The Regressive Demands of Demand-Driven Development
Sarah Baird, Craig McIntosh, and Berk Özlera
April 23, 2013
Despite their explicit focus on reaching the poor, many community driven development (CDD)
initiatives are only partially successful in targeting spending towards them. This paper examines
Tanzania’s flagship CDD program and provides new evidence on the mechanisms by which thedemand-driven components of the program may undermine the goal of pro-poor funding
allocations. We exploit two data sources for the analysis: a census of wards for mainland
Tanzania and a census of households in 100 program villages. These data paint a consistentpicture at both levels: wealth, education, access to media, and political engagement are positively
correlated with the likelihood to apply for the program at the national level, and to be aware of itat the local level. Centrally dictated features of the program – namely predetermined funding
allocations to districts and eligibility rules – combine with the decentralized selection process
within districts to counteract this initially regressive application pattern and produce a programthat is, like many other CDD programs, only mildly pro-poor. Our results suggest thatsensitization and outreach prior to the application process will be a critical dimension in making
CDD programs more progressive.

Keywords: Targeting; Community Driven Development; Poverty; Elite Capture
JEL Classification Codes: H42, I38

We thank seminar participants at UC Berkeley and WGAPE. We are also grateful to Michael Futch and Leah
Nelson for excellent research assistance. 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. Baird: sbaird@email.gwu.edu, Özler: bozler@worldbank.org.
Corresponding author: McIntosh: ctmcintosh@ucsd.edu. Phone: 858 822 1125. Address: 9500 Gilman Drive, La
Jolla CA, 92093-0519.

1. Introduction
Over the past two decades, community driven or community based development(CDD/CBD) has become an increasingly common way to distribute public assistance, and, in
addition to providing much needed infrastructure, is intended to provide a variety of otherbenefits to communities including poverty reduction, improved social capital, and capacity
building at the local government level. Mansuri and Rao (2012) argue that the current wave ofinterest in localized participatory development started as a reaction to “top-down” development
aid that was “… deeply disconnected from the needs of the poor, the marginalized, and theexcluded.” (p. 2) CDD programs are supposed to improve on previous approaches by bettertargeting the available funds to needy communities and also allow those communities todetermine their specific needs – hence community- or demand-driven development.
Despite the fact that the poor are explicitly the target group for most of these programs,
the empirical literature on targeting performance shows that they tend to be only moderately propoor.1 For example, World Bank (2002) found that “…social funds projects have delivered
slightly more than proportional benefits to the poor and the poorest.” (p. xi) Beneficiarycommunities and households are determined by which ones apply and, having applied, getapproved. The literature before us has examined the benefit incidence for program beneficiaries(i.e. targeting performance vis-à-vis the final funding allocations), but we know of no studies ofdecentralized CDD programs that decompose targeting performance into project applications and,conditional on application, selection.2
In this paper, we exploit a unique combination of datasets to examine the application andselection processes for a CDD project at the national and household levels. At the national level,
we combine administrative data on the universe of project applications from villages and thefinal funding allocations to these communities under Tanzania’s Social Action Fund Second
Phase (TASAF II) with a poverty map of Tanzania, as well as census data and voting records todistinguish the pattern of project applications from that of final spending allocations in almost

1

In this paper, we use pro-poor to mean that the share of per capita program spending that reaches poor
communities (or households) is significantly higher than that for the non-poor. For a discussion of commonly used
targeting performance measures, see Ravallion (2009).
2
Coady and Parker (2009) pose a similar question in a different context, and assess the relative contributions of selfselection (i.e. applications) and administrative targeting (i.e. eligibility criteria) at the household level to the final
targeting performance of Oportunidades, Mexico’s urban cash transfer program.

2

2,200 wards.3 We also examine the roles played by the center and the decentralizedadministrative units by decomposing these outcomes into within- and between-district
components. Finally, we complement this analysis with micro level data from 100 program
villages to assess awareness of TASAF II and the likelihood of benefitting from it at thehousehold level. Using data on every household in these 100 communities, we examine the rolesof eligibility, access to information, and political connectedness in determining beneficiary status
in great detail.
We uncover a regressive pattern on the demand side: richer districts produce substantiallymore applications per capita than poorer ones at the national level while richer households are
more likely to be aware of the program among those eligible at the local level. At both of theselevels, independent sources of data paint a consistent picture: access to media, education, andpolitical participation are strongly associated with the likelihood of being aware of and applying
for projects.
Faced with this regressive pool of applicants, TASAF successfully utilizes several tools
available to it to produce an ultimately pro-poor program. First, the effect of an inordinately
large number of applications from richer districts is nullified by a funding formula that is used by
the center to allocate each district a fixed amount of funds before the start of the program.
Second, households eligible to receive grants for income generating activities under TASAF II –determined by criteria also imposed by the center using easily observable household
characteristics – are significantly more likely to be poor than ineligible households. Finally, eventhough eligible households who are aware of the program, i.e. those who can de facto apply for
grants, are no more or less likely to be poor than other households (because program awarenessincreases with income), the selection process within districts is such that the final beneficiarypool is slightly poorer than the population as a whole. The final targeting performance can thus
be decomposed into a strongly regressive demand-driven component, and then a funding
formula, an eligibility rule, and a decentralized beneficiary selection process each of which is
progressive.
In the end, however, the targeting performance of this CDD program, in which
communities and households have to produce applications to receive government support andlocal authorities select beneficiaries under rules imposed by the center, is underwhelming. At the3

The hierarchy of administrative units in Tanzania is Region, District, Division, Ward, and Village.

3

national level, a one standard deviation increase in poverty headcount would imply an increaseof $0.24 in per capita spending in the ward.4 Similarly at the household level, TASAF II
beneficiaries are only marginally more likely to be poor than non-beneficiaries (in our household
data 66% of TASAF II VG group members are poor, while 55% of non-beneficiaries are). At
both levels, these figures appear closer to a neutral targeting scheme (in which every ward orhousehold receives the same amount of funding) than to a perfectly pro-poor allocation of funds.
Furthermore, political activity at the ward level (measured by voter turnout) and proximity to thevillage center, political participation, connections to local elites, and access to other safety net
programs at the household level are strong correlates of beneficiary status – even after
controlling for poverty. Because program awareness is low among poor eligible households, alarge fraction of the population – those who are most likely to be isolated and least likely to haveaccess to other forms of safety nets – are left with no chance of receiving support from theprogram once the applications are in.
There are several takeaway messages from our study. First, even (or perhaps especially)in decentralized programs, careful centralized design is critical. The allocation of funding to subnational units and the designation of eligibility criteria provide direct levers for the center toinfluence the composition of beneficiaries.5 Second, our study provides guidance on how thevery definition of sub-national administrative units and the appropriate choice for the level ofdecentralization can improve targeting: if the regressive pattern of applications is spatiallyclustered (as it is here), then defining sub-national units such that most of this variation is
between units and then funding them using a predetermined formula will neutralize this effect.
4

The increase of $0.24 is calculated by multiplying the standard deviation of the poverty headcount (0.184 as shownin Table 1) with the coefficient estimate of poverty headcount on final per capita spending (1.33 as shown in column
6 of Table 2), i.e. 0.184 x $1.33 = $0.244. By contrast, a ward with a poverty headcount of 0.34, which is equal tothe average headcount rate in our data across 2,177 wards, is expected to receive, on average, $3.07 per person in
TASAF II funding.
5
Several empirical papers have attempted to measure the relative performance of national and local-levelgovernments in poverty targeting (Ravallion, 2000; Alderman, 2002; Galasso and Ravallion, 2005). Mansuri andRao (2012), summarizing the evidence on the performance of the central vs. the local authorities in allocating
private transfers, find that such programs are mildly pro-poor at best with the targeting performance slightly better at
the local level. In contrast, studies of social funds indicate that while the center is somewhat successful in allocating
resources to poor areas, it is less successful in ensuring that poor households (or poorer communities within thesepoor areas) benefit more from these programs (Paxson and Schady, 2002; World Bank, 2002). For example, Paxson
and Schady (1999) found in Peru that “there was no intra-district targeting.” Targeting performance, especially at
the center and the district levels, depends largely on the availability of data and the political will to use those data totarget poor areas. For example, Galasso and Ravallion (2005) report that all 490 Thanas (sub-districts) in
Bangladesh benefited from the Food-for-Education program because of political considerations and note that this is
not uncommon.

4

The same principle also applies to the spatial composition of poverty: progressive allocation offunds to districts will, on average, be more effective in channeling funds to the poor whenpoverty is explained mostly by income differences between districts rather than within them.6 As
the starting point for any demand-driven program is the act of applying, a robust information
campaign seems to be a necessary, but likely insufficient, condition for potential beneficiaries tojoin what is intended to be a participatory process. Our results suggest that informationally andpolitically marginalized groups start at a disadvantage when development is demand-driven.
The primary argument that has emerged from the literature so far as to why CDD
programs fail to reach the poor is the idea of elite capture (Bardhan and Mookherjee 2000, 2005).
A number of studies have examined the differences in policy preferences across elite and nonelite groups (Platteau and Gaspart, 2003; Rao and Ibanez, 2003; Dasgupta and Beard, 2007;
Olken, 2007) and the role of local inequality in permitting elite capture (Conning and Kevane,
2002; Galasso and Ravallion, 2005; Araujo et al., 2008). When community development funds
are used for political purposes they can also influence targeting performance (Cox and
McCubbins, 1986; Dixit and Londregan, 1996; Schady, 2000; Khemani, 2010), while corruption
can divert scarce resources away from their intended targets (Reinikka and Svensson, 2004).
Most relevant to this paper, local political elites can steer funds towards themselves, theirextended families, kinship networks, and constituencies (Arcand et al., 2006; Besley et al., 2007;
Camacho and Conover, 2011). Alatas et al. (2013), using a recent experiment in Indonesia, findthat local officials and their relatives, who are slightly richer than non-elites, are more likely toreceive benefits than non-elites but also that the welfare losses from such elite capture are small.
However, the strong roles played by access to information and political involvement inour data raise the possibility that a different form of capture, an informational one, is at play.
Such asymmetries in access to information are potentially welfare reducing even when local
authorities are better able to take advantage of idiosyncratic information at the local level(Alderman, 2002), or when their priorities or their definition of who is poor, vulnerable, ordeserving of social assistance are different than those of the center (Alatas et al., 2012). In
practice, social funds typically engage in promotion campaigns to try to disseminate information
6

Note, however, that Ravallion (2009), studying the relationship between traditional targeting performanceindicators and poverty reduction in China’s Di Bao cash transfer program, finds that the correlation between
targeting performance and poverty reduction is low at best and recommends focusing on estimable outcome
measures that are most directly relevant to the policy problem at hand.

5about the availability of funds and program rules as broadly as possible. Many also provide
outreach (and facilitation) to help poor communities through the project application process(World Bank, 2002). Existing evidence on the effects of widespread dissemination of
information suggests that such sensitization efforts should reduce capture (Francken et al., 2009;
Reinikka and Svensson, 2011; Shankar et al., 2011).7
However, further inspection suggests that such sensitization can be subject to perverse
incentives. If, for example, central or district level officials rely on local leaders and/or elites topromote these programs, they may be reluctant to spread the word extensively in order to steer
benefits towards themselves, their extended families, kinship networks, or individuals withwhom they have patron client relationship – creating a conflict of interest. Such conflicts ofinterest are likely to be felt most strongly at the local level when sensitizers are themselves
potential beneficiaries and when the beneficiary pool is small. This reasoning suggests that evenin the case of sensitization, centralized implementation or incentive compatible structures may beneeded to neutralize the potentially regressive nature of demand-driven development.8 Our
results suggest that demand-driven programs are unlikely to achieve real progressivity without amajor effort in this direction, when large portions of the population lack access to informationand to local elites or government officials.9 We return to a discussion of this issue in the
concluding section.
The remainder of the paper is structured as follows. We describe TASAF II in more detailin Section 2, and then discuss the data and the estimation strategy in Sections 3 and 4. We
present our findings at the national and the local levels in Sections 5 and 6, respectively, andSection 7 concludes.

7

Coady and Parker (2009), studying the targeting performance of Mexico’s Oportunidades program, find that theknowledge of the program decreased with increased per capita consumption, implying that the government’s efforts
in disseminating information about eligibility was successful. They find that the information campaign was
instrumental in reducing coverage among richer households but had little role in increasing the concentration oftransfers among the poorest households.
8
Ravallion (2000) describes improvements in Argentina’s Trabajar II program due to a reallocation of resources
across provinces using provincial poverty indicators while also giving incentives to provinces for reaching the poor.
Olken (2007) finds that while central audits significantly reduced corruption in road construction projects inIndonesia, community monitoring had negligible effects. Kochar et al. (2009) find that central mandates could be
effectively used in the North Indian state of Punjab to target funds towards public goods primarily used by the poor.
Dutta et al. (forthcoming) finds that awareness of right to work and how to get work is also low in India’s National
Rural Employment Guarantee Scheme and suggest using enhanced administrative capacity to improve local publicawareness of the rights and rules of the scheme, as well as social audits for local monitoring.
9
See Labonne and Chase (2009) for similar evidence from the Philippines.

62. Tanzania’s Social Action Fund Second Phase (TASAF II)
TASAF II is a USD 120 million social funds program – fairly typical of such large
programs in Sub-Saharan Africa. In this second phase of TASAF projects, up to one third of all
Tanzanian villages were expected to receive a sub-project by the end of 2010. TASAF II features
three components: Service Poor (SP), Food Insecure (FI), and Vulnerable Groups (VG). Our
analysis at the national level includes the universe of projects in all three components of TASAF
II. The Service Poor component mainly provides public goods in the form of infrastructure
projects, while the Food Insecure component is a workfare program, in which very low daily
payments are mostly self-targeted among able-bodied individuals.10 Our analysis at the national
level includes all three components. Because the VG program has a particularly well-defined
beneficiary group and provides excludable private benefits, we focus on this sub-component in
our analysis of the effect of eligibility rules on the targeting performance of the program at thehousehold level. The beneficiaries of VG projects are ‘vulnerable’ households, i.e. thosecontaining elderly individuals, people with disabilities, widows, orphans, and those affected by
HIV/AIDS, who form small groups and receive grants for income generating activities.11
Application and awareness in this CDD program are each outputs of a complex
institutional process. Local government officials initiate project applications, but a successful
application must pass technical review by district-level officials and, in the case of VG
applications, must include an informed and eligible beneficiary group. Villages that do not apply
cannot receive funding. Similarly, knowledge of the program is the primary responsibility of thesensitization conducted at the beginning of the program by district and local government
officials, but individuals who do not participate in village information dissemination activities

10

However, in lean season, the demand for these low-paying jobs can be high enough to warrant rationing at the
local level. In such cases, communities come up with lists of able-bodied individuals who are eligible to benefit
from these local workfare projects.
11
Vulnerable Groups projects provide grants of US$6,000-10,000 to groups of roughly 10 individuals fromvulnerable households in order to finance an entrepreneurial activity. Typical activities for these groups are animalhusbandry, beekeeping, tailoring, or the operation of milling machines. These projects thus generate an unusually
private form of benefit relative to typical CDD programs. Anecdotal evidence collected by the authors suggests thatthe formation of these groups across study villages is not uniform. In some cases, pre-existing groups (such as a selfhelp group) can apply to receive funding under this component; in others, newly-formed groups seek to receive
funding; yet in other cases, village chiefs help form vulnerable groups (by grouping the elderly or widows or
disabled together) and propose the income-generating activity (hence providing coordination across groups andincome-generating activities within the village). In all cases, applications must have the explicit approval of thevillage executive officer before being submitted to TASAF.

7

will be difficult to reach. Those who are unaware of the existence of the program will not be
included as beneficiaries, even if they qualify.
District- and village-level officials are the key actors in this decentralized setting. The
well-defined roles for different actors in the TASAF II funding process give us the ability todifferentiate the effects of their actions. Here, we describe the project selection process in detailby providing the ‘waterfall’ of steps through which a proposal must go and linking each layer ofdecision-making to a specific part of the empirical analysis. As with many such decentralized
programs, the funding waterfall features a mixture of centralized design features with decisionmaking at lower administrative levels, with clearly distinguished roles for national and subnational actors. The initial decisions in this waterfall, illustrated in Figure 1, are two key
centralized features: the eligibility rules for the program, and the formula that will be used to
distribute funding across sub-national units, i.e. districts in the case of Tanzania.
The allocations from the center to the districts under the predetermined funding formulawere based on a weighted score calculated using three variables: population, geographical size,
and a poverty index. The poverty index used by TASAF II administrators was based on
indicators of access to basic social services such as health, education and water. It also included
variables, such as food security, disability, number of orphans, and the HIV/AIDS burden in the
district.12 Still, since using funding formula alone could result in large differences between
district councils’ allocations, 25% of the National Village Fund (NVF, the main spending vehicle
under TASAF II) was first distributed equally to all councils. The remaining amount was thendistributed using the funding formula described above.
In order to initiate the possibility of funding, village officials work with TASAF district
officials to sensitize the local population as to the existence and nature of the program, and thenvillage officials must complete an application, called a sub-project interest form or SPIF, to theprogram. TASAF II created an elaborate screening process in order to guard against the types of
elite capture documented in other community development programs. Recognizing theimportance of outreach in this process, every one of Tanzania’s 11,000 villages were supposed to
be visited by an official from the district and given information about the program and how to
12

The data used came from NBS (HBS, Demographic and Health Survey), Ministry of Agriculture and fromadministrative data that were gathered from each Local Government Authority. This funding formula is only weakly
correlated with the measure of consumption poverty used in this paper once we control for district population andarea size.

8

apply. TASAF district officials then work with the key village level officials (the Village
Executive Officer and Village Chairman) to receive applications from villages that choose topursue funding. The application is forwarded through the district TASAF office to be registeredat the central offices in Dar es Salaam, the capital of Tanzania. This is the definition of‘application’ in our data.
Once an application is registered, it is then subjected to an evaluation process by districtlevel TASAF officials and by the elected District Council. First, it goes through a ‘technicalreview’ by district-level sector experts. Promising proposals are then subjected to an ‘extended
participatory rural appraisal’ (EPRA) by trained TASAF facilitators, a village meeting during
which they undergo a business plan and budget review, an environmental review, and a pairwise
ranking exercise that guarantees that the project applied for is indeed the one desired by thecommunity.13 Because an initial application only specifies the project type and the village, theparticipatory appraisal may result in the clarification of or changes to the intended nature andscope of the project, as well as the identities of beneficiaries as appropriate.14 The beneficiaries
of Vulnerable Groups project applications are finalized at this stage.15 Surviving proposals arethen sent to District Council’s Finance Committee (a non-TASAF political entity at the districtlevel) for final selection.16 The center plays no role whatsoever in this selection process, anddisburses funding once the districts have decided which projects they would like to undertake
within the budget they had been allocated from the center.
The clean segregation of this decentralized program into a formulaic cross-district
component and a complex decision-making process within districts provides us with aparticularly exact econometric analogy. The between-effects regression on spending per person
across districts recovers the final effective spending progressivity as determined by the cross13

In the pairwise ranking exercise the whole village is called to a meeting, divided into groups by demographics,
asked to come forward with a number of different project suggestions, and then village votes on pair-wisecombinations of these potential projects. If the outcome of this exercise is different than the application the village
has submitted, then the village is expected to go back to the drawing board and submit another application that is inline with the participatory appraisal. However, TASAF officials, through email communication with the authors,
suggest that such cases were extremely rare.
14
A SPIF for a VG project contains minimal information, usually the vulnerable group (such as widows or elderly)
and the proposed income-generating activity (such as animal husbandry or tailoring). It does not include a list of
beneficiaries. Hence, the list of beneficiaries for this component is finalized during the village meetings that takeplace as part of the EPRA process.
15
While a trained facilitator from the TASAF District Office conducts the EPRA, the decisions regarding the detailsof the project and, where relevant, list of beneficiary households are meant to be made through a participatory
discussion.
16
The District Council is composed of elected councilors from each ward.

9

district funding allocation at the national level. All within-district targeting of spending, then, isthe sole product of the decentralized district-level bureaucracy of TASAF working in concert
with village-level officials under the eligibility constraints imposed by the center. Figure 1
provides a detailed schematic of the roles played by different actors in the sensitization andscreening process, and the regression analysis that corresponds to each step in the waterfall.
3. Data
3.1. Poverty maps
We construct the national database beginning with a poverty map created by the authors
using the method described in Elbers et al. (2003). This exercise uses the household surveys
from Tanzania’s 2000/01 Household and Budget Survey (HBS) and the 2002 Population andHousing Census, both conducted by the National Bureau of Statistics (NBS). The HBS is anationally representative sample of 22,178 households sampled between May 2000 and June
2001. The HBS is a much richer survey than the population census, containing information on awide range of outcomes including demographics, education, health status, and ownership ofdurable assets, and most importantly a measure of per capita household consumption. However,the HBS is cluster-sampled and hence cannot be used to understand poverty in every sub-regionof the country. The small area estimation technique used here allows us to be able to constructpoverty estimates for smaller geographic units of aggregation, such as districts orwards.17Poverty rates were estimated at the level of each ward on the basis of a methodology thatis described in detail in Elbers et al. (2003). PovMap 2.0 was used to estimate poverty rates andtheir standard errors for the wards and districts of Tanzania.18 We estimate poverty based on ameasure of per capita household consumption, which is estimated using the HBS, with the set of
explanatory variables restricted to those that are also found in, and strictly comparable to, thepopulation census. We then use these estimates to calculate the poverty rate of wards and districtsin the population census using the vector of parameters from this model, including those that
describe the distribution of the disturbances. Using this procedure, our estimate of the poverty
headcount differs from its true value due to two types of errors: idiosyncratic error, which is dueto the unobserved component of household consumption and declines rapidly with the size of the17

The poverty mapping data are missing for the islands of Zanzibar and Pemba, and so we restrict our analysis tomainland Tanzania.
18
See iresearch.worldbank.org/PovMap/ for documentation of the software for poverty mapping.

10

target population; and model error, which is due to the fact that the true (unknown) parametersof the model are estimated.19
Because estimating our regressions using a predicted rather than observed value ofpoverty rate as an explanatory variable would typically underestimate the true variance of thecoefficient estimate for poverty, we follow a simulation procedure very similar to that suggested
by Elbers et al. (2005). We utilize poverty estimates from the underlying simulations conductedin PovMap that explicitly include all three types of error.20 We bootstrap standard errors for all
regressions using poverty estimates as an explanatory variable, iterating 500 times and for eachdraw selecting both a new poverty estimate and sampling with replacement from our own data.
This bootstrap distribution thus contains all three sources of error in the poverty map estimates aswell as the downstream sampling error.
3.2. Institutional Data from TASAF II
This paper utilizes two main databases from the management information system (MIS)of TASAF II. The first contains every application received by TASAF II between May of 2004
and October of 2007, for a total of 102,606 applications. More than 95% of the 2,542 wards inmainland Tanzania submitted at least one application, with the median ward submitting 14. Thisapplication, called a sub-project interest form (or SPIF), constitutes the first step in seeking
TASAF II funding and records only the location, beneficiary group, and sector of the project but
specifies neither the amount of funding requested nor a list of the would be beneficiaries. The
second institutional database describes every TASAF II project funded through August 2008,
and gives details of the beneficiaries, project type, and budgets for each of the 4,037 projects
funded. This database also shows the composition of funding provided by the NVF, local
government authorities, and the community itself. NVF spending typically makes up about 80%
of total project costs, and is never below 50%. We merge these datasets at the ward level tocalculate the number of applications, the percentage of applications funded, and the total amountspent from each source per ward.

19

A third type of error, namely computation error, is made negligible through a sufficiently large number ofsimulations.
20
The average of the poverty rate for each administrative unit across these simulations yields the poverty mapping
estimate for that unit.

11

3.3. Electoral Data
The electoral data used in the national analysis are from the 2005 presidential election.
All data are available online at the website of the National Electoral Commission of Tanzania.21
The presidential results are at the constituency level22, and the electoral data is merged with the
TASAF II institutional data and the poverty maps as a weighted average vote by theconstituencies overlapping each ward (weighted by fraction of surface area of the ward in eachconstituency). The elections took place prior to the announcement of the awards of TASAF II
projects, and hence we take political outcomes as predetermined, and seek to understand how
voter registration and turnout, as well as voting patterns, relate to application, approval, andfunding patterns. We calculate two core political variables: ward-level votes as a fraction of
population, a variable that combines voter registration and turnout and serves as our measure ofpolitical engagement. For brevity, we refer to this variable as ‘turnout.’ The second indicator isthe share of the electorate that voted for the dominant Chama Cha Mapinduzi (CCM) candidate
Jakaya Kikwete. This variable is intended to measure patronage; the extent to which programs
may be targeted at core or opposition regions.
3.4. Household Survey Data
The household survey data were collected in 2008 as a part of a cluster randomized
impact evaluation of the Vulnerable Groups component of the project conducted by the authors.
First, five districts were selected: Moshi, Kwimba, Lushoto, Makete, and Nzega.23 Then, withinthese districts the TASAF district officials, who are called village fund coordinators or VFC,
were asked to take the first 20 villages in which VG projects had been newly approved (butproject funds not yet disbursed), and these villages were then enrolled in the study. This sample
of 100 villages is explicitly not a representative sample of villages even in these five districts(because every one of them would receive a VG project), but may be considered as
representative of the types of villages that are selected for VG funding.
21

Data available from http://www.nec.go.tz/
The constituency is a region defined for electoral purposes. The 232 constituencies in Tanzania are typically larger
than a ward but smaller than a division.
23
These five districts were intended to give a broad representation of the 119 districts in mainland Tanzania as awhole. Compared with the average poverty headcount in Tanzania, our estimates suggest that poverty rates arelower than average in Lushoto and Moshi, roughly average in Nzega, and slightly higher than average in Kwimba
and Makete. They were selected for inclusion in a cluster randomized impact evaluation of the vulnerable groups
component of TASAF II with the help of TASAF officials and it is important to note that they were not selected
randomly from the list of all districts in Tanzania.
2212

We then conducted a comprehensive study of this sample of 100 villages. First, we
conducted a listing exercise that gives us a census of all 61,611 households in thesecommunities. The short listing survey collected basic demographic information about thehousehold (e.g. household size and age of the eldest household members), GPS data (allowing us
to calculate distance to village center for every household), and determined whether or not thehousehold contained a vulnerable, i.e. eligible to receive funding from a VG project, individual.
This information allowed us to sort households into four strata for the study: village elites(Village Executive Officer or Village Chairman), TASAF II VG beneficiaries, eligible nonbeneficiaries (households that contain a vulnerable individual but do not benefit from theprogram) and ineligible households. At the time of the listing exercise, the person answering thesurvey was also asked whether he or she was familiar with TASAF. This question provides thedata that underlies our ‘heard/aware of TASAF’ indicator.24
For the detailed household survey, the two village leaders and the three group leaders(chairperson, secretary, and treasurer) were always sampled with probability equal to 1. In
addition, three other VG beneficiaries, three eligible non-beneficiaries, and three ineligible
households were randomly sampled to produce a sample of 14 households in a typical village
with only one TASAF II VG project awaiting funding for an income generating activity – or a
final sample of 1,544 households in 100 study villages.25 The main advantage of the household
survey sample for this study is that it contains detailed consumption data at the household level
allowing us to construct a poverty measure based on a comprehensive consumption aggregate.
The effort to carefully stratify the sample into beneficiaries, eligible non-beneficiaries, andineligible households and collect data from all of them allows us to rigorously analyze thetargeting performance of the program at a very micro level. These data form the basis of ouranalysis of targeting performance in Table 4.
As the household survey sample was relatively small, all households with vulnerable
members (including VG beneficiaries and eligible non-beneficiaries – 38,871 households intotal), and a large randomly selected sample of ineligible households were also given a long
listing survey. This survey was significantly shorter than the household survey and, importantly,
24

Since the vulnerable individual in a household may or may not have been the one answering the listing survey,
this question does not provide a perfect measure of the information among the eligible individuals, but provides areasonable proxy for the extent to which the existence of the program was understood in the household as a whole.
25
The sample size is larger than 1,400 (14x100) because some villages had more than one VG project.

13

did not collect detailed consumption data in each household. However it collected limited dataon consumption of proteins (number of days fish, meat, or eggs were consumed), education of
the household head, asset ownership, participation in village meetings, whether household
members hold political office in the village, blood relationships between household members andvillage officials, and whether they benefited from other safety net programs. These data are used
to analyze awareness of TASAF among eligible households and the determinants of being a
program beneficiary among eligible households aware of TASAF in Tables 5 and 6.
In summary, two detailed databases are available for our study. The first one is a wardlevel database covering virtually all of mainland Tanzania to study targeting performance of
TASAF II at the national-level. There are 2,542 wards in 119 districts in mainland Tanzania. The
poverty mapping data is unavailable for 86 of these wards. Out of these, the ward councilor
elections were uncontested in 254 wards, depriving us of any electoral outcome data other thanthe political party of the winner. The poverty maps cannot be calculated for an additional 25
wards, meaning that regressions using vote shares and turnout are conducted on the 2,177 wards
for which both poverty and electoral data are available.26 66 of these wards submitted no
applications to TASAF II, and so the analysis of the percentage of applications funded isconducted in 2,111 wards. The second one is a household-level database from the 100 villages
with VG projects that allows us study benefit incidence within villages. Table 1 provides
summary statistics on the ward-level sample in the left panel, and on the household-level sample
in the right.
These two datasets provide us within an unusually comprehensive view of the benefit
incidence of TASAF II all the way from the center to the household level. However, there areseveral important respects in which our data have limitations. First, we are unable to study thetargeting of villages within wards because we are missing this link in the data chain.27 Second,
while the ward is not a unit of great importance in the implementation of TASAF II, we use it as
the unit for the national targeting analysis simply because it is the smallest unit at which ourvarious national databases can be merged. Third, our study of within-village targeting focuses
only on the VG component of TASAF II and not on the Service Poor or Food Insecure
26

Uncontested wards are slightly less likely to submit applications than other wards, but are not different in terms ofpoverty or funding than the contested wards included in the expanded analysis.
27
Matching village names from SPIFs to census data would have been very difficult, if not impossible. Even if we
could do this, we would not have poverty estimates at this level, nor would we have electoral data.

14

components. The eligibility for the VG program, which provides private goods to households, ismore clearly defined, hence making it easier to address the question of national- versus locallevel targeting criteria, but this limitation is important to note.
4. Estimation Strategy
Our estimation strategy is influenced by Galasso and Ravallion (2005), which provides
an empirical structure for testing the additional contribution of local information gained throughdecentralization. In particular, they define the information set held by the central planners andthen use a household dataset to construct a much richer definition of ‘eligibility’ for the program
than was available to central bureaucrats. They then attribute the additional poverty targetingachieved above and beyond that coming from the planners’ information set as the benefits arisingfrom decentralized targeting. Our approach is inspired by this structure in the sense that the only
components of TASAF II that were centrally dictated were eligibility rules, the allocation offunds to the districts, and some guidelines provided to the districts for the selection of subprojects and beneficiaries. Therefore most within-district targeting arises from the actions ofdecentralized agents. We therefore decompose the variation in targeting efficiency into a crossdistrict (centralized) and a within-district (decentralized) component. Using this structure, we canseparately isolate the role of the clearly defined funding formula that drives allocation to the
districts, and the complex decentralized process through which the districts allocate funding to
the lower administrative levels.
Analyzing TASAF II applications received and final TASAF II spending per capita at theward level, we present the univariate correlation between the headcount index and the outcomeof interest and econometrically decompose this relationship into a between-district and a withindistrict component (Table 2). In Appendix Table 1 and Table 3, we present a multivariate linear
regression using an additional set of controls to examine the underlying correlates of theseoutcomes. Specifically, for ward i in district d, we estimate:

Within districts only:yid  d  1Pid  2 X id  3Zid   id

(1)

Between districts:

yd   0  1 Pd   2 X d  3 Z d   d(2)Pooled OLS:yid  0  1Pid  2 X id  3Zid   id

(3)

15

where yid is one of the three outcomes of interest, Pid is the poverty headcount ratio at the ward
level, and X id and Z id (used only in the specifications for Table 3 and Appendix Table 1)
represent a broader set of socio-economic and political controls. Equation (1) is estimated with
district-level fixed effects and hence is identified only by within-district variation at the ward
level; equation (2) is estimated at the district level using district averages, and equation (3) pools
these two sources of variation to estimate the total effect.
An important empirical issue relates to the estimation of the marginal effects of poverty
when the right-hand side variable comes from a Poverty Map and hence is estimated with error.
If the first-stage regression is the one that creates the poverty map estimate and the second stage
regresses a final outcome on poverty, the standard errors in the second stage must reflect theprediction error in the first stage. We therefore conduct 100 runs of the poverty map, each time
drawing a new set of model parameters and estimating a new predicted log per capita
consumption figure for each household in the population census. This provides us with 100
replications of the poverty estimate that contain the idiosyncratic and model error. We thencollapse these 100 estimates to the appropriate unit for analysis (ward or district), merge themwith our institutional data from TASAF II, and then resample with replacement 500 times, eachtime randomly selecting one of the 100 poverty simulations. The distribution of the resulting
marginal effects contains both the first stage and the second stage regression error, and thestandard errors reported in all regressions are calculated from the distribution of thesebootstrapped coefficients.
We construct two core dependent variables for the ward-level analysis. The first indicatoris the number of applications submitted from a ward per 1,000 individuals. Fewer than 5% ofwards submitted no applications, with a median of 1.2 and a mean of 3 applications per thousandpeople.28 The second dependent variable is the per capita spending in US dollars in each ward,
which is used to measure the incidence of transfers under the program. To support this analysis,
we also examine the fraction of applications that are approved in Table 3 and Appendix Table 1.
This is an outcome defined only for wards that submitted at least one application, thus we lose
the 67 wards from our analysis, from which no applications were made.

28

The average number of applications submitted by villages is 39 per ward and 834 per district.

16

5. National Targeting Performance using Ward-level Data.
The national incidence of applications and spending are presented in Table 2. Columns(1-3) demonstrate that the number of applications emanating from a ward decreases strongly
with its poverty rate. The coefficient estimate in column (3) indicates that moving from a ward at
the 25th percentile of poverty headcounts to one at the 75th percentile (moving from a poverty
headcount rate of 19% to 46%) would predict a decrease of approximately 1.3 applications per
thousand inhabitants – equivalent to 0.25 standard deviations. Interestingly, column (2) suggeststhat this effect is almost entirely due to the fact that more applications come from richer districts.
This much stronger cross-district variation indicates that a similar increase in poverty would
decrease the expected number of applications per thousand by 2.8, or by more than 0.8 standard
deviations.
While applications are strongly regressive with respect to district-level poverty levels, wefind no evidence that poorer wards submit fewer applications within districts (column 1). This
statistical distinction proves to be consequential given the unit of decentralization in Tanzania:
the center is responsible for allocating funds to each district, while district officials are
responsible for targeting within each of these units. As TASAF II allocations to districts arepredetermined using the center’s funding formula, applications from districts with higher
application rates will mechanically have lower probabilities of success.29 It is therefore
significant that despite the large cross-district regressive pattern in application rates, applications
are orthogonal to poverty status within districts. This indicates that predetermined funding to
districts will have nullified the regressive nature of the applicant pool, which would have been
the job of district officials had applications been coming primarily from richer wards within
districts. We return to this point later in this section.
To examine the final incidence of spending under TASAF II, we turn to columns (4-6) of
Table 2. The within-district targeting incidence is mainly the responsibility of the decentralized
agents in TASAF. Column (4) shows that these decentralized actors prove successful in targeting
spending within their districts: the final spending per capita increases with the poverty headcount29

This can be seen in Columns (1) and (2) of Appendix Table 1, which shows that just as applications were much
more likely to come from richer districts, applications in such districts were much less likely to get funded. Figure 2
shows this inverse relationship between number of applications per capita and funding probability at the ward level.
This finding is also suggestive that the heterogeneity in applications is not simply arising from the expected
probability of funding: application rates are highest in places where the success rates are lowest and vice versa.

17ratio in the ward. This effect is sufficiently large as to drive the total incidence seen in column(6), which indicates that the final spending per capita is pro-poor under TASAF II – even if
mildly so. Column (5) shows that, despite the fact that the center’s allocation formula included
proxies of poverty, almost all of the pro-poor final spending is explained by variation within
districts rather than between them. It seems that the central funding formula was successful innullifying the potentially detrimental effect of a much larger number of applications from richer
districts and left the job of steering funds to poor communities to the district governments.30
Hence, a strongly regressive pool of applications is turned into a progressive final
incidence of spending by the combination of two forces: a central funding formula that nullified
the strongly regressive and largely cross-district variation in application rates and a decentralized
within-district selection process that pushed funds towards poorer wards. The marginal effects
indicate that a ward going from being universally non-poor to universally poor would see
spending per person increase by only $1.33 from a base of $2.62 (the lowest poverty headcountrates observed in the data is 0, and the highest 0.986). In other words, a one standard deviation
increase in the poverty rate of a ward (an approximately 20 percentage point increase in theheadcount ratio) would cause an increase of only $0.25 per person or approximately $3,300 total
in a ward of average population size. These results are typical of the broader literature on CDD
that show benefit-incidences that are only moderately pro-poor.31
Before moving to an examination of within-district performance, we make a few morepoints on the role of between-district effects on project applications and selection. First,
applications are more likely to be coming from districts with better access to media (measured by
the fraction of households with a radio or phone) and with higher voter turnout – highlighting therole of information and political engagement in this demand-driven program (Appendix Table 1,30

Comparing the coefficient estimate in column 5 of Table 2 to that in column 4 of Appendix Table 1 indicates thatthe actual pattern of expenditures appears to be slightly more progressive than the budgeted pattern, meaning thatdeviations from the original spending plan may have served to make the program somewhat more pro-poor.
31
Another way to consider why these estimates indicate a mildly pro-poor performance is by considering what the
regression coefficients would be if the program were neutral (i.e. allocated its funds evenly per person) and underthe extreme scenario of the program being perfectly pro-poor in the sense of allocations proportional to theheadcount ratio. Compared with our estimates of a constant term equal to US$2.62 and a coefficient estimate of
1.33, a neutral program would have allocated everyone US$3.07 and produced a coefficient estimate of 0. A
perfectly pro-poor program would have allocated no funds to areas with a poverty headcount of zero (i.e. theconstant term would be equal to zero) and produced a coefficient estimate of 9.06. Hence, the final spending
allocation per capita across wards that we find in Table 2 is fairly close to a neutral program.

18

column 3).32 In Section 6, we will show that the correlates of awareness of the program at thehousehold level are very similar and consistent with the evidence from these ward-level data.
Similarly, the final allocations from the center are higher for districts with higher voter turnout(Appendix Table 1, columns 5 and 6).
Second, given that the unit of decentralization in Tanzania is the district, it would be hardfor the center to improve its targeting performance without making significant changes in its
procedures. The center could have given more weight to poverty in its funding formula, or
implicitly used a higher inequality aversion parameter to give poorer districts more funds perpoor person (i.e. treating equally poor households/communities in different districts differently),
but given its decision to first allocate 25% of the NVF evenly across districts before using amildly pro-poor formula to distribute the remaining funds, it seems likely that this may not havebeen politically feasible.33 The center could have also tried to give funding directly to divisions
or wards, bypassing the districts, but administrative structure and low capacity at lower levels ofgovernment likely stood in the way of such an approach. Or, the center could have tried to give
incentives to districts for poverty reduction (Ravallion, 2000). While the center’s funding
formula was simple and amounted to a little better than allocating equal amounts to districts perperson and geographic area, it was still effective in nullifying the effect of an inordinately large
number of applications from richer districts – even if this success may have been largely
circumstantial and context-specific.
Third, just because the regressive pattern of project applications was across districts inthe case of Tanzania rather than within them, it does not mean that we should expect the same
pattern to be repeated elsewhere. An important feature of Tanzanian political geography is thatthe country has split urban areas from the surrounding rural areas to constitute their own districts.
Of the 119 districts in the data, 14 cities have been split off from their surrounding districts toform separate urban and rural districts.34 The 14 urban districts plus the major city of Dar es
Salaam comprise only 3.6% of the area and 13% of the population of the country, and yet make32

For purposes of comparison, the intra-district correlation in applications per capita is very similar to that ofpoverty itself (0.354) and of voter turnout (0.308). On the other hand, it is substantially lower than that of ownershipof radio or phones (0.614), which is presumably increased by the clustered nature of radio and phone masttransmitters.
33
As mentioned before, Galasso and Ravallion (2005) report that all 490 Thanas (sub-districts) in Bangladesh
benefited from the Food-for-Education program because of political considerations and note that this is notuncommon.
34
This pattern is different from neighboring countries such as Uganda or Kenya, where only the two largest cities of
Nairobi and Mombasa are set in their own districts.

19

up almost a third of the total applications to TASAF II. While it is possible that all urban areas
simply had superior district-level TASAF II sensitization processes, a more likely explanation is
the greater access to information and government bureaucracy are responsible for the higher
demand. In a different setting where the regressive nature of the applications was due to variation
within the units of decentralization rather than across them, local officials would have had towork harder to channel funds to poorer areas without the benefit of a predetermined funding
formula based on detailed information on each constituency.
Before concluding the targeting analysis at the national level, we return to a moredetailed examination of targeting performance within districts. In Table 2, we showed that while
per capita applications within districts are uncorrelated with the poverty level of the wards, final
per capita spending is higher in poorer wards. Table 3 examines, within districts, the correlates ofapplications received per 1,000 people from each ward (column 1), spending per application(column 2), and final per capita TASAF II spending in each ward (column 3) by adding
covariates to the same specification used in Table 2 (columns 1 & 4, respectively). Column (1)shows that the number of applications per capita from wards within districts is as good as
random, reinforcing the earlier finding. None of the variables we include in our regression model
is significantly associated with the number of applications per capita. In response, districts seem
more likely to select applications coming from more rural wards (column 2), although thisrelationship is not statistically significant. Column (3) suggests that final spending is channeledtowards wards that are rural and sparsely populated – consistent with the aim of social funds toreach such areas that are usually underserved in terms of service delivery. However, even within
districts, the pattern of wards with higher voter turnout receiving more funding per capita
remains. Thus the district- and local-level actors overall achieve a funding incidence that isprogressive in education and pushes money towards rural and sparsely-populated wards (which
therefore have poor access to radio and mobile phone networks), but remains regressive inpolitical involvement at the ward level (voters/population).35
35

Appendix Table 2 presents some robustness checks on our analysis of the ward-level data. Panel A presents
findings when poverty headcount is replaced by the poverty gap. While the point estimates increase (because theunits of the dependent variable change) the interpretation remains the same. Panel B addresses the concern thatdivision bias may be pushing our coefficients upwards if population were measured with error (Borjas, 1980). While
we use a population census to measure this variable, which reduces the likelihood of measurement error, we firstcontrol for the inverse of population, the quantity in which error may enter the equation. The purely between-district
regressive nature of applications remains with this control, but the per-capita funding no longer appears progressive.
Given that funding favors sparsely populated districts and wards, the control for inverse population effectively takes

20

These results provide national evidence of a theme that manifests itself in very similar
ways in the household-level analysis that follows: demand-driven community development
projects put the onus on local actors to pull funding towards themselves. They therefore face thedanger of rewarding constituencies that are more informed, mobilized, and capable ofovercoming collective action problems to successfully seek funding.
6. Targeting Performance at the Village Level
To examine the targeting performance of the program at a very micro level we selected asample of 100 villages in five districts, which had just been chosen to receive TASAF II funding(but had not yet received these funds). We used a comprehensive listing exercise to enumerateeach household and collected detailed data for a stratified random sample of households that isrepresentative of village elites, program beneficiaries, eligible non-beneficiaries, and ineligible
households before any VG funds were disbursed in these study villages. To isolate within-village
effects, we use village-level fixed effects in the analysis that follows.36
The national bureaucracy still has some influence at this level, mainly in setting theeligibility criteria for the program. In order to study a well-defined ‘eligible’ group, we focus on
the Vulnerable Groups (VG) component of TASAF II. This program had clearly articulatedhousehold-level eligibility rules and thus permits a direct examination of this central targeting
component. At the same time, such rules will only have bite (and result in a progressive final
beneficiary group) if the decentralized bureaucracy is willing and able to enforce them. So it isinteresting to consider both the de jure eligible group relative to the population, as well as the defacto comparison of the beneficiaries to the nominally eligible.
We use the household data to conduct a village-level targeting analysis with as close aparallel as possible to the national-level analysis. Just as the central government controls the
cross-district funding formula, it controls the de jure eligibility. Analysis of the targeting of
eligibility itself therefore isolates the role of the center. The sensitization and selection of actual
beneficiaries within this eligible population is the responsibility of decentralized actors. As withthe regression out of per capita units. TASAF II gives a similar sum of money to each ward, meaning that poor
wards are overfunded only on a per-capita basis. This can be seen more clearly in Panel C, where we movecompletely out of per-capita terms and regress the total spending per ward on the population and the poor population
per ward. Again, in absolute terms TASAF II applications are regressive and the funding process is very weakly propoor across wards.
36
We have also conducted all of the regression analysis in this section using ward-level fixed effects. The results arevery similar and can be obtained from the authors upon request.

21

the role of applications at the national level our study introduces an empirical measure of the‘demand-driven’ component of CDD programs. The same sensitization campaign that solicits
applications must first ‘spread the word’ so that eligible individuals come into the projectpipeline as potential beneficiaries. In practice, universal awareness of this new program did notoccur, and our results indicate that the requirement that individuals have to produce applications
again proves regressive.
Our analysis of within-village targeting makes use of baseline data collected for arandomized evaluation of the VG projects that was designed to assess their impact on thecreation of small enterprises and improvements in household welfare. We begin the analysis of
the household-level data by analyzing the sample of households with detailed survey data. These
data allow us to calculate household-level consumption aggregates, and therefore to conduct ananalysis of poverty at the local level. We use sampling weights to make this sample
representative of the population in the 100 study villages.
Figure 3 shows the cumulative distribution function (CDF) of monthly per capita
consumption among the entire population of these 100 villages, all eligible non-beneficiaries,
eligible households who heard of TASAF, and VG project beneficiaries.37 The figure shows thatwhile the eligibility criteria for this program is progressive (eligible non-beneficiaries are poorer
than the population as a whole; p-value=0.028 for the Kolmogorov-Smirnov, K-S, test for theequality of distributions), eligible households who are aware of the program’s existence haveroughly the same distribution function as the average household in these villages (K-S pvalue=0.115). Furthermore, the final group of VG project beneficiaries is not significantly poorer
than eligible non-beneficiaries (K-S p-value=0.424).38 These observations indicate that the
progressive eligibility criteria imposed by the center is set back by lower program awarenessamong the poor and that conditional upon eligibility, the selection process at the local level does
not produce a poorer group of beneficiaries. Again, the combination of eligibility criteria fromthe center (vulnerability) and the within-village selection process produces a targeting
performance that is mildly pro-poor at the local level: VG beneficiaries are 10 percentage points
37

Note that this figure includes comparisons across villages, and hence does not isolate the within-village targeting
component as the regressions in Tables 4-6 do.
38
The VG project beneficiary status employed in this analysis is defined using in-depth group surveys of each VG
sub-project at baseline, i.e. after the sub-project was approved for funding (and the group informed) but before the
disbursement of funds. The entire list of group members, i.e. beneficiaries, was enumerated during this group survey
and their names and membership statuses (chairperson, secretary, treasurer, or member) were recorded. These werethen linked to the listing data and the detailed household surveys used in this section.

22

(pp) more likely to be categorized as poor than the average household in these villages using ourlower-bound poverty line of 18,000 Tanzanian Shillings per month per capita in 2008 prices.
Table 4 analyzes receipt of the program in stages that isolate the roles of eligibility(dictated by the center) and the broad within-village process that subsumes the awareness andactions of potential beneficiaries and their selection as final beneficiaries. Column (1) examines
the likelihood of being a TASAF II beneficiary among all households – the final product of theeligibility criterion, the application stage, and the within-village selection process. Poor
households are 1.4 percentage points more likely to be VG project beneficiaries than non-poorhouseholds over a mean of 2.8% for the non-poor. Column (2) repeats these regressions using aricher set of covariates, and demonstrates that program beneficiaries live closer to the village
population center, are more politically active (both in terms of participation and holding local
office) and more likely to benefit from other social safety net programs designed for vulnerable
households – compared to the entire population. Heads of beneficiary households also havelower levels of education.
The next four columns decompose this progressive selection of beneficiaries into two
components: eligibility among all households and beneficiary status among eligible ones.
Column (3) shows the de jure targeting rule, estimating the likelihood of being eligible for
TASAF II VG projects by poverty status: poor households are, on average, 9.3 percentage points
more likely to be categorized as eligible for the program. Column (4) indicates that thevulnerability criterion identifies a group that is poorly educated, with lower access to media andto village officials, rather than one that is poor conditional on these variables. Consistent with
Figure 3, the nationally stipulated piece of within-village targeting is progressive.
A simple manipulation of the estimates in this table indicates that the probability of being
a program beneficiary is the same for those categorized as poor and the non-poor among those
who are eligible. This is confirmed by the analysis in column (5), which shows that poverty
status is not correlated with beneficiary status among the eligible. Column (6) indicates thathousehold heads in beneficiary households have significantly higher levels of education relative
to eligible households – in contrast to the finding that they are less educated compared with theaverage household (column 1). They are also, exactly as in column (2), more likely to live closer
to the village center, be politically active in the village, and benefit from other social protection
programs. In summary, Table 4 paints a picture in which the final beneficiary selection is23

progressive (with respect to poverty) – an effect that is composed of a progressive eligibility rule
imposed by the center and a neutral process of applications and selection among the eligible
within villages.
In Table 4, we decomposed final beneficiary status at the household level into eligibility
and, conditional on eligibility, the within-village selection process. However, we can also
decompose the within-village selection process among eligible households – in the spirit of theanalysis of applications and funding at the national level in Section 5. There are no applications
from households within villages, but we have data on whether the household was aware of
TASAF II, which can be considered as a prerequisite for being able to become a VG
beneficiary.39 Interestingly, our data show that, among all eligible households in the study
villages, just over half of survey respondents had ever heard of the program. Ineligible
households were actually three percentage points more likely to have heard of the program thaneligible ones. These numbers prevail despite the fact that the listing survey was conducted after
VG groups had been formed and were ready to be funded in each of these 100 villages.
Given the potentially regressive role of information in TASAF II, Table 5 presents a
decomposition of the within-village selection process among eligible households into (i)awareness of TASAF II and (ii) likelihood of selection conditional on being aware. Whileimperfect, the first part of this decomposition is akin to examining the demands of CDD projects
from its potential beneficiaries, while the second part sheds light on the decentralized selectionof final program beneficiaries within villages. As the sample of eligible households is small, we
utilize data on all eligible households in the 100 study villages from the listing survey, which
provides us with more statistical power. While all the covariates used in Table 4 are available in
the listing survey, we lose the poverty indicator the underlying data used to create ourconsumption aggregate were not collected in this sample. Therefore, in Table 5, we replace thepoverty indicator with a composite consumption indicator that is equal to the total number ofdays over the past week that the respondent’s household had consumed meat, eggs, and milk.

39

It should be noted that the parallels between this analysis of ‘program awareness’ at the household level and‘applications’ from villages at the national level are inexact for two additional reasons. First, while they are rare,
there are cases where a household is unaware of TASAF while a member is listed as a beneficiary of a VG project.
Second, applications at the national level are for all types of programs, many of which seek funding for the creationof public goods (such as schools, clinics, roads, etc.), while VG projects provide private benefits at the household
level.

24

Columns (1) and (2) in Table 5 replicate the same specification in columns (5) and (6) inTable 4 and describe the correlates of being a beneficiary household among eligible households.
Beneficiary status is negatively correlated with the consumption of meat, eggs, and milk during
the week before the listing survey, indicating that beneficiary selection from the group of eligible
households was more progressive with respect to recent consumption of proteins than it was withrespect to per capita household consumption.40 The next four columns decompose this process
into ‘awareness’ and ‘selection’ conditional on awareness. Column (3) shows that programawareness significantly increases with household food consumption among eligible households,
suggesting, as in the analysis of applications at the national level, a regressive demand-driven
process. Column (4) shows that program awareness increases (conditionally) on almost every
indicator of advantage examined in this table: education, access to media, political activity,
connectedness to village elites, and benefitting from previous social safety net programs. The
majority of poorer, vulnerable, and marginalized households were not even aware of thisprogram designed to assist them. Hence, we see a repeat of the regressive pattern at theapplications stage that carries all the way through to the final beneficiary pool.
Columns (5) and (6) attempt to recreate the spirit of the national-level results on theprobability of funding conditional on application (implicit in Table 2) by examining thecorrelates of being a VG project beneficiary among eligible households aware of TASAF. This is
the group that is potentially capable of attempting to pull demand-driven benefits to themselves,and hence gives a metric of within-village targeting efficiency abstracting away from both
eligibility and informational requirements. Within this group, we see that selection is progressive
on socio-economic variables such as food consumption and ownership of a radio or a mobile
phone. Once again, however, we see that proximity to village center, as well as political activity
and connectedness are powerful correlates of receiving benefits. Within this group, all else equal,
an individual belonging to a household wherein a member holds village office is 10 pp more
likely to be a TASAF II beneficiary, and beneficiaries of previous social protection programs forvulnerable groups are 27 pp more likely to benefit from this new wave of projects. Politically40This slight change in findings when we move from column (5) of Table 4 to column (1) of Table 5 seems due tothe change in the welfare indicator used as an explanatory variable: if we run the same regression in column (1) of
Table (5) restricting the analysis to the household survey sample in column (5) of Table 4, we get very similar
results. That the findings in column (6) of Table 4 and column (2) of Table 5 are qualitatively the same for the
additional covariates further indicates that the findings are robust to switching from using the household survey
sample in Table 4 to using the entire population in Table 5.

25

connected households are both more likely to have heard about the program and more likely tobenefit from it conditional on awareness.

7. Concluding Discussion and Policy Implications
In this study, we provide new quantitative evidence on the separate forces underlying thefinal targeting performance of a major community-driven development program in Tanzania.
The bottom-up, beneficiary-driven dimension of the program results in a regressive intake
sample of individuals that are aware of and apply for the program, setting the selection process
off at a disadvantage relative to the overall population. The requirement that individuals takeaction to be considered as beneficiaries, then, presents a regressive demand on this demanddriven development program. From this starting point, a variety of forces conspire to push thefinal beneficiary distribution back to a moderately progressive state. A cross-district funding
formula set by the government was intended to be progressive but, using an independent poverty
measure, we find it to be poverty-neutral. Interestingly, however, this cross-district formula plays
an important and perhaps unanticipated role, effectively neutralizing the substantial andregressive cross-district variation in application rates. The district- and local-level agents towhom decentralized targeting is delegated are effective at targeting, resulting in higher per capita
allocations to wards that are significantly poorer than their district averages. However, thisselection process does not neutralize the extent to which politically active wards are overrepresented in the applicant pool: per capita TASAF II spending is also higher in wards that are
more politically engaged within districts.
Our analysis at the household level strongly mirrors these findings. The centrally
stipulated eligibility rule for the Vulnerable Groups program is effective in selecting a poor
sample, but that knowledge of the program is highly regressive. In a centrally targeted program
the knowledge that potential beneficiaries hold about the program when it begins may be next toirrelevant, but in a demand-driven project it is critical.41 A household cannot seek inclusion in a
41

There are obviously many cash transfer programs, such as PROGRESA in Mexico, that do not require theirbeneficiaries to submit applications. A newer wave of cash transfer programs that take advantage of idiosyncratic
information (and preferences) at the local level, such as BLT in Indonesia (Alatas et al. 2013) also partially rely on
communities to determine beneficiaries but do not require them to apply. On the other hand, Oportunidades – anexpansion of PROGRESA into urban areas – did use applications as a self-selection mechanism before applying a
proxy-means test to select beneficiaries from the pool of applicants.

26

demand-driven program of which it has not heard. We find that barely half of the eligible
households had ever heard of TASAF at baseline. Similar to the pattern for project applications
at the ward level, we find that households who are more educated, have better access to media
and information, active in village affairs, and related to village elites are substantially more likely
to have heard of TASAF. Again, the decentralized actors are successful at selecting a final
beneficiary pool relative to the universe from which they are allowed to choose: the eligible andinformed. Eligible households that are related to village elites and attend village meetings are
more likely to have heard of the program and more likely to benefit conditional on awareness.
The main contribution of this paper is to document the precise ways in which thedemand-driven nature of community development programs may be hindering their ability toreach the poor. The lack of applications from poor households and communities could be due tovarious factors, among which lack of information, suggested here, is only one. For example, it ispossible that poor households (or communities) are aware of the program but unable to navigate
the system to produce valid applications. The evidence we have at the household level suggeststhat this may be part of the story: group leaders for the proposed income generating activities aresubstantially more educated, more likely to own phones and radios, and less likely to be poor
than the “rank and file” members of these groups.42 It is likely that these individuals are
instrumental in putting together viable project proposals and navigating the application process.43
Another possibility is that households and communities that are aware of TASAF (andable to apply) nonetheless decide against doing so. Low application rates among the poor mightarise if they rationally decide not to apply due to a perceived low probability of being approved.
The data available to us also argue against this idea. Approval rates are higher in poorer wards,
meaning that, if anything, application rates from these areas should be higher. Hence weconclude that the regressive pool of applications is likely to be arising from variation in access to
42

The analysis of VG project leaders is not included in this paper but is available from the authors upon request.
While these households are technically eligible under the criteria imposed by the center, their average consumption
level is closer to ineligible households than eligible ones.
43
It is important to recognize that the Vulnerable Groups component of TASAF II is not a palliative poverty
reduction program, but rather aims to reduce poverty by making grants to groups of vulnerable individuals, who aresupposed to invest the funds into a proposed income generating activity, and thereby creating a sustainable stream ofincome. If the successful creation of such small enterprises is much harder when groups consist entirely of
uneducated, inexperienced, and marginalized individuals, then the inclusion of better educated, well-connected, andricher individuals – i.e. the very definition of group leaders described above – may make sense. Considered in thislight, what may look like ‘capture’ or ‘mistargeting’ may actually be the key to the longer-term success of theseincome-generating activities. An ongoing randomized impact evaluation of these subprojects will permit us toanalyze this issue in great detail in the near future.

27

information (and perhaps in the ability to produce viable applications), connections to local
officials, and civic engagement, rather than the high costs or low perceived benefits of applying.
The presumably accidental way in which the district funding formula neutralized thecross-district variation in applications suggests a way of thinking about how to make targeting
neutral to distortions. The regressive heterogeneity in application rates is like a ‘squeaky wheel,’
whereby well-informed and politically active individuals are trying to appropriate social funds
spending. When a distortion such as this is primarily found across political sub-units, the simple
act of ‘decentralizing’ spending by distributing fixed amounts to each sub-unit will neutralize it.
This suggests that we can use survey data to estimate the intra-cluster correlations of such factors
and target those clusters directly when they are high.44 This relationship represents an interesting
way in which pure statistics can guide policymaking.45
Our study confirms findings in the literature with respect to the advantages enjoyed by
local elites in decentralized programs, while suggesting a new culprit for this pattern. Despite thefact that community development projects are supposed to be designed to address the needs ofthe “poor, the marginalized, and the excluded”, we find that these are exactly the groups amongwhom the awareness of the program is lowest. The importance of civic engagement and politicalconnections permeates our results. Unlike measures of poverty, variables measuring politicalactivity and connectedness increase both the demand-side probability to seek out the program aswell as the supply-side probability of selection. The final distribution of program benefits thus
ends up skewed heavily towards districts and wards with high voter registration and turnout, andtowards households that have direct connections to village political elites. While our data do notallow us to distinguish active ‘informational capture’ by elites from the fact that marginalized
44

Baker and Grosh (1994) and Elbers et al. (2007) both find that targeting smaller administrative units, by using“poverty maps” or similar data, would produce large gains in poverty reduction.
45
Another dimension in which the statistical exercise conducted in this study may be relevant to researchers,
especially downstream users of poverty maps, is in the correction of standard errors for predicted explanatory
variables. While several authors have pointed out that a correction is required (see, for example, Elbers et al. 2005),
such corrections are not common in studies making use of poverty maps in downstream research. One take-awayfrom the bootstrapping exercise in this study is that the effect of this error is modest, and, as expected, decreases
with the scale of aggregation. By conducting the bootstrapping exercise with and without the variation across thepoverty map simulations, we find that the incorporation of the prediction error from the poverty map causes thestandard error of second-stage parameter estimates to increase by 29.6% in our ward-level analysis (average
population 13,370), but by only 11.8% in the district-level analysis (average population 276,000). While
policymakers are rightly worried about using noisy estimates for small administrative units to make important
decisions, such as allocating funds across wards, such estimates can be used for research while fully accounting forthe error inherent in them.

28

groups are simply harder to sensitize, the informational disadvantage among the poor thatpervades this study motivates a strong focus on outreach efforts in CDD programs.46
A variety of community-driven development programs require their potential
beneficiaries to be aware of and fully participate in the entire process, but the ability to do so is
not equitably distributed across the population. Rather, it is significantly lower among the poor,
the vulnerable, and the marginalized. Inducing meaningful participation at the local levels
remains a big hurdle for these programs to truly succeed.

46

For example, the amount of funds devoted to “information, education, and communication” were approximately
1% of the NVF funds. Even a small increase in the availability of such funds for better sensitization and outreach
could make a difference in the application pool and in the knowledge applicants have of their rights.

29

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32

TABLES
Table 1: Summary Statistics
Ward-level analysis:Targeting Outcome variables:

Poverty/Inequality:

Information

TASAF spending (US $ per capita)

Mean
3.072

Std. Dev.
3.800

Obs
2,177

Household is Eligible (Vulnerable)

Mean
0.213

Std. Dev.
0.409

Funded projects per 1000 ward residents

0.096

0.156

2,177

Household is Beneficiary, among eligibles

0.131

0.337

29,882
10,894

Project applications per 1000 ward residents

3.057

5.436

2,177

Heard of TASAF, among eligibles

0.543

0.498

10,894

Percentage of applications funded

12.91725.012

2,111

Obs

Poverty Headcount Ratio

0.340

0.184

2,177

Household is poor (Household survey sample only)

0.597

0.491

1,297

Population, 000

13.37010.4002,177

Cumulative Days milk, meat, eggs eaten in past week

2.932

3.838

29,882

Area (000 sq km)

0.027

0.051

2,177

Fraction Urban

0.221

0.373

2,177

Inverse Distance to Village Population Center

0.032

0.077

29,882Fraction Illiterate

0.364

0.172

2,177

Household Head has Primary Education

0.497

0.500

29,882Fraction with Radio or Phone

0.561

0.179

2,177

Household owns a Radio or Phone

0.676

0.468

29,882

0.807

0.097

2,177

Any household member holds village office

0.053

0.224

29,882

Any HH member related to village elites

0.067

0.250

29,882

Any HH member belongs to other VG group

0.022

0.146

29,882

Attended village meeting past 3 months

0.465

0.499

29,882

Political Connectedness CCM vote share in Presidential Election

Political Activity

Household/Listing survey analysis:

Ward Voter Turnout (votes/population)

0.325

0.070

2,177

33

Table 2
TASAF Applications Received
per 1000 People in Ward:

TASAF Spending per Person in Ward(final in US $):WithinDistricts

BetweenDistricts

Total Effectat WardLevel

WithinDistricts

BetweenDistricts

Total Effectat Ward
Level

(1)

(2)

(3)

(4)

(5)

(6)

0.66

-10.22***-4.60***

1.30***

0.88

1.33***(0.61)(2.52)(0.61)(0.45)(1.72)(0.44)

Observations

2177

119

2177

2177

119

2177

R-Squared

0.375

0.146

0.024

0.389

0.002

0.004

Poverty Headcount Ratio (P0)District-Level Fixed Effects:
Y
N
N
Y
N
N
*** p<0.01, ** p<0.05, * p<0.1. Ward-level analysis weighted by ward population, between regression run at the districtlevel and weighted by district population to be representative of mainland Tanzania. Standard errors calculated by abootstrapping exercise in which we drew estimates of the poverty headcount from the sub-estimations of the poverty map
for each unit, sampled from our data with replacement, and calculated marginal effects 500 times. These standard errors
reflect the fact that the core RHS variable is estimated.

34

Table 3
Within-District Analysis

Poverty Headcount Ratio
Population, 000
Area, 0000 sq km.
Fraction Urban
Fraction Illiterate
Fraction with Radio or Phone
Presidential vote share for CCM
Ward Voter Turnout (votes/population)

TASAF Applications
Received per 1000
People in Ward

TASAF Spending per
Application(1000 US $)

(1)

(2)

TASAF Spending
per capita in
Ward (final in US
$)
(3)

0.79

-3.17

0.69

(0.73)(2.00)(0.55)

-0.03

0.07

-0.03***(0.02)(0.05)(0.01)

1.21

-9.58

-0.99

(1.51)(6.44)(1.38)

-0.31

-4.59

-1.24***(0.58)(3.10)(0.34)

-1.00

0.23

-0.33

(1.00)(3.88)(0.70)

0.96

1.92

0.50

(1.45)(4.03)(0.78)

-3.35

-6.26

-0.38

(4.08)(14.21)(1.58)

4.02-0.9

5.43***(3.13)(6.40)(1.35)

Observations

2177

2111

2177

R-Squared

0.387

0.136

0.428

Mean of Dependent Variable

3.05
5.59
3.07
Y
Y
Y
District-Level Fixed Effects:*** p<0.01, ** p<0.05, * p<0.1. Ward-level analysis weighted by ward population to be representative of
mainland Tanzania. Standard errors calculated by a bootstrapping exercise in which we drew estimates of thepoverty headcount from the sub-estimations of the poverty map for each unit, sampled from our data withreplacement, and calculated marginal effects 500 times. These standard errors reflect the fact that the core RHS
variable is estimated.

35

Table 4
Household-level Targeting of TASAF VG Membership.
TASAF beneficiary(estimated among all
households)
Household is poor

Eligible(estimated among all
households)

TASAF beneficiary(estimated among
eligible households)

(1)

(2)

(3)

(4)

(5)

(6)

0.0139**0.0101

0.0927**0.07080.00830.0114(0.008)(0.046)(0.051)(0.031)(0.033)(0.007)
Inverse Distance to Village Population Center

0.298***

0.245

0.325*(0.076)(0.294)(0.186)

Household Head has at least Primary Education-0.0211***-0.285***0.0687*(0.008)(0.042)(0.035)

Household owns a Radio or Phone-0.00742-0.110**0.0193(0.007)(0.048)(0.032)

Attended village meeting past 3 months

0.0261***

0.05130.105***(0.007)(0.056)(0.031)

Any household member holds village office0.0330*-0.105*0.268**(0.017)(0.060)(0.118)Any HH member related to village elites

0.0231

0.063

0.0699(0.015)(0.066)(0.060)Any HH member belongs to other VG group0.134***

0.342***

0.163*(0.044)(0.114)(0.090)Observations

1297

1297

1297

1297

863

863

R-Squared

0.051

0.078

0.13

0.248

0.135

0.199

Mean of Dep. Var in Sample for Regression:
0.028
0.214
0.130
*** p<0.01, ** p<0.05, * p<0.1. Regressions are linear probability models with village-level fixed effects and standard errors
clustered at the village level. All analyses weighted by household-level sampling weights to make them representative of the
100 study villages.

36

Table 5
Awareness of TASAF: (estimated among all eligible households)

Dependent Variable: Ever Heard of TASAF.

Cumulative Days milk, meat, eggs eaten in past week

TASAF beneficiary(estimated among eligible
households)

Aware of TASAF(estimated among eligible
households)

TASAF beneficiary(estimated among eligible
households aware of TASAF)

(1)

(2)

(3)

(4)

(5)

(6)

-0.00599***-0.00706***

0.00958***

0.00247-0.0124***-0.0115***(0.001)(0.001)(0.002)(0.002)(0.001)(0.001)Inverse Distance to Village Population Center

0.0331-0.01940.178*(0.037)(0.044)(0.091)

Household Head has at least Primary Education

0.0414***0.181***

0.0149(0.009)(0.013)(0.014)

Household owns a Radio or Phone

0.000112

0.115***-0.0406***(0.008)(0.012)(0.014)

Attended village meeting past 3 months

0.0666***0.138***

0.0693***(0.011)(0.012)(0.016)

Any household member holds village office

0.126***

0.197***

0.0984***(0.024)(0.027)(0.028)Any HH member related to village elites

0.01270.0502***0.0018(0.014)(0.018)(0.019)Any HH member belongs to other VG group0.228***

0.111***

0.269***(0.035)(0.024)(0.037)

Observations

10,894R-squared

0.134

Mean of Dep. Var in Sample for Regression:

0.132

10,894

10,894

0.173

0.11
0.543

10,894

5916

0.194

0.154

5916
0.188
0.219

*** p<0.01, ** p<0.05, * p<0.1. Linear Probability Model with village fixed effects and standard errors clustered at the village level. Regression weighted to be
representative of population of 100 study villages. Columns (1) and (2) present the same specification in Columns (5) and (6) of Table 4 for the larger sample ofthe listing survey.

37

FIGURES
Figure 1: ‘Waterfall’ of TASAF Application and Disbursal.
Selection of Beneficiary Villages

Selection of Beneficiary Households(All 3 types of project, ward-level analysis)(Vulnerable Groups Only, household-level analysis)

National TASAF
OfficeCenter announces:

District TASAF
OfficesVillage-Level
Officials

Cross-District Funding
Formula(Table 2, Col 5)

National TASAF
OfficeDistrict TASAF
OfficesVillage-Level
Officials

Selection Step:

Progressivity of Step:

Entire Population

Program Design:
Progressive

Eligibility Criteria(Table 4, Col 3 & 4)
TASAF Eligible Population
Household-level sensitization(Table 5, Col 3 & 4)

Village-level sensitization

Sensitization:

Application
Application
Preparation, Selection
Preparationof beneficiary
Assistancehouseholds(Table 2, Col 1-3)

Application:

Demand-Driven
Component:
Regressive

Application
Preparation
Aware of/Apply for Program

TASAF District
Officials and District
Council select projects(Table 2, Col 6)

Project Selection:

Disbursement:

Selection of
beneficiaryhouseholds(Table 4, Col 5 & 6,
Table 5, Col 1 & 2)

National Officedisburses money toDistrict TASAF office

Money disbursed to beneficiaries, project
implemented on the ground.

National Officedisburses money toDistrict TASAF office

Money disbursed to beneficiaries, project
implemented on the ground.

Targeting Among
Applicants:
Progressive
Actual Beneficiaries

38

Figure 2a. Applications & Acceptance Rates, by Poverty Headcount

0

0

4
8
12
% Projects Funded

Applications per '000 pop.
2
4

6

16

Application & Acceptance Rates, by Poverty Headcount

0

.2

.4

.8

% Projects Funded

Fraction
0 .025 .05

Applications per '000 pop.

.6

0

.2

.4
Ward-level Poverty Headcount Ratio.6.8

Histogram of Poverty Headcounts given in bottom panel, 5% tails of poverty distribution trimmed

39

Figure 2b. Spending per Capita, by Poverty Headcount0

Spending per Capita, US$
1
2
3
4

5

Spending per capita, by Poverty Headcount

0

.2

.4
Ward-level Poverty Headcount.6

95% CI
Smoothed Spending per Capita, US$.8
Fitted values

Local Polynomial smoother and Linear regression weighted by ward population

40

Figure 3: Consumption CDFs by Group, Household Data.

Cumulative Density
.2
.4
.6
.8

1

Cumulative Density Functions (CDFs) of Consumption0

Poverty Lines:
Lower

Middle Upper

1
1.5
2
2.5
3
4
monthly per capita household expenditure in TSh 10,000
Population
Eligible & Heard of TASAF

Eligible Non-Beneficiaries
Beneficiaries

Figure plots the 10th through 90th population percentile of expenditure

41

APPENDIX TABLE I
Between-District Analysis
TASAF Applications

TASAF Spending per capita (US $)Budgeted

Budgeted

ActualApplications Percentage of Applications
TASAF
TASAF
TASAF
Received (per Applications Received (per Spending per Spending per Spending per
1,000 people)
Funded1,000 people) capita (US $) capita (US $) capita (US $)Poverty Headcount Ratio

(1)

(2)

(3)

(4)

(5)

(6)

-10.22***

17.59***

-4.04

-0.12

0.04

1.09

(2.52)(6.68)(2.98)(1.59)

Population, 000
Area, 0000 sq km.
Fraction Urban
Fraction Illiterate
Fraction with Radio or Phone
Presidential vote share for CCM
Ward Voter Turnout (votes/population)(1.18)(1.50)

0.000-0.01***-0.00***(0.002)(0.002)(0.002)-0.36

0.97***

0.88*(0.36)(0.33)(0.46)

1.85

1.26*

1.46*

(2.57)(0.69)(0.88)

3.67

0.04

0.93

(4.28)(1.27)(1.66)

8.55**

1.19

-1.43

(4.04)(1.15)(1.60)

0.73

-0.29

-2.24

(4.37)(1.39)(2.09)

16.59**

7.25*

19.16***(6.37)(3.81)(4.98)

Observations

119

119

119

119

119

119

R-Squared

0.146

0.038

0.343

0.0000

0.455

0.412

Mean of Dependent Variable
3.02
8.05
3.02
3.58
3.58
3.15
*** p<0.01, ** p<0.05, * p<0.1. Ward-level analysis weighted by ward population, between regression run at the district level andweighted by district population to be representative of mainland Tanzania. Standard errors calculated by a bootstrapping exercise inwhich we drew estimates of the poverty headcount from the sub-estimations of the poverty map for each unit, sampled from our datawith replacement, and calculated marginal effects 500 times. These standard errors reflect the fact that the core RHS variable isestimated.

42

APPENDIX TABLE II

Panel A: Alternate measure of Poverty:

Poverty Gap (P1)

TASAF Applications Received
per 1000 People in Ward:

TASAF Spending
per Person in Ward (US $):Within Districts

BetweenDistricts

BetweenDistricts

Total Effect at
Ward Level

(1)

(2)

(3)

(4)

(5)

(7)

Total Effect at
Within Districts
Ward Level

1.28-22.21***-9.03***

3.02**-0.66

3.42***(1.21)(6.55)(1.40)(1.13)(4.94)(1.17)

Observations

2177

119

2177

2177

119

2177

R-Squared

0.375

0.111

0.017

0.389

0.0001

0.005

District-Level Fixed Effects:

Y

N

N

Y

N

N

*** p<0.01, ** p<0.05, * p<0.1. Bootstrapped standard errors in parentheses. Ward-level analysis weighted by ward population.
Between regression run at the district level and weighted by district population. Standard Errors are bootstrapped.

Panel B: Controlling for 1/Population:
TASAF Applications Received
per 1000 People in Ward:

TASAF Spending
per Person in Ward (US $):BetweenDistricts(5)

Total Effect at
Ward Level
(7)

0.95

0.51

(1)

BetweenDistricts
(2)

0.22

-10.20***Within Districts

Poverty Headcount Ratio (PO)
1/Population

Observations

Total Effect at
Within Districts
Ward Level(3)(4)-4.86***

0.38(0.61)(2.85)(0.59)(0.34)(1.01)(0.43)

12.99***

123.1

11.04***

26.91***

760.76***35.64***(2.92)(118.92)(2.53)(1.86)(78.95)(2.13)

2177

119

2177

2177

119

2177

R-Squared
0.386
0.153
0.036
0.482
0.578
0.251
District-Level Fixed Effects:
Y
N
N
Y
N
N
*** p<0.01, ** p<0.05, * p<0.1. Bootstrapped standard errors in parentheses. Ward-level analysis weighted by ward population.
Between regression run at the district level and weighted by district population. Standard Errors are bootstrapped.

Panel C: Regression in Absolute Numbers:
TASAF Applications Received
per 1000 People in Ward:

TASAF Spending
per Person in Ward (US $):(1)

BetweenDistricts(2)

BetweenDistricts(5)

Within Districts

Number of Poor People
Population

Observations

Total Effect at
Within Districts
Ward Level(3)(4)

Total Effect at
Ward Level
(7)

0.001

-0.011***-0.006**0.760-0.0100.327(0.003)(0.002)(0.003)(0.597)(0.398)(0.605)0.001

0.007***

0.004***

0.119

0.888***

0.209(0.001)(0.001)(0.001)(0.191)(0.192)(0.177)

2177

119

2177

2177

119

2177

R-Squared
0.31
0.513
0.104
0.308
0.508
0.02
District-Level Fixed Effects:
Y
N
N
Y
N
N
*** p<0.01, ** p<0.05, * p<0.1. Bootstrapped standard errors in parentheses. Ward-level analysis weighted by ward population.
Between regression run at the district level and weighted by district population. Standard Errors are bootstrapped.

43