metadata: authors: 0: author: Dervisevic,Ervin
1: author: Garz,Seth Aaron Levine
2: author: Mannava,Aneesh
3: author: Perova,Elizaveta
count: Lao People's Democratic Republic
docna: 0: docna: In Light of What They Know : How do Local
            Leaders Make Targeting Decisions?
docty: Policy Research Working Paper
subtopic: Inequality,Nutrition,Urban Governance and Management,Municipal Management and Reform,Urban Housing,Urban Housing and Land Settlements,Services & Transfers to Poor,Economic Assistance,Access of Poor to Social Services,Disability,Roads & Highways
teratopic: Macroeconomics and Economic Growth,Communities and Human Settlements,Social Protections and Labor,Public Sector Development,Urban Development,Health, Nutrition and Population,Poverty Reduction,Transport
abstracts: cdata!: This paper analyzes how local leaders
            make targeting decisions in the context of a public workfare
            program in the Lao People~^!!^s Democratic Republic. The
            study finds that village heads are progressive in their
            targeting, prioritizing the poorer households in their
            villages. The study benchmarks this decentralized selection
            to the common alternative proxy means test method and finds
            that village heads are at least as progressive as a proxy
            means test method approach. To illuminate what
            poverty-related information village heads could plausibly be
            incorporating into their internal selection decisions, the
            study designs and administers a set of exercises for village
            heads to rank villagers on land ownership, access to
            nutrition, and experience with recent shocks -- indicators
            that are likely to differ in their observability to village
            heads and could plausibly be associated with need for public
            support.  The study finds that village heads~^!!^
            perceptions, as revealed through the ranking exercise,
            differ substantially from actual levels reported in surveys
            of the villagers themselves. The study then uses a
            data-driven machine learning approach to identify the
            predictors of village head selection.  It concludes that
            village heads rely on a combination of easily observable
            household characteristics, forming a holistic impression of
            household welfare, rather than specific indicators like
            actual land ownership, nutrition, or economic shocks.
geo_reg_key: 517198,517191
repnme: repnme: In Light of What They Know : How do Local
            Leaders Make Targeting Decisions?
display_title: In Light of What They Know : How do
            Local Leaders Make Targeting Decisions
listing_relative_url: /research/2020/11/32550167/light-know-local-leaders-make-targeting-decisions
topicv3: Poverty m1326687 695,Nutrition m1326779 644,Cash Transfers m1327437 1608,Land and Housing m1327648 817,Road Maintenance m1505354 4031
publishtoextweb_dt: 2020-11-03T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Communities and Human Settlements,Social Protections and Labor,Public Sector Development,Urban Development,Health, Nutrition and Population,Poverty Reduction,Transport
origu: EAP Chief Economist Unit (EAPCE)
no_of_pages: 55
author_keys: 0: author_key: 1204570
1: author_key: 1297140
2: author_key: 1348103
3: author_key: 943628
model_extractions: Policy Research Working Paper 9465 In Light of What They Know How Do Local Leaders Make Targeting Decisions?

_Ervin Dervisevic Seth Garz Aneesh Mannava Elizaveta Perova_ East Asia and the Pacific Region Gender Innovation Lab & Office of the Chief Economist November 2020
Policy Research Working Paper 9465

# **Abstract**

This paper analyzes how local leaders make targeting decisions in the context of a public workfare program in the Lao People’s Democratic Republic. The study finds that village heads are progressive in their targeting, prioritizing the poorer households in their villages. The study benchmarks this decentralized selection to the common alternative proxy means test method and finds that village heads are at least as progressive as a proxy means test method approach. To illuminate what poverty-related information village heads could plausibly be incorporating into their internal selection decisions, the study designs and administers a set of exercises for village heads to rank villagers on land ownership, access to nutrition, and experience with recent shocks—indicators that are likely to differ in their observability to village heads and could plausibly be associated with need for public support. The study finds that village heads’ perceptions, as revealed through the ranking exercise, differ substantially from actual levels reported in surveys of the villagers themselves. The study then uses a data-driven machine learning approach to identify the predictors of village head selection. It concludes that village heads rely on a combination of easily observable household characteristics, forming a holistic impression of household welfare, rather than specific indicators like actual land ownership, nutrition, or economic shocks.

This paper is a product of the Gender Innovation Lab and the Office of the Chief Economist, East Asia and the Pacific Region. 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:// www.worldbank.org/prwp. The authors may be contacted at eperova@worldbank.org.

_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
# In Light of What They Know: How Do Local Leaders Make Targeting Decisions?<sup>1</sup>

Ervin Dervisevic*, Seth Garz^, Aneesh Mannava* and Elizaveta Perova*~ JEL classification: I38, D73, O12 Keywords: Targeting Social protection Public works Decentralization Local leaders Measurement 1 We gratefully acknowledge funding from the Umbrella Facility for Gender Equality (UFGE) and Australia’s DFAT to carry out this work. The data collection protocols were approved by Health Media Labs IRB. We thank Alana Teman for excellent field coordination, and Darathip Sounthavong, Vanvilay Phommalath and Khanithalath Koummarasy for the management of data collection process. We are grateful to Hatthachan, Hongngeun Sadettan and other members of Laos PRF for providing detailed information about the RMG program and sharing their insights about our findings. We thank Aline Coudouel, Patrick Premand, Stephanie Kuttner, Erik Johnson, Norman Loayza, Sybounheung Phandanouvong, Kathleen Beegle, Aaditya Mattoo and East Asia and Pacific Chief Economist Office seminar participants for helpful comments. All errors are our own.

The IE was carried out by the East Asia and Pacific Chief Economist Office, under PASA activity P156793. Elizaveta Perova was the TTL of the PASA. Erik Caldwell Johnson is the TTL of the Poverty Reduction Fund III AF (P168620) operation, which supports the Laos Road Maintenance Groups project.

* World Bank; ^ Bill and Melinda Gates Foundation; ~ corresponding author: eperova@worldbank.org
# 1. Introduction

Targeting of social programs by local leaders, fully or in combination with some other targeting method, has been at the core of development policies and programs for many years. The discussion of the merits and perils of this approach to targeting has been comparably long. Recently, the COVID-19 pandemic has necessitated quick and effective emergency assistance delivery to those most affected, bringing new relevance to the old debate.

Indeed, there are several strong arguments in favor of this approach. Local leaders – individuals deeply embedded in the context of the lives of potential beneficiaries – may have better information about the community and relative needs of its residents (Conning and Kevane 2002; Subbarao et al. 2012). Reliance on local leaders also presents an opportunity to save resources needed for collecting information to implement alternative targeting methods, such as proxy means tests, which are often more data intensive (Alix-Garcia et al., 2019). Finally, selection of beneficiaries by a trusted member of the community may be easier to understand than selection based on a more technical approach,<sup>2</sup> and thus result in greater acceptance of targeting results by the community (Alatas et al., 2012).

On the other hand, vesting power of beneficiary selection in the hands of several community members may result in nepotism and elite capture. This has been an important concern for reliance on targeting by local leaders. There is no consensus in the literature on the extent of and welfare losses from elite capture in targeting of social programs by local leaders. Kilic et al. (2013) find evidence of nepotism when analyzing the distributional effectiveness of the Malawi Farm Input Subsidy Programme (FISP). Their results suggest that the relatively well-off and locally well-connected are more likely to access the program where targeting decisions are largely made by village chiefs. Analyzing the same program but using a different measure of poverty, Basurto, Dupas and Robinson (2019) suggest that the consequences of nepotism are likely to be small: a PMT would not do better. This result corroborates an earlier finding from Alatas et al. (2013) in Indonesia, who suggest that although elite capture exists in the distribution of village welfare programs, eliminating it entirely would only marginally improve welfare gains from governmental programs.<sup>3</sup> Although a key focus of prior research, elite capture is not the only reason why village leader selection for social programs may lead to sub-optimal targeting of beneficiaries. Notably, the dominant approach to evaluating the effectiveness of targeting is comparison of selected beneficiaries with the members of the community deemed the “poorest” according to some poverty indicator. This common metric for effectiveness in the research literature may differ from the metric in the mental model guiding the village leader selection process for several reasons. First, the set of the poorest will clearly depend on the indicator of poverty used, which is ultimately a subjective decision. Alatas et al. (2012) demonstrate that relatively worse performance of community leaders targeting compared to PMT stems from the fact that they use a different definition of poverty. Second, village leaders may use a set of selection criteria other than poverty indicators because they are optimizing for a different metric of effectiveness. For example, Basurto, Dupas and Robinson (2019) show that village chiefs target fertilizer subsidy distribution based on

> 2 Roopnaraine and Adato (2004), Brown et al. (2016) find that complexity of the PMT-based selection leads to challenges of acceptability since non-beneficiaries may find it difficult to understand why they were not selected while others were. Cameron and Shah (2014) find that using PMT in a cash transfer program in Indonesia led to distrust of local administrators, erosion of local social capital, and even an increase in crime rates. 3 By less than 1 percent.

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expected productivity. The authors further demonstrate the presence of meaningful intra-village transfers, suggesting that in the presence of these transfers allocating fertilizer subsidy to more productive households may be a superior selection criterion compared to targeting based on poverty status.

Even if village leaders were optimizing targeting of the poorest community members according to some objective definition of poverty, the effectiveness of their selection will be limited by the information available to them. Although local leaders are immersed in the life of the community, they may not have the same level of detailed information on every household that an enumerator sent to recover this information could retain. Although lack of information is a straightforward and universally plausible constraint on targeting efficiency there is a notable lack of research on the topic. To our knowledge, the only study that tests this information hypothesis is Alix-Garcia et al. (2019) in which the authors find that local leaders actually do provide accurate information on households, but acknowledge that the accuracy of their information may vary by context; for example, their information may be less accurate in larger and more dispersed communities.

In this context, our paper attempts to make a threefold contribution to the literature on targeting by local leaders. First, we contribute to the body of evidence addressing traditional metrics of effectiveness of local leader targeting by answering two questions: i) do they select worse off community members? ii) How well does their selection compare to alternative approaches, including the canonical technical approach of PMT? Second, we attempt to shed light on the process of selection. Specifically, we explore what characteristics and criteria best rationalize local leader led beneficiary selection. This aspect of the paper contributes to the evidence that local leaders may use a different definition of poverty compared to program designer/policy maker. We test whether, in selecting the poorest, local leaders are more likely to rely on information about assets, likelihood of hunger, or subjective perception of immediate need. While assets are likely to capture long-term welfare, likelihood of hunger is a better proxy of current welfare, and perception of immediate need may reflect exposure to shocks. Third, we check whether local leaders rely on accurate information when making targeting decisions: i.e. whether what they know about household’s landholdings corresponds to landholdings as reported by the household.

Another contribution of our paper is in analyzing the effectiveness of targeting by local leaders in the specific context of a public works program. We focus on a program in the Lao People’s Democratic Republic where village heads played an important role in selecting program beneficiaries. Constraints on effective targeting are likely to differ by program type. For example, the likelihood of nepotism may be higher for programs where there are no implicit participation costs (e.g. unconditional cash transfers, food vouchers), compared to programs with strings attached, such as public works programs, especially if working conditions are difficult. In contrast to the workfare setting of our study, much of the existing targeting literature is focused on conditional cash transfers, including work focused on the targeting of health insurance (Alatas et al., 2012), food subsidies (Alatas et al., 2012 and Basurto, Dupas and Robinson, 2019), and fertilizer subsidies (Basurto, Dupas and Robinson, 2019).

We collect detailed welfare data on those who were identified as eligible to participate in the program by the village heads, as well as on a random sample of households from the same village. We also administer a series of ranking exercises to the village heads, aimed to elicit guiding principles for their selection, and to assess the quality of information they rely on. The paper is organized as follows: the next section describes the Road Maintenance Groups program, the process of beneficiary selection and provides 3
background on the position of village heads. Section 3 presents the study’s data. Section 4 analyzes whether the village heads indeed selected poorer households in the village. Section 5 explores what village heads prioritize when making selections. Section 6 analyzes what information they use. Section 7 explores the question of acceptability of the village heads’ selection for other community members. Section 8 concludes.

# 2. Road Maintenance Groups Program and Beneficiary Selection

In rural Lao PDR, remote villages are connected to laterite highways via dirt access roads. Access roads are built by government-hired contractors but typically fall into extreme disrepair and often become unusable within a couple years due to a harsh rainy season. The Road Maintenance Group (RMG) program is a pilot program set up by Lao PDR Poverty Reduction Fund (PRF)<sup>4</sup> with the dual objectives of providing an income-earning opportunity to in-need households and extending the lives of the access roads. The RMG program trains participants to carry out road maintenance activities that do not require heavy machinery and subsequently employs them to provide basic road maintenance services throughout the year. PRF designed the program to be part-time. In order to support women’s empowerment, PRF only hires women to work in RMGs.

The villages participating in the RMG program are poor and have few opportunities for wage work. Compared to rural households in the nationally representative sample in the Lao Expenditure and Consumption Survey (LECS) V, collected in 2013, RMG villages were poorer across a range of indicators including housing composition, ownership of durables, and nutrition.<sup>5</sup> Only 17% of working-age individuals in these villages had paid work outside the household at the start of the program. The RMG jobs were therefore highly attractive to populations living in these villages, but the program aimed to allocate the RMG spots to the poorest households in the villages.

The village heads played an important role in this allocation. First, the village heads were responsible for publicizing the program and inviting households to the registration event, acting to some extent as gatekeepers for information about RMGs. Baird, MacIntosh and Ozler (2013) provide an example of informational elite capture. Their analysis of targeting effectiveness of Tanzania’s flagship CDD program suggests that wealth, education, access to media, and political engagement are positively correlated with awareness of the program, which is a necessary condition for benefitting from it. Such lack of access to information results in only mildly pro-poor targeting.

Second, the village heads contributed to the selection of beneficiaries through updating their poverty ranking. Specifically, beneficiaries for the RMG program were selected through a two-step process in June 2018. In the first step, those eligible to participate in the program were identified. In the second step, as demand for the program exceeded the number of RMG jobs, a lottery (lucky draw) was carried out to

> 4 PRF is housed under the Government Office of Lao PDR. Its mission is to apply a community driven development approach to reduce poverty in Lao PDR, with a focus on rural populations, by improving infrastructure and access to services and resources.

> 5 Authors’ calculations using the baseline data for the RMG impact evaluation and LECS V. Available upon request.

4
divide all the eligible and interested into beneficiaries (RMG) and a waitlist (WL).<sup>6</sup> In this paper, we focus on the first step of the selection process. In order to qualify as ‘RMG-eligible’, registrants had to be at least 18 years old, female, and able-bodied. Women from poorer households were given priority based on the household poverty rank (poorest, poor, middle income and better off). Specifically, women in the bottom two poverty ranks (poorest and poor) were first granted entry. If the village quota for RMG-eligible was met, the process ended. If the quota was not met, women from the next poverty rank (middle-Income) were then entered. If the quota was still not met, women from the next poverty rank (better-off) were also entered. Eligibility was thus determined by poverty bracket; no distinction was made between women of the same bracket.

The poverty status of each household was determined through a process that combined PMT, communityparticipation, and village-head updating. Household poverty ranks were originally constructed by PRF in 2016 based on PMT. In each village, there was a multi-day participatory process of PMT verification and adjustment by which households were assigned poverty ranks. During the registration process for the RMG program in 2018, village heads updated these rankings to account for changes in the two years since the original ranking. Twenty-four percent of households changed their poverty rank as a result of this updating.

Figure 1 plots the original ranking against ranking by the village heads in 2018. Overall, 174 households changed poverty category.<sup>7</sup> Among them, 74 (10 percent) became less poor: moved from poorest to poor or middle income, from poor to middle income, or from middle income to better off. One hundred (14 percent) became poorer, moving from better off to middle income, poor and poorest, from middle income to poor and poorest, or from poor to poorest. If these movements reflect real changes in household welfare, reliance on village heads is efficiency improving. It would allow PRF to allocate the RMG program considering recent changes in household welfare, without carrying out another PMT test and community validation, which at the moment of the RMG program launch were prohibitively expensive. On the other hand, the change in poverty ranking of 24 percent of interested households may reflect nepotism, or as discussed above, be based on a definition of poverty different from the one preferred by PRF.

A priori, we believe the former hypothesis to be more likely. First, village head is an elected position. Specifically, the village head is first elected by villagers, and then approved by district governor. The term lasts for 3 years, and village head can be re-elected. Notably, the villagers can report to the district management office, if they are not happy with the village head, and request re-election/re-appointment. Consequently, there are accountability mechanisms that create disincentives to engage in nepotism.

Second, assessment of poverty status of the villagers is included in responsibilities of village heads. Decree on the Criteria for Poverty Graduation and Development outlines six criteria for graduating from poverty, and village heads should be receiving training on these criteria. These criteria are: (1) have safe and strong housing; (2) have assets and equipment necessary for their livelihoods and income generation; (3) have

> 6 PRF agreed to introduce randomized assignment of jobs with two objectives: (i) fair and transparent allocation of the jobs; (ii) possibility to carry out an impact evaluation of the RMG program. The data analyzed in this paper were collected as part of the IE.

> 7 We have the original 2016 poverty ranks for 716 households. For other 430 households, the rankings were not available. We are confirming the role of village heads in assigning these rankings.

5
labor, stable income or employment; (4) school age family members receive lower secondary school education; (5) have access to clean water and stable sources of energy; (6) have access to primary public health services. Decree suggests that implementing ministries should divulgate its provisions to local governments. The extent to which village heads adhere to these criteria when selecting beneficiaries in practice depends on several factors, such as success in disseminating the decree and village heads training on it, potential differences in interpretation of some of these criteria (e.g. stable employment) and village heads’ access to information about these criteria for all households in the village. Lastly, village heads may still select on a different criterion, as in Basurto, Dupas and Robinson (2019), especially given that there are no formal checks of accuracy in place.

We study the outcomes of village heads selection process in 85 villages across 7 provinces in northern and southern Lao PDR where the RMG program was implemented.<sup>8</sup>

# 3. Data

To understand how village heads determined household eligibility and to assess the quality of their decisions in terms of targeting the poor, RMG baseline data collection included two targeting assessment components. First, in addition to collecting data on RMG and WL households that form the treatment and control groups for the impact evaluation, we administered the same survey to a Random Sample (RS) of village households with a woman of eligible age to participate in the RMG program (limited for simplicity in the random sample to ages 18-55). Households selected into the random sample could be replaced if they did not have a female household member of eligible age, if they had moved out of the village, if they could not be located after multiple attempts spanning at least two days, if they refused to participate, or if they were already part of the RMG or WL sample.<sup>9</sup> This sampling strategy enables comparison of household characteristics between the RMG-eligible and RS groups to establish whether the households granted entry to the lucky draw were truly among the villages’ poorer households. Second, we administered the village heads a targeting survey to probe the key factors influencing their selection of welfare beneficiaries and to understand their underlying perceptions regarding what constitutes poverty.

> 8 Although the RMG program was implemented in 86 villages, one village was dropped from the analysis because recruitment of beneficiaries there deviated from the model described above.

> 9 There was a total of 274 replacements: 151 households were replaced because they were already in the RMG/WL sample. The remaining replacements were due to: absence of women of eligible age in the household (36); households were absent due to harvesting season (20); households were no longer in the village (33); refusal (14) and other reasons, such as mental illness or temporary absence (20).

6
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a total of 1,245 households. There were two types of ranking questions: RMG priority ranking and ranking based on specific criteria:

# _“RMG Priority Ranking”:_

Please rank who you would prioritize to join the RMG if it were solely up to you and not determined by eligibility and a lottery. Start with the highest priority to the least priority. For example, if I were in the list and you really thought I should receive the RMG job, you would rank me number 1. If you think I am the last person in the list you would give the job to, rank me (total number in the list).<sup>12</sup>

# _Criteria Rankings:_

- Rank who owns the least to most square meters of land, agricultural and residential land combined.

- Rank the likelihood that, in the last 12 months, the person or someone in her household was hungry but did not eat because there was not enough rice or food at home, and not enough money or other tradeable goods to purchase rice or food. Start with the person or household most likely to have had been hungry but did not eat.

- Rank who needs the most to least help right now.

The variety of data collected, including actual data on selection, respondent self-reports, and data collected on hypothetical ranking by village heads, combined with an intention sampling design that includes both selected and non-selected among eligible and a random sample of villagers, enable us to explore both the actual targeting by village heads, the proximate inputs into the targeting choices, and the potential information constraints that may limit the ability of village heads to target the poorest households in their villages.

4. Do village heads effectively select poorer households for participation in RMGs? Given the extensive baseline surveys administered to the RMG-eligible and the RS, we can compare them on a wide range of dimensions, including income, assets, land holding, etc. Notably, the baseline questionnaires did not include a comprehensive consumption module, which elsewhere is commonly used for assessing poverty status. Table A1 in the Appendix shows 127 variables describing the participants’ basic demographic characteristics, household composition, income, possession of durables and livestock, housing conditions, nutrition, and experience of economic shocks, available in our survey.

To reduce dimensionality and address the likelihood of Type I errors in our numerous possible comparisons between the RMG-eligible and RS groups, we follow Katz, Kling and Liebman (2007) and Andersen (2008) by constructing composite z-scores and adjusting the p-values for multiple hypothesis testing. The z-scores of the individual variables are calculated and then averaged by category to construct composite z-scores. The z-scores of variables assumed to be negatively correlated with wealth are inverted. Altogether, we construct 7 composite z-scores to encompass broad dimensions of welfare: land and buildings ownership, household composition, durables ownership, livestock ownership, housing conditions, nutrition, and shocks. We also test for differences in household per capita total income. We apply Bonferroni correction to adjust p-values for the differences in means tests, to further reduce the

> 12 Sixteen villages did not have enough RMG, WL, and RS members to rank 15 individuals because of either smaller RMG-eligible memberships or participant absence at time of survey. In these cases, the maximum number of households available for ranking was specified in the instructions. Eight villages had 14 households ranked, 5 villages had 13, 2 villages had 12, and 1 village had 9.

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likelihood of false rejections of the null hypothesis of no difference due to multiple hypotheses testing. Table 1 shows the t-tests and provides more details on variables included in specific z-scores.

**Table 1: Differences in household welfare between RMG-eligible and Random Sample**

||RMG-<br>eligible<br>obs.|Random<br>Sample<br>obs.|RMG-<br>eligible<br>mean|Random<br>Sample<br>mean|Diff. (RMG-<br>eligible –<br>Random<br>Sample)|P-val|Bonferroni<br>corrected<br>P-val|
|---|---|---|---|---|---|---|---|
|Land and buildings<br>ownership|1146|742|-0.035|0.054|-0.0890**|0.004|0.033|
|Household composition|1146|742|-0.0176|0.0272|-0.0448|0.013|0.102|
|Durables ownership|1146|742|-0.0388|0.0599|-0.0987***|0.000|0.000|
|Livestock ownership|1146|742|-0.0449|0.0693|-0.1142***|0.000|0.001|
|Total income per HH<br>member (USD PPP)|1146|742|381.919|480.733|-98.8140**|0.004|0.032|
|Housing conditions|1146|742|-0.0131|0.0202|-0.0333*|0.008|0.066|
|Nutrition|1146|742|0.0097|-0.0149|0.0246|0.084|0.669|
|Exposure to shocks|1146|742|0.0039|-0.0059|0.0098|0.666|1.000|

* p<0.1 ** p<0.05 *** p<0.01

RMG-eligible = 1146 observations. Random Sample = 742 observations. All household characteristics are shared as composite z-scores, except for total income per HH member. Variable included in composite z-scores are: Land and <u>buildings: total area of residential and agricultural land, plus total number of buildings owned by each household. Household composition: dependency ratio, household head age, gender, ethnicity, and education. Durables ownership:</u> ownership of household goods, including cars, TVs, refrigerators, etc. Livestock ownership: numbers of productive animals owned, including cows, buffalos, chickens, etc. Total income per capita: sum of 9 types of income divided by the number of household members. Housing conditions: number of rooms; flooring, roofing, and wall materials; kitchen location and structure; access to water; etc. Nutrition: balls of rice, kg of meat, number of days dairy eaten, etc. Exposure to shocks: experience of health, business, and agricultural shocks.

Table 1 suggests that the RS households are significantly better off than RMG-eligible households, typically owning more possessions, having higher quality housing, and generally experiencing better well-being as viewed through these variables. It is important to remember that the pool of RMG-eligible reflects a combination of targeting methods: self-targeting as well as targeting by village heads. Village heads affected the selection process through (i) sharing information about the program; (ii) updating poverty 9
status.<sup>13</sup> However, only women who were also interested to join RMGs entered the lucky draw and are captured in our sample as RMG-eligible. For simplicity and clarity of exposition, we may refer to this sample as village head selection, though formally speaking this implies village head selection conditional on self-selection.

Overall, Table 1 suggests that the RMG program effectively targeted poorer households within villages. We next compare actual selection to a hypothetical selection based on PMT used by the Government of Lao PDR to identify the poor for participation in social programs. This PMT index was developed by the World Bank and Government of Lao PDR, following administration of LECS V in 2012-2013 (Pimhidzai et al. 2014). It relied on regression analysis of household consumption on surveyed household characteristics. The model aimed to accurately distinguish between poor and non-poor households. Nonsignificant characteristics were removed from the model; characteristics that minimized misidentification of poor and non-poor households were kept. Index variables and weights were determined based on the regression outcomes. Households scoring below 64 were classified as poor. Table A2 in the Appendix provides the complete list of variables used in the PMT index (World Bank forthcoming).

When we compare village head selection with those selected through PMT, we find that, though both methodologies selected approximately the same number of beneficiary households, they disagree on which households should benefit in 44% of cases. The magnitude of this disagreement holds true whether one considers the full sample of 1,888 study households (Table 2) or only 1,245 households ranked in the village head targeting survey (Table A3 in the Appendix). Although we expect differences in how PMT and village heads identify poor households, the consensus on population poverty rates and need for welfare support is striking.

**Table 2: Village heads vs PMT – Full Sample of 1,888 Households**

|RMG-eligible|PMT b<br>No|elow 64<br>Yes|Total|
|---|---|---|---|
|No|300 (16%)|442 (23%)|742 (39%)|
|Yes|401 (21%)|745 (39%)|1,146 (61%)|
|Total|701 (37%)|1,187 (63%)|1,888 (100%)|

The discrepancy in identification of the poor by the two methods leads us to further assess their targeting accuracy. We compare household characteristics typically indicative of wealth status between the eligible and ineligible/ poor and non-poor groups under village heads selection and PMT. We expect the more accurate method to produce a larger difference in observable welfare characteristics.

To reduce the complexity of analyzing overall wealth disparities through numerous household characteristics, we again construct composite z-scores similar to those we used for comparisons of RMG-

> 13 Thus, the selection may also reflect PMT and community validation carried out in 2016. However, based on the field supervisors’ reports, the changes in poverty ranks were substantial.

10
eligible and random sample in Table 1. One important difference in the construction of these z-scores is that now we exclude variables used to construct the PMT index. Including these variables would give unfair advantage to the PMT method: we would expect to observe larger differences between eligible and non-eligible according to PMT on the variables underlying its construction.<sup>14</sup> Figure 1 plots the differences in z-scores between RMG-eligible and RS, against the differences between poor and non-poor according to PMT, including 90% confidence intervals. The gaps between groups according to the two methods suggest that PMT outperforms RMG-eligible selection in identifying poor households. The differences in means are significantly larger under PMT for 4 out of 8 analyzed indicators.<sup>15</sup> RMG-eligible selection performs better than pure random assignment to eligible vs. ineligible groups, but it is less accurate than PMT. T-tests for differences in z-scores for eligible and not eligible according to two selection methods are shown in Table A3 of the Appendix.

However, village heads were identifying the poorest households from within a village, not across the sample. If we replicate this analysis by calculating z-scores within village, rather than across the entire sample, village head selection and PMT appear to provide equivalent levels of accuracy. Figure 2 shows the differences in z-scores between eligible and non-eligible according to the two methods, including confidence intervals.<sup>16</sup> The overlap of confidence intervals suggests that we cannot reject the hypothesis of no difference in the magnitude of the welfare gap between eligible and non-eligible according to the two methods. Table A4 in the Appendix shows corresponding t-tests.

In the analysis based on village-level z-scores, easily observed variables perform better under village heads selection, while factors less visible to the public eye perform better under PMT. For example, livestock and durables ownership are negative and significant for both methodologies, but the difference in means is greater under RMG-eligible selection. Conversely, nutrition and income, the status of which might be known only to household members, perform better under PMT; the difference in the nutrition composite z-score t-test is negative and significant only for PMT, and the difference in means for income is significantly larger under PMT.

The village-level results suggest that when organizing beneficiary selection to meet the needs of the local context, the village heads selection was effective through leveraging the heads’ familiarity with the households. This familiarity, however, was limited when it came to assessing more obscure or private household characteristics. In such cases, the rigorous PMT methodology based on detailed survey data performed better.

We must also note that less-poor households (ranked ‘Middle-income’ or ‘Better-off’) were included in the RMG-eligible list in some villages in order to satisfy village quotas for the program. We would expect the inclusion of these non-poor households to worsen the average performance of the village heads selection. In a setting where the quotas for road maintenance were satisfied by the households from the

> 14 Comparison of RMG eligible observations and random sample using this restricted selection of variables provides results similar to those shown in Table 1. See Table A5 in the Appendix for all comparisons.

> 15 Please note, income per household member is shown as z-scores in Figure 1 in order to display the measure along with the other indicators in the graph, but the actual difference for the RMG-eligible vs. RS is -0.1426 (p<0.01) and, for PMT poor vs. nonpoor, -0.8213 (p<0.01).

> 16 Income per household member is again shown as a z-score to aid the graph. Actual difference between RMGeligible and RS is -0.2160 (p<0.01). Between PMT poor and nonpoor, the difference is -0.5417 (p<0.01).

11
lowest poverty ranks, we would expect the village heads selection to fare slightly better on welfare comparisons.

12
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demonstrates a regression of likelihood of being ranked higher for RMG priority on each of the three criteria in columns (1) – (3) and the three rankings together in column (4). Regression in column (5) controls for RMG-eligibility status: a dummy equal to 1 if the household is RMG-eligible, and 0 if it belongs to the random sample.

Table 3 reveals several interesting features about the process of village heads’ selection. First, the relationship between priority ranking and each of the three ranking is significant, but coefficients are low, ranging between 0.15 (for land holdings) and 0.22 (for the need for help). Notably, association between the ranking of those who need help most and RMG priority ranking is the strongest: not only is the coefficient the highest, association between these rankings is the only one that remains significant once all three rankings are accounted for in a regression. Village heads appear to rely on some notion of the need of help when deciding on how to prioritize for RMGs.

Second, once we account for RMG-eligibility, none of the three criteria remains significant. Only designation as an RMG-eligible or RS household is significant: being eligible for RMGs is associated with decrease in rank, and consequently higher priority for RMGs in village heads’ perception (please note that households are ordered from 1 to 15, one being the highest priority). This may suggest that village heads fully endorse the selection criterion proposed by PRF: household’s poverty status. An opportunity to prioritize based on a different criterion does not lead to a vastly different result than their selection of RMG-eligible. However, as mentioned earlier, we cannot exclude the possibility of an anchoring effect.

**Table 3: Likelihood of being prioritized for an RMG role as a function of other rankings**

||Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|Priority for<br>RMG|
|---|---|---|---|---|---|
|Lowest to highest land holdings|0.1560***|||0.0465|0.0266|
||(0.036)|||(0.038)|(0.028)|
|Highest to lowest likelihood to be<br>hungry||0.1834***||0.0321|-0.0277|
|||(0.036)||(0.047)|(0.037)|
|Highest to lowest need for help|||0.2228***|0.1751***|0.0466|
||||(0.037)|(0.049)|(0.040)|
|RMG-eligible (y/n)|||||-6.2117***<br>(0.420)|
|Village fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|1,245|1,245|1,245|1,245|1,245|

* p<0.1 ** p<0.05 *** p<0.01

Priority for RMG regressions are calculated as highest to lowest priority: the household ranked #1 is the household that should get the highest priority. Standard errors in parentheses. The 3 criteria (land holdings, likelihood to be hungry, need for help) are ranked with #1 as the poorest or worst outcome, and improving in status as the rankings increase until the final slot, #15.

# 6. What information do village heads use in their targeting decisions?

Relatively weak association between RMG priority ranking and the three rankings which may capture different dimensions of poverty begs the question of whether village heads use accurate information in assigning the ranks. To check whether this is the case, we correlate village heads’ ranking and ranking 15
based on the survey data. Specifically, we correlate ranking on land with rank on the sum of land area, ranking on the likelihood of hunger with a nutrition index, and ranking on the need for help with an index constructed based on the survey data about recently experienced shocks. We use Kendall correlations, a special case of standard Pearson correlation evaluating the degree of similarity among sets of ranks (Abdi, 2007).

To check robustness, we use several measures for each of the dimensions of poverty captured in ranking exercise. For land, in addition to the sum of residential and agricultural plots, we create a composite z- score of variables related to land and building ownership, as well as the first principal component of these variables. For the likelihood of being hungry, we use a composite z-score and the first principal component of all nutrition-related questions in the survey. For the likelihood of needing help, we create a composite z-score of all survey variables related to economic shocks, and also use the first principal component of these variables. These variables provide a unique household ranking in nearly all villages. Table 4 presents the results.

Village heads’ rankings of land ownership are weakly but significantly correlated with households’ reported land size. Their rankings of likelihood of hunger are correlated with nutrition measures but the correlation is weaker. Rankings of need for help are not correlated with shocks. The consistent strength of land measures across ranking exercises may be explained by the fact that land is the most directly observable of the three criteria for someone outside the household.

**Table 4: Correlations of rankings and household variables**

||Lowest to highest<br>land holdings|Highest to lowest<br>likelihood to be<br>hungry|Highest to lowest<br>need for help|
|---|---|---|---|
|**Land measures**||||
|Size of all plots (res. and agr.)|0.149***|0.098***|0.103***|
|Composite z-score of land and buildings|0.150***|0.133***|0.136***|
|Principal component 1 of land and buildings|0.148***|0.133***|0.137***|
|**Nutrition measures**||||
|Composite z-score of nutrition|0.008|0.004|0.017|
|Principal component 1 of nutrition|0.088***|0.108***|0.090***|
|**Shocks measures**||||
|Composite z-score of shocks (all inverted)|-0.049**|-0.031|-0.023|
|Principal component 1 of shocks (all inverted)|-0.031|-0.028|-0.008|
|Observations|1,245|||

* p<0.1 ** p<0.05 *** p<0.01

The weakness of the correlations between the three criteria and their corresponding observable characteristics indicates that village heads do not determine these rankings based solely on observable land, nutrition, and shock characteristics. Rather, the village heads may base their rankings on other information about household welfare that overlaps with the information on land, nutrition, and shocks.

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context-specific understandings of poverty and of who needs help the most. The holistic measures are based on visible household characteristics and potentially on observable community characteristics, as well.

# 7. Is the village head’s role in selection acceptable to the population?

While discussing the random forest analysis of ranking predictors, we observed that there appears to be little or no evidence of elite capture in the village head selection process. Analyzing the performance of village head targeting against PMT suggested that the village heads are better able to incorporate local knowledge, tailoring beneficiary selection to the local context. To access effectiveness of village heads targeting, one more question must be answered: do community members consider village head targeting to be a legitimate method for allocating community resources?

Our survey included two questions that facilitate this inquiry: 1) Do you feel that your village head has a good understanding of how rich or poor most of the households are in your village? 2) When the RMG in this village was selected, the village head advised the selection team on the poverty status of the applicants. Is this a fair practice? These questions were administered to WL and RMG households only – unfortunately, we did not ask the RS households. Below we present the results separately for RMG and WL women, considering that being selected into/out of the program, even if as a result of lottery, may affect RMG/WL women’s view on the overall fairness of the selection process and the role of village heads.

Both RMG and WL women indicate strong confidence in the accuracy and fairness of village head targeting. Sixty-five % of RMG and 67% of WL women thought the village head “absolutely” had a good understanding of the villagers’ wealth. An additional 22% of WL and 19% of RMG members thought the village head understood “to some degree.” There was no significant difference between their responses.

Similarly, the RMG and WL women considered the village head’s role in beneficiary selection to be fair. Sixty-eight % of WL and 70% of RMG thought it was “absolutely” fair, and 18% of WL and 17% of RMG thought it was fair “to some degree.” Again, there was no significant difference in answers between WL and RMG.

Unfortunately, we did not ask the RS households these questions. This leaves open the possibility that those who were admitted to the lucky draw were satisfied with village heads involvement in the process even if they did not ultimately benefit from the program (i.e. were in the control group), whereas those denied the chance to enter the lucky draw could be dissatisfied with the village heads’ role. It is also possible that our respondents indicated satisfaction out of acquiescence bias or hesitation to admonish authority figures. Future studies can inquire more deeply into community-wide acceptance of village head targeting.

# 8. Conclusion

We studied the targeting performance of a multi-province workfare program in rural Lao PDR that used a combination of methods to target poorer women interested in the opportunity to do paid work. To identify poorer women, the program used poverty ranks constructed using PMT and community ranking in 2016 and updated by village heads during the program registration process in 2018. Our results suggest that the program was successful in identifying beneficiaries significantly worse off than a random sample 20
of households from their villages. Moreover, comparison with a hypothetical pure-PMT demonstrates the two methods to be roughly equivalent in their ability to identify poorer households within a village.

Further, we shed light on what information village heads use to assess the welfare status of households. We asked village heads to rank households by different welfare criteria and then compared these data to household survey data. We find that village heads seem to base their assessments of specific criteria like household land ownership, likelihood of being hungry and likelihood of needing assistance on a holistic impression of household welfare, rather than specific indicators like actual land ownership, nutrition or economic shocks. Using random forest analysis, we find that their rankings are best explained by easily observable indicators of welfare like household characteristics and asset ownership.

In this program, updating poverty ranks by village heads provided a much quicker and less expensive alternative to a full PMT plus community-ranking and validation process, without compromising the targeting efficacy of the program and produced outcomes acceptable for the community. The results suggest that the performance of village heads compared to alternative methods may be particularly strong in remote areas with varying poverty levels. There, the village heads’ familiarity with residents and local circumstances can help them distinguish among community members’ relative welfare statuses. In these cases, a flexible targeting led by village heads may be successful at achieving pro-poor targeting objectives.

There are caveats about the applicability of these findings in other contexts. First, village heads only updated prior, community-supported poverty rankings in this program. This reduced the reliance on village heads for determination of eligibility and may have also improved community acceptability, since applicants may have perceived the updated rankings as a revision of a system they had already endorsed. Other populations without a comparable experience may view village head targeting as less legitimate. Second, the selection for the RMG program also relied on self-selection into the program by the neediest households. This may explain some of the targeting success of the program. Third, our analysis suggests that village heads hold a holistic impression of household welfare built on easily observable household and individual characteristics. This type of assessment may be less useful when targeting households with specific, less observable needs, such as the need for nutrition support.

21
# **References**

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2. Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., and Tobias, J. (2012). Targeting the poor: evidence from an experiment in Indonesia. _American Economic Review_ , 102(4):1206–40.

3. Alix-Garcia, J.M., Sims, K.R.E., and Costica, L. (2019). Better to be indirect? Testing the accuracy and cost-savings of community leader surveys for poverty targeting. Working paper, University of California Davis.

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6. Brown, C., Ravallion, M., and Van de Walle, D. (2016). A poor means test? Econometric targeting in Africa. Technical report, The World Bank, Washington, DC.

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9. Friedman, J., Hastie, T., and Tibshirani, R. (2001). _The elements of statistical learning_ , Volume 1. Springer series in statistics: New York.

10. Kilic, T., Whitney, E., Winters, P., 2013. Decentralized beneficiary targeting in largescale development programs: insights from the Malawi farm input subsidy program. Journal of African Economies, Volume 24, Issue 1, January 2015, Pages 26–56.

11. <mark>Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(1), 83-119.</mark> 12. Pimhidzai, O., Fenton, N. C., Souksavath, P., and Sisoulath, V. (2014). Poverty Profile in Lao PDR. Technical report, The World Bank.

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# **Appendix**

# Table A1: T-tests of RMG-eligible and RS Group Descriptive Statistics

Note: Yellow highlighting indicates that the variable was excluded during construction of composite z-scores to compare VHT against PMT, since these variables were used in construction of the PMT index.

Note: Variables marked "(inverted)" were not inverted in Table A1, but their individual z-scores were inverted when computing composite z-scores.

||RMG-eligible<br>mean<br>(N=1,146)|RMG-eligible<br>SD|Random<br>Sample mean<br>(N=742)|Random<br>Sample<br>SD|Diff. (RMG-<br>eligible –<br>Random<br>Sample)|P-val|
|---|---|---|---|---|---|---|
|**Land and buildings ownership**|||||||
|Size of agr. plots (thousand sqm)|19.3528|21.4625|19.6314|24.212|-0.2785|0.799|
|Size of res. plots (thousand sqm)|0.3287|0.6168|0.4269|0.7434|-0.0982***|0.003|
|Num. of buildings owned (res. and business)|1.0785|0.2969|1.1146|0.3805|-0.0360**|0.029|
|**Household composition**|||||||
|HH dependency ratio (inverted)|0.9138|0.6953|0.8761|0.7146|0.0377|0.258|
|Head of HH age (years)|41.5175|12.8011|43.6739|13.2874|-2.1564***|0.000|
|Head of HH education (years)|3.2818|2.9826|3.5957|3.3598|-0.3138**|0.039|
|Head of HH is female (inverted)|0.0925|0.2899|0.0472|0.2121|0.0453***|0.000|
|Head of HH is divorced (inverted)|0.0244|0.1545|0.0175|0.1313|0.0069|0.298|
|Head of HH is a widow (inverted)|0.0742|0.2622|0.0418|0.2002|0.0324***|0.002|
|Head of HH is married|0.8988|0.3018|0.9407|0.2363|-0.0419***|0.001|
|Head of HH was never married (inverted)|0.0026|0.0511|0|0|0.0026*|0.083|
|Head of HH ethnicity: Lao|0.1309|0.3374|0.1173|0.3219|0.0136|0.378|
|Head of HH ethnicity: Khmu|0.4319|0.4956|0.3733|0.484|0.0586**|0.011|
|Head of HH ethnicity: Katang (inverted)|0.1169|0.3215|0.1658|0.3721|-0.0488***|0.003|
|Head of HH ethnicity: Other (inverted)|0.3202|0.4668|0.3437|0.4753|-0.0234|0.292|
|**Durables ownership**|||||||
|Num. of vehicles (car, vans, etc.)|0.0253|0.1626|0.0499|0.2413|-0.0246**|0.015|
|Num. of motor cycles|0.7452|0.7451|0.8814|0.7789|-0.1362***|0.000|
|Num. of bicycles|0.0497|0.233|0.066|0.2933|-0.0163|0.202|
|Num. of refrigerators/freezers|0.0908|0.3272|0.1685|0.4346|-0.0777***|0.000|
|Num. of sewing machines|0.055|0.228|0.0714|0.2577|-0.0165|0.157|

23
|Num. of washing machines|0.0035|0.059|0.0216|0.1454|-0.0181***|0.001|
|---|---|---|---|---|---|---|
|Num. of electric rice cookers|0.0628|0.2567|0.097|0.3265|-0.0342**|0.016|
|Num. of steam rice cookers|1.4572|0.645|1.3989|0.6341|0.0583*|0.053|
|Num. of food processors|0.007|0.0833|0.0202|0.1501|-0.0132**|0.029|
|Num. of two- and four-wheel tractors|0.2373|0.4398|0.3369|0.4898|-0.0996***|0.000|
|Num. of agr. equipment|9.6152|5.909|10|6.3484|-0.3848|0.187|
|Num. of boats|0.0175|0.131|0.0377|0.1976|-0.0203**|0.014|
|Num. of fishing nets|0.548|0.8836|0.6402|1.0147|-0.0922**|0.043|
|Num. of rice mills|0.2784|0.4523|0.3113|0.4662|-0.033|0.129|
|Num. of TVs|0.3229|0.4807|0.4205|0.5334|-0.0976***|0.000|
|Num. of radios/VCD|0.1457|0.3928|0.1752|0.4078|-0.0295|0.120|
|Num. of telephones|0.1178|0.3609|0.1226|0.3482|-0.0048|0.771|
|Num. of mobile phones|1.4145|1.395|1.4569|1.3667|-0.0424|0.514|
|Num. of satellite discs/connections|0.3054|0.4608|0.3814|0.4861|-0.0760***|0.001|
|Num. of computers|0.0087|0.093|0.0216|0.1544|-0.0128**|0.042|
|Num. of air conditioners|0.0009|0.0295|0.0027|0.0519|-0.0018|0.384|
|Num. of jewelry|0.301|0.854|0.3383|0.9524|-0.0372|0.388|
|Num. of mosquito nets|2.9206|1.7012|3.0418|1.8323|-0.1212|0.149|
|Num. of solar panels|0.2286|0.4617|0.2776|0.5653|-0.0490**|0.049|
|**Livestock ownership**|||||||
|Num. of cows|1.1745|2.7296|1.973|3.896|-0.7985***|0.000|
|Num. of buffalos|0.5567|1.3927|0.9151|2.0999|-0.3584***|0.000|
|Num. of goats|0.5192|1.838|0.8518|2.2621|-0.3326***|0.001|
|Num. of pigss|1.9188|2.464|2.0526|2.7296|-0.1337|0.280|
|Num. of chickens|9.7164|14.5332|9.9447|14.0601|-0.2283|0.734|
|Num. of ducks|2.1195|4.4355|2.1132|4.4888|0.0063|0.976|
|**Income**|||||||
|Agriculture income|382.5096|1035.4527|406.5878|1085.7145|-24.0782|0.632|
|Livestock income|390.1721|916.5445|513.3494|1135.7117|-123.1773**|0.013|
|Fishes income|6.4066|26.6132|7.2247|29.5335|-0.8181|0.541|
|Forest income|159.6157|307.38|137.9027|323.628|21.713|0.147|

24
|Handicraft income<br>Services income|94.7048<br>30.8876|340.7649<br>181.66|89.1626<br>49.3024|360.8131<br>232.1116|5.5421<br>-18.4148*|0.739<br>0.068|
|---|---|---|---|---|---|---|
|Salary income|435.9056|1741.3517|809.0677|2522.6435|-<br>373.1622***|0.000|
|Extra income|150.4058|520.9682|114.4544|448.0776|35.9515|0.111|
|Other income|114.9503|380.2719|102.258|360.7227|12.6924|0.465|
|Total income|1933.775|2951.829|2410.1417|3614.1872|-<br>476.3667***|0.003|
|Total income per HH member (USD PPP)|381.919|620.3777|480.733|789.1612|-98.8140***|0.004|
|**Housing conditions**|||||||
|Dwelling: num. of rooms|1.9171|1.1149|2.0782|1.1176|-0.1611***|0.002|
|Dwelling walls: brick|0.0654|0.2474|0.0755|0.2643|-0.01|0.409|
|Dwelling walls: concrete|0.0637|0.2443|0.0903|0.2868|-0.0266**|0.037|
|Dwelling walls: wood|0.5663|0.4958|0.624|0.4847|-0.0577**|0.012|
|Dwelling walls: bamboo (inverted)|0.2862|0.4522|0.1873|0.3904|0.0989***|0.000|
|Dwelling roof: concrete|0.0052|0.0722|0.0013|0.0367|0.0039|0.123|
|Dwelling roof: wood|0.0271|0.1623|0.0216|0.1454|0.0055|0.444|
|Dwelling roof: metal (inverted)|0.5977|0.4906|0.6173|0.4864|-0.0195|0.396|
|Dwelling roof: tile|0.3028|0.4597|0.3167|0.4655|-0.0139|0.524|
|Dwelling roof: grass (inverted)|0.0611|0.2396|0.0418|0.2002|0.0193*|0.059|
|Dwelling floor: tile|0.1213|0.3266|0.1402|0.3474|-0.0189|0.238|
|Dwelling floor: concrete|0.0733|0.2607|0.0768|0.2665|-0.0035|0.777|
|Dwelling floor: wood (inverted)|0.5672|0.4957|0.6011|0.49|-0.0339|0.144|
|Dwelling floor: bamboo (inverted)|0.0864|0.2811|0.0606|0.2388|0.0257**|0.033|
|Dwelling floor: earth/clay (inverted)|0.1518|0.359|0.1213|0.3267|0.0305*|0.057|
|Rainy season water: piped water|0.178|0.3827|0.1752|0.3804|0.0028|0.876|
|Rainy season water: protected well|0.5332|0.4991|0.4704|0.4995|0.0628***|0.008|
|Rainy season water: unprotected well (inverted)|0.0515|0.2211|0.0687|0.2532|-0.0172|0.129|
|Rainy season water: river/lake (inverted)|0.1344|0.3412|0.1132|0.3171|0.0212|0.169|
|Dry season water: piped water|0.1806|0.3849|0.1792|0.3838|0.0014|0.939|
|Dry season water: protected well|0.5663|0.4958|0.5162|0.5001|0.0501**|0.033|

25
|Dry season water: unprotected well (inverted)|0.0812|0.2732|0.1065|0.3086|-0.0253*|0.069|
|---|---|---|---|---|---|---|
|Dry season water: river/lake (inverted)|0.1571|0.364|0.1482|0.3556|0.0088|0.602|
|Toilet: flush|0.4066|0.4914|0.4003|0.4903|0.0064|0.783|
|Toilet: no facility (inverted)|0.4171|0.4933|0.4097|0.4921|0.0074|0.750|
|Toilet: shared (inverted)|0.3709|0.4832|0.3693|0.4829|0.0016|0.945|
|Kitchen type: inside the house (inverted)|0.5061|0.5002|0.4394|0.4966|0.0668***|0.005|
|Kitchen type: outside roofed|0.4738|0.4995|0.5404|0.4987|-0.0666***|0.005|
|Kitchen type: outside unroofed (inverted)|0.014|0.1174|0.0148|0.1209|-0.0009|0.878|
|Cooking fuel: wood (inverted)|0.993|0.0833|0.9744|0.1581|0.0186***|0.003|
|Cooking fuel: charcoal|0.007|0.0833|0.0256|0.1581|-0.0186***|0.003|
|Lighting source: electric network|0.3839|0.4866|0.4623|0.4989|-0.0783***|0.001|
|Lighting source: generator (inverted)|0.0271|0.1623|0.0202|0.1408|0.0068|0.332|
|Lighting source: battery (inverted)|0.0183|0.1342|0.0243|0.154|-0.0059|0.390|
|Lighting source: kerosene lamp (inverted)|0.062|0.2412|0.0553|0.2286|0.0067|0.543|
|Lighting source: solar panel (inverted)|0.1894|0.392|0.2102|0.4078|-0.0209|0.270|
|**Nutrition**|||||||
|Num. of balls of glutinous rice for women (prior day)|8.8194|8.0422|8.6482|8.1434|0.1711|0.654|
|Num. of balls of glutinous rice for kids (prior day)|1.4508|3.2564|1.5086|3.3957|-0.0579|0.713|
|Num. of bowls of ordinary rice for women (prior day,<br>inverted)|3.4171|2.8494|3.2884|2.678|0.1287|0.320|
|Daily num. of bowls of ordinary rice for kids (prior day,<br>inverted)|0.2203|0.8912|0.1157|0.608|0.1046***|0.002|
|Num. of days woman eats meat (weekly)|2.7635|2.2208|2.7102|2.1872|0.0533|0.607|
|Num. of days woman eats eggs (weekly)|0.5445|1.1778|0.5013|1.0882|0.0432|0.415|
|Num. of days woman eats dairy (weekly)|0.1091|0.6484|0.0768|0.5125|0.0323|0.230|
|Num. of days child eats meat (weekly)|0.9223|1.8741|0.746|1.6267|0.1764**|0.030|
|Num. of days child eats eggs (weekly)|0.2757|0.9997|0.219|0.7868|0.0567|0.170|
|Num. of days child eats dairy (weekly)|0.0881|0.6897|0.062|0.5705|0.0261|0.371|
|Kg of meat per HH member (weekly)|0.2385|0.2637|0.2367|0.2939|0.0018|0.892|
|Kg of fish per HH member (weekly)|0.2021|0.2522|0.2036|0.2487|-0.0015|0.899|
|Kg of vegetables per HH member (weekly)|0.907|1.0112|0.827|0.8545|0.0801*|0.065|

26
|Kg of fruits per HH member (weekly)|0.3822|0.5745|0.3181|0.4934|0.0641***|0.010|
|---|---|---|---|---|---|---|
|**Exposure to Shocks**|||||||
|Family death shock (inverted)|0.1239|0.3296|0.124|0.3298|-0.0001|0.996|
|Illness shock (inverted)|0.5646|0.496|0.5593|0.4968|0.0053|0.822|
|Business or unemployment shock (inverted)|0.0654|0.2474|0.0512|0.2206|0.0142|0.192|
|Natural disaster shock (inverted)|0.178|0.3827|0.1941|0.3957|-0.0161|0.383|
|Crop loss shock (inverted)|0.4171|0.4933|0.4461|0.4974|-0.029|0.215|
|Robbery shock (inverted)|0.6614|0.4734|0.6752|0.4686|-0.0138|0.535|
|**Woman characteristics**|||||||
|Woman age (years)|33.0995|10.5313|33.8491|10.2717|-0.7496|0.125|
|Woman education (years)|2.4485|2.7485|2.6361|3.3249|-0.1876|0.201|
|Woman is divorced (inverted)|0.0288|0.1673|0.0189|0.1362|0.0099|0.158|
|Woman is a widow (inverted)|0.0567|0.2314|0.0202|0.1408|0.0365***|0.000|
|Woman is married|0.8743|0.3316|0.9205|0.2707|-0.0461***|0.001|
|Woman was never married (inverted)|0.0401|0.1964|0.0404|0.1971|-0.0003|0.975|
|Woman ethnicity: Lao|0.1283|0.3345|0.1186|0.3235|0.0097|0.531|
|Woman ethnicity: Khmu|0.4319|0.4956|0.372|0.4837|0.0600***|0.009|
|Woman ethnicity: Katang (inverted)|0.1134|0.3173|0.1617|0.3684|-0.0483***|0.003|
|Woman ethnicity: Other (inverted)|0.3264|0.4691|0.3477|0.4766|-0.0214|0.339|
|Woman works on HH farm (inverted)|3.5855|2.8454|3.1927|2.8634|0.3928***|0.004|
|Woman works in HH business|0.1667|0.9789|0.283|1.3187|-0.1164**|0.039|
|Woman works in non-HH business|0.9145|2.0791|0.628|1.8519|0.2865***|0.002|
|**Connections with village head**|||||||
|Woman often or sometime had conversations with the<br>village head in the past month|0.7033|1.1038|0.4946|0.9744|0.2087***|0.000|
|Someone in HH is a close friend/relative with the village<br>head|0.7042|0.4566|0.6644|0.4725|0.0398*|0.071|

* p<0.1 ** p<0.05 *** p<0.01

27
Table A2: Variable Weights for PMT Index PMT Variables **Employment and Income** Someone in the HH owns a business Someone in the HH is an employee Someone in the HH is self-employed HH has non-labor income (rent, remittances, pension) **HH Members 15 to 64 years old** None 1 person 2 persons 3 persons 4 persons 5 or more persons **HH members 0-14 years old and elderly over 64 years** None 1 person 2 persons or more **Highest education completed by hh members** Vocational school or university Secondary school or lower **Housing Conditions** Roof material: concrete or wood Wall material: brick or concrete Floor material: marble, ceramic, or tiles Drinking water: pipe water or bottled water Toilet ownership: own toilet **Asset Ownership** Washing machine Refrigerator Air conditioner TV Mobile phone Telephone Computer Car Motorcycle Bicycle **Village Public Services** Access to permanent/daily market Access to periodical market at least twice a week Access to lower secondary school Access to upper secondary school Access to road Accessible road during wet season 28
Table A3: Village heads vs PMT – Ranked Sample of 1,245 Households

|RMG-eligible|PMT b<br>No|elow 64<br>Yes|Total|
|---|---|---|---|
|No|193 (16%)|294 (24%)|487 (39%)|
|Yes|266 (21%)|492 (40%)|758 (61%)|
|Total|459 (37%)|786 (63%)|1,245 (100%)|

29
Table A3: T-Test comparison of village head and PMT targeting methods – Across the full sample

||RMG-eligible<br>obs.|Random<br>Sample<br>obs.|RMG-eligible<br>mean|Random<br>Sample mean|Diff. (RMG-<br>eligible –<br>Random<br>Sample)|PMT poor<br>obs.|PMT nonpoor<br>obs.|PMT poor<br>mean|PMT nonpoor<br>mean|<br>Diff. (PMT<br>poor -<br>nonpoor)|
|---|---|---|---|---|---|---|---|---|---|---|
|Household composition|1146|742|-0.0151|0.0233|-0.0384**|1187|701|-0.0039|0.0066|-0.0106|
|Exposure to shocks|1146|742|0.0039|-0.0059|0.0098|1187|701|-0.0047|0.0080|-0.0128|
|Land and buildings<br>ownership|1146|742|-0.0312|0.0482|-0.0794**|1187|701|-0.0259|0.0438|-0.0696*|
|Nutrition|1146|742|0.0097|-0.0149|0.0246*|1187|701|-0.0423|0.0716|-0.1140***|
|Livestock ownership|1146|742|-0.0449|0.0693|-0.1142***|1187|701|-0.0535|0.0906|-0.1442***|
|Housing conditions|1146|742|-0.0207|0.0320|-0.0527***|1187|701|-0.0640|0.1084|-0.1725***|
|Durables ownership|1146|742|-0.0345|0.0533|-0.0879***|1187|701|-0.0646|0.1093|-0.1739***|
|Total income per HH<br>member|1146|742|-0.0560|0.0865|-0.1426***|1187|701|-0.3049|0.5164|-0.8213***|

Note: Variables are rendered as composite z-scores, computed by excluding variables used in the creation of the PMT index, and by estimating means and standard deviations for the whole sample of 1,888 observations.

* p<0.1 ** p<0.05 *** p<0.01

30
Table A4: T-test comparison of village head and PMT targeting methods – Grouped by village

||RMG-eligible<br>obs.|Random<br>Sample<br>obs.|RMG-eligible<br>mean|<br>Random<br>Sample mean|Diff. (RMG-<br>eligible –<br>Random<br>Sample)|PMT poor<br>obs.|PMT nonpoor<br>obs.|PMT poor<br>mean|PMT nonpoor<br>mean|Diff. (PMT<br>poor -<br>nonpoor)|
|---|---|---|---|---|---|---|---|---|---|---|
|Household composition|1146|742|-0.0231|0.0357|-0.0589***|1187|701|0.0149|-0.0252|0.0400***|
|Exposure to shocks|1146|742|0.0024|-0.0037|0.006|1187|701|-0.0115|0.0195|-0.031|
|Land and buildings<br>ownership|1146|742|-0.0178|0.0275|-0.0453|1187|701|-0.0213|0.0361|-0.0574|
|Woman characteristics|1146|742|-0.0134|0.0207|-0.0342***|1187|701|-0.0079|0.0133|-0.0211*|
|Nutrition|1146|742|0.0061|-0.0095|0.0156|1187|701|-0.0213|0.0360|-0.0573***|
|Livestock ownership|1146|742|-0.0548|0.0846|-0.1394***|1187|701|-0.0348|0.0589|-0.0936***|
|Housing conditions|1146|742|-0.0156|0.0240|-0.0396***|1187|701|-0.0151|0.0256|-0.0408***|
|Durables ownership|1146|742|-0.0283|0.0436|-0.0719***|1187|701|-0.0230|0.0390|-0.0620***|
|Total income per HH<br>member|1146|742|-0.0849|0.1311|-0.2160***|1187|701|-0.2011|0.3406|-0.5417***|

Note: Variables are rendered as composite z-scores, computed by excluding variables used in the creation of the PMT index, and by estimating means and standard deviations for each village in the sample of 1,888 observations. * p<0.1 ** p<0.05 *** p<0.01

# Table A5: T-tests of composite z-cores for RMG selection

RMG-eligible Random RMG-eligible obs. Sample obs. mean Diff. (RMGP-val eligible – 31
|||||Random<br>Sample<br>mean|Random<br>Sample)||Bonferroni<br>corrected p-<br>val|
|---|---|---|---|---|---|---|---|
|Land and buildings ownership|1146|742|-0.0312|0.0482|-0.0794|0.027|0.213|
|Household composition|1146|742|-0.0151|0.0233|-0.0384|0.041|0.326|
|Durables ownership|1146|742|-0.0345|0.0533|-0.0879***|0.000|0.000|
|Livestock ownership|1146|742|-0.0449|0.0693|-0.1142***|0.000|0.001|
|Total income per HH member (USD PPP)|1146|742|381.919|480.733|-98.8140**|0.004|0.032|
|Housing conditions|1146|742|-0.0207|0.032|-0.0527***|0.000|0.002|
|Nutrition|1146|742|0.0097|-0.0149|0.0246|0.084|0.669|
|Exposure to shocks|1146|742|0.0039|-0.0059|0.0098|0.666|1.000|

RMG-eligible = 1146 observations. Random Sample = 742 observations. All household characteristics are shared as composite z-scores, except for total income per HH member. Variables included are those used in comparisons of RMG and PMT scores.

32
# Construction of individual and composite z-scores

There are two challenges when analyzing a large number of variables. First, the number of variables and outcomes of t-tests can make it difficult to interpret results. Second, analysis of multiple outcomes increases the risk of Type I error unless the significance tests are adjusted appropriately. Following Kling et al. (2007), we compute composite z-scores as means of individual z-scores for all variables in each group of variables, while inverting variables assumed to be negatively correlated with welfare.

We employ the following strategies:

- We identify a set of primary outcomes under each of the following groups, noted in Table A1: `o` Land and buildings ownership;

- Household composition;

- Durables ownership;

- Livestock ownership;

- Income;

- Housing conditions;

- Nutrition;

- Exposure to shocks;

- Woman characteristics

- For income, we use aggregate income per household member since it is better at capturing the underlying notion of welfare than could be achieved through a mean z-score of different types of income. For all other categories of outcomes, we compute composite z-scores through a uniform procedure:

- Calculate the z-score of all variables by subtracting the mean and dividing by the standard deviation;

- Convert all outcomes so that the signs of all variables in a category go in the same direction of being positively correlated with improved welfare;

- In Table A1, all variables negatively correlated with welfare are shown as “inverted,” meaning that their z-scores would be inverted for the composite z-score calculation, though they are not inverted for descriptive statistics or individual z-scores;

- Compute composite z-scores for 8 (eight) categories by taking an average of the z-scores of all variables in a given category.

- In the computation of composite z-scores used to compare VHT and PMT targeting methods in Figures 6 and 7, and Tables A3 and A4, we exclude variables used in the construction of the PMT index. Hence, the computation procedure is the same as above, but the following variables are excluded when calculating the composite z-scores for their categories: `o` Land and buildings ownership:

- Number of buildings owned (residential and business)

- Household characteristics:

- HH dependency ratio

- Head of household education (years)

- Durables ownership:

- Num. vehicle (car, van...)

- Num. many motorcycle

- Num. many bicycle

- Num. refrigerator/freezer

33
- Num. washing machine

- Num. television

- Num. telephone

- Num. mobile phone

- Num. computer

- Num. air conditioner

- Housing conditions:

- Dwelling walls: brick

- Dwelling walls: concrete

- Dwelling roof: concrete

- Dwelling roof: wood

- Dwelling floor: tile

- Dry season water: piped water

- Dry season water: protected well

- Dry season water: unprotected well (inverted)

- Dry season water: river/lake (inverted)

- Toilet: flush

- Toilet: no facility (inverted)

- Toilet: shared (inverted)

- Woman characteristics:

- Woman education (years)

- In Figure 7 and Table A4, we compute individual variables’ z-scores by subtracting village-level means and dividing by village-level standard deviations. Composite z-scores are thus averages of village-based z-scores of variables in each category.

34
# **Importance of vars: Village heads ranking by RMG priority**

|Woman works in non-hh business|1.0000|
|---|---|
|Kitchen type: outside unroofed|0.7218|
|Dwelling walls: concrete|0.6918|
|Num. of days woman eats dairy (weekly)|0.5555|
|Services income|0.5455|
|Woman was never married|0.5240|
|Woman's conversations with village head|0.4975|
|Dwelling floor: tile|0.4411|
|Dry season water: unprotected well|0.4381|
|Head of hh is a widow|0.4305|
|Lighting source: kerosene lamp|0.4297|
|Fishes income|0.4256|
|Lighting source: solar panel|0.4143|
|Dwelling roof: grass|0.3987|
|Dwelling walls: brick|0.3832|
|Dwelling roof: concrete|0.3762|
|Family death shock|0.3704|
|Lighting source: battery|0.3699|
|Business or unemployment shock|0.3615|
|Head of hh is female|0.3569|
|Num. of days kids eat eggs (weekly)|0.3565|
|Num. of days kids eat dairy (weekly)|0.3561|
|Kitchen type: outside roofed|0.3555|
|Rainy season water: unprotected well|0.3549|
|Dry season water: piped water|0.3539|

35
|Woman is a widow|0.3487|
|---|---|
|Daily num. of balls of ord. rice for kids|0.3479|
|Toilet: no facility|0.3446|
|Rainy season water: piped water|0.3396|
|Num. of goats|0.3383|
|Head of hh was never married|0.3344|
|Dry season water: river/lake|0.3315|
|Num. of buildings owned|0.3283|
|Dwelling floor: earth/clay|0.3270|
|Cooking fuel: wood|0.3268|
|Natural disaster shock|0.3251|
|Lighting source: generator|0.3249|
|Woman ethnicity: Katang|0.3220|
|Num. of days woman eats eggs (weekly)|0.3201|
|Woman works in hh business|0.3160|
|Dwelling roof: tile|0.3147|
|Salary income|0.3145|
|Num. of jewelry|0.3139|
|Head of hh age (years)|0.3116|
|Toilet: flush|0.3091|
|Woman ethnicity: Lao|0.3082|
|Lighting source: electric network|0.3077|
|Num. of sewing machines|0.3063|
|Woman ethnicity: Other|0.3052|
|Num. of days kids eat meat (weekly)|0.3019|
|Num. of days woman eats meat (weekly)|0.2992|
|Woman works on hh farm|0.2989|
|Total income per hh member|0.2977|

36
|Rainy season water: protected well|0.2958|
|---|---|
|Head of hh ethnicity: Khmu|0.2935|
|Toilet: shared|0.2895|
|Dwelling walls: bamboo|0.2892|
|Kg of veggies per hh member (weekly)|0.2861|
|Kg of meat per hh member (weekly)|0.2858|
|Dwelling floor: wood|0.2857|
|Crop loss shock|0.2850|
|Dwelling walls: wood|0.2848|
|Daily num. of balls of ord. rice for women|0.2842|
|Cooking fuel: charcoal|0.2839|
|Illness shock|0.2830|
|Daily num. of balls of glut. rice for kids|0.2825|
|Kg of fruits per hh member (weekly)|0.2813|
|Head of hh ethnicity: Katang|0.2811|
|Kitchen type: inside the house|0.2795|
|Extra income|0.2770|
|Num. of computers|0.2747|
|Robbery shock|0.2738|
|Num. of solar panels|0.2732|
|Kg of fish per hh member (weekly)|0.2731|
|Dry season water: protected well|0.2722|
|Num. of telephones|0.2721|
|Dwelling: num. of rooms|0.2715|
|Total income|0.2700|
|Head of hh ethnicity: Lao|0.2684|
|Handicraft income|0.2680|
|Rainy season water: river/lake|0.2665|

37
|Head of hh ethnicity: Other|0.2657|
|---|---|
|Woman ethnicity: Khmu|0.2648|
|Head of hh education (years)|0.2643|
|Dwelling floor: concrete|0.2637|
|Someone in hh is a friend/relative with village head|0.2596|
|Agr. income|0.2566|
|Daily num. of balls of glut. rice for women|0.2551|
|Head of hh is divorced|0.2549|
|Woman age (years)|0.2507|
|Other income|0.2494|
|Dwelling floor: bamboo|0.2492|
|Num. of cows|0.2455|
|Num. of ducks|0.2441|
|Dwelling roof: metal|0.2410|
|Woman is divorced|0.2374|
|Num. of buffalos|0.2320|
|Num. of washing machines|0.2319|
|Woman education (years)|0.2285|
|Forest income|0.2279|
|Woman is married|0.2251|
|Num. of TVs|0.2221|
|Livestock income|0.2205|
|Num. of satellite discs/connections|0.2158|
|Num. of radios/vcd|0.2154|
|Num. of rice mills|0.2134|
|Num. of food processors|0.2087|
|HH dependency ratio|0.2085|
|Num. of chickens|0.2082|

38
|Head of hh is married|0.2067|
|---|---|
|Dwelling roof: wood|0.2028|
|Num. of pigss|0.1946|
|Num. of mosquito nets|0.1870|
|Num. of fishing nets|0.1747|
|Num. of air conditioners|0.1720|
|Num. of mobile phones|0.1715|
|Num. of boats|0.1696|
|Num. of refrigerators/freezers|0.1620|
|Num. of agr. equipments|0.1391|
|Num. of two- and four-wheel tractors|0.1359|
|Num. of motor cycles|0.1354|
|Num. of electric rice cookers|0.1334|
|Num. of vehicles (car, vans, etc.)|0.1330|
|Num. of steam rice cookers|0.1204|
|Num. of bicycles|0.1049|
|Size of res. plots (thousand sqm)|0.0852|
|Size of agr. plots (thousand sqm)|0.0627|

# **Importance of vars: Village heads ranking by land**

|Dwelling walls: bamboo|1.0000|
|---|---|
|Dwelling: num. of rooms|0.8370|
|Dwelling walls: concrete|0.8223|
|Services income|0.8071|
|Total income|0.7809|
|Num. of cows|0.7534|
|Head of hh is a widow|0.7211|
|Dwelling floor: earth/clay|0.6908|

39
|Dwelling roof: tile|0.6863|
|---|---|
|Head of hh age (years)|0.6840|
|Woman works in hh business|0.6806|
|Dwelling floor: bamboo|0.6672|
|Num. of satellite discs/connections|0.6315|
|Dwelling floor: concrete|0.6170|
|Head of hh was never married|0.6102|
|Family death shock|0.6090|
|Head of hh is female|0.5884|
|Num. of buffalos|0.5786|
|Num. of days kids eat eggs (weekly)|0.5781|
|Dwelling floor: tile|0.5667|
|Woman ethnicity: Lao|0.5510|
|Dry season water: unprotected well|0.5469|
|Num. of mobile phones|0.5369|
|Lighting source: generator|0.5282|
|Cooking fuel: wood|0.5274|
|Rainy season water: river/lake|0.5260|
|Num. of days kids eat meat (weekly)|0.5000|
|Total income per hh member|0.4987|
|Kitchen type: outside roofed|0.4969|
|Woman is a widow|0.4947|
|Num. of mosquito nets|0.4938|
|Daily num. of balls of ord. rice for kids|0.4909|
|Dry season water: piped water|0.4894|
|Num. of rice mills|0.4892|
|Lighting source: solar panel|0.4821|
|Lighting source: kerosene lamp|0.4805|

40
|Head of hh ethnicity: Lao|0.4799|
|---|---|
|Woman was never married|0.4797|
|Kitchen type: outside unroofed|0.4779|
|Crop loss shock|0.4775|
|Woman's conversations with village head|0.4767|
|Natural disaster shock|0.4739|
|Num. of days woman eats meat (weekly)|0.4692|
|Business or unemployment shock|0.4681|
|Kitchen type: inside the house|0.4593|
|Dry season water: river/lake|0.4590|
|Num. of days kids eat dairy (weekly)|0.4579|
|Num. of TVs|0.4574|
|Dwelling walls: brick|0.4565|
|Toilet: flush|0.4551|
|Dry season water: protected well|0.4531|
|Lighting source: electric network|0.4514|
|Rainy season water: piped water|0.4499|
|Num. of computers|0.4467|
|Toilet: no facility|0.4458|
|Woman works in non-hh business|0.4425|
|Someone in hh is a friend/relative with village head|0.4425|
|Num. of days woman eats dairy (weekly)|0.4419|
|Dwelling roof: wood|0.4403|
|Daily num. of balls of glut. rice for kids|0.4398|
|Daily num. of balls of ord. rice for women|0.4392|
|Robbery shock|0.4358|
|Num. of ducks|0.4339|
|Kg of fruits per hh member (weekly)|0.4298|

41
|Rainy season water: protected well|0.4268|
|---|---|
|Num. of chickens|0.4255|
|Salary income|0.4244|
|Livestock income|0.4242|
|Kg of veggies per hh member (weekly)|0.4238|
|Toilet: shared|0.4218|
|Lighting source: battery|0.4188|
|Dwelling walls: wood|0.4185|
|Other income|0.4175|
|Kg of fish per hh member (weekly)|0.4171|
|Illness shock|0.4150|
|Agr. income|0.4132|
|Kg of meat per hh member (weekly)|0.4119|
|Num. of days woman eats eggs (weekly)|0.4085|
|Daily num. of balls of glut. rice for women|0.4081|
|Woman ethnicity: Other|0.4062|
|Dwelling floor: wood|0.4008|
|Woman ethnicity: Khmu|0.4004|
|Num. of buildings owned|0.3969|
|Head of hh is divorced|0.3962|
|Woman age (years)|0.3948|
|Woman education (years)|0.3874|
|Dwelling roof: grass|0.3811|
|Handicraft income|0.3792|
|Woman is divorced|0.3744|
|Dwelling roof: metal|0.3711|
|Woman works on hh farm|0.3704|
|Fishes income|0.3679|

42
|Head of hh education (years)|0.3659|
|---|---|
|Extra income|0.3625|
|Head of hh ethnicity: Katang|0.3610|
|Head of hh ethnicity: Other|0.3608|
|Head of hh ethnicity: Khmu|0.3578|
|Woman is married|0.3560|
|Rainy season water: unprotected well|0.3546|
|Num. of goats|0.3424|
|Woman ethnicity: Katang|0.3386|
|Head of hh is married|0.3375|
|Forest income|0.3323|
|Num. of telephones|0.3304|
|Cooking fuel: charcoal|0.3215|
|HH dependency ratio|0.3186|
|Num. of radios/vcd|0.3164|
|Num. of jewelry|0.3115|
|Num. of bicycles|0.3063|
|Num. of solar panels|0.3060|
|Num. of pigss|0.3059|
|Num. of motor cycles|0.2988|
|Num. of refrigerators/freezers|0.2928|
|Num. of two- and four-wheel tractors|0.2913|
|Dwelling roof: concrete|0.2858|
|Num. of boats|0.2667|
|Num. of agr. equipments|0.2454|
|Num. of fishing nets|0.2410|
|Num. of electric rice cookers|0.2291|
|Num. of food processors|0.2195|

43
|Num. of sewing machines|0.2171|
|---|---|
|Num. of vehicles (car, vans, etc.)|0.1867|
|Size of agr. plots (thousand sqm)|0.1704|
|Num. of steam rice cookers|0.1670|
|Size of res. plots (thousand sqm)|0.1662|
|Num. of washing machines|0.1430|
|Num. of air conditioners|0.0760|

|**Importance of vars: Village heads ranking by hunger**|
|---|
|Dwelling walls: concrete<br>1.0000|
|Dwelling floor: tile<br>0.9515|
|Dwelling: num. of rooms<br>0.7716|
|Num. of motor cycles<br>0.7146|
|Total income<br>0.6791|
|Dwelling walls: bamboo<br>0.6732|
|Woman works in hh business<br>0.6621|
|Head of hh is female<br>0.6372|
|Num. of buffalos<br>0.6142|
|Dwelling roof: tile<br>0.6003|
|Head of hh ethnicity: Katang<br>0.5282|
|Num. of mobile phones<br>0.5218|
|Dwelling floor: concrete<br>0.5099|
|Rainy season water: unprotected well<br>0.5055|
|Services income<br>0.4988|
|Toilet: no facility<br>0.4792|
|Head of hh is a widow<br>0.4777|
|Woman is a widow<br>0.4772|
|Dwelling floor: earth/clay<br>0.4763|

44
|Dwelling roof: grass|0.4707|
|---|---|
|Total income per hh member|0.4702|
|Lighting source: kerosene lamp|0.4684|
|Dry season water: unprotected well|0.4680|
|Num. of refrigerators/freezers|0.4678|
|Kitchen type: inside the house|0.4620|
|Daily num. of balls of ord. rice for kids|0.4541|
|Num. of cows|0.4441|
|Num. of buildings owned|0.4438|
|Kitchen type: outside unroofed|0.4417|
|Kitchen type: outside roofed|0.4413|
|Livestock income|0.4412|
|Lighting source: battery|0.4354|
|Business or unemployment shock|0.4239|
|Dwelling floor: bamboo|0.4231|
|Num. of TVs|0.4214|
|Dry season water: protected well|0.4199|
|Toilet: flush|0.3972|
|Fishes income|0.3971|
|Dwelling walls: brick|0.3963|
|Lighting source: generator|0.3942|
|Lighting source: solar panel|0.3939|
|Family death shock|0.3935|
|Num. of days kids eat eggs (weekly)|0.3928|
|Rainy season water: protected well|0.3914|
|Daily num. of balls of ord. rice for women|0.3897|
|Num. of days woman eats meat (weekly)|0.3851|
|Lighting source: electric network|0.3845|

45
|Toilet: shared|0.3839|
|---|---|
|Num. of days woman eats dairy (weekly)|0.3827|
|Rainy season water: piped water|0.3806|
|Woman is divorced|0.3798|
|Woman education (years)|0.3782|
|Woman ethnicity: Lao|0.3778|
|Robbery shock|0.3751|
|Num. of rice mills|0.3723|
|Someone in hh is a friend/relative with village head|0.3718|
|Kg of fruits per hh member (weekly)|0.3683|
|Woman's conversations with village head|0.3681|
|Woman was never married|0.3668|
|Illness shock|0.3646|
|Natural disaster shock|0.3610|
|Dry season water: piped water|0.3609|
|Head of hh ethnicity: Lao|0.3571|
|Head of hh is divorced|0.3557|
|Num. of satellite discs/connections|0.3549|
|Kg of veggies per hh member (weekly)|0.3534|
|Woman ethnicity: Katang|0.3533|
|Dry season water: river/lake|0.3532|
|Num. of days kids eat meat (weekly)|0.3528|
|Crop loss shock|0.3519|
|Num. of chickens|0.3497|
|Cooking fuel: wood|0.3484|
|Rainy season water: river/lake|0.3472|
|Woman works on hh farm|0.3467|
|Kg of fish per hh member (weekly)|0.3460|

46
|Daily num. of balls of glut. rice for women|0.3447|
|---|---|
|Num. of mosquito nets|0.3439|
|Woman ethnicity: Other|0.3405|
|Kg of meat per hh member (weekly)|0.3403|
|Dwelling floor: wood|0.3393|
|Num. of days woman eats eggs (weekly)|0.3379|
|Daily num. of balls of glut. rice for kids|0.3365|
|Head of hh education (years)|0.3319|
|Head of hh ethnicity: Khmu|0.3296|
|Dwelling walls: wood|0.3290|
|Woman ethnicity: Khmu|0.3268|
|Woman works in non-hh business|0.3240|
|Agr. income|0.3203|
|Head of hh was never married|0.3201|
|Handicraft income|0.3183|
|Num. of sewing machines|0.3109|
|Extra income|0.3071|
|Head of hh ethnicity: Other|0.3053|
|Num. of jewelry|0.2993|
|Salary income|0.2979|
|Woman is married|0.2957|
|Woman age (years)|0.2932|
|Head of hh is married|0.2905|
|Other income|0.2899|
|Head of hh age (years)|0.2878|
|Dwelling roof: metal|0.2846|
|Num. of pigss|0.2796|
|Num. of goats|0.2701|

47
|Forest income|0.2689|
|---|---|
|Num. of ducks|0.2685|
|Num. of days kids eat dairy (weekly)|0.2653|
|Num. of solar panels|0.2541|
|HH dependency ratio|0.2532|
|Cooking fuel: charcoal|0.2510|
|Num. of telephones|0.2484|
|Num. of vehicles (car, vans, etc.)|0.2388|
|Num. of boats|0.2347|
|Num. of radios/vcd|0.2324|
|Num. of fishing nets|0.2307|
|Num. of two- and four-wheel tractors|0.2216|
|Dwelling roof: wood|0.2153|
|Num. of bicycles|0.2066|
|Num. of agr. equipments|0.2030|
|Num. of food processors|0.1928|
|Num. of electric rice cookers|0.1919|
|Num. of computers|0.1732|
|Dwelling roof: concrete|0.1640|
|Num. of steam rice cookers|0.1598|
|Size of agr. plots (thousand sqm)|0.1547|
|Size of res. plots (thousand sqm)|0.1530|
|Num. of washing machines|0.1488|
|Num. of air conditioners|0.1318|

|**Importance of vars: Village heads ranking by need**|
|---|
|Dwelling: num. of rooms<br>1.0000|
|Services income<br>0.9838|

48
|Head of hh is female|0.9774|
|---|---|
|Num. of mobile phones|0.8470|
|Woman works in hh business|0.7672|
|Dwelling walls: concrete|0.7033|
|Dwelling walls: bamboo|0.6860|
|Total income|0.6722|
|Woman is a widow|0.6459|
|Dwelling roof: tile|0.6241|
|Head of hh is a widow|0.6015|
|Lighting source: kerosene lamp|0.5926|
|Dry season water: unprotected well|0.5660|
|Num. of motor cycles|0.5571|
|Dwelling floor: tile|0.5508|
|Num. of days kids eat meat (weekly)|0.5202|
|Num. of days kids eat dairy (weekly)|0.5187|
|Kitchen type: inside the house|0.5035|
|Livestock income|0.4971|
|Dwelling roof: grass|0.4965|
|Daily num. of balls of ord. rice for kids|0.4933|
|Toilet: no facility|0.4900|
|Kitchen type: outside roofed|0.4886|
|Num. of buildings owned|0.4823|
|Num. of buffalos|0.4807|
|Lighting source: generator|0.4652|
|Business or unemployment shock|0.4580|
|Num. of cows|0.4559|
|Total income per hh member|0.4530|
|Lighting source: battery|0.4505|

49
|Lighting source: solar panel|0.4494|
|---|---|
|Dwelling floor: concrete|0.4437|
|Kg of fish per hh member (weekly)|0.4431|
|Num. of days kids eat eggs (weekly)|0.4424|
|Dwelling floor: bamboo|0.4421|
|Kitchen type: outside unroofed|0.4418|
|Cooking fuel: wood|0.4339|
|Dwelling floor: earth/clay|0.4311|
|Num. of rice mills|0.4292|
|Family death shock|0.4283|
|Woman works in non-hh business|0.4189|
|Woman was never married|0.4182|
|Num. of refrigerators/freezers|0.4153|
|Dry season water: piped water|0.4148|
|Head of hh ethnicity: Katang|0.4130|
|Dry season water: protected well|0.4114|
|Toilet: flush|0.4083|
|Toilet: shared|0.4066|
|Head of hh is divorced|0.4013|
|Woman ethnicity: Other|0.3978|
|Fishes income|0.3969|
|Daily num. of balls of glut. rice for kids|0.3958|
|Head of hh ethnicity: Lao|0.3876|
|Rainy season water: piped water|0.3874|
|Head of hh ethnicity: Other|0.3872|
|Rainy season water: unprotected well|0.3858|
|Natural disaster shock|0.3817|
|Kg of veggies per hh member (weekly)|0.3811|

50
|Salary income|0.3805|
|---|---|
|Woman's conversations with village head|0.3778|
|Num. of satellite discs/connections|0.3775|
|Woman ethnicity: Lao|0.3744|
|Num. of days woman eats dairy (weekly)|0.3729|
|Num. of days woman eats eggs (weekly)|0.3711|
|Rainy season water: river/lake|0.3710|
|Kg of meat per hh member (weekly)|0.3702|
|Num. of days woman eats meat (weekly)|0.3698|
|Kg of fruits per hh member (weekly)|0.3666|
|Illness shock|0.3645|
|Robbery shock|0.3643|
|Daily num. of balls of glut. rice for women|0.3632|
|Extra income|0.3627|
|Someone in hh is a friend/relative with village head|0.3624|
|Other income|0.3618|
|Dwelling floor: wood|0.3608|
|Rainy season water: protected well|0.3591|
|Dry season water: river/lake|0.3570|
|Woman is divorced|0.3558|
|Daily num. of balls of ord. rice for women|0.3556|
|Dwelling walls: brick|0.3521|
|Lighting source: electric network|0.3510|
|Dwelling roof: wood|0.3510|
|Crop loss shock|0.3483|
|Dwelling walls: wood|0.3474|
|Num. of TVs|0.3452|
|Num. of mosquito nets|0.3441|

51
|Num. of chickens|0.3389|
|---|---|
|Head of hh ethnicity: Khmu|0.3378|
|Woman ethnicity: Katang|0.3358|
|Woman education (years)|0.3292|
|Agr. income|0.3260|
|Dwelling roof: metal|0.3256|
|Num. of ducks|0.3252|
|Num. of pigss|0.3247|
|Woman works on hh farm|0.3234|
|Handicraft income|0.3220|
|Woman age (years)|0.3167|
|Head of hh education (years)|0.3143|
|Num. of goats|0.3099|
|Head of hh age (years)|0.3098|
|Woman ethnicity: Khmu|0.3060|
|Forest income|0.3047|
|Head of hh is married|0.3014|
|Woman is married|0.2927|
|HH dependency ratio|0.2851|
|Num. of sewing machines|0.2806|
|Cooking fuel: charcoal|0.2797|
|Num. of telephones|0.2779|
|Num. of air conditioners|0.2762|
|Num. of jewelry|0.2650|
|Num. of solar panels|0.2641|
|Num. of vehicles (car, vans, etc.)|0.2447|
|Num. of two- and four-wheel tractors|0.2382|
|Num. of radios/vcd|0.2298|

52
|Num. of electric rice cookers|0.2189|
|---|---|
|Head of hh was never married|0.2178|
|Num. of boats|0.2130|
|Num. of fishing nets|0.1945|
|Num. of agr. equipments|0.1837|
|Num. of food processors|0.1681|
|Num. of steam rice cookers|0.1552|
|Num. of washing machines|0.1524|
|Num. of computers|0.1514|
|Dwelling roof: concrete|0.1501|
|Num. of bicycles|0.1399|
|Size of agr. plots (thousand sqm)|0.1173|
|Size of res. plots (thousand sqm)|0.1170|

53