam.� 2011 Association for Comparative Economic Studies Published by Elsevier Inc. All rights

reserved.

1. Introduction

Poverty alleviation is a major policy goal in developing countries. Numerous poverty reduction programs have beenimplemented throughout the world, and the impact of these programs on poverty depends heavily on their poverty target-ing. Poverty maps – a geographical visualization of poverty estimates – are an important tool for poverty targeting of anti-poverty programs. Elbers et al. (2007) found that the impact of budget transferring on poverty is larger when geographictargeting units are smaller. Other studies such as Baker and Grosh (1994), Bigman and Fofack (2000) also highlight the roleof geographic targeting in poverty reduction. Other positive impacts and applications of poverty maps can be found in Bediet al. (2007).

Yet, estimation of poverty measures at different disaggregation levels is not simple. Data on expenditure (or income)which are used for poverty estimation are available in household surveys. However, household surveys often have smallsample sizes that are not representative for small areas. Population censuses, on the other hand, cover all households butdo not contain information on expenditure and income.

Fortunately, a so-called small area estimation method proposed by Elbers et al. (2002, 2003) can overcome the problem ofdata shortage in estimating disaggregated poverty measures by combining a household survey and a census. According tothis method, a relation between household expenditure (or income) and household characteristics is modeled using datafrom a household survey. Then, this modeled relation is applied into a census to estimate expenditure for all householdscovered in this census, and these estimated expenditure data are used to compute poverty measures for small areas.

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N.V. Cuong / Journal of Comparative Economics 39 (2011) 368–382 369

Poverty trends are also of interest for policy makers as well as researchers. Regular construction of poverty maps can-not be done by straightforward application of the method of Elbers et al. (2002, 2003), since censuses are not carried outon a regular basis. In most countries, censuses are often carried out every 10 years. Several studies produce povertymaps for non-census years by combining an old census and new household surveys (e.g., Emwanu et al., 2006; Nguyen,2009). However, there have been no attempts to project a poverty map for the years to come. Information on povertyforecast can be useful for not only monitoring poverty changes but also planning poverty reduction programs in the nextperiods.

In this paper, we propose a simple way to project poverty measures at the small area level and discuss several assump-tions required by this estimation way. The poverty map projection uses the same estimation method of Elbers et al. (2002,2003). However, its idea is different. In the poverty map projection, we use panel data from household surveys to estimatethe relation between expenditure in the second period and household characteristics in the first period. Then this estimaterelation is applied to a census in the second period to predict expenditure and poverty measures in the third period (a futureperiod). Using the same estimation strategy, we can also predict the poverty estimate for a past period. Thus the main objec-tive of the project method is to predict poverty estimates in a period when both a household survey and a census are notavailable.

Vietnam has committed itself to a ‘‘growth with equity’’ strategy of development. The country has achieved high eco-nomic growth, with annual GDP growth rates of around 7% over the past 10 years. Poverty rates have declined remarkablyfrom 58% to 16% between 1993 and 2006. The government of Vietnam has implemented an extensive public safety net with alarge number of poverty alleviation programs. Accurate maps of poverty estimates can be helpful for the government inimproving the poverty targeting of the antipoverty programs.

Poverty maps have been receiving attention from policy makers and researchers in Vietnam. Up to now, several povertymaps have been constructed in Vietnam using the small area estimation method. Most studies rely on similar estimationmethods but using different data sets. Minot (2000) combined the 1993 Vietnam Living Standard Survey (VLSS) and the1994 Agricultural Census to estimate rural poverty maps in 1994. Minot et al. (2003) and Gian and van der Weide (2007)combined the 1998 VLSS and the 1999 Population and Housing Census to construct maps of poverty and inequality at theprovince and district levels in 1999. Nguyen (2009a, 2009b) used the 2002 and 2004 Vietnam Household Living StandardSurveys (VHLSS) and the 1999 Population and Housing Census to estimate the disaggregated poverty measures for the years2002 and 2004. Recently, Nguyen et al. (2009) construct rural poverty maps using the 2006 VHLSS and a 50% sample of the2006 Rural, Agricultural and Fishery Census.

This paper aims to project a poverty map for rural Vietnam in the year 2008 using a 50% sample of the 2006 Rural, Agri-cultural and Fishery Census and the 2004 and 2006 Vietnam Household Living Standard Surveys (VHLSS). Since the 2008VHLSS is also available, we can also construct a poverty map for 2008 using panel data of VHLSSs 2006 and 2008, and datafrom the 2006 Rural, Agricultural and Fishery Census (using a similar method of Emwanu et al., 2006). The poverty estimatesfrom the updating method can be used for validation of the poverty estimates from the projection method.

This study focuses on the rural population, since there are only data on rural households in the 2006 Rural, Agriculturaland Fishery Census. In addition, poverty in Vietnam is mostly a rural phenomenon, with 95% of all poor living in rural areas.

The paper is structured in five sections. Section 2 introduces the data sets used for poverty mapping in Vietnam. Section 3presents the methods of small area estimation and poverty map projection. Next, empirical findings are presented in Section4. Finally, Section 5 concludes.

2. Data set

This study relies on two data sets. The first is the Vietnam Household Living Standard Surveys (VHLSS) in 2002, 2004,2006 and 2008. These surveys were conducted by General Statistics Office of Vietnam (GSO) with technical supports fromthe World Bank. The 2002 covered around 29,530 households, while the 2002, 2004 and 2006 VHLSSs each covered around9189 households. The number of rural households in the 2002, 2004, 2006 and 2008 VHLSSs is 22,621, 6938, 6882 and 6837,respectively. The collected information on household characteristics includes income, expenditure, employment status,education level, housing, fixed assets, credit and households’ participation in poverty alleviation programs. The surveysare designed to be representative at the regional level.

It is interesting that two consecutive surveys set up panel data. More specifically, the 2002 and 2004 VHLSSs contain apanel data set consisting of 4008 households. The 2004 and 2006 VHLSSs have panel data of 4126 households, and the2006 and 2008 VHLSSs have panel data of 4090 households. These surveys are not designed for panel data from more thantwo rounds. Thus there are few households who were covered by three as well as four surveys.

The second data set is a 50% sample of the Rural, Agricultural and Fishery Census (RAFC) for 2006. The 2006 RAFC was alsocarried out by GSO.1 The 2006 RAFC covers all households in rural areas, and is conducted every 5 years. The 2006 RAFC con-tains information on household demography, education, dwelling unit characteristics and asset ownership, farming character-istics of households such as rice cultivation, aquatic cultivation, household ownership of farming tools and machinery.

1 GSO has not released full household data of the 2006 RAFC.

Table 1Estimates of the poverty indexes using the projection method in 2006. Source: Poverty estimates in 2006 are from Nguyen et al. (2009), and poverty estimatesin 2008 are estimated by the author.

VHLSS 2006 Projection using small areas estimation

P0 P1 P2 P0 P1 P2

Red River Delta 11.0 0.0187 0.0051 9.4 0.0165 0.0046[1.1] [0.0025] [0.0009] [1.3] [0.0031] [0.0011]

North East 29.9 0.0670 0.0222 28.9 0.0675 0.0230[1.8] [0.0060] [0.0028] [1.6] [0.0055] [0.0025]

North West 56.4 0.1811 0.0751 53.7 0.1662 0.0557[3.7] [0.0146] [0.0075] [3.2] [0.0129] [0.0069]

North Central Coast 33.1 0.0881 0.0335 31.9 0.0764 0.0276[2.4] [0.0093] [0.0050] [2.0] [0.0069] [0.0033]

South Central Coast 17.1 0.0362 0.0117 20.0 0.0500 0.0178[2.1] [0.0065] [0.0027] [2.0] [0.0055] [0.0026]

Central Highlands 34.4 0.1097 0.0476 37.0 0.1179 0.0489[3.7] [0.0156] [0.0093] [2.3] [0.0091] [0.0053]

South East 9.9 0.0261 0.0108 7.1 0.0211 0.0033[1.5] [0.0054] [0.0028] [1.1] [0.0026] [0.0010]

Mekong River Delta 11.8 0.0206 0.0055 13.4 0.0245 0.0086[1.0] [0.0022] [0.0007] [1.3] [0.0034] [0.0014]

Standard errors in brackets.

370 N.V. Cuong / Journal of Comparative Economics 39 (2011) 368–382

3. Methodology

3.1. Small area estimation method

The small area estimation method developed by Elbers et al. (2002, 2003) can be described by two main steps as follows.Firstly, a functional relation between expenditure (or income if the poverty indexes are calculated based on income) andhousehold characteristics is estimated using a household survey.

2 A c3 Det

lnðyhÞ ¼ Xhbþ eh; ð1Þ

where yh is per capita expenditure of household h. X is a vector of explanatory variables including household and communitycharacteristics which are available in both the survey and the census.

It should be noted that in the full presentation of Elbers et al. (2002, 2003), the error terms are decomposed into a house-hold idiosyncratic component, uch, and a cluster component, gc, which is used to capture correlation of the error terms withincluster c.2 If the intra-cluster correlation of the error terms is not accounted for, standard errors of poverty estimates will beunderestimated. For illustration of ideas, in this section we write the total error terms eh.3 In the empirical section, when esti-mating poverty measures and their standard errors, we follow the same method of Elbers et al. (2002, 2003).

In this step, model (1) is estimated by Generalized Least Squares regressions in which the error terms are allowed to haveheterogeneous variances and a within cluster correction. The parameters of the distribution of coefficients and error termsare also estimated.

Secondly, the estimated expenditure model is applied into the census and a series of Monte Carlo simulations are carriedout to estimate the poverty and inequality indexes using the census data. In each simulation, specific values of regressioncoefficients and error terms are randomly drawn from their estimated distributions. Let bs, es

h, denote the drawn values ofthe coefficients, idiosyncratic and cluster in the s-th simulation, respectively. Then, the predicted expenditure for a house-hold in the census is given by:

ysh ¼ expðXhb

s þ eshÞ: ð2Þ

Next, we can estimate the three Foster–Greer–Thorbecke poverty indexes (see Foster et al., 1984) for a small area asfollows:

bPsa ¼

1n

Xn

h¼1

z� ysh

z

� �a

1ðysh < zÞ; ð3Þ

luster can be defined by a district, a commune or a village.ailed description of the method is presented in Elbers et al. (2002, 2003).

Table 2Estimates of the poverty indexes in 2006 and 2008. Source: Poverty estimates in 2006 are from Nguyen et al. (2009), and poverty estimates in 2008 areestimated by the author.

Region Poverty estimates in 2006using the 2006 VHLSS

Poverty estimates in 2008using the 2008 VHLSS

Poverty estimates in 2008using the projection method

Poverty estimates in 2008using the updating method

P0 (%) P1 P2 P0 (%) P1 P2 P0 (%) P1 P2 P0 (%) P1 P2

Red River Delta 11.0 0.0187 0.0051 5.2 0.0088 0.0025 7.1 0.0107 0.0026 8.1 0.0155 0.0049[1.1] [0.0025] [0.0009] [0.9] [0.0022] [0.0009] [0.9] [0.0018] [0.0005] [0.6] [0.0016] [0.0006]

North East 29.9 0.067 0.0222 23.4 0.0577 0.0200 22.2 0.0502 0.0165 26.0 0.0654 0.0231[1.8] [0.0060] [0.0028] [2.7] [0.0081] [0.0033] [1.4] [0.0047] [0.0020] [1.2] [0.0044] [0.0021]

North West 56.4 0.1811 0.0751 51.3 0.1371 0.0486 40.1 0.0992 0.0341 45.1 0.1258 0.0490[3.7] [0.0146] [0.0075] [5.7] [0.0195] [0.0091] [2.8] [0.0106] [0.0049] [2.2] [0.0093] [0.0048]

North Central Coast 33.1 0.0881 0.0335 20.7 0.0552 0.0213 21.7 0.0486 0.0163 19.3 0.0453 0.0161[2.4] [0.0093] [0.0050] [3.0] [0.0122] [0.0066] [1.7] [0.0048] [0.0020] [1.1] [0.0030] [0.0013]

South Central Coast 17.1 0.0362 0.0117 13.6 0.0307 0.0101 14.5 0.0312 0.0102 12.5 0.0310 0.0116[2.1] [0.0065] [0.0027] [3.1] [0.0095] [0.0041] [1.3] [0.0033] [0.0013] [0.9] [0.0023] [0.0010]

Central Highlands 34.4 0.1097 0.0476 28.6 0.0855 0.0364 27.8 0.0722 0.0266 32.7 0.1012 0.0401[3.7] [0.0156] [0.0093] [5.1] [0.0178] [0.0100] [1.5] [0.0059] [0.0029] [1.3] [0.0055] [0.0029]

South East 9.9 0.0261 0.0108 7.0 0.0148 0.0058 6.4 0.0118 0.0034 4.9 0.0079 0.0020[1.5] [0.0054] [0.0028] [2.0] [0.0051] [0.0026] [0.7] [0.0016] [0.0006] [0.7] [0.0014] [0.0005]

Mekong River Delta 11.8 0.0206 0.0055 9.2 0.0186 0.0049 7.4 0.0123 0.0032 9.7 0.0158 0.0041[1.0] [0.0022] [0.0007] [1.4] [0.0034] [0.0011] [0.7] [0.0016] [0.0005] [1.0] [0.0023] [0.0008]

Standard errors in brackets.


where n is the number of household in a small area, z is the poverty line, and 1ðysh < zÞ denotes an indicator function that

equals 1 if ysh < z, and 0 otherwise. a can be interpreted as a measure of inequality aversion. When a = 0, we have the poverty

incidence (P0), which measures the proportion of people below the poverty line. When a = 1 and a = 2, we obtain the povertygap index (P1), which measures the depth of poverty, and the squared poverty gap (P2), which measures the severity of pov-erty, respectively.

After S simulations, say 500, we can obtain the estimates of the poverty indexes by computing the mean of simulatedvalues:

bPa ¼1S

XS

s¼1

bPsa: ð4Þ

Finally, the variance of bPa is calculated directly from the sample of S simulated values.It is worth noting that Tarozzi and Deaton (2007) state explicitly two assumptions for validity of the small area estimation

method. This first is called the ‘‘measurement of predictors’’ assumption, which requires that the explanatory variables in theexpenditure equation are the same for households in the census and the survey. The second is referred as an assumption on‘‘area homogeneity’’ (conditional independence), which states that the conditional distribution of expenditure given theexplanatory variables in small areas and large regions is the same. The second assumption means that the expenditure func-tion should be the same for small and large areas so that expenditure models of large areas such as regions that are estimatedfrom the household survey can be applied validly to small areas in the census to estimate welfare measures for these smallareas.

3.2. Projection of poverty indexes

For poverty projection, in addition to census data, we need panel data from household surveys. Suppose that we have acensus at time t2, and two household surveys at time t1 and t2 which set up two-period panel data. Our objective is to con-struct a poverty map at time t3 that equals [t2 + (t2 � t1)]. For example, in this study, we aim to estimate a poverty map in2008 using panel data of VHLSS 2004–2006 and the 2006 RAFC.

Suppose there is a functional relationship between household expenditure in the current period and household charac-teristics in a previous period as follows:

lnðyh2Þ ¼ Xh1b12 þ eh2 ð5Þ

where subscript ‘‘2’’ refers to time t2. Subscript ‘‘12’’ in the regression coefficients indicates the relation between expenditurein the second period and the independent variables in the first period.

Similarly, we can assume a functional relation between the dependent variable in time t3 and the independent variablesin time t2 as follows:

lnðyh3Þ ¼ Xh2b23 þ eh3 ð6Þ

Table 3Estimates of the poverty incidence in 2006 and 2008. Source: Poverty estimates in 2006 are from Nguyen et al. (2009), and poverty estimates in 2008 areestimated by the author.

Province 2006 2008

Poverty mapping The projection method The updating method

Estimate Std. Err. Estimate Std. Err. Estimate Std. Err.

Red River DeltaHa Noi 4.8 1.5 3.8 1.6 2.6 0.5Vinh Phuc 13.6 2.4 9.4 2.3 7.7 0.9Bac Ninh 9.4 1.6 6.4 1.7 6.1 0.8Ha Tay 11.7 1.8 7.8 1.5 10.4 1.0Hai Duong 10.9 1.9 5.9 1.3 8.0 0.9Hai Phong 12.0 2.2 7.6 2.4 7.8 1.2Hung Yen 12.0 2.0 6.5 1.6 8.4 1.0Thai Binh 11.5 2.2 6.3 1.7 9.2 1.0Ha Nam 14.6 2.9 7.4 2.3 11.7 1.4Nam Dinh 10.9 2.0 7.1 1.9 6.6 0.8Ninh Binh 15.6 3.0 9.8 2.5 8.4 1.0

North EastHa Giang 62.7 3.9 55.7 4.5 57.0 3.8Cao Bang 48.2 3.2 35.0 3.9 41.2 2.7Bac Kan 36.9 4.2 24.0 4.0 30.8 3.5Tuyen Quang 28.6 4.8 21.6 4.8 22.7 4.3Lao Cai 53.9 3.9 45.7 4.6 48.7 3.9Yen Bai 38.8 4.4 29.2 4.4 33.3 4.1Thai Nguyen 21.9 3.3 13.7 3.0 18.3 2.9Lang Son 40.4 3.8 28.2 3.6 32.2 3.3Quang Ninh 20.3 2.9 11.3 2.0 16.2 2.0Bac Giang 17.6 2.7 12.3 2.4 14.6 2.6Phu Tho 20.9 3.2 12.1 2.7 16.1 2.5

North WestDien Bien 69.9 3.8 53.9 5.8 60.7 4.5Lai Chau 84.6 2.9 74.3 5.8 73.3 4.3Son La 52.8 3.8 35.7 4.5 43.0 3.7Hoa Binh 44.1 4.3 22.2 4.5 25.6 3.5

North Central CoastThanh Hoa 35.7 2.4 23.3 2.6 22.8 1.8Nghe An 32.5 2.4 23.0 2.8 21.7 2.0Ha Tinh 30.7 3.3 19.2 3.3 14.5 2.4Quang Binh 30.7 3.7 20.7 4.2 14.3 3.0Quang Tri 35.7 3.8 24.3 3.7 18.2 2.1Thua Thien Hue 24.5 2.5 14.4 3.1 9.0 1.8

South Central CoastDa Nang 8.0 3.0 5.4 3.5 2.8 2.2Quang Nam 18.0 1.6 16.6 2.2 12.0 1.3Quang Ngai 20.6 1.8 17.3 2.4 16.3 1.4Binh Dinh 15.2 1.9 11.7 2.7 8.6 1.6Phu Yen 19.1 2.2 14.0 2.9 14.4 2.2Khanh Hoa 18.5 2.0 13.6 2.8 13.6 2.3

Central HighlandKon Tum 58.7 3.9 45.3 4.0 52.5 3.4Gia Lai 50.0 2.8 43.6 2.8 51.0 2.1Dak Lak 33.9 3.5 25.1 2.6 30.0 2.2Dak Nong 37.9 4.6 27.4 4.3 31.7 3.1Lam Dong 31.3 3.3 17.2 2.6 18.7 2.0

South EastNinh Thuan 39.0 5.4 24.7 4.7 17.3 3.8Binh Thuan 16.9 2.9 11.9 2.2 9.3 1.9Binh Phuoc 16.1 2.8 8.0 2.0 2.9 1.1Tay Ninh 6.2 1.6 3.2 0.9 3.2 0.9Binh Duong 1.3 0.5 1.2 0.4 0.9 0.4Dong Nai 8.3 1.6 6.1 1.2 5.1 1.2Vung Tau 5.9 1.9 3.6 1.2 4.0 1.3Ho Chi Minh 2.3 0.9 1.4 0.5 1.7 0.8

Mekong River DeltaLong An 4.9 1.3 3.5 0.8 5.2 1.1Tien Giang 6.2 1.8 4.6 1.3 4.8 1.4


2008: updating method2008: projection method2006

Fig. 2. Estimates of the provincial poverty incidence. Source: Poverty estimates in 2006 are from Nguyen et al. (2009), and poverty estimates in 2008 areestimated by the author.

Table 3 (continued)

Province 2006 2008

Poverty mapping The projection method The updating method

Estimate Std. Err. Estimate Std. Err. Estimate Std. Err.

Ben Tre 8.8 2.3 5.2 1.5 7.9 1.8Tra Vinh 16.7 3.9 11.4 2.5 11.6 2.5Vinh Long 8.7 2.7 5.5 1.5 8.5 2.0§ong Thap 11.7 2.3 5.9 1.2 9.9 1.8An Giang 15.4 3.4 7.3 1.3 12.5 2.2Kien Giang 18.6 3.5 10.5 1.9 13.8 2.5Can Tho 11.1 3.4 7.3 2.2 8.1 2.6Hau Giang 10.8 3.3 7.7 2.2 9.7 2.6Soc Trang 20.8 3.4 12.4 2.4 13.5 3.0Bac Lieu 13.3 2.8 7.2 2.1 9.7 2.2Ca Mau 17.0 3.1 10.1 2.0 11.4 2.4

The poverty incidence of provinces (%) The poverty incidence of districts (%)

020

4060

8010

0Es

timat

es fr

om th

e up

datin

g m

etho

d

0 20 40 60 80 100

Estimates from the projection method

020

4060

8010

0Es

timat

es fr

om th

e up

datin

g m

etho

d

0 20 40 60 80 100


Fig. 1. Estimates of the poverty rate using projection and updating methods. Source: Author’s estimation.


2008: updating method2008: projection method2006

Fig. 3. Estimates of the district poverty incidence. Source: Poverty estimates in 2006 are from Nguyen et al. (2009), and poverty estimates in 2008 areestimated by the author.


The forecast of poverty depends on the following functions:

b23 ¼ b12 ð7Þ

Varðeh3Þ ¼ Varðeh2Þ: ð8Þ

Condition (7) means that the correlation between current expenditure and past characteristics is unchanged over time. Itis more likely to be satisfied if the duration t1 � t2 is equal to the duration t2 � t3, i.e., t3 = [t2 + (t2 � t1)]. Condition (8) isrequired so that a simulated value of the error terms at time t3 can be randomly drawn from the distribution of the errorterms at time t2.

To project a poverty map at time t3, we run regression of expenditure model (5) using panel data from household surveys,then apply this estimated model to a census to predict household expenditure and poverty indexes at small areas (the povertymap). More specifically, in this paper, we will estimate the following model using the panel data from VHLSSs 2004 and 2006:

lnðy2006Þ ¼ X2004b2004 2006 þ e2006; ð9Þ

Next, we estimate expenditure in 2008 for all households covered in the 2006 RAFC:

y2008 ¼ expðX2006b2004 2006 þ e2008Þ; ð10Þ

and we use the predicted expenditure and the simulation method to estimate the poverty indexes of a small area for the year2008 and their standard errors.

It should be noted that the condition specified in Eq. (7) is more likely to be satisfied if the economic growth rate between2004 and 2006 is similar to that between 2006 and 2008. The economic growth pace in Vietnam is rather stable during theperiod 2004–2008. The annual growth rate of GDP in the years 2004, 2005, 2006, 2007 and 2008 is 7.8, 8.4, 8.2, 8.5 and 6.3,respectively. The economic slowdown only happened in Vietnam in final months of 2008 year. In addition, expenditure datain the 2004 and 2006 VHLSSs refers to expenditure for past 12 months before the interview date, thus expenditure data inthe 2004 and 2006 VHLSSs can be interpreted as the 2003/2004 expenditure and the 2005/2006 expenditure. As a result, thepoverty map is projected for the 2007/2008 time which was before the economic slowdown in Vietnam.

In addition to the three assumptions mentioned above, we need a so-called ‘‘population growth independent of poverty’’assumption to project a poverty map (see Nguyen, 2009b), since the census at time t2 cannot capture population changesrelated to poverty during t2 and t3. This assumption requires that the population growth (including natural growth andnet immigration growth) of small areas between time t2 and time t3 is independent of the areas’ poverty. This assumptionmeans that the population growth of a small area should be exogenous to poverty. For example, this assumption does nothold for an area in which poor households are more likely to migrate to other areas than the non-poor, and we can overes-timate poverty for this area.

This assumption is more plausible for a not long time period such as the period 2006–2008 in this study. The 2004 and2006 VHLSSs show that the proportion of rural population is almost the same during the 2004–2006 period, at 73%.


In this paper, we will illustrate the project method by projecting a poverty map for rural Vietnam in the year 2008 using a50% sample of the 2006 RAFC and the 2004 and the 2006 VHLSS. Since the 2008 VHLSS is also available, we can also constructa poverty map for 2008 using panel data of VHLSSs 2006 and 2008, and data from the same census using a similar method ofEmwanu et al. (2006). Then, the poverty estimates from the updating method can be used to validate of the poverty esti-mates from the projection method.

A reason why the updating method can be used as a benchmark for the projection method in this paper can be explainedas follows. In this updating method, expenditure in time t3 is observed, and we can run a regression of expenditure in time t3on explanatory variable in time t2. In other words, the expenditure model in Eq. (6) (i.e., ln (yh3) = Xh2b23 + eh3) can be esti-mated directly. Assumptions (7) and (8) are not needed in the updating methods. If our objective is to estimate the povertymap in time t3 and a household survey is available in time t3, the updating method is always preferred to the projectionmethod. The main objective of the project method is to predict the poverty map to the time when both a household surveyand a census are not available. We can predict the poverty estimate for not only the future period but also a past period usingthe same estimation strategy.

3.3. Idea illustration and validation method

We can use VHLSSs to illustrate the idea of the projection method and examine its validation. More specifically, we usethe panel data from VHLSSs in 2002 and 2004 to project the poverty indexes in 2006 and compare these projected values tothe actual estimates based on the 2006 VHLSS. Firstly, we run the GLS regression of logarithm of per capita expenditure in2004 on the explanatory variables in 2002.4 Secondly, we use this estimated expenditure model and the explanatory variablesfrom the 2004 VHLSS to project the poverty measures for 2006. Table 1 compares the poverty indexes estimated by the projec-tion method with the poverty indexes calculated directly from the 2006 VHLSS. The results are very encouraging. The differencein the estimates of the poverty incidence between the 2006 VHLSS and the projection method is just around 2% points. For allthe regions, we cannot reject the hypothesis that the poverty estimates from the projection method are equal to the povertyestimates based on the 2006 VHLSS.

To test the projection method at the smaller areas such as provinces and districts, we will compare the poverty estimates,which are obtained from the projection method using data from the panel of VHLSSs 2004 and 2006, and the 2006 RAFC, tothose which are estimated by the updating method using the panel of VHLSSs 2006 and 2008, and the 2006 RAFC. Accordingto the updating method proposed by Emwanu et al. (2006), we will estimate an equation in which logarithm of per capitaexpenditure in 2008 is a function of explanatory variables in 2006. Then, the estimated equation is applied into the 2006RAFC to predict the poverty rates of small areas in 2008.

4. Empirical results

4.1. Consumption models

4.1.1. Consumption models in the poverty projectionThe first step in the projection of a poverty map is to construct a model of per capita expenditure using household survey

data. A main problem in the model construction is to select explanatory variables. Data on these variables must be also avail-able in a census, and data on the explanatory variables should be comparable between the household survey and the census.To select the explanatory variables, we compare not only summary statistics on these variables but also the questionnairesbetween the 2006 VHLSS and the 2006 RAFC. After checking the comparison, we select 29 household variables for the expen-diture model and six geographical variables. The list of the explanatory variables used for estimation of the expendituremodel is presented in Table A1 in Appendix A.

The consumption models are estimated using panel data of the VHLSSs in 2004 and 2006. The dependent variable is log-arithm of per capita expenditure in 2006, and the explanatory variables are household variables in 2004. There are eight geo-graphical regions in Vietnam, and ideally there should be a separate expenditure regression for each region. However, sincesome regions have a small number of observations, some similar regions are combined together. More specifically, we esti-mate an expenditure regression with a regional dummy variable for North East and North West, and a regression for NorthCentral Coast, South Central Coast and Central Highlands, and a regression for South East and Mekong River Delta. Thus,there are four expenditure regressions in total.

The GLS regressions of logarithm of per capita expenditure are presented in Tables A2 and A3. It shows that all the sig-nificant explanatory variables have expected signs.5 The value of adjusted-R2 ranges from 0.32 to 0.57. The highest R2 is forNorth West and North East, whereas it is lowest for Red River Delta. Given that there is a time difference between the dependentvariable and the explanatory variables, these results of R2s are very encouraging. Tables A2 and A3 also report the model of het-eroskedasticity of error terms in the expenditure models.

4 The regressions are not reported in this paper, since similar estimation strategy and regressions will be presented in details in Section 4. Readers who areinterested in the regressions in this section can contact the author to have the regression results.

5 We use the PovMap program to estimate poverty. Districts are specified as clusters in modeling location effect. We do not use communes as clusters sincein many communes there are only 1 or 2 observations.


4.1.2. Consumption models in the poverty updatingTo update the poverty using panel data of VHLSSs 2006 and 2008, we run regressions of logarithm of per capita

expenditure in 2008 on explanatory variables in 2006. In addition to the household and geographic variables, wecan also construct commune level data from the 2006 ARFC and merge the commune data with household data.The list of commune variables is presented in Table A1 in Appendix A. Similar to the poverty projection method, thereare four expenditure regressions for different regions. It should be noted that the 2008 expenditures are deflated interms of the 2006 price using food and non-food CPIs. The GLS regressions of logarithm of per capita expenditureand the error term heteroskedasticity regressions are presented in Tables A4 and A5. All the significant explanatoryvariables have expected signs. The value of adjusted-R2 ranges from 0.33 for Red River Delta to 0.51 for North Westand North East.

4.2. Poverty estimates

In the projection method, the consumption models which are estimated from the panel data of VHLSSs 2004 and 2006 areapplied into the 2006 RAFC to project poverty measures at different desegregation levels for the year 2008. In the updatingmethod, the consumption models which are estimated from the panel data of VHLSSs 2006 and 2008 are also applied intothe 2006 RAFC to estimate poverty measures for the year 2008.

In this paper, a household is defined as poor if their per capita expenditure is below the expenditure poverty line con-structed by World Bank and GSO. The poverty line is equivalent to the expenditure level that allows for nutritional needs,and some essential non-food consumption such as clothing and housing. The expenditure poverty line in 2006 is equal to2560 thousand VND.

Table 2 presents the regional estimates of the poverty rate, poverty gap index, and the poverty severity index in 2006 and2008. The 2006 estimates are calculated from the 2006 VHLSS, while the 2008 estimates are obtained from three ways: di-rectly from the 2008 VHLSS; the projection method; and the updating method. It shows that the poverty estimates in 2008from there estimation ways are quite similar. For all regions, we cannot reject the hypothesis on equality of the poverty inci-dence from different estimation methods. The poverty indexes estimated directly from the 2008 VHLSSs have higher stan-dard error than those which are estimated from the two poverty map methods. The Table 2 shows that all regionsexperienced reduction in poverty estimates during the period 2006–2008. North West remains the poorest region, followedby Central Highlands.

The poverty rate of provinces in 2006 and 2008 are presented in Table 3. The poverty estimates for 2006 are obtainedfrom Nguyen et al. (2009).6 The poverty estimates in 2006 are presented to examine the poverty reduction during 2006–2008 at the district and province level. The comparison of poverty estimates between the projection method and the updatingmethod at the province and district levels is examined in Fig. 1. The projection method yields the poverty incidence quite sim-ilar as the updating method. The estimates of the poverty gap and the poverty severity indexes at the provincial levels are pre-sented in Table A6 in Appendix A. The comparison of the estimates of the poverty gap and severity indexes is examined inFig. A1 in Appendix A.

The spatial visualization of the province and district poverty incidences in 2006 and 2008 are presented in Figs. 2and 3. Poverty in Vietnam still has a rather strong geographical dimension with high poverty in mountainous andhighland areas. Within some regions and provinces, there exists a high variation in poverty across areas. Again, thepoverty map projection method and the poverty map updating method give similar spatial patterns of povertyincidences in Vietnam.

5. Conclusions

The small area estimation method proposed by Elbers et al. (2002, 2003) estimates disaggregated poverty measuresby combining a household survey and a census. According to this method, a functional relation between householdexpenditure and household characteristics is modeled using data from a household survey. Then, this modeled relationis applied into a census to estimate expenditure for all households covered in this census and poverty measures of smallareas.

In this paper, we propose a simple method to project poverty measures at the small area level in a periodwhen both a census and a household survey are not available. Although, the projection method uses the sameestimation method of Elbers et al. (2002, 2003), its idea is different. More specifically, we use panel data fromtwo household surveys to estimate expenditure in the second period as a function of household characteristicsin the first period, then apply this estimated function into a census in the second period to predict expenditureand poverty measures in the third period. This projection method can be also used to predict a poverty map in apast period.

In this paper, the small area estimation method is applied to project a poverty map in rural Vietnam for the year 2008using a 50% sample of the 2006 Rural, Agricultural and Fishery Census and the 2004 and 2006 Vietnam Household Living

6 Nguyen et al. (2009) construct rural poverty maps using the 2006 VHLSS and a 50 percent sample of the 2006 Rural, Agricultural and Fishery Census.


Standard Surveys (VHLSS). More specifically, the logarithm of per capita expenditure in 2006 is assumed to be a linearfunction of household characteristics in 2004, and this function is estimated by Generalized Least Squares using the paneldata of the 2004 and 2006 VHLSSs. Then, this estimated function is inserted into the 2006 RAFC to project the poverty in-dexes of rural provinces and districts for the year 2008.

To assess the poverty estimates from this projection method, we compare them to the poverty estimates which are esti-mated by the updating method using the panel of VHLSSs 2006 and 2008, and the 2006 RAFC. Since the updating methodrelies on the actual data on expenditure in 2008, it can produce more accurate poverty estimates in 2008. Thus in this study,the poverty estimates which are obtained from the updating method can be regarded as a benchmark to validate the povertyestimates from the projection method.

It is found that the poverty estimates using the projection method are very encouraging. The poverty projection methodand the poverty updating method produce quite similar estimates, especially the estimates of the poverty incidence at theregional and provincial level. The estimation results show that poverty in Vietnam remains to have a strongly geographicaldimension with high poverty in mountainous and highland areas in 2008. Within some regions and provinces, there exists ahigh variation in poverty across areas. These findings on the poverty pattern are consistent to previous poverty map studies(e.g., Minot et al., 2003; Nguyen et al., 2009).

Appendix A

Fig. A1 and Tables A1–A6.

The poverty gap index of provinces The poverty gap index of districts

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Fig. A1. Estimates of the poverty indexes using projection and updating methods. Source: Author’s estimation.

Table A1Common household variables between the 2006 VHLSS and the 2006 RAFC.

Variables Type

Household variablesEthnic minorities (yes = 1) BinaryHousehold size DiscretePermanent house Binary

(continued on next page)

Table A1 (continued)

Variables Type

Semi-permanent house BinaryTemporary house BinaryTap water BinaryClean water BinaryOther water BinaryFlush toilet BinaryOther toilets BinaryNo toilet BinaryHave radio BinaryHave computer BinaryHave motorbike BinaryHave color television BinaryHave mobile BinaryHave telephone BinaryHave fridge BinaryHave fan BinaryFraction of female members to working members ContinuousFraction of working member to household size ContinuousFraction of service members to working members ContinuousFraction of working members without vocational training ContinuousFraction of working members with vocational training ContinuousFraction of working members with college/university ContinuousLog of per capita living area (log of m2) BinaryUse or own annual land (yes = 1) BinaryArea of annual crop land (1000 m2) ContinuousUse or own water surface (yes = 1) Binary

Geographic variables at the district levelPercentage of area elevation lower than 250 m in total area ContinuousPercentage of area slope lower 4 degree in total area ContinuousMean elevation (m) ContinuousMean sunshine (annual hours) ContinuousMean temperature (degree Celsius) ContinuousMean rainfall (mms) Continuous

Commune variables (from the 2006 RAFC)Commune have national electricity system cover all villages BinaryThe road to this commune center is concrete and always available in year BinaryFraction of concrete road in commune ContinuousNumbers of primary schools per 1000 households DiscreteNumbers of secondary schools per 1000 households DiscreteNumber of irrigation per 1000 households DiscreteNumber of extension staff per 1000 households DiscreteNumber of markets per 1000 households DiscreteNumber of concrete markets per 1000 households DiscreteHave bank branch Binary

Table A2Poverty map projection: GLS regression on per capita expenditure for Red River Delta and North Central Coast, South Central Coast and Central Highlands.Source: Author’s estimation.

Explanatory variables Red River delta North East and North West

Coef. Std. Err. |Prob|>t Coef. Std. Err. |Prob|>t

Expenditure model (Beta model)Intercept 7.9425 0.1139 0.0000 7.7975 0.1046 0.0000Having clack television 0.1520 0.0654 0.0204Having color television 0.2250 0.0416 0.0000 0.2891 0.0342 0.0000Household size squared �0.0051 0.0016 0.0018Living in permanent house 0.0741 0.0346 0.0328Log of living area per capita (m2) 0.0779 0.0351 0.0268 0.1762 0.0284 0.0000Having motorbike 0.0884 0.0354 0.0128 0.1863 0.0318 0.0000Fraction of household members without education degree �0.2029 0.0537 0.0002 �0.3292 0.0557 0.0000Fraction of female household members 0.1275 0.0643 0.0479Fraction of working household members 0.2978 0.0664 0.0000 0.2671 0.0723 0.0002Having telephone 0.2459 0.0523 0.0000Have flush toilet 0.2010 0.0534 0.0002



Explanatory variables Red River delta North East and North West


Have no toilet �0.1652 0.0468 0.0004Ethnic minorities �0.1311 0.0380 0.0006Having electric fan 0.0914 0.0394 0.0206Having mobile phones 0.4313 0.1183 0.0003Percentage of area elevation lower than 250 m in total area 0.0000 0.0000 0.0229Mean elevation (m) �0.0002 0.0001 0.0013Number of obs. 676 604Number of cluster 82 103Adjusted R squared 0.3271 0.5667Rhoa 0.1066 0.1069

Heteroskedasticity model (Alpha model)Intercept �3.0984 0.2276 �13.6120 �9.301 1.4889 �6.2467Fraction of service members to working members ⁄ yhat �0.1012 0.0347 �2.9186Other toilet �0.7383 0.2316 �3.1882Harvester⁄yhat⁄yhat �0.0095 0.0034 �2.774Household size⁄yhat⁄yhat_ 0.002 0.0008 2.439Mean temperature 0.2044 0.0622 3.2842Fraction of working members with vocational training �1.7209 0.6873 �2.5039Adjusted R squared 0.0182 0.0328

Note: what is the predicted value of the dependent variable in Beta model (i.e., predicted logarithm of per capita expenditure).a Rho is the ratio of r2

g=r2u , which measures the relative component of location errors in the total errors in the model.

Table A3Poverty map projection: GLS regression on per capita expenditure for North Central Coast, South Central Coast and Central Highlands, and South East andMekong River Delta. Source: Author’s estimation.

Explanatory variables North Central Coast, South CentralCoast and Central Highlands

South East and MekongRiver Delta


Expenditure model (Beta model)Intercept 7.5080 0.1189 0.0000 8.0136 0.0959 0.0000Having color television 0.2223 0.0317 0.0000Having annual crop land (yes = 1) 0.0000 0.0000 0.0094Ethnic minorities �0.2344 0.0544 0.0000 �0.1925 0.0603 0.0014Having electric fan 0.1603 0.0415 0.0001Household size squared �0.0028 0.0008 0.0006Living in temporary house �0.0920 0.0375 0.0142 �0.1561 0.0310 0.0000Log of living area per capita (m2) 0.1458 0.0307 0.0000 0.2096 0.0255 0.0000Having motorbike 0.1786 0.0328 0.0000 0.2402 0.0300 0.0000Fraction of household members without education degree �0.2497 0.0532 0.0000 �0.2523 0.0531 0.0000Fraction of working household members 0.4024 0.0701 0.0000 0.2430 0.0607 0.0001Having telephone 0.3028 0.0622 0.0000Have flush toilet 0.1279 0.0510 0.0123 0.1267 0.0390 0.0012Tap water 0.3125 0.0805 0.0001Other clean water sources 0.1020 0.0432 0.0186 �0.0687 0.0312 0.0277Having fridge 0.2771 0.0483 0.0000Mean elevation (m) �0.0003 0.0001 0.0007 �0.0008 0.0003 0.0056Percentage of area elevation lower than 250 m in total area 0.0000 0.0000 0.0252Number of obs. 825 941Number of cluster 134 128Adjusted R squared 0.5399 0.4389Rho 0.1109 0.1061

Heteroskedasticity model (Alpha model)Intercept �4.5541 0.5139 �8.8623 �2.8888 0.2885 �10.0118Log of living area per capita (m2) 0.5311 0.1435 3.7021Tap water⁄yhat⁄yhat �0.0072 0.0027 �2.6414Permanent house �0.7016 0.2633 �2.6645Tap water⁄yhat �0.0468 0.0158 �2.9611Adjusted R squared 0.0201 0.0135


Table A4Poverty map updating: GLS regression on per capita expenditure for Red River Delta and North Central Coast, South Central Coast and Central Highlands.Source: Author’s estimation.

Explanatory variables Red River Delta North East and North West


Expenditure model (Beta model)Intercept 10.1484 0.9667 0.0000 9.3132 0.2498 0.0000Having electric fan 0.2056 0.0744 0.0059 0.1654 0.0438 0.0002Household size 0.1112 0.0579 0.0552 �0.0567 0.0119 0.0000Household size squared �0.0171 0.0062 0.0061Log of living area per capita (m2) 0.1764 0.0440 0.0001Having motorbike 0.1389 0.0426 0.0012 0.2446 0.0403 0.0000Fraction of household members without education degree �0.1129 0.0564 0.0458 �0.3511 0.0735 0.0000Fraction of working household members 0.3193 0.0738 0.0000 0.2321 0.0842 0.0060Having fridge 0.0992 0.0522 0.0578 0.1764 0.0836 0.0352Having telephone 0.1611 0.0475 0.0007 0.1255 0.0767 0.1022Having toilet (not flush) �0.0826 0.0435 0.0579 0.1529 0.0487 0.0018Not using clean water (Other water) �0.3003 0.1442 0.0377Semi-permanent house �0.0760 0.0403 0.0599Temporary house �0.1428 0.0452 0.0017Having mobile phone 0.1972 0.0992 0.0473Numbers of secondary schools per 1000 households 0.2094 0.0891 0.0191Fraction of service members to working members 0.2477 0.0814 0.0025Fraction of households living in semi�permanent house in communes 0.2895 0.1186 0.0149Fraction of households having fridge in communes 1.1231 0.2165 0.0000Fraction of households using clean water in communes �0.2521 0.1277 0.0488Fraction of people without education degree in communes �1.2270 0.2632 0.0000Mean temperature (degree Celsius) �0.0001 0.0000 0.0094Mean temperature �0.1004 0.0421 0.0173Number of observations 677 586Number of clusters 86 112Adjusted R squared 0.3332 0.5126Rho 0.0269 0.1015

Heteroskedasticity model (Alpha model)Intercept �4.4038 0.3545 �12.4229 �4.2724 0.3604 �11.8545Log of living area per capita (m2) 0.0037 0.0018 2.0719Motorbike �0.4402 0.1643 �2.6796Computer �2.9063 0.8773 �3.3129Television ⁄yhat⁄yhat_ 0.0243 0.0069 3.4991Flush toilet �4.0078 1.8044 �2.2211Proportion of female members �1.1553 0.4773 �2.4204Adjusted R squared 0.0129 0.0066

Table A5Poverty map updating: GLS regression on per capita expenditure for North Central Coast, South Central Coast and Central Highlands, and South East andMekong River Delta. Source: Author’s estimation.

Explanatory variables North Central Coast, South Central Coast andCentral Highlands

South East and Mekong RiverDelta


Expenditure model (Beta model)Intercept 7.0990 0.1399 0.0000 7.5992 0.1473 0.0000Ethnic minorities �0.4761 0.0585 0.0000Log of living area per capita (m2) 0.2059 0.0302 0.0000 0.1953 0.0359 0.0000Having mobile phone 0.1274 0.0709 0.0725 0.1365 0.0504 0.0069Having motorbike 0.2024 0.0324 0.0000 0.2148 0.0367 0.0000Fraction of working members without vocational training �0.1432 0.0729 0.0498Fraction of working members with vocational training 0.3883 0.0889 0.0000Fraction of working members with college/university 0.3754 0.1828 0.0403 0.4888 0.1600 0.0023Fraction of working member to household size 0.3268 0.0663 0.0000 0.2478 0.0740 0.0008Having fridge 0.1833 0.0707 0.0097 0.1819 0.0509 0.0004Having telephone 0.1579 0.0553 0.0044 0.1574 0.0464 0.0007Not having toilet �0.1058 0.0406 0.0093Having annual crop land (yes = 1) 0.0580 0.0343 0.0916Having electric fan 0.1253 0.0376 0.0009Household size �0.0217 0.0114 0.0580Number of markets per 1000 households 0.1146 0.0584 0.0501Fraction of households having mobile phone in communes 0.6222 0.2490 0.0127



Explanatory variables North Central Coast, South Central Coast andCentral Highlands

South East and Mekong RiverDelta


Fraction of households having color television in communes 0.4655 0.1264 0.0002Percentage of area slope lower 4 degree in total area 0.0000 0.0000 0.0176Percentage of area elevation lower than 250 m in total area 0.0000 0.0000 0.0572 0.0000 0.0000 0.0505Mean temperature (degree Celsius) 0.0001 0.0000 0.0060Number of observations 807 895Number of clusters 143 150Adjusted R squared 0.4996 0.4257Rho 0.0932 0.0840

Heteroskedasticity model (Alpha model)Intercept �3.6896 0.2333 �15.8116 �4.8979 0.0896 �54.6558Fraction of working members without vocational training �0.8602 0.2554 �3.3676Telephone⁄yhat⁄yhat 0.0065 0.0022 2.8998Adjusted R squared 0.0127 0.0082

Table A6Estimates of the poverty gap and severity indexes in 2006 and 2008. Source: Author’s estimation.

Province Estimates in 2006 using thestandard method

Estimates in 2008 usingthe projection method

Estimates in 2008 usingthe updating method

P1 Std. Err. ofP1

P2 Std. Err. ofP2

P1 Std. Err. ofP1

P2 Std. Err. ofP2

P1 Std. Err. ofP1

P2 Std. Err. ofP2

Red River DeltaHa Noi 0.0080 0.0031 0.0022 0.0010 0.0057 0.0029 0.0014 0.0008 0.0049 0.0011 0.0016 0.0004Vinh Phuc 0.0242 0.0053 0.0068 0.0017 0.0152 0.0045 0.0040 0.0013 0.0162 0.0021 0.0058 0.0008Bac Ninh 0.0160 0.0032 0.0043 0.0010 0.0096 0.0031 0.0023 0.0009 0.0111 0.0017 0.0033 0.0006Ha Tay 0.0208 0.0040 0.0058 0.0013 0.0123 0.0029 0.0031 0.0009 0.0215 0.0025 0.0072 0.0010Hai Duong 0.0185 0.0039 0.0050 0.0012 0.0085 0.0023 0.0020 0.0006 0.0145 0.0021 0.0043 0.0007Hai Phong 0.0214 0.0050 0.0059 0.0016 0.0116 0.0046 0.0029 0.0013 0.0147 0.0027 0.0045 0.0010Hung Yen 0.0210 0.0045 0.0058 0.0015 0.0094 0.0029 0.0022 0.0008 0.0154 0.0023 0.0045 0.0008Thai Binh 0.0195 0.0047 0.0052 0.0015 0.0090 0.0031 0.0021 0.0008 0.0168 0.0025 0.0049 0.0009Ha Nam 0.0262 0.0065 0.0073 0.0021 0.0109 0.0042 0.0026 0.0012 0.0222 0.0036 0.0067 0.0013Nam Dinh 0.0187 0.0042 0.0051 0.0014 0.0106 0.0035 0.0026 0.0010 0.0119 0.0019 0.0035 0.0007Ninh Binh 0.0287 0.0072 0.0082 0.0025 0.0158 0.0051 0.0041 0.0016 0.0165 0.0023 0.0053 0.0009

North EastHa Giang 0.1765 0.0197 0.0655 0.0100 0.1499 0.0213 0.0544 0.0106 0.1692 0.0189 0.0675 0.0101Cao Bang 0.1279 0.0152 0.0464 0.0077 0.0826 0.0148 0.0276 0.0067 0.1159 0.0128 0.0450 0.0070Bac Kan 0.0886 0.0142 0.0305 0.0061 0.0500 0.0117 0.0154 0.0045 0.0732 0.0114 0.0254 0.0049Tuyen Quang 0.0628 0.0138 0.0204 0.0053 0.0455 0.0130 0.0142 0.0048 0.0480 0.0119 0.0152 0.0045Lao Cai 0.1480 0.0180 0.0549 0.0088 0.1134 0.0192 0.0384 0.0091 0.1374 0.0176 0.0530 0.0090Yen Bai 0.0969 0.0156 0.0341 0.0069 0.0707 0.0145 0.0244 0.0063 0.0826 0.0135 0.0296 0.0059Thai Nguyen 0.0438 0.0085 0.0132 0.0030 0.0257 0.0073 0.0074 0.0025 0.0361 0.0075 0.0108 0.0027Lang Son 0.0956 0.0132 0.0323 0.0056 0.0489 0.0101 0.0147 0.0038 0.0726 0.0103 0.0239 0.0042Quang Ninh 0.0425 0.0072 0.0134 0.0026 0.0215 0.0046 0.0063 0.0016 0.0340 0.0056 0.0108 0.0021Bac Giang 0.0341 0.0067 0.0102 0.0024 0.0172 0.0058 0.0050 0.0021 0.0332 0.0072 0.0102 0.0028Phu Tho 0.0405 0.0087 0.0119 0.0032 0.0229 0.0073 0.0067 0.0028 0.0318 0.0068 0.0096 0.0026

North WestDien Bien 0.2559 0.0245 0.1191 0.0154 0.1429 0.0247 0.0510 0.0119 0.1917 0.0230 0.0802 0.0127Lai Chau 0.3551 0.0292 0.1789 0.0211 0.2142 0.0339 0.0787 0.0175 0.2417 0.0281 0.1032 0.0165Son La 0.1562 0.0181 0.0634 0.0095 0.0816 0.0148 0.0268 0.0062 0.1106 0.0135 0.0405 0.0062Hoa Binh 0.1132 0.0174 0.0410 0.0082 0.0448 0.0120 0.0135 0.0044 0.0542 0.0106 0.0170 0.0042

North Central CoastThanh Hoa 0.0847 0.0082 0.0291 0.0035 0.0529 0.0075 0.0180 0.0031 0.0541 0.0052 0.0190 0.0023Nghe An 0.0802 0.0082 0.0288 0.0036 0.0526 0.0076 0.0180 0.0030 0.0558 0.0054 0.0212 0.0024Ha Tinh 0.0676 0.0104 0.0220 0.0042 0.0373 0.0086 0.0110 0.0031 0.0241 0.0054 0.0063 0.0017Quang Binh 0.0716 0.0115 0.0245 0.0047 0.0447 0.0113 0.0148 0.0043 0.0287 0.0063 0.0096 0.0021Quang Tri 0.0983 0.0136 0.0385 0.0064 0.0609 0.0109 0.0225 0.0049 0.0510 0.0069 0.0204 0.0038Thua Thien

Hue0.0582 0.0082 0.0203 0.0036 0.0296 0.0081 0.0092 0.0031 0.0167 0.0039 0.0049 0.0013

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Province Estimates in 2006 using thestandard method

Estimates in 2008 usingthe projection method

Estimates in 2008 usingthe updating method

P1 Std. Err. ofP1

P2 Std. Err. ofP2

P1 Std. Err. ofP1

P2 Std. Err. ofP2

P1 Std. Err. ofP1

P2 Std. Err. ofP2

South Central CoastDa Nang 0.0131 0.0058 0.0035 0.0017 0.0089 0.0069 0.0024 0.0021 0.0044 0.0041 0.0011 0.0012Quang Nam 0.0411 0.0040 0.0143 0.0016 0.0379 0.0056 0.0130 0.0023 0.0314 0.0034 0.0122 0.0016Quang Ngai 0.0495 0.0053 0.0175 0.0023 0.0407 0.0067 0.0142 0.0029 0.0470 0.0047 0.0189 0.0025Binh Dinh 0.0284 0.0045 0.0084 0.0015 0.0211 0.0060 0.0060 0.0020 0.0164 0.0030 0.0052 0.0009Phu Yen 0.0410 0.0055 0.0134 0.0020 0.0273 0.0067 0.0083 0.0023 0.0327 0.0053 0.0115 0.0021Khanh Hoa 0.0427 0.0052 0.0148 0.0020 0.0298 0.0073 0.0100 0.0028 0.0330 0.0055 0.0121 0.0023

Central HighlandsKon Tum 0.1979 0.0229 0.0857 0.0134 0.0849 0.0142 0.0289 0.0061 0.1538 0.0162 0.0598 0.0083Gia Lai 0.1670 0.0168 0.0727 0.0100 0.1126 0.0131 0.0450 0.0070 0.1712 0.0131 0.0738 0.0079Dak Lak 0.0962 0.0141 0.0378 0.0070 0.0636 0.0088 0.0230 0.0040 0.0802 0.0081 0.0299 0.0038Dak Nong 0.1054 0.0188 0.0409 0.0094 0.0658 0.0140 0.0228 0.0060 0.0844 0.0120 0.0312 0.0057Lam Dong 0.0881 0.0125 0.0346 0.0060 0.0374 0.0074 0.0120 0.0029 0.0439 0.0065 0.0148 0.0028

South EastNinh Thuan 0.1061 0.0202 0.0404 0.0094 0.0563 0.0137 0.0190 0.0055 0.0330 0.0093 0.0096 0.0032Binh Thuan 0.0353 0.0081 0.0112 0.0032 0.0223 0.0053 0.0065 0.0018 0.0156 0.0042 0.0041 0.0013Binh Phuoc 0.0341 0.0077 0.0110 0.0031 0.0137 0.0043 0.0037 0.0014 0.0041 0.0019 0.0010 0.0005Tay Ninh 0.0094 0.0032 0.0023 0.0010 0.0047 0.0016 0.0011 0.0004 0.0044 0.0016 0.0010 0.0004Binh Duong 0.0017 0.0009 0.0004 0.0002 0.0016 0.0006 0.0004 0.0002 0.0011 0.0005 0.0002 0.0001Dong Nai 0.0156 0.0037 0.0046 0.0013 0.0103 0.0026 0.0028 0.0008 0.0080 0.0025 0.0020 0.0008Vung Tau 0.0095 0.0037 0.0025 0.0011 0.0056 0.0022 0.0014 0.0006 0.0059 0.0023 0.0014 0.0006Ho Chi Minh 0.0035 0.0017 0.0009 0.0005 0.0019 0.0008 0.0004 0.0002 0.0023 0.0014 0.0005 0.0004

Mekong River DeltaLong An 0.0077 0.0025 0.0020 0.0007 0.0052 0.0013 0.0012 0.0004 0.0078 0.0020 0.0019 0.0006Tien Giang 0.0104 0.0037 0.0028 0.0011 0.0079 0.0029 0.0023 0.0011 0.0070 0.0026 0.0016 0.0007Ben Tre 0.0155 0.0050 0.0043 0.0016 0.0079 0.0028 0.0020 0.0008 0.0122 0.0036 0.0030 0.0011Tra Vinh 0.0321 0.0096 0.0095 0.0034 0.0201 0.0057 0.0055 0.0018 0.0193 0.0055 0.0050 0.0018Vinh Long 0.0144 0.0056 0.0038 0.0018 0.0085 0.0028 0.0021 0.0008 0.0134 0.0041 0.0033 0.0012Dong Thap 0.0205 0.0050 0.0057 0.0016 0.0091 0.0023 0.0023 0.0007 0.0162 0.0040 0.0042 0.0013An Giang 0.0291 0.0083 0.0084 0.0029 0.0121 0.0027 0.0031 0.0008 0.0212 0.0048 0.0056 0.0016Kien Giang 0.0365 0.0089 0.0109 0.0032 0.0179 0.0041 0.0048 0.0013 0.0233 0.0056 0.0062 0.0018Can Tho 0.0190 0.0074 0.0051 0.0023 0.0117 0.0044 0.0030 0.0013 0.0125 0.0053 0.0031 0.0017Hau Giang 0.0179 0.0068 0.0047 0.0021 0.0123 0.0043 0.0031 0.0013 0.0151 0.0052 0.0037 0.0015Soc Trang 0.0431 0.0094 0.0135 0.0036 0.0220 0.0054 0.0061 0.0018 0.0228 0.0068 0.0060 0.0022Bac Lieu 0.0251 0.0067 0.0074 0.0023 0.0114 0.0041 0.0029 0.0012 0.0157 0.0044 0.0040 0.0013Ca Mau 0.0351 0.0081 0.0111 0.0030 0.0166 0.0042 0.0043 0.0013 0.0183 0.0049 0.0046 0.0015


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Poverty projection using a small area estimation method: Evidence from Vietnam

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