GiveDirectly

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Published: November 2019

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's work has been studied in multiple randomized controlled trials (RCTs). (More)

What do you get for your dollar? The proportion of total expenses that GiveDirectly has delivered directly to recipients is approximately 83% 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. With additional funding, it could significantly increase the number of cash transfers it delivered in its six countries of operation and potentially expand to additional countries. Over 2020-2022, we estimate that GiveDirectly could productively use several hundred million dollars more than we expect it to receive. Update: In November 2019, we recommended that Open Philanthropy grant $2.5 million to GiveDirectly, which leaves it with a funding gap of ~$450 million. See this page for a summary of GiveDirectly's expected use of additional funding. (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:

• There is limited evidence on the long-term impact of the type of transfers (large, one-time transfers; and, going forward, unconditional long-term income transfers) that GiveDirectly generally provides, as well as the impact of such transfers on local economies. We expect further research on these questions to be available in the future. We have reviewed some evidence relevant to the question of the effect of cash transfers on non-recipients here.

Our review process

We began reviewing GiveDirectly in 2011. Our review process has consisted of

• Extensive communications 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).

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 low-income countries primarily via mobile phone-linked payment services. It has operated since 2009 and is currently active in Kenya, Uganda, Rwanda, Liberia, Malawi, the Democratic Republic of the Congo (DRC), and Morocco.1 To date, GiveDirectly has primarily provided large, one-time transfers. It also operates a basic income guarantee program, in which recipients 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 non-governmental organizations (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
• GiveDirectly's spending breakdown by country and program

Standard cash transfer program

Grant size

GiveDirectly's standard model involves grants of approximately $1,000 (USD)2 delivered over several months in two payments. We estimate that the average family receives $288 per capita from GiveDirectly. 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 of all households in each village.
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 to all recipients as transfers are being sent to ask various questions about recipients' experiences. They also make in-person visits to vulnerable recipients. 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 have 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.3

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.4 When choosing which evaluations to run, GiveDirectly also considers the potential impact on policymakers.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 An RCT examining the macroeconomic effects of GiveDirectly's program in Kenya was completed in 2018. Details of the study are in this footnote.9 In October 2018, GiveDirectly shared early results from the RCT on spillover effects of its program, which we discuss here. As of April 2019, full results of the study were expected in June 2019.10

Basic income guarantee study

GiveDirectly began a study of providing long-term, ongoing cash transfers sufficient for basic needs ("basic income guarantee") in 2017. As of April 2019, the first endline data collection for this study was expected to begin in late 2019.11 The study is expected to provide transfers to about 20,000 individuals; 5,000 individuals will receive a basic income for 12 years, while others will receive a basic income for 2 years or a lump sum transfer for the same amount. Basic income recipients will receive about $0.75 per adult per day (more details in footnote).12

GiveDirectly has told us that policymakers, academics, and others have shown an increased interest in universal basic income experiments and GiveDirectly believes the project could have significant policy impact.13 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).14

Refugee program

In December 2017, GiveDirectly launched a $3.5 million pilot program distributing cash transfers to refugees in Uganda. The program targeted refugees who had been displaced for at least five years, as well as households in the communities hosting them; in the pilot, 51% of beneficiaries were refugees.15 GiveDirectly believes that the households targeted by this program are at a similar level of poverty as the recipients in its standard cash transfer program; we have not seen data on the poverty levels of recipients in the refugee program.16 As of late March 2018, the pilot had reached 4,371 households with transfers of about $650.17 In September 2018, GiveDirectly published a report on the results of the pilot study. We have not reviewed the results in-depth. GiveDirectly concluded that it is operationally feasible to deliver large cash transfers to refugee and host communities and that the program was well-received.

GiveDirectly began work on a scale-up of the refugee program in Uganda, with a planned cost of $18.7 million, in September 2018, with enrollment expected to begin in June 2019. GiveDirectly aims to reach all households in the Kiryandongo settlement of Uganda with transfers of roughly $1,000. This program will continue to target long-term refugees, as well as households in the communities hosting them.18 GiveDirectly plans to evaluate the impact of this program through an RCT,19 partly with a goal of generating evidence for policymakers about the use of cash transfers in refugee programs. As of April 2019, GiveDirectly had raised over $10 million for this program and was continuing to fundraise to close the remaining gap. RCT results are expected in March 2021, and the program will conclude in early 2022.20

GiveDirectly also launched a refugee program in Rwanda, with an initial planned cost of $1.97 million. Enrollment for the program began in May 2019. GiveDirectly will deliver transfers of roughly $700 to 2,276 long-term refugee households in the Mugombwa refugee camp in Rwanda, with the goal of testing this program model in a new context. As of May 2019, this project was fully funded, and the final report was expected in early 2020.21

Partnership work

GiveDirectly has partnered with a number of institutional partners and foundations to implement cash transfers to populations of specific interest to those funders.

GiveDirectly is matching funding with USAID to deliver cash transfers and run studies on its impact in Rwanda, Liberia, Malawi, and DRC. In Rwanda, GiveDirectly conducted a study to compare the impact of a nutrition program and two sizes of cash transfers, results of which were published in September 2018.22 As of April 2019, GiveDirectly had started distributing cash transfers in Liberia and Malawi and had established an office in DRC. In April 2019, GiveDirectly signed an agreement for an additional project with USAID in Morocco.23

GiveDirectly is running additional partnership projects with other funders, generally foundations. These projects include:24

• In partnership with FSD Africa, GiveDirectly is piloting a cash transfer program targeting urban youth (aged 18-35) in Nairobi. Recipients will also receive access to digital financial tools aimed at encouraging entrepreneurial behavior.
• In partnership with the Benckiser Stiftung Zukunft Foundation, GiveDirectly piloted a cash transfer program targeting coffee farmers in the Iganga district of Uganda. This program is complete, and results were published in May 2019.25
• Both the Uganda and Rwanda refugee programs (described above) are funded by a number of foundations.

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.

Cash transfers breakdown

The following table shows GiveDirectly's committed cash transfers by country and program in 2018 and targeted for 2019.

Committed cash transfers by country and program (millions USD)26

Country	Program	2018	2019 targeted
Kenya	Standard program	$14.6	$9.0
Kenya	Basic income study	$17.5	-
Kenya	Urban youth partnership project	$1.1	$0.2
Kenya (total)		$33.2	$9.2
Uganda	Standard program	$6.6	$6.4
Uganda	Refugee program	$2.3	$3.8
Uganda	Coffee-growing areas partnership project	$1.6	-
Uganda (total)		$10.5	$10.2
Rwanda	Standard program	$8.3	$6.0
Rwanda	Refugee program	-	$1.5
Rwanda	Cash benchmarking partnership project	$0.4	-
Rwanda (total)		$8.7	$7.5
Liberia	Two partnership projects (one with USAID)	$0.9	$1.7
DRC	Partnership project with USAID	-	-
Malawi	Partnership project with USAID	-	$5.4
United States	Hurricane relief	$6.0	-
Total committed transfers		$59.2	-
Total number of households		58,705	-

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.
• Is GiveDirectly effectively targeting very poor households? The evidence we have suggests that GiveDirectly effectively targets low-income recipients. GiveDirectly uses two models to identify beneficiaries: in some places where it works, it distributes cash transfers to all households in selected villages, and in others it distributes cash transfers to only the households in a village that it identifies as meeting a threshold for being among the poorest. We believe that both models are likely to target very poor households.
• 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,27 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.

Is GiveDirectly effectively targeting very poor households?

GiveDirectly selects beneficiaries using one of two methods. In some locations, it selects villages with high poverty levels and distributes cash transfers to all households in those villages (the "village saturation" model). In other locations, it selects villages with high poverty levels and distributes cash transfers to the households in those villages that it identifies as meeting a threshold for being among the poorest (the "household targeting" model).28 Under both models, villages are selected primarily based on local poverty levels, though other factors, such as security, population density, accessibility, and the presence of other NGOs in the area, are also considered.29 We think it is likely that both models successfully identify very poor households.

The village saturation model

GiveDirectly is using the village saturation model in all villages where it works in Kenya and all villages in Uganda except for those involved in an ongoing research project. Due to the high cost of doing so, GiveDirectly does not routinely collect data on the absolute levels of poverty (i.e. average assets and consumption) of households enrolled in its program. Preliminary results from GiveDirectly's general equilibrium study indicate that households in the area targeted for that study (in Kenya) are very poor. Endline results from this study in July 2018 found that mean consumption per capita per day in the full population of the control villages was $0.79.30 We would guess that this population is fairly similar to current beneficiaries of GiveDirectly's program in Kenya. We have less information about recipients in Uganda, though prior research conducted when GiveDirectly used the household targeting method in both Kenya and Uganda suggested that recipients in Uganda were only slightly less poor than recipients in Kenya.31

Given our estimate that households in places where GiveDirectly previously used the targeting model had a mean per capita consumption level of approximately $0.78 per day,32 the results from the general equilibrium study suggest that the income levels of recipients under the targeting and saturation models are very similar. Prior to seeing the new data, we expected recipients under the village saturation model to be somewhat wealthier, on average, than the average recipient under the household targeting model. This result may be an indication that the targeting model was not particularly effective at identifying the poorest households in a village and/or that GiveDirectly is now working in areas that are poorer overall than where it worked previously.

Anecdotal evidence from our site visit to GiveDirectly's program in Kenya in 2012 is consistent with the idea that a large portion of households in villages targeted by GiveDirectly are very poor (more detail in footnote).33

Additional results from GiveDirectly's ongoing research studies may help to inform our understanding of the absolute levels of poverty of current recipients in areas where it uses the village saturation model (though our understanding is that GiveDirectly was using the household targeting model when some of its ongoing studies began).

We note that given our understanding that a high percentage of households in the areas where GiveDirectly uses the village saturation model are very poor (and may in fact not be wealthier than recipients under the targeting model, as we had previously assumed), it seems likely to us that the benefits of the village saturation model (e.g. reducing overhead costs associated with targeting individual households, reducing the potential for conflict between village residents, and reducing the potential for negative effects on the well-being and economic outcomes of people who live near transfer recipients and do not receive transfers) outweigh the possibility that transfers may go to recipients with somewhat higher average income.

The household targeting model

GiveDirectly uses the household targeting model in Rwanda, largely because the Rwandan government requires that the program use eligibility criteria to identify recipients rather than giving cash transfers to all households in a village. Based on the monitoring data we have seen, we believe that this model is generally effective at identifying very poor households.34

In selected villages, households are deemed eligible if they both:

1. Belong to one of the bottom two government-defined poverty tiers ("ubudehe").35 Ubudehe status is collected during the census survey and verified via either government-issued health insurance cards or a government database, if a health insurance card is not available.36
2. Have a score of 45 or below on the Poverty Probability Index (PPI), an independently-created poverty measurement tool managed by Innovations for Poverty Action that uses a respondent's answers to ten questions to generate a score.37 GiveDirectly estimates that a PPI score of 45 in Rwanda corresponds to a median daily consumption level of $0.48 per person.38 The ubudehe criterion disqualifies many households that have a qualifying PPI score, so the average score of eligible households is significantly lower.39 In 2017, households that were deemed eligible and successfully enrolled in the program had an average PPI score of 21.5 (which GiveDirectly estimates corresponds with a median daily consumption level of about $0.38 per person), while ineligible households had an average score of 44.3 (which GiveDirectly estimates corresponds with a median daily consumption level of about $0.56 per person).40 We have not vetted the PPI methodology.

Using these two criteria, in the second half of 2017 (the most recent period for which we requested data), 57% of censused households in Rwanda were deemed eligible.41

The process for determining eligibility includes a census of all households to identify those that meet the eligibility criteria and at least one subsequent visit to each selected household to confirm that it meets these criteria. For more detail on this process, see our page with additional information about GiveDirectly.

Reservations about the targeting model

As noted above, in general we are unsure whether attempting to target only the poorest members of a community is worth the costs, given that we expect almost everyone in the communities that GiveDirectly works in to be quite poor, though we understand that, in the case of Rwanda, targeting is a legal requirement. For more detail on our reservations about the household targeting model, see below.

Verifying eligibility

In all of the countries in which it works, GiveDirectly audits a subset of registered households to verify their eligibility; it aims to audit 25-40% of registered households.42 GiveDirectly audited 58% of registered households in Kenya in the first quarter of 2017 (after which transfers were put on hold for the rest of 2017 due, in part, to the launch of GiveDirectly's universal basic income program). In the second half of 2017, it audited about 40% of registered households in Rwanda and between 25% and 40% of registered households in Uganda.43 GiveDirectly reports that in the second half of 2017, 1.2% of audited households were removed after audit.44 In Rwanda, households are removed if they do not meet the relevant poverty criteria. Registered households in Uganda and Kenya are only removed if GiveDirectly believes that they have falsely claimed to be legitimate households within village boundaries.45

For more detail on the verification process, see our page with additional information about GiveDirectly.

Eligible households declining to participate

In the Homa Bay region of Kenya, GiveDirectly has encountered high rates of households declining to participate in the program regardless of whether they are eligible. In 2015-16, 45% of censused households in Homa Bay declined to participate.46 In the first quarter of 2017, 22% of households censused for GiveDirectly's standard cash transfer program in Kenya declined to participate; we are unsure what proportion of these refusals were in Homa Bay.47 In GiveDirectly's programs in other areas, including its universal basic income (UBI) program in Kenya, under 5% of households declined to participate in the second half of 2017.48

For more detail on refusals in Homa Bay, see our page with additional information about GiveDirectly.

GDLive

In 2016, GiveDirectly launched GDLive, 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 unedited49 and include answers to such questions as:50

• "What is the biggest hardship you've faced in your life?"
• "What is the happiest part of your day?"
• "What did you spend the payment you received on?"

Based on the responses we have seen, we believe that the survey respondents are very poor. We would guess that the profiles are reasonably representative of GiveDirectly's recipients. The selection of recipients that GiveDirectly asks to participate is largely, though imperfectly, random, and a high portion of those asked agree to participate (84% as of early 2018).51

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

Of recipients reached for follow-up during the second half of 2017 (the most recent period for which we have requested data), 99.7% report having received all transfers.52 (GiveDirectly has generally been able to reach the vast majority of recipients for follow-up surveys; details in footnote.53) Of all transfers made during the same time period, 75.6% of first transfers were received within ten weeks of being censused and 73.5% of second transfers were received within twenty weeks of being censused.54

Below we discuss GiveDirectly's methods for distributing funds to recipients and two cases of staff fraud that GiveDirectly has uncovered.

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.55 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.56 GiveDirectly has told us that recipients are generally able to withdraw cash from mobile money agents located in or near their villages.57 Recipients must pay a small fee when they withdraw a portion of their transfer (around 1% for large withdrawals, and higher for small withdrawals).58

GiveDirectly works with a mobile money provider called MTN in Uganda.59 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.60

In Uganda, the agent network is less robust; however, GiveDirectly has found that recipients are still able to withdraw cash from mobile money agents.61 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.62

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.63

We have not yet asked GiveDirectly for the details of how recipients access funds in Rwanda; information on speed of cash deployment in Rwanda is in this footnote.64

Staff fraud

GiveDirectly has discovered and written publicly about two cases of staff fraud in its Uganda program and one case in its Liberia 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 of 2018, GiveDirectly is conducting "internal audits" designed to identify fraud perpetrated by non-recipients as part of its monitoring process; more information can be found on our supplementary page.

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.65 This could increase the risk of large-scale crime.66 GiveDirectly believes that additional security measures are unlikely to be particularly useful (details in footnote).67 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.68 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.69 GiveDirectly has taken multiple measures to address the vulnerabilities exposed by this case of fraud (see footnote for details).70

Uganda 2015-2016

GiveDirectly estimates that, between September 2015 and December 2016, GiveDirectly staff in Uganda stole up to 0.5% of transfers delivered (up to $60,000). The funds were stolen "primarily through staff enrolling ineligible households with the expectation of receiving part of their transfers."71

GiveDirectly became suspicious that there might be a problem based on data from its audit team, whose role is to survey past recipients about key aspects of the program. A whistleblower within the organization also suggested that there might be fraud; the whistleblower came forward after a field director held a meeting to encourage whistleblowing.72

In response, GiveDirectly conducted interviews with staff and with 8,000 recipients.73 It dismissed the staff that it believes were responsible, as well as complicit management staff.74 GiveDirectly notes that, going forward, it has made changes to its processes to reduce the risk of future fraud (details in footnote).75

Liberia 2019

In Liberia, GiveDirectly determined that recipient families had been enrolled as multiple households in order to receive multiple cash transfers.76 GiveDirectly estimates that less than $20,000 was lost, primarily through cash transferred and phones distributed to ineligible households.77 GiveDirectly's response included re-training staff78 and terminating staff who were involved in the fraud.79

Other issues

Other possible issues with GiveDirectly's process for sending cash to recipients include:

• In Kenya, M-PESA agents could be overcharging or stealing some of recipients' funds.80 GiveDirectly recognizes that this is a common criticism from recipients who call into GiveDirectly's hotline, but believes it is likely that many recipients with this complaint are not fully aware of how to use their mobile money accounts.81 Results from GiveDirectly's follow-up surveys indicate that this problem is fairly rare.82
• In Uganda, some recipients have experienced delays in accessing their funds due to MTN not activating their accounts immediately.83
• Recipients who are unfamiliar with mobile phones or mobile money accounts may not know how to keep their information secure.
• Some of the recipients that GiveDirectly serves are not able to fully understand how to use the mobile money payments system on their own, or do not have the mobility to go to agents or cash out days to withdraw their funds.84 For these recipients, GiveDirectly finds a trustee or helper who aids them with their cash transfers; GiveDirectly tries to ensure that this person is someone the recipient trusts.85

How do recipients spend their cash, and how does this spending impact their lives?

Findings from the RCT

In the RCT, researchers collected data by surveying members of the treatment and control groups about their recent spending. GiveDirectly recipients increased the value of their non-land assets and their monthly consumption and did not increase spending on alcohol or tobacco. The RCT also found increases in food security, revenue, psychological well-being, and female empowerment for recipients of cash transfers. There was no significant effect found on health and education outcomes, profits, or cortisol levels.

We write extensively about the results from GiveDirectly's RCT in our intervention report on cash transfers.

Data from follow-up surveys

For several of GiveDirectly's past campaigns, GiveDirectly staff surveyed recipients on how they used their cash transfers during the follow-up calls, but has since discontinued the practice due to the limitations of self-reported data.86 Recipients reported spending a large portion of their transfer on "building." The next largest expenditure categories were household items, livestock, and school. See our page with additional information about GiveDirectly for more detail.

GiveDirectly has also presented some limited data on spending in a single village on its website.87 These data indicate that the vast majority of recipients (over 75%) in the village used their transfer to buy an iron roof.88 The next three largest categories of spending were on other home improvements, livestock, and furniture.89

Since late 2016, GiveDirectly has been sharing some of the data it collects on how recipients report spending their transfers through its GDLive tool (more above).

Anecdotal evidence from our site visit

In our site visit to Kenya, we asked recipients about the value of items commonly purchased with transfer funds.90 Recipients reported that their thatched-roofs frequently leak when it rains and require replacement every 3-4 months at a cost of 1,000 Kenyan shillings ($11.68 based on the exchange rate as of November 15, 201291) as well as time/labor. One recipient also reported that when it rains, she moves her family and their belongings into other structures to stay dry. Recipients reported buying livestock as an investment/savings device, hoping that they could (a) use the milk from the cow or goat for additional income and (b) sell the cow or goat and any offspring in the future if/when they needed additional funds (for e.g., secondary school fees for their children which are approximately 15,000 Kenyan shillings per year92 [$175.13 based on the exchange rate as of November 15, 201293]).

Will the results be different in other campaigns?

GiveDirectly's RCT was conducted in Rarieda, Kenya. GiveDirectly now primarily works in Homa Bay, Kenya, Uganda, and Rwanda.94 We guess that these contexts are similar enough that the impact of cash transfers on recipients will be roughly similar.

GiveDirectly has informed us that most potential recipients in Homa Bay County already have iron roofs.95 Additionally, Rwanda recently banned thatched roofs, so recipients are more likely to already have iron roofs there.96 To date, our estimate of investment returns from GiveDirectly's cash transfers has been based on the return to buying an iron roof (due to this being a particularly common purchase). The fact that iron roofs are already common in Homa Bay and Rwanda raises questions about how recipients will spend transfers and what returns on their investments they will get. GiveDirectly has noted that Homa Bay County is geographically very close to Rarieda and that the poverty rate in Homa Bay County is higher than it was in Rarieda, which could indicate that cash transfers will do more good in Homa Bay.97 We expect to learn more about the impact of cash transfers on recipients in Homa Bay from the results of the "Aspirations" study (more).

Are the size and structure of the cash transfers well-thought-through and appropriate?

We discuss GiveDirectly's rationale for setting the grant size at $1,000 per household and our reservations about it on our page with additional information about GiveDirectly. In short, 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 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 in Kenya in November 2012, during which we spoke with recipients and non-recipients about potential problems.

Does distribution to some community members and not others result in jealousy, conflict, or related issues?

This is of greater concern in places where GiveDirectly uses the household targeting model than in places where it uses the village saturation model, but there is also the possibility of across-village effects in areas with the village saturation model. Across-village effects may be mitigated in cases where GiveDirectly provides transfers in all villages in a selected region. GiveDirectly notes that it is also possible that cash transfers could lead to positive externalities or spillover effects.98

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.99

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:100

• 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 2017.101 In follow-up surveys from the second half of 2017, recipients were asked about their experience with customer service and 2.7% reported receiving no customer service after trying.102

Data from follow-up surveys

GiveDirectly has sent us results from follow-up surveys conducted in multiple transfer campaigns.103 Note that GiveDirectly surveys only cash recipients, not non-recipients, and all data are self-reported.

In 2018, we asked for data for the second half of 2017 from GiveDirectly's follow-up calls, in which GiveDirectly call center agents asked all recipients whether they had heard complaints about GiveDirectly. The data we have seen suggest that recent complaint rates are very low.104 Recent rates of bribery and theft, as reported in follow-up surveys, are also fairly low.105

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

Data from hotline calls

Records of calls made to GiveDirectly's hotline 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, though such complaints appear to be relatively infrequent.

In 2018, we asked for aggregated data from call center logs for the second half of 2017. During that time period, GiveDirectly received 6,631 calls to its center in Kenya and 931 calls to its center in Rwanda. GiveDirectly reports that the 931 calls in Rwanda included 75 reports of adverse events, such as theft and household conflict; calls in Kenya were categorized differently and reports of adverse events are not clearly demarcated.106

See footnote for data from earlier periods.107

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

We revisited the relevant evidence on this question and shared our updated analysis in November 2018. We concluded:

For more, see our full report on this question.

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.108 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.109

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

• People stealing cash and cellphones from recipient households110
• People contacting recipients and posing as GiveDirectly staff to defraud recipients of funds111
• Mobile money agents defrauding recipients of funds112
• GiveDirectly staff defrauding recipients of funds (we discuss two cases 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.113 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.114

For the first quarter of 2017 (the period for which we requested data on this metric in 2017), 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.115 Across all programs in the second half of 2017 (the period for which we requested data on this metric in 2018), 1.8% of recipients reported having experienced theft themselves.116

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.117 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.

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.

Cash deployment in the first quarter of 2017 (the period for which we requested data on this metric in 2017) 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 in Kenya and Uganda whose transfers had been delayed due to reasons such as registration issues and loss to follow-up and that, for Rwanda, payments were delayed from Q1 to Q2 due to local regulatory constraints.118 In the second half of 2017 (the period for which we requested data on this metric in 2018), cash deployment appears to have proceeded more quickly, with 75.6% of all recipients receiving the first transfer within 70 days of census and 73.5% receiving the second transfer within 140 days of census.119 GiveDirectly reports that over 90% of recipients who were censused in 2017 (i.e., excluding recipients who were registered for the program earlier and received payments in 2017) received payments within 70 days of the census.120

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.121

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.122 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).123

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.124 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. Evidence from a later follow up on the Rarieda RCT is more difficult to interpret and may point to some distortion; we plan to do more research on this question in the future (more in this blog post). 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.

Does GiveDirectly divert skilled labor away from other areas?

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.125 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.126 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.127 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.128 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.129 GiveDirectly has provided evidence that weakly suggests that the international aid sector is moving towards benchmarking programs against cash.130 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:131

• Anecdotally, GiveDirectly has heard that some large funders are asking themselves "Is this better than cash?" before making grants.132 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.133
• GiveDirectly believes there has been an increase in demand from policymakers for evidence that compares programs to cash.134
• 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).135
• Anecdotally, GiveDirectly has heard that several new cash transfer programs, new evaluations, and increased transparency practices were inspired by GiveDirectly.136 GiveDirectly believes that, by executing an excellent program, it may put competitive pressure on other implementers to also perform effectively.137
• GiveDirectly has provided informal advice to new cash programs and studies.138
• GiveDirectly has participated in several high-level panels and roundtables.139
• 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.140

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).141 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.142 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 83.0% of GiveDirectly's all-time incurred expenses.143

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.144 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).145 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. In late 2017, 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.146 For comparison, GiveDirectly's total spending through February 2018 was $133 million; including, for example, $4 million in additional research costs147 would decrease the portion of funding that has reached households to 81%.148
• It excludes costs of following up with households who have received transfers recently (and so who have not yet been followed up with).
• It includes fundraising costs that are expected to generate revenue in the future.

A breakdown of GiveDirectly's spending from August 2016 to February 2018 is in GiveWell's analysis of GiveDirectly financial summary through February 2018. 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.

In summary:

• Total opportunities to spend funds productively: GiveDirectly believes it could spend roughly $160 million in 2020, $200 million in 2021, and $250 million in 2022, if it had sufficient funding to do so. (More)
• Cash on hand: As of June 2019, GiveDirectly held $86 million. For all currently available funding (as of June 2019), GiveDirectly has either earmarked the funds for supporting core costs and projects other than its standard cash transfer program, or expects to commit the funds through its standard cash transfer program to specific households by the end of 2019. (More)
• Expected additional funding: We estimate that GiveDirectly will raise $44-50 million per year in each of the next three years. (More)
• Track record of scalability: GiveDirectly has a track record of being able to scale up quickly and effectively and our understanding is that in recent years it has been constrained by funding, not capacity;149 it does not yet have a track record of operating at the size it believes it could scale to in 2020. (More discussion of potential risks to GiveDirectly's ability to scale up below.)

Over 2020-2022, we estimate that GiveDirectly could productively use several hundred million dollars more than we expect it to receive—we roughly estimate $450 million based on GiveDirectly's expectations of its capacity to scale. Update: In November 2019, we recommended that Open Philanthropy grant $2.5 million to GiveDirectly, which leaves it with a funding gap of ~$450 million. See this page for a summary of GiveDirectly's expected use of additional funding.

More detail in the sections below and in this spreadsheet.

Uncommitted and expected funding

As of June 2019, GiveDirectly held $85.7 million. GiveDirectly has earmarked all of this funding for core costs or specific projects, or expects to allocate it to specific households through its standard cash transfer program by the end of 2019. It has made the following commitments and allocations (which include $2.1 million in expected future revenue):150

• $21.4 million committed to recipient households that have already been enrolled in the program.
• $29.6 million expected to be committed to recipient households that will be enrolled in its standard cash transfer program by the end of 2019.
• $23.2 million earmarked for cash transfers through other projects: UBI, the refugee project, and partnership projects in Uganda, Kenya, Liberia, Malawi, DRC, and Morocco.
• $6.6 million earmarked for matching funding from institutional partners (more below on these types of projects).
• $1.9 million earmarked for fundraising activities.
• $5.1 million held in reserve for staff salaries and fundraising costs.

We roughly estimate that GiveDirectly will raise $50 million in the next year and $44 million in the following two years. This is based on:151

• Funding independent of GiveWell: In the past year, GiveDirectly has received $44 million that we do not attribute to GiveWell's recommendation. We note that we previously estimated that GiveDirectly would receive $22 million independent of GiveWell's recommendation of GiveDirectly in that period, suggesting that using past revenue to project future revenue may lead to underestimates; we have not corrected for that here because we do not expect it to change our conclusions about GiveDirectly's overall room for more funding.
• Funding due to being a GiveWell top charity: GiveWell maintains both a list of all top charities that meet our criteria and a recommendation for which charity or charities to give to in order to maximize the impact of additional donations, given the cost-effectiveness of remaining funding gaps. Based on the amount of funding that we tracked to GiveDirectly in 2018 as being due to our recommendation, we estimate that GiveDirectly will receive $7.9 million in the next year from donors who use our top charity list but don't follow our recommendation for marginal donations.152

Additional spending opportunities

GiveDirectly expects to spend a total of $44 million in 2019153 and believes it could increase spending up to $161 million in 2020, $196 million in 2021, and $249 million in 2022. GiveDirectly could put additional funding toward:154

1. Top priorities for 2020:
   • Operate standard cash transfer programs in five countries (Kenya, Uganda, Rwanda, Malawi and DRC),155 each at an estimated minimum level to achieve $0.85 per dollar donated transferred to households ($5 to $10 million per country for a total of $36 million). GiveDirectly notes, "The rolling programs keep country offices open, enabling us to say 'yes' to more types of projects delivering cash to poor people and produce more types of research."
   • Fill the funding gap for an RCT of its refugee program in Uganda ($2.4 million)
   • Fundraising work ($3.5 million)
2. Additional opportunities in 2020:
   • Higher volumes of cash transfers in the six countries in which it operates (up to an additional $109 million)
   • Expansion to an additional country ($10 million)
3. Similar opportunities, with further expansion possible, in 2021-2022

Separately, GiveDirectly has set aside $6.6 million to provide matching funds for partnerships with institutional partners for specific cash transfer projects.156 As of July 2019, GiveDirectly was in discussions with five potential partners about projects in several countries, including three of its current countries of operation. In total, GiveDirectly estimates that, if it proceeds with these projects, it would need to spend $7.7 million and its partners would spend a total of $14.6 million, however it estimates a low likelihood (5-40%, varying between projects) that any given discussion will lead to a confirmed agreement.157 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.158

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. Risks include:

• Refusals: As discussed on our page with additional information about GiveDirectly, there have occasionally been periods over the past few years when GiveDirectly experienced a fairly high rate of people in Kenya refusing to be enrolled. GiveDirectly has had low rates of refusal in Uganda and Rwanda.159 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).160 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.161 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. Refusal rates in the UBI program over the second half of 2017 were considerably lower than in the standard Kenya program in the first quarter of 2017.162
• 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.163 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.164
• 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.
• 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 others 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 primarily works.
• 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.165 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 some difficulties in the partnership.166 GiveDirectly also notes that in late 2017 and early 2018, it successfully partnered with another alternative provider in Uganda (Post Bank) in order to serve some of the households enrolled in its refugee program in Uganda.167
• 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.168 GiveDirectly believes that its hiring processes have been successful and that new staff are taking on responsibility quickly and competently.169 In 2017, GiveDirectly laid off some staff due to lower than projected revenue170; 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 use qualitative assessments of our top charities to inform our funding recommendations. See this page for more information about this process and for our qualitative assessment of GiveDirectly as an organization.

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
Center for Global Development blog post, April 2018	Source (archive)
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 Joe Huston, GiveDirectly, April 20, 2018	Unpublished
Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013	Unpublished
GDLive	Source (archive)
GDLive example page	Source (archive)
GiveDirectly blog, An update on fraud management in Uganda	Source (archive)
GiveDirectly blog, Fighting fraud in Uganda	Source (archive)
GiveDirectly census data, standard Rwanda, July-November 2017	Source
GiveDirectly Disbursement figures for 2018 and targets for 2019	Source
GiveDirectly financial summary through February 2018	Source
GiveDirectly financial summary through July 2017	Source
GiveDirectly revenue summary for 2018	Source
GiveDirectly staff, conversation with GiveWell, October 6, 2016	Unpublished
GiveDirectly staff, responses to monitoring questions, October 11, 2016	Source
GiveDirectly website, Basic Income	Source (archive)
GiveDirectly, Blog post, "Announcing cash for refugees," March 27, 2018	Source (archive)
GiveDirectly, Blog post, "How facial identification technology could help our field ops," April 4, 2019	Source (archive)
GiveDirectly, Blog post, "How participants opt in to GDLive," November 17, 2016	Source
GiveDirectly, Blog post, "Our take on HS18, revisited," April 20, 2018	Source
GiveDirectly, Blog post, September 22, 2016	Source (archive)
GiveDirectly, Blog post, September 5, 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, April 2018	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 Survey, May 2018	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, Matching fund summary	Unpublished
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, Refugee pilot study results	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
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 analysis of GiveDirectly financial summary through February 2018	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
Government of Rwanda, "Community-led Ubudehe categorisation kicks off"	Source
Ground Truth Solutions, Survey of affected people and field staff in GiveDirectly's refugee program in Uganda, January 2018	Source (archive)
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
IGIHE, "Ubudehe undergoes reforms, poverty numbers worrying," April 2016	Source (archive)
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.
  • "GiveDirectly was founded by Paul Niehaus, Michael Faye, Rohit Wanchoo and Jeremy Shapiro [...] They created GiveDirectly as a private giving circle in 2009 and opened it to the public in 2011 after two years of operational testing." GiveDirectly, FAQs 2015
  • As of November 2016, GiveDirectly had provided partial or full cash transfers to approximately 52,000 households in western Kenya and eastern Uganda, and was continuing to transfer funds to additional households in both places. Ian Bassin, edits to GiveWell's review, November 10, 2016
  • "In October 2016, GiveDirectly will launch a $5-million-dollar retail campaign in Rwanda." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, pg. 6.
  • "As of April 2019...GiveDirectly has started partnership projects in three new countries—Liberia, Malawi, and the Democratic Republic of the Congo." Joe Huston, GiveDirectly CFO, comments on review, April 2019.
  • "...Morocco, where we're in the process of getting set up." Joe Huston, GiveDirectly CFO, comments on review, October 31, 2019.
• 2.

  "Transfer sizes for the standard lump sum projects: Kenya: $1,085, Rwanda: $970, Uganda: $963." Joe Huston, GiveDirectly CFO, email to GiveWell, February 20, 2018.

• 3.
  • GiveDirectly, Siaya enrollment database
  • GiveDirectly, Google enrollment database
  • GiveDirectly, Nike enrollment database
  • GiveDirectly, RCT Enrollment Database
  • GiveDirectly, Kenya 2M enrollment database, September 2013
  • GiveDirectly, Uganda pilot enrollment database - Kosile
  • GiveDirectly, Uganda pilot enrollment database - Kawo
  • GiveDirectly, Uganda pilot enrollment database - Kanyamutamu
  • GiveDirectly, Uganda pilot enrollment database - Akumure
  • GiveDirectly, Kenya 1.2M enrollment database
  • GiveDirectly, Kenya 2M census results, July 2013
  • GiveDirectly, Kenya, Uganda, and Rwanda enrollment database, 2016
  • GiveDirectly gave permission to publish the following databases on the condition that all personally identifying information was removed. Due to the size of the databases, we have not yet anonymized them and therefore have not published them. If you are interested in the content of these databases, please contact us at info@givewell.org.
    • GiveDirectly, Kenya rolling campaign enrollment database - Homa Bay
    • GiveDirectly, Kenya rolling campaign enrollment database - Siaya
    • GiveDirectly, Uganda 2M campaign enrollment database
• 4.

  GiveDirectly, Update for GiveWell, July 2014, Pg 5.

• 5.

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

• 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.
  • 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).
    • GiveDirectly, GE research and measurement plan, Pg 6.
    • "The GE study in Siaya is an example of how research studies can affect GiveDirectly’s timeline. In that case, GiveDirectly moved more quickly than anticipated, and so had to delay the token transfers for some of its recipients to give the GE team enough time to conduct its baseline survey, which had to be completed before the token transfers were sent." GiveWell's non-verbatim summary of a conversation with Paul Niehaus and Carolina Toth, September 7, 2015, Pg 8.
  • 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.
• 10.

  See row 15 in this spreadsheet.

• 11.

  See row 19 in this spreadsheet.

• 12.

  Details on the study design (GiveDirectly website, Basic Income):

  • "Working in rural Kenya, we'll conduct a randomized control trial comparing 4 groups of villages:
    • Long-term basic income: 40 villages with recipients receiving roughly $0.75 (nominal) per adult per day, delivered monthly for 12 years
    • Short-term basic income: 80 villages with recipients receiving the same monthly amount, but only for 2 years
    • Lump sum: 70 villages with recipients receiving the same amount (in net present value) as the short-term basic income group, but all up front as a 'lump sum'
    • Control group: 100 villages not receiving cash transfers"
  • "More than 21,000 people will receive some type of cash transfer, with more than 5,000 receiving a long-term basic income."
    • The total number of people receiving some type of cash transfer was later updated to 20,000. Joe Huston, GiveDirectly CFO, comments on review, July 13, 2018.
  • "We will use an independent contractor for all research surveying, publicly register the study to mitigate publication bias, and publish a pre-analysis plan that will guide how analysis is conducted to prevent cherry-picking."
  • "While payments for the long-term group will continue for 12 years, we'll have results on how long-term cash transfers influence short-term decisions and welfare within the first 1-2 years."

  Details on GiveDirectly's plans for study evaluation (GiveDirectly website, Basic Income):

  • "Comparing the first and second groups of villages will shed light on how important the guarantee of future transfers is for outcomes today (e.g. taking a risk like starting a business). The comparison between the second and third groups will let us understand how breaking up a given amount of money affects its impact.
  • "We will assess the impact of a basic income against a broad set of metrics, including:
    • economic status (income, assets, standard of living)
    • time use (work, education, leisure, community involvement)
    • risk-taking (migrating, starting businesses)
    • gender relations (especially female empowerment)
    • aspirations and outlook on life"

  Eligibility criteria:

  • GiveDirectly will attempt to enroll everyone in the village. GiveDirectly wants its study to be as helpful to policymakers as possible, and policymakers interested in basic income programs are especially interested in universal programs. Paul Niehaus and Ian Bassin, conversation with Givewell, September 15, 2016
  • "Adult individuals, not households, are the relevant unit for GiveDirectly for all arms (i.e., each adult in a household has their own line)
    All full-time resident adults (>=18) will receive payments
    All individuals >=15 years old will begin to receive payments once they turn 18 for whatever remains of the payment period (e.g., 9 years for a 15 year old)
    Migration: Eligible recipients who migrate out of the village will continue to receive. Recipients that migrate into the village after enrollment has begun are not eligible to receive." GiveDirectly, Update for GiveWell on experimentation, September 2016, Pg 2.
• 13.
  • "GiveDirectly hopes to launch a test of a program that would guarantee a basic income to participants over the course of several years. The idea for a universal basic income has gained some traction around the world but has never been done in its full form and evaluated, so GiveDirectly believes this is a good time to test it." Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016, p. 1.
  • Paul Niehaus and Ian Bassin, conversation with Givewell, September 15, 2016
• 14.

  "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.

• 15.
  • Joe Huston, GiveDirectly CFO, conversation with GiveWell, April 6, 2018
  • "We're working with refugees who are in protracted exile, i.e. people who fled their homes 5 years ago or more. We’re also supporting local communities hosting these refugees, the majority of whom are themselves living in extreme poverty. The goal is not only to empower these families with cash, but also to strengthen social and economic bonds between the two groups." GiveDirectly, Blog post, "Announcing cash for refugees," March 27, 2018
  • GiveDirectly intended for a higher proportion of beneficiaries to be refugees, but encountered constraints in the number of refugees it could enroll. Joe Huston, GiveDirectly CFO, comments on review, July 13, 2018
• 16.

  Joe Huston, GiveDirectly CFO, conversation with GiveWell, April 6, 2018

• 17.
  • GiveDirectly, Blog post, "Announcing cash for refugees," March 27, 2018:
    • "We’ve reached 4,400 households, (21,500 individuals) with our pilot. With your support, we’re targeting 8,000 households in the project’s next phase."
    • "So far, households in our pilot received lump sum transfers of around US $650."
  • In a comment on a draft of this review on July 13, 2018, Joe Huston (GiveDirectly CFO) updated the number of households reached to 4,371.
• 18.

  Joe Huston, GiveDirectly CFO, comments on review, April 2019

• 19.

  See row 20 in this spreadsheet.

• 20.

  Joe Huston, GiveDirectly CFO, comments on review, April 2019

• 21.

  Joe Huston, GiveDirectly CFO, comments on review, June 2019

• 22.

  See row 14 in this spreadsheet.

• 23.

  Joe Huston, GiveDirectly CFO, comments on review, April 2019

• 24.

  Joe Huston, GiveDirectly CFO, comments on review, April 2019

• 25.

  See row 4 in this spreadsheet.

• 26.

  @Disbursement figures for 2018 and targets for 2019@

  In 2018, GiveDirectly provided cash transfers to people affected by hurricanes in the United States.

• 27.

  Note that in this context, "consumption" refers to total spending, rather than being limited to food consumption.

• 28.

  Until 2017, GiveDirectly used the household targeting model in all countries in which it worked. For more information on its historical processes for identifying eligible households, see the previous versions of our review of GiveDirectly.

• 29.

  For more information on GiveDirectly's process for selecting villages, see our page with additional information about GiveDirectly.

• 30.
  • "For GE [general equilibrium] pure control villages surveyed at endline, mean per capita consumption was $0.79. That estimate takes total consumption and divides by the total number of people. If, instead, you weight the estimate by household (i.e. calculate per-capita income at the hhd level and examine the distribution of those values), median per capita consumption was $0.73 and mean was $0.99 (with the difference from the 'true' per capita estimate driven by larger households having lower per capita consumption)." Email from Joe Huston, GiveDirectly CFO, July 13, 2018
  • GiveDirectly confirmed that, while GiveDirectly used the household targeting method in treatment villages included in the general equilibrium study, the average and median consumption figures reported above were based on the entire population of the control villages rather than just households that met GiveDirectly's eligibility criteria. Conversation with Joe Huston, GiveDirectly CFO, August 6, 2018.
• 31.

  GiveDirectly, What We Do - Operating Model, "Uganda" tab

• 32.

  Control group households in Haushofer and Shapiro 2013 had a mean monthly non-durable consumption level of $157.40 USD PPP (Table 1, p. 49). In our cost-effectiveness model, we use this figure to roughly estimate baseline annual consumption per capita at $286 (nominal USD; see here for more detail). This translates to a daily consumption rate of approximately $0.78 (nominal USD).

• 33.

  At the time of our site visit, GiveDirectly's program in Kenya was using a household targeting model. We visited five locations (three in Siaya and two in Rarieda) where GiveDirectly was either in the process of enrolling recipients or had already transferred 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 the program, which was using thatched roofs and mud building materials as its criteria at the time. During our site visit, we got the impression that both recipient and non-recipient households in the treatment villages and other households in the surrounding areas were at similar levels of extreme poverty.

• 34.

  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 and 2018, we requested only summary data on key metrics, rather than household-level data.

  Note that prior to 2017, GiveDirectly used the household targeting model in Uganda and Kenya in addition to Rwanda. For more information on its historical processes for identifying eligible households, see the previous versions of our review of GiveDirectly.

  • GiveDirectly, Siaya enrollment database
  • GiveDirectly, Google enrollment database
  • GiveDirectly, Nike enrollment database
  • GiveDirectly, RCT Enrollment Database
  • GiveDirectly, Kenya 2M enrollment database, September 2013
  • GiveDirectly, Uganda pilot enrollment database - Kosile
  • GiveDirectly, Uganda pilot enrollment database - Kawo
  • GiveDirectly, Uganda pilot enrollment database - Kanyamutamu
  • GiveDirectly, Uganda pilot enrollment database - Akumure
  • GiveDirectly, Kenya 1.2M enrollment database
  • GiveDirectly gave permission to publish the following databases on the condition that all personally identifying information was removed. Due to the size of the databases, we have not yet anonymized them and therefore have not published them. If you are interested in the content of these databases, please contact us at info@givewell.org.
    • GiveDirectly, Kenya rolling campaign enrollment database - Homa Bay
    • GiveDirectly, Kenya rolling campaign enrollment database - Siaya
    • GiveDirectly, Uganda 2M campaign enrollment database
  • GiveWell, spot checks of Segovia registration sample 2016
  • GiveDirectly staff, responses to monitoring questions, October 11, 2016
  • GiveDirectly, Dashboard Metrics for GiveWell, May 2017
  • GiveDirectly, Dashboard Metrics for GiveWell, August 2017
  • GiveDirectly, Dashboard Metrics for GiveWell, April 2018
• 35.

  The Rwandan government describes the bottom two ubudehe categories as follows:

  • "Category 1: Families who do not own a house and can hardly afford basic needs.
  • Category 2: Those who have a dwelling of their own or are able to rent one but rarely get full time jobs."

  Government of Rwanda, "Community-led Ubudehe categorisation kicks off"

• 36.

  Joe Huston, GiveDirectly CFO, email to GiveWell, May 21, 2018

• 37.
  • "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)."

  Poverty Probability Index, FAQs

• 38.

  Joe Huston, GiveDirectly CFO, email to GiveWell, May 21, 2018

• 39.

  Joe Huston, GiveDirectly CFO, email to GiveWell, May 10, 2018

• 40.

  "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)."

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

• 41.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 3.

• 42.

  "After registration, a portion of recipients are randomly chosen to be audited.
  "We have and will continue to experiment with using specific risk factors to select recipients, but currently most audited recipients are selected randomly and we target auditing 25-40% of all recipients."
  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 13.

• 43.
  • "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
  • GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 13:
    • "After registration, a portion of recipients are randomly chosen to be audited.
      "We have and will continue to experiment with using specific risk factors to select recipients, but currently most audited recipients are selected randomly and we target auditing 25-40% of all recipients."
    • A graph shows that audit rates in Rwanda hovered around 40%, while audit rates in Uganda ranged from about 25% to about 40% over the course of the second half of 2017.
• 44.
  • GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 13.
  • Email from Joe Huston, GiveDirectly, April 20, 2018
• 45.

  "In Rwanda, eligibility is determined by poverty indicators collected at census (Ubudehe score and PPI), which means that a portion of every village is usually deemed ineligible. In Kenya Standard and Uganda Standard, we saturate villages, meaning that people are only deemed ineligible if we believe they have lied about their status as a legitimate household living within the village boundaries." GiveDirectly census data, standard Rwanda, July-November 2017

• 46.

  "In general, an overwhelming majority of eligible recipients opt to receive cash transfers from GiveDirectly. In Siaya, where GiveDirectly Kenya has operated from 2011 to the beginning of 2016, over 95% of recipients who are given the opportunity to be a part of the program accept it. In Uganda and Rwanda more than 96% of eligible recipients have opted in, respectively.

  "These figures are high relative to participation rates in typical development programming, which is not surprising given the unconditional nature of our transfers. For example, Manuela Angelucci and Orazio Attanasio found that Oportunidades, a conditional cash transfer program in Mexico, had take up rates of roughly 50%. Oriana Bandiera et al. report that a BRAC training program targeted at adolescent girls in Uganda expected participation rates of roughly 20%.

  "Recently, however, we’ve seen lower than usual participation rates in parts of Kenya. In July 2015 we entered Homa Bay, a new county and our first venture outside of Siaya. In Homa Bay and the neighboring areas, roughly 45% of the households we speak with decline to be enrolled into the program. As it turns out these challenges have been common for NGOs working in the area. Other development programs focused on HIV, water and sanitation, agricultural development, education, and female empowerment have also faced community resistance."
  GiveDirectly, Blog post, September 5, 2016

• 47.

  GiveDirectly, Dashboard Metrics for GiveWell, May 2017, cell E7.

• 48.
• Data on refusal rates from the second half of 2017 (the most recent period for which we requested data):
  • Overall: 2.4%
  • Standard Uganda: 1.4%
  • Standard Rwanda: 0.2%
  • UBI Kenya: 4.8%
  • Refugees Uganda: 0.1%

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018

• 49.

  "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." GiveDirectly, email newsletter, December 27, 2016, Pg 2.

• 50.

  GDLive example page

• 51.

  "We choose to ask GDLive questions for certain projects (currently, Kenya standard lump sums, Uganda standard lump sums, the coffee project with BSZ, UBI, the remote project in Uganda), and within those projects we randomly offer recipients the option to participate. Across the projects, 84% of recipients asked choose to participate.

  "I should note the randomization isn't always 100% pure - it's spurred by a random process in the surveys for those projects, but in some cases the step it appears in (e.g., audit) isn't a totally random selection itself or in one non-study village with UBI-style payments the percentage of GDLive questions is higher."

  Joe Huston, GiveDirectly CFO, email to GiveWell, February 20, 2018.

• 52.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018

• 53.

  The following details relate to data from the second half of 2017.

  • More than 99% of households were reached for at least one follow-up call. Recipients were reached for follow-up after 94% of transfers. "By project, we reached recipients for follow-up after 100% of payments for Uganda and Rwanda lump sum and Uganda refugees projects, and 70% of the Kenya lump sum project. In the Kenya project, >99% of recipients were reached for at least 1 follow-up with the 30% of post-transfer follow-ups dropped coming entirely from not successfully following up after the last payment." Joe Huston, email to GiveWell, April 13, 2018
  • Regarding the lower follow-up rate among recipients in the standard program in Kenya, "We found this cohort of recipients more difficult to reach over the phone, potentially because [some people were boycotting Safaricom (the telco related to M-Pesa) for political reasons] or because coverage was just worse in the areas where these recipients were from. Given this, we will likely need to reach these recipients via in-person follow-up, which we have deprioritized until after UBI enrollment (as rapid UBI enrollment has increased demands on the follow-up team)." Joe Huston, email to GiveWell, April 17, 2018
• 54.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 8. Details on speed of transfer deployment broken down by program are available in this source.

• 55.
  • Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012
  • GiveWell visit to M-PESA agent, November 8, 2012
• 56.

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

• 57.

  "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

• 58.
  • "Mobile money payments are subjected to a small, less than 1%, withdrawal fee; GiveDirectly absorbs this cost by increasing the transfer amount accordingly." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 4.
  • The withdrawal fee table for M-PESA is here (archive).
• 59.

  "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.

• 60.
  • Observations from GiveWell site visit to GiveDirectly, October 2014, Pg 5.
  • "Mobile money payments are subjected to a small, less than 1%, withdrawal fee; GiveDirectly absorbs this cost by increasing the transfer amount accordingly." GiveWell's non-verbatim summary of a conversation with Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016, Pg 4.
  • The withdrawal fee table for MTN withdrawals is here (archive).
• 61.

  "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.

• 62.
  • For example, see GiveWell, spot checks of Segovia follow-up data sample, 2016 and GiveDirectly, Follow-up Survey, May 2018.
  • 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.
• 63.

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

• 64.

  In the second half of 2017, 74.0% of first transfers were received within ten weeks of being censused and 50.2% of second transfers were received within twenty weeks of being censused. GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 11.

• 65.
  • 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.
• 66.

  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.

• 67.

  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

• 68.
  • GiveDirectly 's blog post on the staff fraud: GiveDirectly blog, Fighting fraud in Uganda
  • "Two GD staff members stole 2% of transfers on Google campaign ($20,500 in total) in form of 50,000 UGX deductions from penultimate or final transfers (85%) or theft of entire final transfers (15%)"GiveDirectly, Check in with GiveWell, September 2014
• 69.

  "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

• 70.

  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

• 71.
  • Joe Huston, GiveDirectly CFO, conversation with GiveWell, November 10, 2017
  • GiveDirectly blog, An update on fraud management in Uganda
• 72.
  • Joe Huston, GiveDirectly CFO, conversation with GiveWell, November 10, 2017
  • GiveDirectly blog, An update on fraud management in Uganda
• 73.

  Joe Huston, GiveDirectly CFO, conversation with GiveWell, November 10, 2017

• 74.

  GiveDirectly blog, An update on fraud management in Uganda

• 75.

  "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

• 76.

  “Prior to signing recipients up for our program, we had sent a team to count households in target villages. When we returned to enroll those households, we saw population numbers 75% in excess of what we’d seen the first time around. What was driving this sudden spike in residency? Two options seemed likely: (1) community members were showing up in multiple villages to try to qualify for more than one transfer, or (2) legitimate residents were presenting their families as more than one household in order to receive multiple transfers...Through on the ground investigation, we’ve since determined the low rate of duplicates largely stems from the fact that most fraudulent activity was of the second variety (i.e. single households pretending to be multiple).” GiveDirectly, Blog post, "How facial identification technology could help our field ops," April 4, 2019

• 77.
  • "We were able to identify something was wrong earlier in the process in this case -- between the research mapping stage and our census, which allowed us to respond/investigate earlier." Joe Huston, GiveDirectly CFO, comments on review, June 2019.
  • "Total losses were kept small ([less than] $20K). We estimate $8.5K was transferred to ineligible households, and an additional $8.8K were lost in the form of phones distribute to ineligible households. $2K was paid out by GiveDirectly to staff who were dismissed early, corresponding to the remaining salary on their contracts." Joe Huston, GiveDirectly CFO, comments on review, October 31, 2019.
• 78.

  “While the numbers weren’t sizable, the cases we did identify provided fairly strong evidence of misconduct on the part of multiple staff members...We’ve since re-trained our staff to better discern such cases.” @GiveDirectly blog, How facial identification technology could help our field ops@

• 79.

  Joe Huston, GiveDirectly CFO, comments on review, June 2019

• 80.

  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

• 81.
  • Ms. Tala (an Assistant Field Manager, see footnote above) believes that these reports are incorrect and that approximately 10% of recipients are not fully aware of how to use M-PESA, so they may withdraw funds without checking their balance, ultimately being surprised when they have drawn down their account. Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012
  • GiveDirectly told us that it recognizes this issue and maintains a hotline to provide recipients with assistance in navigating the M-PESA system: GiveDirectly helps recipients with issues using M-PESA via our hotline. Staff sometimes contact M-PESA agents to notify them one of our clients will be visiting and may need assistance. Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012
• 82.

  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.

  • GiveDirectly, Siaya verification stats
  • GiveDirectly, Rarieda verification stats
  • GiveDirectly, Siaya follow-up data - disaggregated
  • GiveDirectly, Nike follow-up data - disaggregated
  • GiveDirectly, Google follow-up data - disaggregated (LS - long)
  • GiveDirectly, Rarieda Top-up Verification (short)
  • GiveDirectly, Rarieda verification (top ups), May 26, 2013
  • GiveDirectly, Siaya verification, June 15, 2013
  • GiveDirectly, Google verification, September 2013
  • GiveDirectly, Nike verification (final), September 2013
  • GiveDirectly, Kenya follow up data, November 2014
  • GiveDirectly, Uganda pilot follow up data, April 2014
  • GiveDirectly gave permission to publish the following databases on the condition that all personally identifying information was removed. Due to the size of the databases, we have not yet anonymized them and therefore have not published them. If you are interested in the content of these databases, please contact us at info@givewell.org.
    • GiveWell, GiveDirectly follow up surveys summary - Uganda, September 2015
    • GiveWell, GiveDirectly follow up surveys summary - Kenya, September 2015

  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.

• 83.

  "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.

• 84.

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

• 85.
  • 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.

• 86.

  "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.

  • GiveDirectly, Kenya follow up data, November 2014
  • GiveDirectly, Uganda pilot follow up data, April 2014
  • GiveDirectly, Rarieda verification (top ups), May 26, 2013
  • GiveDirectly, Siaya verification, June 15, 2013
  • GiveDirectly, Google verification, September 2013
  • GiveDirectly, Nike verification (final), September 2013
  • GiveWell, GiveDirectly follow up surveys summary - Uganda, September 2015
  • GiveWell, GiveDirectly follow up surveys summary - Kenya, September 2015 (Note: GiveDirectly gave permission to publish this database on the condition that all personally identifying information was removed. Due to the size of the database, we have not yet anonymized it and therefore have not published it. If you are interested in the content of this database, please contact us at info@givewell.org.)
• 87.

  GiveDirectly, What We Do - Who We Serve, September 2016

• 88.

  GiveDirectly, What We Do - Who We Serve, September 2016 See the chart in the upper left section of the web page.

• 89.

  GiveDirectly, What We Do - Who We Serve, September 2016 See the chart in the upper left section of the web page.

• 90.

  For full details of our interviews with recipients, see GiveWell Site visit notes.

• 91.

  Provided by Google via Citibank N.A. on November 15, 2012.

• 92.

  Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012.

• 93.

  Provided by Google via Citibank N.A. on November 15, 2012.

• 94.

  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.

• 95.
  • "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.
• 96.

  Paul Niehaus and Ian Bassin, conversation with Givewell, September 15, 2016

• 97.

  "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

• 98.

  Comments on a draft of this review, September 9, 2018.

• 99.
  • 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.
• 100.
  • GiveDirectly has shared 9 versions of the survey forms it used, as the form was modified over time. The most recent version of the survey we have seen is GiveDirectly, Follow-up Survey, May 2018.
  • For earlier versions, see GiveDirectly, Kenya verification template, August 2013, GiveDirectly, Verification template (October 1, 2012) and GiveDirectly, Verification template (November 7, 2011) (corresponding data for the latter: GiveDirectly, Verification data (November 17, 2011))
• 101.
  • "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
• 102.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 6.

• 103.

  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.

• 104.
  • Follow-up surveys include a question about whether the respondent has heard complaints within their community: "Has the recipient heard complaints about GD in their community?" GiveDirectly, Follow-up Survey, May 2018
  • About 20,000 recipients were reached for follow-up surveys across GiveDirectly's three countries of operation, of whom 36 reported hearing complaints within their communities. Categories of complaints include "some eligible houses were skipped," "GD is evil/from the devil," and "GD should not use different criteria in different villages." GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 16.
  • More than 99% of households were reached for at least one follow-up call. Recipients were reached for follow-up after 94% of transfers. Joe Huston, email to GiveWell, April 13, 2018
• 105.

  0.3% of recipients reported being asked for bribes, and 0.2% paid bribes. 1.8% of recipients reported experiencing theft. GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 16.

• 106.
  • Of inbound calls to the hotline in Kenya, none are clearly categorized as adverse events; the most common types of calls are returning missed follow-up calls, general inquiries about GiveDirectly, and inquiries about transfer dates.
  • Of 75 adverse events reported to the hotline in Rwanda, there were 22 calls categorized as being about an imposter, 21 about household conflict, and 14 about theft. Other adverse events received less than 10 calls each.
  • We have not seen data from Uganda because GiveDirectly only began formally tracking call center data in Uganda in December 2017.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pgs 6-7.

• 107.

  In the most recent complete hotline call data that we have seen (from October 2014), the most common type of adverse event recorded is household conflict, followed by theft (GiveDirectly, Follow-up tracker, October 2014 Sheet: "Summary"). 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). (GiveDirectly, Follow-up tracker, October 2014 Sheets: Summary, cell H22). 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. Additional sources:

  • GiveDirectly, Kenya hotline log, July 2013 (May 2012 – July 2013)
  • GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)
  • GiveDirectly, Follow-up tracker, October 2014 (January 2013-September 2014). This file also contains issues reported through other channels, such as to field staff during field visits and during follow up surveys.
  • GiveDirectly, Kenya top 10 adverse events 2015 (Note: we are unsure if all of the issues reported in this file were reported through the hotline.)
  • GiveDirectly, Uganda top 10 adverse events 2015 (Note: we are unsure if all of the issues reported in this file were reported through the hotline.)
  • GiveDirectly, Kenya randomized sample of adverse events, 2014-2015
  • GiveDirectly, Uganda randomized sample of adverse events, 2014-2015
  • We have not checked to see whether the issues reported in these files are overlapping, though note that the time ranges are overlapping.
• 108.

  Haushofer and Shapiro 2013 Policy Brief, Table 10, Pg 38.

• 109.
  • 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
• 110.

  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.)

• 111.

  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.)

• 112.

  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.)

• 113.

  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.

• 114.
  • Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 (unpublished)
  • GiveDirectly, Follow-up tracker, October 2014;GiveDirectly, Kenya hotline log, July 2013 (May 2012 – July 2013); GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)
• 115.

  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, sheet “Summary Dashboard Metrics,” Cells E11, F11.
  GiveDirectly, Dashboard Metrics for GiveWell, August 2017, cell E12.

• 116.

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pg 16.

• 117.

  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.

• 118.

  GiveDirectly, Dashboard Metrics for GiveWell, May 2017

• 119.
  • Overall: 75.6% (transfer 1), 73.5% (transfer 2)
  • Standard Uganda: 62.2% (transfer 1), 94.6% (transfer 2)
  • Standard Rwanda: 74.0% (transfer 1), 50.2% (transfer 2)
  • Standard Kenya: 95.5% (transfer 1), 95.5% (transfer 2)
  • Refugees Uganda: 100% (transfer 1)

  GiveDirectly, Dashboard Metrics for GiveWell, April 2018, Pgs 8-12.

• 120.

  Joe Huston, GiveDirectly CFO, email to GiveWell, November 8, 2017

• 121.

  "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.

• 122.
  • Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012.
  • Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013
• 123.
  • The hotline records also document the steps that GiveDirectly has taken to follow up on recipients' concerns; we have reviewed some of these cases and find the steps taken to resolve issues to be reasonable and appropriate.
  • GiveDirectly, Kenya hotline log, July 2013
  • GiveDirectly, Follow-up tracker, July 2013
  • GiveDirectly, Follow-up tracker, October 2014
  • Data from GiveDirectly indicate that on average 11% of recipients in Kenya campaigns are delayed due to registration issues. GiveDirectly, Monthly operations report, August 2015
• 124.

  "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

• 125.

  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)

• 126.
  • 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).
• 127.

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

• 128.

  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

• 129.

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

• 130.

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

• 131.

  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).

• 132.

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

• 133.

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

• 134.

  For example.

• 135.

  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"

  GiveDirectly, Update for GiveWell, September 2015, pgs. 5-6.

• 136.

  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.

• 137.

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

• 138.

  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.

• 139.

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

• 140.

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

• 141.

  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

• 142.

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

• 143.

  GiveWell's analysis of GiveDirectly financial summary through February 2018

• 144.
  • "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
• 145.

  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

• 146.

  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.

• 147.

  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.

• 148.

  GiveWell's analysis of GiveDirectly financial summary through February 2018

• 149.

  GiveDirectly scaled from committing $2.8 million to households in 2013 to $10.1 million in 2014, $13.9 million in 2015, $30.1 million in 2016, $29.3 million in 2017, and $59.2 million in 2018 (including $17.5 million in commitments through the Basic Income project). GiveDirectly estimates that it will commit $34 million to households in 2019. GiveWell's analysis of GiveDirectly financial summary through February 2018, "Rate of transfers per year" sheet and @Disbursement figures for 2018 and targets for 2019@.

• 150.

  See this spreadsheet, sheets "Funding commitments", "Available and expected funding", and "Source: Allocated funding."

• 151.

  Calculations in this spreadsheet, sheet "Available and expected funding"

• 152.

  See this spreadsheet, sheet "Available and expected funding," row 21. $7.9 million is the amount that we tracked to GiveDirectly as being due to GiveWell, less the grant for $2.5 million that we recommended Good Ventures make to GiveDirectly in November 2018 (see this blog post, section "Our recommended allocation for Good Ventures"). Note that the 2021 and 2022 projections do not include this assumption, i.e. does not assume that GiveWell will continue to include GiveDirectly on our top charity list past this year, which is the reason they are lower than the 2020 projection.

• 153.

  @Disbursement figures for 2018 and targets for 2019@

• 154.

  See this spreadsheet, sheets "Spending opportunities" and "Source: Funding gaps"

• 155.

  According to GiveDirectly's list of spending opportunities, it does not plan to operate a standard cash transfer program in Liberia in 2020 unless it raises sufficient funding to operate a minimum level in its other countries of operation. See this spreadsheet, sheet "Source: Funding gaps."

• 156.

  See this spreadsheet, sheet "Source: Allocated funding," row 16

• 157.

  GiveDirectly, Matching fund summary, 2019, unpublished

• 158.

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

• 159.
  • In 2017, 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%)

    GiveDirectly, Dashboard Metrics for GiveWell, May 2017
    GiveDirectly, Dashboard Metrics for GiveWell, August 2017

  • In 2018, we asked for data on refusal rates for the second half of 2017.
    • Overall: 2.4%
    • Standard Uganda: 1.4%
    • Standard Rwanda: 0.2%
    • UBI Kenya: 4.8%
    • Refugees Uganda: 0.1%
    • Very few people were censused for the standard program in Kenya during that time period.

    GiveDirectly, Dashboard Metrics for GiveWell, April 2018

• 160.

  GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 161.

  GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 162.

  4.8% in the UBI program in the second half of 2017, GiveDirectly, Dashboard Metrics for GiveWell, April 2018. 22.1% in standard Kenya program in the first quarter of 2017, GiveDirectly, Dashboard Metrics for GiveWell, May 2017.

• 163.

  "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.

• 164.

  "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.

• 165.
  • "Dependency on a single provider: low risk. In Uganda, we have Ezee Money and Airtel as a back-up, and in Kenya could use Equity Bank."
    GiveDirectly, Update for GiveWell, July 2014 Pg 12.
  • "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.
  • GiveDirectly continues to seek other mobile money providers who it might want to partner with for future campaigns in Uganda (e.g., Airtel).
    Conversation with GiveDirectly, April 8, 2014 Pg 5.
• 166.

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

• 167.

  Joe Huston, comment on this review, September 10, 2018

• 168.

  GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 169.

  GiveDirectly staff, conversation with GiveWell, October 6, 2016

• 170.

  "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