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GiveDirectly – November 2016 version


GiveDirectly is one of our top-rated charities and we feel that it offers donors an outstanding opportunity to accomplish good with their donations.

More information: What is our evaluation process?

Published: November 2016


What do they do? GiveDirectly ( 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. It appears that GiveDirectly has been effective at delivering cash to low-income households. GiveDirectly has one major randomized controlled trial (RCT) of its impact and took the unusual step of making the details of this study public before data was collected (more).

What do you get for your dollar? The proportion of total expenses that GiveDirectly has delivered directly to recipients is approximately 82% overall (more).

Is there room for more funding? We believe that GiveDirectly is highly likely to be constrained by funding next year. We expect GiveDirectly to have $19.8 million to spend on its standard cash transfer campaigns in its 2017 budget year. We estimate that if it received an additional $46 million (allowing it to commit $65.8 million) its chances of being constrained by funding would be reduced to 50%. (more).

GiveDirectly is recommended because of its:

  • Focus on a program with a low burden of proof and a strong track record (more).
  • Strong process for ensuring that cash is well-targeted and consistently reaches its intended targets (more).
  • Documented success in transferring a high portion of funds raised directly to recipients (more).
  • Standout transparency (more).
  • Room for more funding. We believe that GiveDirectly can use substantial additional funding productively (more).

Major open questions include:

  • While GiveDirectly has one major RCT of its activities in Kenya, there is still limited evidence on the impact of the type of transfers (large, one-time transfers; and, in the future, unconditional long-term income transfers) that GiveDirectly generally provides, particularly the long-term impact of such transfers. There are currently several ongoing experimental evaluations of GiveDirectly's programs, including a long-term RCT.
  • GiveDirectly chooses who should receive cash on a household-by-household basis, as opposed to simply giving cash transfers to everyone in a village. We have doubts about the efficiency of this strategy, given the difficulties of finding criteria that effectively target the poorest households, the large amount of staff time that goes into vetting each household, and the possible offsetting impact of conflict and jealousy. GiveDirectly will soon test giving cash to every recipient in a village in its basic income guarantee program.
  • We believe GiveDirectly's basic income guarantee program is likely less cost-effective than GiveDirectly's standard cash transfer campaigns. In 2016, GiveDirectly chose to fundraise extensively for the basic income study rather than for its standard cash transfers, leaving it with a large funding gap for standard cash transfer campaigns in 2017.
Table of Contents

Our review process

To date, our review process has consisted of

  • Conversations with GiveDirectly staff: Paul Niehaus (Director and President), Piali Mukhopadhyay (COO, International), Ian Bassin (COO, Domestic), Joy Sun (former COO, Domestic), Carolina Toth (Manager, Finance and Operations), and Stuart Skeates (former Uganda Field Director).
  • Conversations with GiveDirectly board members: Rohit Wanchoo (Director), Michael Faye (Director), and Jeremy Shapiro (former Director).
  • 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.

All content on GiveDirectly, including updates, blog posts and conversation notes, is available here.

What do they do?


GiveDirectly transfers cash to poor households in developing countries primarily via mobile phone-linked payment services.1 It has operated since 2009 and is currently active in Kenya, Uganda, and Rwanda (launched in October 2016).2 To date, GiveDirectly has primarily provided large, one-time transfers. It expects to soon start a basic income guarantee program, in which recipients will receive long-term (over two or twelve years in the initial study), ongoing cash transfers sufficient for basic needs (more).

GiveDirectly's work of providing cash transfers to poor households may also include:3

  • 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.4
  • 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.5

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 research or policy impact its programs might have. We focus on direct impact for historical and pragmatic reasons: in the past, GiveDirectly's direct work was the primary use of additional unrestricted donations, and direct impact is more quantifiable and evidence-backed than research or policy impact. More recently, a greater proportion of GiveDirectly's focus has been on research and policy impact; we are not sure if this trend will continue.

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

Grant structure

GiveDirectly's standard model involves grants of approximately $1,000 (USD) over about four months, after which recipients become ineligible for further grants.6 GiveDirectly has told us that it adjusts its transfer sizes for purchasing power; as of late 2015, in Kenya, GiveDirectly transferred approximately $1,040 to each enrolled household, while in Uganda, it transferred approximately $875.7 We are not sure what the size of transfers in Rwanda will be, but expect it to also be about $1,000 per household.

This is a different approach from the approach we've seen in government cash transfer programs. One way of putting the difference (which has been reflected in GiveDirectly's communications with us) is that government programs aim for "income transfers" (small supplements to income over many years), whereas GiveDirectly's standard program aims for "wealth transfers" (large, one-off transfers that hopefully give people more flexibility to make large purchases and investments). GiveDirectly's basic income guarantee program (more below; expected to launch in late 2016) will be structured as "income transfers."

GiveDirectly's standard transfer schedule involves a small initial transfer (or "token" payment) of about $90 (USD), followed by two larger transfers of about $475 (USD).8 GiveDirectly aims to send these transfers over a period of approximately 4 months.9 GiveDirectly has an ongoing study of behavioral interventions that will allow some recipients the ability to choose when they receive their transfers.

Note that when we reviewed household data from Kenya several years ago, we found that household size varies substantially: while the mean household size was ~4.7 and the median size was 4, 16% of households had 1 or 2 people, ~20% had 6 or more, and the maximum household size was 16.10 We estimate that the average family receives $288 per capita from GiveDirectly, which is 121% of baseline annual consumption per capita for recipients in Kenya.11

GiveDirectly's process

GiveDirectly currently operates in Kenya, Uganda and Rwanda (more details about how GiveDirectly chose those countries in the footnote).12 GiveDirectly is not prioritizing expansion to other countries, as there remain many poor households to serve in the countries in which it operates.13 It does, however, consider opportunities in other countries when compelling reasons are presented, such as the opportunity to impact policy in a way that could only be done in that country or the opportunity to serve a significant number of poor households but only by operating in a donor’s preferred country.14

GiveDirectly's typical process is as follows:

  1. Selection of a local region: Once GiveDirectly has selected a country, it narrows down the geographic region in which it would like to work based on a variety of factors, heavily weighting poverty statistics. For example:
    • GiveDirectly told us that it initially chose to work in western Kenya and eastern Uganda based on poverty statistics.15
    • GiveDirectly considers poverty data, population density, logistical and security factors, and the presence of other poverty-focused NGOs when it selects a district or county to work in.16
    • In early 2015, when selecting sub-counties and sub-locations in Kenya, GiveDirectly considered poverty data, the number of potentially eligible households, how easily it could transfer staff capacity to the new locations, and how rural each area was.17

    Note that we have reviewed the data GiveDirectly used in some of the examples above (see footnotes).

  2. Selection of villages: GiveDirectly selects villages primarily based on poverty level and location.18 For details on how GiveDirectly has targeted villages historically, see this footnote.19 For recent campaigns in Kenya and Uganda, GiveDirectly has estimated poverty levels through census data.20
  3. Obtaining permission from local officials: Before beginning to work in a given area, GiveDirectly obtains permission from local officials. This process can involve officials from the national to the village level and generally requires a series of conversations to get all the relevant stakeholders on board.21 GiveDirectly signs written agreements with or obtains approval letters from local officials to formalize permissions.22
  4. Village meeting: A village meeting is held "introduce GiveDirectly and its programming to the village residents, to answer questions anyone may have about the program, and to clarify that [GiveDirectly is not] affiliated with a political party, government agency, etc."23
  5. Enrollment process:
    • Census: GiveDirectly has field staff from its census team visit the village to create a census of all households.24 The field staff collect data about each household and note if the household is eligible for transfers (the criteria for eligibility in a campaign depend on where the campaign is located – more).25
    • Registration: GiveDirectly has a separate set of field staff from its registration team visit households marked as eligible in the census and register them.26 Registration involves 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 (more detail in footnote).27 A registered household is formally enrolled only after all phases of enrollment (census, registration, back check, and audit) have been completed and the household has obtained a mobile money account (if necessary).28
    • Back check: GiveDirectly sends a separate team of field staff from its back check team to revisit every registered household and collect data about that household that can be compared to data collected during census and registration.29 GiveDirectly field staff also ask households if they were asked to pay a bribe to register.30 GiveDirectly is currently testing a more streamlined version of its program that does not include the back check step; GiveDirectly hopes to know by the end of 2016 if it can maintain the quality of its program without this step.31
    • Audits: GiveDirectly sends field staff to revisit a portion of the registered households for audits.32 GiveDirectly determines which households to audit based on the extent of the discrepancies between data collected at different phases in enrollment.33 GiveDirectly field staff resolve discrepancies during audits to determine whether households are eligible or ineligible. Households found to be eligible through this process are then considered formally enrolled, in addition to the households considered eligible after back check and not selected for audit.34

    GiveDirectly aims to enroll all eligible households.35 If eligible members of the household are not home during a phase of enrollment, GiveDirectly staff revisit the household several times until they can be found.36

    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.37

  6. Sending transfers to recipients: GiveDirectly sends transfers to recipients via mobile money providers (and, in one campaign, via a bank) (more).38 See above for more on the grant structure. GiveDirectly is currently testing a more streamlined version of its program that does not include the token payment; GiveDirectly hopes to know by the end of 2016 if it can maintain the quality of its program without this element.39
  7. Conducting follow up calls: GiveDirectly field staff make multiple phone calls and, for vulnerable recipients, in-person visits, to all recipients as transfers are being sent.40 The schedule of follow up calls has varied somewhat by campaign.41 In 2016, GiveDirectly changed its policies such that recipients cannot receive their next transfer installment until they have been reached for follow-up.42 In addition to the follow-up calls, GiveDirectly maintains a phone "hotline" for recipients to call if they have any questions about the transfers or issues in obtaining funds.43 Recipients can also report issues to GiveDirectly field staff when they are in the village; GiveDirectly created a formal mechanism for recording these reports.44

Staff structure

GiveDirectly delivered its first cash transfers in 2011.45 Starting in January 2011 it had one full-time staff member.46 In early 2013 it hired a second full-time staff member to serve as COO (Domestic).47 GiveDirectly has since expanded its staff significantly. As of February 2016, its organizational structure in East Africa included:48

  • Chief Operating Officer International (COO-I): The COO-I provides oversight and quality control of cash transfer programming and international operations. The COO-I oversees the Country Directors.
  • Country Directors (CDs) and Field Directors (FDs): Both CDs and FDs are primarily in charge of overseeing field operations. The Country Directors oversee operations in a given country; the Field Director position is a slightly more junior role. Combined, GiveDirectly had four CDs and FDs in early 2016.49
  • Field Managers and Associate Field Managers: The Field Managers supervise Associate Field Managers, focusing on quality control, management, and training of Field Officers.50 Associate Field Managers manage the logistics of transfer rounds and oversee Field Officers, as well as conduct high-level analysis of field operations and work on technology integration.51 GiveDirectly had 10 Field Managers and Associate Field Managers in early 2016.52
  • Field Officers (FOs): 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 is a separate group of FOs for each of the first three pre-transfer stages: census, registration, and back checks. 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, registration, or back check phases.53 GiveDirectly had 71 Field Officers in early 2016.54

It is our impression that GiveDirectly has grown substantially in 2016 and the numbers above may no longer accurately represent its current size.


In mid-2014, three members of GiveDirectly's board of directors began the for-profit technology company Segovia, which develops software that NGOs and developing-country governments can use to help implement their cash transfer programs.55 Paul Niehaus and Michael Faye, co-founders of GiveDirectly and Segovia, split their time between the two organizations.56 They previously told us that they track their time allocation to projects and would be able to share details of how much time they each spend on GiveDirectly and Segovia; however, when we requested this information, GiveDirectly told us that it had previously understood that we no longer wanted this information so it had stopped explicitly time tracking. This was a miscommunication, but we decided not to ask GiveDirectly to track this going forward. GiveDirectly estimates that the time Faye and Niehaus collectively spend on both organizations amounts to each organization having the equivalent of a full-time CEO.57 One other staff member who was previously working full-time at GiveDirectly now works part-time for each entity.58 We discuss potential risks from the overlap in staff in this blog post.

When Segovia was created, it expected to provide its services to GiveDirectly for free, in order to avoid conflicts of interest between the two organizations.59 However, in 2016, after realizing that providing free services to GiveDirectly was too costly for Segovia (customizing the product for GiveDirectly required much more Segovia staff time than initially expected), the two organizations negotiated a new contract under which GiveDirectly will compensate Segovia for its services.60 GiveDirectly wrote about this decision here. GiveDirectly told us that it recused all people with ties to both organizations from this decision and evaluated alternatives to Segovia.61 We have seen information on what portion of Segovia's revenues GiveDirectly accounts for, but we do not have permission to share that figure publicly.62

Although we believe that there are possibilities for bias in this decision and in future decisions concerning Segovia, and we have not deeply vetted GiveDirectly's connection with Segovia, overall we think GiveDirectly's choices were reasonable. However, we believe that reasonable people might disagree with this opinion, which is in part based on our personal experience working closely with GiveDirectly's staff for several years.

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.63 When choosing which evaluations to run, GiveDirectly also considers the potential impact on policymakers.64 GiveDirectly has told us that it has increased its experimentation to the point where it aims to enroll every recipient in a study or a campaign variation.65 Below, we list the studies and campaign variations that GiveDirectly is currently working on, has completed, or has considered.

Ongoing experimentation

  • 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.66 GiveDirectly is working to conduct an RCT examining the macroeconomic effects of GiveDirectly's program in Kenya.67 Details of the study are in this footnote.68 Endline data collection was expected to be completed by the end of 2016; as of September 2016, midline data was still being analyzed.69
  • Behavioral interventions (Ideas42 study): GiveDirectly is conducting an RCT of two main behavioral interventions: (a) enabling recipients to decide when and how to receive their transfer payments, and (b) providing more information to recipients about spending options.70 Details of the study are in this footnote.71 This study began in late October 2014 and endline results are expected to be available in early 2017.72
  • Gender contracts: GiveDirectly ran a small pilot of informal contracts between spouses receiving cash transfers in the spring of 2015.73 External research partners are evaluating the impacts of the contracts on domestic violence and female empowerment.74 After the initial study group was completed, GiveDirectly piloted a second round in early 2016.75 GiveDirectly has said that if the pilot is successful it will be expanded into a larger-scale project.76
  • Aspirations study: GiveDirectly is running an RCT in 180 villages looking at the effects of showing recipients a motivational video before their participation in GiveDirectly's program.77 A pilot of the intervention was completed, and baseline data collection was nearly finished as of September 2016.78 GiveDirectly does not expect results from this study for several years.79
  • Coffee study: GiveDirectly is implementing an RCT to study the effect of cash transfers on coffee farming communities, and as of September 2016 it was finishing enrollment for the study.80 The study is intended to provide insight into how recipients with high investment return opportunities (i.e., coffee farms) are affected by cash transfers.81 Results are expected in 2018.82
  • High throughput campaign: GiveDirectly is currently testing a more streamlined version of its program that removes the back check and token payment steps of its process.83 It hopes to use the streamlined process in 2017 to increase the amount of cash it can transfer per staff member.84 GiveDirectly expects to have finished the majority of its testing by the end of 201685 and intends to implement this model in Rwanda.86

Previous experimentation

  • 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.87 These transfers were made in Rarieda, Kenya in 2011-2012.88 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.
  • Small-scale RCT of cash transfers to young women: IPA conducted an RCT of GiveDirectly's Nike campaign, which provided transfers exclusively to young women ages 18-19.89 GiveDirectly shared IPA's survey instrument with us prior to the study.90 We did not see an analysis plan prior to the study, as we did with the Rarieda RCT.91 The study is now complete, and GiveDirectly has shared its write-up, as well as a qualitative piece on the perspectives of the young women involved in the study, which was prepared for GiveDirectly by an independent researcher; we have reviewed these documents.92
  • Extended data collection by phone: IPA received a $30,200 grant to extend data collection in a sub-sample of participants from the Rarieda RCT using mobile phone-based data collection techniques.93 The goals of the project were to generate data on longer-term effects of cash transfers (up to two years after completion of the RCT), as well as to study the potential for using mobile phones as cost-effective, easily adaptable tools for data gathering.94 GiveDirectly has sent us the results from this study; they include information on the follow-up rates achieved by different types of surveys and on what participants in the study were thinking about before they were called or texted.95
  • Broadening eligibility with more inclusive targeting: GiveDirectly conducted a small-scale study in Kenya to see whether more inclusive targeting criteria could reduce tension and conflict within villages. Details of the study are in this footnote.96 GiveDirectly found that data collected on adverse events was inconclusive, and that when faced with the decision of how to allocate limited resources, focus groups preferred to prioritize thatched-roof households.97 We put limited weight on these results due to the small sample size of the study and would be interested in seeing further research on this question.
  • Community-based targeting: GiveDirectly piloted community-based targeting, where village residents help determine who should receive cash transfers. GiveDirectly does not expect to implement this targeting method more broadly.98
  • Index-based crop insurance program: GiveDirectly and The Rockefeller Foundation developed a strategy for offering index-based insurance to cash transfer recipients (details on index-based insurance in footnote).99 GiveDirectly then ran a small-scale test of the program in western Kenya, simulating a government cash transfer program.100 GiveDirectly found that the cost of the program was lower than the cost of previous index-based insurance programs and a higher rate of people bought insurance.101
  • Biometrics: GiveDirectly has tested the use of biometrics to enhance security in Uganda.102 GiveDirectly may continue to use biometrics in contexts where national IDs are uncommon.103
  • Eligibility requirements in Homa Bay: GiveDirectly experimented with new eligibility requirements because a) it needed new eligibility requirements for Homa Bay County, where grass is scarce and thus thatch roofs are less common, and b) knowing how to use a number of different eligibility requirements increases GiveDirectly's ability to work in new areas.104 GiveDirectly chose new eligibility requirements for Homa Bay in October 2015 (more).

Future experimentation

Below we describe experimentation that GiveDirectly is planning for or might implement in the future.

Basic income guarantee study

GiveDirectly is planning to begin a study of providing long-term, ongoing cash transfers sufficient for basic needs ("basic income guarantee") in 2017; it launched a pilot of the program in October 2016.105 The study is expected to include approximately 30,000 individuals and provide a basic income for either 2 or 12 years to every adult enrolled (more details in footnote).106 The income will likely be close to $0.75 per day.107 GiveDirectly may solicit input from recipients when determining the timing of the basic income transfers; GiveDirectly suspects most recipients will want to receive larger, more infrequent payments.108

GiveDirectly told us that recently policymakers, academics, and others have shown an increased interest in universal basic income experiments and GiveDirectly believes the project could have significant policy impact.109 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).110

Other future experimentation

Other ideas that GiveDirectly has considered or is considering for future experimentation include:

  • Providing cash transfers in an urban setting111
  • Providing cash transfers as humanitarian relief112
  • Providing cash transfers to sex workers, in part to examine the impact of cash transfers on HIV outcomes113
  • Facilitating the pooling of recipient funds for public goods projects114

Partnership work

GiveDirectly has been exploring projects with a number of partners. The projects that GiveDirectly has partnered on or considered generally involve implementing cash transfers as part of a study funded by an institutional partner. GiveDirectly has also provided informal advice to those considering cash transfer programs. For a sample of smaller potential partnership projects that GiveDirectly has considered, see this footnote.115

In 2015 and 2016, GiveDirectly's President spent approximately 25% of his time on developing partnership projects.116 GiveDirectly has signed agreements or MOUs for the following partnership projects:

  • Rwanda benchmarking project: In 2015, GiveDirectly finalized an agreement for a partnership project in Rwanda. GiveDirectly will be implementing cash transfers in two randomized controlled trials; the studies will cost $4 million and are co-funded by an institutional funder and The studies will test cash transfers as a benchmark against other aid programs funded by the institutional funder.118 GiveDirectly started enrollment for one of the studies in 2016, for which it expects results in late 2017, and as of August 2016 was finalizing the structure of the second study.119 GiveDirectly has provided us with some additional details about the studies, which are not yet public.
  • MOU with large institutional funder: In 2016, GiveDirectly signed an agreement with an institutional funder (whom it is partnering with for the Rwanda benchmarking project) which provides a mechanism through which multiple benchmarking projects (projects comparing cash transfers to other types of aid programs) can be launched.120 The funder and GiveDirectly have each offered to contribute up to $15 million (for a total of $30 million) to support four different benchmarking projects with GiveDirectly acting as the implementer for the cash arm.121 We describe what we know about the process of setting up the benchmarking projects in this footnote.122 We do not yet have details of which aid programs will be evaluated or how the evaluations will be carried out. By the end of 2016, GiveDirectly hopes to identify two countries in which it would like to do a benchmarking project.123

Additionally, GiveDirectly has told us that it has made progress in conversations with several other institutional funders about potential projects.124 If all of the partnership projects GiveDirectly is discussing came through (which GiveDirectly believes is unlikely), GiveDirectly would need $23 to $30 million to support all of them.125

Although partnership projects are now taking up a significant portion of Dr. Niehaus' time, GiveDirectly does not believe this has negatively affected its core operations.126 Over the last year, GiveDirectly has hired two additional high-level staff to help with its partnership work: Ian Bassin and Jo Macrae.127 We expect partnerships to continue to take up the President's time and to involve a significant portion of GiveDirectly’s funding over the next few years.128

We have not yet made a strong attempt to assess the value of the partnership projects beyond their direct impact (more). We can imagine cases where partnership projects might be very high leverage (e.g., enabling another organization to "benchmark" its current programming against cash, perhaps ultimately directing funding away from a less effective intervention to cash transfers) and also cases that may have more limited value (e.g., providing cash transfers at a higher cost given the coordination and other costs of partnership projects).

Does it work?

This section discusses the following questions:

  • Generally speaking, are unconditional cash transfers a promising approach to helping people? We believe that this approach faces an unusually low burden of proof and that the available evidence is consistent with the idea that unconditional cash transfers help people.
  • How effective and well-founded are GiveDirectly's criteria? The evidence we have suggests that GiveDirectly targets low-income recipients. We have reservations about the approach of giving cash transfers to only those who meet GiveDirectly's criteria.
  • Is GiveDirectly effectively targeting people who meet its criteria? We believe GiveDirectly's enrollment process is a relatively effective way of targeting people who meet its criteria, although we note that GiveDirectly has experienced difficulties recently with some people in certain geographic areas refusing to enroll in its program.
  • Does GiveDirectly have an effective process for getting cash to recipients? GiveDirectly's process seems to have been successful so far, with one notable exception.
  • 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 raise 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. These studies generally show substantial increases in short-term consumption, especially food, and little evidence of negative impacts (e.g., increases in alcohol or tobacco consumption). It is important to note that most of these studies are of income transfers; there is more limited evidence for programs with wealth transfer models like GiveDirectly's. This is a potential cause for concern and one of the reasons that we are particularly interested in GiveDirectly experimenting with and evaluating different approaches.
  • There is also some evidence that recipients are able to invest cash transfers at high rates of return (e.g., ~20% per year), leading to long-term increases in consumption.
  • We feel that this intervention faces an unusually low burden of proof, given that short-term poverty reduction is an outcome by definition, though donors' intuitive reactions to it may vary widely.

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

GiveDirectly currently uses two different sets of eligibility criteria for its standard campaigns:

  • Assets and vulnerability status: In its campaign in Homa Bay County, Kenya, GiveDirectly uses an algorithm to determine eligibility; this algorithm uses a number of inputs related to household assets and the vulnerability of recipients.129 GiveDirectly developed this algorithm after testing a number of new potential criteria and expects to use similar algorithms for its other campaigns in the near future.130 It is our understanding that GiveDirectly is working to test and develop a similar algorithm for its eligibility criteria in Rwanda.131
  • Thatched roofs: Until 2015, GiveDirectly used housing materials to select recipients in all of its standard campaigns, enrolling households who live in a house made of organic materials (thatched roof, mud walls, and a mud floor) and excluding households with iron roofs, cement walls, or cement floors.132 GiveDirectly still uses these criteria in Uganda.133

The assets and vulnerability status criteria

In 2015, GiveDirectly started to work in Homa Bay County in Kenya, where families are less likely to have thatch-roofed houses due to a scarcity of grass.134 Consequently, GiveDirectly changed its eligibility criteria for Homa Bay County to better capture the poorest households.135 The algorithm GiveDirectly uses to determine eligibility in Homa Bay takes into account a range of factors including household assets and the vulnerability status of potential recipients; we are unable to elaborate because GiveDirectly would prefer to keep the criteria confidential so as to prevent households from gaming the system (more detail in footnote).136 More detail on how the algorithm was developed is in this footnote.137 Note that GiveDirectly may adjust its eligibility criteria for other campaigns based on its experience in Homa Bay: for example, it is currently developing eligibility criteria using a similar process in Rwanda.138

GiveDirectly tried to choose criteria that (a) included recipients who would benefit the most from the transfer, (b) were difficult to fake, (c) were low cost to implement, and (d) were perceived as fair both by community members and by GiveDirectly staff.139 GiveDirectly believes that, compared to its previous criteria, the assets and vulnerability status criteria are more difficult to fake, somewhat more expensive to administer, and more difficult to explain (which might lead to people believing the criteria are not fair).140 Note that recipients are not made aware of the full criteria (as a measure to prevent cheating), which may also contribute to decreased perceptions of fairness.141 However, because the criteria explicitly put weight on vulnerability, they could also increase perceptions of fairness, or at least offset other fairness concerns.142

GiveDirectly's assets and vulnerability criteria may help GiveDirectly expand to new areas more easily and could provide valuable guidance for other cash transfer programs (although we are unsure if GiveDirectly will be able to share learnings from this project since it hopes to keep its algorithm confidential). However, our evidence for GiveDirectly's impact and for low rates of conflict within villages is based on previous campaigns in which GiveDirectly used different eligibility criteria, and it is possible—although we think unlikely143—that these criteria will substantially change these outcomes.

The thatched roof and mud house criteria

As part of the baseline survey for the RCT of its program, researchers collected in-depth information on poverty levels of recipients. GiveDirectly has shared the full survey form used to interview participants, as well as its own summary of the data collected as of March 2012:144

Well over half of adults skip meals, less than half of household members eat until they are content, people commonly go to sleep hungry and a paltry 18% report having enough food for tomorrow in their household. Those living in eligible homes are even worse-off than the average household, consuming less and holding fewer assets. Overall, mean and median daily per capita consumption among eligible households are $0.65 and $0.55 at nominal rates, and 74% are below the Kenyan poverty line, indicating a very poor population.

GiveDirectly reports that recipients in Uganda have a slightly higher average daily consumption of $0.83.145

GiveDirectly also provided charts that show a clear difference in the consumption, expenditures, and assets of households in mud and thatch homes compared to those in cement homes, but fairly small differences between those living in mud and thatch homes and those living in mud and iron roof homes.146

End-line data on food consumption among control group recipients from GiveDirectly's RCT also suggests that the thatched-roof eligible households are extremely poor.147 This data shows that "20% [sic] of the control group reports that not all household members usually eat until they are content, 23% of respondents report sleeping hungry in the last week, and only 36% report having enough food in the house for the next day."148 Other results related to food consumption are measured as well, which are, in our view, consistent with the notion that recipients are extremely poor.

Concerns about GiveDirectly's eligibility criteria

How much poorer are those selected by GiveDirectly's criteria?
It is not clear to us that people in thatched-roof homes (eligible for transfers) are substantially and consistently poorer than people in iron-sheet-roofed homes with mud walls and floors (not eligible for transfers in a standard campaign). In community-based targeting pilots, GiveDirectly recipients identified households that did not meet GiveDirectly's standard targeting criteria but seemed comparably poor.149 GiveDirectly has also received feedback from field staff and recipients that using housing materials as the targeting criteria systematically misses some households that are viewed within communities as comparably poor to those in thatched-roof houses.150 GiveDirectly still feels that housing materials are an effective means of targeting the poorest of the poor, on average, in areas where it has worked to date.151

Note that the concern that GiveDirectly's criteria do not select the poorest households could also apply to the assets and vulnerability status criteria. However, from a sample of 423 people, GiveDirectly found that its algorithm selected recipients with an average consumption of $0.50 per day, compared to a community average consumption of $0.86 per day.152 We don't believe these numbers are highly reliable, but they lend some support to the claim that GiveDirectly on average targets poorer households.153

What do housing materials or assets indicate about financial management?
To the extent that there are differences in income or wealth between residents of eligible homes and those who live in non-eligible homes, it seems possible that these differences come down to fortune/luck (e.g., people in iron-sheet homes have been more fortunate and thus able to afford iron sheets), but we also think it may come down to differences in choices regarding financial management (e.g., people in iron-sheet homes may have demonstrated better financial management and planning, thus allowing them to acquire iron sheets). If the latter is the case, there is a potential risk that GiveDirectly is systematically targeting the people who are less likely to use additional money well. GiveDirectly comments: "The most informative data available on this point are the differential impacts we’re seeing within the set of eligible households – specifically, poorer families seeing bigger impacts on nutrition while richer households see bigger impacts on tangible investment."154

Are the benefits of targeting the poorest worth the costs?
We also wonder if attempting to target only the poorest members of a community (with any eligibility criteria) is worth the costs, given that we expect almost everyone in the communities that GiveDirectly works in to be quite poor. In addition to the cost of staff time needed to select eligible households and verify their eligibility, giving cash transfers to some members of a community and not others has the potential for increased conflict. GiveDirectly's follow up surveys demonstrate that cash transfers can lead to tension between recipients and non-recipients.155 Though follow up surveys report low levels of tension and conflict, we would expect these to be underreported by recipients to GiveDirectly staff, a dynamic that GiveDirectly has seen play out in past cases.156 GiveDirectly conducted a small-scale study in Kenya to see whether more inclusive targeting criteria could reduce tension and conflict within villages. We find the results inconclusive (more). However, we note that GiveDirectly will be testing universal enrollment again as part of its basic income guarantee study.157 When we spoke with three field staff in Uganda, two of them suggested that it would be better for GiveDirectly's transfers to reach more people in a village, even if it meant reducing the size of a standard transfer. According to the Assistant Field Manager, the current targeting model causes bragging and unrest in the communities, potentially motivating those who don't benefit to steal from those who do. He said it would be better for GiveDirectly to provide transfers to everyone in a village, even if some transfers were small.158

Anecdotal evidence from GiveWell's site visit to Kenya

In November 2012, GiveWell staff visited Kenya to view GiveDirectly's program in the field. See our notes and photographs from the site visit. We visited five locations (three in Siaya and two in Rarieda) where GiveDirectly had transferred funds or was in the process of enrolling recipients to receive funds. We visited approximately 15 households across the five locations (including two non-recipient households with metal roofs and cement walls and floors that did not qualify for GiveDirectly's program). For details on how homes we visited were selected, see this footnote.159 Note that when we visited, GiveDirectly was using thatched roofs and mud building materials as its criteria.

We would characterize the ~15 households we visited (as well as other households we saw while walking but did not speak with directly) as extremely poor. We summarize characteristics of these households in this footnote.160

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

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

Is GiveDirectly effectively targeting people who meet its criteria?

GiveDirectly's process for identifying and enrolling households is described above. It involves multiple unannounced visits by different staff to each recipient home in order to confirm that recipients meet the criteria. (That is, if someone were to temporarily occupy a mud and thatch home in order to be enrolled, they would be unlikely to be sure of being present for future re-checks.) We have examined data collected by GiveDirectly from its enrollment process (registration, back checks, remote checks and audits) for most transfer campaigns; we have only spot-checked the data GiveDirectly shared with us in 2015 and 2016.161

Historically, if the information collected about a household at different stages of enrollment is inconsistent, GiveDirectly staff revisit the household for an audit.162 GiveDirectly tracks the percentage of households found to be ineligible at the back check and audit stages on its website; as of October 2016, it reported that 3% of initially registered participants in Kenya were found to be ineligible by the end of GiveDirectly's enrollment process, while that figure was higher at 6% in Uganda.163 We are not sure over what time period these figures are calculated.164

We believe GiveDirectly's current process to be generally effective at identifying households that meet its criteria. However, GiveDirectly has told us that in the future it plans to experiment with streamlining its enrollment process by excluding the back check step from its process.165 It is possible that this change will allow a greater number of recipients to game the system. However, given that we expect almost everyone in the communities that GiveDirectly works in to be quite poor, we do not believe this is cause for much concern.

Refusals in Homa Bay, Kenya

While we believe GiveDirectly currently has robust systems in place to identify recipients who are attempting to game its enrollment process, GiveDirectly has recently struggled to persuade some potential recipients to start the enrollment process, even if they are eligible.166

GiveDirectly started enrolling recipients from Homa Bay county, Kenya in mid-2015.167 There, it encountered unexpectedly high rates of refusals from potential recipients; while refusal rates in Uganda and Siaya, Kenya have historically been low (around 5%), refusal rates in Homa Bay have been about 45%.168 GiveDirectly believes the refusals are due to widespread skepticism towards GiveDirectly's program and rumors that GiveDirectly is associated with the devil.169

Although GiveDirectly has created an outreach team to address the issue, in part to prevent the problem of refusals from spreading, as of September 2016 it was still facing high rates of refusals.170 GiveDirectly told us that while the refusals have reduced GiveDirectly's efficiency somewhat, GiveDirectly was still ahead of its enrollment targets as of late August 2016.171 GiveDirectly plans to run its basic income guarantee experiment in Kenya, but not in Homa Bay county.172 We intend to continue to follow this issue, as we are concerned about what it could mean for GiveDirectly's ability to scale up in the future (more).173

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

Mobile money providers and distribution models

GiveDirectly transfers funds to recipients through mobile money providers. In Kenya, the mobile money provider, M-PESA, allows users to receive, send, deposit, and withdraw funds on their mobile phones. When withdrawing funds, recipients must present ID along with their mobile phone number and a user-specified M-PESA PIN number to an M-PESA agent.174 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.175 GiveDirectly has told us that recipients are generally able to withdraw cash from mobile money agents located in or near their villages.176 Recipients must pay a small fee when they withdraw a portion of their transfer (around 1% for large withdrawals, and higher for small withdrawals).177

GiveDirectly works with a mobile money provider called MTN in Uganda.178 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.179

In Uganda, the agent network is less robust; however, GiveDirectly has found that recipients are still able to withdraw cash from mobile money agents.180 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.181

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.182

Staff fraud

The most significant issue that GiveDirectly has had in making sure that cash gets to recipients is a case of staff fraud in its Uganda pilot campaign. In mid-2014, GiveDirectly experienced a case of large-scale crime, when two of its 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 deductions from 85% of recipients and $100 deductions from 15% of recipients.183 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.184 GiveDirectly has taken multiple measures to address the vulnerabilities exposed by this case of fraud (see footnote for details).185 We consider fraud to be an ongoing risk to the success of GiveDirectly's programs, but feel that the risk is mitigated by these measures as well as by GiveDirectly's monitoring. It shifted to a distributed cash out model in Uganda in late 2015, which may be somewhat more secure.186

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.187 This could increase the risk of large-scale crime.188 GiveDirectly believes that additional security measures are unlikely to be particularly useful (details in footnote).189 In addition to harming recipients, crime would likely cause delays for GiveDirectly's work.

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.190 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.191 Results from GiveDirectly's follow-up surveys indicate that this problem is fairly rare.192
  • In Uganda, some recipients have experienced delays in accessing their funds due to MTN not activating their accounts immediately.193
  • Recipients who are unfamiliar with mobile phones or mobile money accounts may not know how to keep their information secure. Field Officers may provide assistance during back check visits.194 GiveDirectly checks the quality of its Field Officers' interactions with potential recipients by administering "quality audits" that test how well recipients understand GiveDirectly's program and ask how the Field Officer conducted himself or herself.195
  • 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.196 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.197

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

Findings from the RCT

We write extensively about the results from GiveDirectly's RCT in our intervention report on cash transfers. In the RCT, researchers collected data by surveying members of the treatment and control groups about their recent spending. All data that follows comes from participant self-reports. GiveDirectly recipients increased the value of their non-land assets and their monthly consumption.198 Their spending is broken down in more detail below (all dollar amounts are adjusted for US purchasing power).

  • Total non-land assets.199 Receipt of large transfers increased households’ non-land assets by an average of $463 (95% CI: $378 to $549).200 Households receiving transfers (small or large) were 23 percentage points (95% CI: 17% to 29%) more likely to have an iron roof than the control households.201 Haushofer and Shapiro 2013 estimated that iron roofs cost about $564 based on a survey of one respondent in each of 20 villages.202 GiveDirectly ran a survey that sampled a respondent from each of 20 villages and found that iron roofs cost $418 on average.203 We do not know what explains this discrepancy.
  • Business expenses. Households receiving large transfers spent about $13 per month (95% CI: $1 to $25) more than control households on business expenses, which were primarily made up of non-durable expenses on non-agricultural businesses.204 Recipients of small transfers also spent about $13 more per month (95% CI: $4 to $22).205
  • Health expenditures. Recipients of large transfers spent about $3 (95% CI: -$1 to $6) per month more than control households on health expenditures.206 Recipients of small transfers also spent about $3 (95% CI: $1 to $5) more.207 This spending was also included within the estimate of spending on consumption, below.
  • Education expenditures. Haushofer and Shapiro 2013 reports that treatment households receiving large transfers spent $1.89 (95% CI: $0.20 to $3.58) more than the control households on education expenditures and treatment households receiving small transfers spent $0.79 (95% CI: -$0.31 to $1.89) more.208 Education expenditures were also included within the estimate of spending on consumption, below.
  • Consumption. Treatment households consumed about $51 more per month (95% CI: $32 to $70) than control households.209 About half of this additional consumption was on food.210 This additional consumption also included increased spending on social expenditures and various other expenditures.211
  • Alcohol and tobacco. Treatment households did not increase their spending on alcohol or on tobacco.212

The RCT also found increases in food security, revenue, psychological well-being, and female empowerment for recipients of cash transfers.213 There was no significant effect found on health and education outcomes, profits, or cortisol levels.214

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.215 The surveys were conducted at different points in the transfer cycle of each campaign.216 We summarize the data from the more recent campaigns in Kenya and Uganda below. The spending data from Kenya covers portions of the Kenya 2M, Kenya 1.2M, and Kenya rolling campaigns, and covers dates from February 2014 to September 2015. The spending data from Uganda covers some of the Uganda pilot campaign from October 2013 to April 2014.217 Note that we do not put much weight on this data, as it is all self-reported and we have no control group to compare it to.

Amount of reported funds spent, by category

Kenya Uganda
Category Amount of funds reported to be spent in category (KES) % of total funds reported to be spent in category Amount of funds reported to be spent in category (UGX) % of total funds reported to be spent in category
Food 8,996,160 5.0% 20,667,800 4.4%
Clothing 1,448,061 0.8% - -
Household items 8,590,151 4.8% 56,122,240218 11.9%
Building 100,863,660 55.9% 194,449,559 41.2%
Land 5,499,000 3.0% 19,603,000 4.1%
Livestock 13,621,595 7.6% 66,344,250 14.0%
Farm business 1,896,405 1.1% 10,536,000 2.2%
Non-farm business 8,007,323 4.4% 8,414,000 1.8%
School 9,664,617 5.4% 49,246,000 10.4%
Medical 1,421,347 0.8% 13,434,010 2.8%
Water 25,800 0.0% 0 0.0%
Debt 837,951 0.5% 6,444,000 1.4%
Savings 8,551,415 4.7% 19,258,500 4.1%
Life event 5,571,655 3.1% 750,000 0.2%
Family 1,429,030 0.8% 866,000 0.2%
Church 105,450 0.1% 141,000 0.0%
Transport 1,448,285 0.8% - -
Alcohol - - 5,000 0.0%
Other 2,331,600 1.3% 6,190,000 1.3%
Total 180,309,505 100.0% 472,471,359 100.0%

Note that GiveDirectly has also presented some limited data on spending in a single village on its website.219 This data indicates that the vast majority of recipients (over 75%) in the village used their transfer to buy an iron roof.220 The next three largest categories of spending were on other home improvements, livestock, and furniture.221

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.222 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, 2012223) 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 year224 [$175.13 based on the exchange rate as of November 15, 2012225]).

Will the results be different in other campaigns?

GiveDirectly's RCT was conducted in Rarieda, Kenya. GiveDirectly now primarily works in Homa Bay, Kenya and Uganda, and recently started a standard campaign in Rwanda (in October 2016).226. 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.227 Additionally, Rwanda recently banned thatched roofs, so recipients are more likely to already have iron roofs there.228 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 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.229 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?

GiveDirectly’s standard model is to grant about $1,000 (USD) to households over approximately four months, after which recipients become ineligible for future transfers.230 GiveDirectly has also experimented with different transfer sizes and structures and plans to continue doing so in the future.231 In the past, GiveDirectly has given the following rationale for the size of its standard transfers:232

GiveDirectly sends each recipient household $1,000, or $200 per person for an average household. These payments are spaced out in time to respect limits imposed by the M-PESA system and to give recipients time to plan for them, but should be thought of as wealth and not income transfers. GiveDirectly sized transfers at this level to ensure that they are fair, well-understood, and potentially transformative.
  • Fair. Transfers are calibrated to be large enough to enable eligible households to raise their incomes to the level of their least well off but ineligible neighbors. This calculation was made using baseline data from our ongoing impact evaluation and assuming a 25% annual rate of return. (Our estimate of the return on capital was triangulated using average micro-credit loan charges, academic studies on the returns to capital in developing countries and interviews with recipients.) Calculations based on equalizing net worth, as opposed to income streams, led us to a similar ballpark figure. [GiveDirectly further notes, "'fair' is a subjective concept and we are not arguing for a particular concept of 'fairness' per se but rather that we think many would consider it 'unfair' to transfer so much to eligible [households] that we re-order the wealth distribution. We do not make the claim that non-recipients or particular donors agree that any particular transfer policy is fair."]
  • Well-understood. Transfers are sized to be within the range of transfers issued by other well-studied cash transfer programs. Examples of transfer sizes from other well-known programs include:
    • $406 per household per year for participants in Progressa / Opportunidades, and up to $4,059 in total over ten years.
    • $524 per household per year for participants in Bolsa Familia (Brazil) in 2011, and a maximum of $7,855 in total over five years.

    If anything we would lean towards transferring more than these programs do, since they serve people starting from a higher level of wealth.

  • Potentially transformative. Because cash transfers are flexible by design there are a number of relevant ways to think about what they could do for a recipient.
    • If invested at a 25% real rate of return, the transfer would allow the average recipient to permanently increase his/her [daily] consumption by $0.14 over a baseline level of $0.65, a 22% increase.
    • The transfer is enough to purchase
      • 5.5 years of secondary schooling (estimated returns on a year of education for rural Kenya are around 15%)
      • 5.2 years of basic food requirements for one adult.
      • 1.2 acres of land, which is 1.8 times average baseline landholdings among eligible households.
      • Tin roofs for 4 houses (estimated financial rate-of-return: 17%, not including health and comfort benefits.)

We have reservations about the above reasoning:

  • Regarding "fair:" Pre-cash-transfer wealth/income differences between eligible and ineligible recipients may exist for a number of reasons; we don't believe it's warranted to assume that a fair world would see the two groups with the same wealth/income due to an equalizing transfer, and more to the point, we don't believe that the ineligible households are likely to see the situation as fair. In addition, we are concerned that by aiming to equalize eligible and ineligible households, GiveDirectly takes on a substantial risk of its calculations being off in a way that leads to eligible households becoming systematically better off than ineligible households, which could distort incentives and lead to conflict.
  • Regarding "well-understood:" GiveDirectly notes that its transfers are similar—in dollar terms—to those of government programs, but that they are likely much larger in "percentage of income" terms. We note that the cash transfer programs that have been studied to date seem to be in the range of 9-27% of recipients' annual consumption; by contrast, if GiveDirectly's recipients average $0.65 in daily per capita consumption and receive an average of $288 per person over the course of a year (see above), this implies that people receive an average of 121% of their annual consumption in the year in which they receive the transfer.233 The quote above states that the lower level of initial income is an argument for making the cash transfer larger, but to us, it also means that the risks of distorting incentives, causing conflict, etc. are likely to be greater than those of previously-studied programs, since the transfers are a substantially greater percentage of consumption. This issue is somewhat mitigated by the fact that GiveDirectly's transfers are designed as "wealth transfers" rather than as "income transfers": recipients receive funds over the course of a few months and then become ineligible, whereas the government programs GiveDirectly points to have longer periods of eligibility. GiveDirectly has also told us that its decision to make larger transfers over a shorter period of time is based on recipients' reported preferences.234

Perspectives of recipients and field staff

During our site visit to Uganda in 2014, we spoke with a small number of recipients and field staff about the size of transfers. We asked whether people felt it would be better for GiveDirectly to keep the transfer size the same or reduce the transfer size but provide transfers to more people.235 3 out of 4 recipients told us that the transfer size should stay the same (or be increased).236 One GiveDirectly field officer also held this view, saying that $1,000 is enough to help someone advance, but is not so much that it would distort incentives to work. Two other field officers suggested that it would be better for GiveDirectly's transfers to reach more people in a village, even if it meant reducing the size of a standard transfer.237

Merits of further research

GiveDirectly has considered experimenting with transfer size but does not view this as a high priority, in part because it feels that although further research on this question may improve GiveDirectly's program, it would be unlikely to influence other cash transfer programs.238 GiveDirectly is not concerned that people will run out of good uses of funds from $1,000 transfers.239 The Rarieda RCT included both a $300 transfer treatment group and a $1,000 transfer treatment group, but did not provide strong evidence on what the best transfer size would be, because of small sample sizes.240

Are there negative or offsetting impacts?

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

The RCT that Innovations for Poverty Action conducted of GiveDirectly's transfers in Rarieda found no significant effects of transfers on the rate of crime in treatment villages or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages.241

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:242

  • 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 has informed us that recently its hotline service was not effectively responding to everyone who called in; it is in the process of upgrading its hotline.243

Data from follow-up surveys
GiveDirectly has sent us results from follow-up surveys conducted in multiple transfer campaigns. In 2016, we asked for a sample of recent follow-up survey data. GiveDirectly sent us a database covering 3,329 follow-up calls from late July to mid-August 2016 across its campaigns in Kenya, Uganda, and Rwanda.244 This data indicated that reported issues were low: 7% of recipients reported some regrets about how they spent their transfer, 2% reported hearing complaints, and 1% reported thefts.245

Below, we summarize older survey data from several campaigns in Kenya and the pilot campaign in Uganda for some of the questions included in these surveys.

This table includes follow-up survey data primarily from the Kenya 2M, Kenya 1.2M, Kenya rolling enrollment, and Kenya behavioral optimization campaigns (survey results are from 2014 and 2015) and from the Uganda pilot campaign, the Uganda 2M campaign, and the Uganda model variations campaign (survey results are from 2013, 2014, and 2015). Note that recipients may have been surveyed more than once and would therefore be included more than once in the data presented.246 Percentages reported in this table represent the number of recipients who are marked as having responded "yes" (that they had the issue) out of those for whom a response is recorded in the data.247

Kenya Uganda
Issue # of reports/# of respondents % reports of total respondents # of reports/# of respondents % reports of total respondents
Trouble collecting 141 / 17,289 0.8% 39 / 1,950 2%
Complaints 2,314 / 39,554 5.9% 159 / 5,467 2.9%
Theft248 490 / 18,802 2.6% 18 / 5,511 0.3%
Bribes249 67 / 39,547 0.2% 33 / 5,552 0.6%
Shouting 558 / 39,547 1.4% 69 / 5,521 1.2%
Crime 311 / 39,544 0.8% 24 / 5,530 0.4%
Domestic violence 428 / 17,905 2.4% 1 / 3,555 0.0%
Household argument 182 / 39,546 0.5% 34 / 5,547 0.6%

Note that GiveDirectly surveys only cash recipients, not non-recipients, and all data is self-reported.

Data from hotline calls
We have reviewed records of calls made to GiveDirectly's hotline from May 2012 – August 2015, which provide anecdotal evidence of tension and conflict caused by the cash transfers, according to recipient reports, including marital disputes, fraud committed by helpers, trustees, or family members, and Village Elders requesting funds from recipients.250 In the most recent complete hotline call data that we have seen (from October 2014; in 2015 we asked for sample data only and in 2016 we did not ask for a sample), the most common type of adverse event recorded is household conflict, followed by theft.251 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).252

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

There is suggestive evidence that cash transfer programs may have moderate negative short-term effects on the well-being and economic outcomes (e.g., consumption, assets, and business revenue) of non-recipient households living in the same areas as similar households that receive transfers.253 However, the evidence for these effects primarily comes from studies of a variant of GiveDirectly’s program that may differ from its core program in important ways. GiveDirectly notes that even though it has not identified significant evidence of negative effects on non-recipients, it now generally avoids conducting experiments that randomize at the individual level, to avoid situations in which one eligible household receives transfers while a similarly situated neighbor does not.254

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

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.255 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.256

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

  • People stealing cash and cell phones from recipient households257
  • People contacting recipients and posing as GiveDirectly staff to defraud recipients of funds258
  • Mobile money agents defrauding recipients of funds259
  • GiveDirectly staff defrauding recipients of funds (we discuss one particularly large case of this above)

To mitigate the risk of small-scale crime, in its communications with recipients GiveDirectly emphasizes ways that recipients can keep their mobile money accounts and phones secure.260 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.261

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.262 The one-off nature of transfers (recipients are not eligible for a second round of transfers) may help to mitigate these effects among past and current recipients, though there is information to suggest that some recipients believe transfers could be given again in the future.263

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.264 It is possible that this will become more of an issue in the future, as 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, and it is possible this will increase delays.265

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.266 In Kenya, for recipients receiving their first transfer in February 2016 (the last time we checked this), the average time for recipients between the census survey and their first payment was 67 days and 2.5% of recipients had transfers that had been delayed for over 3 months.267 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).268

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.269 This may hamper recipients' ability to execute plans for how and when to use funds. In late 2015 (the last time we checked this), 81% of recipients in GiveDirectly's Uganda model variations campaign had received their transfers on time (within 15 weeks of enrollment) and 14% had experienced registration problems.270 In early 2016, GiveDirectly reported that transfers in Uganda were delayed due to elections, but did not state by how much.271

Do grants distort local markets?

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

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

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

Does GiveDirectly divert skilled labor away from other areas?

In February 2016, GiveDirectly had 94 total field staff members across Kenya, Uganda, and Rwanda: 4 Country Directors and Field Directors, 2 Data Managers and Operations Managers, 7 Administration and Finance staff, 10 Field Managers and Associate Field Managers, and 71 Field Officers.272

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 reports that it receives approximately six times the number of resumes as openings for Field Officer positions.273 Regarding its field staff in Kenya, GiveDirectly explained that successful candidates generally have a college education and are paid approximately $12 per day, in addition to expenses for travel and lodging while working.274 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.275

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.276 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.277 GiveDirectly has provided evidence that weakly suggests that the international aid sector is moving towards benchmarking programs against cash.278 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:279

  • Anecdotally, GiveDirectly has heard that some large funders are asking themselves "Is this better than cash?" before making grants.280 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.281
  • GiveDirectly believes there has been an increase in demand from policymakers for evidence that compares programs to cash.282
  • 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).283
  • Anecdotally, GiveDirectly has heard that several new cash transfer programs, new evaluations, and increased transparency practices were inspired by GiveDirectly.284 GiveDirectly believes that, by executing an excellent program, it may put competitive pressure on other implementers to also perform effectively.285
  • GiveDirectly has provided informal advice to new cash programs and studies.286
  • GiveDirectly has participated in several high-level panels and roundtables.287
  • 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 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.288

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).289 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.290 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 82.3% of GiveDirectly's all-time incurred expenses.291 This figure includes fundraising costs that are expected to generate revenue in the future and excludes some of the costs of following up with recent recipients.292 We do not have a detailed breakdown of projected future campaign costs, so we are unsure if the ratio of direct grant to total spending will look similar in the future. We believe it's likely to be slightly lower: for spending since June 2015, transfers have been closer to 80% of GiveDirectly's total spending.293

2015 response from GiveDirectly:294 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.

Below we break down GiveDirectly's total spending through July 2016 by activity; however, note that the table does not include data for the period July 2015 - February 2016, because GiveDirectly's breakdown of the data for that time period did not match the categories used at other times.295 Costs not included in GiveDirectly's total spending were at least some of the research costs of the independently-run studies of GiveDirectly's program (these costs are not funded by GiveDirectly)296 and the reserves that GiveDirectly had set aside to cover staff salaries in the event that GiveDirectly has a funding shortfall.297

Breakdown of GiveDirectly's total spending by activity - through July 2016, excluding July 2015 - February 2016298
Cost category Spending % of total costs
Direct grants to recipients $30,430,766 83.2%
Enrollment costs $723,748 2.0%
Transfer costs $458,022 1.3%
Follow-up costs $185,773 0.5%
Core operations299 $2,574,585 7.0%
Other (excluding fundraising) $54,379 0.1%
Fundraising $1,727,153 4.7%
Value of President's time pre-FY 2014 $400,000 1.1%
Total $36,554,425 100.0%

For the period that is missing from the table above, we know that GiveDirectly spent $13,576,343 and that $10,832,798 of that went to direct transfers (79.8%).300

Note that GiveDirectly expects its efficiency to be higher in its Rwanda standard cash transfer campaign.301

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 and bednets. (In the case of the comparison with bednets, for instance, this means quantifying the estimated impact of bednets 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 child mortality and improving incomes.

We guess that in purely programmatic terms, and given our values, bednet distributions and deworming are both 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, and we’re not entirely confident that the figures themselves are adding substantial information beyond the intuitions we have from examining the details of them.

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?

Basic income guarantee cost-effectiveness

We have limited information on the cost-effectiveness of GiveDirectly's basic income guarantee program. While we have modeled the basic income guarantee program in our cost-effectiveness analysis, we do not have a high degree of confidence in our results. Our model indicates that GiveDirectly's standard cash transfer campaigns are roughly 1.5 to 2 times more cost-effective than the basic income guarantee program.302 The difference in cost-effectiveness is partially driven by the fact that GiveDirectly is raising funding for the basic income guarantee study upfront and investing it in a low-risk portfolio. On average, because one arm of the study will receive cash transfers for 12 years, this funding won't be spent for multiple years, so the benefits from the funding are discounted.

It is possible that the basic income model will be less cost-effective than GiveDirectly's standard model because long-term, smaller transfers may reduce incentives or ability to invest the funds, or because it is possible that it will be more expensive per dollar transferred for GiveDirectly to deliver funds.303 However, it is also possible that the program will be significantly more cost-effective, perhaps by allowing participants to make longer-term plans or through influencing other funders and governments to implement basic income guarantees. We have not incorporated estimates of this type of impact into our model. GiveDirectly has told us that while it expects the cost-effectiveness of direct transfers through this project to be lower than its standard program, it believes the potential for beneficial policy impact, which is hard to quantify, outweighs any difference.304

Benchmarking partnership projects cost-effectiveness

As we have mentioned above, it is our impression that GiveDirectly has increased its focus on experimentation and partnerships. We expect the structure of benchmarking cash transfer programs could look substantially different from GiveDirectly's standard model in some cases. For example, GiveDirectly has told us that if it is benchmarking cash against a program that distributes food stamps, GiveDirectly might disburse smaller and more frequent payments (which recipients are more likely to spend on food) to make the programs more comparable.305 As with the basic income guarantee program, and for similar reasons, benchmarking projects could be more or less cost-effective than GiveDirectly's standard program.

Is there room for more funding?

We believe that GiveDirectly could effectively use more funding than it expects to receive and is very likely to be constrained by funding next year. We estimate that if it received an additional $46 million (allowing it to commit $65.8 million) its chances of being constrained by funding would be reduced to 50%.

In short, we calculate this from (more detail in the sections below):

  • Total opportunities to spend funds productively: We believe that GiveDirectly could productively use between $66 million (50% chance of scaling to this level) and $144 million (5% chance of scaling to this level) for cash transfers in its 2017 budget year. This excludes consideration of what GiveDirectly could productively use on activities other than standard cash transfer campaigns (such as fundraising) because GiveDirectly has told us its other activities are fully funded. This estimate includes the costs of enrollment, transferring funds, and follow-up.
  • Cash on hand: GiveDirectly holds approximately $66 million. It expects to spend or allocate all of this before February 2017.
  • Expected additional funding: We estimate that GiveDirectly will raise $19.8 million in additional funding for its 2017 standard cash transfer campaigns.

Below, we also discuss:

  • Past spending: In recent months, GiveDirectly has enrolled recipients at a rate corresponding to transferring $21 million per year.
  • Additional considerations: GiveDirectly has a track record of success in scaling its operations quickly. Recently, it grew its capacity for cash transfers by a factor of almost two in a year. It is not clear whether it will be able to continue this trend. Over the last year, GiveDirectly has experienced a high rate of targeted households refusing to be enrolled in an area GiveDirectly was expanding into.

Details follow.

Available and expected funds

As of July 2016, GiveDirectly had $66 million on hand.306 By the end of GiveDirectly's current budget year (February 2017), GiveDirectly expects to have spent or allocated all of this funding, along with an additional $17.5 million that it expects to raise for its basic income study (collectively, $83.5 million):307

  • $30 million will be allocated to the basic income guarantee project and granted out over the next 12 years.
  • $14 million is allocated to partnership projects.
  • $8.5 million is allocated to GiveDirectly's fundraising activities for the next three years.
  • $2 million is set aside for salary reserves.
  • $29 million will be committed to households for standard cash transfers by the end of February 2017.

Excluding GiveWell-influenced donors, we predict that GiveDirectly will raise $15.8 million in unrestricted funding through the first half of its 2017 budget year that it could use for its standard cash transfer campaigns in 2017.308 We expect that GiveDirectly will receive an additional $4 million from GiveWell-influenced donors who do not follow our recommendation exactly.309

Funding priorities

In the table below, we've briefly summarized the details of GiveDirectly's funding gaps; further detail follows the table. All figures in this section are inclusive of the costs of enrollment, transferring funds, and follow-up.310

Note that:311

  • A standard cash transfer team consists of one "team lead" (a Field Director or Country Director) and a team of Field Managers, Associate Field Managers, and Field Officers. GiveDirectly expects that each team in 2017 will be able to transfer $12 million per year (up from a pace of $7 million per team per year in mid-2016 and $11 million per team per year that GiveDirectly expects to transfer by the end of 2016; more below). Half of a team in the table below represents that team working on standard cash transfer campaigns for half of the year.
  • GiveDirectly has estimated its "throughput"—the amount of cash that GiveDirectly can commit to households within a given time frame—will be $33 million in 2016.

GiveDirectly's funding gaps for 2017312

Opportunity Additional cost (millions USD) Cumulative funding need (millions USD) GiveWell's prioritization
Standard cash transfer campaigns operate at approximately 2/3 the size of 2016 throughput 20 0.2 Execution level 1
Standard cash transfer campaigns operate slightly below 2016 throughput 10 10 Execution level 1
3 full standard cash transfer teams, operating slightly above 2016 throughput 6 16 Execution level 1
4 full standard cash transfer teams, at planned 2017 throughput 12 28 Execution level 1
5.5 full standard cash transfer teams (includes adding a second full team to Uganda) 18 46 Execution level 1
10 full standard cash transfer teams 54 100 Execution level 2
12 full standard cash transfer team 24 124 Execution level 3
Total 144 124 --

Additional detail:313

  • Operating below 2016 throughput: GiveDirectly expects to transfer $33 million in its 2016 budget year (3.1 fully trained teams) and to be on pace, if it raises enough funding, to transfer $48 million in 2017 (4 teams).314 We discussed several scenarios with GiveDirectly about what it would look like if it scaled down its operations below its 2016 level in 2017:
    • If GiveDirectly raises a total of $20 million for its standard cash transfer campaigns in 2017, it will have to downsize its staff for standard cash transfer campaigns by approximately 33%.315
    • At a total of $30 million, GiveDirectly would operate 2.5 full teams (likely one full team in Kenya, one full team in Uganda, and a half team in Rwanda).316 GiveDirectly told us that it would still need to downsize its staff.317 GiveDirectly estimates that it would take half a year to scale back up to its current pace after the losses in staff it would experience at the $30 million level.318
  • Funding level at which GiveDirectly has a 50% chance of being constrained by funding: GiveDirectly is currently on pace (with no additional hiring) to have four full teams operating its standard cash transfer model in 2017 (details in footnote).319 However, it believes that it could easily scale to 5.5 teams (details in footnote) and, if it receives enough funding to do so, has a 70-80% chance of scaling to this level successfully.320 We guess that this probability is lower (about 50%), given that (a) GiveDirectly has other major priorities as well in 2017 (e.g., partnership projects and the large basic income study), and (b) that GiveDirectly does not yet have the ability to raise enough funds to maintain an operation of this size in the future, which might make it more hesitant to scale to that size in 2017—GiveDirectly has expressed concerns about the negative attention that might come with reducing its size.321
  • Funding level at which GiveDirectly has a 20% and 5% chance of being constrained by funding: We asked GiveDirectly to estimate the point at which it believes it would only have a 5% chance of succeeding at scaling to that level in 2017. While this estimate is highly uncertain, GiveDirectly estimated that scaling to 16 additional full teams, 20 total, would have a low likelihood of success.322 We estimate that the 5% level is at 8 additional teams, 12 total. This estimate is very rough and relies on our intuitions. Assuming a linear relation between team size and chance of success, we estimate that GiveDirectly has a 20% chance of success at a scale of 10 teams.323

GiveWell's prioritization of GiveDirectly's funding gaps

We have tried to rank our top charities' funding gaps based on:

  • Capacity relevance: how important the funding is for the charity's development and future success.
  • Execution relevance: how likely it is that the charity's activities will be constrained if it does not receive the funding.

We believe that "capacity-relevant" gaps are the most important to fill, and "execution"-related gaps vary in importance. More explanation of this model is in this blog post.

We consider all of the funding gaps for GiveDirectly's current priorities to all be "execution" gaps.324 We assign execution level gaps a level (1, 2 or 3) that corresponds with how likely we believe it is that GiveDirectly would be constrained by funding (rather than other factors, such as an inability to grow staff capacity quickly enough) if it is unable to fill the funding gap. Level 1 is 50% chance of funding being the constraint, level 2 is 20% chance, and level 3 is 5% chance. These judgements are rough and largely based on intuitions formed from following GiveDirectly's scale up over several years (more in the next section).

Past enrollment rate

GiveDirectly's past rate of committing funds to recipients is lower than its projected rate for the remainder of 2016 and 2017. Its enrollment rate from March 2016 - July 2016 (the period of GiveDirectly's current budget year for which we have information) implies a transfer rate of about $21 million per year,325 or, assuming three full teams were in operation (two in Kenya and one in Uganda), about $7 million per team per year.326 Including the costs of delivering transfers, GiveDirectly has been transferring about $7.7 million per team per year.327

Note that GiveDirectly expected to transfer $29 million in the period August 2016 - February 2017; assuming that it has 4 teams during that time period, that would require a pace of $12.4 million per team per year, much faster than its pace in the first half of the year.328 When we asked GiveDirectly about this, it noted that it is on pace with its plan, which had included significantly more transfers in the second half of the year.329 If GiveDirectly manages this pace for the second half of 2016, then it should be able to transfer $12 million in cash transfers per team per year (which includes the costs of delivering transfers) in 2017.

In the past, GiveDirectly has successfully scaled up over time, recently increasing its rate of transfers by about a factor of 1.5 to 2 in a year,330 but it is unclear if it will be able to continue this trend. In the table below, we show how GiveDirectly's rate of commitments has increased recently.

Rate of funds committed331

Time period Funds committed to recipients per month (millions)
March 2013 - August 2013 0.09
September 2013 - February 2014 0.54
March 2014 - August 2014 0.58
September 2014 - February 2015 1.13
March 2015 - August 2015 1.18
September 2015 - February 2016 1.52
March 2016 - July 2016 1.78

In the past, with a lag of about four months, distributed transfers have generally kept pace with committed transfers.332

To scale up to any point beyond 4 full teams on standard cash transfer campaigns, GiveDirectly will need to hire additional team leads (Country Directors or Field Directors), and it takes GiveDirectly several months to hire a team lead.333 Historically, GiveDirectly has not expected hiring more junior staff to be a challenge.334

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. The following are concerns identified by GiveWell or GiveDirectly:

  • Refusals: As discussed above, GiveDirectly has experienced a high rate of people refusing to be enrolled in Kenya over the last year. 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 further 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).335 However, it is possible that the high rates of refusals 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.336 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). Additionally, similar challenges in other locations in the future might also reduce GiveDirectly's ability to scale as quickly as it hopes to.
  • 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 with expertise in navigating such government relationships and who could intervene if there were a problem.337 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.338 In fall 2016, GiveDirectly was beginning the process of assessing how many households remained for it to enroll in the areas it has historically worked in and considering which areas it should enter next.339 GiveDirectly told us that there were sufficient eligible households to enroll over the next year, even if GiveDirectly worked with 5.5 full teams (we did not ask about whether there were sufficient households for more teams than this).340
  • Crime: Incidents of large-scale crime could cause delays and reduce GiveDirectly’s ability to transfer funds to recipients. The risk of crime could increase as GiveDirectly becomes better known in the regions in which it works. We discuss this risk more above. We consider this a low to moderate risk.
  • Security: GiveDirectly notes that political violence and terrorism could hamper its ability to work in an area, and while these are risks in Kenya, they have not impacted Western Kenya (where GiveDirectly works) since 2008. GiveDirectly has attempted to mitigate this risk by working in multiple locations, so that it could shift its operations to Uganda or Rwanda if there were an issue.341 We know very little about security risks in Kenya, Uganda and Rwanda, but would guess based on GiveDirectly's assessment that it presents a low risk. As GiveDirectly continues to expand to other countries (e.g., Rwanda), we think this risk will be reduced because GiveDirectly will have more areas to redirect its work if necessary.
  • 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.342 We would guess that this risk is low, as the mobile money providers that GiveDirectly uses are national networks, and GiveDirectly has identified alternatives. However, we note that GiveDirectly once tried working with an alternative provider in Uganda (Centenary Bank) and had difficulties in the partnership.343
  • Maintaining staff quality as the organization grows: GiveDirectly noted that it has hired a number of new staff over the last year to scale up and prepare for additional scale-up in the future.344 It is possible that GiveDirectly will face issues if the new staff members learn slowly or turn out to be poor fits for their positions.345 So far, GiveDirectly believes that its hiring processes have been successful and that new staff are taking on responsibility quickly and competently.346
  • Supporting operations as the organization grows: As GiveDirectly has grown in size, it has needed to expand its internal operations to support its larger team and activities. For example, GiveDirectly now uses Segovia (which we discuss above) to manage many of its enrollment, transfer, and follow-up activities. In 2015, GiveDirectly intended to hire a fundraising team leader to build out its fundraising operations.347 However, it did not manage to find someone for this position until late 2016, possibly affecting the amount of funding GiveDirectly will have available for standard cash transfer campaigns in 2017.348 We are unsure if GiveDirectly's internal operations and fundraising will be able to grow quickly enough to support its current size and continued expansion.
  • Political instability or regulation: GiveDirectly has noted that there is some risk of political instability in the countries it works in. Elections could change how the government works with GiveDirectly or regulates NGOs.349 In the worst case scenario, GiveDirectly might be forced to move its operations out of one of the countries it is operating in.350 However, because GiveDirectly now operates in three different countries, it believes that this would be doable.351

Unrestricted vs. restricted funds

We prefer that GiveDirectly spend funds in the way that it believes will maximize its potential 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

GiveDirectly is a relatively young organization. It was founded in 2009 when its founders were graduate students in economic development; Paul Niehaus, President and co-founder of GiveDirectly, is also an Assistant Professor of Economics at the University of California, San Diego.352 Professor Niehaus was on sabbatical from his teaching position and working full time on GiveDirectly in 2014-2015.353 He returned to his professorship in fall 2015.354

We believe GiveDirectly to be an exceptionally strong and effective organization:

  • Self-evaluation: GiveDirectly has invested heavily in self-evaluation from the start, and furthermore, the study design of its Rarieda RCT was pre-registered for additional accountability and credibility. It continues to demonstrate a strong commitment to rigorous analysis of its work.
  • Track record: Although it is relatively young, we feel that GiveDirectly's first few years have gone well; GiveDirectly has successfully accomplished its goal of transferring cash to extremely low-income people at a fairly low expense ratio. We have also seen GiveDirectly refine its process over the years and take thoughtful measures in response to problems that arise, demonstrating a commitment to continuous improvement.
  • Communication: GiveDirectly has always communicated extremely clearly and directly with us and given thoughtful answers to our critical questions. Generally, GiveDirectly seems to come to conclusions that we find reasonable on key questions.
  • Transparency: GiveDirectly appears to value transparency as much as any organization we’ve encountered. We have not seen it hesitate to share information publicly (unless it had what we consider a good reason).

More on how we think about evaluating the leadership of organizations at our 2012 blog post.


Document Source
Carolina Toth, conversation with GiveWell, November 12, 2015 Unpublished
Carolina Toth, email to GiveWell, October 20, 2015 Unpublished
Carolina Toth, email to GiveWell, September 14, 2015 Unpublished
Carolina Toth, email to GiveWell, September 25, 2015 Unpublished
Carolina Toth, email to GiveWell, November 10, 2015 Unpublished
Carolina Toth, email to GiveWell, May 3, 2016 Unpublished
Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013 Unpublished
Conversation with Carolina Toth, GiveDirectly, November 20, 2014 Unpublished
Conversation with GiveDirectly field staff, October 20-21, 2014 Source
Conversation with GiveDirectly, April 8, 2014 Source
Conversation with GiveDirectly, December 7, 2013 Source
Conversation with GiveDirectly, July 7, 2014 Source
Conversation with GiveDirectly, October 6, 2014 Source
Conversation with GiveDirectly, September 5, 2014 Source
Conversation with Paul Niehaus, November 14, 2014 Unpublished
Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013 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 Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013 Unpublished
Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, October 6, 2012 Source
Conversation with Paul Niehaus, President, and Rohit Wanchoo, Director, GiveDirectly, March 18, 2013 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)
Carolina Toth, GiveDirectly, email to GiveWell, November 14, 2014 Unpublished
Carolina Toth, GiveDirectly, email to GiveWell, September 12, 2014 Unpublished
Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 Unpublished
Email conversation with anonymous funder, May 2016 Unpublished
Faizan Diwan, Innovations for Poverty Project Associate, conversation with GiveWell, November 8, 2012 Source
GiveDirectly - Evidence - Research at GiveDirectly Source (archive)
GiveDirectly blog, Fighting fraud in Uganda Source (archive)
GiveDirectly FY 2011 Form 990 Source (archive)
GiveDirectly, 100,000 households plan Unpublished
GiveDirectly, Aspirations study proposal Unpublished
GiveDirectly, Blog post, August 21, 2015 Source (archive)
GiveDirectly, Blog post, August 25, 2015 Source (archive)
GiveDirectly, Blog post, January 21, 2016 Source (archive)
GiveDirectly, Blog post, September 5, 2016 Source (archive)
GiveDirectly, Blog post, September 22, 2016 Source (archive)
GiveDirectly, Budget summary, July 2013 Unpublished
GiveDirectly, Check in with GiveWell, September 2014 Source
GiveDirectly, clarifications on GiveWell's draft review of GiveDirectly Source
GiveDirectly, Consumption data for targeting work Unpublished
GiveDirectly, Contextualizing transfer size Source
GiveDirectly, Coffee study design Source
GiveDirectly, Distributed cash out follow up with vulnerable recipients Source
GiveDirectly, Eligibility check Source
GiveDirectly, Eligibility criteria presentation Unpublished
GiveDirectly, Enrollment speed of distributions - Siaya and Rarieda Source
GiveDirectly, Estimate of personnel 2015 Source
GiveDirectly, FAQs 2015 Source (archive)
GiveDirectly, Final report Nike girls study Source
GiveDirectly, Follow-up tracker, July 2013 Source
GiveDirectly, Follow-up tracker, October 2014 Source
GiveDirectly, GE research and measurement plan Unpublished
GiveDirectly, GE study design Source (archive)
GiveDirectly, Give now Source
GiveDirectly, Google enrollment database Source
GiveDirectly, Google follow-up data - disaggregated (LS - long) Source
GiveDirectly, Google transfer schedule, July 2013 Source
GiveDirectly, Google verification, September 2013 Source
GiveDirectly, GW scratch sheet Source
GiveDirectly, How it works 2013 Source (archive)
GiveDirectly, How it works 2014 Source (archive)
GiveDirectly, Inflation analysis - Kenya Source
GiveDirectly, Kenya 1.2M enrollment database Source
GiveDirectly, Kenya 2M census results, July 2013 Source
GiveDirectly, Kenya 2M enrollment database, September 2013 Source
GiveDirectly, Kenya follow up data, November 2014 Source
GiveDirectly, Kenya hotline log, July 2013 Unpublished
GiveDirectly, Kenya randomized sample of adverse events, 2014-2015 Source
GiveDirectly, Kenya rolling campaign enrollment database - Homa Bay Unpublished
GiveDirectly, Kenya rolling campaign enrollment database - Siaya Unpublished
GiveDirectly, Kenya top 10 adverse events 2015 Source
GiveDirectly, Kenya verification template, August 2013 Source
GiveDirectly, Kenya, Uganda, and Rwanda enrollment database, 2016 Source
GiveDirectly, Monthly operations report, August 2015 Source
GiveDirectly, Monthly operations report, February 2016 Source
GiveDirectly, Monthly operations report, October 2014 Source
GiveDirectly, Nike enrollment database Source
GiveDirectly, Nike follow-up data - disaggregated Source
GiveDirectly, Nike instrument Source
GiveDirectly, Nike verification (combined), May 2013 Source
GiveDirectly, Nike verification (final), September 2013 Source
GiveDirectly, Nike verification (short version), June 2013 Source
GiveDirectly, Offering Memorandum (January 2012) Unpublished
GiveDirectly, Operational process overview Source
GiveDirectly, Performance - Quality of Service, September 2016 Source (archive)
GiveDirectly, Rachuonyo S. Villages Unpublished
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, Revenue by referral source 2015 Unpublished
GiveDirectly, Rockefeller index insurance update, July 2015 Unpublished
GiveDirectly, Room for funding update for GiveWell, October 2016 Source
GiveDirectly, Rwanda technical application Unpublished
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 criteria analysis summary Unpublished
GiveDirectly, Targeting focus group results Unpublished
GiveDirectly, Targeting process overview Source
GiveDirectly, Team Source (archive)
GiveDirectly, UBI cost-effectiveness estimate Unpublished
GiveDirectly, Uganda 2M campaign enrollment database Unpublished
GiveDirectly, Uganda model variations quality audits - census Unpublished
GiveDirectly, Uganda model variations quality audits - registration Unpublished
GiveDirectly, Uganda pilot enrollment database - Akumure Source
GiveDirectly, Uganda pilot enrollment database - Kanyamutamu Source
GiveDirectly, Uganda pilot enrollment database - Kawo Source
GiveDirectly, Uganda pilot enrollment database - Kosile Source
GiveDirectly, Uganda pilot follow up data, April 2014 Source
GiveDirectly, Uganda randomized sample of adverse events, 2014-2015 Source
GiveDirectly, Uganda targeting data, July 22, 2013 Source
GiveDirectly, Uganda top 10 adverse events 2015 Source
GiveDirectly, Update for GiveWell on experimentation, September 2016 Source
GiveDirectly, Update for GiveWell, April 2014 Source
GiveDirectly, Update for GiveWell, February 2015 Source
GiveDirectly, Update for GiveWell, February 2016 Source
GiveDirectly, Update for GiveWell, July 2013 Source
GiveDirectly, Update for GiveWell, July 2014 Source
GiveDirectly, Update for GiveWell, May 2015 Source
GiveDirectly, Update for GiveWell, October 2014 Source
GiveDirectly, Update for GiveWell, September 2015 Source
GiveDirectly, Update on process changes, August 28, 2013 Source
GiveDirectly, Updated data (March 31, 2012) Source
GiveDirectly, Verification data (November 17, 2011) Source
GiveDirectly, Verification template (November 7, 2011) Source
GiveDirectly, Verification template (October 1, 2012) Source
GiveDirectly, Village selection process Kenya Source
GiveDirectly, Village targeting regression Source
GiveDirectly, What We Do - Operating Model Source (archive)
GiveDirectly, What We Do - Operating Model, October 2016 Source (archive)
GiveDirectly, What We Do - Who We Serve, September 2016 Source (archive)
GiveDirectly staff, conversation with GiveWell, October 6, 2016 Unpublished
GiveDirectly staff, responses to monitoring questions, October 11, 2016 Source
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 2015 Source
GiveWell, GiveDirectly financials - May 2016 Source
GiveWell, GiveDirectly financials - 2016 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 registration sample 2016 Source
GiveWell, spot checks of Segovia follow-up data sample, 2016 Source
GiveWell's non-verbatim summary of a conversation with Carolina Toth, GiveDirectly, October 1, 2014 Source
GiveWell's non-verbatim summary of a conversation with 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, February 23, 2016 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 Ian Bassin and Piali Mukhopadhyay, GiveDirectly, August 23, 2016 Source
Haushofer and Shapiro 2013 Source (archive)
Haushofer and Shapiro 2013 Appendix Source (archive)
Haushofer and Shapiro 2013 Policy Brief Source (archive)
Ian Bassin and Carolina Toth, email to GiveWell, June 14, 2016 Unpublished
Ian Bassin and Piali Mukhopadhyay, conversation with GiveWell, August 23, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, August 17, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, August 25, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, October 8, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, October 11, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, October 14, 2016 Unpublished
Ian Bassin, COO, Domestic, GiveDirectly, email to GiveWell, October 17, 2016 Unpublished
Ian Bassin, edits to GiveWell's review, November 10, 2016 Unpublished
Jean Junior, The Perspectives of Young Women in Siaya County, Kenya: Their Lives and Their Thoughts on Cash Transfer Programs Source
Johannes Haushofer and Jeremy Shapiro, Welfare Effects of Unconditional Cash Transfers: Pre-Analysis Plan, June 27, 2013 Source (archive)
Johannes Haushofer and Paul Niehaus, DIL Demonstration Proposal Source
Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012 Unpublished
Michael Faye and Paul Niehaus, Slate article, April 14, 2016 Source (archive)
Paul Niehaus and Carolina Toth, conversation with GiveWell, May 28, 2015 Unpublished
Paul Niehaus and Carolina Toth, conversation with GiveWell, September 7, 2015 Unpublished
Paul Niehaus and Ian Bassin, conversation with GiveWell, September 15, 2016 Unpublished
Paul Niehaus and Johannes Haushofer, Optimizing Impact for the Mobile Era - Final Report Source
Paul Niehaus, AMA on Reddit, May 31, 2016 Source (archive)
Paul Niehaus, Carolina Toth, and Ian Bassin, conversation with GiveWell, February 23, 2016 Source
Paul Niehaus, Carolina Toth, and Ian bassin, conversation with GiveWell, August 12, 2016 Unpublished
Paul Niehaus, email to GiveWell, October 11, 2016 Unpublished
Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012 Unpublished
Paul Niehaus, Michael Faye, and Piali Mukhopadhyay, conversation with GiveDirectly supporters, August 11, 2015 Unpublished
Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012 Unpublished
Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012 Unpublished
Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012 Unpublished
Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, August 25, 2016 Unpublished
Richard Sedlmayr, conversation with GiveWell, February 19, 2016 Unpublished
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)