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Targeting maps:

An asset-based approach to geographic targeting*

Corey Lang,** Christopher B. Barrett and Felix Naschold Cornell University

August 3, 2009

Abstract Proper targeting of policy interventions requires reasonable estimates

of the benefits of the various alternative interventions. In order to inform such decisions,

we develop an integrated approach that estimates the marginal returns to a range of assets

across geographically defined subpopulations allowing returns to vary by household and

by geography. We then create a series of maps illustrating the estimated marginal returns

to specific assets and the proportion of an area’s population that would benefit from

increased holdings of a specific asset. These maps can then be overlaid with traditional

poverty maps to identify areas that are strong candidates for a particular development

intervention. We develop a general method and demonstrate its potential with an

application using Ugandan data.

JEL classification: R12, O2, C15, I32 Keywords: geographic targeting, assets, poverty maps, spatial variation, Uganda

* We appreciate helpful discussions with Nancy Johnson, GIS data assistance from John Owuor and Ugandan data advice from Thomas Emwanu. Useful comments were received from seminar participants at Cornell University. This research was made possible through the support of the International Livestock Research Institute. ** Contact author. Address: Department of Economics, 404 Uris Hall, Ithaca, NY, 14853. Email addresses: CL395@cornell.edu (Lang), CBB2@cornell.edu (Barrett), FN23@cornell.edu (Naschold)

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1 Introduction

Improved targeting of development interventions has long been recognized as

central to achieving greater impact from poverty reduction efforts. However, effective

targeting requires reasonable estimates of where the returns to various programs are

likely to be highest. Currently, no means exist for estimating and comparing expected

benefits across space and across alternative interventions. In this paper, we develop a

method that, first, estimates the marginal returns to a range of assets allowing returns to

vary by household and by geography and, second, maps the estimated marginal returns

creating a visual tool that can inform the targeting decisions of an in-kind transfer

scheme.

There are several methods of targeting, such as a means test, community-based

targeting, categorical or indicator targeting and self-targeting, each with its own

advantages and disadvantages.1 The empirical evidence suggests that geographic

targeting is particularly effective for poverty alleviation (Coady et al. 2004, Baker and

Grosh 1994) and is easier and less expensive to monitor and administer than other

methods (Bigman and Fofack 2000). The idea of geographic targeting is to determine a

subset of geographic regions most in need and then transfer benefits to individuals within

the chosen regions and exclude all others. The benefits to this method are intuitive as

there is ample evidence that individuals living in close geographic proximity tend to have

similar livelihoods and face the same constraints and risks (e.g., Bigman and Fofack

2000, Doss et al. 2008).

The major disadvantages to geographic targeting are that non-poor individuals

living in targeted regions receive benefits (leakage) and poor individuals not living in 1 Coady et al. (2004) discuss these targeting methods and more in detail.

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targeted regions do not receive benefits (undercoverage). One remedy is to target more

finely partitioned regions. As regions become increasingly disaggregated, within region

heterogeneity decreases and targeting performance increases (Elbers et al. 2007, Baker

and Grosh 1994). A second solution is to combine geographic targeting with additional

targeting tools to limit leakage. Coady et al. (2004) surveyed 122 targeted transfer

programs and found the mean number of tools used is more than two – for example,

Mexico’s celebrated PROGRESA/Opportunidades program uses four (Coady 2006).

In this paper, we build on the proven successes of geographic targeting to propose

an enhanced, asset-based approach. In general, transfers can be monetary or in-kind,

where in-kind transfers usually come in the form of subsidies for food, education, or

health services. Here, we explore the possibility of transfers from an entire range of

private and public assets, such as livestock, mobile phones, means of transportation, and

access to roads or microfinance institutions. Our focus on assets stems from the

importance of a household’s asset portfolio in determining the nature and extent of

poverty and vulnerability (Moser 1998, Ellis and Freeman 2004, Adato et al. 2006).

Further, asset transfers may push households beyond an asset poverty threshold and allow

them to engineer their own escape from income poverty (Carter and Barrett 2006).

An obvious criticism of in-kind or asset transfers is that unlike with a cash

transfer, a household is constrained and cannot consume or invest in whatever they think

will best help them.2 While in-kind transfers can appear paternalistic, there are several

reasons why an asset-based approach could perform better than a monetary approach.

First, asset transfers can act as a natural self-selection mechanism to reduce leakage;

2 Currie and Gahvari (2008) review the debate over monetary versus in-kind transfers, though mainly from the perspective of developed countries.

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whereas virtually everyone would accept a cash transfer, only those who benefit from a

given asset would accept it as a transfer. Second, in-kind transfers may stick to the

targeted households better than cash because of the well-established endowment effects

associated with physical goods but not with cash. The findings of Hoffman et al. (2009)

suggest that in-kind transfers of mosquito nets would result in greater use of the nets than

would equivalent cash transfers. Third, monetary transfers, due to their ready divisibility,

may also be subject to a high rate of social taxation compared to a lumpy asset, perhaps

undoing efforts to control leakage. Fourth, imperfect markets can make it difficult to

procure specific, desired assets; this is a common rationale for in-kind food or seed aid in

many remote or disaster-affected regions.

The targeting maps tool improves the information set informing geographic

targeting. Given substantial spatial heterogeneity in poverty incidence and its causes

(Emwanu et al. 2007, Okwi et al. 2007, Kam et al. 2005), there is little reason to believe

that any single poverty alleviation strategy is best suited for all places in a country.

Likewise, spatially heterogeneous asset valuation appears the norm, given the place-

specificity of many complementary inputs – e.g., agro-ecological conditions that affect

livestock value, urban proximity that affects the returns to land, etc. If poverty measures

and the returns to assets both vary markedly across space for a variety of geographic,

institutional, policy and technological reasons, then it seems desirable to exploit the

predictable component of such variation in targeting development interventions. Previous

research has found considerable intra-regional variation in expected returns to different

development investments in Africa and Asia (Fan and Hazell 2001, Fan and Chan-Kang

2004). By customizing asset-based interventions to specific geographic areas, significant

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gains could be made in efficiently and cost-effectively addressing poverty. Our approach

integrates spatially-explicit estimates of the marginal benefits to multiple assets into a

single framework such that inter-asset comparisons of expected marginal benefits can be

made for each region. The output can then be used as one of several components

informing a targeted transfer plan.

Our method draws on the small-area estimation technique pioneered by Elbers et

al. (2003). Their method combines detailed, nationally representative household survey

data with national census data to estimate poverty rates at fine levels of disaggregation

for an entire country.3 First, they derive a relationship between household expenditure

and various demographic and asset variables using the survey data. Second, they predict

out-of-sample estimates of expenditure for the census data using the coefficient estimates

from the relationship derived with the survey data. By projecting expenditure estimates

onto the full population, the Elbers et al. (2003) method enables estimation of poverty

rates in places where no survey data exist and at finer levels of disaggregation than when

using household survey data alone, as these are typically statistically representative only

at relatively coarse scales of aggregation.

Once estimated, the poverty rates for the various regions of a country can be used

to create a poverty map – a visual illustration of the spatial distribution of poverty. This

simple tool is popular and widely used by governments, NGOs and donors in low-income

countries to guide poverty reduction efforts. Poverty maps can significantly bolster

3 All of the Foster-Greer-Thorbecke measures of poverty as well as inequality can be estimated using this method.

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geographic targeting efforts because, as mentioned above, geographic targeting methods

are greatly improved as the geographic scale becomes finer.4

Although poverty maps illustrate problems well and can facilitate policy

discussions, they offer no explicit recommendation as to the best means of alleviating

poverty. If a government is trying to reach a specific welfare target such as the

Millennium Development Goals, poverty maps can at best guide the government to

regions with high poverty rates. What exactly the government should do in that region,

however, remains uncertain.

Targeting maps address these shortcomings by answering two general questions:

1) for a given region, which asset building activity will have the largest marginal gross

benefit? and 2) for a given type of asset building activity, in which regions are the

marginal gross benefits to such an investment highest? Both of these questions address

how to improve the efficacy of targeted, asset-based development programs. Answers to

the first question are paramount for those wishing to cut poverty by the most efficient

means possible. The second question appeals to groups interested in investments of a

specific type, such as Heifer International in building livestock holdings or The Nature

Conservancy in safeguarding natural resources. With scarce aid resources available,

targeting maps can help identify where the most bang-for-the-buck exists.

The construction of targeting maps involves several distinct steps similar to those

involved in creating a poverty map. Using detailed household survey data and spatially

explicit environmental and infrastructure data, we apply multivariate regression and

4 Small-area poverty estimates can additionally be used in subsequent regression analysis as either the key dependent variable to investigate the causes of poverty (Kam et al. 2005, Okwi et al. 2007) or as an explanatory variable to investigate its consequences (Demombynes and Ozler 2005).

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bootstrapping to estimate the returns to various assets and how these estimated returns

vary across space. We then project the parameter estimates onto the broader national

census data and calculate the marginal returns as a function of projected estimates and

household asset holdings. Finally, we aggregate the estimated marginal returns across

households for small geographic areas and, using Geographic Information Systems (GIS),

generate maps that highlight both the magnitude and scope of benefits.

We illustrate our approach using Ugandan household survey and census data.

The results are encouraging; estimated and projected marginal benefits to asset transfers

seem reasonable and show remarkable variation across space. Our results clearly identify

promising areas to target as well as indications of key assets to use in a geographic

targeting scheme. These findings reinforce the value of geographic targeting and the

importance of spatial analysis in general.

The next section describes the methodology in detail, explains how it builds on

poverty mapping, and discusses concerns with the framework. Section three gives the

specifics of the Ugandan data. Section four reports the results including: several

examples of types of targeting maps, a simplified benefit-cost analysis for several assets,

and selection of areas that would be strong candidates for a hypothetical asset transfer.

Section five concludes and discusses ideas for future work.

2 Method

We estimate average expected marginal household-level returns to various assets

across geographically defined subpopulations. In the context of this paper, assets will be

taken as anything whose stock can affect a household’s income or expenditure. We

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classify assets along two dimensions: private vs. public and targetable vs. non-targetable.

Private and public goods follow traditional definitions; public goods are non-rival and

non-excludable; private goods represent the rest. The distinction between targetable and

non-targetable concerns whether an asset’s quantity, quality or existence can be changed

by an intervention. This classification results in four categories: private targetable assets

(e.g., livestock holdings, literacy, land holdings), public targetable assets (e.g., source of

potable drinking water, access to health clinics, road access), private non-targetable assets

(e.g., education of household head, gender of household head) and public non-targetable

assets (e.g., rainfall, temperature). Our method estimates the returns to all types of assets,

but ultimately we are only interested in those that are targetable.

The minimum data necessary to create a targeting map are a nationally

representative household survey and a census taken at about the same time. Additional

environmental or public good variables can and should be added when available to

supplement both the survey and census data. In the first step of our analysis, we compare

the data available in the household survey and the census to generate a set of variables

that are common to both data sets, such as demographic variables, livestock and durable

goods. We restrict the data in this way because we must use a specification that is

replicable in the census for all independent variables.

The second step is to estimate the relationship between per capita equivalent

household expenditure and asset holdings, which include the variables selected in the first

stage as well as relevant environmental and public good variables. We assume that

household expenditure is a function of asset holdings and place-specific asset returns.5

5 This specification can be thought of as permanent or structural income (Carter and May 2001, Adato et al. 2006, Carter and Barrett 2006).

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We remain agnostic about the functional form of the asset returns equation and model the

relationship between expenditure and asset holdings using a second order flexible

functional form. For household i in location c, letting icy = per adult male equivalent

household expenditure, icA = private, targetable assets, cA = place specific means of the

private, targetable assets, cB = public, targetable assets, icY = private, non-targetable

assets, cY = place specific means of the private, non-targetable assets, cZ = public, non-

targetable assets, and icX = additional household control variables, we can write the

assumed functional form as:6

(1) '),,,('),,,,(' ),,,('),,,,('ln

icicciccicZccciccicXic

ciccicBccicccicAicic

XZYBARZZYYBARYZYBARBZYBAARAy

)(jR is a vector of returns to asset type j = A, B, Y, Z, which is the object of estimation.

The functional form of asset returns implies that the expected returns to each asset can

depend on the stock of every other asset. For example, the returns to a head of cattle may

depend on the household head’s level of education, the average number of cattle owned

in that region, the existence of a nearby livestock market and/or local precipitation levels.

Place specific asset means are only interacted with household levels of the same variable

(i.e., average cattle holding is interacted with each household’s cattle holdings, but not

with each household’s pig holdings or mobile phone ownership). Further, we assume the

error term is composed of a location component and a household specific component.

(2) M')(M icciccc ic

where ],,,[ ccccc ZYBAM .

6 The place specific means, cA and cY , are derived from the census.

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Our principal goal in this second step in constructing the targeting map is to

accurately estimate the coefficients in the expenditure asset relationship. We bootstrap

200 iterations of the regression, using weighted least squares (weighted by population

expansion factors) with errors clustered at the enumeration area level. We save the

coefficient estimates from each iteration of the bootstrapped regressions.

Having thus estimated the shape of asset returns (many times), in the third step we

project the estimated coefficients from the first stage regressions onto the census data.

Ultimately, however, we are not interested in the coefficient point estimates, but in the

expected marginal household-level return for a given targetable asset, k:

(3) )(ˆ

')(ˆ

')(ˆ

')(ˆ

'][ln ick

Zc

ick

Yic

ick

Bc

ick

Aic

ick

ic

ARZ

ARY

ARB

ARA

AyE

For each iteration of the bootstrap, we project the coefficient estimates onto the census

data and calculate the derivatives for all targetable assets. Aggregating all of the

estimates generates an empirical distribution of estimated marginal household-level

returns to specific assets. The mean estimated marginal return across bootstrapped

iterations yields our best estimate of a household’s expected marginal return for each

asset. We then aggregate households over geographically defined areas and calculate

statistics fundamental to the final product. First, we compute the mean and standard error

of the expected marginal returns for every geographic area and determine which areas

have returns that are statistically significantly greater than zero. The estimated average

marginal returns and their statistical significance inform essential questions about the

expected magnitude of average benefits associated with specific asset transfers in

particular areas. Second, we calculate the proportion of households with positive

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expected marginal returns for every geographic area, which reflects the scope of benefits

from specific asset transfers in particular areas.

Finally, using GIS techniques, we display the results. Unlike with poverty

mapping, no one map can summarize all of the results; instead this targeting method

requires a series of maps. One map can display the most beneficial asset, as judged either

by the highest expected average marginal returns of any asset or the highest proportion of

positive expected marginal returns of any asset, for each geographic area. This map

would address question one above: for a given region, which asset building activity will

have the largest marginal gross benefit? Then, maps can be made for each asset, showing

either the expected average marginal returns or the proportion of households with

positive expected marginal returns to that asset for each geographic area. These maps

would address question two above: for a given type of asset building activity, in which

regions are the marginal gross benefits to such an investment highest? Two estimated

objects, two broad targeting questions, and many assets make for a large number of maps,

each catering to a different audience or targeting question.

2.1 Comparing our method with poverty mapping methods

No standard poverty mapping methodology exists, but there are common

practices from which our method deviates slightly, thus it is useful to contrast and justify

our approach. One common practice is to partition the data into the smallest regions for

which the survey data are statistically representative and run regressions for each of those

regions separately. For example, Okwi et al. (2006) and Emwanu et al. (2007) split

Ugandan data into nine strata and Demombynes and Ozler (2005) split South Africa into

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nine provinces. The idea behind this step is to allow coefficient estimates to vary over

space. In contrast, we pool all survey data into a single regression. While our method

does not allow coefficient estimates to vary over space, asset returns can vary

dramatically over space via the large number of place-specific interaction terms. Our

motivation for this choice is to explicitly take into account the influence of place-specific

characteristics on asset returns. If the geographic scope of regressions is limited, the

variation in some variables, especially the place-specific variables such as climate, is

necessarily very limited. This constraint could lead to biased and inconsistent parameter

estimates.

Another common methodological step in poverty mapping is to use stepwise

regression to reduce the number of right hand side variables (Okwi et al. 2006, Emwanu

et al. 2007, Demombynes et al. 2007). When no underlying theory exists about which

variables belong on the right hand side, this method can be used to iteratively delete

variables based on a criterion such as adjusted-R2 or t-statistics. This approach would

likely exclude several asset variables, both limiting the scope of inter-asset comparisons

and potentially biasing the estimated returns of included assets (via omitted relevant

variable bias). Thus, we take a more structured approach and place priority on the

inclusion of all asset variables.

The most common way to estimate the error surrounding the poverty estimates is

to use parametric bootstrapping (Elbers et al. 2003, Demombynes et al. 2007).

Parametric bootstrapping projects coefficient estimates onto census households by taking

random draws from the distribution defined by a single set of regression coefficient

estimates and their associated covariance matrix. The poverty status of individual

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households are then averaged by geographic areas. This process is repeated many times

to obtain a distribution of each area’s poverty. We choose instead to bootstrap the first

stage estimation in order to reduce bias in the estimates, since our method puts a greater

premium on the regression coefficient estimates themselves.

2.2 Endogeneity concerns

The major pitfall of our targeting maps methodology is the obvious endogeneity

of several asset variables, which can affect results in several ways. First, there is the

basic, natural correlation between expenditure and assets. Ideally, we could use an

accurate measure of income as the key left hand side variable of interest, but of course

such a measure rarely exists (and this would not fully assuage endogeneity concerns

anyway). As a result, there could be a natural positive correlation between asset holdings

and expenditure – in order to acquire assets, one must spend money. On the flip side,

there may be a negative correlation between assets and expenditure in a static setting; for

example, if a household has just sold lots of livestock, it may have increased consumption

but lower ex post livestock holdings. An additional confounding factor is that not all

asset ownership is motivated by current productive value. Some assets are acquired not

because they will produce more current expenditure, but because they enhance welfare in

some other way or at some future date. For example, some livestock may be held for risk

prevention or social status, and educational investments are aimed at increasing future,

not current, income.

Estimated returns to assets may be additionally biased due either to omitted

relevant variables or unobserved heterogeneity. Differences in preferences, ability and

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various idiosyncratic features specific to households are all potential sources of bias. We

include in our specifications a rich set of place-specific covariates and a complete set of

interaction terms in order to pick up as much variation as possible and diminish the effect

of this kind of bias.

These are clearly serious concerns. They are inherent, however, to any analysis

that tries to answer the questions posed above. Short of running hundreds or thousands of

identical field experiments across vast spaces, there is no practical way to estimate

marginal returns to multiple assets across a large geographical space with ironclad

identification. While it is impossible to argue a purely causal relationship, knowing how

households change their asset portfolios as their welfare increases and how welfare is

related to the environment and infrastructure around them can nonetheless provide quite

useful insights to inform development policy. Given the considerable policy and

operational importance of the questions targeting maps aim to address, we think this

tradeoff is acceptable and hope and expect that future research can ameliorate this

problem somewhat.

3 Data

We apply our method to the 2002 Ugandan National Household Survey, the 2002

Ugandan Population and Housing Census and the 2002 Ugandan community survey, all

administered by the Ugandan Bureau of Statistics (UBOS). The household survey and

census are stratified by four regions (Central, East, North, and West) and an urban-rural

split. For the purposes of this paper, we restrict our attention to rural households only

(5,648 households in the survey and nearly 4.4 million in the census), although urban

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households could be included easily. The hierarchy for Ugandan administrative units,

from largest to smallest, is nation, district, county, sub-county, and parish. Table 1 lists

how many administrative units of each type exist and the average and median number of

households in each unit. There are one or two enumeration areas (EA) per parish. The

household survey clustered observations at the EA level and randomly sampled

households within the EA.

The private asset variables all come from the household survey and the census.7

We use the census, the community survey and several GIS layers to create location

specific public asset variables. From the census, we calculate measures of ethnic and

religious diversity and population density, as well as means of all variables at the parish

level.8 The community survey includes information on drought, livestock and crop

extension services, markets, microfinance and violence against women. These variables

are aggregated to both the parish and sub-county level, necessary since not all parishes

contained a community that was surveyed. In addition, we use GIS to derive variables

such as average distance to secondary school, average distance to urban areas, average

distance to water, proportion of a region composed of various agro-ecological zones,

average annual rainfall, average variation in rainfall, average annual temperature and

average variation in temperature, among others.9 Data layers for school location, urban

areas, agro-ecological zones and water location were provided by the International

Livestock Research Institute (ILRI). Weather data were downloaded from

7 As stated above, we are constrained to only use variables that appear in both the census and the survey. There are several instances where a variable that would be fantastic to include (e.g., mosquito net coverage of all household members) is only contained in one data set. This underscores the importance of planning and coordinating household surveys and censuses. 8 Diversity is calculated (as in Easterly and Levine 1997) as the probability that two people of different ethnicity/religion meet. 9 Euclidean, or straight-line, distance is used.

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www.worldclim.org at a resolution of 30 arc-seconds. These geographic variables are

aggregated at the sub-county level, due to limitations with the GIS software.10 Table 2

gives summary statistics for each asset variable for the survey and census. Once all

interaction and second order variables are added, there are a total of 1120 right hand side

variables in our specification of equation 1. Appendix 1 lists all variables used.

In addition to numerical comparability of the data, geographic comparability is

important. Table 3 gives the percentage of each administrative unit represented in the

survey and community census and Appendix 2 shows the geographic location of the

survey data. The survey data appear well dispersed and thus we have confidence that our

estimates are representative of many different geographies.

4 Results

Appendix 3 presents complete results from the bootstraped regressions.

As a first step in analyzing the results, we determine the appropriate level of

aggregation for the expected marginal returns. In standard poverty mapping exercises,

there is a tradeoff between geographic aggregation and precision (Elbers et al. 2003).

The goal is to aggregate households into the smallest possible geographic area without

sacrificing precision, which enables inter-regional comparison.

We aggregate derivatives and calculate means and standard errors of all targetable

assets at three different administrative levels: county, sub-county, and parish. Table 4

gives the estimated standard errors of four assets – bicycles, chickens, microfinance

10 Due to the small size (in terms of area) of some of the parishes and the relatively larger size of the weather raster data, the zonal statistics could not be calculated for all parishes.

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access and road access11, which we use as examples throughout – as well as the average

across all targetable assets. Clearly, as the area of aggregation grows so does the

standard error. This finding contrasts with the standard inverse relationship found in

poverty mapping due to the difference in our method, which first estimates household

level marginal returns via simulation and then aggregates over geographic areas. Our

error estimates are a composite of ordinary imprecision plus inter-household variation.

As the geographic scale grows, more inter-household heterogeneity is introduced and the

standard errors increase. The empirical findings clearly indicate that parish is the

appropriate level of aggregation for our estimates, especially since the efficiency of

geographic targeting increases as the geographic area decreases in size (Baker and Grosh

1994, Elbers et al. 2007).

Figures 2a-2d plot the estimated average marginal returns that are significantly

greater than zero for chickens, bicycles, access to microfinance and road access,

respectively, at the parish level. The magnitude of the returns is not readily apparent; the

units are the expected additional natural log of monthly per adult male equivalent

expenditure associated with an additional unit of that asset. For purposes of comparison,

the average of the natural log of per adult male equivalent monthly expenditure across the

whole household survey sample is 10.15. The ordinality of the magnitudes of returns

seems reasonable with road access being most valuable, then microfinance access, then

bicycle ownership and finally chickens. There is also considerable spatial variation in the

estimates. We see pockets of high returns, like those in the northwestern Uganda for

11 Microfinance access is a binary variable indicating whether at least one community within a parish indicated having access to microfinance. Road access is measured as an index from 0 to 2, where 0 represents “no roads,” 1 is “seasonal roads” and 2 is “all weather roads”. Values for a parish are averaged responses from all communities surveyed within that parish.

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chicken, and spatial patterns, such as the remarkable regularity in which the returns to

road access increase with proximity to urban areas. For each asset, a considerable portion

of the country does not exhibit statistically significant returns, reflecting both relatively

large standard errors and several negative point estimates. It is reasonable that some

returns are actually negative because we estimate marginal returns comprehensively,

including areas that are completely unsuitable for certain assets.12

Figures 2a-2d present the proportion of households with estimated marginal

returns greater than zero for chickens, bicycles, access to microfinance and road access,

respectively, at the parish level. The chicken map largely reinforces the information

contained in Figure 1b; areas with high returns, notably the northwest, also have a large

proportion of households with statistically significant positive expected returns. The road

access map offers little way to distinguish between areas as almost all areas have a

beneficiary proportion over 85%, signaling near-universal benefits from improved road

access. The bicycle map offers the greatest additional insight. Whereas coverage for

significantly greater than zero returns was only 25% for bicycles (Figure 1a), Figure 2a

clearly shows that the scope of benefits is wide ranging and over 90% in over half of

parishes.

Next, we derive which asset offers the largest benefits for each parish. In Figure

3a we map which asset, other than road access – which is nearly-universally the highest

return investment – is expected to generate the maximum marginal expected benefit.

Similarly, Figure 3b shows which asset is expected to generate the maximum proportion

of positive marginal expected returns, by parish. Consistent with the observed high

magnitude and near-universality of benefits to improved road access, private transport 12 Kam et al. (2005) also find that estimated returns to assets can be negative in some areas.

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assets dominate these maps. Motor vehicle ownership and motorcycle ownership often

offer the maximum expected return. Motor vehicle ownership and bicycle ownership are

the most prominent assets on the map of maximum proportion of positive household-

level expected returns. These targeting maps make clear the high magnitude and spatial

extent of expected benefits to improved transport systems in rural Uganda.

It should not be the least bit surprising that motor vehicles offer the highest

average marginal return in a large number of parishes; motor vehicles are extremely

valuable and expensive. These targeting maps depict estimated marginal gross returns;

information about the costs of supplying different assets has been conspicuously absent

from our analysis thus far. In order to address this deficiency and enable explicit benefit-

cost comparisons (albeit simplistically and incompletely), we compare estimated benefits

with estimated costs based on the mean price of livestock purchased or sold, as reported

in the household survey (values of other assets are unavailable in the data).13 Cost data

do not include the marginal costs of maintaining stocks, thus total costs would be higher.

Table 5 presents the findings.

Because it is unclear what time horizon the stream of benefits would accrue, we

take an extremely conservative approach and report only the expected increase in

expenditure for a single month. Since durable assets typically affect monthly

expenditures over a period of many months, depending on their rate of depreciation, this

necessarily understates the benefits, in some cases by orders of magnitude. This

13 The expected household marginal benefit was calculated with the following formula,

)( )(ln)(ln yey eehsehmb where ehmb is the expected household marginal benefit, hs is the average household size (in adult male equivalents), y is the average household monthly expenditure, and e is the estimated average marginal return (like those displayed in Figure 1). Expected household marginal benefit is the expected increase in monthly expenditure per household that receives a one unit asset transfer.

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extremely conservative approach underscores, however, the considerable marginal

returns to investment in rural Uganda. Three of the four livestock assets clearly pass a

benefit-cost test, and the other (cattle) passes for time horizons of three years or more,

even just one year in areas with expected returns on the high end of the distribution.

While detailed exploration of the behavioral and institutional reasons for these findings is

beyond the scope of this methodological paper, the results clearly underscore apparent

underinvestment in productive assets in rural Uganda. Targeting maps of this sort can

help development agencies identify best bet forms for asset transfers given such apparent

underinvestment, and especially preferred geographic locations for a specific asset

transfer program (e.g., livestock), since the costs of provision typically vary only

modestly across space for a given asset.

Beyond looking at estimated marginal benefits of an asset, we examine how those

benefits relate to existing holdings of that asset and to the poverty headcount rate by

parish.14 The correlation between benefits and holdings explores whether there are

positive or negative network externalities associated with each asset, i.e., are marginal

returns increasing or decreasing in total parish holdings. The correlation between the

marginal returns to an asset and the poverty rate reveals prospective tradeoffs or

synergies between efficiency and equity objectives.

The central column of Table 6 shows the correlations between estimated average

marginal returns and asset holdings for all targetable assets, while Table 7 presents

14 The poverty headcount rate is the percentage of the population that is poor. In Uganda, a household is deemed poor if their estimated monthly expenditure falls below the expenditure thresholds set by Emwanu et al. (2007). As a check on our method, we compare our poverty estimates to those previously estimated for Uganda using the same data from Emwanu et al. (2007), who estimated the poverty headcount rate at the sub-county level. The correlation between the two estimated poverty headcount rates is 0.64 and the rank correlation is 0.66. The poverty map created using our method is shown in Appendix 4.

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analogous correlations with the estimated proportion of households with positive

marginal returns. Most correlations are qualitatively similar between the two measures of

estimated benefits. Significant positive network effects appear for literacy, mobile phone

ownership and road access, reflecting how these assets become more valuable when

others in the area already possess them. Conversely, congestion effects are evident with

respect to home and motorcycle ownership as well as for some livestock (chickens and

goats).

Tables 6 and 7 also display the correlations between estimated marginal benefits

to asset transfers and poverty. A positive (negative) correlation implies efficiency and

equity aims are mutually reinforcing (competing). The results point to chickens and

microfinance as good candidates for geographic targeting in the sense that poverty

headcount rates are positively correlated with estimates of both the magnitude and

breadth of benefits to asset transfers.

As the final step in illustrating the potential utility of targeting maps, we identify

parishes that might be especially strong candidates for receiving asset transfers. Explicit

decision criteria based on expected returns do not exist at present; targeting maps can fill

that void. We focus on a hypothetical chicken transfer program that a NGO might

implement, since chickens clearly pass a simple benefit-cost test and the expected

benefits are highest in areas of greatest poverty. We select parishes based on the

following three attributes: 1) expected average marginal returns to a chicken ≥0.13 and

statistically significantly greater than zero, 2) at least 90 percent of households have

positive expected marginal returns to chickens, and 3) a poverty headcount rate ≥0.51.15

A total of 58 parishes meet these criteria and are mapped in Figure 4. Of those, we 15 The numeric thresholds were chosen based on the mapping results; there were no a priori levels.

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highlight two parishes that show particular promise for this sort of development

intervention. Bulumba parish (in the southeast) offers large expected marginal returns

and an intermediate amount of poverty. Moli parish (in the northwest), on the other hand,

has one of the highest poverty rates in the sample and above average expected marginal

returns. All households in both parishes have expected positive returns to chicken

transfers.

5 Conclusions

This paper presents a novel method that has the potential to greatly advance the

efficacy of asset-based, geographically targeted transfer schemes. We add to the

substantial literature of small-area estimation and go beyond estimating poverty and

begin to address the best means of alleviating it. Our method first estimates the marginal

returns to various assets and then creates a series of maps that can address a variety of

questions regarding the magnitude and scope of benefits and the efficient spatial

allocation of development programs. The results produced using Ugandan data are

promising; estimated and projected asset returns seem reasonable and show substantial

variation across space.

Continued work with additional inputs is needed to complement targeting maps.

First, even if a policy maker has a targeting map in hand, there are still unanswered

questions about net benefits to and final effects of various asset transfers. We addressed

some of these concerns with a limited benefit-cost analysis. A more thorough analysis

for all assets with more precise information on procurement costs is a natural and

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straightforward exercise for agencies intending to implement a transfer scheme using

targeting maps as an input.

Second, while targeting maps estimate the best means to an end, policy makers

are most often interested in the end itself – i.e. poverty reduction. A natural extension of

the targeting maps method is to use panel data to determining the expected impacts of an

asset transfer program on poverty (or other outcome variables of interest).

The maps and other results produced in this paper serve mainly to demonstrate the

potential usefulness of this method. Our hope is that the method can be improved upon

and eventually implemented in development programming, complementing the well-

established use of poverty maps in less developed countries. The promise of these

methods might also help encourage organizers of household surveys and censuses to

better coordinate future questionnaires with poverty and targeting maps in mind.

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References

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Ellis, F and Freeman, A, 2004 “Rural livelihoods and poverty reduction strategies in four African countries” Journal of Development Studies, 40(4), 1-30. Emwanu, T., Okwi, P. O., Hoogeveen, J. G., Kristjanson, P., and Henninger, N., 2007. Nature, distribution and evolution of poverty and inequality in Uganda 1992-2002. Uganda Bureau of Statistics and the International Livestock Research Institute. Fan, S and Hazell, P, 2001. “Returns to public investments in the less-favored areas of India and China” American Journal of Agricultural Economics, 83(5), 1217-1222. Fan, S and Chan-Kang, C, 2004. “Returns to investment in less-favored areas in developing countries: a synthesis of evidence and implications for Africa” Food Policy, 29, 431-444. Hoffman, V, Barrett, C and Just, D, 2009 “Do free goods stick to poor households? Experimental evidence on insecticide treated bednets” World Development, 37(3), 607-617. Kam, S., Hossain, M., Lal Bose, M., and Villano L.S., 2005. “Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh” Food Policy, 30, 551-567. Moser, C, 1998 “The asset vulnerability framework: Reassessing urban poverty reduction strategies” World Development, 26(1), 1-19. Okwi, P.O., Ndeng’e, G., Kristjanson, P. Arunga, M., Notenbaert, A. Omolo, A., Henninger, N., Bensen, T., Kariuki, P., and Owuor, J., 2007. “Spatial determinants of poverty in rural Kenya”. Proceedings of the National Academy of Sciences of the United States of America, 104 (43), 16769-16774. Tarozzi, A. and Deaton, A., 2008. “Using Census and Survey data to estimate poverty and inequality for small areas” forthcoming, Review of Economics and Statistics.

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Tables Table 1: Hierarchy of Ugandan administrative units and associated number of households

Administrative unit Total units

Number of households per unitmean median

District 56 90,797 79,024 County 163 31,194 27,650 Sub-county 958 5,308 4,584 Parish 5,234 971 818

Notes: Data come from the census and include both rural and urban households.  

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Table 2: Summary statistics Survey Census Number of households 5,648 4,376,978 Monthly household expenditure ( in Ugandan Shillings) 144,345   ‐            Variable (assets) Mean St. dev. Mean St. dev. Household head male 0.80 0.40 0.78 0.42 Household head education (years) 5.19 3.69 4.55 3.85 Household head age 41.19 13.77 41.62 16.26 Household head married 0.84 0.36 0.74 0.44 Proportion of household literate 0.45 0.25 0.45 0.32 Cattle 2.15 14.34 1.19 12.49 Goats 0.41 4.56 1.00 7.22 Pigs 0.12 1.30 0.15 1.24 Chicken 2.15 26.33 2.37 16.93 Land ownership (1=yes) 0.32 0.47 0.16 0.36 House ownership (1=yes) 0.91 0.29 0.85 0.36 Motor vehicle ownership (1=yes) 0.01 0.11 0.01 0.10 Motorcycle ownership (1=yes) 0.04 0.19 0.02 0.15 Bicycle ownership (1=yes) 0.53 0.50 0.35 0.48 Mobile phone ownership (1=yes) 0.03 0.17 0.03 0.16 Derived from census Population density (people per sq. km) 292.10 463.80 396.86 875.49 Ethnic diversity of parish 0.28 0.26 0.29 0.27 Religious diversity of parish 0.55 0.14 0.56 0.15 Derived from Community Survey Existence of livestock market in parish (1=yes) 0.26 0.44 0.30 0.46 Existence of crop market in parish (1=yes) 0.51 0.50 0.51 0.50 Microfinance access (1=yes) 0.80 0.40 0.79 0.41 Road access index 1.10 0.27 1.12 0.42 Existence of cattle rustling in parish (1=yes) 0.23 0.42 0.22 0.41 Existence of rebel activity in parish (1=yes) 0.15 0.36 0.17 0.38 Existence of drought in parish (1=yes) 0.86 0.34 0.85 0.36 Existence of animal disease in parish (1=yes) 1.00 0.05 0.99 0.11 Existence of crop disease in parish (1=yes) 0.99 0.08 0.99 0.12 Existence of animal extension services in parish (1=yes) 0.92 0.26 0.91 0.28 Existence of crop extension services in parish (1=yes) 0.94 0.24 0.93 0.26 Existence of human epidemic in parish 0.96 0.20 0.95 0.21

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(Table 2 continued) Derived from GIS Average distance to secondary school in parish (km) 4.32 4.03 4.73 4.89 Average distance to Kampala in parish (km) 185.30 96.89 187.29 99.35 Average distance to an urban area in parish (km) 15.64 10.63 15.98 11.40 Average distance to freshwater in parish (km) 1.92 3.48 1.85 3.16 Percentage of parish agro-ecological zone 1 0.08 0.26 0.08 0.26 Percentage of parish agro-ecological zone 2 0.10 0.30 0.11 0.30 Percentage of parish agro-ecological zone 3 0.38 0.47 0.38 0.46 Average annual temperature (°C) 21.83 2.02 21.86 2.02 Mean Diurnal Range Temperature 12.03 0.65 12.03 0.71 Temperature seasonality 65.79 26.68 66.49 27.54 Annual temperature range 14.65 1.26 14.66 1.34 Average annual total precipitation (mm) 1226.57 178.95 1224.53 182.93 Average precipitation in driest month (mm) 34.29 15.22 34.32 16.12 Precipitation seasonality 42.75 6.51 43.05 6.77

Table 3: Geographic coverage of data (percent) Administrative

unit Survey Community

census Census District 100 100 100 County 100 100 100 Sub-county 56.3 100 100 Parish 11.5 98.4 100

Table 4: Average standard deviation of estimated average marginal returns

Administrative unit Bicycle Chicken

Microfinance access

Road access index

Average over all assets

County 0.28 0.03 0.46 2.41 0.53 Sub-county 0.23 0.02 0.32 1.19 0.38 Parish 0.17 0.01 0.11 0.37 0.23

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Table 5: Simplified benefit cost analysis (all numbers in Ugandan Shillings)

Asset Cost Expected marginal monthly

benefit Average 95th percentile

Cattle 343,502 10,470 29,448 Chicken 4,848 26,087 65,820 Goats 23,920 54,066 116,162 Pigs 24,600 333,269 1,049,289

Table 6: Correlation of estimated average marginal returns

Asset Correlation with

Average existing holding in parish

Poverty headcount

Cattle -0.02 -0.08 Goats -0.18 -0.08 Pigs -0.06 0.12 Chicken -0.05 0.03 Land ownership 0.03 0.17 House ownership -0.42 0.02 Motor vehicle ownership 0.15 0.13 Motorcycle ownership -0.17 -0.05 Bicycle ownership 0.17 -0.02 Mobile phone ownership 0.37 0.08 Proportion of household literate 0.67 0.05 Existence of livestock market in parish 0.07 0.01 Existence of crop market in parish 0.15 -0.07 Microfinance access -0.09 0.06 Road access index 0.31 -0.13 Household head education (years) 0.29 -0.19

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Table 7: Correlation of estimated proportion of households with positive marginal returns

Asset Correlation with

Average existing holding in parish

Poverty headcount

Cattle 0.02 -0.10 Goats -0.26 -0.19 Pigs 0.16 -0.05 Chicken -0.09 0.09 Land ownership 0.07 -0.10 House ownership -0.26 -0.15 Motor vehicle ownership 0.05 -0.05 Motorcycle ownership -0.34 0.23 Bicycle ownership 0.19 0.19 Mobile phone ownership 0.23 -0.11 Proportion of household literate 0.73 -0.32 Existence of livestock market in parish 0.04 -0.01 Existence of crop market in parish 0.05 -0.14 Microfinance access -0.06 0.05 Road access index 0.15 -0.05 Household head education (years) 0.15 -0.14

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Figures Figure 1: Examples of maps of estimated average marginal returns that are significantly greater than zero for the given asset.

Fig. 1a. A map of estimated average marginal returns to bicycles that are significantly greater than zero.

Fig. 1c. A map of estimated average marginal returns to microfinance access that are significantly greater than zero.

Fig. 1b. A map of estimated average marginal returns to chickens that are significantly greater than zero.

Fig. 1d. A map of estimated average marginal returns to road access that are significantly greater than zero.

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Figure 2: Examples of maps of proportion of households with estimated positive marginal return for the given asset.

Fig. 2a. A map of the proportion of households with estimated positive marginal returns to bicycles.

Fig. 2c. A map of the proportion of households with estimated positive marginal returns to microfinance access.

Fig. 2b. A map of the proportion of households with estimated positive marginal returns to chickens.

Fig. 2d. A map of the proportion of households with estimated positive marginal returns to road access.

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Figure 3: Maximum asset returns

Fig. 3a. A map of which asset offers the largest expected average marginal return that is significantly greater than zero. In the legend, the number in parentheses indicates the number of parishes for which that asset offers the maximum return that is significantly greater than zero. Note that road access has been excluded from this analysis.

Fig. 3b. A map of which asset offers a positive expected marginal return to the largest proportion of households. In the legend, the number in parentheses indicates the number of parishes for which that asset offers the maximum proportion of households with expected positive return. Note that road access has been excluded from this analysis.

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Figure 4: Sample targeting exercise