metadata: authors: 0: author: Fiala,Nathan V.
1: author: Premand,Patrick
count: Uganda
docna: 0: docna: Social accountability and service delivery :
            experimental evidence from Uganda
docty: Policy Research Working Paper
subtopic: Social Accountability,Livestock and Animal Husbandry,Public Sector Administrative & Civil
            Service Reform,Public Sector Administrative and Civil
            Service Reform,De Facto Governments,Democratic Government,Health Care Services Industry
teratopic: Social Development,Governance,Industry,Agriculture
abstracts: cdata!: Corruption and mismanagement of public
            resources can affect the quality of government services and
            undermine growth. Can citizens in poor communities be
            empowered to demand better-quality public investments? This
            paper looks at whether providing social accountability
            training and information on project performance can lead to
            improvements in local development projects. It finds that
            offering communities a combination of training and
            information on project quality leads to significant
            improvements in household welfare. However, providing either
            social accountability training or project quality
            information by itself has no welfare effect. These results
            are concentrated in areas that are reported by local
            officials as more corrupt or mismanaged. The impacts appear
            to come from community members increasing their monitoring
            of local projects, making more complaints to local and
            central officials, and cooperating more. The paper also
            finds modest improvements in people's trust in the
            central government. The study is unique in its size and
            integration in a national program. The results suggest that
            government-led, large-scale social accountability programs
            can strengthen communities' ability to improve service delivery.
geo_reg_key: 517191
repnme: repnme: Social accountability and service delivery
            : experimental evidence from Uganda
display_title: Social accountability and service
            delivery : experimental evidence from Uganda
listing_relative_url: /research/2018/05/29910746/social-accountability-service-delivery-experimental-evidence-uganda
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Social Accountability m1326920 733,Public Administration m1327723 723,Livestock m1504490 4024,Health m1504661 3480
publishtoextweb_dt: 2018-05-21T00:00:00Z
historic_topic: Social Development,Governance,Industry,Agriculture
no_of_pages: 72
author_keys: 0: author_key: 947516
1: author_key: 921098
model_extractions: WPS8449 Policy Research Working Paper 8449 Social Accountability and Service Delivery Experimental Evidence from Uganda _Nathan Fiala Patrick Premand_ Social Protection and Jobs Global Practice May 2018
Policy Research Working Paper 8449

# **Abstract**

Corruption and mismanagement of public resources can affect the quality of government services and undermine growth. Can citizens in poor communities be empowered to demand better-quality public investments? This paper looks at whether providing social accountability training and information on project performance can lead to improvements in local development projects. It finds that offering communities a combination of training and information on project quality leads to significant improvements in household welfare. However, providing either social accountability training or project quality information by itself has no welfare effect. These results are concentrated in areas that are reported by local officials as more corrupt or mismanaged. The impacts appear to come from community members increasing their monitoring of local projects, making more complaints to local and central officials, and cooperating more. The paper also finds modest improvements in people’s trust in the central government. The study is unique in its size and integration in a national program. The results suggest that government-led, large-scale social accountability programs can strengthen communities’ ability to improve service delivery.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at nathan.fiala@uconn.edu and ppremand@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Social Accountability and Service Delivery: Experimental Evidence from Uganda**<sup>**1**</sup>

Nathan Fiala (University of Connecticut) Patrick Premand (World Bank)<sup>2</sup> JEL codes: D7, H4, O1 Keywords: Social accountability; community training; scorecards; corruption; service delivery.

> 1 This study was pre-registered under AEARCTR-0001115. We are very thankful to Suleiman Namara and Endashaw Tadesse, who led the design and supervision of the program at the World Bank; and James Penywii and Munira Ali, who managed it at the Ugandan Inspectorate of Government. We thank Filder Aryemo and Jillian Larsen for outstanding research and operational contributions; Iker Lekuona, Kalie Pierce, Simon Robertson, Areum Han, Mariajose Silva Vargas and Leticia Riva for excellent research assistance; the study participants for generously giving their time; as well as the field officers of Innovations for Poverty Action for their dedication to the quality of data collected. Data collection was funded by a Vanguard Charitable Trust and the World Bank, including grants from the i2i and NTF Trust Funds. We are grateful for comments provided at various points during this study by Colin Andrews, Chris Blattman, Bénédicte de la Brière, Robert Chase, Deon Filmer, Vincenzo Di Maro, Christina Malmberg Calvo, Ezequiel Molina, Obert Pimhidzai, Pia Raffler, Ritva Reinikka, Dena Ringold, Danila Serra and Lynne Sherburne-Benz, as well as audiences at Harvard University, Makerere University, GIGA, RWI-Essen, DIW Berlin, the University of Connecticut and the World Bank. All findings, interpretations, and conclusions in this paper are those of the authors and do not necessarily represent the views of the World Bank or the government of Uganda.

> 2 Fiala ( _corresponding author_ ): University of Connecticut, Makerere University and RWI Essen; <u>nathan.fiala@uconn.edu.</u> Premand: World Bank; ppremand@worldbank.org.
# **1 Introduction**

Corruption and mismanagement of public resources can undermine development by generating costs for society. Those costs can range from an increase in bureaucratic hurdles in order to extract payments from citizens, to the creation of an unappealing economic environment for foreign investments, or a reduction of human capital stemming from low-quality delivery of health or education services (Bertrand et al., 2007; Woo, 2010; Reinikka and Svensson, 2004; Bjorkman and Svensson, 2009). Corruption and mismanagement can also increase inequality by affecting more severely those with less voice but greater need for public services (Olken, 2006; Hunt, 2007).

Recent research has suggested that empowering local populations and promoting transparency on the performance of local leaders and service providers can improve public governance by increasing the demand for accountability. A recent systematic review by Molina et al. (2016) finds that local monitoring can improve health services, though the evidence is limited due to a small number of studies. There is particularly little empirical evidence on the effectiveness of promoting social accountability in the context of large-scale national programs (Devarajan et al., 2011).

Community and government officials may misuse or divert funds from local populations. When combined with collective action problems and lack of information and skills to address these issues, corruption could lead to significant problems in service delivery. Can citizens in poor communities be empowered to demand better-quality public services and programming from local officials and bureaucrats? To answer this, we worked with the Inspectorate of Government of Uganda to conduct an experiment with a large sample of communities to test whether providing monitoring skills and encouraging the reporting of cases of mismanagement, as well as disseminating information on the absolute and relative performance of community projects, pushes citizens to demand and obtain more from local development projects.

Communities from across the broad north of Uganda were selected by the central government to receive a community-driven development program called the Second Northern Uganda Social Action Fund (NUSAF2). From a list of 940 communities that received a NUSAF2 project, we randomly selected 634 to receive an intensive, six-day training on how to monitor community projects, including how to identify and make complaints about corruption 2
and mismanagement to implementing partners and local, sub-national, or national leaders. The trainings were managed by the Inspectorate of Government (IG), an independent arm of the government responsible for fighting corruption, and implemented in partnership with local civil society organizations (CSOs). The sample size for this intervention is between five and 20 times larger than sample sizes in similar research, covering more than 45 districts and 485 sub-counties throughout the northern half of the country of Uganda, with more than 10,000 direct beneficiaries. The design thus allows for a minimum detectible effect size of less than 10% for most outcomes.

As NUSAF2 comprised a wide range of project types, including building teachers’ houses, providing livestock to households, putting up fencing, and establishing enterprise development, we developed a normalized index of project quality obtained through physical assessments of the projects (similar to audits). These data were collected about six months after the mean completion of the local NUSAF2 projects and were used to measure the immediate impacts of the training. We then used the information collected from this assessment to create a scorecard that ranks the performance of the community projects relative to other community projects within a district. We randomly selected 283 communities to be given this information during a community meeting, which included a facilitated discussion about why communities did or did not perform well relative to others.

This produced a 2x2 design where communities received training, a scorecard, both training and a scorecard, or no intervention. This design allows us to test directly whether training communities on social accountability or simply providing information on relative project quality can lead to improved service delivery, or if a combination of the two is needed.

We conducted individual surveys with community members six months after the initial assessment and scorecards were delivered to measure household welfare impacts. The sample includes over 6,900 individuals. Almost two-thirds of the projects provided livestock to households, making these projects more easily comparable to one another and more likely to have welfare implications for individual households. For these reasons, we focus our welfare analysis on these projects, though we also present results from the full sample.

We find that the social accountability training led to a small increase in the overall quality of projects by 0.135 standard deviation. From a follow-up household survey conducted six months later, we find that neither the training nor the project quality scorecard alone had any 3
impact on household welfare. However, the combination of the two led to very large household welfare increases: households in communities that received both training and information scorecards have approximately 0.6 more head of cattle per household, or 27% more than the control group. This is equivalent to approximately $114 per household ($1,140 per community) worth of animals. These findings indicate that for rural Ugandans, who often have limited interactions with the government, providing training alone or information about the quality of a project alone is not sufficient to increase the quality of service delivery. Rather, the combination of training on how to identify issues and report problems with information on the performance of projects leads to large welfare improvements.

We explore mechanisms for the observed impacts and find that the training and information increased community monitoring of the projects and cooperation among community members. People report spending more time visiting and monitoring projects and making complaints to various levels of government. Individuals also report an increased ability of communities to solve collective action problems and a modest increase in trust in the central government.

During a survey conducted before the experiment, we asked local leaders to identify areas near them that they thought had more corruption or mismanagement issues. We conduct heterogeneity analysis using these responses. We find that program impacts are concentrated in areas that local officials report as being more likely to be corrupt or mismanaged. We do not find spillovers across communities on our outcomes of interest, but we do find increased rates of monitoring of other projects or government services within treatment communities, suggesting the impacts observed here could expand to other public investments in treated communities.

Uganda, like many developing countries, faces significant challenges with service delivery. For example, though lowering child mortality and increasing rates of primary school enrollment are major goals of the government, both measures of service delivery are poor (Bold and Svensson, 2013). Low-quality services can obviously be related to a lack of funding for programs, but even when money is available, service provision can also be a problem. Hard data on the sources of these issues are rare, though corruption and mismanagement by officials or service providers, as well as citizens’ behaviors are often blamed.

An active body of research seeks to identify the most cost-effective approaches to reduce corruption and improve management of development projects. Research on the impact of 4
community-based monitoring can be broadly divided into two types of interventions. The first involves providing trainings for communities to learn to identify issues on local development projects and how to act on them. The second involves providing information to communities on the quality or process of local development projects.

The evidence for the first type of intervention is extremely limited. In the only study we are aware of that provides trainings to communities, Bjorkman and Svensson (2009) experimentally tested a program that combines information on the quality of providers and two half-day trainings to communities to improve the provision of health care in Uganda. They find communities receiving this combined intervention monitored providers more, and these providers increased their effort levels. This led to reductions in child mortality and increased child weight. Nyquist, De Walque, and Svensson (2017) find that these results were sustained four years after the program. They also introduced another treatment arm with training only, but their findings suggest that this was not enough to lead to sustained changes in the communities. They did not, however, have an information-only treatment. Note that both studies have a relatively small sample size, with 50 communities per treatment arm, which means there could be low statistical power for some tests.

Evidence for providing information to communities is a bit more developed, though the results obtained thus far are mixed. In a well-known experiment, Olken (2007) tested the effect of dramatically increasing top-down audit rates and encouraging citizen monitoring of road projects in Indonesia. The community monitoring was done through accountability meetings, where local leaders explained how funds were used. Communities received no other trainings or support to monitor that spending. Olken found significant decreases in leakages from the audits, but no effects from the community monitoring. Andrabi, Das, and Khwaja (2017) randomly provided report cards on school performance to communities in Pakistan. They found the report cards led to increases in test scores and enrollment, and decreases in school fees. Banerjee et al. (2010) conducted a randomized evaluation of a program that tested whether community-created scorecards could lead to increased community participation in child education in India. They found this program had little impact. In another study, however, Banerjee et al. (2018) mailed information on a rice distribution program in Indonesia to inform households about the program, and find beneficiaries received significantly more rice. Finally, Barr et al. (2012) tested community-created scorecards on school performance in Uganda. Their findings indicate that the 5
use of the scorecards increased student test scores and decreased teacher absenteeism. These varied results suggest that providing information can lead to improved service delivery, but information alone may not be enough, and the mechanisms and distributional effects are not well known.

Our contributions to this literature are as follows. First, we provide evidence that social accountability training and information on project performance can empower communities to improve the public investments they receive. Our design allows us to conclude that project quality information or accountability training alone is not sufficient to improve services in a lowcapacity environment; instead, both interventions need to be used together.

Second, these interventions were part of a large-scale, government-run program managed by the Inspectorate of Government and implemented in cooperation with local civil society organizations. As such, the scope, delivery mechanism, and scale of the program make it particularly relevant for learning about policy effectiveness. Recent evidence on the differences in approach and impact of interventions by governments, NGOs, and small tightly designed experiments has led to concerns about external validity (Bold et al., 2015). Results show that large-scale, government-led versions of social accountability programs can be effective.

Third, we provide evidence of the mechanisms behind these effects. While we cannot rule out all potential mechanisms, we present evidence that increased empowerment obtained through the combination of training and information led to greater monitoring by the community and increased complaints to all levels of government. Our findings also suggest significant spillovers from the intervention onto other government services in these communities.

Fourth, we estimate the distributional impacts of corruption and mismanagement by using a novel data collection strategy. We asked local officials to identify areas that were more likely to have problems with corruption and mismanagement before the start of the program. We find that increases in household welfare are concentrated in areas identified as being more likely to be corrupt, suggesting the program is more effective where government services are performing worse. While research has shown there can be important distributional implications of corruption and mismanagement, this has not been well tested in the context of social accountability interventions.

These findings also suggest that local officials are aware of where there are corruption and service delivery problems, to a better extent than the central government. Our results thus 6
add to recent research on the information asymmetries of principals and agents, including work on organization decentralization (Dal Bo et al, 2018; Aghion and Tirole, 1997; Bloom et al., 2012; Mookerjee, 2006), manager decision making for deployment of monitoring systems (de Rochambeau, 2017) and who to target for programming (Hussam et al., 2017).

The results of this experiment suggest that low-income citizens can successfully demand better services, when empowered with both proper skills and information. Large-scale, government-led versions of social accountability programs can increase the returns on investments in local development projects and improve citizen engagement with government. This can happen when social accountability training is combined with information about the performance of local development projects. The effects can be especially strong in areas where local service delivery is particularly poor. Recent calls by international organizations for greater accountability are leading some to argue for reducing investments in areas where corruption and mismanagement can be high. Our results suggest that programs can instead implement a community-based approach, thus “shrinking the [black] box [of corruption] by minimizing the impact that corruption can have on aid outcomes” (Kenny 2017).

The remainder of this paper proceeds as follows. In the next section, we describe the NUSAF2 program, training and scorecard interventions. In section 3 we present the experimental design. In section 4 we present the data. We examine the results in section 5. Section 6 then concludes with a discussion of the implications of this work and a cost-benefit analysis.

# **2 The NUSAF2 program and interventions**

NUSAF2 was a large-scale community-driven development program implemented by the Office of the Prime Minister (OPM) in coordination with local, sub-county, and district authorities, with $135 million funding from the World Bank and the UK’s Department for International Development (DFID) to the Government of Uganda. We present a simple representation of the various levels of government in Uganda in the context of NUSAF2 in Figure 1. As part of the program, communities were invited to formulate projects and submit proposals to project offices based in the sub-counties.<sup>3</sup> Once approved by the sub-county, the proposals were then passed to

> 3 ”Community” refers to either a village or a collection of villages that come together to propose a NUSAF2 project. They are thus not legal designations but are official designations under NUSAF2.

7
the district, which assessed the feasibility of the projects before passing them on to OPM for final approval and funding. The submitted projects fell under three categories: (i) public works, (ii) livelihood investment, and (iii) infrastructure rehabilitation.

Once projects were approved by OPM, funds were managed directly by the communities themselves through a variety of committees. The Community Project Management Committee and Community Procurement Committee were responsible for the delivery of the selected projects. Community Social Accountability Committees were created to oversee and monitor project progress and provide oversight within the community. Sub-county and district authorities were then expected to undertake monitoring and supervision in coordination with NUSAF2 project staff.

A highly decentralized project like NUSAF2 can create a range of transparency and accountability challenges.<sup>4</sup> Some concerns include that community and government officials may potentially misuse, or divert funds from community projects. Anecdotal evidence from a previous phase of the program suggests some cases of misappropriation of funds by officials. If transparency is limited, communities may lose control over how money is spent. Officials may insist on low-quality suppliers for community projects, potentially expecting kickbacks. Community elites may try to engage in similar behavior to attempt to manage funds with little oversight or to induce fellow community members to hire low-quality suppliers.

At the same time, it is often impossible to separate corruption from general mismanagement of resources. Communities and local governments may simply not have the capacity to make optimal decisions, and so funds may be used inefficiently or ineffectively. It is also possible that there may be issues with collective action, where communities may fail to implement a project well because it is too difficult to organize community members to complete the activities. This could be an especially large problem in the case of public works programs, which require local labor, or construction projects where monitoring of suppliers is difficult and time consuming. Finally, beneficiaries themselves may simply fail to take sufficient care of the public investments they receive.

To address these potential concerns, a Transparency, Accountability, and Anti-Corruption (TAAC) component was included in the design of the NUSAF2 project. We worked with the

> 4 Evidence from Fisman and Gatti (2002) suggests that decentralization can actually _reduce_ corruption. We do not take a position on whether decentralization in Uganda has increased or decreased corruption, only that a highly decentralized program can create a range of potential challenges.

8
Inspectorate of Government to embed a randomized control trial as part of the component. In the seventh and eighth rounds of NUSAF2 funding (out of a total of 12 rounds), communities were trained on the details of project implementation and how to identify and prevent cases of corruption and mismanagement. The training was implemented by seven different CSOs across the broad north of Uganda,<sup>5</sup> which sent representatives to communities to implement detailed training on social accountability and community monitoring of NUSAF2 projects. The program also organized follow-up visits by CSO representatives to provide ongoing training and advise the communities on how to monitor implementation of NUSAF2 projects.

When the CSO trainers first entered a community, they organized community assemblies. The assemblies discussed the principles of social accountability and community monitoring. As part of this mobilization phase, each community elected additional representatives to add to an existing social accountability committee. The existing committees were generally considered to be untrained and poorly prepared to monitor issues in the project. The social accountability training was thus designed to give them new capacity and revive their mandate. Members of the new committees made a public pledge to participate in the training program, undertake monitoring of the project on behalf of the community, and report back to the community.

The training provided background on social accountability and the NUSAF2 program, taught participants community-monitoring skills, and provided tools to monitor NUSAF2 projects. The training also provided hands-on skills in writing reports, providing feedback to the community, generating a community action plan, and applying monitoring skills to projects other than NUSAF2 in the community. The training gave special focus to encouraging communities to reach out and make complaints to the local and central governments, including the IG if necessary. People could contact the IG either by approaching a local office in their district or by texting a new national corruption hotline. A detailed description of the program components is presented in the appendix, including some of the visual training materials used for illiterate populations (Figures A1 and A2).<sup>6</sup>

> 5 Due to the size of the program, one civil society organization managed the implementation of the program but sub-contracted to seven individual CSOs that were present in the districts where the training was implemented.

> 6 In addition to the main training treatment, an additional treatment was also attempted in a random sub-sample of communities. This additional treatment was supposed to increase incentives for individuals to monitor projects through non-monetary rewards. These took the form of pins provided to participants showing they served as community monitors. These individual incentives were low value. In addition, group rewards were considered for communities who completed the entire training, conducted the community monitoring and produced timely monthly reports. However, these group rewards were not implemented.  We compare the treatment effects between the different treatments and do not observe a meaningful difference in coefficients and significance.

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Approximately six months after the mean completion date of these projects, from December 2015 through January 2016, we conducted an assessment of the quality and quantity of the community projects. This was done through physical observations. We then used this information to construct a score for the projects in each community. In February 2016, individual community facilitators, trained by the research team but identifying themselves as representing the IG, went to communities to present these scores. The facilitators also provided communities a ranking of their performance, relative to other NUSAF2 communities in their district. The scorecard stated that their project was ranked X out of Y projects in the district based on their performance in the assessment. An example of a scorecard is presented in Figure 2.

To ensure comparability of scores, the scorecard was done only for livestock projects. (Due to operational issues, we also had to exclude the Karamoja region.)<sup>7</sup> Treatment communities were presented summary information on the health of animals, animal productivity, assistance from the district veterinary officer (who was supposed to assist communities with their animals but was not always present), and a constructed value for money score that was calculated by multiplying the number of animals received by the productivity score of all the animals, divided by the total money received for the sub-project.

During the dissemination of the scorecards, the communities were invited to discuss the results. This discussion was supported by the community facilitator and included opening remarks from community leaders and a speech introducing the goals of the meeting. The scorecard results were then announced, with each component of the score fully explained. The meeting ended with a discussion about how communities could use the results of the score to improve service delivery and accountability in the community. Some of the actions suggested by communities that were discussed during these meetings included: (1) voicing concerns to the sub-county and district leadership; (2) participating actively in the community projects; (3) voting for local politicians whom they believe can best help the community develop; (4) selecting the best possible sub-project leaders and monitoring them closely; and (5) working together as a community to resolve issues whenever they can.

> For the analysis presented here, we thus do not differentiate between the different treatments and instead present results of the pooled treatment.

> 7 The focus on livestock means that the information treatment was conducted only in projects that were a private good, as opposed to infrastructure projects that were a public good. We provide evidence below that the training treatment had similar impacts in livestock and other project types, but we do not have direct evidence on whether project quality information could have led to improvements in public good projects.

10
The facilitator brought to each community five copies of the scorecard in English and five copies in the local language, a number line to graphically show the ranking of the community project, and sodas and soap as gifts to participants. Once the facilitators left, they did not return to the community.

The interventions we study here were based on a well-defined curriculum that was directly relevant for projects being implemented in communities. The training intensity was relatively long compared to other studies cited above. The scorecard information was also tailored to the projects and meant to encourage specific action by communities and presented direct comparisons to other communities in their area.

# **3 Experimental design**

Due to the large size of the NUSAF2 program, it was implemented in 12 rounds over five years. Working with the IG, we were given a list of all projects to be funded in the seventh and eighth rounds and randomized which communities would be given the social accountability and community monitoring training. The randomization was done in Stata. Due to the limited amount of administrative data from the government that had been digitized, we were only able to observe the location, budget, and rough classification of projects _ex ante_ (whether public works, livelihood investments, or infrastructure rehabilitation projects). The communities’ choice of project type was based on an endogenous process that we were not able to observe. We present some discussion in the next section about what predicts the type of project that is chosen. Note that every community in our sample received a NUSAF2 project. As the interventions we study here were randomly distributed across projects, and project types are well balanced for each treatment and control condition, the type of project chosen by a community does not bias our inference of impacts from the training and information treatments.

The timeline for this study is presented in Figure 3. An initial survey of local officials, discussed below, was conducted in early 2013. In November 2013, we received the list of NUSAF2 projects from the seventh and eighth funding rounds. We randomly assigned communities into social accountability training treatment or control in January 2014, with the NUSAF2 program and social accountability trainings beginning in June 2014. In December 2014, 80% of the funds were distributed, with the other 20% funded in the preceding six months.

11
Some projects were completed quickly, such as livestock projects, while road and building construction took up to one year to complete. All projects were completed by October 2015, with a mean completion of June 2015. We conducted the project quality assessment from December 2015 to early February 2016. From this assessment we constructed the project quality information scorecard and randomized communities to receive the scorecard intervention in February. We then distributed the scorecards from February to March 2016. Six months after the assessment, in June to July 2016, we completed the final household survey. The final household data collection was done on a rolling basis to coincide with the timing of the project assessment and ensure that communities were visited on a consistent timeline.

The design and number of projects by type and treatment status for the social accountability training intervention are presented in Table 1. A total of 940 projects were included in the sample. However, our main outcomes are not easily comparable across each of the project types. In the project types with the smallest number of communities, we were unable to create a reliable index of outcomes from the first project assessment, and so we focus on the most common project types: enterprise development, fencing, livestock, road and housing construction, and tree planting. This reduces our sample to 895 projects.<sup>8</sup> For household-level welfare outcomes, we include all project types.

In Table 2 we present the information scorecard design. As described previously, we developed and delivered the project quality information scorecard only to communities with livestock projects to improve comparability. Due to operational difficulties, we did not include the northeast part of the country, the Karamoja region, with 61 communities. A total of 574 communities are thus included in the sample. The end design is a 2x2 that includes both social accountability training treatment and control communities.

The NUSAF2 program and the social accountability training were implemented across the broad north of Uganda. We present a map of training treatment intensity in Figure 4. The figure shows the number of NUSAF2 communities that received training by parish across the

> 8 Because we had limited information on project type before selecting communities for the social accountability training treatment, we were not able to pre-drop project types that were implemented in numbers too small to allow for reasonable comparison of project quality, as is commonly done in similar experiments. We instead drop them in our analysis here. As the number of such projects is small (less than 5% of the sample in total), and given that we target all the projects delivered by NUSAF2 in two funding tranches, this post-dropping should not affect our internal or external validity.

12
entire sample.<sup>9</sup> In some areas, there is a high concentration of projects, but for the most part they are distributed across the broad region. We also look at spillovers at the sub-county level to test if the number of treated projects within a local area affects outcomes for the control group.

Before data were analyzed, all the outcomes were pre-registered with the American Economic Association registration system, number AEARCTR-0001115. The main outcomes of interest are the quality of the NUSAF2 project<sup>10</sup> and household welfare as captured by assets. To explore potential mechanisms, we also look at whether accountability training and project quality information scorecards affected the procurement and contracting process for communities, the level of monitoring by community members, and the interaction with local officials and technical staff. Our secondary effects of interest are whether the program changed individuals’ perceptions of the legitimacy of local and central government, and whether there were spillovers to other programs in communities.

We measure household welfare using an index of assets. We use both total household assets and the number of cattle owned by the household, as the latter is a direct outcome of the livestock projects and is the most common way households store wealth in the area studied. We explore these effects for all projects but do not expect animal ownership to change in the nonlivestock projects. We therefore constrain some analysis to livestock projects only.

While we were able to confirm that all of the selected communities received training, and that training was of satisfactory quality overall, there were delays in some communities receiving the training. The expectation was that communities would receive training either before or within a few months of receiving the NUSAF2 project funds. However, there are three reasons why this did not always happen. First, training implementers had limited information from the NUSAF2 program office about the timing of fund disbursements. Second, funds went from the central government to the districts before going to communities, and there was little information from the districts about their fund disbursement schedule. These two issues meant that timing the training precisely was very difficult in practice. Finally, the local CSOs often had difficulties organizing their activities to implement the training on time, and so delivered training later than originally planned in some cases.

> 9 Administrative units in Uganda, from largest to smallest, go from the central government to the district, then sub-county, parish, and village. We present the intensity of projects by parish as it is a medium level of administration and best displays the intensities across the area.

> 10 We describe in the next section and in Tables A1 and A2 the construction of this indicator.

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Soon after the trainings were completed, we conducted a short process evaluation in a randomly selected 96 projects to determine when funds were received relative to when the trainings were conducted. We found that 17 projects received their training after they started using their funds, with 11 receiving training within two weeks of using their funds. Four projects (4.2% of the randomly selected sample) began using their funds at least a month before they received training. We consider this late treatment to be non-compliance. Given the low rate of late trainings, we do not make corrections for non-compliance and so focus on the intention-totreat (ITT) estimates.

# **4 Data and balance**

# **4.1 Data**

The data for the analysis presented here come from several sources. Before the program began, we were given limited administrative data on what projects were to be funded by NUSAF2. From this list, we obtained information on the location, budget, and general types of projects.

We conducted a survey of local officials between January and March 2013 in which we included all 45 districts and 485 sub-counties in areas where NUSAF2 operated at that time. Subcounty officials interviewed in the survey include elected and appointed officials, as well as local NUSAF2 officers. We were interested in obtaining information on levels of corruption or mismanagement. To measure this, we asked each respondent the following question: “In your personal opinion, within your district, which sub-county has the biggest problem with corruption?” We then counted the number of times a sub-county was mentioned. Of the subcounties in the sample, 47% were never mentioned by an official and 20% were mentioned only once. We created an indicator if the sub-county was mentioned more than once.<sup>11</sup> As mentioned above, outcome data were obtained from two separate surveys: first, a project quality assessment captures effects on community project outputs, and second, a survey of individual households conducted six months later that captures effects on the households.

> 11 It is possible that communities select project types based on local prevalence of corruption. We in fact observe this. Communities that are in areas cited as corrupt choose livestock projects 58% of the time, while those in areas not cited as corrupt choose livestock 70% of the time.

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The first source of follow-up data collected is a project assessment conducted between December 2015 and February 2016. The project quality assessment includes observations of community projects by a team of enumerators. For projects with a single output (e.g., a staff house or a borehole), enumerators directly observed characteristics of the output. For livelihood support projects where outputs were distributed to beneficiaries, a sample of beneficiaries was drawn and beneficiary-level outputs were observed. For example, for livestock projects, a sample of beneficiaries was selected, and enumerators visited the sampled beneficiaries to observe the animals provided by the project. The project assessment data allow for the measurement of a set of core outcomes for the impact evaluation, but also of intermediary outcomes (or main underlying mechanisms) that can lead to changes in final outcomes. For each domain, the project assessment allows us to capture a range of variables, which can later be aggregated into indices. The next sub-sections provide additional information on the main outcomes and intermediary outcomes to be tested and the indicators that were collected to measure them. The appendix provides tables with the full list of variables composing the indices (Tables A1 and A2).

The primary outcome is a measure of a project overall score, which is composed of indices that measure the quality of the project and the quantity of outputs delivered. The project overall score is the main outcome for the analysis. It is built as an interaction of a quality measure and a quantity measure. This allows us to account for situations in which a community received more output from a project but at lesser quality, and vice versa. The quality and quantity indices are also analyzed separately. As the quality and quantity indicators are created across different project types, the indices constructed are normalized within each project type to ensure comparability.

Project quality is measured within each project type through direct observation of a range of attributes of the project output. For livestock, the project quality score is an additive index of whether the animal received was of the appropriate age, whether it was a local or improved breed of animal, whether the animal was productive when visited by the survey team, and whether the animal displayed any signs of illness. For staff houses, the quality is measured in terms of how well the walls, roof, windows, doors, ceilings, and floors meet quality standards. For enterprise projects, quality is determined by whether individuals have access to materials, transportation, credit, labor, and markets. Road quality is measured by the material used in the construction. The 15
quality of tree planting projects is determined by whether the seeds or seedlings are certified by the government or other NGOs.

The quantity measure captures the outputs delivered as part of the community project. It is determined by the number of animals received, length and height of the building constructed, number of people engaged in the enterprise, length of the road constructed, and the number of trees planted. These measures are obtained from direct observations of the outputs by enumerators at the time of the project assessment. In cases where the output could not be observed, the quantity measure takes a value of zero. This happens for livestock projects, for example, when the livestock have died or are otherwise missing at the time of the follow-up project assessment. We provide the full list of quality and quantity indicators in the appendix. In addition, Figures A3 and A4 in the appendix illustrate how some project assessments were conducted in practice.

To complement the observed measures of project quality and quantity of outputs, we also constructed an index of project implementation. This score is composed of subjective questions asked of the community about whether they felt the project was useful, whether it was completed as expected, and whether the materials met expectations and were not deemed to be too expensive.

The final indicator considered is whether the project could be located for the project assessment. When the survey team was unable to find a project during data collection, a research assistant was sent to confirm whether the project existed. In total, 23 of the projects, or 2.6% of the sample, could not be found by the survey team during any of the attempts at data collection and so were considered missing projects. At the end of the data collection, the IG was notified of these missing projects. The IG office sent a team to verify their existence, which reported that they had identified each of the missing projects and confirmed they had been operating. It is unclear how these projects should be considered in our analysis. Significant efforts were made by the survey team to locate the projects and confirm their existence. In addition, the missing projects were livestock and enterprise projects, which can be hard to identify because most households had multiple animals and income-generating activities prior to the projects. It is possible that communities did not declare these projects to the survey team. It is also possible that, when the IG team arrived to confirm the existence of the projects, some communities presented similar types of output as coming from NUSAF2, even though these outputs may have 16
previously existed. For our analysis, we test whether the share of these missing projects varies between treatment and control. For our measures of quality, we code these projects as zeros. Most importantly, the results are also robust when treating these projects as survey attrition and dropping them from the analysis entirely.

In addition to the primary outcomes, the project assessment also measures three sets of intermediary outcomes that capture the main underlying mechanisms that can explain changes in final outcomes. These include (i) the procurement and contracting process, (ii) community monitoring, and (iii) community interactions with local leaders. These three domains relate to some of the key areas covered by the social accountability training curriculum. Indicators on the procurement and contracting process include an index of challenges faced by communities in the procurement process, an index of satisfaction with suppliers of goods and materials, and whether the community hired a contractor. For communities that did hire a contractor, indicators also include an index of challenges faced by communities in the contracting process and an index of satisfaction with the contractor. The second main domain for intermediary outcomes includes indicators of community monitoring, such as an index of the intensity of project community monitoring, and an index of the intensity of social accountability committee (SAC) project monitoring. Finally, the third main domain for intermediary outcomes captures interactions between communities and local officials. This domain includes indicators of whether a payment was made to a district official or staff, and an index of satisfaction with the sub-county NUSAF2 official and district veterinarian officer.

The second source of follow-up data is an endline survey conducted with households in the sample communities in June and July 2016. The sample surveyed was a selection of individuals who directly benefited from the NUSAF2 project, as well as a sample of the broader community. Eight people per community were surveyed, including the chairpersons of each of the executive committees in the project, two members of the original community social accountability committee, two members from the expanded community accountability committee (called the CMG) in the social accountability training treatment group, and two regular members. In social accountability training control communities, the CMG does not exist and so this group was replaced with two regular members. The sample is thus composed of eight beneficiaries in social accountability training control communities and six in social accountability training 17
treatment communities. The remaining two individuals in the treated communities are people associated with the program but not necessarily directly affected by it.

The data from the household survey include assets, including animals and household durables; whether the individuals had made complaints to local leaders about their NUSAF2 project or other projects in the community; and the individuals’ level of trust in local leaders. The descriptive statistics for the project assessment and household data collections are presented in Table 3. The description is separated by whether data were collected at the project or household level. While NUSAF2 targeted very low-income households, most had livestock in their home, with the mean household having 2.45 cattle at the endline.<sup>12</sup> The sample size for the household survey was determined to provide the highest statistical power given a fixed budget. The intra-cluster correlation (ICC) for the main outcome of interest, number of cattle, is 0.045. For the scorecard sample, which includes 574 clusters, the minimum detectible effect (MDE) size is below 10%. For total assets, the ICC is 0.35 and so the MDE is approximately 15%.

# **4.2 Balance tests**

Tables 4 and 5 present balance tests for the social accountability training and project quality scorecard samples, respectively. Due to the project timeline and funding, a full baseline with communities was not feasible. We do have four indicators that were available before the beginning of the NUSAF2 projects in the sample: the amount of money approved per community, the type of project, when the program grants were received, and the level of corruption and mismanagement in the areas where the communities exist. We also present tests for whether the randomly drawn respondent in the household survey was a man, whether that person could write or read, and the distance from the respondent’s household to the sub-county headquarters. We include these last four measures because we believe they are not likely to have changed due to the program and so reflect the characteristics of the communities before the social accountability training treatment.

> 12 As part of a separate experiment, the enumeration teams were randomly assigned to villages during the second data collection. This was done to test for enumerator effects on reported household characteristics and outcomes. There is no or very little enumerator bias introduced on the main outcomes of interest, especially number of animals. While the experiment is not able to directly test for Hawthorne effects, the lack of enumerator bias and the fact that the enumeration team was separate from the implementation team reduces the likelihood of such issues impacting the main results.

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We do not find a statistically or economically significant difference between the social accountability training treatment and control groups for any of the project-level indicators. For the broader sample (Table 4), the amount of funding received by project averaged over 20 million USH (a little over USD 6,000).<sup>13</sup> The test for balance shows a difference of less than 1% from the control group and is not statistically significant. Similarly, there is no difference in the likelihood of the project being livestock, or in the date when the funding was received in the communities. There is a small difference in whether participants were men and whether they could write, but while these differences are significant, they are relatively small. The scorecard sample, presented in Table 5, is likewise well balanced, with no characteristic being significantly different between scorecard treatment and control communities. We conclude, therefore, that the characteristics of the communities and the people within the communities are generally well balanced due to randomization.

# **5 Results**

To identify the impact of the programs on project- and household-level outcomes, we run the following intention to treat (ITT) OLS regression model: where _i_ refers to a project or household and _Yi_ is the outcome of interest. _Ti_ is whether a community was randomly selected to the social accountability training treatment, _Si_ is the scorecard treatment, and _Ti*Si_ refers to communities assigned to both social accountability trainings and scorecard distribution. _R_ is a matrix of region dummies and _εit_ is the error term. The coefficient _β_ thus presents the impact of the social accountability training treatment only, _γ_ the impact of the scorecard treatment only, and _β1 + β2 + β3_ the impact of combining social accountability training and scorecard treatments. For household-level outcomes, we cluster the standard errors at the project level.

> 13 This average included very large projects such as teachers’ houses. The amount for livestock and enterprise projects averaged just less than half this amount.

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We begin by discussing the impacts of the intervention on project quality as measured through the project assessment. This analysis is done on data collected before the scorecard intervention, and so we do not consider the _β2Si_ and _β3Ti*Si_ terms in Equation (1).<sup>14</sup> We then present the results of the household survey conducted six months later, including the scorecard effects. To explore the potential mechanisms, we discuss impacts on monitoring of projects and reporting to government officials as well as other community activities as reported by respondents. We end by looking at important heterogeneities in treatment and local spillovers.

Note that we do not use multiple hypothesis correction for outcomes. This is because we have only two main outcomes: project score (measured during the assessment) and household assets (measured at the household endline). Both of these are indices of family of outcomes. We present analysis on the individual family components and test for mechanisms without correction as we consider these to be exploratory analyses. Note that the endline survey was powered to identify a minimum detectible effect size of 8% on our main household outcome and mechanisms. Due to the endline survey being very well powered and the high level of significance of estimations, most of the results from that survey would pass conservative correction procedures.

# **5.1 Initial impacts on project performance**

The impacts for the main outcomes from the project assessment survey are presented in Table 6. These include the overall score for each of the NUSAF2 projects in the sample (columns 1 and 2), which is created by multiplying the project quality score (columns 3 and 4) and quantity score (columns 5 and 6) together. We also look at whether the project could not be located (columns 7 and 8). Each of these indicators is from the first project assessment and is estimated at the community level. The indicators are standardized, as discussed previously.

Odd-numbered columns report results for all project types in the sample. We find a small positive and statistically significant impact on the overall score of the project of 0.135 standard deviation. This effect appears to be coming mostly from the quantity indicator (column 5) and is not driven by whether the project could not be located. The results suggest that the training led to

> 14 However, we estimate equation (1) for some outcomes measured before the scorecard intervention to provide additional balance tests.

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an increase in quantity of outputs delivered by projects by approximately 0.177 standard deviation. There are no statistically significant effects on the quality score. In appendix Table A9, we also look at these results when trimming the top and bottom 0.5% outcomes and find broadly similar results.<sup>15</sup> Even-numbered columns report outcomes for interacting treatment with whether the NUSAF2 project was non-livestock. We look at this difference specifically as we are interested in whether the results are being driven by a specific project type. As most project types are a small portion of our total sample, we are only able to look at livestock projects, which are about two-thirds of the total sample. Livestock projects are also the project type most likely to directly lead to welfare impacts at the household level, which we discuss in the next sub-section. The coefficients for treatment effects remain about the same size. However, most likely due to decreases in power, the project overall score is not significant at the 10% level, though the quantity score is of the same significance as the non-interacted results. None of the interaction terms are significant. We conclude that there is likely very little difference between the impact of the program by project type, at least when comparing to livestock.

To help reduce power concerns and further explore impacts, we present in Table 7 the scores and score components for livestock-only projects. The livestock projects offer an opportunity to further improve our power as we have five observations per project as opposed to just one data point for the road, fencing, and house projects. The analysis conducted in Table 7 is thus at the animal level, rather than at the project level. All of the analysis includes clustered standard errors at the project level.

Similar to the results in Table 6, we find a small but statistically significant effect from the training on the quantity score in column 3. Treatment increased livestock quantity by 0.125 standard deviation, significant at the 10% level. The total score and quality score are not statistically significant. We further explore the components of the score in columns 4 to 8 and find no statistically significant impacts on the age of the animal when it was purchased by the community, the breed of the animal, whether the animal was deemed productive, or the health of

> 15 A randomized inference test produces results similar to the OLS results, and so we only present the results of the OLS specification.

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the animal.<sup>16</sup> We do find a decrease in whether the animal was reported to the project assessment team as dead, stolen, or sold.

To provide additional information on the balance of the scorecard randomization, we present in Table 8 the same analysis as Table 7 but include the scorecard treatment and interaction terms. This is a form of balance test, as the data from Table 8 are from before the scorecard treatment. We thus expect no statistically significant coefficients from the scorecard and interaction terms. We do not find anything significant except for a small positive effect on the interaction term for whether the animal was productive. The coefficient is small relative to the control and significant at the 10% level. Combining this and the results from Table 5, we conclude that the scorecard sample is well balanced.

# **5.2 Impacts on welfare**

Six months after the initial project assessment, we conducted an additional household survey in the communities to measure household-level welfare outcomes. The household survey allows us to go beyond the measure of project quality obtained from the project assessment and estimate potential welfare impacts of the program at the household level.

In Table 9 we present the main outcome of interest for the household survey: the number of cattle and an index of total household asset ownership. Columns 1 and 2 present the asset outcomes for all project types, columns 3 and 4 present these outcomes only for livestock projects, and columns 5 and 6 present results for projects in the information scorecard sample. Note that we do not expect impacts on the number of household animals for any but the livestock projects. We prespecified a focus on this outcome as livestock projects represent 68% of the sample, and we believe these are the projects that are most likely to lead to direct changes at the household level. They are also the focus on the scorecard intervention. For this analysis, we restrict the sample to those who were direct beneficiaries of the program, i.e., those who were selected to receive animals from NUSAF2.<sup>17</sup>

> 16 Note that the illness index is reweighted as 1 minus the mean number of illnesses, so the positive coefficient means fewer observed illnesses.

> 17 The endline survey was conducted on eight beneficiaries per community in the control group. In the treatment group, we included six beneficiaries, as well as two non-beneficiaries who were selected to join the community management committee as part of the training intervention. We do not include non-beneficiaries in this analysis as we do not expect impacts from the treatment on household welfare.

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We do not find statistically significant impacts on the number of animals from all of the projects in the sample, though the coefficient for cattle is large. However, we do find statistically significant and very large effects on the total number of assets in the household. For livestockonly projects, we find statistically significant effects for both cattle and total household assets. The effects on cattle are large at 0.31 additional animal per household, an increase of 15% relative to the control group.<sup>18</sup> As there were 10 households per community that received cattle as part of livestock projects, this represents an increase of approximately 3 cattle per community, on average.

Looking at the impacts for the scorecard sample in columns 5 and 6, we test the full model presented in Equation (1), which includes a dummy for social accountability training treatment, scorecard treatment, and the interaction of the two treatments. None of the coefficients in column 5 is statistically significant. However, a joint test of the social accountability training plus scorecard plus interaction terms is significant at the 2% level for cattle. We cannot reject equality of the social accountability training and scorecard treatments. We find similar results for the total assets indicator.

The results from the household survey analysis show that the interaction of the two treatments drives impacts, not training or the scorecard alone. Households that received both the social accountability training and project quality information scorecard interventions obtain an increase in their number of animals of 0.61, or approximately 27% relative to the control group.<sup>19</sup> This is a highly statistically significant effect of large economic magnitude.

# **5.3 Potential mechanisms: Impacts on community monitoring and reporting**

During the project assessment survey, we measured the actions taken by communities to monitor their projects. Table 10, column 1 presents the results for an index of how much the broader community monitored the program, while column 2 presents the monitoring activities of the local accountability group that is present in all communities. The broader community does not

> 18 While NUSAF2 delivered only one animal to households, a goal of the project was to lead to new animal births, thus theoretically multiplying the number of animals beneficiaries have over time. Thus, the comparison of treatment effects should not be made to the one animal delivered, but to the number of animals in the control group. 19 We also test for whether impacts are concentrated in communities that had the lowest scores (not shown) and do not find a relationship between the absolute score and the number of animals in households. The impact of the training and scorecard information appears to exist across the distribution of scores.

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report statistically significant changes in monitoring. However, for the social accountability committee group, we find very large and significant increases in the monitoring activities they conduct.

In columns 3 to 7 we explore the impacts on reporting of issues in the community to officials at different levels of government. This measure comes from the household survey conducted six months after the project assessment. The results show significant increases in the number of reports at all levels of government. Reports to the lowest level of government, LC1 and sub-county officials, increase by approximately 10%. Reports to officials at a slightly higher level of local government, the district, increases by 14%. Most strikingly, reporting to the central government through the IG increases by 59%. In appendix Table A3 we find similar results when looking at the full list of project types and only the social accountability training treatment.

We also look at reported issues during the procurement and contracting process and the quality of interactions with bureaucrats in Tables A4 and A5 in the appendix. We do not find statistically significant impacts on whether communities reported issues with procurement or contracting, their satisfaction with suppliers, whether they hired a contractor, or their satisfaction with the contractor. We also do not find impacts on whether they made a payment to a district representative, their satisfaction with the local NUSAF2 coordinator, or their satisfaction with the local veterinarian officer.

In Table 11 we explore several additional outcomes and potential mechanisms. In columns 1 and 2 we test whether treatment affected individuals’ perceptions of the performance of the project leaders. In columns 3 and 4 we ask about general satisfaction with the quality of the project and management committee, respectively. We do not find meaningful changes in any of these measures. In columns 5 and 6 we look at two measures of the ability of communities to deliver public goods and find there does appear to be a significant, though small, effect on whether people believe members of the community can come together to solve issues in the community. In appendix Table A6 we look at the full sample and only social accountability training treatment status. Here we find statistically significant decreases in whether people report choosing the same project leadership, increases in public goods provision, and an increase in the perception of being able to make their community a better place to live. However, all of these effects are very small relative to control group means. These results suggest that the social 24
accountability training and project information scorecard may have affected the ability of communities to cooperate, but the effects are relatively small.

There are of course several other mechanisms that could have led to the outcomes we observe. In particular, the trainings could have changed information asymmetries by supporting communities in understanding better what was to be delivered to their communities and how that was to be done. The trainings could have also led to changes in bargaining power by communities. For instance, a local newspaper reported on the arrest of a NUSAF2 official by the IG, instigated at the request of a treatment community. The trainings may have also had a social impact by increasing pressure on participants to take better care of their animals. Overall, we see large and significant effects on monitoring of projects and complaints to officials, though our data do not allow us to rule out other mechanisms.

# **5.4 Impacts on trust in community leaders and government**

We also analyze whether the program changed the way people view local and government officials. In Table 12 we present the results from asking respondents whether they thought their leaders acted in the interests of local communities.<sup>20</sup> In columns 1 to 6 we look at the village leaders for the NUSAF2 program, the elected sub-county official, sub-county bureaucrats, the elected district official, the district bureaucrats, and the central government, respectively.

We do not find significant changes in how people perceive project leaders or the subcounty and district elected and appointed officials. We do find a small but statistically significant increase in trust in the central government. This effect is only a 2.5% increase over the control group. We find a similar result in appendix Table A7 on the full sample, looking only at social accountability training treatment effects. We believe this effect reflects the increased visibility of the IG, the agency from the central government that managed the training delivered to these communities.

> 20 The analysis presented in Tables A7 and A8 is for the entire sample. We also look at just livestock projects and find the same results.

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# **5.4 Heterogeneities by local levels of corruption**

To determine which communities in our sample had the largest issues with corruption and mismanagement, we conducted a survey of all local officials in the areas that would be part of the study before the start of the experiment. As described above, we asked officials to name the most corrupt or mismanaged sub-county in their district.<sup>21</sup> We then count the number of times a sub-county is listed and create an indicator of whether a given sub-county is in the top most cited sub-counties. If a sub-country is mentioned more than once we consider it to have high reported corruption. This is the case in 33% of the sample sub-counties.

In Table 13 we present the results of dividing the sample by this indicator and testing for the impacts on the number of cattle owned by individuals. The impact of the treatments is concentrated entirely in communities in the sub-counties noted by local officials as most corrupt or mismanaged. The social accountability training treatment indicator is marginally not statistically significant, while the interaction between social accountability training and project information scorecard is significant at the less than 1% level and very large. Households in areas that are reported more corrupt or mismanaged that received both treatments have, on average, an additional 0.77 to 1.19 animals. This is an increase of between 35% and 59% over the control group. This result suggests there are substantial distributional effects from the interventions, and so there could be large benefits from targeting such a program to areas that have the biggest issues with corruption or mismanagement. We also test for differential mechanism effects (not shown), which we presented in Tables 10 to 12, and find no difference in these outcomes between areas of high or low reported corruption. We conclude that people made similar levels of complaints in these areas, but they were most effective in the areas reported as more corrupt.

While we do not observe a large difference in control means across the high and low reported corruption groups in Table 13, we cannot rule out that this measure could also be correlated with other community characteristics, including performance of local government and overall poverty levels. We compare the results of the reporting of officials and the scores in the scorecards and find a significant relationship between the two. Communities in sub-counties that are most more likely to be corrupt have a lower absolute score of 2.39 points, out of an average

> 21 A district is composed of approximately five sub-counties.

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of 70.85, significant at the less than 1% level. The results suggest that local officials likely have very useful information on the level of corruption and mismanagement in their areas.

# **5.5 Spillovers**

The randomization process for the selection of treatment communities was not stratified. However, it led to natural variation in the number of treated communities within sub-counties. We utilize this variation to look at the spillovers of treatment to control communities. To do this, we focus on the treatment group that received both the training and information treatments, and calculate the total number of treated communities by sub-county, divided by the total number of projects in our sample. This indicator is our intensity of treatment. Spillover effects could be positive if local officials feel pressure from communities and so improve all of their operations. They could also be negative if officials shift corruption or mismanagement from treatment to control communities.

We present the results in Table 14. The size and significance of the coefficient for treatment is identical to that found in Table 9, column 5. The coefficient for the intensity of treatment in a sub-county is large, but not significant. It appears that there are no observable spillovers from the program on control communities.

However, within communities there could be spillovers from treatment on other projects not related to the NUSAF2 projects. In Table 15 we recreate the analysis on reporting of issues conducted in Table 10, columns 3 to 7, but for other community projects that are not related to NUSAF2. Like the results for NUSAF2-related projects, we find statistically significant and large effects on whether people report issues to local leaders or central officials. Reports from individuals about making complaints to officials at all levels about non-NUSAF2 projects (column 5) go from 22% in the control group to 32% in in the scorecard treatment – an increase of 34% – significant at the 3% level. This effect is only in scorecard treatment communities. However, when looking at the full sample of communities in appendix Table A8, we find significant effects from the unconditional social accountability training treatment on all levels of reporting, although the effect sizes relative to a control group are generally small.

We also look at heterogeneities by the region where the community is located and distance from individual beneficiaries’ homes to the main sub-county office, as well as 27
beneficiary sex and education level for the full sample using the unconditional social accountability training treatment. The results of these tests are presented in appendix Table A10. Overall, we do not find significant variation in outcomes on the individual characteristics, though there does appear to be some variation in outcomes across regions. However, this variation is no longer significant when we interact the region and treatment with the measure of local corruption. It appears that the quality of the sub-county leadership matters more than the specific region a community is in.

# **6 Discussion**

The impacts from the social accountability training and scorecard information treatments on household welfare in communities that received livestock projects are quite large. We estimate that there are over five additional cattle in communities that received both treatments. Cattle are valued at approximately 800,000 USH, or about $230 each. The program thus led to approximately $114 worth of additional animals per household, or $1,140 per community. However, the cost of the program was significant, given the geographic spread and relative intensity of the training. We estimate that the total cost of the program, measured by the amount paid to the CSOs that ran the trainings, was between $900 and $1,200 per community, depending on how costs are accounted.<sup>22</sup> There are two points to keep in mind with this cost/benefit calculation. The first is that we find small but potentially important impacts on attitudes toward the central government, and potentially large impacts on other, non-NUSAF2-related programs being implemented in communities. We are not able to monetize these additional impacts, but we expect they are not trivial. The second point is about the potential distributional effects, which we observed through heterogeneity by the local levels of corruption. The impacts we observe are concentrated in communities that were considered by local leaders as more corrupt or mismanaged: the effects are up to four times larger than the estimated average treatment effects. For communities that are particularly likely to be affected by corruption or mismanagement, the combination of social accountability training and information has especially large effects.

> 22 These costs reflect the time spent developing the material; training of the CSO representatives; transport, materials, and drinks for participants during the trainings; and scorecard dissemination.

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Overall, we present evidence that increasing engagement in poor communities can produce higher returns from public investments. The social accountability training combined with a project quality information scorecard intervention resulted in individuals owning a significant number of additional animals. These effects appear to come from increased monitoring by communities, as well as an increase in the reporting of issues to officials from the local to the central government. We also find that the program led to some modest improvements in people’s trust in the central government.

The results suggest a positive role and significant potential for programs that seek to promote citizen engagement and increase local populations’ participation in the development process. This approach is becoming popular, with similar interventions being conducted in government programs in Liberia and Sierra Leone, as well as being expanded considerably in Uganda. We show that this approach is feasible, impactful, and, under some conditions, of good value. But it is clear that communities in this context need more than training on how to identify and report issues alone, or simple information about their project’s performance. Rather, it is necessary to combine these interventions, especially in areas where citizen’s interactions with government are difficult or not the norm.

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32
# **Figure 1. Levels of government involved in NUSAF2**

<!-- Start of picture text --> Central<br>Government<br>(OPM and IG)<br>District<br>(LC5 and bureaucrats)<br>Sub-county<br>(LC3 and NUSAF2 representative)<br>Villages<br>(LC1 leaders)<br><!-- End of picture text --> 33
# Community Scorecard NUSAF?2 Livestock Sub-Project

Sub-Project Name: We have compared your sub-project with 33 other sub-projects in your district.

## Your Rank

<!-- Start of picture text -->
1. Health When Animals Arrived: 21 out of 33<br>2. Health and Animal Productivity: 22 out of 33<br>3. Assistance from your DVO: 7 out of 33<br>4. Value for Money: 20 out of 33<br>Overall Score: 69 out of 100<br>Overall Position in the District: 12 of 33<br>Your<br>Points<br>0 100<br><!-- End of picture text -->
# **Figure 3. Study Timeline**

<!-- Start of picture text --> NUSAF2 and<br>Obtain NUSAF2 and social Information<br>list of social accountabili scorecard<br>NUSAF2 accountabil ty trainings<br>presented to<br>projects ity end communities<br>Nov 2013 trainings Oct 2015 Feb to March<br>begin 2016<br>June 2014<br>Survey Randomizatio Mean Project Household<br>with n of completion assessment level<br>local projects date of Dec 2015 survey<br>official Jan 2014 NUSAF2 to Feb June to<br>s projects 2016 July 2016<br>Jan 2013 June 2015<br><!-- End of picture text --> 35
**<u>Table 1. Social accountability training design</u>**

|<br>**Project type**|<br>**Control**|<br>**Treatment**|<br>**Total**|
|---|---|---|---|
|Enterprise|23|58|81|
|Fencing|9|18|27|
|Livestock|212|423|635|
|Road|9|22|31|
|Staff House|11|36|47|
|Tree Planting|27|47|74|
|Borehole|8|10|18|
|Classroom|2|5|7|
|Dormitory|2|7|9|
|OPD|3|8|11|
|Total|306|634|940|

Notes: This table reports the total number of communities in the social accountability training program. Due to low numbers of project types, communities below the middle line are not included in the initial analysis (Tables 3 and 4).

37
**Table 2. Scorecard information design**

||Scorecard<br>control|Scorecard<br>treatment|Total|
|---|---|---|---|
|Training control|99|95|194|
|Training treatment|192|188|380|
|Total|291|283|574|

Notes: This table reports the total number of communities in the scorecard information program, by social accountability training status. As described in the text, to ensure comparability of the projects, the scorecard was designed for the livestock projects only and was implemented everywhere except for the Karamoja region.

38
# **Table** **<u>3. Descriptive Statistics</u>**

||(1)<br>Mean|(2)<br>SD|(3)<br>Min|(4)<br>Max|(5)<br>Obs|
|---|---|---|---|---|---|
|**Project level:**||||||
|Project Funds (in 1,000 ugx)|22750.6|32741.9|7612|162670|895|
|Livestock project (0/1)|0.709|0.454|0|1|895|
|Project start date (Period when grants were received)|38.188|3.935|1|48|812|
|Project overall score (std)|0.032|0.949|-2.9|3.28|872|
|Project quality score (std)|0.043|1.021|-2.86|3.23|871|
|Project quantity score (std)|0.016|0.928|-5.71|12.1|863|
|Project is missing (0/1)|0.027|0.162|0|1|895|
|Project Implementation Quality Index|2.38|0.905|0|4|705|
|Satisfaction with supplier Index|5.629|1.366|0|8|660|
|Hired a Contractor to Implement Project|0.388|0.487|0|1|800|
|Index of challenges in Contracting Process|3.332|1.868|0|9|301|
|Satisfaction with contractor Index|5.375|1.77|0|8|459|
|Index for Intensity of Project Community Monitoring|2.957|1.139|0|4|821|
|Index for Intensity of Social Accountability Committee Project Monitoring|0.831|0.954|0|4|863|
|Satisfaction with NDO Index|5.912|1.407|0|8|839|
|Satisfaction with District Vet Index|10.638|1.463|6|15|572|
|**Animal level:**||||||
|Animal dead (0/1)|0.13|0.336|0|1|6891|
|Animal sold (0/1)|0.051|0.22|0|1|6891|
|Animal stolen (0/1)|0.017|0.131|0|1|6891|
|Animal dead/sold/stolen (0/1)|0.198|0.399|0|1|6891|
|**Beneficiary level:**||||||
|Number of Cattle (Total)|2.452|10.5|0|800|6961|
|Number of Goats (Total)|4.206|7.018|0|230|6966|
|Number of Livestock in Tropical Livestock Unit|1.816|5.523|0|406.5|6952|
|Reporting NUSAF-related issues (total)|1.052|1.310|0|4|6966|
|Reporting NUSAF-related issues to LC1|0.405|0.491|0|1|6964|
|Reporting NUSAF-related issues to Subcounty|0.305|0.460|0|1|6961|
|Reporting NUSAF-related issues to District|0.203|0.402|0|1|6963|
|Reporting NUSAF-related issues to IG|0.141|0.348|0|1|6957|
|<br>Trust Project Leaders (1-4)|3.582|0.712|1|4|6952|
|Trust LC3 Chairperson (1-4)|3.181|0.93|1|4|6937|
|Trust Subcounty Bureaucrats (1-4)|3.295|0.809|1|4|6921|
|Trust LC5 Chairperson (1-4)|2.997|1.011|1|4|6892|
|Trust District Bureaucrats (1-4)|3.297|0.859|1|4|6933|
|TrustGovernment(1-4)|3.635|0.66|1|4|6932|

39
**Table 4. Social accountability training balance tests**

||(1)<br>Project Funds|(2)<br>Livestock<br>project|(3)<br>Project<br>start date|(4)<br>High<br>reported<br>corruption|(5)<br>Male|(6)<br>Can write|(7)<br>Can read|(8)<br>Distance to<br>s/c<br>headquarter|
|---|---|---|---|---|---|---|---|---|
|Training|152,054|0.002|-0.030|0.018|0.050***|0.051***|0.036**|0.760|
||[458,459]|[0.026]|[0.314]|[0.035]|[0.015]|[0.016]|[0.016]|[9.737]|
|Control means|20,700,000|0.729|38.38|0.33|0.425|0.563|0.563|81.642|
|N|895|895|812|846|5,915|5,913|5,914|5,840|
|R-squared|0.977|0.624|0.346|1.000|0.088|0.183|0.156|0.072|

Notes: This table reports a balance test for project and individual-level characteristics. Columns 1 to 4 are measured at the project level during the project assessment. Columns 5 to 8 are from the endline household survey and represent participant characteristics that were not expected to change due to the treatment. Standard errors are reported in brackets below the coefficients. *** p< 0.01, ** p< 0.05, *p< 0.1.

40
**Table 5. Scorecard information balance tests**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Project Funds|Project start<br>date|High<br>reported<br>corruption|Male|Can write|Can read|Distance to<br>s/c<br>headquarter|
|Training|60,239|0.380|0.017|0.021|0.041|0.027|32.033|
||[59,693]|[0.484]|[0.058]|[0.025]|[0.027]|[0.028]|[25.551]|
|Scorecard|78,426|0.277|0.043|-0.030|0.004|-0.002|21.557|
||[71,073]|[0.574]|[0.067]|[0.028]|[0.028]|[0.029]|[15.474]|
|Training x scorecard|-8,340|-0.060|-0.026|0.025|0.013|-0.005|-42.160|
||[86,583]|[0.701]|[0.083]|[0.036]|[0.036]|[0.037]|[28.271]|
|Training + Scorecard +|0.028|0.213|0.560|0.478|0.023|0.437|0.415|
|interaction=0||||||||
|Training=Scorecard|0.770|0.838|0.661|0.051|0.156|0.282|0.396|
|Control means|11,700,000|38.16|0.271|0.441|0.615|0.622|87.804|
|N|574|542|528|3,853|3,851|3,853|3,797|
|R-squared|0.764|0.316|0.001|0.075|0.075|0.065|0.059|

Notes: This table reports a balance test for project and individual-level characteristics. Columns 1 to 4 are measured at the project level during the project audit. Columns 5 to 8 are from the endline household survey and represent participant characteristics that were not expected to change due to the treatment. Standard errors are reported in brackets below the coefficients. *** p< 0.01, ** p< 0.05, *p< 0.1.

41
|||**Ta**<br>|**ble 6. Projec**<br>|**t Score**<br>|||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Project ov|erall score|Quality|score|Quantity|score|Project no|t located|
|Training|0.135*|0.127|0.094|0.097|0.177**|0.164*|-0.003|-0.008|
||[0.071]|[0.085]|[0.074]|[0.084]|[0.074]|[0.089]|[0.012]|[0.011]|
|Training*non-livestock||-0.031||0.015||-0.053||-0.017|
|||[0.156]||[0.188]||[0.167]||[0.038]|
|Control means|-0.034|-0.034|0.012|0.012|-0.066|-0.066|0.027|0.027|
|N|859|859|867|867|863|863|895|895|
|R-squared|0.384|0.384|0.389|0.389|0.359|0.359|0.280|0.280|

Notes: This table reports the OLS regression results for the treatment effect on project-level outcomes. Odd columns are the total treatment effect, while even columns include an interaction with whether the project was livestock. The dependent variable in columns 1 and 2 is an aggregate index of columns 3 to 6. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

42
**Table 7. Components of livestock overall score at the beneficiary level**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Livestock<br>Score|Livestock<br>Quality<br>Score|Livestock<br>Quantity<br>Score|Animal<br>Bought at<br>The Correct<br>Age (0/1)|Animal Is an<br>Improved/Crossed/Hybrid<br>Breed (0/1)|Animal Is<br>Productive<br>(0/1)|Animal<br>Health by<br>Mean<br>Number of<br>Illnesses|Fraction of<br>Animals Not<br>Observed<br>(Dead/Stolen/Sold)|
|Training|0.067|0.035|0.125*|-0.014|0.020|-0.032|0.032|-0.045**|
||[0.080]|[0.077]|[0.073]|[0.040]|[0.013]|[0.042]|[0.025]|[0.018]|
|Control means|0.055|0.06|-0.029|0.354|0.161|0.448|0.892|0.184|
|N|5,061|5,127|5,471|5,524|5,515|5,127|5,127|5,524|
|R-squared|0.403|0.453|0.353|0.346|0.821|0.356|0.293|0.391|

Notes: This table reports the OLS regression results for the treatment effect on household-level outcomes for livestock projects only at the first endline survey. The dependent variable in column 1 is an aggregate index of columns 2 and 3. Column 2 is composed of columns 4 to 8. Column 3 is an indicator of the number of animals a participant received. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

43
**Table** **<u>8. Livestock score with scorecard information treatment for additional balance test</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Livestock<br>Score|Livestock<br>Quality<br>Score|Livestock<br>Quantity<br>Score|Bought at<br>The Correct<br>Age (0/1)|Animal Is<br>Improved<br>Breed (0/1)|Animal Is<br>Productive<br>(0/1)|Animal Health|Fraction of<br>Animals Dead,<br>Stolen or Sold|
|Training|0.004|-0.065|0.117|-0.006|0.024|0.117**|-0.009|-0.018|
||[0.120]|[0.119]|[0.096]|[0.060]|[0.024]|[0.059]|[0.029]|[0.024]|
|Scorecard|0.054|-0.124|0.172|0.024|-0.025|-0.095|-0.061|0.023|
||[0.141]|[0.137]|[0.122]|[0.072]|[0.021]|[0.072]|[0.042]|[0.029]|
|Training x scorecard|0.072|0.141|0.006|-0.027|0.001|0.161*|0.067|-0.022|
||[0.175]|[0.168]|[0.168]|[0.087]|[0.028]|[0.089]|[0.046]|[0.034]|
|Control Mean|0.052|0.101|-0.061|0.323|0.188|0.442|0.917|0.155|
|N|3,486|3,517|3,753|3,775|3,769|3,517|3,517|3,775|
|R-squared|0.402|0.459|0.357|0.337|0.826|0.351|0.246|0.380|

Notes: This table reports the OLS regression results for the treatment effect on household-level outcomes for livestock projects only at the first endline survey. In this table, we have included the scorecard interaction. As the scorecard treatment was not conducted until after this data collection, this analysis is presented as a balance test for the scorecard treatment. Note that the scorecard was conducted on a subsample of the livestock projects. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

44
# **Table** **<u>9. Endline household animals and assets</u>**

||All pr<br>|ojects<br>||Livestock<br>|projects<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Cattle|All assets|Cattle|All assets|Cattle|All assets|
|Training|0.753|0.275***|0.310**|0.152*|0.128|0.089|
||[0.515]|[0.080]|[0.157]|[0.081]|[0.208]|[0.120]|
|Scorecard|||||0.097|0.025|
||||||[0.223]|[0.135]|
|Training x scorecard|||||0.382|0.155|
||||||[0.349]|[0.172]|
|Total interaction effect|||||0.607**|<br>0.306**|
|Training + Scorecard + interaction=0|||||0.017|0.030|
|Training + interaction=0|||||0.058|0.039|
|Scorecard + interaction=0|||||0.055|0.093|
|Training=Scorecard|||||0.877|0.576|
|Control means|2.262|-0.181|2.14|-0.219|2.237|-0.060|
|N|5,909|5,831|4,214|4,149|3,851|3,791|
|R-squared|0.044|0.249|0.091|0.209|0.085|0.134|

Notes: This table reports the OLS regression results for the treatment effect on household-level animal and total asset outcomes at the final endline survey. Columns 1 and 2 are for the full sample. Columns 3 and 4 are for all the livestock projects in the sample. Columns 5 and 6 is only communities in the scorecard sample. All analysis includes only direct program beneficiaries and the top 0.5% of responses have been trimmed. Standard errors are reported in brackets below the coefficients. Regressions include sub-county controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

45
# **Table 10.** **<u>Community monitoring</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||<br>Index for<br>Intensity of<br>Project<br>Community<br>Monitoring|<br>Index for<br>Intensity of<br>Social<br>Accountability<br>Committee<br>Project<br>Monitoring|<br>Reporting<br>NUSAF-related<br>issues to LC1|<br> <br>Reporting<br>NUSAF-related<br>issues to<br>Subcounty|<br>Reporting<br>NUSAF-<br>related issues<br>to District|<br>Reporting<br>NUSAF-<br>related issues<br>to IG|<br>Reporting<br>NUSAF-<br>related issues<br>(total)|
|Training|-0.047|0.404***|0.058|0.069|0.075*|0.139***|0.340**|
||[0.139]|[0.112]|[0.051]|[0.048]|[0.043]|[0.044]|[0.162]|
|Scorecard|||0.095*|0.063|0.102**|0.155***|0.415**|
||||[0.055]|[0.056]|[0.049]|[0.050]|[0.180]|
|Training x scorecard|||-0.038|-0.023|-0.041|-0.063|-0.163|
||||[0.071]|[0.067]|[0.059]|[0.063]|[0.224]|
|Training + Scorecard + interaction=0|||0.018|0.023|0.002|0.000|0.000|
|Training=Scorecard|||0.443|0.910|0.511|0.712|0.616|
|Control means|3.174|0.959|0.681|0.540|0.341|0.256|1.816|
|N|559|574|3,850|3,848|3,850|3,849|3,852|
|R-squared|0.380|0.454|0.168|0.172|0.168|0.185|0.210|

Notes: This table reports the OLS regression results for the treatment effect on project- and individual-level outcomes for the training and scorecard information samples. The sample comprises all individuals, including those that are not direct beneficiaries. Columns 1 and 2 are at the project level and were collected during the first assessment survey. Columns 3 to 7 were collected at the individual level at the final household survey. Column 7 is an aggregate measure of columns 3 to 6. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

46
**Table 11. Community monitoring**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||How would you<br>rate the<br>performance of<br>the CPC overall?|How would<br>you<br>rate the<br>performance<br>of<br>the CPMC<br>overall?|How would<br>you<br>rate the<br>performance<br>of<br>the CPMC<br>overall?|How likely<br>would<br>you be to choose<br>the same<br>subproject<br>management<br>committee again?|It is easy to get<br>many<br>members of the<br>community to come<br>together to solve<br>issues in the<br>community|It can be hard to get<br>members of the<br>community to come<br>together to solve<br>issues in the<br>community|
|Training|0.013|-0.005|0.036|-0.015|0.094*|-0.037|
||[0.037]|[0.036]|[0.047]|[0.065]|[0.057]|[0.059]|
|Scorecard|-0.066|-0.047|-0.094|-0.022|-0.028|0.023|
||[0.043]|[0.041]|[0.063]|[0.077]|[0.072]|[0.074]|
|Training x scorecard|0.048|0.020|0.008|-0.047|0.016|-0.095|
||[0.055]|[0.053]|[0.074]|[0.094]|[0.088]|[0.091]|
|Training + Scorecard + interaction=0|0.892|0.381|0.294|0.210|0.152|0.068|
|Training=Scorecard|0.035|0.259|0.013|0.901|0.048|0.352|
|Control means|3.441|3.488|3.512|3.469|3.046|2.402|
|N|3,613|3,710|3,852|3,845|3,851|3,848|
|R-squared|0.148|0.144|0.176|0.094|0.119|0.117|

Notes: This table reports the OLS regression results for the treatment effect on individual-level outcomes at the final endline survey using the training and scorecard information samples. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

47
**Table 12. Trust in leaders, local officials, and government**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Project<br>Leaders|LC3<br>Chairperson|Sub-county<br>Bureaucrats|LC5<br>Chairperson|District<br>Bureaucrats|Central<br>Government|
|Training|0.008|0.062|0.080|0.033|0.040|0.105***|
||[0.044]|[0.059]|[0.049]|[0.063]|[0.058]|[0.037]|
|Scorecard|-0.009|0.023|0.013|0.030|-0.033|0.094**|
||[0.053]|[0.067]|[0.057]|[0.074]|[0.066]|[0.043]|
|Training x scorecard|-0.054|-0.095|-0.127*|-0.061|-0.095|-0.129**|
||[0.064]|[0.083]|[0.070]|[0.093]|[0.085]|[0.052]|
|Training + Scorecard + interaction=0|0.215|0.866|0.470|0.964|0.107|0.059|
|Training=Scorecard|0.687|0.519|0.177|0.964|0.210|0.765|
|Control means|3.693|3.225|3.382|3.044|3.377|3.647|
|N|3,845|3,836|3,822|3,808|3,837|3,831|
|R-squared|0.119|0.141|0.097|0.083|0.102|0.113|

Notes: This table reports the OLS regression results for the treatment effect on individual-level outcomes at the final endline survey. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

48
# **Table 13. Heterogeneity** **<u>by level of reported corruption</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||High reported c|orruption|Low reported c|orruption|
|Training||0.792||-0.126|
|||[0.525]||[0.225]|
|Scorecard||0.370||0.064|
|||[0.507]||[0.265]|
|Training x scorecard|1.186***|0.488|0.309|0.331|
||[0.407]|[0.644]|[0.296]|[0.462]|
|Training + Scorecard + interaction=0||0.006||0.354|
|Training=Scorecard||0.267||0.465|
|Control means|2.303|2.436|2.233|2.22|
|N|1,011|1,011|2,550|2,550|
|R-squared|0.137|0.141|0.063|0.064|

Notes: This table reports the OLS regression results for the treatment effect on household-level animal outcomes at the endline survey. The sample only includes the social accountability training and scorecard information samples. The sample is split by the whether the sub-county is perceived to have issues of corruption, as reported to the research team during a survey of local officials. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

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|**Table 14. Spillovers across **|**communities**<br>|
|---|---|
|Training x scorecard|0.525**<br>[0.226]|
|Intensity of treatment|23.241<br>[51.952]|
|Control means|2.207|
|N|3,851|
|R-squared|0.085|

Notes: This table reports the OLS regression results for the treatment effect on household-level cattle outcome at the endline survey. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

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**Table 15.** **<u>Spillovers of reporting issues to officials within communities</u>**

||(1)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to LC1|(2)<br>Reporting<br>other (non-<br>NUSAF) to<br>Sub-county|(3)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to<br>District|(4)<br>Reporting<br>other (non-<br>NUSAF)<br>issues to IG|(5)<br>Reporting<br>other<br>(non-<br>NUSAF)<br>issues<br>(total)|
|---|---|---|---|---|---|
|Training|0.002|0.007|0.023**|0.018**|0.018|
||(0.025)|(0.019)|(0.010)|(0.009)|(0.029)|
|Scorecard|0.046|0.018|0.042***|0.036***|0.080**|
||(0.032)|(0.025)|(0.013)|(0.013)|(0.036)|
|Training x scorecard|-0.024|0.013|-0.040**|-0.039**|-0.059|
||(0.037)|(0.030)|(0.016)|(0.015)|(0.043)|
|Training + Scorecard + interaction=0|0.327|0.064|0.011|0.089|0.169|
|Training=Scorecard|0.099|0.587|0.121|0.105|0.048|
|Control means|0.214|0.132|0.045|0.023|0.234|
|N|3,810|3,810|3,809|3,809|3,853|
|R-squared|0.130|0.093|0.065|0.057|0.121|

Notes: This table reports the OLS regression results for the treatment effect on household-level outcomes at the final endline survey. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

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