You are here

GiveDirectly - 2015 Review

We have published a more recent review of this organization. See our most recent report on GiveDirectly.

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

More information:

Published: November 2015



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 85% overall (more).

Is there room for more funding? We are reasonably confident that GiveDirectly could effectively use an additional $15 million beyond what it already expects to receive and could potentially absorb up to an additional $75 million (more).

GiveDirectly is recommended because of its:

  • Focus on a program with a low burden of proof and a strong track record (more).
  • Strong (and evolving) 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 and commitment to self-evaluation (more).
  • Room for more funding - we believe that GiveDirectly can use substantial additional funding productively (more).

Major unresolved issues 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) 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.
  • Through 2014 and 2015, GiveDirectly has increased the amount of time it spends networking with potential government and NGO partners. This time involves senior management taking meetings to discuss GiveDirectly's work, the impact of cash transfers, and potential shared objectives. This work has the potential to significantly increase GiveDirectly's impact, but could divert GiveDirectly's resources away from a proven program to work on one whose payoff is much more uncertain. We are continuing to monitor how GiveDirectly's resources are allocated and to think about how we should evaluate GiveDirectly's partnership work in the future.

Our full review, below, discusses our full assessment of GiveDirectly, including what we see as its strengths and weaknesses as well as issues we have yet to resolve. All content on GiveDirectly is available here.

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), 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 via mobile phone-linked payment services.1 It is currently active in Kenya and Uganda, and will soon be starting cash transfers in Rwanda.2

GiveDirectly's work includes:3

  • Direct impact: Providing cash directly to poor households.
  • 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 GiveDirectly's direct impact.

GiveDirectly's standard model involves grants of approximately $1,000 (USD) per household over approximately four months to one year, after which recipients become ineligible.6 GiveDirectly aims to help the poorest households, targeting those that are in "acute poverty."7 Recipient households are currently identified by (a) selecting poor villages based on poverty data, and (b) determining household eligibility. In the past, household eligibility was based on building materials of homes. Recently, GiveDirectly started to use new, more complex eligibility criteria in Homa Bay county in Kenya (more).

Below, we discuss:

  • The structure of GiveDirectly's transfers
  • The status of GiveDirectly's transfer campaigns
  • GiveDirectly's process for identifying recipient households and delivering cash transfers
  • GiveDirectly's staff structure
  • GiveDirectly's experimental work
  • GiveDirectly's work on partnerships

Grant structure

GiveDirectly's standard model involves grants of approximately $1,000 (USD) over about four months to one year, after which recipients become ineligible.8 In Kenya, GiveDirectly transfers $1,040 to each enrolled household, while in Uganda, it transfers $875; these transfer amounts are based on GiveDirectly's standard transfer size, but are adjusted for purchasing power.9 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 aims for "wealth transfers" (large, one-off transfers that hopefully give people more flexibility to make large purchases and investments).

Note that when we reviewed household data 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.11

Frequency and sizing of transfers

In Kenya, GiveDirectly's standard transfer schedule involves a small initial transfer of about $90 (USD), followed by two larger transfers of about $475 (USD).12

In Uganda, GiveDirectly's transfer schedule until recently involved 9 monthly transfers of about $97 (USD) each.13 GiveDirectly told us that the transfer schedule in Uganda was designed to be more manageable for the mobile money providers than two lump-sums, and also that it would provide more frequent opportunities to evaluate the performance of the mobile money providers.14 In the most recent campaigns in Uganda, GiveDirectly has used a structure more like Kenya's with three transfers.15

GiveDirectly has an ongoing study of behavioral interventions that will allow some recipients the ability to choose when they receive their transfers.

Status of transfer campaigns

As of August 2015, GiveDirectly had provided partial or full cash transfers to almost 25,000 households in western Kenya and eastern Uganda, and was continuing to transfer funds to additional households in both places.16

GiveDirectly's work to-date can be grouped into 13 campaigns (note that to keep this table easy-to-read, we have included many details in footnotes):17

Campaign Households enrolled (#)18 Start date Transfer funds sent19 Special features
Rarieda (Kenya)20 498 June 2011 100% Evaluated with an RCT (more)
Siaya (Kenya)21 199 July 2012 100% -
Nike (Kenya)22 77 September 2012 100% Transfers to young women as part of a RCT
Google (Kenya)23 861 January 2013 100% -
Kenya 2M24 2055 October 2013 100% -
Uganda pilot25 965 June 2013 100% -
Kenya 1.2M26 1195 January 2014 100% -
Kenya rolling enrollment27 14,74328 May 2014 64% GiveDirectly's first campaign testing a rolling model in Kenya29
Uganda 2M30 2103 September 2014 73% -
Kenya behavioral optimization (Ideas42 study)31 58532 July 2014 48% Transfers are part of a RCT on behavioral interventions (more)
Rockefeller index insurance study (Kenya)33 No info November 2011 No info Small-scale investigation into how cash transfers could support index insurance programs; $200 transfers (more)
Uganda model variations34 1662 June 2015 4% Testing biometrics, new mobile money partner, and new cash out model (more)
Uganda rolling enrollment35 N/A September 2015 0% GiveDirectly's first campaign testing a rolling model in Uganda36

We have created a summary table of the campaigns noting the documentation we have for each here.

GiveDirectly's process

The steps of GiveDirectly's process are as follows:

  1. Selection of a country. GiveDirectly told us that it chose to work in Kenya due to the robustness of M-Pesa as a mobile banking platform and the large population of people meeting its criteria who have access to mobile technology.37 In choosing a second country in which to work, GiveDirectly said that it considered whether there was a mobile money provider accessible to the very poor, how costly it would be to operate in the country, how politically stable the country is, and how common corruption is in government affairs. The ease of moving staff between Kenya and Uganda was also a factor, as GiveDirectly's current COO (International) oversees the work in both places.38 GiveDirectly chose to work in Rwanda to pursue a partnership with a bilateral aid donor after staff at the Rwanda office of that donor approached GiveDirectly about a possible cash transfer program there.39 Rwanda has a growing mobile money system and a large population of extremely poor potential recipients.40 GiveDirectly notes that it currently does not direct individual donor contributions to Rwanda.41
  2. Selection of a region. GiveDirectly told us that it initially chose to work in western Kenya and eastern Uganda based on poverty statistics.42 We have reviewed poverty data that GiveDirectly sent us for Uganda; we have not reviewed poverty data across districts for Kenya.43 GiveDirectly has not yet selected regions within Rwanda.
  3. Selection of districts or counties. For Kenya, GiveDirectly told us that its executive staff uses data on poverty, population density, security, and presence of poverty-focused NGOs (with the goal of avoiding overlapping with these) to select counties.44 We have reviewed poverty data that GiveDirectly sent us for divisions within Siaya district, but not the data used to select other counties in Kenya.45 For Uganda, GiveDirectly told us that it chose a county to target initially based on poverty statistics, logistical factors and security considerations.46 We have reviewed the poverty data for Uganda.47
  4. Selection of villages. GiveDirectly states that it selects villages based on poverty level and location.48 GiveDirectly shared the full details of its village selection process for an early campaign in Kenya, including data for each village and the method for weighting the different factors used to select villages in that campaign.49 We have not reviewed recent data used to select villages in Kenya. For the pilot campaign in Uganda, GiveDirectly relied on publicly available poverty data, which we have reviewed, as well as data that it received from local officials, which we have not reviewed.50 GiveDirectly also sent field staff to test cell phone reception and measure proximity to market centers, and used these as inputs into its village selection (optimizing for good reception and longer distances from market centers).51 GiveDirectly also considered population size in the villages, so that it could enroll all eligible households in each village and not exceed the budget for the Uganda pilot campaign, which was funded by part of its Google Global Impact Award.52 For recent campaigns in Kenya and Uganda, GiveDirectly has used various methods to estimate poverty levels through census data and the proportion of thatch-roof to iron-roof households.53 For details on how GiveDirectly has targeted villages historically, see this footnote.54
  5. 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.55 GiveDirectly signs written agreements with or obtains approval letters from local officials to formalize permissions.56
  6. Village meeting. A village meeting is held "to answer questions anyone may have about the program, clarify that [GiveDirectly is not] affiliated with a political party, etc."57 Village meetings were first implemented in the Google campaign.58
  7. Enrollment process.
    • Census: GiveDirectly has field staff visit the village to create a census of all households.59 In conducting the census, field staff collect data about each household and note if the household is eligible for transfers (the criteria for eligibility in a campaign depends on where the campaign is located – more).60 The census process was different in GiveDirectly's early campaigns.61
    • Registration: GiveDirectly has a separate set of field staff visit households marked as eligible in the census and register them.62 In Kenya, registration involves giving the household member a SIM card (if they do not already have an M-PESA account), which is used to transfer funds through the M-PESA system, and collecting other data that can be checked against the initial data from the census.63 Recipients are given the option of purchasing a cell phone from GiveDirectly at the time of registration, the cost of which is removed from the recipient's transfer.64 In Uganda, GiveDirectly uses a similar registration process.65 Additionally, GiveDirectly helps recipients in Uganda obtain national ID cards and arranges for mobile money agents to visit villages to register recipients in the mobile money system.66 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).67 Registration was different in early transfer campaigns.68
    • Back check: GiveDirectly sends a separate team of field staff to revisit every registered household and collect data about that household that can be compared to data collected during census and registration.69 GiveDirectly field staff also ask households if they were asked to pay a bribe to register.70
    • Audits: GiveDirectly sends field staff to revisit a portion of the registered households for audits.71 In Kenya, the field staff who do audits are not involved in earlier enrollment activities.72 In Uganda, the field staff who have done audits in past campaigns were from earlier enrollment teams.73 GiveDirectly determines which households to audit based on the extent of the discrepancies between data collected at different phases in enrollment.74 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 backcheck and not selected for audit.75 The procedure for deciding which households to audit and determining eligibility was different in prior campaigns.76

    GiveDirectly aims to enroll all eligible households.77 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.78 GiveDirectly also 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.79

    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.80

  8. Sending transfers to recipients. GiveDirectly sends transfers to recipients via mobile money providers (more).81 In Kenya, transfers are sent in an initial installment of approximately $90, then two larger installments of approximately $475.82 In Uganda, transfers have historically been sent in nine installments of approximately $100 each. Recently, GiveDirectly has begun to use a three transfer model similar to what it does in Kenya.83 See above for more on grant structure.
  9. Holding "cash out" days (Uganda only): In Uganda, the mobile money agent network is less robust, so GiveDirectly coordinates cash out days for recipients to withdraw funds after each of the installments.84 Cash out days are overseen by the Uganda Field Director, who coordinates with mobile money agents to travel to the village and set up stations for recipients to withdraw cash. In each village, there are two recipients nominated by the community to assist the Uganda Field Director in monitoring cash out day activities. GiveDirectly's call center staff also conduct phone surveys with a randomly selected 10% of recipients in the village to ask if there are any issues at the payday. We have not reviewed the logs of these calls. GiveDirectly previously changed its cash out day procedure in response to a case of staff fraud in Uganda.85 GiveDirectly is experimenting with a model in Uganda that more closely matches the model in Kenya; if this model is successful, GiveDirectly may eliminate cash out days from Uganda (more).86
  10. Conducting follow up calls. GiveDirectly field staff make multiple phone calls to recipients as transfers are being sent.87 There are short verification calls to confirm that the transfer was received and ask if the recipient experienced any problems after each transfer.88 There are also two longer surveys administered to a randomly selected sample of recipients after larger portions of the transfers have been sent.89 In the longer surveys, GiveDirectly staff ask recipients a number of questions including whether they received the transfers or had any trouble withdrawing funds, how they spent the funds, and whether there were any problems in their community relating to the transfers.90 The schedule of follow up calls has varied somewhat by campaign.91 We have reviewed and made public data from these calls for ongoing transfer campaigns in Kenya.92

    In addition to these calls, GiveDirectly maintains a phone "hotline" for recipients to call if they have any questions about the transfers or issues in obtaining funds.93 We have reviewed the records of calls made to GiveDirectly's Kenya hotline from May 2012 – September 2014.94 Recipients can also report issues to GiveDirectly field staff when they are in the village; GiveDirectly created a formal mechanism for recording these reports.95

Key differences in some past campaigns were (a) the lack of a "census" (instead, GiveDirectly asked village officials to take them to eligible households, and thus conducted two in-person checks of each house rather than three); and (b) the lack of a village meeting.96

Staff structure

GiveDirectly delivered its first cash transfers in 2011.97 Starting in January 2011 it had one full-time staff member.98 In early 2013 it hired a second full-time staff member to serve as COO (Domestic).99 GiveDirectly has since expanded its staff significantly. Its current organizational structure in East Africa includes:100

  • Chief Operating Officer International (COO-I): The COO-I provides oversight and quality control of cash transfer programming and international operations. The COO oversees the Field Directors.
  • Field Directors (FDs): FDs are in charge of overseeing field operations, as well as approving transfer schedules and rosters. GiveDirectly estimated that it would have four Field Directors by the end of 2015.101
  • Field Manager: The Field Managers supervise Assistant Field Managers, focusing on quality control, management and training of Field Officers.102 GiveDirectly estimated that it would have 3 Field Managers by the end of 2015 (one each in Kenya, Uganda, and Rwanda).103
  • Assistant Field Manager: Assistant 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.104 GiveDirectly estimated that it would have 13 Assistant Field Managers by the end of 2015.105
  • 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 backchecks. 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 backcheck phases.106 GiveDirectly estimated that it would have 69 Field Officers by the end of 2015.107


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.108 One other staff member who was previously working full-time at GiveDirectly switched to working part-time for each entity. We discuss potential risks from the overlap in staff in this blog post.

GiveDirectly deployed several versions of Segovia in 2015, which have automated some processes and led to slight time savings.109 GiveDirectly expects moderate efficiency gains from Segovia in the future.110

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.111 GiveDirectly has told us that it has increased its experimentation to the point where every recipient is enrolled in a study or a campaign variation.112 Below, we list the studies that GiveDirectly has completed, is currently working on, 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.113 GiveDirectly is working to conduct an RCT examining the macroeconomic effects of GiveDirectly's program in Kenya.114 This study includes a long-term follow up component that will provide information on the impacts of cash transfers several years after the transfer.115 The study is randomized at the village level, will involve 325 villages, and is expected to survey approximately 3,900 households and 4,875 enterprises.116 Baseline data collection for the study began in August 2014 and was still in progress as of September 2015.117 Endline data collection was expected to be completed by the end of 2016, although this may be delayed since baseline data collection has taken longer than expected.118 Paul Niehaus, GiveDirectly's President, is serving as one of the Principal Investigators on this study, along with Edward Miguel (UC Berkeley), Johannes Haushoffer (Princeton), and Michael Walker (UC Berkeley).119 In order to mitigate potential bias from his involvement with the research, GiveDirectly has applied a number safeguards, including preregistration of plans for measurement and analysis.120
  • 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.121 The main outcome of interest in this study is the rate of return on spending.122 GiveDirectly is conducting the data collection for this study internally, and the analysis will be done by independent researchers.123 This study began in late October 2014 and endline results are expected mid-2016; as of May 2015 baseline results and the initial cash transfers were completed.124 It is fully funded by an anonymous donor.125
  • Gender contracts: GiveDirectly ran a small pilot of informal contracts between spouses receiving cash transfers in the spring of 2015.126 External research partners are evaluating the impacts of the contracts on domestic violence and female empowerment.127 After the initial study group was completed, GiveDirectly began a second round, and expected to have results in fall 2015.128 GiveDirectly has said that if the pilot is successful it will be expanded into a larger-scale project.129
  • Biometrics: GiveDirectly is testing the use of biometrics to enhance security in Uganda.130 GiveDirectly has purchased palm readers, collected palm prints during registration, and used palm scans as an additional identification measure during recipients' first cash out day; it is also collecting information on the level of comfort that recipients feel towards biometrics during its follow-up surveys.131 GiveDirectly may continue to use biometrics in contexts where national IDs are uncommon and cash out days are necessary (more).132
  • Distributed cash out model in Uganda: GiveDirectly would prefer not to run cash out days in Uganda, so it is currently piloting a model of distributed cash outs there (the same model it uses in Kenya).133 More detail is below.
  • Aspirations study: GiveDirectly is running an RCT looking at the effect of showing recipients a motivational video before their participation in GiveDirectly's program.134 The main outcome being examined is the rate of return on investments.135 Approximately 6,500 recipients are being enrolled in the study.136

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.137 These transfers were made in Rarieda in 2011-2012.138 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 a RCT of GiveDirectly's Nike campaign, which provided transfers exclusively to young women ages 18-19. This campaign was funded by the Nike Foundation. GiveDirectly shared IPA's survey instrument with us prior to the study.139 We did not see an analysis plan prior to the study, as we did with the Rarieda RCT. GiveDirectly writes that "the pilot was designed at a small scale with the expectation that it would not produce statistically robust evidence, but would provide directional learnings to guide future investment and experimentation."140 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.141
  • 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.142 The grant was from USAID via the Policy Design and Evaluation Lab at UCSD.143 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.144 We don't know when results from this study will be available.
  • 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. In treatment villages, GiveDirectly applied a "saturation" model, which included households with iron roofs and mud walls as eligible. In total, about 85% of households in saturation villages received transfers. GiveDirectly collected its standard follow up survey data, which includes questions about tension and conflict, in the 19 treatment and 18 control villages, where GiveDirectly used its standard targeting criteria. It also conducted focus groups in 3 treatment and 3 control villages to elicit opinions about targeting strategies.145 GiveDirectly found that the data 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.146 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. Note: GiveDirectly separately piloted "community-based targeting," but is not planning to implement it more broadly.147
  • 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).148 GiveDirectly then ran a small-scale test of the program in Western Kenya, simulating a government cash transfer program.149 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.150 Note that we have only seen a summary of the results. This study and associated cash transfers were fully funded by the Rockefeller Foundation.151
  • Eligibility requirements in Homa Bay: GiveDirectly recently experimented with new eligibility requirements because a) it needs 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.152 Recipients from approximately 55 villages were enrolled using several different targeting methods.153 GiveDirectly chose new eligibility requirements for Homa Bay in October 2015 (more).154

Possible future experimentation

Other ideas that GiveDirectly has considered for future experimentation include:

  • Providing cash transfers in an urban setting155
  • Providing cash transfers as humanitarian relief156
  • Facilitating the pooling of recipient funds for public goods projects157
  • Implementing a lifetime basic income guarantee158
  • Serving as the payment provider at cash out days159


GiveDirectly has been exploring projects with a number of partners. The projects that GiveDirectly has partnered on or considered involve implementing cash transfers funded by an institutional partner. They have also provided informal advice to those considering cash transfer programs. For a partial list of GiveDirectly’s partnership activities, see this footnote.160

In 2015, GiveDirectly finalized an agreement for its largest partnership project to date: GiveDirectly will be implementing cash transfers in a randomized controlled trial in Rwanda; the study costs $4 million and is co-funded by an institutional funder and The study will test cash transfers as a benchmark for another still-to-be-chosen aid program.162 GiveDirectly is currently in several preliminary conversations with partners for similarly large projects in the future; it currently is hoping to use the funding provided by Good Ventures earlier this year to encourage matching funds from potential partners (more).163

In 2014, GiveDirectly’s President and COO (International) spent time networking and developing potential partnerships with government officials and international aid agencies.164 In 2015, GiveDirectly's President spent approximately 25% of his time on developing partnership projects, primarily focused on Rwanda and replicating the Rwanda model.165 Although partnership projects are now taking up a significant portion of his time, GiveDirectly does not believe this has negatively affected its core operations.166 We expect partnerships to continue to take up the President's and Chairman's time and to involve a significant portion of GiveDirectly’s funding over the next few years.167

We have not yet attempted to assess the value of the partnership projects. 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 limited value (e.g., implementing a program that would have been implemented effectively without GiveDirectly’s involvement).

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 GiveDirecly has used in the past (and continues to use in Uganda) of targeting people based on the materials their houses are made of, and we do not yet have a good sense of how effective GiveDirectly's new targeting criteria are.
  • 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.
  • Does GiveDirectly have an effective process for getting cash to recipients? GiveDirectly's process seems to have been relatively 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.
  • Do the cash transfers cause problems and complications that offset their positive impact? 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.
  • 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.
  • Does GiveDirectly divert skilled labor away from other areas? Our best guess is that this is not a serious concern.

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

We discuss this question more extensively at 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 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 the household assets and the vulnerability of recipients.168 GiveDirectly recently 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.169
  • Thatched roofs: To date, GiveDirectly has 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.170 In GiveDirectly's campaigns in Kenya, about 35-45% of households have been eligible based on these criteria, while in Uganda about 80% of households have been found to be eligible.171 GiveDirectly still uses this criteria in Uganda, but expects to soon adjust the Uganda eligibility requirements based on some of its learnings from recent experimentation with requirements in Homa Bay county, Kenya.172

The assets and vulnerability status criteria

GiveDirectly recently 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.173 For these reasons, GiveDirectly has changed its eligibility criteria for Homa Bay county to better capture the poorest households.174 The new criteria algorithm takes into account a range of factors including a household's assets and vulnerability status; we are unable to elaborate because GiveDirectly would prefer to keep the new criteria confidential so as to prevent households from gaming the system.175

To test possible proxies for poverty to use as its new criteria, GiveDirectly told us that it attempted to determine the validity and replicability for each metric.176 For example, GiveDirectly would test the same criterion on the same group of people at different times to see if respondents gave consistent answers that led to that criterion producing the same results each time about who should be considered eligible.177 We have not yet had an opportunity to fully review how GiveDirectly ultimately analyzed all of its options and arrived at its new criteria, as the new criteria were finalized as we were finishing this report. We expect to discuss this more with GiveDirectly next year. Note that GiveDirectly expects to adjust its eligibility criteria for other campaigns based on its experience in Homa Bay and GiveDirectly told us that it is currently enrolling most of its recipients in Homa Bay, so we expect these eligibility criteria to be the main ones GiveDirectly uses for the foreseeable future.178

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.179 GiveDirectly believes that the new criteria will be 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).180 Note that recipients will not be made aware of the full criteria (as a measure to prevent cheating), which may also contribute to decreased perceptions of fairness. Because the criteria explicitly put weight on vulnerability, they could also increase perceptions of fairness, or at least offset other fairness concerns.

GiveDirectly's development of new eligibility 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 new criteria 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 that the new eligibility criteria will change these outcomes.

The thatched roof and mud house criteria

As part of the baseline survey for the RCT of its program, researchers collected more 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 2012:181

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 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.182

End-line data from the RCT on food consumption among control group recipients also suggests that eligible households are extremely poor.183 This data shows that "20% 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."184 Other results related to food consumption are measured as well which are, in our view, consistent with the notion that recipients are extremely poor.

GiveDirectly has 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 thatch-roof houses.185

We have reservations about the approach of targeting people based on the materials their houses are made of:

  • 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.186 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. It is considering modifying its targeting criteria to catch some of the outlier cases in these regions.187
  • To the extent that there are differences in income or wealth between residents of thatched-roof homes and those who live in iron-sheet-roofed homes, it seems possible that these differences come down to fortune/luck (i.e., 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 (i.e., 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."188
  • GiveDirectly's follow up surveys demonstrate that cash transfers can lead to tension between recipients and non-recipients.189 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.190 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). In addition, 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.191

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 5 locations (3 in Siaya and 2 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 5 locations (including 2 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.192 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 as follows:

  • Most homes are made up of three rooms.
  • The main room is a sitting area. In the homes we visited, this room varied in size from approximately 10'x12' to 12'x20'. This room has 3 doors: one to the outside; one to what appeared to be a storage room; one to a bedroom. The husband and wife sleep in the bedroom and the children sleep in the storage room or in the kitchen.
  • The kitchen is often a separate structure, most often thatched-roof (even for homes that have tin roofs). Some households have no kitchen structure and cook outside. Others have a small kitchen in place of a storage room.
  • There are no doors in between rooms in the house, just hung curtains.
  • The living room has many chairs and couches for sitting. They often almost fully cover the wall area (aside from doors). There are also coffee tables in the middle of the room. Poorer homes had less furniture; one home we saw had a single chair and a single broken table.
  • People have wall hangings for decoration. The most common hanging we saw was old calendars (e.g., from 2003) that have pictures and can be used for decoration.
  • Most houses had 1-2 kerosene lamps that provide light since they don't have electricity. One home (a non-recipient we visited) had two electric lights, which were powered with a solar panel.
  • People we spoke with reported walking 5-20 minutes daily to obtain drinking water, which one recipient reported doing 1-2x per day.
  • Most households owned a bicycle.
  • Some households have radios (both of the non-recipients we saw had radios; one had a TV). Of the others, 3/14 had radios. These were most often powered with what looks like a car battery.
  • Most people owned one cell phone pre-GiveDirectly.
  • Households tend to own some livestock. Most commonly, we saw 1-2 cows and 4-5 chickens each. They reported selling the milk or saving it for personal use, and many mentioned being able to sell their cows in the future to pay for their kids to attend secondary school.

Note that among the households we visited, many had already received part or all of their transfer from GiveDirectly, so these characterizations 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, backchecks, remote checks and audits) for most transfer campaigns; we have only spot-checked the data GiveDirectly shared with us in 2015.193

If the information collected about a household at different stages of enrollment is inconsistent, GiveDirectly staff revisit the household for an audit.194 GiveDirectly tracks the percentage of households found to be ineligible at the backcheck and audit stages on its website; between September 2013 and July 2015, 3.5% of recipients initially eligible after registration were found to be ineligible after the backcheck or audit stage.195 We believe this process to be generally effective at identifying and enrolling households that meet its criteria.

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.196 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.197 GiveDirectly has told us that recipients are generally able to withdraw cash from mobile money agents located in or near their villages.198

In Uganda, the agent network is less robust, so GiveDirectly has coordinated cash out days for recipients to withdraw funds after each payment.199 On cash out days, the mobile money provider that GiveDirectly works with will send an armoured vehicle with large amounts of cash, security personnel, and multiple agents to a location close to recipients' villages, so that recipients can easily come and withdraw their funds.200

GiveDirectly worked with two different mobile money providers in its pilot campaign in Uganda: EZEE Money and MTN (745 recipients were assigned to EZEE Money, 215 recipients to MTN).201 After assessing the relative performance of these two providers, GiveDirectly chose to work exclusively with MTN in the next campaign.202 MTN also has the advantage of having a more robust agent network than EZEE Money, so MTN recipients are somewhat less dependent on cash out days.203 MTN has similar security measures as M-PESA: a user must present ID to an agent before making withdrawals, provide their phone or SIM card, and enter their PIN number. Confirmation messages are sent after withdrawals.204 GiveDirectly is also testing the use of biometrics to increase security during cash out days.205

GiveDirectly recently tested working with a different payment provider (Centenary Bank) in Uganda and experienced difficulties.206 While GiveDirectly will continue to work with MTN in Uganda, it is also piloting a distributed cash out model (the same model used in Kenya).207 GiveDirectly hopes that communicating intensively to recipients about where the nearest MTN mobile money agents are will cause the distributed cashout model to work in Uganda.208

Staff fraud

The most significant issue that GiveDirectly has had in making sure that cash gets to recipients is the case of staff fraud in the 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.209 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.210 GiveDirectly has taken multiple measures to address the vulnerabilities exposed by this case of fraud (see footnote for details).211 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.

As GiveDirectly scales, we would expect greater awareness of its program and more attention to be paid by people outside of the villages in which it works. This could increase the risk of large-scale crime. The cash out days that GiveDirectly administers in Uganda seem to be particularly easy targets for large-scale theft, as there is a substantial amount of cash in one location (although it is our understanding that GiveDirectly's partners send security personnel to the cash out days to mitigate this risk).212 We are not aware of security measures that GiveDirectly has implemented to mitigate the risk of large-scale crime beyond its response to the staff fraud, although it has piloted a few measures.213 In addition to harming recipients, crime would likely cause delays for GiveDirectly's work.

Other issues

Despite the mobile money security measures, Lydia Tala, an Assistant Field Manager who has been responsible for making post-transfer phone calls to recipients in Kenya, reports that one of the most common client complaints is the belief that M-PESA agents are overcharging or stealing funds.214 Ms. Tala believes that these reports are incorrect and that approximately 10% of recipients are not fully aware of how to use M-PESA, so they may withdraw funds without checking their balance, ultimately being surprised when they have drawn down their account.215 GiveDirectly told us that it recognizes this issue and maintains a hotline to provide recipients with assistance in navigating the M-PESA system.216

In Uganda, some recipients have experienced delays in accessing their funds due to MTN not activating them immediately.217

Another issue that GiveDirectly has noted is that recipients who have not previously had a mobile phone or mobile money account are often less familiar with how to use them and how to keep their account information secure. GiveDirectly field staff explained that they provide training to recipients in how to use their phones and accounts, and reminders are also given at cash out days.218 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.219 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.220

After the transfers are sent, GiveDirectly also administers follow up surveys that ask recipients if they have collected their funds and if they had any trouble doing so.221 The percentage of recipients who report issues withdrawing funds is consistently low (<5%) across campaigns. See the table below for details. Follow up surveys also ask recipients what size of transfer they received. These amounts generally appear to vary slightly among cohorts of recipients. For example, in follow-up surveys of recipients in Kenya from 2014, recipients reported receiving various amounts between 37,000 KES – 40,000 KES.222 Other than the mobile phone purchase deduction, we do not know the causes of this variance.

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

Findings from the RCT

72% of treatment group households in the evaluation received just $287; the rest received $1,085.223 In the sections below, we use the outcomes from the larger transfer group unless otherwise specified, because GiveDirectly typically gives transfers of roughly $1,000. (More above. For every outcome, the larger transfer led to more spending compared to the smaller transfer with a few exceptions, where we have noted the outcome for the smaller transfer group below, and for tobacco and alcohol and indices of health and education, where the effects were not statistically different from zero. Though we report transfer sizes in exchange-rate adjusted terms, we report the outcomes in purchasing power parity (PPP) adjusted U.S. dollars.224

How GiveDirectly transfers were spent
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.225 Their spending is broken down in more detail below.

  • Total non-land assets.226 Receipt of large transfers increased households’ non-land assets by an average of $463 (95% CI: $378 to $549).227 The largest categories of asset increases were livestock ($131, 95% CI: $79 to $183), durable goods ($100, 95% CI: $71 to $129; primarily furniture), and savings ($18, 95% CI: $9 to $27).228 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.229 Though Haushofer and Shapiro 2013 doesn't report the change in likelihood for recipients of large transfers alone, recipients of large transfers were 23 percentage points (95% CI: 13% to 33%) more likely to have iron roofs at end-line than recipients of small transfers.230 Haushofer and Shapiro 2013 estimated that iron roofs cost about $564 USD PPP based on a survey of one respondent in each of 20 villages.231 GiveDirectly ran a survey that sampled a respondent from each of 20 villages and found that iron roofs cost $418 USD PPP on average.232 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.233 Recipients of small transfers also spent about $13 more per month (95% CI: $4 to $22).234
  • Health expenditures. Recipients of large transfers spent about $3 (95% CI: -$1 to $6) per month more than control households on health expenditures.235 Recipients of small transfers also spent about $3 (95% CI: $1 to $5) more.236 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.237 We're not sure of the time period over which this estimate is calculated. Haushofer and Shapiro 2013 also reports that treatment households receiving large transfers spent $16.26 (95% CI: -$6.50 to $39.02) more than control households on education expenditures in the past month and treatment households receiving small transfers spent $19.41 (95% CI: -$12.22 to $44.74) more.238 We're not sure if the difference between the two estimates is due to the difference in the samples used to calculate them (they have different sample sizes) or the different time periods over which they might be calculated or some other explanation.239 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.240 About half of this additional consumption was on food.241 This additional consumption also included increased spending on social expenditures and various other expenditures.242
  • Alcohol and tobacco. Treatment households did not increase their spending on alcohol or on tobacco.243

Impacts of GiveDirectly transfers on recipients

  • Food security. At baseline, food security was low among participants.244 Program participants reported a 0.37 standard deviation (95% CI: 0.17 to 0.57) increase in a food security index over controls.245
  • Health and education. The study did not detect an effect on indices of health and educational outcomes.246
  • Revenue and profits. Receipt of large transfers lead to a $15 per month (95% CI: -$1 to $32) increase in total revenues and receipt of small transfers lead to a $17 (95% CI: $4 to $30) increase but neither resulted in a detectable increase in profits.247 We emphasize that these are very short-run effects and we do not know whether participants’ business investments might lead to profits in the longer run.

Researchers also considered more subjective measures of impact on recipients' quality of life:

  • Psychological well-being. Treatment improved an index of psychological wellbeing by 0.45 standard deviations (95% CI: 0.25 to 0.65).248 There was no observable effect on cortisol for the treatment group as a whole although cortisol, an indicator of stress, was slightly lower in the large transfer group than the small transfer group, a difference that was statistically significant at the 10% level when controls were included in the model.249
  • Female empowerment. Control households in treatment villages measure 0.23 standard deviations (95% CI: 0.05 to 0.41) higher on an index of female empowerment than control households in control villages.250 This suggests that cash transfers to a village unexpectedly empowered females in both recipient and non-recipient households. The researchers propose potential mechanisms for this effect, but are explicit that these measured results are surprising and warrant further investigation.251 Note that we report this result for the sake of comprehensiveness but would guess that it is more likely to be random than real.

Data from follow-up surveys

GiveDirectly staff surveyed recipients on how they used their cash transfers. This data was collected at different points in the transfer cycle of each campaign.252 We summarize the data from recent campaigns in Kenya and Uganda below. The spending data from Kenya covers portions of the Kenya 2M, Kena 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; we have not seen more recent data from Uganda.253

Number of recipients who reported spending, by category

Kenya Uganda
Category # of respondents who reported spending in category % of respondents who reported spending in category # of respondents who reported spending in category % of respondents who reported spending in category
Food 2,351 53.5% 618 31.0%
Clothing 605 13.8% - -
Household items 1,372 31.2% 628254 31.5%
Building 3,421 77.8% 1,075 53.9%
Land 170 3.9% 147 7.4%
Livestock 939 21.4% 496 24.8%
Farm business 332 7.6% 116 5.8%
Non-farm business 490 11.1% 48 2.4%
School 1,259 28.6% 424 21.2%
Medical 268 6.1% 186 9.3%
Water 4 0.1% 0 0.0%
Debt 170 3.9% 68 3.4%
Savings 807 18.4% 176 8.8%
Life event 303 6.9% 5 0.3%
Family 341 7.8% 17 0.9%
Church 36 0.8% 14 0.7%
Transport 309 7.0% - -
Alcohol - - 1 0.1%
Other 199 4.5% 57 2.9%
Total respondents 4,397 1,996

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% 56,122,240255 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%

Anecdotal evidence from our site visit

In our site visit to GiveDirectly recipients in Kenya, we asked about the value of items commonly purchased with transfer funds.256 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, 2012257) 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 year258 ($175.13 based on the exchange rate as of November 15, 2012259)).

We have not visited Homa Bay county. GiveDirectly has informed us that most potential recipients in Homa Bay county already have iron roofs.260 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.

Do the cash transfers cause problems and complications that offset their positive impact?

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

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.261

GiveDirectly surveys recipients (post-transfer) on the following questions:262

  • Have you heard complaints about GD 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.

GiveDirectly has sent us results from follow-up surveys conducted in multiple transfer campaigns. Below, we summarize the survey data from recent 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.263 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.264

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 / 2,103 1.9%
Complaints 2,314 / 39,554 5.9% 159 / 5,467 2.9%
Theft265 490 / 18,802 2.6% 18 / 5,511 0.3%
Bribes266 67 / 39,547 0.2% 33 / 5,552 0.6%
Shouting 558 / 39,547 1.4% 69 / 5,552 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.

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.

We have also 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.267 In the most recent complete hotline call data that we have seen (from October 2014; in 2015 we asked for sample data only), the most common type of adverse event recorded is household conflict, followed by theft.268 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).269

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.270 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.271

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

  • People stealing cash and cell phones from recipient households272
  • People contacting recipients and posing as GiveDirectly staff to defraud recipients of funds273
  • Mobile money agents defrauding recipients of funds274
  • GiveDirectly staff defrauding recipients of funds (we discuss one particularly large case of this above).

To mitigate the risk of small-scale crime, GiveDirectly emphasizes ways that recipients can keep their mobile money accounts and phones secure.275 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.276

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. 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.277

Another way in which grants may distort decisionmaking 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.278 In the Rarieda campaign, 67% (359 of 536) of recipients waited less than a month, 84% (448 of 536) waited 3 months or less, and 6% (34 of 536) waited 6 months or more. In the Siaya campaign (a later campaign), 188 of 193 recipients waited less than a month, and the remaining 5 waited 2-3 months.279

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.280 In GiveDirectly's Kenya campaigns, approximately 49% of recipients have received their first transfer on time (within 9 weeks of being enrolled) and the average time for recipients between the census survey and their first payment was 71 days.281 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).282

In Uganda, the agent networks of mobile money providers are not as robust, which means that recipients must either travel farther, on average, to reach an agent or must withdraw cash only on days when GiveDirectly has arranged for agents to visit the villages (which GiveDirectly announces in advance).283 These challenges and the lack of flexibility may hamper recipients' ability to execute on plans for how and when to use funds. GiveDirectly told us that so far, the vast majority of recipients have been able to collect their transfers, with a few delays of up to a few hours on days when transfers are scheduled due to agents needing to replenish their cash stocks.284 In the most recent Uganda campaign (Uganda model variations), 81% of recipients have received their transfers on time (within 15 weeks of enrollment) and 14% have so far had registration problems.285

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 or decreased spending on these goods.

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 to one year, after which recipients become ineligible for future transfers.286 GiveDirectly has also experimented with different transfer sizes and structures, plans to continue doing so in the future.287 In the past, GiveDirectly has given the following rationale for the size of its transfers:288

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.289 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 year and then become ineligible, whereas the government programs it alludes 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.290

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.291 4 out of 4 recipients said that the transfer size should stay the same (or be increased).292 One GiveDirectly Field Officer also held this view, saying that $1000 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.293

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.294 GiveDirectly is not concerned that people will run out of good uses of funds from $1000 transfers.295 The Rarieda RCT included both a $300 transfer treatment group and a $1000 transfer treatment group, but did not provide strong evidence on what the best transfer size would be, because of small sample sizes.296

Does GiveDirectly divert skilled labor away from other areas?

GiveDirectly predicted that by the end of 2015 it would have 89 total field staff members across Kenya, Uganda, and Rwanda: 4 Field Directors, 3 Field Managers, 13 Assistant Field Managers, and 69 Field Officers.297 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.298 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.299 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.300 Based on GiveDirectly's current staffing situation, we do not see diversion of skilled labor as a serious concern.

What do you get for your dollar?

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

Cash grants make up 84.5% of GiveDirectly's all-time incurred expenses.301 This figure includes some fundraising costs that are expected to generate revenue in the future and excludes some of the costs of following up with recent recipients.302 Note that this an improvement from last year: by October 2014, 80.4% of GiveDirectly's total expenses had been transferred directly to recipients.303

When including projected future costs for current campaigns (transfers for still-to-be-enrolled recipients and the costs of sending and following those transfers) and core operations for 2016, the ratio of direct grants to total spending is 80.8%.304 Note that this analysis is based on financial documents GiveDirectly sent us before its transfer sizes were updated for inflation.305

Response from GiveDirectly:306 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.

Over the course of its existence, GiveDirectly has spent a total of $24.9 million (through June 2015).307 Below we break down GiveDirectly's total spending through June 2015, and its future budgeted costs through December 2016.308 Costs not included in GiveDirectly's total spending are at least some of the research costs of the independently-run studies of GiveDirectly's program (these costs are not funded by GiveDirectly)309 and the roughly $2.4 million that GiveDirectly has set aside as reserves to cover staff salaries in the event that GiveDirectly has a funding shortfall.310

Breakdown of GiveDirectly's total spending by category311

Cost category Incurred (through June 2015) Future (June'15 - Dec'16) Total Cost (incurred + future) % of incurred costs % of total costs
Direct grants to recipients $21,363,392 $7,935,583 $29,298,975 84.5% 80.8%
Enrollment costs $468,995 $159,359 $628,354 1.9% 1.7%
Transfer costs $370,985 $97,362 $468,347 1.5% 1.3%
Follow-up costs $124,109 $136,718 $260,827 0.5% 0.7%
Core operations312 $1,305,727 $1,517,677 $2,823,404 5.2% 7.8%
Other (excluding fundraising) $14,446 - $14,446 0.1% 0.0%
Fundraising $1,241,346 $1,122,845 $2,364,191 4.9% 6.5%
Value of President's time pre-FY 2014 $400,000 - $400,000 1.6% 1.1%
Total $25,289,000 $10,969,544 $36,258,544 100.0% 100.0%

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 are more cost-effective than deworming, which is 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. 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 2015 cost-effectiveness spreadsheet is available here. See also, our 2012 discussion of the cost-effectiveness of cash transfers and other interventions.

Is there room for more funding?

We are reasonably confident that GiveDirectly could effectively use an additional $15 million beyond what it already expects to receive and could potentially absorb up to an additional $75 million. In short:

  • Estimated maximum: In the next year, GiveDirectly estimates that it can scale up so that it can enroll recipients at a distribution rate of $94 million in cash transfers per year. It estimates that it will cost up to an additional 10% ($9.4 million, total $103.4 million) to support the costs of enrollment, transferring funds, and follow-up.
  • Cash on hand: GiveDirectly expects to have at least $30-35 million available for cash transfers in its 2016 budget year (March 2016 to February 2017). About half of this are funds GiveDirectly would like to hold until 2017 in order to attract matching funds.
  • Other sources of funds: GiveDirectly expects to receive a $6 million grant at the end of 2015.
  • Past spending: In recent months, GiveDirectly has enrolled recipients at a rate corresponding to transferring $16 million per year. Funds transferred to recipients has generally kept pace with commitments.
  • 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 two in a year. It is not clear whether it will be able to continue this trend.

Details follow.

GiveDirectly's projections for 2016

GiveDirectly estimates that it can enroll recipients for a total of $94 million in cash transfers in its 2016 budget year (March 2016 to February 2017).313 This assumes that each Field Director can oversee disbursement of $17.25 million per year in Kenya and Uganda and $8 million in Rwanda.314 This represents the amount each Field Director could commit in the next year (i.e., specific recipients enrolled) rather than the amount they could transfer in that time. (We discuss below how this compares to GiveDirectly's past enrollment rate.) It also assumes that GiveDirectly will be able to hire two additional Field Directors (going from 4 to 6).315 GiveDirectly told us that it does not expect hiring Field Directors, or more junior staff, to be a challenge.316 GiveDirectly plans to first use additional funding for its campaigns in Kenya and Uganda, and only enroll additional recipients in Rwanda once its capacity in those two countries has been filled.317

Note that the above estimates refer only to GiveDirectly's room for more funding for cash transfers (the estimates exclude, for example, the cost of GiveDirectly's staff next year). GiveDirectly estimates that the costs of delivering the transfers will be approximately an additional 5-10% of what it transfers.

Available and expected funds

Absent additional funding due to a GiveWell recommendation, GiveDirectly expects to have $30-35 million available for cash transfers in its next budget year (March 2016 to February 2017):

  • $16-19 million of a $25 million grant from Good Ventures. The grant is unrestricted, but GiveDirectly expects to use a portion to support fundraising (more below). GiveDirectly has told us that it expects to hold these funds at least through 2016 because it hopes to use the grant funds to attract matching funds from other funders, and such partnership agreements can take a significant amount of time to negotiate.318
  • $0.6 million raised in July and August 2015.319
  • $4 million GiveDirectly expects to raise between September 2015 and February 2016.320
  • $6 million from a grant GiveDirectly expects to receive before the end of 2015.321
  • $4 million from a grant for cash transfers in Rwanda.322

In addition, GiveDirectly held, as of June 2015:

  • About $6 million committed to cash transfers for enrolled recipients.323
  • $16 million designated for ongoing campaigns in Kenya and Uganda. GiveDirectly expected to commit all of these funds to recipients enrolled between June 2015 and February 2016.324
  • $6-9 million of a $25 million grant from Good Ventures. This grant was unrestricted. GiveDirectly plans to use this portion of the grant to expand its fundraising capacity over the next few years.325
  • Approximately $2.4 million that has been officially set aside for salary reserves and $1.1 million that has been officially designated for fundraising.326

Past enrollment rate

GiveDirectly's past rate of committing funds to recipients is much lower than its projected rate for 2015. Its enrollment rate in March 2015 to June 2015 implies a transfer rate of about $16 million per year.327 GiveDirectly has until recently been operating with two Field Directors,328 implying a commitment rate of $8 million per year per Field Director.

Rate of funds committed329

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 - June 2015 $1.34

With a lag of about four months, distributed transfers have generally kept pace with committed transfers.330

Bottom line

There is significant uncertainty about the maximum funding GiveDirectly could use in 2016. GiveDirectly estimates that it can scale up to several times its current enrollment rate in the next year implying the ability to utilize an additional ~$75 million.331 Note that GiveDirectly has successfully scaled up over time, recently increasing its rate of transfers by about a factor of two in a year,332 but it is unclear if it will be able to continue this trend. If we assume a doubling of GiveDirectly's current rate of commitments, this implies that GiveDirectly would be able to commit $32 million in cash transfers in its 2016 budget year; excluding funds it may hold through 2016, GiveDirectly expects to have at least $15 million available for cash transfers in that year.

Risks to room for more funding

GiveDirectly believes it can grow extremely quickly. GiveDirectly has previously identified the following risks, which might impede its ability to grow as fast as it believes it can. We do not find any of the items below particularly concerning now given GiveDirectly's progress:

  • Crime: Incidences 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 and plan to continue to check up on it.
  • 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 could intervene if there were a problem.333 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.334 We do not consider this to be a limiting factor for FY 2016, as GiveDirectly has already obtained permissions to enroll a cumulative capacity of about 100,000 households across Kenya and Uganda.335
  • 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 if there were an issue.336 We know very little about security risks in Kenya and Uganda, 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 by giving GiveDirectly 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.337 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 recently tried working with an alternative provider in Uganda (EZEE Money), and was not impressed by the partnership (more).338

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). Donations made directly via GiveDirectly’s website can be designated for any county, Kenya, Uganda, or experimental.339

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.340 Professor Niehaus was on sabbatical from his teaching position and working full time on GiveDirectly in 2014-2015.341 He returned to his professorship in fall 2015.342

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 roughly a 90% 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, October 9, 2015 Unpublished
Carolina Toth, email to GiveWell, September 14, 2015 Unpublished
Carolina Toth, email to GiveWell, September 22, 2015 Unpublished
Carolina Toth, email to GiveWell, September 25, 2015 Unpublished
Carolina Toth, email to GiveWell, November 10, 2015 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
Email from Carolina Toth, GiveDirectly, November 14, 2014 Unpublished
Email from Carolina Toth, GiveDirectly, September 12, 2014 Unpublished
Email from Joy Sun, COO, Domestic, GiveDirectly, August 9, 2013 Unpublished
Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 Unpublished
Faizan Diwan, Innovations for Poverty Project Associate, conversation with GiveWell, November 8, 2012 Unpublished
GiveDirectly - Evidence - Research at GiveDirectly Source (archive)
GiveDirectly blog, Fighting fraud in Uganda Source
GiveDirectly FY 2011 Form 990 Source
GiveDirectly, 2013 Annual Report Source
GiveDirectly, Aspirations study proposal Unpublished
GiveDirectly, Blog post, August 21, 2015 Source (archive)
GiveDirectly, Blog post, August 25, 2015 Source (archive)
GiveDirectly, Budget summary, July 2013 Unpublished
GiveDirectly, Chart of accounts Source
GiveDirectly, Check in with GiveWell, September 2014 Source (archive)
GiveDirectly, clarifications on GiveWell's draft review of GiveDirectly Source (archive)
GiveDirectly, Contextualizing transfer size Source (archive)
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 2012 Source
GiveDirectly, FAQs 2015 Source
GiveDirectly, Final report Nike girls study Source
GiveDirectly, Financials 2014 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 Q&A, April 2013 Source
GiveDirectly, GW scratch sheet Source
GiveDirectly, How it works 2013 Source
GiveDirectly, How it works 2014 Source (archive)
GiveDirectly, Inflation analysis - Kenya Source
GiveDirectly, Inflation analysis spreadsheet - 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, Monthly operations report, April 2015 Source
GiveDirectly, Monthly operations report, August 2015 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 (archive)
GiveDirectly, Nike verification (final), September 2013 Source
GiveDirectly, Nike verification (short version), June 2013 Source (archive)
GiveDirectly, Offering Memorandum (January 2012) Unpublished
GiveDirectly, Operational process overview Source
GiveDirectly, Performance - Efficiency Source (archive)
GiveDirectly, Rarieda Top-up Verification (short) Source (archive)
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, Rockefeller index insurance update, July 2015 Unpublished
GiveDirectly, Room for more funding summary 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 (archive)
GiveDirectly, Siaya verification, June 15, 2013 Source (archive)
GiveDirectly, Siaya village index Source
GiveDirectly, Survey for randomized controlled trial Source
GiveDirectly, Team Source
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, Uganda transfer schedule Unpublished
GiveDirectly, Update for GiveWell, April 2014 Source
GiveDirectly, Update for GiveWell, February 2015 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 2013 Source
GiveDirectly, Update for GiveWell, September 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, Values Source
GiveDirectly, Verification data (November 17, 2011) Source
GiveDirectly, Verification template (November 7, 2011) Source
GiveDirectly, Verification template (October 1, 2012) Source
GiveDirectly, Village targeting regression Source
GiveDirectly, What We Do - Operating Model Source (archive)
GiveWell Household size analysis Source
GiveWell Site visit notes Source
GiveWell site visit to GiveDirectly, October 2014 Source
GiveWell visit to M-PESA agent, November 8, 2012 Source
GiveWell, GiveDirectly financials 2015 Source
GiveWell, GiveDirectly follow up surveys summary - Kenya, September 2015 Unpublished
GiveWell, GiveDirectly follow up surveys summary - Uganda, September 2015 Source
GiveWell's non-verbatim summary of a conversation with Carolina Toth, GiveDirectly, October 1, 2014 Source
Haushofer and Shapiro 2013 Source
Haushofer and Shapiro 2013 Appendix Source
Haushofer and Shapiro 2013 Policy Brief Source
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
Johannes Haushofer and Paul Niehaus, DIL Demonstration Proposal Source
Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012 Unpublished
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, 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
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)