metadata: authors: 0: author: Lanjouw,Peter F.
1: author: Marra,Marleen
2: author: Nguyen, Cuong
count: Vietnam
docna: 0: docna: Vietnam's evolving poverty map :
            patterns and implications for policy
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Disability,Urban Governance and Management,Urban Housing,Educational Sciences,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Services & Transfers to Poor,Economic Assistance,Access of Poor to Social Services,Municipal Management and Reform,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping
teratopic: Macroeconomics and Economic Growth,Poverty Reduction
abstracts: cdata!: This paper uses small area estimation
            techniques to update Vietnam's province and
            district-level poverty map to 2009. It finds that poverty
            rates continue to be highest in the northern and central
            mountainous regions, where ethnic minorities make up a large
            fraction of the population. Poverty has fallen in most
            provinces and districts over this decade, but the pace of
            poverty reduction has been least pronounced in those
            localities with high initial poverty or inequality levels.
            As a result, poverty rates have become more spatially
            concentrated over time, which is consistent with widely
            observed growth processes linked to agglomeration. The
            authors hypothesize that this makes geographic targeting of
            the poor more relevant as a means to re-balance growing
            welfare disparities between geographic areas. Simulations
            indicate that in both 1999 and 2009, geographic targeting
            for poverty alleviation improves upon a uniform lump-sum
            transfer and this becomes more evident the more spatially
            disaggregated the target populations. The analysis further
            indicates that the gains from geographic targeting have
            become more pronounced over time in Vietnam. Although
            poverty reduction in Vietnam has been impressive, further
            progress may thus warrant increased attention to geographic targeting.
geo_reg_key: 517191,517198
repnme: repnme: Vietnam's evolving poverty map :
            patterns and implications for policy
display_title: Vietnam's evolving poverty map
            : patterns and implications for policy
listing_relative_url: /research/2013/02/17211672/vietnams-evolving-poverty-map-patterns-implications-policy
topicv3: Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Education m1327048 470,Social Assistance m1327201 1466,Land and Housing m1327648 817
publishtoextweb_dt: 2013-02-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Poverty Reduction
no_of_pages: 50
author_keys: 0: author_key: 95180
1: author_key: 1135655
model_extractions: WPS6355 Policy Research Working Paper 6355 Vietnam’s Evolving Poverty Map Patterns and Implications for Policy _Peter Lanjouw Marleen Marra Cuong Nguyen_ The World Bank Development Research Group Poverty and Inequality Team February 2013
Policy Research Working Paper 6355

# **Abstract**

This paper uses small area estimation techniques to update Vietnam’s province and district-level poverty map to 2009. It finds that poverty rates continue to be highest in the northern and central mountainous regions, where ethnic minorities make up a large fraction of the population. Poverty has fallen in most provinces and districts over this decade, but the pace of poverty reduction has been least pronounced in those localities with high initial poverty or inequality levels. As a result, poverty rates have become more spatially concentrated over time, which is consistent with widely observed growth processes linked to agglomeration. The authors hypothesize that this makes geographic targeting of the poor more relevant as a means to re-balance growing welfare disparities between geographic areas. Simulations indicate that in both 1999 and 2009, geographic targeting for poverty alleviation improves upon a uniform lump-sum transfer and this becomes more evident the more spatially disaggregated the target populations. The analysis further indicates that the gains from geographic targeting have become more pronounced over time in Vietnam. Although poverty reduction in Vietnam has been impressive, further progress may thus warrant increased attention to geographic targeting.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at planjouw@worldbank.org, mmarra1@worldbank.org, and c_nguyenviet@yahoo.com.

_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 paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Vietnam’s Evolving Poverty Map: Patterns and Implications for Policy**

Peter Lanjouw, Marleen Marra and Cuong Nguyen<sup>1</sup> JEL classification: I31, I32, O15 Keywords: Poverty, inequality, small area estimation, population census, household survey, targeting efficiency, Vietnam.

> 1 Development Economics Research Group (DECRG) World Bank, DECRG,World Bank, and National Economics University, Hanoi.  This study is an outgrowth of analysis undertaken as part of the Vietnam Poverty Assessment managed by Valerie Kozel under the POV Sector Board.  We are grateful for her advice and guidance. The study is funded by the World Bank. We would like to thank Tran Ngoc Truong and Lo Thi Duc (GSO) for assistance in data analysis.

Email: planjouw@worldbank.org; mmarra1@worldbank.org; c_nguyenviet@yahoo.com
# **1. Introduction**

Vietnam represents one of the great international success stories in achieving poverty reduction during recent decades. Concerns have been raised, however, that the pace of poverty reduction has slowed in recent years. In response, the Government of Vietnam has launched a variety of policies and initiatives to restore momentum in poverty reduction, and it is perceived that in order to buttress the impact of these policy measures, programs must become better targeted at the poor. One route towards better targeting is to exploit geographic variation in welfare outcomes. International evidence suggests that transfers will have a larger impact on poverty when they are targeted to finely delineated communities and localities (Baker and Grosh, 1994; Bigman and Fofack, 2000; Elbers _et al._ 2007).

Geographic targeting of the poor may be most effective in those settings where economic growth originates in areas with high factor productivity, which then results in the emergence of lagging regions. The 2009 World Development Report published by the World Bank, entitled “Spatial Disparities and Development Policy”, demonstrates that economic activity and growth is spatially concentrated in many developing countries due to agglomeration benefits deriving from networks, technological change and human capital externalities. The report argues that countries should embrace this development rather than insist on geographically balanced growth. However, the report further argues that policy makers should explore opportunities to ensure that the benefits from such spatially concentrated growth are distributed broadly across the population. One means to that end is to promote the movement of people; barriers to trade and factor mobility should be removed. Another is to implement spatially targeted re-distribution policies that permit countries to support these growth processes while pursuing an equitable distribution of wellbeing.

A major obstacle to detailed spatial targeting is scarcity of reliable information on welfare levels at the local level. Estimation of poverty in small geographical units such as districts and communes poses considerable data demands. Income or expenditure data are commonly used as indicators of economic wellbeing and such information are routinely collected in household sample surveys. While representative at a national level, their sample 2
sizes are typically too small to yield reliable estimates of poverty at the level of districts or communes. Censuses do not suffer from small sample problems as they cover the entire population. But while censuses also collect valuable information on individual and household characteristics that provide insights into living standards, they rarely include the income or expenditure information needed to measure poverty directly.

Small area estimation techniques have been developed to estimate poverty at the small area level. One popular approach, introduced by Elbers, Lanjouw and Lanjouw (2002, 2003) – henceforth ELL - combines household survey data and census data at the unit record level. The approach exploits the census’s coverage of the entire population and the household survey’s detailed information on income and expenditure. First, an expenditure (or income) model is estimated using the household survey data. The dependent variable is expenditure (or income), and the explanatory variables are a set of household and community characteristics that are comparable and that are available in both the household survey and the census. Subsequently, the parameter estimates from the expenditure model are applied to the census data in order to predict expenditure of all households in the population. From there it is a straightforward procedure to estimate poverty measures in small areas such as communes and districts.

The small area estimation method has been applied in a large number of countries to produce maps of not only poverty measures but also other welfare indicators (see Bedi _et al._ , 2007 for a review of applications). In Vietnam, a number of poverty maps have been developed using the ELL small area estimation method. Minot (2000) combines the 1993 Vietnam Living Standard Survey (VLSS) and the 1994 Agricultural Census to estimate poverty at the local level in rural areas of Vietnam. Minot _et al._ (2003) construct a poverty map using a 1998 VLSS and a 33% sample of the 1999 Population and Housing Census. Nguyen (2009) applies the 2002 VHLSS to the 33% sample of 1999 Population and Housing Census to produce a poverty map for 2002. Nguyen et al. (2009) further update the rural poverty map for 2006 using the 2006 VHLSS and the 2006 Rural Agriculture and Fishery Census.

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The General Statistics Office of Vietnam recently published the 2009 Population and Housing Census and the 2010 Vietnam Household Living Standard Survey. These datasets permit a new updating of the Vietnam poverty and inequality maps. This paper presents new estimates of expenditure poverty at the province and district level of Vietnam. The estimates are based on the 15-percent sample of the 2009 Population and Housing Census in combination with the 2010 Vietnam Household Living Standard Survey. In addition, we estimate poverty at the provincial and district level for different groups including rural and urban, Kinh/Hoa and ethnic minority people. We also report small area estimates of inequality and examine in which provinces and districts the richest 15% of Vietnam are to be found. Finally, we compare the 1999 and 2009 poverty maps and analyze how evidence on local level changes can inform social policy making in Vietnam.

We simulate the impact of spatial targeting of a hypothetical budget, directed in turn to localities defined in terms of increasing spatial disaggregation to assess the gains from geographic targeting. The departure point of this analysis is a paper by Ravallion (1993) showing how spatial disaggregation to the regional level in Indonesia improves targeting, albeit to only a modest extent. In contrast, Elbers, Fujii, Lanjouw, Özler, and Yin (2007) apply an approach identical to the one employed here and find that in the case of Ecuador, Madagascar and Cambodia, when the geographic disaggregation is to a considerably finer level the gains from spatial targeting become very pronounced.

Vietnam has previously employed geographic targeting based on a fairly arbitrary list of poor communes, and is currently using a bottom-up approach to define who is poor and eligible for social assistance. Doubts about the effectiveness and transparency of both methods have been raised (Litvack, 1999; Baulch and Minot, 2002; van de Walle, 2002, Nguyen et al., 2010). Although poverty reduction in Vietnam has been impressive over the last decade, geographic targeting based on more clearly objective criteria may be needed to maintain further progress. Our simulations for 1999 and 2009, involving two snap-shots of the spatial distribution of poverty and a variety of alternative administrative levels of targeting, allow us to track the effectiveness of geographic targeting at different levels of spatial disaggregation as well as under alternative settings of the spatial distribution of poverty. At 4
the same time, and as is noted further below, our simulations are highly stylized and predicated on simplifying assumptions, and as such can at best provide suggestive guidance to the design of policy.

The paper is structured as follows. The second and third sections introduce the estimation method and data sources used in this paper, respectively. The fourth section presents the empirical findings on the geographical distribution of poverty. The fifth section presents inequality and wealthy maps. Next, the change in poverty during the period 19992009 at the disaggregated levels is discussed in the sixth section. The simulation of the impact of spatial targeting of transfers on poverty reduction is presented in the seventh section. Finally, the eighth section concludes.

# **2. Methodology**

To estimate the poverty rate in small areas such as districts, we employ the small area estimation method developed by Elbers, Lanjouw and Lanjouw (2002, 2003). The method involves three broad steps. In the first step, we select a set of variables that are common to both the household survey and the population census. The common variables include household characteristics, as well as characteristics of small areas such as villages and/or communes via area mean variables computed from the census. Area mean variables include, for example, the average household size at the commune level as calculated from the census, or the total population of the commune. These variables are then “inserted” into the household survey so that they can be included as candidate variables in the modeling stage. All variables common to the survey and census are subject to careful comparison; they need to be similar in terms of their means and distribution and in terms of the framing of the question.

In the second step, we regress observed expenditure in the household survey on the selected common variables. More specifically, we estimate the following model: 5
where ln( _ych_ ) is log of per capita expenditure of household _h_ in cluster _c_ , _X ch_ the vector of the common variables, β the vector of regression coefficients, η _c_ the cluster-specific random effect and ε _ch_ the household-specific random effect. The subscript _ch_ refers to household _h_ living in cluster _c_ . We estimate different models for the six regions separately, to allow for variation in the relationship between expenditure and the selected variables in these areas. Table 1 in the Appendix lists the selected variables that are included in the regression models, i.e. selected on the basis of being common to both the survey and census and contributing significant explanatory power.

In the third step, we predict expenditure of a household in the census as follows: ˆ ˆ where β<sup>ˆ</sup> , η _c_ and ε _ch_ denote the estimates for β , η _c_ and ε _ch_ . The predicted expenditure is used to calculate the poverty rate of small areas. It should be noted that the point estimates as well as the standard errors of the poverty rate and per capita expenditure are calculated by ˆ ˆ Monte-Carlo simulations. In each simulation, a set of values β<sup>ˆ</sup> , η _c_ and ε _ch_ are drawn from their estimated distributions, and an estimate of expenditure and the poverty rates are obtained. After _k_ simulations, we can obtain average expenditure and its standard deviation over the _k_ different simulated values. Since we are restricted to a 15% sample of the 2009 Population Census (as opposed to the full census), we add an additional sample error component to the standard deviation. For this, we in effect treated the census as a survey, taking account its complex design and sample weights.

The predicted expenditure for all households in the population census are subsequently aggregated to generate district- and province level welfare measures. In 2009, we apply the GSO-World Bank official poverty line of 7,836,000 VND/person/year, and in 1999 the line is 1,789,871 VND/person/year (Minot et al, 2003). As poverty measure we use the popular headcount rate. We also estimate the Gini coefficient as our primary indicator of income inequality at the local level.

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# **3. Data sources**

As described in the methodology section, the 2009 poverty maps are based on two data sets. The first is the 15-percent sample of the Vietnam Population and Housing Census (VPHC). The 2009 VPHC was conducted by the General Statistics Office of Vietnam in April 2009, with (technical) support from the United Nations Population Fund (UNFP), and includes two modules. The first module is used to collect basic demographic and housing data for the whole population. Data include age, gender, race and education of individuals. The second module, which underpins our small area poverty estimates, contains more elaborate data. Individual data include demographics, education, employment, disability and migration. Household data include durable assets and housing conditions. The 15-percent sample is representative at the district level, and is selected on the basis of a cluster sampling technique, covering 3,692,042 households with 14,177,590 individuals (Central Population and Housing Census Steering Committee, 2009).

The second dataset is the 2010 Vietnam Household Living Standard Survey (VHLSS). The 2010 VHLSS is also conducted by GSO with technical support from the World Bank. It includes very detailed data on individuals, households and communes. Individual data consist of information on demographics, education, employment, health and migration. Householdlevel data include information on durables ownership, assets, production, income and expenditure, and participation in government programs. There are 9,402 households with 37,012 individuals covered in this data set. This 2010 VHLSS is representative for rural/urban areas and 6 geographic regions. Only 3 households are sampled from each commune. Thus, 9,402 households are sampled from 3,113 communes which belong to 686 districts. The 2010 VHLSS differs from earlier rounds (2002-2008) in terms of content as well as sampling design. The principal changes include shortening of the questionnaire, reframing of the consumption modules, and drawing of the sample from the 2009 instead of the 1999 Population Census.

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To compare the 2009 results with those of 1999, we used the 1999 SAE poverty rates from Minot et al. (2003).<sup>2</sup> These are based on the 1998 VLSS and a 33% sample of the 1999 VPHC. The survey is implemented by GSO with funding from the Swedish International Development Agency and UNDP and with technical assistance of the World Bank. It includes 6,000 households. The 33% sample of the 1999 census includes 5,553,811 households.

# **4. The 2009 poverty map**

# **_4.1 Consumption models_**

The first step in the poverty mapping method is to select common explanatory variables in the census and household survey. After carefully screening the questionnaires and examining the data (comparing summary statistics), we have 20 household variables (Table A.1 in Appendix). We also constructed commune level data from the census and merged these variables with the household survey. For example, we constructed the percentage of Kinh people per commune, the average household size of communes, the proportion of households per commune having motorbike, etc. Note that these variables are comparable across the census and survey by construction. The total number of explanatory variables for estimation of the expenditure model is 39.

We estimate six separate regressions of log of per capita expenditure for six regions. To allow for the difference in coefficients between urban and rural areas, we interact the urban variable with all the remaining explanatory variables.

A forward stepwise technique is used so that only variables which are significant at least at the 5% level are kept. We select explanatory variables that are robust in explaining expenditure. This means that these variables have unchanged signs and are significant when the models are changing. The regression results of the large models are presented in the tables in Appendix I. The R-squared is very high. The Mekong River Delta has the lowest R-squared

> 2 In Section 6 we note that caution must be exercised in comparing poverty between 1999 and 2009 as the underlying household survey data are not strictly comparable.

8
of 0.5. The Central Highlands have the highest R-squared of 0.74. Other regions have an R- squared above 0.6.<sup>3</sup>

# **_4.2 Spatial patterns of poverty_**

Table 1 presents the regional estimates of the poverty rate and per capita expenditure which are computed directly using per capita expenditure data of the 2010 VHLSS and those estimated from the poverty mapping method. The 2012 VHLSS is representative at the regional level, and the regional poverty rate directly estimated from expenditure data can be thus regarded as the benchmark against which to compare the poverty map estimates. Table 1 reveals that estimates of the poverty rate are quite similar across the two approaches.

Table 1: Per capita expenditure and poverty indexes

||Estimates from<br> <br>|the 2010<br>|VHLSS<br>||Predictions fr<br>|om SAE<br>|||
|---|---|---|---|---|---|---|---|---|
||Per<br>capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|Per capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|
|Northern Mountain|10927.1|44.87|0.1558|0.0701|10826.4|43.85|0.1483|0.0679|
||(250.2)|(1.54)|(0.0069)|(0.0042)|(340.9)|(1.76)|(0.0082)|(0.0046)|
|Red River Delta|21546.0|11.95|0.0265|0.0088|20515.2|10.65|0.0203|0.0060|
||(605.6)|(0.85)|(0.0025)|(0.0010)|(592.2)|(1.02)|(0.0025)|(0.0009)|
|Central Coast|14222.6|23.73|0.0635|0.0251|14002.1|22.48|0.0520|0.0180|
||(267.3)|(1.33)|(0.0051)|(0.0028)|(268.7)|(1.05)|(0.0031)|(0.0013)|
|Central Highlands|13069.0|32.74|0.1149|0.0542|12931.0|33.29|0.1146|0.0536|
||(490.9)|(2.75)|(0.0128)|(0.0077)|(351.8)|(1.25)|(0.0056)|(0.0032)|
|South East|24297.4|7.02|0.0172|0.0064|23350.9|7.07|0.0139|0.0043|
||(935.9)|(0.96)|(0.0036)|(0.0018)|(844.9)|(0.84)|(0.0020)|(0.0007)|
|Mekong River Delta|14858.2|18.71|0.0425|0.0143|14497.9|17.45|0.0359|0.0112|
||(265.8)|(1.10)|(0.0033)|(0.0015)|(280.7)|(1.08)|(0.0029)|(0.0011)|

Standard errors are in parentheses. <u>Source: Estimation from the 2009 VPHC and the 2010 VHLSS.</u> 3 We conduct two exercises to shed light on the question how well the models do in predicting per capita expenditure. Firstly, within the 2010 VHLSS, we randomly split the six regions into two samples, and estimating the regional models in the first sample, we predicted expenditure in the second sample and compared the results to the actual per capita expenditure. We find that the observed poverty rates are in all regions’ subsamples similar to the predicted ones. Secondly, we also examine the sensitivity of the expenditure poverty rate of districts and provinces to different expenditure models. We estimate two expenditure models: one with a large number of explanatory variables and another with a smaller number of explanatory variables. Both models give very similar estimates of poverty indexes at the district and province level. For interpretation in this paper, we will use the estimates from the large model, which give lower standard errors of welfare estimates.

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Table A.8 in the Appendix presents by province, the predicted per capita expenditure, poverty rate, number of poor, and share in the total number of poor in the country. Lai Chau, Ha Giang and Dien Bien are three poorest provinces with the poverty rate of more than 70%. As expected, Hanoi and Ho Chi Minh city are the richest cities, followed by Da Nang, Hai Phong, Quang Ninh, Binh Duong, Ba Ria-Vung Tau.

Figure 1 shows the spatial distribution of poverty by provinces and districts in 2009. Poverty rates are highest in the mountainous Northern areas and lowest in the Mekong and Red River Deltas. Disaggregating down to the district level reveals a greater degree of heterogeneity in terms of both pockets of extreme poverty and pockets with particularly low levels of poverty. As we shall see below, such heterogeneity across sub-national localities translates into gains from spatial targeting of anti-poverty resources.

Figure 2 graphs the density of the poor across the country. Because of their large populations, the Mekong and Red River Delta regions still account for a significant number of the poor in the country. However, as we shall see below, the picture in 2009 is much less accentuated than at the time of the preceding census, and as such indicates a clear attenuation of the pattern described in earlier studies of poverty in Vietnam (see Minot et al, 2003) where the distribution of the number of poor people was inversely correlated with the spatial distribution communities’ poverty rates. Then, poverty rates were highest in relative sparsely populated localities and these thus accounted for only a modest fraction of the poor. Today, poverty rates remain spatially concentrated but the distribution of poor people is more evenly spread across the country. Consequently Vietnam’s poorest communities account for a larger share of the poor population.

Despite the ongoing urbanization process, poverty in Vietnam is currently still largely a rural phenomenon: 95 percent of the poor live in rural areas. The poverty rate in urban areas is generally low, and there is a large difference in poverty rates between urban and rural areas even within a province and a district; see Figure 3. Figure 4 shows clearly that poverty is markedly higher amongst ethnic minorities than Kinh and Hoa households. Even within the same mountain or delta region, there remains a large gap in poverty between ethnic minority households and Kinh (Hoa) households.

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# **_4.3  Poverty correlates_**

This section considers a few province- and district-level correlates of local headcount rates, in an attempt to look beyond its spatial distribution to more generalizable patterns. The results refer to figures 1-5 in the appendix.

The data displays a clear positive relationship between poverty rate and Gini index. A more equal distribution is associated with a lower poverty rate (Figure A.1 appendix). However, what is also clear from this Figure is that at any particular poverty rate there is heterogeneity across provinces and districts in terms of inequality outcomes.

Although Vietnam remains a rural country, the urbanization process has been accelerating in recent years. About 30 percent of people now reside in urban areas (General Statistics Office, 2011). Overall, urban areas tend to have lower poverty, and as a result it is a general tendency that poverty is lower as the urban population share increases (Ravallion et al., 2007). In Vietnam, poverty is negatively correlated with the urban population share at the provincial and district level (Figure A.2 Appendix).

Also, poverty is substantially higher in areas with large concentrations of ethnic minorities (Figure A.3 Appendix). This finding relates to the earlier one that the poor are now concentrated in Northern mountain and central highlands where there are large population shares of such ethnic minorities. It also follows that poverty rates are higher in areas with low population and low population density (Figures A.4 and A.5 Appendix).

# **5. The 2009 inequality and wealth maps**

We now turn, briefly, to the spatial distribution of inequality across provinces and districts. Table A.9 in the Appendix presents the Gini index and the ratio of the 90<sup>th</sup> to 10<sup>th</sup> expenditure percentile (a measure of “absolute” inequality) for all provinces. In addition, we also estimate the percentage of people belonging to the richest 20% of the population.

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and Chile, it is generally difficult to avoid errors of _leakage_ (reaching the non-poor) and of _under-coverage_ (not reaching the poor). Improving targeting aims to reduce these errors, resulting in a more effective poverty reduction policy.

The current process for targeting the poor in Vietnam employs a “bottom up” approach. At the aggregate level, overall progress in tracking poverty over time is undertaken by the Ministry of Labor, War Invalids, and Social Affairs (MOLISA) on the basis of an income poverty line<sup>4</sup> applied it to the periodic rounds of VHLSS survey data. Identification of households eligible to receive targeted assistance is undertaken at the village level. Village committees use their own widely varying criteria to compose a list of poor households. Criteria may include food security, housing, assets, etc. This list is submitted for review to a commune-level committee of Hunger Eradication and Poverty Reduction. The committee checks that households on the list are below MOLISA’s income poverty line by conducting simple surveys of randomly selected households from the list. The refined list is then sent to the People’s Committee and the People’s Council, which are empowered to make further adjustments to it as well (Nguyen et al., 2010). Households that make it to the final ‘poor list’ are then in principle eligible for certain social assistance programs such as free health insurance or subsidies.

Previously, Vietnam would redistribute funds by means of geographic targeting. However, it is generally felt that there was a large margin of error in this approach; the “list of poor communes” that MOLISA used to target the poor was constructed somewhat arbitrarily and failed to capture the majority of poor people. One study estimates that although leakage was limited with this practice – about 8% of non-poor were misclassified as being poor – under-coverage was substantial with 80% of the poor not being reached (Baulch and Minot, 2002).

Concerns have also been voiced about the way that funding is distributed once the poor are identified. Although funds are allocated in a redistributive way to the provinces, intraprovincial distribution is to a large extent left to the discretion of provincial authorities and it

> 4  Which is different – usually lower - from the expenditure poverty line set by the General Statistics Office and The World Bank that is used by the international research community to study poverty.

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is suggested that this is the first of many bottlenecks to a fair and transparent system (Litvack, 1999; van de Walle, 2002). Often, communities are urged to collect funds through fees and levees themselves, which is obviously hardest for the most deprived communities. It is not surprising that statistics show a large gap between eligible households and actual beneficiaries (van de Walle, 2002).

As we have shown in previous sections, poverty has become much more spatially concentrated over time in Vietnam. We have noted in the introductory section that this is likely a common consequence of spatial concentration of economic growth due to agglomeration externalities. Absent an effective means to redistribute incomes to lagging regions this process is likely to result in a growing spatial concentration of poverty. Geographic targeting of resources to combat poverty offer one possible means to attenuate such a process.

To assess effectiveness of geographic targeting in Vietnam, we consider the geographic distribution of a hypothetical budget to the population of Vietnam. We assume that we have no information about the poverty status of this population other than the geographic location of residence and the level of poverty in each location. As a benchmark case we make the extreme assumption of no knowledge whatsoever about the spatial distribution of poverty – in which case our given budget is distributed uniformly to the entire population.<sup>5</sup> We set up a series of comparisons to this benchmark, where we assume knowledge about poverty levels in progressively smaller sub-populations. For a given level of disaggregation, we ask how knowledge about poverty outcomes across localities can be incorporated into the design of a transfer scheme so as to improve the overall targeting performance relative to the benchmark case. In light of the observations made above concerning the evolving spatial distribution of poverty (in Vietnam and beyond), we ask whether and how our conclusions differ between 1999 and 2009.

> 5 Actually, comparing this no-knowledge benchmark against the successfulness of the geographic targeting approach that Vietnam exercised a decade ago as assessed by Baulch and Minot (2002), it is not that extreme. They found that only 20% of the poor were reached. If the total population gets distributed an equal part of the budget, 100% the poor would be reached by definition. The only thing is that the amount received is probably so little in that scenario that it is questionable how many people would get lifted out of poverty.

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The transfer scheme makes use of our knowledge of the spatial distribution of poverty in such a way that poverty is minimized at the national level. We consider the gains from spatial targeting at alternative levels of disaggregation. As poverty measure we use the squared poverty gap, which is particularly sensitive to the severity of poverty by calculating the distance between a poor person’s income level and the poverty line and giving more weight to larger distances.<sup>6</sup> We specify a poverty line that accords with a poverty rate of around 20% nationally, in each respective year, and we postulate a modest hypothetical budget that would be insufficient, in and of itself, to eliminate all poverty, even if it were perfectly targeted at the household level. Our results show that in both 1999 and 2009 there are potentially large gains in targeting performance from disaggregating to the local level. These benefits are even more clearly seen when we examine the squared poverty gap as our poverty measure of choice. The impact on the headcount rate is, unsurprisingly, more muted, given that we are not able to “optimize” our transfer scheme with respect to this poverty measure.

The benefits from spatial targeting become increasingly evident as one makes use of more and more disaggregated data on poverty. We show that a given impact on poverty can be achieved at considerably less expense with detailed spatial targeting than with a uniform transfer. Importantly, we find that the benefits from spatial targeting, at any level of disaggregation, are more evident in 2009 rather than 1999. This follows from our earlier finding that poverty in 2009 is more spatially concentrated than was the case in 1999, and supports our assertion that in developing countries that experience uneven spatial progress geographic targeting can be a successful policy to reduce growing wealth disparities.

# **_7.1. Transfer scheme_**

> 6 We focus on the squared poverty gap because of its appealing properties from both a conceptual and technical point of view.  The basic approach explored here would also work for other poverty measures, particularly FGT measures with values of parameter α greater than 1.  However, with the headcount measure (the FGT measure with α=0) welfare ‘optimization’ is not well defined and the approach taken here is thus less obviously applicable (see for example Ray, 1998, pg 254-255).

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We postulate that the government has a budget, _S_ , available for distribution and wishes to transfer this budget in such a way as to reduce poverty. We specify a baseline case in which the government is assumed to have no knowledge of who the poor are or where they are located. It is therefore unable to distribute its budget in any manner other than a lump-sum transfer to the entire population of size _N_ . We thus calculate the impact of transferring _S/N_ to the entire population.

Kanbur (1987) shows that to minimize poverty summarized by the Foster-GreerThorbecke (FGT) class of poverty measures with parameter value _α>1,_ the group with the higher FGT( _α-1_ ) should be targeted on the margin.<sup>7</sup> Hence, to minimize the squared poverty gap (equal to a poverty measure from the FGT class with _α_ =2), target populations should be ranked by the poverty gap (FGT with _α_ =1) and lump-sum transfers made until the poverty gap of the poorest locality becomes equal to that in the next poorest one, and so on, until the budget is exhausted.

# **_7.2. Budget and Poverty Lines_**

We assume that the budget available for distribution has been exogenously set. As is intuitively clear, the potential benefits from targeting will vary with the overall size of budget. In the limit, as the budget goes to infinity, there is no need for targeting, as even a uniform transfer will eliminate poverty. As a benchmark, we identify the per capita consumption value of the 25<sup>th</sup> percentile of the consumption distribution.<sup>8</sup> We scale this consumption value by the total population. Our benchmark budget is set to equal 5% of this total value.

7 Following Foster, Greer and Thorbecke (1984) the FGT class of poverty measures take the following form: where xi is per capita expenditure for those individuals with weight wi who are below the poverty line and zero for those above, z is the poverty line and ∑<sup>_w_</sup> _i_<sup>is total population size.αtakes a value of 0 for the</sup> Headcount Index, 1 for the Poverty Gap and 2 for the Squared Poverty Gap. For further discussion, see Ravallion (1994).

8 The consumption distribution is constructed on the basis of the average, across _r_ replications, of householdlevel predicted per-capita consumption in the population census.

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Gains from targeting also vary with the choice of poverty line. The higher the poverty line, the less need for targeting, as leakage to the non-poor diminishes to zero. In this study, we select as benchmark the a poverty line that yields a poverty rate of exactly 20% in both 1999 and 2009, respectively.

# **_7.3. Simulating the impact of uniform transfers_**

Our policy simulation in the benchmark case of uniform transfers is calculated in a very straightforward manner. Budget _S_ is divided by total population _N_ . The resulting transfer _a_ is ( _r_ ) added to each predicted expenditure in our database, to yield _ych +a_ . For each replication _r_ we estimate post-transfer national poverty. The average across the _r_ replications of the estimated post-transfer poverty rates yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer scheme. This new estimated poverty rate can be compared to the original national-level poverty estimate from the poverty map to gauge the impact of the transfer.

# **_7.4. Simulating the impact of “optimal” geographic targeting_**

Simulating the impact of the “optimal” targeting scheme is a bit more complicated. Following Kanbur (1987) we want to equalize the following expression across the poorest locations of a country: which is _z_ times the poverty gap in location _c_ , after every person in the location has received a transfer _ac. Fc(y)_ is the average of the R simulated expenditure distributions of _c_ . The function ( _x_ )<sup>_+_</sup> gives the ‘positive part’ of its argument, i.e. ( _x)_<sup>_+_</sup> _=x,_ if _x_ is positive, otherwise 0. Transfers _ac_ (which must be nonnegative) add up to a given budget _S_ : where _Nc_ is the population size of location _c_ . After transfers there is a group of locations all sharing the same (maximum) poverty gap rate in the country. These are the only locations 26
receiving transfers. We describe in the Appendix how this problem is solved given that we are working with a database of incomes for every household in the population census.

# **_7.5. Results of spatial targeting simulation_**

Table 2 presents the basic results from our simulations. There are a number of conclusions to be drawn. First, the availability of disaggregated data on poverty can help to improve on a uniform lump-sum transfer across the entire population. Targeting transfers to poor localities, in accordance with the optimization scheme outlined above, yields lower values of the national FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire population. Second, the more disaggregated the poverty map, the greater the improvement over the uniform lump-sum transfer. The simulations here suggest that with estimates of poverty at the province, district and commune levels, further improvements in terms of impact on the FGT2 with a given budget are attainable, and are non-negligible. Third, while the general patterns we observe are similar across our two poverty maps for 1999 and 2009 respectively, they are not identical. Notably, we see in Table 2 that while communelevel targeting in 1999 would reduce the FGT2 from a level of 0.0110, following a uniform transfer, to 0.0058 with commune level targeting (a 43 percentage point reduction), the improvement from commune level targeting in 2009 would be 66 percentage points – the FGT2 declining from 0.0166 to 0.0057 (Table 3). With district level targeting rather than commune-level targeting, the gains are slightly less marked but nonetheless a striking 58 percentage point reduction compared to uniform transfers.

Table 3 repeats the simulations presented in Table 2 but focuses now on the headcount, or FGT0, measure of poverty. As mentioned above the optimization procedure outlined in Kanbur (1987) applies to the squared poverty gap or FGT2 measure. There is no analogous optimization algorithm for the FGT0 measure. We report in Table 3, however, the resulting FGT0 estimates from having applied the procedure to allocate our budget in such a way as to minimize the resulting FGT2 measure. Table 3 reveals that the gains in terms of the FGT0 of geographic targeting are far less marked than was observed when the FGT2 measure was our reference measure.

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Table 2: Impact on FGT2 of Targeting at Different Levels of Geographic Disaggregation Optimal Targeting Scheme

||1999|2009|1999|2009|
|---|---|---|---|---|
|Original FGT2|0.0159|0.0234|1.00|1.00|
|FGT2 after:|||||
|Uniform transfer|0.0110|0.0166|0.69 (1.00)|0.71 (1.00)|
|Province Level Targeting|0.0080|0.0096|0.50 (0.72)|0.41 (0.58)|
|District-level targeting|0.0066|0.0070|0.42 (0.61)|0.30 (0.42)|
|Commune-level targeting|0.0058|0.0057|0.36 (0.57)|0.24 (0.34)|

Note: Budget=5% of (Total Population * 25<sup>th</sup> Percentile Per Capita Expenditure). Poverty Line= Per capita expenditure defining bottom quintile of population (pre-transfer). The two last columns are the poverty gap indexes normalized to one. The figures in parentheses are the ratio of normalized FGT2 in the case of targeting transfer to the normalized FGT2 in the case of uniform transfer. The number of provinces, districts and communes in 1999 is 61, 614, and 10474, respectively. The number of provinces, districts and communes in 2009 is 63, 685, and 10896, respectively.

Table 3: Impact on FGT0 of Targeting at Different Levels of Geographic Disaggregation Optimal Targeting Scheme

||1999|2009|1999|2009|
|---|---|---|---|---|
|Original FGT2|0.2000|0.2000|1.00|1.00|
|FGT2 after:|||||
|Uniform transfer|0.1673|0.1724|0.84 (1.00)|0.86 (1.00)|
|Province Level Targeting|0.1522|0.1555|0.76 (0.90)|0.78 (0.91)|
|District-level targeting|0.1443|0.1465|0.72 (0.86)|0.73 (0.85)|
|Commune-level targeting|0.1390|0.1372|0.70 (0.83)|0.69 (0.80)|

Note: Budget=5% of (Total Population * 25<sup>th</sup> Percentile Per Capita Expenditure). Poverty Line= Per capita expenditure defining bottom quintile of population (pre-transfer). The two last columns are the poverty gap indexes normalized to one. The figures in parentheses are the ratio of normalized P0 in the case of targeting transfer to the normalized P0 in the case of uniform transfer. The number of provinces, districts and communes in 1999 is 61, 614, and 10474, respectively. The number of provinces, districts and communes in 2009 is 63, 685, and 10896, respectively.

We only have access to a 15% sample of the Population Census, which is not representative on a commune-level. Therefore, it must be noted that the commune-level estimates of poverty may not be representative of the actual welfare levels, making our district-level results more on the point. However, as the government does have the full Census in its possession, a potential geographic targeting policy could successfully target aggregation levels as low as commune or even village, reaping the benefits of more disaggregated spatial 28
targeting. After distributing funds on the village level though, the localities must implement their own intra-village distribution; making this a hybrid approach of geographic targeting and possibly the same village-level lists of poor that are extant now. The reason is that with this methodology, estimates on a household level are probably too noisy to yield useful results.

A related note of concern must be raised on the issue of _elite capture_ . The presented gains from targeting are obtained assuming that whichever spatial unit is targeted, resources distributed within the unit are distributed uniformly. In reality, the power of the local elites is known to affect the way decentralized benefits reach the poor. For instance, Galasso and Ravallion (2005) show that the results of the Food for Education program in Bangladesh is better in communities with more land equality and they argue that this reflects greater elite capture when the poor are less powerful. Araujo et al., (2008) find that localities with more inequality (as measured by the expenditure share of the top 1%, 3%, 5%, 10% and 20% of the population) reduces the probability that latrine projects that are especially pro-poor are implemented in Ecuador, holding the level of poverty in the community constant. These findings may be especially worrying as we show in this paper that poverty is correlated with inequality in Vietnam. Also numerous studies show that that even at the lowest administrative levels the scope for political elite capture should not be assumed away<sup>9</sup> .

On the one hand, one may say that elite capture will challenge our findings as presented in Tables 2-3, but on the other hand it enforces the need to establish an objective framework for poverty targeting. Plus, as this phenomenon occurs at all levels, distributing social assistance from the commune- or village level down to poor households would still be an improvement from the current provincial level.

# **8. Conclusions**

This paper combines the 2009 Vietnam Population Census and the 2010 Vietnam Household Living Standard Survey to estimate poverty and inequality indexes for all the

> 9 See for example the World Bank Policy Research Report, “Localizing Development: Does Participation Work?” (World Bank, 2012).

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provinces and districts of Vietnam. Then, using the disaggregated data on poverty indexes, we simulate the effect of cash transfers on poverty under different targeting scenarios.

It is found that there is a large variation in poverty between districts and provinces. Mountainous and highland districts have high poverty incidence and severity in 2009. Rural and ethnic minority people account for a large proportion of the poor. There is a strong relationship between inequality and poverty: areas with low inequality are also more likely to have low poverty rates. The spatial distribution of the poor population has changed significantly during the period 1999-2009. In 1999, the poor were highly concentrated in delta regions such as Red River Delta and Mekong River Delta, partly because of the high population density in these areas. Since 1999, the number of the poor people has decreased remarkably in delta regions, but much less markedly in the Northern mountains and central highlands. While the distribution of poor people seems more evenly distributed across the country in 2009, areas with high poverty rates are more concentrated in Mountainous areas than they were a decade ago. Most provinces and districts experienced poverty reduction during 1999-2009, but the extent of poverty decline has been greatest in areas with average poverty levels in 1999. Provinces and districts with high initial poverty achieved smaller reductions in poverty.

We show that poverty rates have become more concentrated in Vietnam between 1999 and 2009. This phenomenon is likely not unique to Vietnam and may be widespread in developing countries. Economic activity and growth are becoming more spatially concentrated, due to agglomeration benefits related to network, technology and human capital externalities. This suggests that spatially targeted re-distribution policy executed alongside the development of high-potential areas may offer one means for a country to maintain growth while also maintaining an equitable distribution of wealth. As poverty becomes more spatially concentrated, the effectiveness of geographic targeting is likely to improve.

We take advantage of the high resolution available in our poverty maps to show that the more finely defined the beneficiary populations, the greater the gains from geographic targeting of anti-poverty resources over a uniform lump-sum transfer. For instance, in 2009 district level targeting is found to result in a 58 percentage point reduction in poverty severity 30
compared to uniform transfers. This is also a significant improvement over the same level of targeting in 1999, which would ‘only’ improve the lump-sump transfer by 39 percentage points.

It should be emphasized, however, that our stylized analysis of targeting cannot be used to directly evaluate existing poverty alleviation efforts in Vietnam. One possible exercise that could inform policy makers’ deliberations is to compare the hypothetical “optimal” provincial and district-level budgetary distribution deriving from an exercise as has been presented above with the actual provincial and district-level distribution that is currently in place. There is no presumption that these two should line up exactly. However, it could be of interest to follow up with further investigation if such an exercise were to reveal glaring inconsistencies.

There are, furthermore, important caveats that attach to the geographic targeting findings reported here. First, we assume that the government is willing to accept that households with equal pre-transfer per-capita consumption levels might enjoy different posttransfer consumption levels. Second, we assume in this paper that the budget available for distribution is exogenously determined. We abstract entirely from the question of how the transfers are to be financed. Political economy considerations could influence options for resource mobilization (see for example, Gelbach and Pritchett, 2002). Third, we do not address the very real possibility that the costs of administering a given transfer scheme may increase with the degree of disaggregation. Fourth, we do not allow for behavioral responses in the population.

Fifth, we do not address the possibility that inequalities in power and influence that prevail in a community influence how transfers are allocated. Such factors could result in an overestimation of the impact of spatial targeting on poverty reduction, especially as we show that inequality and poverty are correlated in Vietnam. Such elite capture on the other hand enforces the need to establish an objective framework for targeting the poor and for distribution of social funds. As elite capture is known to occur at all administrative levels, distributing social assistance directly to the commune- or village level down to poor households would still be an improvement from the current provincial level distribution.

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For all these reasons, the findings of the geographic targeting exercise should be viewed as illustrative only. At all times, the gains from targeting should be juxtaposed against the potential costs and political-economy considerations, as well as scrutinized against other possible policy objectives. In practice, a hybrid approach combining geographic targeting between villages and means-tested targeting within villages may be the best way forward. Policymakers in Vietnam will need to assess such programs on a case-by-case basis to determine just how far to rely on fine-geographic targeting as the central element in their poverty alleviation strategy.

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