GiveDirectly

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Published: November 2017

Summary

What do they do? GiveDirectly (givedirectly.org) transfers cash to households in developing countries via mobile phone-linked payment services. It targets extremely low-income households. (More)

Does it work? We believe that this approach faces an unusually low burden of proof, and that the available evidence supports the idea that unconditional cash transfers significantly help people. GiveDirectly has a track record of effectively delivering cash to low-income households. GiveDirectly has one major randomized controlled trial (RCT) of its impact and several more RCTs in progress. (More)

What do you get for your dollar? The proportion of total expenses that GiveDirectly has delivered directly to recipients is approximately 82% overall. This estimate averages across multiple program types and relies on several rough assumptions about what costs to include and exclude. (More)

Is there room for more funding? We believe that GiveDirectly is highly likely to be constrained by funding next year. It expects to use additional funding primarily for standard cash transfers and to unlock grants for projects in new countries from other funders who require GiveDirectly to provide matching funding for their contributions. We estimate that GiveDirectly could use more than $200 million in additional funding in 2018-2019. (More) GiveDirectly is recommended because of its: • Focus on a program with a low burden of proof and a strong track record. (More) • Strong process for ensuring that cash is well-targeted and consistently reaches its intended targets. (More) • Documented success in transferring a high portion of funds raised directly to recipients. (More) • Standout transparency (more). • Room for more funding. We believe that GiveDirectly can use substantial additional funding productively. (More) Major open questions include: • While GiveDirectly has one major RCT of its activities in Kenya, there is still limited evidence on the impact of the type of transfers (large, one-time transfers; and, going forward, unconditional long-term income transfers) that GiveDirectly generally provides, particularly the long-term impact of such transfers. There are currently several ongoing experimental evaluations of GiveDirectly's programs, including a long-term RCT. • We believe GiveDirectly's basic income guarantee program is likely less cost-effective than GiveDirectly's standard cash transfer campaigns. In 2016-2017, GiveDirectly chose to fundraise extensively for the basic income study, and some donors who previously supported standard cash transfers supported the basic income project instead. GiveDirectly had a large funding gap for standard cash transfer campaigns in 2017 relative to growth projections. It is unclear if these donors, particularly the largest donors, would have continued to support standard cash transfers in the absence of the basic income project. Our review process To date, our review process has consisted of • Regular (~3-6 times per year) conversations with GiveDirectly staff. • Reviewing documents GiveDirectly sent in response to our queries. • In November 2012, we visited GiveDirectly's operations in Kenya, where we met with beneficiaries of its work and spoke with its local field staff. • In 2014, we retained a journalist to visit GiveDirectly in Kenya. We published his report on our blog. • In October 2014, we visited GiveDirectly's operations in Uganda, where we met with beneficiaries of its work, spoke with local field staff, and observed a cash out day (a cash out day is when a mobile money agent makes a scheduled visit to village that has received transfers by phone from GiveDirectly). Note that in 2017, we decided that since we had followed GiveDirectly for several years and did not have major outstanding questions for them, we would limit the extent of our updating of this review (which we noted in a March 2017 blog post). We asked for key pieces of information on their finances, monitoring results, and room for more funding, and had regular phone calls with GiveDirectly in an attempt to learn about operational changes that might lead us to ask additional questions. There are details that we have for previous years that we did not seek updates on in 2017, as we did not feel that that would be a good use of GiveDirectly or GiveWell's time. All content on GiveDirectly, including updates, blog posts and conversation notes, is available here. We have also published a page with additional, detailed information on GiveDirectly to supplement some of the sections below. What do they do? Overview GiveDirectly transfers cash to poor households in developing countries primarily via mobile phone-linked payment services. It has operated since 2009 and is currently active in Kenya, Uganda, and Rwanda (launched in October 2016).1 To date, GiveDirectly has primarily provided large, one-time transfers. It recently started a basic income guarantee program, in which recipients will receive long-term (over two or twelve years in the initial study), ongoing cash transfers sufficient for basic needs (more). GiveDirectly's work of providing cash transfers to poor households may also include: • Experimentation: GiveDirectly runs or participates in studies on a) the impact of cash transfers and b) the costs and benefits of various program designs, with the goal of improving its own cash transfer program, improving other cash transfer programs, or encouraging the creation of new programs. (More) • Partnership work: GiveDirectly pursues opportunities to partner with other organizations on cash transfer projects. Through these projects, GiveDirectly aims to encourage the evaluation of aid projects (often by using cash transfers as a standard of comparison) and ultimately influence funders to move resources from less effective aid programs to more effective ones. (More) We discuss GiveDirectly's experimentation and partnership work to some extent below, but most of our review focuses on its direct impact, rather than the experimentation or policy impact its programs might have. We focus on direct impact because of the difficulty of predicting the impact of experimentation and partnership work without a demonstrable track record of past success. In 2014, three members of GiveDirectly's board, including founders of the organization, started and are partial owners of a for-profit company, Segovia, which develops software that NGOs and developing-country governments can use to help implement their cash transfer programs. GiveDirectly pays for use of Segovia's software. We discuss the potential for conflicts of interest on our page with additional information about GiveDirectly. Below, we discuss: • The structure of GiveDirectly's transfers • GiveDirectly's process for identifying recipient households and delivering cash transfers • GiveDirectly's staff structure • GiveDirectly's experimentation work • GiveDirectly's work on partnerships Standard cash transfer program Grant size GiveDirectly's standard model involves grants of approximately$1,000 (USD) delivered over several months in multiple payments. We estimate that the average family receives $288 per capita from GiveDirectly, which is 121% of baseline annual consumption per capita for recipients in Kenya. More on GiveDirectly's grant structure can be found on our page with additional information about GiveDirectly. Process GiveDirectly's typical process is as follows: 1. Local area selection: Select local region and then villages based largely on poverty rates. 2. Census: Conduct a census in each village of all households. 3. Registration: Send a separate team to register eligible households. This includes a) helping recipients set up a payment system to receive transfers (if they don't already have such a system in place), and b) collecting an additional round of data from the household that can be checked against the initial data from the census. 4. Audit: Some households are flagged for audit based on discrepancies collected in the previous steps and are revisited to collect additional data. 5. Transfers sent: GiveDirectly sends transfers to recipients via mobile money providers (more). 6. Follow up calls: GiveDirectly field staff make multiple phone calls and, for vulnerable recipients, in-person visits, to all recipients as transfers are being sent to ask various questions about recipients' experiences. In addition to the follow-up calls, GiveDirectly maintains a phone "hotline" for recipients to call if they have any questions about the transfers or issues in obtaining funds. More detail on the above process can be found on our page with additional information about GiveDirectly. We have reviewed (and made public) data collected during each step of the enrollment process for most of GiveDirectly's campaigns, with deletions to preserve anonymity.2 Staff structure In its countries of operation, GiveDirectly's programs are overseen by a Chief Operating Officer International (COO-I), Country Directors (CDs) and Field Directors (FDs). Day-to-day operations are overseen by Field Managers and Associate Field Managers, who focus on quality control, management, training of Field Officers, logistics, and management of Field Officers. Field Officers (FOs) implement the steps required on the ground to enroll and follow up with households. They have the most face-to-face interaction with recipients and are all hired within the country of the transfers. There are separate groups of FOs for census and registration. FOs are also hired to conduct audits and follow-up surveys with recipients post-transfers; some of the FOs hired for these roles may have previously worked on the census or registration phases. More on GiveDirectly's staff structure can be found on our page with additional information about GiveDirectly. Evaluation and experimentation GiveDirectly's goals for experimentation include increasing the evidence base for cash transfers, improving recipient returns and welfare (both in GiveDirectly's program and others), and developing capabilities necessary to implement larger-scale programs or programs in new contexts.3 When choosing which evaluations to run, GiveDirectly also considers the potential impact on policymakers.4 In 2017, GiveDirectly told us that a very high proportion of recipients are part of research studies.5 See this spreadsheet for a full list of GiveDirectly experimentation projects. Below we discuss a few selected projects that are of greatest interest to us. RCT of GiveDirectly's Rarieda campaign Innovations for Poverty Action (IPA) conducted a randomized controlled trial (RCT) of GiveDirectly's program in which eligible households were selected randomly to receive cash transfers.6 These transfers were made in Rarieda, Kenya in 2011-2012.7 GiveDirectly publicly provided the plan for collecting and analyzing data to determine the impact of these transfers. The RCT has been published; we discuss it in detail here. Macroeconomic effects Based on conversations with policymakers, GiveDirectly found that a key question relevant to government cash transfer programs is the impact they have on macroeconomic factors such as inflation and job creation.8 GiveDirectly is working to conduct an RCT examining the macroeconomic effects of GiveDirectly's program in Kenya.9 Details of the study are in this footnote.10 Endline data collection was expected to be completed by the end of 2016; as of November 2017, GiveDirectly expected to share selected results with us by late 2017 or early 2018.11 Basic income guarantee study Note that we last updated this section in late 2016. As of this writing in August 2017, the basic income trial had not yet started – it is scheduled to start in the fall of 2017. We have chosen to wait until the trial is underway to ask GiveDirectly for an update on the study design. As of August 2017, GiveDirectly had raised about$26.6 million out of the $30 million it would need to run the trial with the intended sample size.12 GiveDirectly is planning to begin a study of providing long-term, ongoing cash transfers sufficient for basic needs ("basic income guarantee") in 2017; it launched a pilot of the program in October 2016.13 The study is expected to include approximately 30,000 individuals and provide a basic income for either 2 or 12 years to every adult enrolled (more details in footnote).14 The income will likely be close to$0.75 per day.15 GiveDirectly may solicit input from recipients when determining the timing of the basic income transfers; GiveDirectly suspects most recipients will want to receive larger, more infrequent payments.16

GiveDirectly told us that recently policymakers, academics, and others have shown an increased interest in universal basic income experiments and GiveDirectly believes the project could have significant policy impact.17 We and GiveDirectly believe that the direct impact of the program (excluding any potential policy impact) is likely to be less cost-effective than GiveDirectly's standard campaign (more on our page with additional information about GiveDirectly).18

Partnership work

GiveDirectly has been exploring projects with a number of partners. The projects that GiveDirectly has partnered on or considered generally involve implementing cash transfers as part of a study funded by an institutional partner. GiveDirectly has also provided informal advice to those considering cash transfer programs. For a sample of smaller potential partnership projects that GiveDirectly has considered, see this footnote.19

GiveDirectly has signed an agreement for one partnership project (in Rwanda; costing $4 million) and a memorandum of understanding (MOU) to consider additional projects (GiveDirectly and its partner may each spend up to$15 million).20 The projects will involve running studies to test other interventions against cash transfers or to measure the impact of cash transfers in different contexts (see our page with additional information on GiveDirectly for details).

Additionally, GiveDirectly has told us that it has made progress in conversations with several other institutional funders about potential projects.21

We discuss the question of whether GiveDirectly has a broader impact on the international aid sector through its experimentation and partnership work below, and below we discuss the cost-effectiveness of partnership projects and how additional funding would affect its discussions with potential partners.

Does it work?

This section discusses the following questions:

• Generally speaking, are unconditional cash transfers a promising approach to helping people? We believe that this approach faces an unusually low burden of proof and that the available evidence is consistent with the idea that unconditional cash transfers help people.
• How effective and well-founded are GiveDirectly's criteria? The evidence we have suggests that GiveDirectly targets low-income recipients. GiveDirectly has recently transitioned to making transfers to 100% of households in selected villages in two of its three countries of operation.
• Is GiveDirectly effectively targeting people who meet its criteria? We believe GiveDirectly's enrollment process is a relatively effective way of targeting people who meet its criteria, although we note that GiveDirectly has experienced difficulties recently with some people in certain geographic areas refusing to enroll in its program.
• Does GiveDirectly have an effective process for getting cash to recipients? GiveDirectly's process seems to have been successful so far, with two notable exceptions—we find it encouraging that GiveDirectly was able to detect and respond to these cases.
• How do recipients spend their cash, and how does this spending impact their lives? We present a variety of evidence, including findings from a randomized controlled trial of GiveDirectly's work.
• Are the size and structure of the cash transfers well-thought-through and appropriate? We find GiveDirectly's approach to be defensible, but we look forward to seeing the results of GiveDirectly's experimentation with different approaches in the future.
• Are there negative or other offsetting impacts? GiveDirectly has taken some measures to address this question, and we believe that the evidence so far suggests that while the cash transfers do lead to some problems, these problems are relatively minor.
• Does GiveDirectly have a broader impact on the international aid sector? We have chosen not to look at this question in depth. We have not seen compelling evidence that GiveDirectly has significantly affected the behavior of funders or other organizations, although GiveDirectly has shared some qualitative evidence that we have not followed up on.

Generally speaking, are unconditional cash transfers a promising approach to helping people?

We discuss this question more extensively in our report on cash transfers. In brief:

• The evidence most relevant to GiveDirectly comes from an RCT of a GiveDirectly campaign (available here). We discuss the findings of this RCT in our cash intervention report.
• Cash transfers are among the best-studied development interventions, though questions remain. Studies generally show substantial increases in short-term consumption, especially food, and little evidence of negative impacts (e.g., increases in alcohol or tobacco consumption). It is important to note that most of these studies are of "income transfers" (relatively small, ongoing payments); there is more limited evidence for programs with "wealth transfer" (relatively large, one-time transfers) models like GiveDirectly's. This is one of the reasons that we are particularly interested in GiveDirectly experimenting with and evaluating different approaches.
• There is also some evidence that recipients are able to invest cash transfers at high rates of return (e.g., ~20% per year), leading to long-term increases in consumption.
• We feel that this intervention faces an unusually low burden of proof, given that short-term poverty reduction is an outcome by definition, though donors' intuitive reactions to it may vary widely.

How effective and well-founded are GiveDirectly's eligibility criteria?

Starting in 2017, GiveDirectly began transitioning, in Uganda and Kenya, from a model in which it targets households based on eligibility criteria to a model in which it enrolls all households in selected villages. In particular:22

• In Uganda, GiveDirectly switched from targeted enrollment to enrolling all households in selected villages in June 2017, with the exception of villages involved in an ongoing research project.
• In Kenya, as of late 2017, GiveDirectly's standard cash transfers were on hold as it prepared to start the basic income project. It expects to begin enrolling all households in selected villages when it restarts standard cash transfers. The basic income project enrolls all adults in selected villages.
• In Rwanda, the government required GiveDirectly to use a targeted approach.

Previously in Kenya and Uganda and currently in Rwanda, GiveDirectly has used three different sets of eligibility criteria for its standard campaigns:

• Poverty Probability Index (PPI): In Rwanda, GiveDirectly uses PPI, an independently-created tool managed by Innovations for Poverty Action. The tool uses ten questions to generate a PPI score.23 GiveDirectly notes, "Recipients enrolled using this approach in 2017 scored, on average, 21.5 (mapping to $0.38/day median consumption level), whereas ineligible households scored, on average, 44.3 (mapping to$0.56/day median consumption level)."24 We have not vetted the PPI methodology.
• Assets and vulnerability status: This approach, which GiveDirectly previously used in Kenya, uses an algorithm to determine eligibility; the algorithm uses a number of inputs related to household assets and the vulnerability of recipients.25 GiveDirectly developed this algorithm after testing a number of new potential criteria.26
• Housing materials: GiveDirectly previously used this approach in Uganda and Kenya.27 Households are selected based on housing materials, enrolling households who live in a house made of organic materials (thatched roof, mud walls, and a mud floor) and excluding households with iron roofs, cement walls, or cement floors.28

As part of the first RCT of GiveDirectly's program, during which the housing materials selection criteria were used, researchers collected in-depth information on poverty levels of recipients and found that those selected were extremely poor on a number of measures, including that half of adults skip meals and average per capita daily consumption is $0.65 at nominal rates.29 In a later study, GiveDirectly found that recipients in Uganda had a slightly higher per capita daily consumption of$0.83.30

Anecdotal evidence from GiveWell's site visit to Kenya

In November 2012, GiveWell staff visited Kenya to view GiveDirectly's program in the field. See our notes and photographs from the site visit. We visited five locations (three in Siaya and two in Rarieda) where GiveDirectly had transferred funds or was in the process of enrolling recipients to receive funds. We visited approximately 15 households across the five locations (including two non-recipient households with metal roofs and cement walls and floors that did not qualify for GiveDirectly's program). For details on how homes we visited were selected, see this footnote.31 Note that when we visited, GiveDirectly was using thatched roofs and mud building materials as its criteria.

We would characterize the ~15 households we visited (as well as other households we saw while walking but did not speak with directly) as extremely poor. We summarize characteristics of these households in this footnote.32

Note that among the households we visited, many had already received part or all of their transfer from GiveDirectly, so our observations are based on a selection of households that include some newly-built or renovated structures in addition to older structures. Given that some of the recipients we met used transfers to build larger houses or buy livestock, our observations would likely over-estimate the assets of each household pre-transfer.

In addition, the homes we saw from afar in villages we visited and homes we passed while driving in the area appeared to be at a similar level of extreme poverty.

GDLive

In 2016, GiveDirectly launched GDLive (https://live.givedirectly.org/), an online tool for donors to read recipients' answers to questions about their lives and their reactions to receiving cash transfers from GiveDirectly. Recipients responses are only posted if they opt in to sharing them online. Responses are unedited and include answers to such questions as "What is the biggest hardship you've faced in your life?", "What is the happiest part of your day?" and "What did you spend the payment you received on?"

GiveDirectly notes that, "We’re not editing or curating the feedback to make sure it’s happy, or fits a narrative. We’re letting it hit your screen the moment it hits our database."33 We'd guess that the profiles are reasonably representative of GiveDirectly's recipients, though we haven't followed up with GiveDirectly about questions such as 'are all recipients invited to participate?' and 'what portion opt in to participating?'

Is GiveDirectly effectively targeting people who meet its criteria?

As noted above, GiveDirectly is no longer using targeting criteria in Kenya or Uganda. Therefore, the rest of this section is primarily applicable to GiveDirectly's past operations in Kenya and Uganda and ongoing work in Rwanda.

GiveDirectly's process for identifying and enrolling households is described in short above and in more detail on our page with additional information about GiveDirectly. It involves multiple unannounced visits by different staff to each recipient home in order to confirm that recipients meet the criteria. We have examined data collected by GiveDirectly from its enrollment process (registration, remote checks, and audits) for most transfer campaigns through 2014. In 2015 and 2016, we spot-checked the data GiveDirectly shared with us. In 2017, we requested only summary data on a few key metrics, rather than household-level data.34

We believe GiveDirectly's current process to be generally effective at identifying households that meet its criteria. In mid-2017, GiveDirectly eliminated a step in the process, the "backcheck," in which staff would revisit households after they had been registered to verify their identities, but it will continue to audit a portion of registered households. GiveDirectly audited 58% of registered households in Kenya in the first quarter of 2017; we have not seen recent data on the proportion of households audited in Uganda or Rwanda.35 GiveDirectly reports that in 2017, before back checks were eliminated, roughly 2% of registered households were removed after each the back check and the audit stages.36

In 2016 and 2017, GiveDirectly encountered high refusal rates – i.e., households declining to participate – in the Homa Bay region in Kenya (more on our page with additional information about GiveDirectly).

Does GiveDirectly have an effective process for getting cash to recipients?

Mobile money providers and distribution models

GiveDirectly transfers funds to recipients through mobile money providers. In Kenya, the mobile money provider, M-PESA, allows users to receive, send, deposit, and withdraw funds on their mobile phones. When withdrawing funds, recipients must present ID along with their mobile phone number and a user-specified M-PESA PIN number to an M-PESA agent.37 Users enter the amount they want to withdraw on their own phone, and after each transaction, they can see their remaining balance, reducing the ability of agents to defraud clients of funds.38 GiveDirectly has told us that recipients are generally able to withdraw cash from mobile money agents located in or near their villages.39 Recipients must pay a small fee when they withdraw a portion of their transfer (around 1% for large withdrawals, and higher for small withdrawals).40

GiveDirectly works with a mobile money provider called MTN in Uganda.41 MTN has similar security measures to M-PESA: a user must present ID to an agent before making withdrawals, provide their phone or SIM card, and enter their PIN number. Users must pay a fee to withdraw, and confirmation messages are sent after withdrawals.42

In Uganda, the agent network is less robust; however, GiveDirectly has found that recipients are still able to withdraw cash from mobile money agents.43 GiveDirectly tracks the ability of recipients to withdraw cash in its follow-up surveys, in which it asks recipients if they have withdrawn their transfer, if they experienced any issues, and how long it took them to make the trip to withdraw.44

Additionally, the "coffee RCT" that GiveDirectly is running will be conducted in Uganda (more), and GiveDirectly intends to use data from this study as a more rigorous check on how easily recipients can withdraw their money in Uganda.45

We have not yet asked GiveDirectly for the details of how recipients access funds in Rwanda.

Staff fraud

GiveDirectly has discovered and written publicly about two cases of staff fraud in its Uganda program. We consider fraud to be an ongoing risk to the success of GiveDirectly's programs, but are encouraged that GiveDirectly's monitoring has allowed it to detect and respond to these cases.

As GiveDirectly scales, we would weakly expect greater awareness of its program and more attention to be paid to it by people outside of the villages in which it works.46 This could increase the risk of large-scale crime.47 GiveDirectly believes that additional security measures are unlikely to be particularly useful (details in footnote).48 In addition to harming recipients, crime would likely cause delays for GiveDirectly's work.

Uganda 2014

In mid-2014, two of GiveDirectly’s field staff colluded with mobile money agents to defraud recipients of funds. The staff and mobile money agents were able to steal a total of $20,500 in the form of$20-100 deductions from recipients' payouts.49 GiveDirectly found out about the fraud through follow-up calls to recipients, which were accelerated after a separate issue had been reported to GiveDirectly's hotline.50 GiveDirectly has taken multiple measures to address the vulnerabilities exposed by this case of fraud (see footnote for details).51

Are there negative or offsetting impacts?

Below, we discuss questions about the possible negative effects of cash transfers and GiveDirectly's operations. For more, see our site visit notes from our visit to GiveDirectly's operations Kenya in November 2012, during which we spoke with recipients and non-recipients about potential problems.

The RCT that Innovations for Poverty Action conducted of GiveDirectly's transfers in Rarieda found no significant effects of transfers on the rate of crime in treatment villages or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages.75

We have found very limited information about jealousy and conflict related to other cash transfer programs, but one study that found small levels of hostility towards recipients of an unconditional wealth transfer in Uganda is discussed in our cash transfer intervention report.

GiveDirectly has two primary mechanisms for tracking and resolving conflicts: its follow-up surveys and its hotline. GiveDirectly's follow-up surveys include questions like the following:76

• Have you heard complaints about GiveDirectly in your community? What complaints are you hearing? Who is upset/complaining? Who are they upset with?
• Has there been any shouting or angry arguments among people in your village about these transfers? If yes, describe.
• Has there been any violence, theft, or other crime in your village related to these transfers? If yes, describe.

Recipients can use GiveDirectly's hotline to report issues at any time. GiveDirectly told us in 2016 that its hotline service was not effectively responding to everyone who called in; it moved to a new system in early 2017.77

Data from follow-up surveys

GiveDirectly has sent us results from follow-up surveys conducted in multiple transfer campaigns. In 2016, we asked for a sample of recent follow-up survey data. GiveDirectly sent us a database covering 3,329 follow-up calls from late July to mid-August 2016 across its campaigns in Kenya, Uganda, and Rwanda.78 These data indicated that reported issues were low: 7% of recipients reported some regrets about how they spent their transfer, 2% reported hearing complaints, and 1% reported thefts.79 Note that GiveDirectly surveys only cash recipients, not non-recipients, and all data are self-reported.

In 2017, we asked for data on complaints made to GiveDirectly for the first quarter of the year during GiveDirectly's follow-up calls to recipients. GiveDirectly call center agents asked recipients whether they had heard complaints about GiveDirectly. In Kenya, recipients said they heard complaints in 5.1% of calls and most (68%) of the complaints were categorized as "program is evil/from the devil" (more on this issue here), while 15% were about the program being unfair (primarily in which households were included) and 10% were about jealousy from non-recipients. In Uganda, recipients said they heard complains in only 0.5% of calls and 91% of complaints were about unfairness and jealousy. We have seen only limited information on complaints in Rwanda; the data we have seen suggest that complaint rates are very low so far.80

Data from Kenya and Uganda for 2013-2015 are on our page with additional information about GiveDirectly.

Data from hotline calls
We have reviewed records of calls made to GiveDirectly's hotline from May 2012 – August 2015, which provide anecdotal evidence of tension and conflict caused by the cash transfers, according to recipients, including marital disputes, fraud committed by helpers, trustees, or family members, and Village Elders requesting funds from recipients.81 In the most recent complete hotline call data that we have seen (from October 201482), the most common type of adverse event recorded is household conflict, followed by theft.83 The number of issues reported was about 6% of the total households in the campaigns (though it is possible that single households account for more than one issue recorded).84

Do the cash transfers have negative effects on non-recipients?

We note that in two of the three countries that GiveDirectly works in, it is now enrolling all households in selected villages.

There is suggestive evidence that cash transfer programs may have moderate negative short-term effects on the well-being and economic outcomes (e.g., consumption, assets, and business revenue) of non-recipient households living in the same areas as similar households that receive transfers.85 However, the evidence for these effects primarily comes from studies of a variant of GiveDirectly’s program that may differ from its core program in important ways. GiveDirectly notes that even though it has not identified significant evidence of negative effects on non-recipients, it now generally avoids conducting experiments that randomize at the individual level, to avoid situations in which one eligible household receives transfers while a similarly situated neighbor does not.86

Do the cash transfers lead to more frequent or more serious criminal activity?

The RCT of GiveDirectly's transfers in Rarieda found no significant effects of transfers on the rate of crime in treatment villages.87 It is possible that cash transfers cause more serious crimes (in terms of damages) even if they do not cause more crimes; this seems plausible given that cash transfers create an influx of resources into villages. GiveDirectly notes that crime could become a more serious problem as its program becomes larger and more well-known, but GiveDirectly does not expect to see significantly higher rates of crime in the near future.88

Examples of attempted and/or successful criminal activity relating to GiveDirectly cash transfers include:

• People stealing cash and cellphones from recipient households89
• People contacting recipients and posing as GiveDirectly staff to defraud recipients of funds90
• Mobile money agents defrauding recipients of funds91
• GiveDirectly staff defrauding recipients of funds (we discuss one case of this above)

To mitigate the risk of small-scale crime, in its communications with recipients, GiveDirectly emphasizes ways that recipients can keep their mobile money accounts and phones secure.92 It does not communicate with recipients via text message and tells recipients of this policy in order to protect against mass attempts at fraud, and it follows up with recipients who report crimes to try to resolve the issues.93

For the first quarter of 2017 (the period for which we requested these data), 0.9% of recipients in Kenya reported having heard about or experienced theft. The figure was 0.1% in Uganda and 1.5% in Rwanda.94

Do grants distort incentives and decision-making?

We have not seen information on the question of whether individuals who live in the areas served by GiveDirectly change their behavior in order to increase their chances of receiving transfers – for example, by spending more time at home to increase their chances of being at home when GiveDirectly staff visit, or by choosing to live in poorer quality housing in hopes of receiving transfers.95 The one-off nature of transfers (recipients are not eligible for a second round of transfers) may help to mitigate these effects among past and current recipients, though there is information to suggest that some recipients believe transfers could be given again in the future.96

Another way in which grants may distort decision making is if they are promised and not delivered in time (causing people to make plans that cannot be executed). We do not have data directly addressing this issue, but GiveDirectly provides some statistics on the speed with which transfers are received.97 Cash deployment in 2017 (in the period for which we requested data on this metric) appears to have been quite slow, with 63% of recipients in Kenya and only 9% in Uganda receiving their first payment within 70 days of census. GiveDirectly notes that this was because it made a push in this period to make transfers to or, if necessary, write off commitments to recipients whose transfers had been delayed due to reasons such as registration issues and loss to follow-up. GiveDirectly reports that over 90% of recipients who were censused in 2017 received payments within 70 days of the census.98 For Rwanda, GiveDirectly notes, "Because of Rwandan government constraints in Q1, we did not make payments to recipients who had already been enrolled until Q2. [...] Q2 saw us resume enrollment and payments, and our 'Speed of Cash Deployments' metrics are more in line with expectations.99

GiveDirectly recently changed its model such that recipients cannot receive their next transfer until a GiveDirectly staff member has followed up with them about their previous transfers.100

Previously, GiveDirectly told us that in its Kenya campaigns the key factor determining when a recipient receives funds is when he or she registers for M-PESA; recipients are told that they will not receive transfers until they have registered.101 GiveDirectly's records of calls to its Kenya hotline demonstrate that some recipients are delayed in registering for M-PESA or collecting transfers due to issues outside of their control (e.g., a recipient's SIM number was already registered to someone else's M-PESA account; another recipient reported that an agent mistakenly claimed that the recipient's account had expired).102

In Uganda, the agent networks of mobile money providers are not as robust, which means that recipients must travel farther, on average, to reach an agent.103 This may hamper recipients' ability to execute plans for how and when to use funds.

Do grants distort local markets?

It seems possible to us that a large infusion of cash into an area could alter economic opportunities for both recipients and non-recipients. Such effects could be positive (for example, by spurring investment and job creation or by increasing the availability of retail goods) or negative (for example, by leading primarily to local inflation). The limited evidence addressing this issue in the RCT of GiveDirectly's program in Rarieda and the broader literature on cash transfers points to no distortion. There is an ongoing RCT of GiveDirectly's program that is testing for macroeconomic effects.

Do cash transfers lead to large increases in spending on alcohol and tobacco?

The RCT of GiveDirectly's program in Rarieda did not find an increase in spending on alcohol or tobacco. As discussed in our intervention report on cash transfers, RCTs of other programs that report spending on alcohol or tobacco find no impact on spending on these goods.

GiveDirectly recruits Field Officers through referrals from peer organizations, postings at universities, and job advertisements. The application process involves an interview with a Field Director and a language competency exam. GiveDirectly told us in 2013 that it was receiving approximately six times the number of resumes as openings for Field Officer positions.104 For field staff in Kenya, successful candidates generally have a college education and, as of 2013, the last time we checked, were paid approximately $12 per day, in addition to expenses for travel and lodging while working.105 GiveDirectly reported greater language heterogeneity in the areas in which it works in Uganda, which made it harder to hire qualified field staff who also had the necessary language skills.106 We have not asked GiveDirectly about its experiences hiring field staff in Rwanda. Because GiveDirectly continues to easily hire additional staff and its compensation seems roughly in line with market value, we do not see diversion of skilled labor as a serious concern. Does GiveDirectly have a broader impact on the international aid sector? One of the aims of GiveDirectly's partnership and evaluation work is to influence the broader international aid sector to use its funding more cost-effectively.107 We have not yet seen compelling evidence that GiveDirectly is causing significant shifts within the international aid sector, although GiveDirectly has noted that we might find conversations with some of its partners to be qualitatively persuasive.108 GiveDirectly has provided evidence that weakly suggests that the international aid sector is moving towards benchmarking programs against cash.109 However, it is difficult to understand what portion of that shift is attributable to GiveDirectly. Below, we describe the types of examples GiveDirectly has provided in support of its impact on the sector:110 • Anecdotally, GiveDirectly has heard that some large funders are asking themselves "Is this better than cash?" before making grants.111 Additionally, several large funders partnering with GiveDirectly (or in discussions for future partnerships) have told GiveDirectly that they are having internal policy conversations around the idea of benchmarking programs against cash, in large part due to GiveDirectly.112 • GiveDirectly believes there has been an increase in demand from policymakers for evidence that compares programs to cash.113 • GiveDirectly believes there has been an increase in the number of studies that include cash arms (and GiveDirectly was invited to implement the cash arms of several new evaluations).114 • Anecdotally, GiveDirectly has heard that several new cash transfer programs, new evaluations, and increased transparency practices were inspired by GiveDirectly.115 GiveDirectly believes that, by executing an excellent program, it may put competitive pressure on other implementers to also perform effectively.116 • GiveDirectly has provided informal advice to new cash programs and studies.117 • GiveDirectly has participated in several high-level panels and roundtables.118 • GiveDirectly is used as an example in trainings and university courses. We have created a spreadsheet with the examples of GiveDirectly's potential impact on the international aid sector that we are aware of. It was last updated in 2016. It is easier to evaluate GiveDirectly's role in causing unique projects to happen, as opposed to its impact on the broader sector. We believe that the Rwanda project, which caused large donors to give$4 million to a study that will benchmark an intervention against cash transfers, would not have occurred without GiveDirectly and the media attention that GiveDirectly has attracted.119

We would guess that a large portion of any sector impact attributable to GiveDirectly comes from the fact that GiveDirectly has functioned as a proof of concept for cash transfers. Because GiveDirectly has already shown that implementing cash transfers broadly is feasible, we are unsure whether or not additional growth would have a similar sector impact. It is possible that some activities, such as policy-relevant experimentation or partnership projects, could cause significant sector impact in the future; we have not looked in-depth at the impact of these activities (beyond the direct impact on recipients).120 We remain highly uncertain of our ability to determine how much these activities sway policymakers' or funders' decisions, even if we put substantial time and effort into the question.

GiveDirectly notes that its standard cash transfer campaigns could also contribute to sector impact by attracting additional attention which later leads to partnership projects or changes in funders' behavior.121 While this is plausible, we do not see any clear way to verify the suggested causal connection.

What do you get for your dollar?

What percentage of GiveDirectly's expenses end up in the hands of recipients?

Cash grants make up 81.5% of GiveDirectly's all-time incurred expenses.122

While we believe that this is a reasonable estimate of the percentage of funds that will reach recipient households in the future, it is an imperfect estimate in a few ways:

• Depending on GiveDirectly's future revenue, it may operate at a smaller or larger scale in the future, which would likely affect its cost structure.
• GiveDirectly has several different program types, which differ in their cost structures. GiveDirectly has told us that donations driven by GiveWell's recommendation are used for standard cash transfers (other than some grant funding from Good Ventures and cases where donors have specified a different use of the funds). GiveDirectly has told us in the past that a higher percentage of funds that are used for standard cash transfers are spent on transfers (89% across Kenya, Uganda, and Rwanda standard cash programs), than for the average dollar that GiveDirectly receives.123 This seems plausible to us, but we have not attempted to determine whether that is the case.
• It excludes costs incurred by external researchers to study GiveDirectly's programs, with one exception (details in footnote).124 We believe this is appropriate in some cases (where GiveDirectly would not have chosen to do the project if the research funds were instead given as an unrestricted grant to GiveDirectly, and where the study does not significantly contribute to our confidence in the program); there are other cases where we believe this decision is more questionable. We asked GiveDirectly for the information it had on hand about these costs. For most research projects, GiveDirectly told us that it was not involved in the fundraising or spending and had limited information, on hand, on total research costs. (We have not yet sought this information from GiveDirectly's research partners or asked GiveDirectly to do so.) Based on the information GiveDirectly was able to share, we have excluded at least $3.5 million in partners' research costs (though this includes some future costs) and likely the full amount is closer to double that.125 For comparison, GiveDirectly's total spending through July 2017 was$95 million; including, for example, $4 million in additional research costs126 would decrease the portion of funding that has reached households to 78%.127 • It excludes costs of following up with households who have received transfers recently. • It includes fundraising costs that are expected to generate revenue in the future. A breakdown of GiveDirectly's spending from August 2016 to July 2017 is in GiveDirectly financial summary through July 2017. A breakdown of funding through July 2016 is on our page with additional information about GiveDirectly. Does GiveDirectly offer a large amount of humanitarian impact per dollar? We have not conducted a cost-effectiveness analysis that attempts to quantify the benefits of cash transfers in humanitarian terms. Instead, in comparing cash transfers to the interventions conducted by our other top charities, we have attempted to monetize some of the benefits of the latter, in particular the “developmental effects” of deworming, insecticide-treated nets and seasonal malaria chemoprevention. (In the case of the comparison with the two malaria prevention programs, for instance, this means quantifying the estimated impact of malaria prevention programs on later-in-life income of children through a comparison with the effects of deworming, and then subjectively comparing the cost per life saved with the value of that amount of money as a cash transfer.) In practice, these calculations are highly sensitive to assumptions, especially regarding: • the investment returns to cash transfers; • how much confidence one places in the developmental impacts of deworming; and • the subjective assessment of the relative value of averting deaths and improving incomes. We guess that in purely programmatic terms, and given our values, distributions of insecticide-treated nets, seasonal malaria chemoprevention, and deworming are all more cost-effective than cash transfers. However, we think there are plausible values for these assumptions that would permit any ordering of these three programs. We encourage readers who find formal cost-effectiveness analysis important to examine the details of our calculations and assumptions, and to try putting in their own values. To the extent that we have intuitive preferences and biases, these could easily be creeping into the assumption- and judgment-call-laden work we’ve done in generating our cost-effectiveness figures. Our full cost-effectiveness model is available here. See also, our 2012 discussion of the cost-effectiveness of cash transfers and other interventions. Are there significant differences in cost-effectiveness between GiveDirectly's various types of programs? On our page with additional information about GiveDirectly, we discuss how the cost-effectiveness of GiveDirectly's basic income guarantee program may differ from that of its standard cash transfers. In the section below, we discuss the possibility that future funding to GiveDirectly may be used to support programs in which GiveDirectly partners with a government aid agency or other institutional funder to co-fund a cash transfer project. These projects would mostly take place in countries GiveDirectly has not worked in before. There are several ways in which these programs could be more or less cost-effective than GiveDirectly's standard cash transfers: 1. They would generate additional revenue for GiveDirectly that otherwise likely would have gone to activities other than cash transfers—these other activities could be more or less cost-effective than cash transfers, though given the relatively few giving opportunities that we prefer to cash transfers, we'd guess that in most cases we'd consider this reallocation to be positive. 2. Such partnership programs may be more expensive to administer and/or serve populations that can benefit more or less from cash transfers compared with the populations GiveDirectly has served in the past. 3. By demonstrating the value of cash transfer programs to institutional funders, partnership projects could lead to significantly more funding for cash transfer programs in the future. Possibility (3) is likely the most important for cost-effectiveness, as the institutional funders GiveDirectly is in conversations with control very large amounts of funding, and even a fairly small possibility of a modest percentage change in how much these funders allocate to cash transfers would imply that partnership projects are highly cost-effective. But estimating the expected value of possibility (3) relies on several poorly-informed guesses, and we do not feel that we can create a reasonable estimate at this time. Is there room for more funding? We believe that GiveDirectly could effectively use more funding than it expects to receive and is very likely to be constrained by funding next year. We have completed only a rough estimate of GiveDirectly's room for more funding because over the past few years, GiveDirectly's capacity to deliver cash transfers has far exceeded the amount of funding it has received (including funding due to GiveWell's recommendation of GiveDirectly as a top charity). We expect this to continue to be the case and our rough estimate for 2018-2019 is consistent with that expectation. In short, we have considered: • Total opportunities to spend funds productively: Roughly, GiveDirectly believes it could spend$120 million in 2018 ($70 million on standard cash transfers and$50 million on partnership projects) and $200 million in 2019 ($100 million on each standard transfers and partnership projects), if it had sufficient funding to do so. Partnership projects are dependent on sufficient interest from partners, in addition to available funding.
• Cash on hand: GiveDirectly expects to commit all current funding (as of September 2017) to cash transfers by the end of 2017, with the exception of $5.4 million earmarked for fundraising expenses through mid 2019,$14 million earmarked for matching funds for partnership projects, and funding held for its UBI project.
• Expected additional funding: GiveDirectly roughly estimates that it will raise $28 million (range:$12 to $45 million) in additional funding for its work in 2018. This includes revenue expected as a result of being on GiveWell's top charity list .128 • Track record of scalability: GiveDirectly has a track record of scaling quickly;129 it does not yet have a track record of operating at the size it believes it could scale to in 2018-2019. In the year ending July 31, 2017, GiveDirectly transferred$36 million to households; it was constrained by available funding rather than staff capacity.130

In sum, our best guess is that GiveDirectly will have about $42 million to spend on cash transfers in 2018 (excluding UBI) and GiveDirectly estimates that it has the capacity to spend$120 million ($70 million on standard cash transfers and$50 million on partnership projects). Over 2018-2020, we estimate that GiveDirectly could productively use over $200 million more than we expect it to receive. Calculations and more details in this spreadsheet. Partnership projects In October 2017, GiveDirectly told us that raising funds for partnership projects is a high priority. GiveDirectly was in discussions about 11 partnership projects at the time.131 For each project, GiveDirectly believes it would not be possible to move forward with the project without the ability to commit its own funds to match what the other funder would put in. For example, it was in discussions with several country offices of a large institutional funder. GiveDirectly told us that this funder's rules require it to run a request for proposals process for new grants, unless the grantee is able to match the funds that the funder provides. GiveDirectly does not think it is well-positioned to compete in the funder's request for proposals process and notes that that process can take a long time.132 GiveDirectly estimates that if all discussions were successful, it would need to provide$43.7 million in match funds to unlock $63 million in funding from other funders. Assigning probabilities to each potential partnership, GiveDirectly estimates that, if it were able to pursue all discussions, it would end up spending$14.8 million on matching grants from institutional funders. There is considerable uncertainty in this estimate. It currently has $14 million set aside for matching (from a 2015 grant from Good Ventures, made on GiveWell's recommendation).133 GiveDirectly told us that without additional funding for partnerships, it expects to need to slow discussions with potential partners because these partners want a commitment from GiveDirectly before they proceed with more detailed discussions.134 In its overall estimate of its room for more funding, GiveDirectly estimated that it could spend$50 million on partnership projects in 2018.135 Based on what GiveDirectly has told us about the partnership projects it is currently in discussions about, we believe that $50 million is more than GiveDirectly could spend on partnership projects in 2018, but that additional funding available for partnerships would allow it to pursue more of these projects in 2018 (and beyond) and that if GiveDirectly were to have more funding that it could use on partnership projects, it would use the funds to deliver more cash transfers outside of partnership projects. Risks to room for more funding GiveDirectly believes it can grow extremely quickly. However, there are some risks that might impede its ability to grow as fast as it believes it can. We consider the overall risk to be low, in large part because we'd guess that the following factors might slow GiveDirectly's ability to transfer funds, but that in most scenarios funds would simply reach households somewhat later. The following are concerns identified by GiveWell or GiveDirectly: • Refusals: As discussed on our page with additional information about GiveDirectly, GiveDirectly has experienced a fairly high rate of people refusing to be enrolled in Kenya over the last couple of years (with low rates of refusal in Uganda and Rwanda).136 GiveDirectly has told us that this has not slowed down its productivity because (a) the refusals only affect the activities of one team (the census team; though this doesn't take into account increased travel time as other teams have to travel farther on average between each house) and (b) GiveDirectly is flexible enough that it can pivot to new areas when refusals are high and come back later if refusal rates seem like they will decrease (perhaps due to outreach efforts).137 It is possible that the high rates of refusal could create challenges for GiveDirectly in its relationship with the Kenyan government; GiveDirectly has been working to build relationships with the government to mitigate this possibility.138 High refusal rates could also force GiveDirectly to move to new areas sooner than it expected, which could cause challenges if GiveDirectly struggles to obtain permission from local leaders to work in new areas (see next bullet). These risks may be mitigated in part by GiveDirectly's ability to move some of its capacity from Kenya to Uganda and Rwanda. • Government permissions: In order to expand into new areas, GiveDirectly must obtain permission from government officials at many levels. This process could be held up by an official who refused to grant permission, causing delays and possibly preventing GiveDirectly from expanding into an area indefinitely. GiveDirectly has attempted to mitigate this risk by networking with people who have expertise in navigating such government relationships and who could intervene if there were a problem.139 GiveDirectly feels that it now has a good understanding of the process for seeking government approvals and does not see this as a major risk.140 • Crime: Incidents of large-scale crime could cause delays and reduce GiveDirectly’s ability to transfer funds to recipients. The risk of crime could increase as GiveDirectly becomes better known in the regions in which it works. We discuss this risk more above. We consider this a low to moderate risk. • Security: GiveDirectly notes that political violence and terrorism could hamper its ability to work in an area. GiveDirectly has attempted to mitigate this risk by working in multiple locations, so that it could shift its operations from one country to the other two that it works in if there were an issue, though it is possible that insecurity could affect more than one country at a time, given the proximity of the countries in which GiveDirectly works. We know very little about security risks in Kenya, Uganda, and Rwanda, but would guess based on GiveDirectly's assessment that they present a low risk. • Payment provider: Relying on one payment provider in each country introduces a risk that problems with the payment provider could cause delays. GiveDirectly feels that this risk is low, because if there were problems, it could switch to alternative providers.141 We would guess that this risk is low, as the mobile money providers that GiveDirectly uses in Kenya and Uganda (we haven't asked GiveDirectly about this for Rwanda) are national networks, and GiveDirectly has identified alternatives. However, we note that GiveDirectly once tried working with an alternative provider in Uganda (Centenary Bank) and had difficulties in the partnership.142 • Maintaining staff quality as the organization grows: It is possible that GiveDirectly would face issues hiring high quality staff if it were to scale up quickly.143 GiveDirectly believes that its hiring processes have been successful and that new staff are taking on responsibility quickly and competently.144 In 2017, GiveDirectly laid off some staff due to lower than projected revenue145; it is possible that these layoffs could affect its ability to hire high quality staff in the future. Unrestricted vs. restricted funds We prefer that GiveDirectly spend funds in the way that it believes will maximize its impact and, accordingly, do not recommend that GiveWell donors restrict their donations in any way. We plan to grant funds to GiveDirectly unrestricted (such that GiveDirectly may use funds for all purposes, including experimenting with its model and process and organizational capacity building). GiveDirectly as an organization We believe GiveDirectly to be an exceptionally strong and effective organization: • Self-evaluation: GiveDirectly has invested heavily in self-evaluation from the start, and furthermore, the study design of its Rarieda and general equilibrium RCTs were pre-registered for additional accountability and credibility. It continues to demonstrate a strong commitment to rigorous analysis of its work. • Track record: GiveDirectly has successfully accomplished its goal of transferring cash to extremely low-income people at a fairly low expense ratio. We have also seen GiveDirectly refine its process over the years and take thoughtful measures in response to problems that arise, demonstrating a commitment to continuous improvement. • Communication: GiveDirectly has always communicated extremely clearly and directly with us and given thoughtful answers to our critical questions. Generally, GiveDirectly seems to come to conclusions that we find reasonable on key questions. • Transparency: GiveDirectly appears to value transparency as much as any organization we’ve encountered. We have not seen it hesitate to share information publicly (unless it had what we consider a good reason). More on how we think about evaluating the leadership of organizations at our 2012 blog post. Sources Document Source Carolina Toth, conversation with GiveWell, November 12, 2015 Unpublished Carolina Toth, email to GiveWell, November 10, 2015 Unpublished Carolina Toth, email to GiveWell, October 20, 2015 Unpublished Conversation with Carolina Toth, GiveDirectly, November 20, 2014 Unpublished Conversation with GiveDirectly, April 8, 2014 Source Conversation with GiveDirectly, December 7, 2013 Source Conversation with GiveDirectly, July 7, 2014 Source Conversation with GiveDirectly, September 5, 2014 Source Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 Source Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 (unpublished) Unpublished Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 Unpublished Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 Source Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 (unpublished) Unpublished Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 Source Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 (unpublished) Unpublished Dylan Matthews, Vox article, April 15, 2016 Source (archive) Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 Unpublished GiveDirectly blog, An update on fraud management in Uganda Source (archive) GiveDirectly blog, Fighting fraud in Uganda Source (archive) GiveDirectly financial summary through July 2017 Source GiveDirectly staff, conversation with GiveWell, October 6, 2016 Unpublished GiveDirectly staff, responses to monitoring questions, October 11, 2016 Source GiveDirectly, Blog post, September 22, 2016 Source (archive) GiveDirectly, Budget summary, July 2013 Unpublished GiveDirectly, Check in with GiveWell, September 2014 Source GiveDirectly, Coffee study design Source GiveDirectly, Contextualizing transfer size Source GiveDirectly, Dashboard Metrics for GiveWell, August 2017 Source GiveDirectly, Dashboard Metrics for GiveWell, May 2017 Source GiveDirectly, Distributed cash out follow up with vulnerable recipients Source GiveDirectly, Eligibility check Source GiveDirectly, email newsletter, August 15, 2017 Source GiveDirectly, email newsletter, December 27, 2016 Source GiveDirectly, Enrollment speed of distributions - Siaya and Rarieda Source GiveDirectly, Estimate of personnel 2015 Source GiveDirectly, FAQs 2015 Source (archive) GiveDirectly, Final report Nike girls study Source GiveDirectly, Follow-up tracker, July 2013 Source GiveDirectly, Follow-up tracker, October 2014 Source GiveDirectly, GE research and measurement plan Unpublished GiveDirectly, GE study design Source (archive) GiveDirectly, Give now Source GiveDirectly, Google enrollment database Source GiveDirectly, Google follow-up data - disaggregated (LS - long) Source GiveDirectly, Google transfer schedule, July 2013 Source GiveDirectly, Google verification, September 2013 Source GiveDirectly, GW scratch sheet Source GiveDirectly, How it works 2013 Source (archive) GiveDirectly, How it works 2014 Source (archive) GiveDirectly, Inflation analysis - Kenya Source GiveDirectly, Kenya 1.2M enrollment database Source GiveDirectly, Kenya 2M census results, July 2013 Source GiveDirectly, Kenya 2M enrollment database, September 2013 Source GiveDirectly, Kenya follow up data, November 2014 Source GiveDirectly, Kenya hotline log, July 2013 Unpublished GiveDirectly, Kenya randomized sample of adverse events, 2014-2015 Source GiveDirectly, Kenya rolling campaign enrollment database - Homa Bay Unpublished GiveDirectly, Kenya rolling campaign enrollment database - Siaya Unpublished GiveDirectly, Kenya top 10 adverse events 2015 Source GiveDirectly, Kenya verification template, August 2013 Source GiveDirectly, Kenya, Uganda, and Rwanda enrollment database, 2016 Source GiveDirectly, Monthly operations report, August 2015 Source GiveDirectly, Monthly operations report, February 2016 Source GiveDirectly, Monthly operations report, October 2014 Source GiveDirectly, Nike enrollment database Source GiveDirectly, Nike follow-up data - disaggregated Source GiveDirectly, Nike instrument Source GiveDirectly, Nike verification (combined), May 2013 Source GiveDirectly, Nike verification (final), September 2013 Source GiveDirectly, Nike verification (short version), June 2013 Source GiveDirectly, Offering Memorandum (January 2012) Unpublished GiveDirectly, Operational process overview Source GiveDirectly, Performance - Quality of Service, September 2016 Source (archive) GiveDirectly, Rarieda Top-up Verification (short) Source GiveDirectly, Rarieda transfer schedule, August 2013 Source GiveDirectly, Rarieda verification (top ups), May 26, 2013 Source GiveDirectly, Rarieda verification stats Source GiveDirectly, RCT Enrollment Database Source GiveDirectly, Room for funding update for GiveWell, October 2016 Source GiveDirectly, Saturation analysis Source GiveDirectly, Siaya enrollment database Source GiveDirectly, Siaya follow-up data - disaggregated Source GiveDirectly, Siaya poverty data by location Source GiveDirectly, Siaya verification stats Source GiveDirectly, Siaya verification, June 15, 2013 Source GiveDirectly, Siaya village index Source GiveDirectly, Survey for randomized controlled trial Source GiveDirectly, Targeting process overview Source GiveDirectly, Team Source (archive) GiveDirectly, UBI cost-effectiveness estimate Unpublished GiveDirectly, Uganda 2M campaign enrollment database Unpublished GiveDirectly, Uganda pilot enrollment database - Akumure Source GiveDirectly, Uganda pilot enrollment database - Kanyamutamu Source GiveDirectly, Uganda pilot enrollment database - Kawo Source GiveDirectly, Uganda pilot enrollment database - Kosile Source GiveDirectly, Uganda pilot follow up data, April 2014 Source GiveDirectly, Uganda randomized sample of adverse events, 2014-2015 Source GiveDirectly, Uganda targeting data, July 22, 2013 Source GiveDirectly, Uganda top 10 adverse events 2015 Source GiveDirectly, Update for GiveWell on experimentation, September 2016 Source GiveDirectly, Update for GiveWell, April 2014 Source GiveDirectly, Update for GiveWell, February 2015 Source GiveDirectly, Update for GiveWell, February 2016 Source GiveDirectly, Update for GiveWell, July 2013 Source GiveDirectly, Update for GiveWell, July 2014 Source GiveDirectly, Update for GiveWell, May 2015 Source GiveDirectly, Update for GiveWell, October 2014 Source GiveDirectly, Update for GiveWell, September 2015 Source GiveDirectly, Update on process changes, August 28, 2013 Source GiveDirectly, Updated data (March 31, 2012) Source GiveDirectly, Verification data (November 17, 2011) Source GiveDirectly, Verification template (November 7, 2011) Source GiveDirectly, Verification template (October 1, 2012) Source GiveDirectly, Village selection process Kenya Source GiveDirectly, Village targeting regression Source GiveDirectly, What We Do - Operating Model Source (archive) GiveDirectly, What We Do - Operating Model, October 2016 Source (archive) GiveDirectly, What We Do - Who We Serve, September 2016 Source (archive) GiveWell Household size analysis Source GiveWell Site visit notes Source GiveWell site visit to GiveDirectly, October 2014 Source GiveWell visit to M-PESA agent, November 8, 2012 Source GiveWell, GiveDirectly financials - 2016 Source GiveWell, GiveDirectly financials - May 2016 Source GiveWell, GiveDirectly financials 2015 Source GiveWell, GiveDirectly follow up surveys summary - Kenya, September 2015 Unpublished GiveWell, GiveDirectly follow up surveys summary - Uganda, September 2015 Source GiveWell, spot checks of Segovia follow-up data sample, 2016 Source GiveWell, spot checks of Segovia registration sample 2016 Source GiveWell's non-verbatim summary of a conversation with Carolina Toth, GiveDirectly, October 1, 2014 Source GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016 Source GiveWell's non-verbatim summary of a conversation with Matt Johnson and Paul Niehaus, June 28, 2017 Source GiveWell's non-verbatim summary of a conversation with Paul Niehaus and Carolina Toth, September 7, 2015 Source GiveWell's non-verbatim summary of a conversation with Paul Niehaus, Carolina Toth, and Ian Bassin, August 12, 2016 Source GiveWell's non-verbatim summary of a conversation with Paul Niehaus, Carolina Toth, and Ian Bassin, February 23, 2016 Source Haushofer and Shapiro 2013 Source (archive) Haushofer and Shapiro 2013 Appendix Source (archive) Haushofer and Shapiro 2013 Policy Brief Source (archive) Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016 Unpublished Ian Bassin and Piali Mukhopadhyay, conversation with GiveWell, August 23, 2016 Unpublished Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, August 25, 2016 Unpublished Ian Bassin, edits to GiveWell's review, November 10, 2016 Unpublished Jean Junior, The Perspectives of Young Women in Siaya County, Kenya: Their Lives and Their Thoughts on Cash Transfer Programs Source Johannes Haushofer and Jeremy Shapiro, Welfare Effects of Unconditional Cash Transfers: Pre-Analysis Plan, June 27, 2013 Source (archive) Johannes Haushofer and Paul Niehaus, DIL Demonstration Proposal Source Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012 Unpublished Michael Faye and Paul Niehaus, Slate article, April 14, 2016 Source (archive) Paul Niehaus and Carolina Toth, conversation with GiveWell, May 28, 2015 Unpublished Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015 Unpublished Paul Niehaus and Ian Bassin, conversation with GiveWell, September 15, 2016 Unpublished Paul Niehaus and Johannes Haushofer, Optimizing Impact for the Mobile Era - Final Report Source Paul Niehaus, AMA on Reddit, May 31, 2016 Source (archive) Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, August 12, 2016 Unpublished Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016 Source Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012 Unpublished Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012 Unpublished Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012 Unpublished Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, August 25, 2016 Unpublished Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012 Unpublished Poverty Probability Index, FAQs Source (archive) UCSD, Policy Design and Evaluation Lab, "Tracking the Impact of GiveDirectly Transfers with Mobile Surveys in Kenya" Source (archive) XE currency converter, Kenya shillings to US dollars, September 25, 2015 Source (archive) XE currency converter, Uganda shillings to US dollars, September 25, 2015 Source (archive) • 1. • 2. • 3. • 4. Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016 • 5. "A very high proportion (though not 100%) of GiveDirectly's transfer recipients are involved in a research study. For example, in Rwanda, many participants are involved in studies, but GiveDirectly also runs a core operations program with recipients who are not part of a study. In Kenya, the UBI study will be a major part of GiveDirectly's work for the next year, and many of its other Kenyan operations are associated with the aspirations study." • 6. • "We conducted a randomized controlled trial (RCT) of the unconditional cash transfer program implemented by the NGO GiveDirectly in western Kenya between 2011 and 2012, in which poor rural households received unconditional cash transfers through the mobile money system M-Pesa." Pg 1. • "In each chosen village, GD conducted a census, usually with the help of the village elder, which identified all households in the village that met this targeting criterion. Among the eligible households, treatment households were chosen randomly (details are described in Section 2). Households were aware that recipients would be chosen by lottery, but the actual selection was done privately by means of random number generation." Pg 4. • "We are grateful to the study participants for generously giving their time; to Marie Collins, Faizan Diwan, Conor Hughes, Chaning Jang, Bena Mwongeli, Joseph Njoroge, Kenneth Okumu, James Vancel, and Matthew White for excellent research assistance; to Innovations for Poverty Action for implementation." Pg 1. Haushofer and Shapiro 2013 Policy Brief • 7. GiveDirectly, Rarieda transfer schedule, August 2013 • 8. "Based on conversations with policymakers, GiveDirectly has identified gaps in the evidence-base for cash transfers that currently limit policymakers’ ability to implement cash transfer programs or to do so effectively. GiveDirectly has spoken with policymakers in Kenya and Indonesia, as well as representatives of the UK’s Department for International Development (DFID). The leading question that came out of these conversations was about the macroeconomic impacts, or “general equilibrium effects,” of cash transfers when conducted at a national scale. This includes factors such as enterprise structure, prices, local public finance, and local government activities. This question is the primary motivation for GiveDirectly’s top research priority: a study of general equilibrium effects." Conversation with GiveDirectly, July 7, 2014, Pg 2. • 9. "Objective: • Understand macro-economics impacts of transfers at scale (in-flation, job creation, etc.) • Measure impacts over a long time horizon (e.g., [less than[sic]] 5 years) Status: • Started baseline, with long term follow up mechanisms in place • Not fully funded– facing a gap of ~8M Partners: • Edward Miguel, Berkeley • Johannes Haushofer, Princeton Potential impact: • Increase government use of CT programs • Increase support for our particular model in proving LT impact" • 10. • This study may include a long-term follow up component that will provide information on the impacts of cash transfers several years after the transfer: "This study will potentially include long-term follow up as well, to address a separate question raised by policymakers about the long-term impacts of cash transfers. Professor Miguel previously worked on a study of the impacts of deworming (Miguel and Kremer 2004) that involved follow-up over a period of ten years and obtained a high response rate, so he has experience in setting up effective systems for tracking study participants over time." Conversation with GiveDirectly, July 7, 2014, Pg 3 • The study is randomized at the village level, will involve 325 villages, and is expected to survey approximately 3,900 households and 4,875 enterprises. GiveDirectly, GE study design, Pgs. 4-5. Update: "Currently in the midst of endline data collection. Plan is to finish endline data collection by the end of the year, and hope to survey over 9,000 households, 700 village elders, 80 assistant chiefs, 200 school head teachers and 3,000 enterprises." GiveDirectly, Update for GiveWell on experimentation, September 2016, Pg 3. • Baseline data collection for the study began in August 2014 and was still in progress as of September 2015. Baseline data collection was slightly slower than expected, which meant that GiveDirectly had to delay some transfers (so that researchers could complete the baseline survey before recipients had received cash). • Endline data collection was expected to be completed by the end of 2016, although this may be delayed since baseline data collection has taken longer than expected. GiveDirectly, GE research and measurement plan, Pg 6. • Midline data were scheduled to be collected from November 2014 to early 2016. GiveDirectly, GE research and measurement plan, Pg 6. • Paul Niehaus, GiveDirectly's President, is serving as one of the Principal Investigators on this study, along with Edward Miguel (UC Berkeley), Johannes Haushofer (Princeton), and Michael Walker (UC Berkeley). GiveDirectly, GE study design, Pg 2, and Carolina Toth, email to GiveWell, November 10, 2015. • In order to mitigate potential bias from his involvement with the research, GiveDirectly has applied a number of safeguards, including preregistration of plans for measurement and analysis: "Paul Niehaus, GiveDirectly’s Co-Founder and President, will serve as a Principal Investigator on the general equilibrium effects study. To mitigate potential bias when GiveDirectly staff are involved in research on the impacts of its programs, GiveDirectly has decided on a few safeguards: the research team conducting the study will 1) preregister their plans for measurement and analysis 2) use a (non-GiveDirectly staff) third party for measurement, and 3) include academic PIs who are not involved in GiveDirectly and have a reputation for honesty." Conversation with GiveDirectly, July 7, 2014, Pgs 2-3. • 11. • "Currently in the midst of endline data collection. Plan is to finish endline data collection by the end of the year, and hope to survey over 9,000 households, 700 village elders, 80 assistant chiefs, 200 school head teachers and 3,000 enterprises. • Pre-analysis plan for the midline data has been filed, and are actively working on finalizing pre-analysis plans for the local leader survey data and the rest of the endline data. Analysis has begun on the midline data and is ongoing." GiveDirectly, Update for GiveWell on experimentation, September 2016, Pg 3. • Updated timeline provided as a comment on this review in November 2017. • 12. GiveDirectly, email newsletter, August 15, 2017 • 13. • 14. • 15. • 16. Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016 • 17. • 18. "We think the direct impact of the UBI program will be about ~60% as effective as the lump sum program, but that the potential policy impact more than makes up for that difference" GiveDirectly, Update for GiveWell on experimentation, September 2016, Pg 2. • 19. Some of GiveDirectly's potential partnership projects have at various points in time included (note that these projects are small compared to the projects GiveDirectly is currently interested in): • 20. "GiveDirectly is offering up to$15 million in matching funds on a first-come, first-served basis to fund these types of projects. GiveDirectly hopes that offering the matching funding will incentivize large funders to move quickly on partnership projects."

• 21.

List of partnership discussions underway from GiveDirectly, as of October 2017 (unpublished source)

• 22.

Comments provided by GiveDirectly in response to a draft of this review in November 2017

• 23.
• "The Poverty Probability Index (PPI®) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household’s characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line – or above by only a narrow margin."
• "On July 15, 2016, the PPI moved from Grameen Foundation as its organizational ‘home’ to Innovations for Poverty Action (IPA)."
• 24.

Comment provided by GiveDirectly in response to a draft of this page in November 2017

• 25.
• 26.
• Carolina Toth, conversation with GiveWell, November 12, 2015
• "A full set of asset questions are oftentimes only relevant when we are re-assessing poverty of a particular region, or assessing it for the first time when we enter a new region. In addition to assets, we may consider other factors such as housing materials, facilities (latrine, roof, floor) etc. The exact set of factors considered changes across communities to reflect the complex variations of poverty. We also continuously test and tweak our set of criteria based on our analysis. This is why you may see different sets of information (assets/facilities etc.) collected across different geographies." GiveDirectly staff, responses to monitoring questions, October 11, 2016, pg. 1.
• 27.

Carolina Toth, conversation with GiveWell, November 12, 2015

• 28.
• "We typically use building materials as eligibility criterion—organic materials like a thatched roof, mud walls, or mud floor have the advantage of being (a) a strong predictor of poverty, (b) easy for community members to understand, and (c) relatively easy to audit in a number of ways, including both digital imagery captured by our field staff and satellite imagery captured remotely." GiveDirectly, FAQs 2015
• GiveDirectly has tweaked these criteria in the past, e.g., "Dropped mud walls as eligibility requirement." GiveDirectly, Update for GiveWell, October 2014
• Carolina Toth, email to GiveWell, November 10, 2015
• 29.
• GiveDirectly, Survey for randomized controlled trial
• "Well over half of adults skip meals, less than half of household members eat until they are content, people commonly go to sleep hungry and a paltry 18% report having enough food for tomorrow in their household. Those living in eligible homes are even worse-off than the average household, consuming less and holding fewer assets. Overall, mean and median daily per capita consumption among eligible households are $0.65 and$0.55 at nominal rates, and 74% are below the Kenyan poverty line, indicating a very poor population." GiveDirectly, Offering Memorandum (January 2012), Pgs 23-24.
• 30.

GiveDirectly, What We Do - Operating Model, see the Uganda tab.

• 31.
• Several recipient households had been selected by GiveDirectly for our visit as representative of how recipients use funds. 2 locations (and the households within them) were selected as a function of GiveDirectly's activities that day -- an area of Rarieda where the end-line survey for the RCT was being implemented and an area of Siaya where enrollment was being undertaken. In both cases, we don't know whether enrollment and surveying activities were taking place elsewhere that day which would have given GiveDirectly discretion in choosing these areas.
• On the final day of our visit, we asked GiveDirectly whether we could randomly select 3 households to visit. GiveDirectly sent us a list of 15 households in a location in Rarieda where the end-line survey for the RCT was complete and therefore we could question recipients without interfering with the RCT. We don't know whether GiveDirectly had discretion in choosing these 15 households. We selected 5 households from the list using Excel's RAND() function and visited 3 of them. (GiveDirectly made appointments with the households in advance and could not reach 2 of them.)
• We would characterize all the households we visited -- those that GiveDirectly fully selected for us, those over which GiveDirectly had less discretion, and those we selected randomly -- as extremely poor. We did not see any significant differences in wealth between them.
• 32.
• Most homes are made up of three rooms.
• The main room is a sitting area. In the homes we visited, this room varied in size from approximately 10'x12' to 12'x20'. This room has 3 doors: one to the outside; one to what appeared to be a storage room; one to a bedroom. The husband and wife sleep in the bedroom and the children sleep in the storage room or in the kitchen.
• The kitchen is often a separate structure, most often thatched-roof (even for homes that have tin roofs). Some households have no kitchen structure and cook outside. Others have a small kitchen in place of a storage room.
• There are no doors in between rooms in the house, just hung curtains.
• The living room has many chairs and couches for sitting. They often almost fully cover the wall area (aside from doors). There are also coffee tables in the middle of the room. Poorer homes had less furniture; one home we saw had a single chair and a single broken table.
• People have wall hangings for decoration. The most common hanging we saw was old calendars (e.g., from 2003) that have pictures and can be used for decoration.
• Most houses had 1-2 kerosene lamps that provide light since they don't have electricity. One home (a non-recipient we visited) had two electric lights, which were powered with a solar panel.
• People we spoke with reported walking 5-20 minutes daily to obtain drinking water, which one recipient reported doing 1-2x per day.
• Most households owned a bicycle.
• Some households have radios (both of the non-recipients we saw had radios; one had a TV). Of the others, 3/14 had radios. These were most often powered with what looks like a car battery.
• Most people owned one cell phone pre-GiveDirectly.
• Households tend to own some livestock. Most commonly, we saw 1-2 cows and 4-5 chickens each. They reported selling the milk or saving it for personal use, and many mentioned being able to sell their cows in the future to pay for their kids to attend secondary school.
• 33.

GiveDirectly, email newsletter, December 27, 2016

• 34.
• 35.
• "Backcheck: Uganda and Kenya Standard Programs all include the Backcheck stage. Backcheck was used as an additional step after registration, and before audit or payment to:
1) Meet all registered households again and verify identity
2) Share more about GD's program, provide some guidance and education on mobile money literacy
3) Correct data entry error
Backcheck was a necessary step for all registered recipients-- 100% of recipients must have completed backcheck before receiving payment. Following an internal evaluation, we will be removing the backcheck stage in Q3 and Q4 2017 and achieving those 3 aims through other more cost-efficient methods

Audits: This involves visiting a percentage of registered households to identify their legitimacy and program eligibility. The households are selected based on weighted data discrepancy factors (difference in GPS location of household at our various points of visit etc.) In Kenya Q1, 956 households (~58% of registered households), were audited."
GiveDirectly, Dashboard Metrics for GiveWell, May 2017

• [GiveWell]: What is GiveDirectly checking to determine the quality of the campaigns with removed steps (especially the Kenya campaign with no backcheck and token payment)?
[GiveDirectly]:
• "Recipient Comprehension: With the removal of back check, we are tracking any deviation in recipient comprehension below 90% (our preferred rate). We are tracking this through additional survey questions in both audit and our follow up surveys where we ask recipients their understanding of our program. Additionally, Associate Field Managers will be conducting quality check surveys at audit stage for another data point on recipient comprehension.
• Transfer Integrity & Adverse Events Detection. Given additional risk of two payment structure, we will be assessing (on a bi-weekly basis) the percent of transfers reversed and percentage of adverse events happening after first payment and after second payment. This will then be compared to our standard quality bar to assess significant deviations. Given a greater need for over the phone assistance, we will also be tracking through our follow up program the percentage of recipients who received no customer service and tried.
• Fraud Detection. With the removal of back check and a two payment structure that makes each payment riskier, we will be checking the percentage of recipients flagged for additional audit stage and then the percentage of those deemed ineligible after audit is conducted. We also have an internal audit team in place that will be conducting surveys post-payment to see if fraud occurred."
• 36.

"2017 Backcheck and audit exclusion (Refusals + suspicious information gathered)

• Kenya Backcheck excluded ~2%
• Kenya Audit excluded ~2%
• Uganda Backcheck excluded ~ 2%
• Uganda Audit excluded ~0.6%

Email from GiveDirectly, June 7, 2017.

• 37.
• 38.

GiveWell visit to M-PESA agent, November 8, 2012

• 39.

"We then transfer money directly to the recipient's account […] The recipient collects the transfer from a local M-Pesa agent, who is typically a shopkeeper in the village or the nearest town. The recipient transfers his or her electronic M-Pesa balance to the agent using his or her mobile phone in return for cash." GiveDirectly, How it works 2014

• 40.
• 41.

"Selected MTN as preferred provider in Uganda after assessing performance of Ezee/MTN (building relationship with Airtel so as to have an additional hedge)" GiveDirectly, Update for GiveWell, July 2014, Pg 9.

• 42.
• 43.

"Recipients and agents were able to overcome liquidity constraints, and in Uganda, GiveDirectly distributes $700,000 to$1 million per month without any increases in fraud." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 5.

• 44.
• For example, see GiveWell, spot checks of Segovia follow-up data sample, 2016
• When GiveDirectly first started its "distributed cash out" model in Uganda (instead of hosting "cash out days" GiveDirectly conducted some quick follow-up phone calls with vulnerable recipients in Uganda; in the sample of 67 call records GiveDirectly sent us, only 9 vulnerable recipients had already withdrawn their funds successfully (although many had received the transfer and were planning to withdraw it soon, and 22 responses were ambiguous). GiveDirectly, Distributed cash out follow up with vulnerable recipients. Note that some of the comments indicate that the person surveyed was not the recipient, but someone close to the recipient or the recipient's helper. We are not sure how indicative these data are of difficulties obtaining funds.
• 45.

Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016

• 46.
• Note that sometimes as GiveDirectly scales and moves into new areas, it could end up being less well known. For example, when GiveDirectly moved most of its Kenya operations from Siaya County to Homa Bay County, it experienced a high rate of people refusing to be enrolled. GiveDirectly thinks this may be because many people in Homa Bay had not heard of GiveDirectly before and were suspicious of the program.
• "GiveDirectly has seen an uptick in the rate of refusal to participate in its cash transfer program in Homa Bay. The root of this development is not clear, and GiveDirectly has not yet identified a solution. In some cases, community members are led by local religious leaders or local government to mistrust the program. In Siaya County, this issue did not arise, possibly because GiveDirectly covered such a large portion of the county that in any new area it entered, people were already aware of the program and knew that it was trustworthy." Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016, Pg 5.
• 47.

The cash out days that GiveDirectly used to administer in Uganda seemed to be particularly easy targets for large-scale theft, as there was a substantial amount of cash in one location (although it is our understanding that GiveDirectly's partners sent security personnel to the cash out days to mitigate this risk). Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015. If GiveDirectly decides to run cash out days again, either in Uganda or a new location, we will slightly increase our concern about theft.

• 48.

GiveDirectly notes: "...we see two possible forms of larger scale crime/interference. The first is interference or expropriation by other institutional actors. As we have relayed previously we have tried to hedge against this by improving governmental contacts. The second is some larger scale organized crime, but we do not see this as a threat that meaningfully increases as we scale as any attempt at theft would still need to target small individual disbursed transactions, which is the same fundamental risk to our current program that we believe our current structure has had a strong track record in mitigating." Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016

• 49.
• 50.

"Discovery of the fraud

• A GiveDirectly recipient had given their SIM card to the SFO [Senior Field Officer] (whose contract had been terminated in April due to an unrelated issue involving a fraudulent receipt he brought to GiveDirectly for reimbursement). The recipient asked the SFO to replace his SIM card (recipients have to travel about 4 hours round trip to get this done) and the SFO had not returned it. This report was made to the hotline that the OM [Office Manager] was answering three months after the recipient had given over their SIM card. Mr. Skeates audits the logs of these hotline calls.
• GiveDirectly had been planning a full round of follow-up surveys as part of its normal process, but accelerated the follow-up surveys in response to this issue. GiveDirectly’s backcheck team paused their work on enrollment for the Uganda 2M campaign and called all the recipients in that village (Kosile) to ask whether they had received all of their transfers, had any problems withdrawing, and whether GiveDirectly currently had any of their documents (e.g., SIM cards, IDs).
• During this process, there were some reports of problems during paydays. Recipients were initially hesitant to come forward.
• Because of the reports of payday problems, GiveDirectly began calling another village, Kawo, the following day to gather more information. Recipients in Kawo were far more forthcoming with information when asked specific questions about payday problems.
• GiveDirectly continued follow-ups (conducted by a new Field Officer brought on after the SFO's and OM’s dismissals) until it had spoken to about 92% of its recipients across all villages. GiveDirectly also conducted in-person visits to villages."

Conversation with GiveDirectly, September 5, 2014

• 51.

Changes implemented in response to staff fraud
In the Uganda pilot campaign, GiveDirectly cash out days were managed by the Uganda Senior Field Officer, the Uganda Office Manager who also managed the GiveDirectly hotline, and mobile money agents. After these people fraudulently diverted funds from recipients, GiveDirectly implemented a series of changes:

• Terminated the GiveDirectly staff who had been involved in the fraud; started working with new mobile money agents.
• Removed all of its staff from the cash out day process except the Uganda Field Director. The Uganda Field Director had previously been making planned visits to oversee some of the cash out days; he now actively manages all of them along with new mobile money agents.
• Appointed community-nominated monitors to assist the Uganda Field Director on the cash out day with translation, observe transactions between recipients and mobile money agents, and report any issues they see. GiveDirectly compensates the monitors with 10,000 UGX (~$4) for their time during a cash out day. • Developed networks of English-speaking informants who are not formally announced within the villages, but are tasked with also reporting any issues they see regarding transfers. To date, 4 of the 9 informants have provided GiveDirectly with helpful information, such as identifying that households in the enrollment process were actually ineligible, and telling GiveDirectly that someone had taken a recipient's phone after the recipient passed away. • Moved the GiveDirectly call center (hotline) to Kampala, to increase the separation of call center staff from field staff, who are based in Mbale. • Tasked the call center with calling a randomly selected 10% of the village during a cash out day to see if it is going smoothly. • Changed the contractual agreement GiveDirectly has with mobile money agents to include an indemnity clause, so that in the case of stolen funds, GiveDirectly could remove funds directly from a mobile money agent's account. Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 • 52. • 53. • 54. Joe Huston, GiveDirectly CFO, conversation with GiveWell, November 10, 2017 • 55. GiveDirectly blog, An update on fraud management in Uganda • 56. "The fraud we found did not exploit any single vulnerability in our processes but instead required multiple, concurrent failures. Our focus is therefore on incrementally strengthening each check rather than redesigning the overall process. Specific changes we have made include: • Operations: enforcing stricter independence between enrollment teams; reexamining household-level targeting • Technology: built automated dashboards running field data checks to expose suspicious patterns • Culture: instituted recurring reviews of our whistleblower policies; added more explicit field staff pledges, including an honor code, a commitment to survey device accountability, and a conflict of interest declaration • Management: created a security committee comprised of our Chief Operating Officer, Chief Financial Officer, and Chief Technology Officer to examine and manage risks across every facet of the organization" GiveDirectly blog, An update on fraud management in Uganda • 57. Despite the mobile money security measures, Lydia Tala, an Assistant Field Manager who has been responsible for making post-transfer phone calls to recipients in Kenya, reports that one of the most common client complaints is the belief that M-PESA agents are overcharging or stealing funds. Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012 • 58. • 59. After the transfers are sent, GiveDirectly also administers follow up surveys that ask recipients if they have collected their funds and if they had any trouble doing so. The percentage of recipients who report issues withdrawing funds is consistently low (<5%) across campaigns. See the table below for details. Follow up surveys also ask recipients what size of transfer they received. These amounts generally appear to vary slightly among cohorts of recipients. For example, in follow-up surveys of recipients in Kenya from 2014, recipients reported receiving various amounts between 37,000 KES – 40,000 KES. This is based on data GiveWell reviewed in 2014. GiveDirectly, Kenya follow up data, November 2014. Other than the mobile phone purchase deduction, we do not know the causes of this variance. • 60. "There were four types of issues that were responsible for most of the people who were unable to withdraw funds at the cash out day: […] MTN (the payment provider) had not yet activated the funds in the person's account. Mr. Skeates said that this was not a common problem in the previous campaign in Uganda, but it affected many people at this cash out day. He estimated that people affected by this issue would receive their first installment of funds in another 2-3 weeks. […]" GiveWell site visit to GiveDirectly, October 2014, Pg 6. • 61. Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015 • 62. • A "trustee" is someone who is registered for the mobile money payments on behalf of the recipient. A "helper" is someone who is not registered for the payments, but who helps the recipient with the process (e.g. assisting with transportation to the locations one can withdraw cash or helping to use the phone properly). • Roughly, the process for choosing a trustee or helper is to get the recipient alone (out of earshot of family) and ask who that recipient trusts the most. This choice is typically validated with some neighbors (ensuring that that person is regarded as trustworthy). Generally, GiveDirectly prefers to choose trustees and helpers who are already recipients themselves, so that they have less of an incentive to steal the transfer and so that GiveDirectly can stop transfers to them if they are not performing their role appropriately. Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015. Note that GiveDirectly has offered to send us the protocol used to determine helpers and trustees; we have not yet reviewed this protocol. • 63. "Following its Google-funded campaign, GiveDirectly surveyed recipients in detail on how they spent their transfers. Given the limitations of this kind of self-reported data, GiveDirectly has not continued this practice. It prefers to rely on more accurate data gleaned through randomized controlled trials (RCTs), and expects to collect more [of] this type of information in future studies or campaigns, such as its ongoing RCT in coffee farming communities." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 4. • 64. • 65. GiveDirectly, What We Do - Who We Serve, September 2016 See the chart in the upper left section of the web page. • 66. GiveDirectly, What We Do - Who We Serve, September 2016 See the chart in the upper left section of the web page. • 67. For full details of our interviews with recipients, see GiveWell Site visit notes. • 68. Provided by Google via Citibank N.A. on November 15, 2012. • 69. • 70. Provided by Google via Citibank N.A. on November 15, 2012. • 71. Ian Bassin and Piali Mukhopadhyay, conversation with GiveWell, August 23, 2016. Based on conversations in 2017, our understanding is that GiveDirectly's main locations of operation did not change in 2017. • 72. • "The ‘thatch roof, mud walls, mud floor’ eligibility criteria was not going to work in Homa Bay, as <3% households had thatch roofs." Carolina Toth, email to GiveWell, October 20, 2015 • GiveDirectly has told us that this is because there is very little grass in Homa Bay County. GiveDirectly thinks that people in Homa Bay have spent more money historically on their buildings (because the cost of thatch roofs was not cost-competitive). • "However, more people in Homa Bay have metal roofs than in Siaya. This is likely because the grass for thatched roofs does not grow in Homa Bay, so the price of thatch is less competitive." Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016, Pg 6. • 73. Paul Niehaus and Ian Bassin, conversation with GiveWell, September 15, 2016 • 74. "Physically closer to Rarieda than Siaya was Poverty rate is higher than in Rarieda (50% vs 46%)" GiveDirectly, Update for GiveWell, February 2016, Pg 13 • 75. • Haushofer and Shapiro 2013 Policy Brief, Table 10, Pg 38. • "Additional variables in table 9 show the frequency of any episode of physical, sexual or emotional violence in the last six months, and the percentage of respondents who believe that domestic violence is justified in some instances. The point estimates for these variables suggest a reduction in domestic violence, although none are individually different from zero at conventional significance levels." Haushofer and Shapiro 2013 Policy Brief, Pg 21. • 76. • 77. • "For example, following concerns about missed inbound calls, it decided to upgrade its call center technology. This process is ongoing; GiveDirectly expects to see progress in this area within the next few months." Ian Bassin and Piali Mukhopadhyay, conversation with GiveWell, August 23, 2016, Pg 2. • [GiveWell]: "How did GiveDirectly become aware that it might have been missing some incoming calls to the hotline and how will the new call center technology fix this issue?" [GiveDirectly]: • "Hotline phones keep a record of missed calls and we were seeing more than usual • FOs in the field would hear anecdotally that recipients tried the hotline number and failed to reach • The new call center will have a centrally controlled hotline system where inbound calls are routed directly to the first available agent (right now they are being routed sequentially). New technology will also allow us to monitor call volumes and staff the hotline dynamically as certain times of days and days of the month see higher volumes" Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, August 25, 2016 • Conversation with Eric Friedman, GiveDirectly Country Director for Uganda, March 22, 2017 • 78. GiveWell, spot checks of Segovia follow-up data sample, 2016 Note that we did not specify to GiveDirectly which sample to send and they did not say how they selected this particular sample. • 79. • GiveWell, spot checks of Segovia follow-up data sample, 2016. While there were at least 2 events reported for every adverse event that GiveDirectly staff asked about, the rate of other adverse events was effectively 0%. • The rates of issues we've seen reported by GiveDirectly are typically also quite low: • GiveDirectly's website reports that only 0.3% of recipients in Kenya were asked for a bribe; we are not sure over what time period or from what sample this figure was calculated. GiveDirectly, Performance - Quality of Service, September 2016 • In August 2016, GiveDirectly mentioned that its Uganda campaign only has a 0.52% complaint rate from follow-up calls; again, we are not sure from what sample this figure is calculated: "The most common complaints/comments are from people seeking transfers, either people hoping GD will come to their area, people seeking a greater transfer, or ineligibles or people whose transfers have been delayed for some reason seeking to receive. The other recently common complaint are people saying the money is evil in some way. For context on size, Uganda rolling currently has a 0.52% complaint rate of for all follow up calls." Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, August 25, 2016 • Note that GiveDirectly has told us complaints tend to be higher when it first enters a new area: "When GiveDirectly enters a new area, complaint rates tend to be relatively high. This is because GiveDirectly records as "complaints" callers who request payments but are not eligible for its program. When GiveDirectly initially enters a new area, word spreads that GiveDirectly is distributing funds but people misunderstand the program, so these call volumes tend to be high. For example, the initial rate in GiveDirectly's new Rwanda campaign was 30.94%. Field teams are responsible for identifying the source of and addressing high complaint rates." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 2. • 80. • 81. • 82. In 2015 we asked for sample data only. In 2016 we did not ask for a sample. In 2017 GiveDirectly was transitioning to a new call logging system and told us that it would be time-consuming to send us data so we did not request these data. • 83. GiveDirectly, Follow-up tracker, October 2014 Sheet: "Summary" In 2015 we did not ask GiveDirectly to send us a follow-up tracker because it is our understanding that sharing the follow-up tracker databases take a significant amount of effort on GiveDirectly's part (much of the effort goes into anonymizing the entries). Instead, we requested a random sample of adverse events and a sample of some of the most serious adverse events from the previous year. We have reviewed these samples, but have not made them public (they are not anonymized); the issues they cover are broadly similar to the types of issues we have seen in previous years. • 84. GiveDirectly, Follow-up tracker, October 2014 Sheets: Summary; GiveWell notes. • 85. • 86. Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016 • 87. Haushofer and Shapiro 2013 Policy Brief, Table 10, Pg 38. • 88. • The RCT of GiveDirectly’s program in Rarieda did not find an increase in crime, so at that scale it does not seem to be an issue. It’s possible that crime would be a more serious problem if GiveDirectly became a substantially larger and better-known organization. Conversation with GiveDirectly, December 7, 2013 • GiveDirectly has become very well-known in Siaya County, Kenya, but has not seen a significant increase in crime rates there. As GiveDirectly begins to work in Homa Bay County, it expects crime rates to be lower, because the context is similar to Siaya but fewer people know about GiveDirectly in Homa Bay. If GiveDirectly were to start working in more urban areas, where crime rates tend to be higher, GiveDirectly would put more time into strategizing about crime and security. Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015 • 89. Example: "The recipient was given all the cash withdrawn as she requested.. then as she {deidentified} she was just outside the house since she's blind and her door is not lockable, she came to find her money missing but she doesn't know who might have stolen the KES."GiveDirectly, Follow-up tracker, October 2014 Sheet: "Tracker" (text removed in deidentification.) • 90. Example: "He was phoned by unknown person who posed as GD staff and requested for 500/= bribe to hasten the processing of his transfer."GiveDirectly, Follow-up tracker, October 2014 Sheet: "Tracker" (text removed in deidentification.) • 91. Example: "She lost the phone, and in the process of renewing the line the Agent transfer the money to another line in order to withdraw later."GiveDirectly, Follow-up tracker, October 2014 Sheet: "Tracker" (text removed in deidentification.) • 92. Some recipients, especially elderly ones, have to learn how to use cell phones for the first time in order to manage the GiveDirectly transfers in mobile money accounts. These people have a more difficult time understanding how to keep their phones secure; for example, they often keep the phone in its original packaging and do not conceal it. Another problem with security is that some recipients will share the PIN numbers for their mobile money accounts, either intentionally or unintentionally by handing the phone to a mobile money agent before pressing "Send" (so the PIN number is still apparent on the screen of the phone.) This makes recipients more vulnerable to people who wanted to steal money from their accounts. Teaching PIN safety has long been a priority, and GiveDirectly has added additional emphasis on the topic (e.g., emphasis during village meetings, additional trainings given by the mobile provider) Improved security is a reason why GiveDirectly is interested in piloting biometric authentication for mobile money accounts, though it does not currently have plans to do so. Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014, Pgs 2-3. • 93. • 94. The data from Rwanda are from March to June 2017. We first requested first quarter data from all three countries, but first quarter data were not yet available from Rwanda, so we followed up for second quarter data later in the year. GiveDirectly, Dashboard Metrics for GiveWell, May 2017 GiveDirectly, Dashboard Metrics for GiveWell, August 2017 • 95. In Siaya, GiveDirectly experienced some difficulty with people pretending to live in poorer quality housing: "[GiveWell]: There were multiple comments about recipients switching from iron-roofed houses to grass-roofed houses in order to be enrolled. Is this becoming a more common problem? How does GiveDirectly discover these instances? [GiveDirectly staff]: This was a common problem in Siaya -- where everyone knew, from our work there, that our criteria relied on housing materials, therefore they’d try to pose as living in such a house to be eligible. There are a number of ways this can be discovered -- either by asking neighbors, or observing that a recipient does not have at hand items (like vaccination forms) that would be in their possession if they were actually at their home, suggesting they don’t actually live where they are claiming to live." GiveDirectly staff, responses to monitoring questions, October 11, 2016, pg. 3. • 96. • 97. In the Rarieda campaign, 67% (359 of 536) of recipients waited less than a month, 84% (448 of 536) waited 3 months or less, and 6% (34 of 536) waited 6 months or more. In the Siaya campaign (a later campaign), 188 of 193 recipients waited less than a month, and the remaining 5 waited 2-3 months. • GiveDirectly, Enrollment speed of distributions - Siaya and Rarieda • GiveDirectly commented: "We were able to accelerate [the time it took for recipients to register for M-PESA] significantly for two reasons: (a) we gave clearer instructions, and (b) we let recipients designate which household member they wanted to receive the transfers, which gives them flexibility to choose someone who already has a National ID; in the Rarieda round we could not do this as we were randomizing recipient gender. I expect the Nike cohort will take longer to register as that project focuses on 18-19 year old women, many of whom will not yet have IDs." GiveDirectly, Updated data (March 31, 2012) • 98. Joe Huston, GiveDirectly CFO, email to GiveWell, November 8, 2017 • 99. GiveDirectly, Dashboard Metrics for GiveWell, August 2017 • 100. "For its standard Kenya and Uganda programs, GiveDirectly has implemented a rule in Segovia to block payments until the previous one has been confirmed: for example, first and second lump sum payments are blocked until token and first lump sum payments, respectively, have been confirmed via a follow-up call or visit." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 4. • 101. • 102. • 103. "The main downside to both of the mobile money services in [Uganda] as compared to MPESA in Kenya is that there are fewer mobile money agents in the rural areas that GiveDirectly is targeting. In response, GiveDirectly has been more proactive in coordinating with the mobile money service for the transfers that have begun, for example, by giving the mobile money service advanced notice before sending the funds so that agents could be prepared. In some cases, agents traveled to the villages in which recipients live to reduce recipient travel time." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 • 104. GiveDirectly has so far received about six applications for every one FO [Field Officer] position it has open, which it sees as an indicator that the necessary talent is available for it to scale its operations. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 (unpublished) • 105. • Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012. • GiveDirectly, Budget summary, July 2013 •$12 per day seems very roughly to be in line with market value:
• $12 per day * 5 days a week * 52 weeks per year =$3,120 per year
• This salary site indicates that lower-skilled workers are paid ~20,000 - 50,000 KES per month ($198 -$497 per month, according to Google as of May 5, 2016), which comes out to $2,376 -$5,964 per year.
• We would guess that GiveDirectly's Field Officer position is generally lower-skilled (e.g., it involves significant surveying of recipients, which we'd expect to be paid similarly to other types of administrative assistant roles).
• 106.

Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013

• 107.

GiveDirectly has told us that it seeks to influence both the official development assistance that high-income countries provide and individual donor contributions. Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016

• 108.

Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016

• 109.

Some of that evidence must be kept confidential. Note that we have not vetted the examples GiveDirectly has provided.

• 110.

GiveDirectly, Update for GiveWell, May 2015 (the slide with details of the examples mentioned has been redacted).
GiveDirectly, Update for GiveWell, September 2015 (two slides with details of the examples mentioned have been redacted).

• 111.

Note that this includes Good Ventures: see this blog post.

• 112.

Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016

• 113.

For example.

• 114.

For example:

• "Survey of 31 ongoing studies found that 6 have a cash arm currently and 20 would like to add one."
• "GD declined to participate in impact evaluation of cow distribution; study will proceed, may or may not include a cash arm"
• "GD declined to pursue implementation of nutrition benchmarking study, but will provide advice."
• "Discussing multi-country comparison of current conflict & jobs programming to cash transfers, using matching funds"
• "[W]e are seeing growing momentum behind cash transfers"
• 115.

For example: "Indonesian government and World Bank ([Redacted]). WB pushing for an RCT comparing cash to other approaches with conditional funding from DIV and GDL. Pending Indonesian government’s buy-in. Motivated by GD model, GD visited to present on impacts & methods" GiveDirectly, Update for GiveWell, September 2015, pg. 6.

• 116.

Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016

• 117.

For example: "Indonesian government and World Bank ([Redacted]). WB pushing for an RCT comparing cash to other approaches with conditional funding from DIV and GDL. Pending Indonesian government’s buy-in. Motivated by GD model, GD visited to present on impacts & methods" GiveDirectly, Update for GiveWell, September 2015, pg. 6.

• 118.

For example: the Brookings Blum Roundtable and a DFID high-level panel on cash transfers. GiveDirectly, Update for GiveWell, September 2015, pg. 6.

• 119.

This is based on GiveDirectly's description of how the project started. Paul Niehaus and Carolina Toth, conversation with GiveWell, May 28, 2015

• 120.

Note that GiveDirectly has told us that, although comparing the cost-effectiveness of the programs involved in the Rwanda benchmarking experiment is part of the experiment, doing so is challenging, in part because one of its partner organizations may not have high quality data on its expenses. Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, August 12, 2016

• 121.

Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016

• 122.

GiveDirectly financial summary through July 2017

• 123.
• "GiveDirectly delivers 91% of donations from the public directly to recipients in Kenya, and 85% in Uganda. These figures differ from GiveWell's estimate of the overall breakdown of past spending in three ways. First, GiveDirectly's figures refer to standard campaigns for which public donations are used, which differ from bespoke campaigns that GiveDirectly conducts for institutional funders (e.g. to study effects on niche groups like young women) and which have different cost structures. Second, GiveDirectly's figures reflect the costs of transfers to recipients who have completed the process, while GiveWell's include the costs for recipients who have not yet received their transfers. Third, they do not include money spent on fundraising, which GiveDirectly budgets and measures efficiency for separately." Carolina Toth, email to GiveWell, November 10, 2015
• Updated figure of 89% provided by GiveDirectly as a comment on a draft of this page in November 2017
• 124.

Research costs excluded from GiveDirectly's financial statements:

• Original RCT in Rarieda, Kenya. Full cost unknown.
• Ideas42 - Behavioral aspects of cash transfers. GiveDirectly noted that the research costs were at least $158,863. • General equilibrium study. GiveDirectly notes that it was not involved in the fundraising or spending for this research study, though it did incur costs due to coordinating with researchers. It did not have an estimate on hand of the total research costs. • Aspirations study. GiveDirectly notes that it was not involved in the fundraising or spending for this research study, though it did incur costs due to coordinating with researchers. It did not have an estimate on hand of the total research costs. We note that GiveWell recommended a grant of$350,000 for part of the costs of this research study.
• Rwanda benchmarking study. GiveDirectly notes that it was not involved in the fundraising or spending for this research study, though it did incur costs due to coordinating with researchers. GiveDirectly estimates that the research costs were just over $1 million. Research costs included in GiveDirectly's financial statements: •$264,101 for research on cash in coffee farming communities. Our understanding is that this research was primarily carried out by GiveDirectly, with some funding granted to IDinsight. We have not excluded this from our calculations of what portion of GiveDirectly's all-time incurred expenses were cash grants due to uncertainty about what portion of this was spent in the period of the analysis (i.e. by July 2017) and the relatively small amount (and thus the small impact of seeking out the information needed to make this correction).
• $2 million for research on the UBI project. This amount was granted to a research partner. We have excluded this from our calculations of what portion of GiveDirectly's all-time incurred expenses were cash grants. GiveDirectly notes that it was involved in the initial fundraising for this work, but does not expect to be involved in future fundraising. GiveDirectly research costs summary (November 2017), unpublished source • 125. See previous footnote.$3.5 million is the sum of $158,863 for Ideas42,$2 million for UBI, $1 million for Rwanda benchmarking and$350,000 for the Aspirations study. We are missing cost data for the Rarieda RCT, the general equilibrium study, and have partial cost data for the Aspirations and Ideas42 studies.

• 126.

For illustrative purposes: assuming $1 million for Rarieda RCT,$2 million for the general equilibrium study, and $1 million for the Aspirations study, while excluding studies that may be less relevant to GiveWell's review of GiveDirectly and/or consist primarily of future costs. Note that this is a very rough estimate and is for illustrative purposes only. • 127. GiveDirectly financial summary through July 2017 • 128. "Funding we estimate we will receive by EOY and will be available for 2018:$27.7M

• Retail: $11.6M (assumes we would continue to be a GW recommended charity. Likely meaningfully lower if not) • Partnerships:$16M
• Note - reasonable uncertainty given importance of December raise and unpredictableness of partnerships sales conversations. The above is our median projection for each but we could imagine anywhere between $7 and$15m for retail, and $5 and$30m for partnerships (more detail on partnerships pipeline available if of interest)."

Email from Matt Johnson, GiveDirectly CMO, September 8, 2017.

• 129.

GiveDirectly scaled from committing $2.8 million to households in 2013, to$10.1 million in 2014, $13.9 million in 2015, and$30.1 million in 2016. GiveDirectly financial summary through July 2017, "Rate of transfers per year" sheet

• 130.
• 131.

List of potential partnership projects from GiveDirectly (unpublished source)

• 132.

Paul Niehaus and Mitch Riley, GiveDirectly President and Evaluation Lead, conversation with GiveWell, October 2, 2017.

• 133.

List of potential partnership projects from GiveDirectly (unpublished source)

• 134.

Paul Niehaus and Mitch Riley, GiveDirectly President and Evaluation Lead, conversation with GiveWell, October 2, 2017

• 135.

See this spreadsheet, sheet "Spending opportunities."

• 136.

2017 update: We asked GiveDirectly for first quarter monitoring data, including refusal rates, from all three countries and second quarter monitoring data from Rwanda (because when we originally asked for first quarter data, this was not fully available in an easily shareable format from Rwanda due to the newness of the program in Rwanda). Refusal rates at census:

• Kenya Q1 2017: 764 of 3461 (22.1%)
• Uganda Q1 2017: 2 of 606 (0.3%)
• Rwanda Q1 2017: 0 of 942 (0%)
• Rwanda Q2 2017: 1 of 1648 (0.06%)
• 137.

GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 138.

GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 139.

"GiveDirectly is also meeting with senior or retired government officials who can provide guidance on navigating the government and connect GiveDirectly to allies on the public sector side." Conversation with GiveDirectly, April 8, 2014, Pg 11.

• 140.

"By now, GiveDirectly understands well the process for seeking government approvals in Kenya and does not see acquiring approvals as a major risk." Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014, Pg 3.

• 141.
• 142.

Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015

• 143.

GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 144.

GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 145.

"GiveDirectly recently laid off some staff members due to lack of funding." GiveWell's non-verbatim summary of a conversation with Matt Johnson and Paul Niehaus, June 28, 2017