# GiveDirectly - 2014 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.

Published: December 2014

## Summary

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

Does it work? We believe that this approach faces an unusually low burden of proof, and that the available evidence supports the idea that unconditional cash transfers significantly help people. It appears that GiveDirectly has been effective at delivering cash to low-income households. GiveDirectly has one major randomized controlled trial 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 delivers directly to recipients is approximately 87% (more).

Is there room for more funding? We believe that GiveDirectly has significant room for more funding and could effectively absorb up to an additional $25-30 million (beyond what it already expects to receive) in the next year (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 humanitarian impact of the type of transfers (large, one-time transfers) that GiveDirectly provides, particularly the long-term impact of such transfers. GiveDirectly has one ongoing RCT that includes a long-term follow up component and will hopefully shed light on the question of humanitarian impacts. • GiveDirectly has been rolling out a new technology platform for its cash transfer campaigns. GiveDirectly expects the technology platform will improve efficiency and save staff time. At this point, it is too early for us to tell whether efficiency gains will be realized and what issues may arise. • In 2014, GiveDirectly spent time networking with potential government and NGO partners. This time involved taking meetings and providing advice that was closer to advocacy work than the implementation work that GiveDirectly has been focused on to date. While these potential partnerships are at an early stage, they could develop into projects that take a significant amount of GiveDirectly's executive-level staff time and/or partially determine GiveDirectly's implementation agenda. We have yet to see how this may change GiveDirectly's model or impact. 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. This review was initially written in 2012 and has since been updated. All content on GiveDirectly is available here. ## 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 (COO, Domestic), Carolina Toth (Manager, People & Partnerships), and Stuart Skeates (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 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.2 Its standard model involves grants of$1,000 (USD) per household over approximately one year,3 after which recipients become ineligible.4 GiveDirectly aims to help the poorest households, targeting those that are in "acute poverty."5 Recipient households are currently identified by (a) selecting poor villages based on poverty data, and (b) determining eligibility based on building materials of homes (more). It aims to deliver approximately 90 cents directly to recipients of every $1 in campaign expenses.6 Below, we • Discuss the structure of GiveDirectly's transfers • Report the status of GiveDirectly's transfer campaigns • Summarize GiveDirectly's expenses to date • Discuss GiveDirectly's process for identifying recipient households and delivering cash transfers • Describe GiveDirectly's staff structure ### Grant structure GiveDirectly's standard model involves grants of$1,000 (USD) over approximately one year,7 after which recipients become ineligible.8 This is a very 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 household size varies substantially in the data we've reviewed (from the first round of Siaya transfers): while the mean household size is ~4.7 and the median size is 4, 16% of households have 1 or 2 people, ~20% have 6 or more, and the maximum household size is 16.9 We estimate that the average family receives $288 per capita from GiveDirectly, which is 121% of baseline annual consumption per capita.10 Frequency and sizing of transfers In Kenya, GiveDirectly's standard transfer schedule involves a small initial transfer of about$50 (USD), followed by two larger transfers of about $475 (USD). These transfers are sent over a period of approximately 8 months.11 In Uganda, GiveDirectly's transfer schedule involves 10 monthly transfers of about$100 (USD) each.12 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.13

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 September 2014, GiveDirectly has provided partial or full cash transfers to more than 10,000 households in western Kenya and eastern Uganda, and is continuing to transfer funds to additional households in both places.14

GiveDirectly's work to-date can be grouped into 9 campaigns:

• Rarieda: This campaign includes cash transfers to the treatment groups of a randomized controlled trial (RCT) of the effects of unconditional cash transfers (more); once RCT data collection was complete, GiveDirectly provided "top-up" transfers to all participants who previously received less than $1,000. First transfers were sent in June 2011. • Siaya: This campaign is in Kenya. First transfers were sent in July 2012. • Nike: This campaign includes transfers to young women in Siaya district as part of a RCT funded by the Nike Foundation. First transfers were sent in September 2012. • Google: This campaign is in Kenya, funded by a Google Global Impact Award. First transfers were sent in January 2013. • Kenya 2M: This campaign is in Kenya. First transfers were sent in October 2013. • Uganda pilot: This is GiveDirectly's first campaign in Uganda. It was funded by the Google Global Impact Award. First transfers were sent in June 2013. • Kenya 1.2M: This campaign is in Kenya. First transfers were sent in January 2014. • Kenya rolling enrollment: This campaign is in Kenya. First transfers were sent in May 2014. Starting with this campaign, enrollment in Kenya no longer happens within discrete campaigns but on an ongoing ("rolling") basis. • Uganda 2M: This campaign is in Uganda. First transfers were sent in September 2014. Details of each of these transfer campaigns is provided in this table:15 Campaign Households enrolled (#) Transfer funds sent (as a % of total committed) Status Rarieda 538 100.0% Completed Siaya 199 100.0% Completed Nike 77 100.0% Completed Google 861 98.7% Ongoing Kenya 2M 2055 95.7% Ongoing Uganda pilot 960 99.1% Ongoing Kenya 1.2M 1200 56.2% Ongoing Kenya rolling enrollment 7116 17.5% Ongoing Uganda 2M 2000 5.7% Ongoing ### Expenses to date Based on the financial information that GiveDirectly provided, we calculate that it has spent a total of$17.3 million through the end of October 2014.16 This amount includes:

• Campaign costs (incurred + future): Direct grants to households, plus all associated costs of sending transfers. Incurred campaign costs account for transfers already sent; future campaign costs include transfers that have been committed but not yet sent to enrolled recipients, as well as the associated costs of sending those transfers (e.g., future wages for staff).17
• Set up costs (incurred): costs of setting up in Kenya and Uganda, and of international expansion (e.g., management, infrastructure, equipment, legal and compliance).18
• Marketing costs (incurred): costs of GiveDirectly's outreach and fundraising work (e.g., staff time spent on fundraising, mailing materials, software tools), as well as some domestic office expenses (e.g., New York City office rent).19

Costs not included in GiveDirectly's total spending are the value of its President's time (~40 hours/week), which was unpaid before FY 2014;20 research costs of the independently-run studies of GiveDirectly's program; and the roughly $2 million that GiveDirectly has set aside as reserves to cover staff salaries in the event that GiveDirectly has a funding shortfall.21 The total spending figures also do not include set up and marketing costs from September and October 2014. This is due to a slight mismatch in the time period reported for campaign versus set up and marketing costs. GiveDirectly has stated that marginal donations from the public are used to support transfer campaigns, while set-up and outreach costs are funded separately, by designated gifts from large donors.22 Of GiveDirectly's total incurred costs through October 2014,$6.4 million (or 80.4%) was transferred directly to recipients.23 When including projected future costs for current campaigns, the ratio of direct grants to total spending rises to 86.7%.24

GiveDirectly calculates its "efficiency ratio" by the amount transferred directly to recipients out of total campaign costs. GiveDirectly excludes set-up and marketing costs from total spending in this calculation,25 though it recently started amortizing fixed costs of campaigns across campaigns and including those within total campaign costs.26 Under this condition, GiveDirectly's efficiency ratio for incurred costs in all campaigns is 86.6%, and including future costs for ongoing campaigns the ratio rises to 89.7%.27

Below we break down GiveDirectly's total spending through October 2014.28

Cost category Incurred Future Total Cost (incurred + future) % of incurred costs % of total costs
Direct Grants To Households $6,381,367$8,609,217 $14,990,584 80.4% 86.7% Enrollment Costs$234,698 $45,577$280,275 3.0% 1.6%
Transfer Costs $202,947$271,697 $474,644 2.6% 2.7% Follow-up Costs$45,591 $99,666$145,257 0.6% 0.8%
Core operations $451,804$283,560 $735,365 5.7% 4.3% Core Operations - general$48,602 $38,282$86,884 0.6% 0.5%
Set up $199,962 -$199,962 2.5% 1.2%
Marketing $372,016 -$372,016 4.7% 2.2%
Total $7,936,987$9,348,000 $17,284,987 100.0% 100.0% ### 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.29 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.30 2. Selection of a region. GiveDirectly told us that it initially chose to work in western Kenya and eastern Uganda based on poverty statistics.31 We have reviewed the poverty data for Uganda; we have not reviewed poverty data across districts for Kenya. 3. Selection of districts or counties. For Kenya, GiveDirectly told us that its executive staff32 uses data on poverty, population density, security, and presence of poverty-focused NGOs (with the goal of avoiding overlapping with these) to select counties;33 we have reviewed poverty data for divisions within Siaya district, but not the data used to select other counties in Kenya.34 For Uganda, GiveDirectly told us that it chose a county to target initially based on poverty statistics, logistical factors and security considerations;35 we have reviewed the poverty data for Uganda. 4. Selection of villages. GiveDirectly states that it selects villages based on poverty level and location.36 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.37 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. 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). 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 this campaign, which was funded by part of the Google Global Impact Award.38 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.39 For details on how GiveDirectly has targeted villages historically, see this footnote.40 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.41 GiveDirectly signs written agreements with or obtains approval letters from local officials to formalize permissions.42 6. Enrollment process. 1. Census: GiveDirectly has field staff visit the village to create a census of all households. In conducting the census, field staff collect data about each household (e.g., GPS coordinates, roof materials, household name) and note if the household is eligible for transfers43 (the criterion for eligibility in a standard campaign is that the roof is made of thatch44). The census process was different in GiveDirectly's early campaigns.45 2. Registration: GiveDirectly has a separate set of field staff visit households marked as eligible in the census and register them. 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,46 and collecting other data that can be checked against the initial data from the census.47 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.48 In Uganda, a similar registration process exists. 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.49 A registered household is formally enrolled only after all phases of enrollment have been completed and the household has obtained a mobile money account (if necessary).50 Registration was different in early transfer campaigns.51 3. 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.52 GiveDirectly field staff also ask households if they were asked to pay a bribe to register.53 4. Audits: GiveDirectly sends field staff to revisit a portion of the registered households for audits. In Kenya, the field staff who do audits are from the follow up team.54 In Uganda, the field staff who have done audits in past campaigns were from earlier enrollment teams.55 GiveDirectly determines which households to audit based on the extent of the discrepancies between data collected at different phases in enrollment.56 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.57 The procedure for deciding which households to audit and determining eligibility was different in prior campaigns.58 GiveDirectly aims to enroll all eligible households. 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.59 We have reviewed (and made public) data collected during each step of the enrollment process, with deletions to preserve anonymity.60 We have not yet reviewed enrollment data for the Kenya rolling enrollment campaign or the Uganda 2M campaign. 7. 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."61 Village meetings were first implemented in the Google campaign.62 8. Sending transfers to recipients. GiveDirectly sends transfers to recipients via mobile money providers. In Kenya, transfers are sent in an initial installment of approximately$50, then two larger installments of approximately $475. In Uganda, transfers are sent in ten installments of approximately$100 each.63 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 ten monthly installments.64 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 changed its cash out day procedure in response to a case of staff fraud in Uganda.65
10. Conducting follow up calls. GiveDirectly field staff make multiple phone calls to recipients as transfers are being sent. There are short verification calls to confirm that the transfer was received and ask if the recipient experienced any problems.66 There are also 2 longer surveys administered after larger amounts of the transfers have been sent.67 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.68

The schedule of follow up calls has varied somewhat by campaign.69 We have reviewed and made public data from these calls for ongoing transfer campaigns in Kenya.70

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.71 We have reviewed the records of calls made to GiveDirectly's Kenya hotline from May 2012 – September 2014.72 Recipients can also report issues to GiveDirectly field staff when they are in the village; GiveDirectly created a formal mechanism for recording these reports.73

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.

In October 2014, we spoke with GiveDirectly field staff about the challenges they face in carrying out the enrollment process.74

### Partnerships

In 2014, GiveDirectly’s President and COO (International) spent time networking and developing potential partnerships with government officials and international aid agencies.75 The projects that GiveDirectly is considering partnering on involve varying degrees of responsibility for implementing cash transfers on behalf of another agency. For a partial list of GiveDirectly’s potential partnership projects, see this footnote.76

We have not yet attempted to assess the value of the potential partnership projects, but can imagine cases where they might be very high leverage (e.g., enabling another organization to "benchmark" its current programming against cash) and also cases that may have limited value (e.g., implementing a program that would have been implemented effectively without GiveDirectly’s involvement). We expect partnerships to take up some higher-level staff time but not involve a significant amount of GiveDirectly’s funding over the next year.

### Staff structure

GiveDirectly delivered its first cash transfers in 2011.77 Starting in January 2011 it had one full-time staff member.78 In early 2013 it hired a second full-time staff member to serve as COO (Domestic).79 GiveDirectly has since expanded its staff significantly. It's current organizational structure in East Africa includes:80

• Chief Operating Officer (COO): Oversight and quality control of the entire operation. The COO oversees the Field Directors.
• Field Directors (FDs): This role was created in mid-2013. FDs are in charge of overseeing field operations, as well as approving transfer schedules and rosters. FDs oversee Senior Field Officers.
• Senior Field Officers (SFOs): This role was created in mid-2013. SFOs 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.81
• Project Associate (PA): This role was created in 2014 and currently only exists in Kenya. The PA supervises SFOs, focusing on quality control, management and training of FOs.82
• 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.83

## 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.
• Are the people meeting GiveDirectly's criteria low-income? The evidence we have suggests that they 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 in the scheme of things.
• Are the size and structure of the cash transfers well-thought-through and appropriate? We find GiveDirectly's approach to be defensible, but hope to see more experimentation with different approaches in the future.

### 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.

### Are the people meeting GiveDirectly's criteria low-income?

GiveDirectly has collected data on poverty levels of the people receiving cash transfers as part of its studies (these people were selected using the process detailed above). GiveDirectly has shared the full survey form used to interview participants,84 as well as its own summary of the data collected as of March 2012:85

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.86

The RCT that Innovations for Poverty Action conducted of GiveDirectly's transfers in Rarieda provides end-line data on food consumption among control group recipients.87 This data shows that "20% [sic88] 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."89 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.90 GiveDirectly is considering modifying its targeting criteria to capture more of these households.91

#### 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.92

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.93

If the information collected about a household at different stages of enrollment is inconsistent, GiveDirectly staff revisit the household for an audit.94 GiveDirectly tracks the percentage of households found to be ineligible at registration, backcheck, and audit in its monthly operations reports.95 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

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 the M-PESA agent.96 Users enter the amount they want to withdraw on their own phone, and after each transaction, they can see their remaining balance,97 reducing the ability of agents to defraud clients of funds.

In Uganda, GiveDirectly worked with two different mobile money providers in its pilot campaign (745 recipients were assigned to EZEE Money, 215 recipients to the MTN).98 GiveDirectly said that after assessing the relative performance of these two providers, it chose to work exclusively with MTN in the next campaign.99 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.100 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 also sent after withdrawals.101

Despite these mobile money security measures, Lydia Tala, a Senior Field Officer 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. 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.102 GiveDirectly told us that it recognizes this issue and maintains a hotline to provide recipients with assistance in navigating the M-PESA system.103 In Uganda, some recipients have experienced delays in accessing their funds due to MTN not activating them immediately.104 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 will provide training to recipients in how to use their phones and accounts, and reminders are also given at cash out days.105

In Kenya, recipients withdraw cash from mobile money agents located in or near their villages.106 In Uganda, the agent network is less robust, so GiveDirectly coordinates cash out days for recipients to withdraw funds after each of the ten monthly installments.107

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.108 The percentages 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 a survey for ongoing campaigns in Kenya, recipients reported receiving various amounts between 37,000 KES – 40,000 KES.109 Other than the mobile phone purchase deduction, we do not know the causes of this variance.

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. We discuss this case more below.

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

#### Findings from the RCT

Effects of receiving cash transfers
72% of treatment group households in the evaluation received just $287; the rest received$1,085.110 In the sections below, we use the outcomes from the larger transfer group unless otherwise specified, because GiveDirectly typically gives transfers of similar size.111 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 in the text, 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.112

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.113 Their spending is broken down in more detail below.

• Total non-land assets.114 Receipt of large transfers increased households’ non-land assets by an average of $463 (95% CI:$378 to $549).115 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).116 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.117 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.118 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.119 GiveDirectly ran a survey that sampled a respondent from each of 20 villages and found that iron roofs cost$418 USD PPP on average.120 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.121 Recipients of small transfers also spent about$13 more per month (95% CI: $4 to$22).122
• Health expenditures Recipients of large transfers spent about $3 (95% CI: -$1 to $6) per month more than control households on health expenditures.123 Recipients of small transfers also spent about$3 (95% CI: $1 to$5) more.124 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.125 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.126 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.127 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.128 About half of this additional consumption was on food.129 This additional consumption also included increased spending on social expenditures and various other expenditures.130 • Alcohol and tobacco. Treatment households did not increase their spending on alcohol or on tobacco.131 Impacts of GiveDirectly transfers on recipients • Food security. At baseline, food security was low among participants.132 Program participants reported a 0.37 standard deviation (95% CI: 0.17 to 0.57) increase in a food security index over controls.133 • Health and education. The study did not detect an effect on indices of health and educational outcomes.134 • 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.135 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).136 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.137 • 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.138 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.139 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 provided us with data from responses recipients gave to surveys conducted by GiveDirectly staff concerning how recipients used their cash transfers. This data was collected at different points in the transfer cycle of each campaign.140 We summarize the data from recent campaigns in Kenya and Uganda below.141 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 1205 59.0% 618 31.0% Clothing 274 13.4% - - Household items 634 31.0% 426142 21.3% Building 1595 78.1% 1075 53.9% Land 72 3.5% 147 7.4% Livestock 453 22.2% 496 24.8% Farm business 189 9.3% 116 5.8% Non-farm business 243 11.9% 48 2.4% School 687 33.6% 424 21.2% Medical 132 6.5% 186 9.3% Water 2 0.1% 0 0.0% Debt 84 4.1% 68 3.4% Savings 415 20.3% 176 8.8% Life event 127 6.2% 5 0.3% Family 165 8.1% 17 0.9% Church 22 1.1% 14 0.7% Transport 138 6.8% - - Alcohol - - 1 0.1% Other 94 4.6% 57 2.9% Total respondents 2043 1996 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 4,794,402 5.7% 20,667,800 4.4% Clothing 614,522 0.7% - - Household items 3,760,451 4.5% 28,061,120143 5.9% Building 46,449,810 55.1% 194,449,559 41.2% Land 2,214,850 2.6% 19,603,000 4.1% Livestock 6,438,295 7.6% 66,344,250 14.0% Farm business 1,047,850 1.2% 10,536,000 2.2% Non-farm business 3,971,973 4.7% 8,414,000 1.8% School 5,639,148 6.7% 49,246,000 10.4% Medical 652,657 0.8% 13,434,010 2.8% Water 2,200 0.0% 0,000 0.0% Debt 466,230 0.6% 6,444,000 1.4% Savings 4,107,516 4.9% 19,258,500 4.1% Life event 2,066,035 2.4% 750,000 0.2% Family 750,880 0.9% 866,000 0.2% Church 63,750 0.1% 141,000 0.0% Transport 393,375 0.5% - - Alcohol - - 5,000 0.0% Other 909,025 1.1% 6,190,000 1.3% Total 84,342,969 100.0% 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.144 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, 2012145) 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 year146 ($175.13 based on the exchange rate as of November 15, 2012147)). ### Do the cash transfers cause problems and complications that offset their positive impact? • Does distribution to some community members and not others result in jealousy, conflict, or related issues? 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 villages148 or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages.149 GiveDirectly surveys recipients (post-transfer) on the following questions:150 • 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.151 For a full list of follow-up survey questions, see GiveDirectly, Kenya verification template, August 5, 2013.152 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. Kenya Uganda Issue # of reports/# of respondents % reports of total respondents # of reports/# of respondents % reports of total respondents Trouble collecting 68/6395 1.1% 39/1950 2.0% Complaints 1120/14463 7.7% 139/1949 7.1% Theft153 265/6373 4.2% 8/1950 0.4% Bribes154 34/14458 0.2% 52/7795 0.7% Shouting 265/14459 1.8% 65/1949 3.3% Crime 138/14457 1.0% 21/1949 1.1% Household argument 62/14461 0.4% 31/1948 1.6% 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 the records of calls made to GiveDirectly's hotline from May 2012 – September 2014, which provide anecdotal evidence of tension and conflict caused by the cash transfers, according to recipient reports, including marital disputes and Village Elders requesting funds from recipients.155 In the most recent follow up tracker,156 the most common type of adverse event recorded is household conflict, followed by theft. The number of issues reported for recent campaigns are equal to about 6% of the total households in the campaigns (though it is possible that single households account for more than one issue recorded).157 • 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.158 However, we do not know if the cash transfers cause more serious crimes (in terms of damages), but it seems plausible given that they 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.159 Examples of attempted and/or successful criminal activity relating to GiveDirectly cash transfers include: • People stealing cash and cell phones from recipient households160 • People contacting recipients and posing as GiveDirectly staff to defraud recipients of funds161 • Mobile money agents defrauding recipients of funds162 To mitigate the risk of small-scale crime, GiveDirectly emphasizes ways that recipients can keep their mobile money accounts and phones secure.163 It does not communicate with recipients via text message and tells recipients of this policy in order to protect against mass attempts at fraud,164 and it follows up with recipients who report crimes to try to resolve the issues.165 Case of staff fraud: In mid-2014, GiveDirectly experienced one case of large-scale crime, when 2 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.166 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.167 GiveDirectly has taken multiple measures to address the vulnerabilities exposed by this case of fraud.168 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 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 and no formal security. We are not aware of security measures that GiveDirectly has taken to mitigate the risk of large-scale crime beyond its response to the staff fraud. In addition to harming recipients, crime would likely cause delays for GiveDirectly's work.

• Do grants distort incentives and decisionmaking? 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)169 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.170 We also have seen some very limited evidence (mostly pointing to no distortion) in the broader literature on cash transfers.

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 did provide statistics on the speed with which transfers were received for Rarieda and Siaya.171 In Rarieda, while 67% (359 of 536) of recipients waited less than a month and 84% (448 of 536) waited 3 months or less, 6% (34 of 536) waited 6 months or more. In Siaya (a later group), 188 of 193 recipients waited less than a month, and the remaining 5 waited 2-3 months. GiveDirectly comments:172

We were able to accelerate [the time it took for recipients to register for M-PESA] significantly for two reasons: (a) we gave clearer instructions, and (b) we let recipients designate which household member they wanted to receive the transfers, which gives them flexibility to choose someone who already has a National ID; in the Rarieda round we could not do this as we were randomizing recipient gender. I expect the Nike cohort will take longer to register as that project focuses on 18-19 year old women, many of whom will not yet have IDs.

GiveDirectly told us that for Kenya, the key factor determining when a recipient receives funds is when he or she registers for M-PESA, and that recipients are told from the beginning that they will not receive transfers until they have registered.173 GiveDirectly's records of calls to its Kenya hotline demonstrate that some recipients have been 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). The hotline records also document the steps that GiveDirectly has taken to follow up on recipients' concerns; we have reviewed some of these cases and find the steps taken to resolve issues to be reasonable and appropriate.174

In Uganda, the agent networks of GiveDirectly's two mobile money providers are not very 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).175 These challenges and the lack of flexibility may hamper recipients' abilities 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.176

• 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 do not find large increases in spending on these goods.

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.

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

GiveDirectly’s standard model involves grants of $1,000 (USD) to households over approximately one year,177 after which recipients become ineligible for future transfers.178 It gives the following rationale for the size of its transfers:179 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 clients 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.180 The quote above states that the lower level of initial income is an argument for making the cash transfer larger, but to us, it 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.181 But psychologically, the impact of GiveDirectly's transfers may be very different from those of government programs. Perspectives of recipients and field staff On 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.182 4 out of 4 recipients said that the transfer size should stay the same (or be increased).183 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.184

Discussion of the merits of further research

### Evaluation and experimentation

#### Other evaluation and experimentation

Ongoing

• 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. GiveDirectly is working with Professor Ted Miguel of the University of California, Berkeley, and Assistant Professor Johannes Haushofer of Princeton University, who will conduct an RCT on macroeconomic effects of GiveDirectly's program in Kenya. This study will include a long-term follow up component as well. GiveDirectly told us that the ideal sample size for this study, which is randomized at the village level, would require $15 million for cash transfers. Baseline data collection for the study began in August 2014. Paul Niehaus, GiveDirectly's President, is serving as a Principal Investigator on this study. 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.199 We have not yet reviewed this preregistration, but plan to do so in the next year. • Behavioral interventions: 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.200 The main outcome of interest in this study is the rate of return on spending.201 GiveDirectly is conducting the data collection for this study internally, and the analysis will be done by independent researchers.202 This study began in late October 2014.203 It is fully funded by an anonymous donor.204 • Gender: GiveDirectly is planning to run a small pilot of informal contracts between spouses in the spring of 2015. External research partners will evaluate the impacts of the contracts on domestic violence and female empowerment. GiveDirectly has said that if the pilot is successful it will be expanded into a larger-scale project.205 Past • 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.206 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."207 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.208 We have not vetted either document. • 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. The grant is from USAID via the Policy Design and Evaluation Lab at UCSD. The goals of the project are to generate data on longer-term effects of cash transfers (up to 2 years after completion of the RCT) and how treatment effects unfold over time, as well as to study the potential for using mobile phones as cost-effective, easily adaptable tools for data gathering.209
• 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. It also conducted focus groups in 3 treatment and 3 control villages to elicit opinions about targeting strategies.210 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.211 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.
• GiveDirectly also piloted "community-based targeting,"212 but is not planning to implement it more broadly.213

Future

GiveDirectly is considering many other ideas for future experimentation:

• Providing cash transfers in an urban setting214
• Providing cash transfers as humanitarian relief215
• Facilitating the pooling of recipient funds for public goods projects216
• Implementing a lifetime basic income guarantee217
• Serving the role of payment provider at cash out days218
• Using biometric authentication219

GiveDirectly's goals for experimentation include increasing the evidence base for cash, improving recipient returns and welfare, and developing capabilities necessary to implement larger-scale programs.220 GiveDirectly currently plans to use campaigns in Uganda as a "testing ground" for these experiments.221

## What do you get for your dollar?

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

GiveDirectly aims to deliver a high proportion of campaign expenses directly to recipients: 90% in Kenya, and 87% in Uganda.222 GiveDirectly calculates its "efficiency ratio" by the amount transferred directly to recipients out of total campaign costs. GiveDirectly excludes set-up and marketing costs from total spending in this calculation,223 though it recently started amortizing fixed costs of campaigns across campaigns and including those within total campaign costs.224 Data from GiveDirectly's distributions imply that it has been hitting this target. When all costs are considered, including set-up and marketing, cash grants make up 86.7% of expenses (more).

Costs not included in GiveDirectly's total spending are the value of its President's time (~40 hours/week), which was unpaid before FY 2014;225 research costs of the independently-run studies of GiveDirectly's program; and the roughly $2 million that GiveDirectly has set aside as reserves to cover staff salaries in the event that GiveDirectly has a funding shortfall.226 The total spending figures also do not include set up and marketing costs from September and October 2014. This is due to a slight mismatch in the time period reported for campaign versus set up and marketing costs. ### 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 2014 cost-effectiveness spreadsheet is available here. See also, our 2012 discussion of the cost-effectiveness of cash transfers and other interventions. ## Room for more funding ### Projections for fiscal year 2015 In September 2014, GiveDirectly provided the following statement regarding its room for more funding in FY 2015:227 For 2015, we estimate our room for funding to be roughly$20 M with current resources, pace, and model, and $40 M at the optimal pace: • In Kenya, we are committing ~$1M/month and can create the same structure in Ug
• If we buffer two months for holidays and set-up, that’s $10M/year in both countries • Teams are currently 5 FOs and can be increased to 10 with no other changes in structure. SFOs have previously shown the ability to manage teams of 10. • Teams of 10 would double our throughput to$40M/year

Note that the above statement refers only to GiveDirectly's room for more funding for transfer campaigns.

In October 2014, GiveDirectly sent us a scenario analysis of its room for more funding under different assumptions regarding government permissions and pace of enrollment, demonstrating how it would allocate Field Director time.228

GiveDirectly has successfully scaled up over time, increasing its rate of transfers about 4x every year.229 In FY 2014, GiveDirectly received $17.4 million230 spent$6.1 million, and incurred liabilities (future transfers and transfer costs for currently enrolled recipients) totaling $9.3 million.231 In recent months, GiveDirectly has been enrolling ~900 recipients per month in Kenya, equivalent to$900,000 in committed transfers.232 GiveDirectly intentionally paused its enrollment in Uganda for research-related reasons,233 but believes it could ramp up to a similar pace there within a couple months.234

GiveDirectly has received approximately $23.4 million since it was founded.235 Of this funding: •$16.7 million has been used for campaign costs236 representing transfers sent and committed to approximately 15,000 households as of October 2014.237
• $572,000 has been spent on set-up and fundraising costs as of September 2014.238 • Approximately$2 million was set aside for salary reserves.239
• Approximately $1.8 million was designated for 2015 fundraising budget.240 GiveDirectly told us that it plans to use the remaining funds (approximately$2.3 million) for transfers to additional households in Kenya.241 At its current pace, GiveDirectly would complete this enrollment by the end of February 2015.242

GiveDirectly's past room for more funding statements have over-estimated the amount of funds it was actually able to use on campaigns in the given year, but it has nearly met or exceeded these targets shortly after the projected time periods.243 The lower end of GiveDirectly's FY 2015 room for more funding estimate seems very reasonable to us, given that it is based on the pace at which GiveDirectly is already operating in Kenya.244 GiveDirectly has told us that its median expectation of funding from donors other than GiveWell donors and Good Ventures is $10 million.245 #### Risks to room for more funding GiveDirectly identified the following risks to its room for more funding: • Crime: Incidences of large-scale crime could cause delays and reduce GiveDirectly’s ability to transfer funds to recipients. The risk of crime seems likely to increase as GiveDirectly becomes better known in the regions in which it works. We discuss this risk more above. We consider this a moderate risk and plan to follow up on it over the coming year. • 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.246 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.247 We do not consider this a limiting factor for FY 2015, as GiveDirectly has already obtained permissions for a cumulative capacity of about$30 million across Uganda and Kenya.248
• 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.249 We know very little about security risks in Kenya and Uganda, but would guess based on GiveDirectly's assessment that it is a low risk.
• Payment provider: Relying on one payment provider in each country introduces a risk that problems with the payment provider could cause delays. GiveDirectly feels that this risk is low, because if there were problems, it could switch to alternative providers.250 We would guess that this risk is low, as the mobile money providers that GiveDirectly uses are national networks, and GiveDirectly has identified alternatives.

#### Our bottom line

Based on our understanding of GiveDirectly's future plans, we believe GiveDirectly could absorb an additional $25-30 million beyond what it already expects to receive. GiveDirectly estimates that it will hold approximately$3 million as of January 1, 2015 that could be targeted to additional transfers, and that it will receive $10 million from non-GiveWell-influenced sources over the next year. GiveDirectly has said it could receive and move up to approximately$40 million in a year. Though we do not believe that GiveDirectly will fully hit that target, we also see few risks to providing it with excess capital this year.

### 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. On our "Donate" page, linked from here, we explain to donors that funds given via the link on this page will be treated as unrestricted donations (in which case 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 Kenya, Uganda, either Kenya or Uganda, or as "flexible, including experiments."251

## GiveDirectly as an organization

GiveDirectly is a relatively young organization. It was founded in 2008 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.252 Professor Niehaus has been on sabbatical this year from his teaching position and working full time on GiveDirectly.253

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 three 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.

## Sources

Document Source
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
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Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013 Source
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Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013 Unpublished
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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
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Email from Carolina Toth, GiveDirectly, November 11, 2014 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 Joy Sun, COO, Domestic, GiveDirectly, September 27, 2013 Unpublished
Email from Paul Niehaus, GiveDirectly, November 9, 2014 Unpublished
Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 Unpublished
Email from Piali Mukhopadhyay, COO, International, GiveDirectly, November 25, 2013 Unpublished
Faizan Diwan, Innovations for Poverty Project Associate, conversation with GiveWell, November 8, 2012 Unpublished
GiveDirectly blog, Fighting fraud in Uganda Source (archive)
GiveDirectly FY 2011 Form 990 Source (archive)
GiveDirectly, 2013 Annual Report Source (archive)
GiveDirectly, 20140408 GD-GW update Source
GiveDirectly, 20140420 Uganda combined follow up data Source
GiveDirectly, 20140707 GW - GD quarterly update Source
GiveDirectly, 20140723 GW GD quarterly update Source
GiveDirectly, 20140904 GD – GW check in Source
GiveDirectly, 20140929 GW - GD annual update Source
GiveDirectly, 201410 Monthly Operations report Source
GiveDirectly, 20141010 Follow up tracker Source
GiveDirectly, 20141112 Kenya follow up data Source
GiveDirectly, Akumure, Uganda Enrollment database Source
GiveDirectly, Analysis plan for randomized controlled trial Source
GiveDirectly, Annotated budget projections (May 11, 2012) Source
GiveDirectly, Answers to questions from GiveWell, October 2013 Unpublished
GiveDirectly, Audit log Source
GiveDirectly, Back check instrument Source
GiveDirectly, Balance sheet Unpublished
GiveDirectly, Board spending breakdown Source
GiveDirectly, Budget summary, July 22, 2013 Unpublished
GiveDirectly, Campaign efficiency update for GW v2, October 24, 2013 Source
GiveDirectly, Capacity note Source
GiveDirectly, Cash transfer plan, November 7, 2013 Source
GiveDirectly, Chart of accounts Source
GiveDirectly, clarifications on GiveWell's draft review of GiveDirectly Source
GiveDirectly, Contextualizing transfer size Source
GiveDirectly, Disaggregated Google Follow-up Data (LS - long) Source
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GiveDirectly, Disaggregated Siaya Follow-up Data Source
GiveDirectly, Domestic organizational structure, November 7, 2013 Source
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GiveDirectly, FAQs 2012 Source (archive)
GiveDirectly, FAQs 2013 Source (archive)
GiveDirectly, Final report Nike girls study Source
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GiveDirectly, Follow-up tracker, July 2013 Source
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GiveDirectly, GD – GiveWell update, October 16, 2013 Source
GiveDirectly, GD – GW update, July 17, 2013 Source
GiveDirectly, Give now Source
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GiveDirectly, Google transfer schedule Source
GiveDirectly, Google transfer schedule, December 2013 Source
GiveDirectly, Google transfer schedule, July 2013 Source
GiveDirectly, Google verification, September 21, 2013 Source
GiveDirectly, Government cash transfers follow-up Unpublished
GiveDirectly, GW Q&A, April 26, 2013 Source
GiveDirectly, GW scratch sheet Source
GiveDirectly, How it works 2013 Source (archive)
GiveDirectly, How it works 2014 Source (archive)
GiveDirectly, Ideal budget overview, November 7, 2013 Source
GiveDirectly, Income and expense. Unpublished
GiveDirectly, Kanyamutamu, Uganda Enrollment database Source
GiveDirectly, Kawo, Uganda Enrollment database Source
GiveDirectly, Kenya 1.2M enrollment database Source
GiveDirectly, Kenya 2M census results, July 8, 2013 Source
GiveDirectly, Kenya 2M Enrollment database, September 23, 2013 Source
GiveDirectly, Kenya 2M transfer schedule, November 2013 Source
GiveDirectly, Kenya backcheck template, September 3, 2013 Source
GiveDirectly, Kenya census template, July 8, 2013 Source
GiveDirectly, Kenya hotline log, July 24, 2013 Unpublished
GiveDirectly, Kenya registration template, July 22, 2013 Source
GiveDirectly, Kenya verification template, August 5, 2013 Source
GiveDirectly, Kosile, Uganda Enrollment database Source
GiveDirectly, M-PESA transfer history Source
GiveDirectly, Name collection instrument Source
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GiveDirectly, Nike instrument Source
GiveDirectly, Nike transfer schedule, August 2013 Source
GiveDirectly, Nike transfer schedule, December 2013 Source
GiveDirectly, Nike verification (combined), May 18, 2013 Source
GiveDirectly, Nike verification (final), September 24, 2013 Source
GiveDirectly, Nike verification (short version), June 20, 2013 Source
GiveDirectly, Notes on verification data (November 17, 2011) Source
GiveDirectly, Notes to financial statements. Unpublished
GiveDirectly, Offering Memorandum (January 2012) Unpublished
GiveDirectly, Operating efficiency as of 31 August 2012 Source
GiveDirectly, Operational process overview Source
GiveDirectly, Overview of targeting process Source
GiveDirectly, Overview of update documents Source
GiveDirectly, Post-transfer audit Source
GiveDirectly, Rarieda Top-up Verification (short) Source
GiveDirectly, Rarieda transfer schedule, August 2013 Source
GiveDirectly, Rarieda verification (top ups), May 26, 2013 Source
GiveDirectly, Rarieda verification stats Source
GiveDirectly, RCT Enrollment DB Source
GiveDirectly, RCT mid-line results overview Unpublished
GiveDirectly, RCT midline variables Unpublished
GiveDirectly, Room for more funding summary Source
GiveDirectly, Sample scorecard Source
GiveDirectly, Saturation analysis Source
GiveDirectly, Set-up and domestic expenses, October 2, 2013 Source
GiveDirectly, Siaya enrollment database Source
GiveDirectly, Siaya poverty data by location Source
GiveDirectly, Siaya transfer schedule, June 2013 Source
GiveDirectly, Siaya verification stats Source
GiveDirectly, Siaya verification, June 15, 2013 Source
GiveDirectly, Siaya village index Source
GiveDirectly, Survey for randomized controlled trial Source
GiveDirectly, Team Source (archive)
GiveDirectly, Uganda backcheck template Source
GiveDirectly, Uganda census template Source
GiveDirectly, Uganda monthly forecast, November 18, 2013 Source
GiveDirectly, Uganda name collection template Source
GiveDirectly, Uganda targeting data, July 22, 2013 Source
GiveDirectly, Uganda transfer schedule Unpublished
GiveDirectly, Ugunja-GiveDirectly conditions Source
GiveDirectly, Update on process changes, August 28, 2013 Source
GiveDirectly, Updated data (March 31, 2012) Source
GiveDirectly, Values Source (archive)
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
GiveWell's non-verbatim summary of a conversation with Carolina Toth, GiveDirectly, October 1, 2014 Source
GiveWell Household size analysis Source
GiveWell Site visit notes Source
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Jean Junior, The Perspectives of Young Women in Siaya County, Kenya: Their Lives and Their Thoughts on Cash Transfer Programs Source
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Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 6, 2012 Unpublished
Lydia Tala, GiveDirectly Field Assistant, conversation with GiveWell, November 7, 2012 Unpublished
Paul Niehaus and Jeremy Shapiro, GiveDirectly founders, phone conversation with GiveWell, October 6, 2012 Source
Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, April 1, 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, November 5, 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, email to GiveWell, April 23, 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012 Unpublished
Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 24, 2012 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, January 25, 2012 Unpublished
Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012 Unpublished
• 1.

In Kenya: "We transfer your donation electronically to a recipient's cell phone

We move the money from our US bank to our account with Safaricom's M-Pesa mobile payment system using a foreign exchange broker. We then transfer money from our M-Pesa account to the recipient's M-Pesa account. As a security measure we only transfer funds to a recipient if the name in our records matches the name on the national ID document he or she used to register for M-Pesa. The recipient gets an SMS text message reminding him or her of the transfer and then collects the transfer from a local M-Pesa agent, who is typically a shopkeeper in the recipient's village or in the nearest town. The recipient transfers his or her electronic balance to the agent's phone in return for cash." GiveDirectly, How it works 2013

In Uganda, the primary mobile money provider is MTN, which works in exactly the same way up until the point of finding a local agent. The agent network is less widespread in Uganda and so GiveDirectly helps organize a monthly visit of the mobile money provider's agents to a central and accessible point in or near the villages. GiveDirectly has also previously used a mobile-linked system in Uganda, in which recipients received SMS notifications about their transactions but recipients could not check their balances or conduct transactions using only their phones; these actions required visiting a mobile money agent.

• 2.
• 3.

In recent campaigns, the enrollment process has taken slightly more than 2 months on average, and transfers have been sent over a period of 8 months in Kenya and a period of 10 months in Uganda for the vast majority of recipients. This is the basic schedule followed in standard campaigns including Google, Kenya 2M, Kenya 1.2M, and Kenya rolling enrollment. (Example of transfer schedule: GiveDirectly, Google transfer schedule, July 2013)

The amount of time that it takes for total transfers to be sent has varied between campaigns. Campaigns that involved an experiment (like Rarieda and Nike) and older campaigns like Siaya have differed from the standard schedule.

• 4.

"GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33.

Update [November 2014]: the two year timeline mentioned above is now outdated; transfers are generally sent over a period of 8 months in Kenya and 10 months in Uganda. It is still the case that recipients can only receive one full transfer, and that they are ineligible for additional transfers thereafter. Conversation with Carolina Toth, GiveDirectly, November 20, 2014

• 5.

"GiveDirectly uses objective criteria to determine which households are eligible for transfers. We target households which do not have solid (cement or iron) walls, floors or roofs in their houses. Our research shows this criterion is highly correlated with acute poverty." GiveDirectly, FAQs 2012

• 6.
• 7.

In recent campaigns, the enrollment process has taken slightly more than 2 months on average, and transfers have been sent over a period of 8 months in Kenya and a period of 10 months in Uganda for the vast majority of recipients. This is the basic schedule followed in standard campaigns including Google, Kenya 2M, Kenya 1.2M, and Kenya rolling enrollment. (Example of transfer schedule: GiveDirectly, Google transfer schedule, July 2013)

The amount of time that it takes for total transfers to be sent has varied between campaigns. Campaigns that involved an experiment (like Rarieda and Nike) and older campaigns like Siaya have differed from the standard schedule.

• 8.

"GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33.

Update [November 2014]: the two year timeline mentioned above is now outdated; transfers are generally sent over a period of 8 months in Kenya and 10 months in Uganda. It is still the case that recipients can only receive one full transfer, and that they are ineligible for additional transfers thereafter. Conversation with Carolina Toth, GiveDirectly, November 20, 2014

• 9.

GiveWell Household size analysis

• 10.

GiveWell Household size analysis

Mean daily per capita consumption among eligible households = $0.65 (GiveDirectly, Offering Memorandum (January 2012) Pg 24.) • 11. In recent campaigns, the enrollment process has taken slightly more than 2 months on average, and transfers have been sent over a period of 8 months in Kenya and a period of 10 months in Uganda for the vast majority of recipients. This is the basic schedule followed in standard campaigns including Google, Kenya 2M, Kenya 1.2M, and Kenya rolling enrollment. (Example of transfer schedule: GiveDirectly, Google transfer schedule, July 2013) The amount of time that it takes for total transfers to be sent has varied between campaigns. Campaigns that involved an experiment (like Rarieda and Nike) and older campaigns like Siaya have differed from the standard schedule. • 12. GiveDirectly, Uganda transfer schedule • 13. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013 • 14. GiveDirectly, 201410 Monthly Operations report • 15. GiveWell, GiveDirectly financials 2014 update Sheet: "Campaign efficiency FY 2014" • 16. GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses" • 17. • 18. GiveWell, GiveDirectly financials 2014 update Sheet: "Set up & marketing costs FY2014" • 19. GiveWell, GiveDirectly financials 2014 update Sheet: "Set up & marketing costs FY2014" • 20. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013 • 21. GiveDirectly, GW scratch sheet • 22. GiveDirectly expects to spend a total of$0.5 - $1 million on fixed investments in FY 2014, which it is seeking from major donors, not retail donors whose marginal dollars will continue to be directed exclusively to cash transfers. Its total spending on transfer costs will similarly depend on fundraising. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013. "We anticipate a FY2014 budget for outreach, international expansion and technology investments in the$0.5M-$1.0M range for which we are fundraising separately." GiveDirectly, GD – GW update, July 17, 2013. • 23. GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses" • 24. GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses" • 25. "First, as a general principle we'll always aim to publish spending clearly enough so that anyone can calculate efficiency however they want. Second, when we analyze our efficiency for management and forecasting purposes we distinguish between (1) operating costs that are ongoing but shared across campaigns and (2) one-time setup costs. (1) is included in the efficiency calculation and (2) is not. Examples of (1) are Field Director time (which is "fixed" for the duration of his/her contract) and office space (which is "fixed" with respect to number of ongoing campaigns). Examples of (2) are costs of registering as an NGO in a new country and costs of developing code which can then be re-used indefinitely. We accept donations from the public for (1) but not (2)." Email from Joy Sun, COO, Domestic, GiveDirectly, August 9, 2013 "Set-up costs are excluded because one-time costs (e.g. NGO registration) can be re-used indefinitely, as opposed to marginal costs." Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 • 26. • 27. GiveWell, GiveDirectly financials 2014 update Sheet: "Campaign efficiency FY 2014" • 28. GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses" The timeframe for campaign costs goes through October 2014, but set up and marketing costs are only provided through the formal end of FY 2014 (August 31, 2014), and therefore the set up and marketing cost estimates are likely slightly lower than they actually were through the full time period. • 29. "Kenya was selected due to 1) the robustness of M-Pesa as a mobile banking platform 2) large population of target poor with access to mobile tech" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 • 30. Conversation with Paul Niehaus, President, and Rohit Wanchoo, Director, GiveDirectly, March 18, 2013 • 31. • 32. Paul Niehaus, GiveDirectly Founder, conversation with GiveWell, October 22 2012 • 33. "County level – we look at data on poverty, population density, presence of poverty-focused NGOs (which we try to avoid), and security." GiveDirectly, Operational process overview Pg 1. • 34. GiveDirectly, Siaya poverty data by location • 35. "Factors that informed decision to locate initial campaign in […] County: poverty rate, […] logistical ease for set up activities and cross-country management, […] minimum security for staff" GiveDirectly, Uganda targeting data, July 22, 2013 • 36. "Recently we have been relying on sublocation-level poverty data and looking at village names and locations to omit villages that would be too urban for our current model. We have also been working with satellite data to approximate village-level poverty. " Conversation with Carolina Toth, GiveDirectly, November 20, 2014 • 37. See GiveDirectly, Siaya village index for the calculations that GiveDirectly did to create "poverty scores" for different villages in Siaya. The weights placed on each indicator (in constructing the index) were determined using the process described in GiveDirectly, Village targeting regression: the more detailed "poverty scores" from GiveDirectly's Rarieda study were regressed on indicators such as "village population," "number of boreholes," etc. • 38. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 • 39. In the Kenya 1.2M campaign, GiveDirectly selected villages by manually estimating the proportion of thatch- to iron-roof homes with satellite imagery. In the Kenya rolling enrollment campaign, GiveDirectly used a machine learning algorithm to estimate thatch-iron proportions at the village level based on satellite imagery. In the Uganda 2M campaign, GiveDirectly relied on parish-level census data with poverty measures, as well as mobile money coverage. GiveDirectly, 20140408 GD-GW update, Pg 3. • 40. • In Rarieda (the site of the RCT and the first transfers GiveDirectly provided), GiveDirectly sought poorer districts (based on 2005 census data) that were in places with sufficient accessibility, M-PESA usage, population density to make it more convenient, proximity to Innovations for Poverty Action (the RCT implementer) offices and where there would be a sufficient number of potential recipients in this district (# of thatched roof houses). GiveDirectly chose to work in Rarieda District because it was slightly poorer than the nearby Siaya district. In Rarieda, there are slightly more than 300 villages, and GiveDirectly conducted a census in each village to determine the number of eligible (thatched-roof) and ineligible households in each. It then selected the 100 with the highest proportion of thatched-roof to non-thatched-roof households, and randomly selected 60 of those to serve as the treatment and control groups in its trial. (Faizan Diwan, Innovations for Poverty Project Associate, conversation with GiveWell, November 8, 2012) • GiveDirectly chose to work in Siaya District, the location of the three other sets of transfers GiveDirectly has initiated, because it shared local administration with the Rarieda District, making expansion easier; it chose not to remain in Rarieda because it did not want to overlap in areas in which the RCT was being conducted. Using administrative data, it chose 3 locations within Siaya that it believed had the highest poverty levels. It then carried out the process described in the main text to rank the 100 villages in these locations. GiveDirectly was not able to contact all Village Elders to obtain data (staff estimate they reached 85 out of 100) and it excluded villages whose Village Elders it was not able to reach. In June 2012, it selected the 7 villages which its model ranked as highest poverty to receive the Siaya transfers. For the project funded by the Nike Foundation, GiveDirectly selected the next 36 villages in its ranked list of 100 villages in Siaya District, as this number of villages provided a sample size sufficient to meet their target size. In the Google transfers, GiveDirectly is continuing to work down the list of 100 villages to target those not already targeted in the Siaya or Nike Foundation transfers. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 8, 2012) • 41. Seeking government approvals for GiveDirectly cash transfer campaigns By now, GiveDirectly understands well the process for seeking government approvals in Kenya and does not see acquiring approvals as a major risk. GiveDirectly said that in Kenya it is important to maintain relationships with government officials at the county and district levels; district commissioners introduce GiveDirectly to chiefs, and chiefs introduce GiveDirectly to Village Elders. In Uganda, there are no counties, so GiveDirectly coordinates with a few people at the district level to acquire approvals, and from there connect with officials at the local level […] As part of its networking in Kenya, GiveDirectly staff have met with the Permanent Secretary for Devolution and Planning. This is someone who could help GiveDirectly acquire permission to work in new counties in Kenya. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 "When entering a new area, the COO meets with a series of officials to explain the project, obtain permission, and establish a relationship in case any problems arise: • District Commissioner • Chief • Assistant chiefs • Village elders" In Uganda, GiveDirectly had to spend more time meeting with officials early on in the process, because there is a greater bureaucratic structure than in Kenya. This engagement tapered off after the early stage, though GiveDirectly remains in touch with officials by phone to let them know when GiveDirectly has started conducting field activities. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 • 42. "Govn’t relations: Signed MOUs with local officials to maximize buy-in and formalize relationship" GiveDirectly, Update on process changes, August 28, 2013 Typical approval process Kenya: • "Seek buy-in from County and District Commissioner and sign written agreement w/district • Ensure Governor’s office and relevant Country admin officials informed of expansion activities" Uganda: • "Attain approval letter from Resident District Commissioner for natl renewal • Attain approval letters from RDC, District Security Officer, District Intelligence Officer, and District Development Officer for local renewal" • 43. "Enumerators enter villages, engage a local to serve as guide for the day, and enumerate all households living in the village, noting which homes are eligible." GiveDirectly, Operational process overview, Pg 2. Data collected during census can be found in enrollment databases, for example: GiveDirectly, Kenya 1.2M enrollment database • 44. "Household level – we enroll households living in mud and thatch homes." GiveDirectly, Operational process overview, Pg 2. "Dropped mud walls as eligibility requirement" GiveDirectly, 20140929 GW - GD annual update • 45. In the Rarieda campaign, a census of all households was completed before enrollment. However, in this case, the census process was implemented by Innovations for Poverty Action, the organization conducting the randomized controlled trial of GiveDirectly's program, as opposed to GiveDirectly staff. In the Siaya campaign, GiveDirectly did not complete a full census of all households in the village. Instead, staff went to the Village Elder and asked him or her to take them to each thatched-roof household in the village to verify that it was eligible for the transfer. GiveDirectly discovered that some Village Elders were assisting friends or family members in pretending that they live in thatched-roof houses so that they could receive transfers, so it now has as a policy to work with a village member who can serve as a guide rather than to rely on the Village Elder. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012; Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013) In the Nike campaign, GiveDirectly staff requested that the Village Elder lead them to eligible households: women aged 18-19, living in thatched-roof homes. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012) • 46. "Enrollment. A second, distinct enumerator returns to enroll households identified as eligible, give them a SIM card and instructions on how to register if needed." GiveDirectly, Operational process overview Pg 1. • 47. See process description in GiveDirectly, Operational process overview Pg 3. Data collected during registration can be found in enrollment databases, for example: GiveDirectly, Kenya 1.2M enrollment database • 48. GiveDirectly gave cash transfer recipients the option of spending some of the money that they receive to buy a phone provided by GiveDirectly. Conversation with Paul Niehaus, President, and Michael Faye, Director, GiveDirectly, October 6, 2012 • 49. GiveDirectly told us that in Uganda, it is possible to purchase ID cards that can be formally approved by the signature of one's Local Councilperson. GiveDirectly helped recipients obtain ID cards by purchasing the cards, sending field staff to villages to take photographs of recipients, printing the photographs for the ID cards at a local printer, working with Local Councilpeople to approve the cards, and arranging for recipients to collect their cards. GiveDirectly told us that the mobile money agents did not charge a fee to visit villages to register recipients, and that GiveDirectly field staff were present to supervise the process. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 The logistics are significantly harder in Uganda than in Kenya. For example, when GiveDirectly enters a new village in Uganda, over 90% of recipients need SIM cards because they did not previously have cell phones, and about 70-80% of recipients need national IDs. GiveDirectly coordinates registration drives for people to get national IDs - they buy national ID booklets, print a photo of each recipient to put in the booklets, and have the Local Councilperson stamp the booklets to approve them. GiveDirectly was able to reach 85-90% of people through these registration drives, returning IDs within about 1 week of visiting eligible households. In the Uganda 2M campaign, there are 9 villages, and GiveDirectly was able to put them all through the national ID registration process within 1 month, so that 90% of eligible households were ready to receive transfers when payments started. (The remaining households will receive their transfers on a delayed schedule, once they complete registration.) GiveDirectly also facilitates recipients signing up for a mobile money account with MTN by having an agent visit the villages. Once recipients have signed up for an account, MTN generally activates their line within 2-3 weeks. By the time the backcheck team visits villages, most recipients’ lines are active. Conversation with GiveDirectly field staff, October 20-21, 2014 • 50. GiveWell site visit to GiveDirectly, October 2014 • 51. As noted above, in the Siaya campaign, GiveDirectly's staff initially engaged the Village Elder to lead them to eligible households. Once they had been led to an eligible home, GiveDirectly staff provided the household with a SIM card and enrolled them in the program. Thus, in Siaya, there was one fewer backcheck than exists in the current process. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012) In the Nike campaign, GiveDirectly staff requested that the Village Elder lead them to eligible households, so there was one fewer backcheck in this process as well. (Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012) • 52. Data collected during backchecks can be found in enrollment databases, for example: GiveDirectly, Kenya 1.2M enrollment database • 53. "Back-checking. Another enumerator, distinct from the census and enrollment workers, revisits each enrolled household to check that they eligible, didn’t have to pay a bribe to enroll, etc." GiveDirectly, Operational process overview, Pg 3. • 54. "[In Kenya] the follow up team sends some of its members to do audits and staff are not pulled from prior enrollment teams." Conversation with Carolina Toth, GiveDirectly, November 20, 2014 • 55. Conversation with GiveDirectly field staff, October 20-21, 2014 • 56. GiveDirectly's current procedure for identifying households to audit: • GiveDirectly collects information about recipients during the first three stages of a campaign: census, registration, and backcheck. Some information, such as recipient name, GPS location, housing materials, and identifying photograph, is collected at more than one stage and then checked for mismatches. (These checks are currently conducted using Excel but will eventually be automated through Segovia technology. One exception is comparing identifying photographs of recipients, which is done using a crowdsourced work platform called Mechanical Turk.) • Each mismatch in recipient information is assigned a certain weight depending on how likely it is to be an indication of gaming. (GiveDirectly said that it determined the likelihoods of various mismatches indicating gaming by conducting an analysis of the mismatches present in past cases of gaming. We did not review this analysis.) GiveDirectly said that the mismatches with the highest weights are mismatches in identifying photographs and housing materials. • Each recipient is assigned a total mismatch "score" (the composite of all their weighted mismatches). Recipients with scores above a certain level are audited. Conversation with GiveDirectly field staff, October 20-21, 2014 • 57. • 58. In prior campaigns, GiveDirectly audited all households for which there was a discrepancy in enrollment data collected, and tended to exclude recipients if there was reason to believe that the potential recipient did not meet GiveDirectly's eligibility criteria. [Ms. Mukhopadhyay told us that she had reviewed cases in which some members of the village told GiveDirectly's staff that enrollees were not eligible because they did not live in thatched-roof homes. In these cases, Ms. Mukhopadhyay decided to exclude these potential recipients.] Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012. In enrollment rounds completed before November 2012, GiveDirectly did not use all of these steps [data comparisons] as "hard checks." Recipients were still visited by two independent field teams who verified eligibility, but potential recipients could remain eligible even if they failed one of these steps. Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012. More information in our November 2013 review. • 59. People have to be at home at registration and backcheck so that they can be given phones, have saftey information explained etc. If they are not at home during the first attempt to visit, we re-visit them several times until they can be found." Conversation with Carolina Toth, GiveDirectly, November 20, 2014 • 60. • 61. • 62. Village meetings were implemented for the first time as part of the Google campaign. GiveDirectly, GW Q&A, April 26, 2013 • 63. Example of campaign with GiveDirectly's current transfer structure in Kenya: GiveDirectly, Google transfer schedule, July 2013 In Uganda, transfers are sent in ten equal installments. Conversation with GiveDirectly, April 8, 2014 • 64. In Uganda, transfers are sent in ten equal installments, and GiveDirectly works with the mobile money providers to coordinate pay out days, as their agent networks are less robust than those in Kenya. Conversation with GiveDirectly, April 8, 2014 • 65. GiveDirectly is making the following changes to its structure and procedures in Uganda: • Separating jobs that were previously done by one person (e.g., GiveDirectly has moved the complaint hotline and followup calls to an office in Kamapala, so that the employees in charge of these are in a different part of the country and do not know the field staff). GiveDirectly’s larger network in Kenya is already structured this way. • Increasing payday audits by the Field Director from about 25% to 100% of paydays (2 or 3 per month). • Conducting real-time phone spot-checks, i.e. calling recipients during payday to make sure the event is going smoothly and that recipients are receiving the correct amounts. • Using MTN Mobile Money (MTN) instead of EZEE Money for more transfers. The network of EZEE Money agents is very limited, so the only feasible option for recipients was to withdraw funds on paydays. MTN has a larger network of agents, so while it is still more convenient for recipients to withdraw on paydays, recipients can seek other options if they prefer. • Building a network of local, English-speaking informants (e.g., journalists, well-respected farmers). Several people in civil service roles have told Dr. Niehaus that it is important to build such a network. Having English-speaking informants may have helped prevent the fraudulent translation that occurred in this case. Our observations of a cash out day in Uganda: GiveWell site visit to GiveDirectly, October 2014 • 66. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 • 67. Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012 "For Uganda 201404, the plan is to do monthly short surveys, and then long follow up after payday 2 and 9 or 10." Email from Carolina Toth, GiveDirectly, November 14, 2014 • 68. • 69. "For Uganda 201305, we did long follow up surveys on a rolling basis and most recipients were followed up with twice-- this is the data you've already received. At the end of 201305, there was going to be one short survey, and this survey was accelerated as part of the fraud investigation. These are labelled _short and _short_crosscheck and are attached. There were no monthly short surveys. We've not yet decided when/if to fill in the rest of the short surveys for those not reached during the fraud investigation. For Uganda 201404, the plan is to do monthly short surveys, and then long follow up after payday 2 and 9 or 10." Email from Carolina Toth, GiveDirectly, November 14, 2014 In early transfer campaigns, GiveDirectly transferred a first, full installment to recipients before its first call; in its Google campaign, GiveDirectly implemented the initial small transfer to enable it to identify problems before transferring the larger amount. Piali Mukhopadhyay, COO, International, GiveDirectly, conversation with GiveWell, November 7, 2012. • 70. • 71. • 72. GiveDirectly, Kenya hotline log, July 24, 2013 (May 2012 – July 2013) GiveDirectly, Follow-up tracker, July 2013 (July – October 2013) GiveDirectly, 20141010 Follow up tracker (January 2013-September 2014). This file also contains issues reported through other channels, such as to field staff during field visits and during follow up surveys. • 73. GiveDirectly told us that for the Kenya 2M campaign, its enrollment field staff conducted a short survey with anyone who approached the field staff to complain that they had been unfairly or mistakenly skipped during the census. Though [in campaigns prior to and including Kenya 2M] GiveDirectly [did] not add recipients after the census [had] been conducted, it intends to continue carrying out the surveys for future campaigns, as a way of tracking complaints, recognizing potential issues with the census, and assessing changes intended to improve GiveDirectly's census process. Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013 We have not reviewed the results of this survey. Starting with the Kenya rolling enrollment campaign in early 2014, GiveDirectly now adds into the enrollment process households that complain about having been skipped at census. Conversation with Carolina Toth, GiveDirectly, November 20, 2014 • 74. Conversation with GiveDirectly field staff, October 20-21, 2014 • 75. Paul Niehaus, GiveDirectly's President, transitioned this year from working part time to full time. Ms. Mukhopadhyay said that it has been good to have Prof. Niehaus working full time, and that it has enabled them to spend a lot more time thinking about partnerships and in-country networking. Prof. Niehaus and Ms. Mukhopadhyay have also worked together to plan for GiveDirectly's potential expansion into Rwanda. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 • 76. • International Rescue Committee (IRC): GiveDirectly [participated] in discussions with the IRC about elements of GiveDirectly's model that could be incorporated into the IRC's CT program in Pakistan. Conversation with GiveDirectly, October 6, 2014 • Indonesia: GiveDirectly met with representatives from the World Bank, the Indonesian government, and the Australian Aid Agency to discuss conducting an impact evaluation comparing one time CTs with other interventions in Indonesia. There is strong interest in CTs in Indonesia but it will be some time before GiveDirectly knows the outcome of these discussions. The Indonesian government and their counterparts at the World Bank are also interested in elements of GiveDirectly’s operational model. For example, they are considering setting up call centers to facilitate tracking outcomes of government sponsored CT programs. GiveDirectly believes that direct communication with CT recipients could positively impact bureaucratic programs, though it would be hard to quantify this impact. Conversation with GiveDirectly, October 6, 2014 • Center for Global Development: "Participating and providing thought leadership in a cash transfer working group" GiveDirectly, 20140929 GW - GD annual update • Uganda District: "Request from Muruli Mukasa (MP from Nakosongola District, Cabinet Secretary for Security) to discuss working in his district" GiveDirectly, 20140904 GD – GW check in • UK's Department for International Development (DFID): GiveDirectly has spoken with DFID about a program to which it is considering adding unconditional cash transfers; GiveDirectly or Segovia may be involved in this part of the program if it proceeds. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 (unpublished) • United Nations Children's Fund (UNICEF): GiveDirectly has also spoken with UNICEF, which is considering using cash transfers as a benchmark to assess the impacts of one of its nutrition programs. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014 (unpublished) • 77. • 78. "GD has had one person working full-time since January of 2011. Jeremy Shapiro was full time during 2011 and Piali Mukyopadhyay is full-time now." GiveDirectly, clarifications on GiveWell's draft review of GiveDirectly • 79. Conversation with Paul Niehaus, President, and Rohit Wanchoo, Director, GiveDirectly, March 18, 2013 • 80. • 81. "Greater leverage for FD to work on tech integration, high-level analysis (e.g. “smart” audits)" GiveDirectly, 20140929 GW - GD annual update, Pg 6. • 82. "Introduced Project Associate role to strengthen focus on productivity, QC and professional development and increase FD leverage (Ke)" GiveDirectly, 20140408 GD-GW update • 83. Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013 In Uganda, GiveDirectly hired some of the highest performing FOs from the pre-transfer FO staffs to stay on as FOs who would conduct follow-up surveys. (Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013). It is our impression that FOs who conduct follow-up surveys are often hired out of the pre-transfer FO staffs. • 84. GiveDirectly, Survey for randomized controlled trial • 85. • 86. • 87. Haushofer and Shapiro 2013 Policy Brief • 88. The data reported for this variable in Table 4 of Haushofer and Shapiro 2013 Policy Brief, Pg 32, appears to demonstrate that 21% (not 20%) of the control group reports that not all household members usually eat until they are content. • 89. Haushofer and Shapiro 2013 Policy Brief, Pg 18, corresponding data in Table 4, Pg 32. • 90. "Some (but not all) mabati [iron-roofed] or even permanent HH are as deserving as thatched HH […] 6/6 groups mentioned deserving special cases." GiveDirectly, Saturation analysis, Pg 4. Mr. Ekeu thinks that roofs are too rough a way to target poverty because some people may live under an iron roof but actually be very poor (e.g., someone who inherited an iron-roofed house from his grandfather, or a widow whose late husband built her an iron-roofed house long ago). Ms. Mukhopadhyay said that GiveDirectly hears this kind of feedback from a lot of its field staff, but believes that building materials are still a good criteria on average. Conversation with GiveDirectly field staff, October 20-21, 2014 • 91. [GiveDirectly] is also considering modifying its targeting criteria to include certain types of people who may be especially vulnerable whether or not they live under an iron roof. Conversation with GiveDirectly field staff, October 20-21, 2014 GiveDirectly is considering expanding its eligibility criteria to include: • Widows living in iron-roofed houses • Houses with iron roofs that are severely corroded • Households with partially cemented floors Conversation with GiveDirectly, October 6, 2014 • 92. Several recipient households had been selected by GiveDirectly for our visit as representative of how recipients use funds. 2 locations (and the households within them) were selected as a function of GiveDirectly's activities that day -- an area of Rarieda where the end-line survey for the RCT was being implemented and an area of Siaya where enrollment was being undertaken. In both cases, we don't know whether enrollment and surveying activities were taking place elsewhere that day which would have given GiveDirectly discretion in choosing these areas. On the final day of our visit, we asked GiveDirectly whether we could randomly select 3 households to visit. GiveDirectly sent us a list of 15 households in a location in Rarieda where the end-line survey for the RCT was complete and therefore we could question recipients without interfering with the RCT. We don't know whether GiveDirectly had discretion in choosing these 15 households. We selected 5 households from the list using Excel's RAND() function and visited 3 of them. (GiveDirectly made appointments with the households in advance and could not reach 2 of them.) We would characterize all the households we visited -- those that GiveDirectly fully selected for us, those over which GiveDirectly had less discretion, and those we selected randomly -- as extremely poor. We did not see any significant differences in wealth between them. • 93. • 94. GiveDirectly's current procedure for identifying households to audit: • GiveDirectly collects information about recipients during the first three stages of a campaign: census, registration, and backcheck. Some information, such as recipient name, GPS location, housing materials, and identifying photograph, is collected at more than one stage and then checked for mismatches. (These checks are currently conducted using Excel but will eventually be automated through Segovia technology. One exception is comparing identifying photographs of recipients, which is done using Mechanical Turk.) • Each mismatch in recipient information is assigned a certain weight depending on how likely it is to be an indication of gaming. (GiveDirectly said that it determined the likelihoods of various mismatches indicating gaming by conducting an analysis of the mismatches present in past cases of gaming. We did not review this analysis.) GiveDirectly said that the mismatches with the highest weights are mismatches in identifying photographs and housing materials. • Each recipient is assigned a total mismatch "score" (the composite of all their weighted mismatches). Recipients with scores above a certain level are audited. Conversation with GiveDirectly field staff, October 20-21, 2014 • 95. For example, see "Gaming detection" in GiveDirectly, 201410 Monthly Operations report • 96. • 97. • 98. Conversation with Piali Mukhopadhyay, COO, International, GiveDirectly, October 22, 2013 • 99. "Selected MTN as preferred provider in Uganda after assessing performance of Ezee/MTN (building relationship with Airtel so as to have an additional hedge)" GiveDirectly, 20140723 GW GD quarterly update Pg 9. • 100. The network of EZEE Money agents is very limited, so the only feasible option for recipients was to withdraw funds on paydays. MTN has a larger network of agents, so while it is still more convenient for recipients to withdraw on paydays, recipients can seek other options if they prefer. Conversation with GiveDirectly, September 5, 2014 • 101. Observations from GiveWell site visit to GiveDirectly, October 2014, Pg 5. • 102. • 103. GiveDirectly helps recipients with issues using M-PESA via our hotline. Staff sometimes contact M-PESA agents to notify them one of our clients will be visiting and may need assistance. Piali Mukhopadhyay, COO, International, GiveDirectly, email to GiveWell, November 23, 2012. • 104. There were four types of issues that were responsible for most of the people who were unable to withdraw funds at the cash out day: […] MTN (the payment provider) had not yet activated the funds in the person's account. Mr. Skeates said that this was not a common problem in the previous campaign in Uganda, but it affected many people at this cash out day. He estimated that people affected by this issue would receive their first installment of funds in another 2-3 weeks. […] GiveWell site visit to GiveDirectly, October 2014, Pg 6. • 105. A challenge of backchecks is that field officers often end up teaching recipients how to use their cell phones and mobile money accounts, so that they can access their money and are less likely to be scammed. Field officers will teach recipients how to check their balance and distinguish messages that say they received money from other messages. Field officers will sometimes write out instructions for recipients in the local language that describe step-by-step how to operate the phones and mobile money accounts. Recipients often do not understand the importance of keeping their PIN numbers secure. Some elderly recipients do not want a trustee to manage their transfers, but they are unable to remember their PIN numbers or read the messages on their phone, so they are more likely to have issues receiving transfers. Some people are still skeptical that the money will actually come, even after they have received messages on their phone, so they don’t pay attention to the instructions about how to use the mobile money account. Conversation with GiveDirectly field staff, October 20-21, 2014, Pg 3. Mr. Skeates, the Uganda Field Director, made announcements at the start of the event, translated by the 2 community monitors. The announcements included reminders about how to keep account information secure (e.g., after entering your PIN number, make sure to press "send" before handing your phone back to the agent; make sure you have received a confirmation message after withdrawing and that it states the correct amount; count the cash immediately after receiving it.) GiveWell site visit to GiveDirectly, October 2014 Pg 5. • 106. "We then transfer money directly to the recipient's account […] The recipient collects the transfer from a local M-Pesa agent, who is typically a shopkeeper in the village or the nearest town. The recipient transfers his or her electronic M-Pesa balance to the agent using his or her mobile phone in return for cash." GiveDirectly, How it works 2014 • 107. • 108. • 109. GiveDirectly, 20141112 Kenya follow up data • 110. 72% = 348/(137+348). "Large vs. small transfers Finally, a third treatment arm was created to study the relative impact of large compared to small transfers. To this end, 137 households in the treatment group were randomly chosen and informed in January 2012 that they would receive an additional transfer of KES 70,000 (USD 798, PPP 1,112), paid in seven monthly installments of KES 10,000 (USD 114, PPP 160) each, beginning in February 2012. Thus, the transfers previously assigned to these households, whether monthly or lump-sum, were augmented by KES 10,000 from February 2012 to August 20128, and therefore the total transfer amount received by these households was KES 95,200 (USD 1,085, PPP 1,525). The remaining 348 treatment households constitute the 'small' transfer group, and received transfers totaling KES 25,200 (USD 287, PPP 404) per household. These three treatment arms were fully cross-randomized, except that, as noted above, the 'large' transfers were made to existing recipients of KES 25,200 transfers in the form of a KES 70,000 top-up that was delivered as a stream of payments after respondents had already been told that they would receive KES 25,200 transfers. Section 3.1 outlines how this issue is dealt with in the analysis." Haushofer and Shapiro 2013 Policy Brief, Pgs. 7-8 • 111. GiveDirectly's Grant structure • 112. • The PPP adjusted values of the small, mean, and large transfer are$404, $721, and$1,525 respectively. “28% of the treatment group received a transfer of KES 95,200 (USD 1,085, PPP 1,525), while the remaining 72% received KES 25,200 (USD 287, PPP 404); the average transfer was thus KES 45,016 (USD 513, PPP 721).” Haushofer and Shapiro 2013 Policy Brief, pg. 12.
• "All USD values are calculated at purchasing power parity, using is the 2012 World Bank PPP estimate for private consumption in Kenya: 0.016." Haushofer and Shapiro 2013, Pg. 2
• 113.

Table 28, Haushofer and Shapiro 2013 Appendix, Pg. 54

• 114.
• 115.

Small transfers: $210 (95% CI:$158 to $263). Table 28, Haushofer and Shapiro 2013 Appendix, Pg. 54 • 116. • Small transfers, livestock:$68 (95% CI: $35 to$100)
• Small transfers, durable goods: $36 (95% CI:$18 to $54) • Small transfers, savings:$7 (95% CI: $2 to$13)
• Table 32, Haushofer and Shapiro 2013 Appendix, Pg. 58
• 117.

Table 5, Haushofer and Shapiro 2013, Pg. 53

• 118.

Table 5, Haushofer and Shapiro 2013, Pg. 53

• 119.

"To this end, we conducted a separate survey of one respondent from each of 20 villages to obtain estimates for the costs of purchasing and maintaining metal and thatch roofs. The purchase of a metal roof represents an expenditure of on average USD 564, or 75 percent of the average transfer value." Haushofer and Shapiro 2013, Pg. 34

• 120.

"Based on the anonymized individual-level survey data, an iron roof costs $418 on average, thatch roof replacement (including the cost of grass for making the roof and the labor) costs$95 on average, and thatch roof repair (including the cost of grass for making the roof and the labor) costs $107 on average. These numbers appear to conflict with the full paper and the policy brief. It may be that the results were from a different survey. Haushofer and Shapiro have not yet finished verifying which data were used." GiveWell's non-verbatim summary of a conversation with Carolina Toth, GiveDirectly, October 1, 2014 • 121. Table 44, Haushofer and Shapiro 2013 Appendix, Pg. 70 • 122. Table 44, Haushofer and Shapiro 2013 Appendix, Pg. 70 • 123. Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62 • 124. Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62 • 125. Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62 • 126. Table 52, Haushofer and Shapiro 2013 Appendix, Pg. 78 • 127. • 128. Small transfers:$31 (95% CI: $18 to$43). Table 28, Haushofer and Shapiro 2013 Appendix, Pg. 54

• 129.
• Large transfers: $25 (95% CI:$11 to $39).$25/$51 = about 50% • Small transfers:$18 (95% CI: $9 to$27). $18/$31 = about 60%
• Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62
• 130.
• Large transfers, social: $3 (95% CI:$1 to $5) • Small transfers, social:$2 (95% CI: $1 to$3)
• Large transfers, other: $19 (95% CI:$13 to $24) • Small transfers, other:$7 (95% CI: $3 to$11)
• Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62
• 131.
• Large transfers, alcohol: -$2.07 (95% CI: -$4.6 to $0.5) • Small transfers, alcohol: -$0.51 (95% CI: -$2.7 to$1.7)
• Large transfers, tobacco: -$0.38 (95% CI: -$1.0 to $0.2) • Small transfers, tobacco: -$0.08 (95% CI: -$0.6 to$0.4)
• Table 36, Haushofer and Shapiro 2013 Appendix, Pg. 62
• 132.

“Food security is low in this population. Though instance of skipped meals are not extreme, 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.” Haushofer and Shapiro 2013 Policy Brief, pg. 18.

• 133.

Small transfers: 0.21 (95% CI: 0.07 to 0.35). Table 28, Haushofer and Shapiro 2013, Pg. 54

• 134.
• Large transfers, health index: -0.09 (95% CI: -0.27 to 0.09)
• Small transfers, health index: -0.02 (95% CI: -0.16 to 0.12)
• Large transfers, education index: 0.11 (95% CI: -0.05 to 0.27)
• Small transfers, education index: 0.07 (95% CI: -0.07 to 0.21)
• 135.
• 136.

Small transfers: 0.11 (95% CI: -0.01 to .23). Table 28, Haushofer and Shapiro 2013, Pg. 54

• 137.
• 138.
• "These are generally small and not significant, with one exception: we observe an increase of 0.23 SD in the female empowerment index among the control group in treatment villages. This increase is significant at the 5 percent level using conventional p-values. Together with a non-significant direct treatment effect of SD −0.01 on this measure, this spillover effect suggests that the treatment group shows a significant increase in female empowerment relative to the pure control group, which we confirm in the Online Appendix. However, since this is the only outcome that shows any spillover effect and we do not have a good theory for why spillover effects might occur in female empowerment, we do not offer an interpretation of this result at this stage, and instead note that it needs to be replicated." Haushofer and Shapiro 2013, Pg. 23
• Table 1, Haushofer and Shapiro 2013, Pg. 49
• 139.

“As changes in domestic violence were hypothesized to arise through mechanisms directly associated with cash transfers (such as a change in women’s bargaining power, or a reduction in domestic tension over economic hardships), these spillover effects are somewhat surprising. One possible explanation is that the results are simply an artifact of reporting bias, where the spillover sample believed that a different answer was desired from them than the control group. However, given that we do not find spillover effects in other measures that target unobservable outcomes, we find this explanation implausible. Another possibility is that the presence of the cash transfer program in the village motivated the husbands in untreated households to change their behavior in the hope of receiving transfers in the future. For instance, knowing that the primary female in the household was equally likely to receive the transfer as the primary male, men may have shifted their behavior to establish better relationships with their spouse. Alternatively, the spillover effect may operate through changes in attitudes among either or both husbands and wives in non-treated households. Our data does not distinguish between these possibilities; we find these unexpected results intriguing and believe they warrant further investigation.” Haushofer and Shapiro 2013 Policy Brief, pg. 21.

• 140.

Note that in some cases, we have cleaned this data (where it was obvious what the error was in the data entry). These cases have been marked with cell comments. In other cases, when it was not clear what the error was (for example, when an amount of funds was mistakenly placed in a spending category column, and it was not clear whether the amount was intended to be placed in the former or latter column), we deleted the data and commented on the cell to indicate that data had been deleted. Deletions were made no more than 5 times in any given spreadsheet.

• 141.

The tables include follow up survey data from Kenya 2M, Kenya 1.2M, and Kenya rolling enrollment (GiveDirectly, 20141112 Kenya follow up data) and from the Uganda pilot campaign (GiveDirectly, 20140420 Uganda combined follow up data). Note that recipients may have been surveyed more than once and would therefore be included more than once in the data presented.

• 142. In follow up surveys administered in Uganda, recipients were asked about spending on large household items and small household items. The figure reported here is an average of the total number of recipients who reported spending on large household items (379) and the total number of recipients who reported spending on small household items (473). GiveDirectly, 20140420 Uganda combined follow up data
• 143. In follow up surveys administered in Uganda, recipients were asked about spending on large household items and small household items. The figure reported here is an average of the total amounts that recipients reported spending on large household items (31,968,240 UGX) and the total number of recipients who reported spending on small household items (24,154,000 UGX). GiveDirectly, 20140420 Uganda combined follow up data
• 144.

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

• 145.

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

• 146.
• 147.

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

• 148.

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

• 149.

"Additional variables in table 9 show the frequency of any episode of physical, sexual or emotional violence in the last six months, and the percentage of respondents who believe that domestic violence is justified in some instances. The point estimates for these variables suggest a reduction in domestic violence, although none are individually different from zero at conventional significance levels." Haushofer and Shapiro 2013 Policy Brief, Pg 21.

• 150.

GiveDirectly sent 8 versions of the survey forms it used, as the form was modified over time. For the most recent version we have seen, see GiveDirectly, Kenya verification template, August 5, 2013

For earlier versions, see GiveDirectly, Verification template (October 1, 2012) and GiveDirectly, Verification template (November 7, 2011) (corresponding data for the latter: GiveDirectly, Verification data (November 17, 2011))

• 151.

This table includes follow up survey data from Kenya 2M, Kenya 1.2M, and Kenya rolling enrollment (GiveDirectly, 20141112 Kenya follow up data) and from the Uganda pilot campaign (GiveDirectly, 20140420 Uganda combined follow up data). Note that recipients may have been surveyed more than once and would therefore be included more than once in the data presented.

• 152.

Update [November 2014]: Note that this template may now be out of date.

• 153. In the Uganda follow up data, this issue is denoted "stole_item"
• 154. In the Uganda follow up data, we identified 4 issues that we believe all asked about bribes ("bribe," "pay to collect," "others_bribes," "agent_bribe"). We included all four issues in the total bribes reported in this table for Uganda
• 155.

GiveDirectly, Kenya hotline log, July 24, 2013 (May 2012 – July 2013)
GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)
GiveDirectly, 20141010 Follow up tracker (January 2013-September 2014). This file also contains issues reported through other channels, such as to field staff during field visits and during follow up surveys.

We have not checked to see whether the issues reported in these files are overlapping, though note that the time ranges are overlapping.

• 156.

GiveDirectly, 20141010 Follow up tracker Sheet: "Summary"

• 157.

GiveDirectly, 20141010 Follow up tracker Sheets: Summary; GiveWell notes

• 158.

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

• 159.

The RCT of GiveDirectly’s program in Rarieda did not find an increase in crime, so at that scale it does not seem to be an issue. It’s possible that crime would be a more serious problem if GiveDirectly became a substantially larger and better-known organization.

Conversation with GiveDirectly, December 7, 2013

• 160.
Example: "The recipient was given all the cash withdrawn as she requested.. then as she {deidentified} she was just outside the house since she's blind and her door is not lockable, she came to find her money missing but she doesn't know who might have stolen the KES."
GiveDirectly, 20141010 Follow up tracker Sheet: "Tracker" (text removed in deidentification)
• 161.
Example: "He was phoned by unknown person who posed as GD staff and requested for 500/= bribe to hasten the processing of his transfer."
GiveDirectly, 20141010 Follow up tracker Sheet: "Tracker" (text removed in deidentification)
• 162.
Example: "She lost the phone, and in the process of renewing the line the Agent transfer the money to another line in order to withdraw later."
GiveDirectly, 20141010 Follow up tracker Sheet: "Tracker" (text removed in deidentification)
• 163.

Some recipients, especially elderly ones, have to learn how to use cell phones for the first time in order to manage the GiveDirectly transfers in mobile money accounts. These people have a more difficult time understanding how to keep their phones secure; for example, they often keep the phone in its original packaging and do not conceal it. Another problem with security is that some recipients will share the PIN numbers for their mobile money accounts, either intentionally or unintentionally by handing the phone to a mobile money agent before pressing "Send" (so the PIN number is still apparent on the screen of the phone.) This makes recipients more vulnerable to people who wanted to steal money from their accounts. Teaching PIN saftey has long been a priority, and GiveDirectly has added additional emphasis on the topic (e.g., emphasis during village meetings, additional trainings given by the mobile provider) Improved security is a reason why GiveDirectly is interested in piloting biometric authentication for mobile money accounts, though it does not currently have plans to do so. Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014, Pgs 2-3.

• 164.

Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 (unpublished)

• 165.

GiveDirectly, 20141010 Follow up tracker;GiveDirectly, Kenya hotline log, July 24, 2013 (May 2012 – July 2013); GiveDirectly, Follow-up tracker, July 2013 (July – October 2013)

• 166.
GiveDirectly 's blog post on the staff fraud: GiveDirectly blog, Fighting fraud in Uganda
"Two GD staff members stole 2% of transfers on Google campaign ($20,500 in total) in form of 50,000 UGX deductions from penultimate or final transfers (85%) or theft of entire final transfers (15%)" GiveDirectly, 20140904 GD – GW check in • 167. "Discovery of the fraud • A GiveDirectly recipient had given their SIM card to the SFO [Senior Field Officer] (whose contract had been terminated in April due to an unrelated issue involving a fraudulent receipt he brought to GiveDirectly for reimbursement). The recipient asked the SFO to replace his SIM card (recipients have to travel about 4 hours round trip to get this done) and the SFO had not returned it. This report was made to the hotline that the OM [Office Manager] was answering three months after the recipient had given over their SIM card. Mr. Skeates audits the logs of these hotline calls. • GiveDirectly had been planning a full round of follow-up surveys as part of its normal process, but accelerated the follow-up surveys in response to this issue. GiveDirectly’s backcheck team paused their work on enrollment for the Uganda 2M campaign and called all the recipients in that village (Kosile) to ask whether they had received all of their transfers, had any problems withdrawing, and whether GiveDirectly currently had any of their documents (e.g., SIM cards, IDs). • During this process, there were some reports of problems during paydays. Recipients were initially hesitant to come forward. • Because of the reports of payday problems, GiveDirectly began calling another village, Kawo, the following day to gather more information. Recipients in Kawo were far more forthcoming with information when asked specific questions about payday problems. • GiveDirectly continued follow-ups (conducted by a new Field Officer brought on after the SFO's and OM’s dismissals) until it had spoken to about 92% of its recipients across all villages. GiveDirectly also conducted in-person visits to villages." • 168. Changes implemented in response to staff fraud In the Uganda pilot campaign, GiveDirectly cash out days were managed by the Uganda Senior Field Officer, the Uganda Office Manager who also managed the GiveDirectly hotline, and mobile money agents. After these people fraudulently diverted funds from recipients, GiveDirectly implemented a series of changes: • Terminated the GiveDirectly staff who had been involved in the fraud; started working with new mobile money agents. • Removed all of its staff from the cash out day process except the Uganda Field Director. The Uganda Field Director had previously been making planned visits to oversee some of the cash out days; he now actively manages all of them along with new mobile money agents. • Appointed community-nominated monitors to assist the Uganda Field Director on the cash out day with translation, observe transactions between recipients and mobile money agents, and report any issues they see. GiveDirectly compensates the monitors with 10,000 UGX (~$4) for their time during a cash out day.
• Developed networks of English-speaking informants who are not formally announced within the villages, but are tasked with also reporting any issues they see regarding transfers. To date, 4 of the 9 informants have provided GiveDirectly with helpful information, such as identifying that households in the enrollment process were actually ineligible, and telling GiveDirectly that someone had taken a recipient's phone after the recipient passed away.
• Moved the GiveDirectly call center (hotline) to Kampala, to increase the separation of call center staff from field staff, who are based in Mbale.
• Tasked the call center with calling a randomly selected 10% of the village during a cash out day to see if it is going smoothly.
• Changed the contractual agreement GiveDirectly has with mobile money agents to include an indemnity clause, so that in the case of stolen funds, GiveDirectly could remove funds directly from a mobile money agent's account.
• 169.

"GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33.

Update [November 2014]: the two year timeline mentioned above is now outdated; transfers are generally sent over a period of 8 months in Kenya and 10 months in Uganda. It is still the case that recipients can only receive one full transfer, and that they are ineligible for additional transfers thereafter. Conversation with Carolina Toth, GiveDirectly, November 20, 2014

• 170.

"Attrition was not a significant concern in this study because it became evident early on in GD’s work in Kenya that respondents were highly interested in maintaining relations with GiveDirectly in the hope of receiving future transfers (although these are never promised)." Johannes Haushofer and Jeremy Shapiro, Welfare Effects of Unconditional Cash Transfers: Pre-Analysis Plan, June 27, 2013 Pgs 5-6.

• 171.

GiveDirectly, Enrollment speed of distributions

• 172.

GiveDirectly, Updated data (March 31, 2012)

• 173.

Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 174.
• 175.

"The main downside to both of the mobile money services in [Uganda] as compared to MPESA in Kenya is that there are fewer mobile money agents in the rural areas that GiveDirectly is targeting. In response, GiveDirectly has been more proactive in coordinating with the mobile money service for the transfers that have begun, for example, by giving the mobile money service advanced notice before sending the funds so that agents could be prepared. In some cases, agents traveled to the villages in which recipients live to reduce recipient travel time." Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013

"We do inform recipients in advance when pay-days will be happening" Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 176.

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

• 177.

The amount of time that it takes for total transfers to be sent has varied between campaigns. The Google transfer campaign likely gives the best approximation of timing for a standard campaign, because it does not involve an experiment (like Rarieda and Nike), and it is more recent than the Siaya campaign. In the Google campaign, the planned schedule for transfers lasts approximately eight months for any given recipient, not including the time for census and enrollment. GiveDirectly, Google transfer schedule, July 2013

• 178.

"GiveDirectly stops issuing transfers after two years and clearly informs recipients that they should expect this." GiveDirectly, Offering Memorandum (January 2012) Pg 33.

Update [November 2014]: the two year timeline mentioned above is now outdated; transfers are generally sent over a period of 8 months in Kenya and 10 months in Uganda. It is still the case that recipients can only receive one full transfer, and that they are ineligible for additional transfers thereafter. Conversation with Carolina Toth, GiveDirectly, November 20, 2014

• 179.

GiveDirectly, Contextualizing transfer size

• 180.

$0.65 in pre-cash-transfer income per person per day implies (365*$0.65) = $237.25 per person per year. If each person receives$288 in a year from GiveDirectly, that's (288/237.25) = 121%.

• 181.

Paul Niehaus, GiveDirectly Founder, email to GiveWell, November 20, 2012. We have not reviewed the data GiveDirectly used to reach this conclusion.

• 182.

In our conversations with recipients and field staff, we phrased this question two different ways:

• Do you think it would be better for GiveDirectly to provide $1000 transfers to households in one village or$500 transfers to households in two villages?
• Do you think that GiveDirectly should keep the transfer size the same or reduce the transfer size but provide transfers to a greater number of people?
• 183.

GiveWell site visit to GiveDirectly, October 2014

• 184.

We asked the field officers what they think about the current transfer size ($1000), and whether they’d choose to keep it at that level, increase it, or decrease it, given the effects that an adjustment would have on how many people GiveDirectly would be able to serve. • Mr. Okello: Typically there are multiple households on one compound, each inhabited by relatives of the same family, and any household that meets the targeting criteria can receive transfers. Mr. Okello said that it may make more sense for GiveDirectly to group some households on a compound together so that transfers are shared across them, rather than each eligible household receiving the full$1000.

Mr. Okello also said that if GiveDirectly increased the size of the transfers, that could create a high level of dependency. One of the messages that field officers send is that people should use the $1000 transfers to develop themselves as much as possible, but if someone knew they were getting$2000, they may stop farming, for example. With $1000 people can get some things but not everything; it is the right amount. • Mr. Ekeu: Mr. Ekeu prefers reducing the amount of money in each transfer and expanding the recipient base to reach everyone in the village. He said that the current targeting model causes bragging and unrest in the communities. The people who don’t benefit may be brought to use force to get some of the money, such as by breaking into recipients’ homes. Mr. Ekeu suggested that it would be better for GiveDirectly to provide all households in a village with some amount of money, even if it was less for households that are currently deemed ineligible (e.g.,$100). This way, each of the households would be busy figuring out how they would spend their own money rather than how to get money from another.
• Mr. Olinga: Mr. Olinga said that to reduce extreme poverty the bigger transfer is better, but he didn't have a strong opinion on $1000 transfers to some people versus$500 transfers to twice as many. [This is how we posed the question to Mr. Olinga.]
• 185.

The Rarieda RCT, which included both a $300 transfer treatment group and a$1000 transfer treatment group, did not provide strong evidence on what the best transfer size would be, because it is difficult to assess the relative value of how people use the 1000th dollar versus the 300th. There are also large confidence intervals around any of the findings based on sub-groups because of the relatively small sample size. Dr. Niehaus thinks it is unlikely $1000 is exactly the optimal size for transfers, but expects returns to be relatively flat around the$1000 mark, and so feels the costs to slight variance from the optimal transfer size are low.

GiveDirectly has considered experimenting with different transfer sizes to determine the optimal amount, but feels that other research questions are more important. GiveDirectly is not concerned that recipients will run out of good ways to spend $1000 in transfer funds (e.g., purchasing an iron roof, a cow, and paying school fees for a year will use up most of a transfer). GiveDirectly also believes that while testing transfer sizes could improve its own program, it would be unlikely to affect other cash transfer programs. For example, if a government was considering providing large, one-time transfers, it would not be as concerned with whether$1000 is the right amount, but rather whether the model of large, one-time grants is effective. GiveDirectly prefers to pursue research questions that it sees as likely to influence public policy quickly (e.g., giving people control over the timing of their transfers). GiveDirectly sees the question of optimal transfer size as one that would require rigorous research to learn about, so it is not inclined to simply vary the size of its transfers as an informal experiment.

It is widely accepted that periodic income transfers can reduce human suffering, but it is much less accepted that large one-time transfers can be effective ways of investing in people. There is less literature on the latter, and therefore more to be gained from researching it. Dr. Niehaus believes that some people mistakenly believe that in-kind transfers of low value can significantly change a poor person's life, when in fact it requires significant investment to lift someone out of poverty.

Conversation with GiveDirectly, September 5, 2014

• 186.
Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014
• 187.
• "Majority of households are widows, almost half have one family member that is disabled or very sick.
• Households seem to be as or more needy than typical GD recipients"
• 188.

Mr. Ekeu [the Senior Field Officer in Uganda] thinks that roofs are too rough a way to target poverty because some people may live under an iron roof but actually be very poor (e.g., someone who inherited an iron-roofed house from his grandfather, or a widow whose late husband built her an iron-roofed house long ago). Ms. Mukhopadhyay said that GiveDirectly hears this kind of feedback from a lot of its field staff, but believes that building materials are still a good criteria on average. It is also considering modifying its targeting criteria to include certain types of people who may be especially vulnerable whether or not they live under an iron roof. Conversation with GiveDirectly field staff, October 20-21, 2014, Pg 4.

• 189.
• 190.
• 191.

One adverse event reported in the Kenya follow-up tracker involved a case of domestic abuse resulting in the death of the mother and child, where the particular instance of conflict may have been related to the use of transfer funds. When GiveDirectly investigated the event, the parents of the deceased expressed that there was not anything GiveDirectly could have done to prevent this adverse event. People in the community, including the parents of the deceased, also said that they did not want to report the incident because they were afraid that it would cause GiveDirectly to cancel the cash transfer program, which they did not want to happen. Conversation with GiveDirectly, July 7, 2014 Pg 5

"During this process there were some reports of problems during paydays - recipients were hesitant to come forward initially"
Email from Carolina Toth, GiveDirectly, September 12, 2014

• 192.

We asked the field officers what they think about the current transfer size ($1000), and whether they’d choose to keep it at that level, increase it, or decrease it, given the effects that an adjustment would have on how many people GiveDirectly would be able to serve. • Mr. Okello: Typically there are multiple households on one compound, each inhabited by relatives of the same family, and any household that meets the targeting criteria can receive transfers. Mr. Okello said that it may make more sense for GiveDirectly to group some households on a compound together so that transfers are shared across them, rather than each eligible household receiving the full$1000.

Mr. Okello also said that if GiveDirectly increased the size of the transfers, that could create a high level of dependency. One of the messages that field officers send is that people should use the $1000 transfers to develop themselves as much as possible, but if someone knew they were getting$2000, they may stop farming, for example. With $1000 people can get some things but not everything; it is the right amount. • Mr. Ekeu: Mr. Ekeu prefers reducing the amount of money in each transfer and expanding the recipient base to reach everyone in the village. He said that the current targeting model causes bragging and unrest in the communities. The people who don’t benefit may be brought to use force to get some of the money, such as by breaking into recipients’ homes. Mr. Ekeu suggested that it would be better for GiveDirectly to provide all households in a village with some amount of money, even if it was less for households that are currently deemed ineligible (e.g.,$100). This way, each of the households would be busy figuring out how they would spend their own money rather than how to get money from another.
• Mr. Olinga: Mr. Olinga said that to reduce extreme poverty the bigger transfer is better, but he didn't have a strong opinion on $1000 transfers to some people versus$500 transfers to twice as many. [This is how we posed the question to Mr. Olinga.]
• 193.

GiveDirectly, 201410 Monthly Operations report

• 194.

GiveDirectly has so far received about six applications for every one FO position it has open, which it sees as an indicator that the necessary talent is available for it to scale its operations. Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, July 18, 2013 [Unpublished]

• 195.
• 196.

GiveDirectly, Budget summary, July 22, 2013

• 197.

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

• 198.

GiveDirectly, Rarieda transfer schedule, August 2013

• 199.

Identifying policy-relevant research questions

Based on conversations with policymakers, GiveDirectly has identified gaps in the evidence-base for cash transfers that currently limit policymakers’ ability to implement cash transfer programs or to do so effectively. GiveDirectly has spoken with policymakers in Kenya and Indonesia, as well as representatives of the UK’s Department for International Development (DFID). The leading question that came out of these conversations was about the macroeconomic impacts, or “general equilibrium effects,” of cash transfers when conducted at a national scale. This includes factors such as enterprise structure, prices, local public finance, and local government activities. This question is the primary motivation for GiveDirectly’s top research priority: a study of general equilibrium effects.

Study of general equilibrium effects

Ted Miguel of the University of California, Berkeley will lead the study of general equilibrium effects. The minimum funding required for cash to be transferred as a part of this study is $7.5 million, with an ideal amount of$15 million. GiveDirectly has so far raised $7 million for this study. GiveDirectly's fundraising deadline for these transfers is January 2015, as that is when the baseline measurement will be complete for the first set of villages, so if measurement is going to be extended, the researchers would need to know by January. Increasing the number of villages in the study will also increase measurement costs, so it would be helpful for the research team to have lead-time for their own fundraising before January. Currently, GiveDirectly is enrolling villages in Siaya district that will be a part of the general equilibrium effects study. It is also working to obtain permission from local government officials to conduct transfers in another county from which villages will be drawn to participate in the study. Villages may be assigned to treatment or control groups within zones, so that some zones can have a higher or lower percentage of treatment villages in order to aid in the detection of spillover effects. The researchers will start collecting baseline data in August 2014, before token transfers are sent. This study will potentially include long-term follow up as well, to address a separate question raised by policymakers about the long-term impacts of cash transfers. Professor Miguel previously worked on a study of the impacts of deworming (Miguel and Kremer 2004) that involved follow-up over a period of ten years and obtained a high response rate, so he has experience in setting up effective systems for tracking study participants over time. Paul Niehaus, GiveDirectly’s Co-Founder and President, will serve as a Principal Investigator on the general equilibrium effects study. To mitigate potential bias when GiveDirectly staff are involved in research on the impacts of its programs, GiveDirectly has decided on a few safeguards: the research team conducting the study will 1) preregister their plans for measurement and analysis 2) use a (non-GiveDirectly staff) third party for measurement, and 3) include academic PIs who are not involved in GiveDirectly and have a reputation for honesty. "Objective: • Understand macro-economics impacts of transfers at scale (in-flation, job creation, etc.) • Measure impacts over a long time horizon (e.g., [less than[sic]] 5 years) Status: • Started baseline, with long term follow up mechanisms in place • Not fully funded– facing a gap of ~8M Partners: • Edward Miguel, Berkeley • Johannes Haushofer, Princeton Potential impact: • Increase government use of CT programs • Increase support for our particular model in proving LT impact" • 200. "Objective: • Measure impact of providing information on spending options • Measure impact of getting to choose when and how to receive cash" • 201. Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 (unpublished) • 202. • "We have replaced IPA on this project with an RA hired by ideas42 + GD field staff, for cost reasons • This represents our first in-house data collection, and a change in policy: whenever treatments are variations on how to do cash (rather than: does cash work at all?) we do not face a conflict of interest and can be involved in data collection" GiveDirectly, 20140723 GW GD quarterly update "Partners: • Anandi Mani, Warwick • Sendhil Mullainathan, Harvard • Anuj Shah, Chicago Booth" • 203. Conversation with Stuart Skeates, GiveDirectly, October 20-21, 2014 (unpublished) • 204. • 205. "Objective: • Test if informal contracts can help further reduce domestic violence and improve female empowerment Status: • Small pilot, spring 2015 • If successful, grow into a more large-scale project Partners: • Simone Schaner, Dartmouth • Jessica Leight, Williams" • 206. GiveDirectly, Nike instrument • 207. • 208. • 209. Johannes Haushofer and Paul Niehaus, DIL Demonstration Proposal • 210. In its standard model, GiveDirectly provides cash transfers only to the households that have thatch roofs. GiveDirectly experimented with more inclusive targeting in 19 randomly selected villages, in which nearly all households received transfers (all except those made from fully permanent materials such as cement walls and iron roofs). GiveDirectly compared these villages to 18 villages in the same region where standard targeting was applied. The factors being compared were cases of conflict/tension reported in follow-up surveys and focus groups, and instances of gaming that were discovered by GiveDirectly field staff throughout the cash transfer process. Conversation with GiveDirectly, April 8, 2014 GiveDirectly: As of March 2013, GiveDirectly had received$790,000 from GiveWell donors designated as “flexible funds.” This includes a $500,000 gift from Good Ventures. The research question we are most interested in is whether providing cash transfers to all households in a village, rather than targeting the poorest households, could reduce tension and improve social outcomes of the transfer campaigns. In order to address this question, we’ve created 3 groups of randomly assigned villages for GiveDirectly’s most recent campaign in Kenya: • Villages in which no households will receive transfers • Villages in which only mud-wall and thatch-roof households will receive transfers • Villages in which nearly all households will receive transfers (all households with mud walls and thatch or metal roofs will receive transfers, only households with cement walls and metal roofs will be excluded) We are currently finishing up enrollment for this campaign, so transfers will be sent soon. We plan to collect data by administering our standard phone surveys, which include questions about tension, disagreements with neighbors, etc. We expect to receive the first round of data within the next month or two. The “flexible funds” received from GiveWell donors are going to be used for transfers to villages in group 3, including households with mud walls and either thatch or metal roofs.” Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, August 27, 2013 In GiveDirectly's Kenya 2M campaign, "saturation villages" are villages in which all households with mud walls and thatch or iron roofs will receive transfers. These households make up about 85% of the households in saturation villages. The other 15% of households are built with all man-made materials (e.g., cement walls and iron roof) and will not receive transfers. Conversation with Carolina Toth, Field Director, GiveDirectly, October 24, 2013 • 211. • "Quantitative analysis is inconclusive: some metrics favorable in saturation villages (e.g. fewer overall complaints), others less favorable (e.g. larger % asked to pay a bribe) • FGDs suggests that conflict levels were low across both categories of villages (mainly rumors and awkwardness) and that when faced with the choice we have to make, people prioritize the poorest, who they feel are more deserving" • 212. • 213. In Kenya, GiveDirectly experimented with a community-based targeting process, whereby residents gave input on households that they felt were deserving of transfers but had been excluded by GiveDirectly’s criteria. GiveDirectly felt that to do this process well required significant resources (staff time) and that the benefits were not worth the costs. In addition, some of the villages involved in this experiment gave feedback that they would prefer for GiveDirectly to make the decisions about targeting. Conversation with GiveDirectly field staff, October 20-21, 2014, Pg 4. • 214. • 215. • 216. Cash transfers currently fund private goods. GiveDirectly is considering experimenting with a system whereby transfer recipients could propose public goods projects and individuals could pool their resources to fund projects they consider worthwhile. Conversation with GiveDirectly, September 5, 2014, Pg 5. • 217. GiveDirectly would like to conduct a separate study on the effects of a basic income guarantee. The annual cash transfers involved would be smaller than those in GiveDirectly’s standard program but granted consistently throughout the recipient’s life. GiveDirectly is interested in whether people would take greater risks if they know that their basic needs are met. This study could use the same control-group villages as the GE study. Conversation with GiveDirectly, September 5, 2014, Pg 5. • 218. GiveDirectly has begun discussing a pilot project in Bukedea District as part of this focus on new operational challenges. GiveDirectly is considering managing cash withdrawals for recipients in the pilot rather than relying on an independent mobile money network, pending board approval. Conversation with GiveDirectly, October 6, 2014, Pg 3. • 219. Biometric authentication (This is a possible future operational improvement, though it is not currently in GiveDirectly's workplan.) The government of Uganda started a large cash transfer program called Social Assistance Grants for Empowerment (SAGE), which provides$20 monthly transfers to eligible people in Uganda. The program currently serves 100,000 households in 17 districts and has plans to scale up; it is not currently active in Bukudea, where GiveDirectly operates. The government of Uganda is working with the mobile money provider MTN to build the capability to use biometric authentication (fingerprinting) for transactions and account access. GiveDirectly is interested in running a pilot of biometric authentication with its own cash transfer recipients who are serviced by MTN.

• 220.
• 221.

GiveDirectly is planning to use campaigns in Uganda to try out new approaches to operational challenges. This is intended to allow GiveDirectly to learn more about activities it could undertake in the future. The board asked Piali Mukhopadhyay and the Uganda team to propose appropriate experimental activities, e.g. work in areas where payment infrastructure is less developed or work in humanitarian settings where the speed of payments supersedes other goals.

Uganda naturally presents more operational challenges than Kenya, so it makes sense to experiment with new operational approaches there. In addition, the GE study is the focus in Kenya, so GiveDirectly thinks it is better to locate its operational experimentation elsewhere.

• 222.
• 223.

"First, as a general principle we'll always aim to publish spending clearly enough so that anyone can calculate efficiency however they want. Second, when we analyze our efficiency for management and forecasting purposes we distinguish between (1) operating costs that are ongoing but shared across campaigns and (2) one-time setup costs. (1) is included in the efficiency calculation and (2) is not. Examples of (1) are Field Director time (which is "fixed" for the duration of his/her contract) and office space (which is "fixed" with respect to number of ongoing campaigns). Examples of (2) are costs of registering as an NGO in a new country and costs of developing code which can then be re-used indefinitely. We accept donations from the public for (1) but not (2)." Email from Joy Sun, COO, Domestic, GiveDirectly, August 9, 2013

"Set-up costs are excluded because one-time costs (e.g. NGO registration) can be re-used indefinitely, as opposed to marginal costs." Email from Paul Niehaus, President, GiveDirectly, and Joy Sun, COO, Domestic, GiveDirectly, November 18, 2013

• 224.
• 225.

Conversation with Paul Niehaus, President, and Joy Sun, COO, Domestic, GiveDirectly, October 16, 2013

• 226.

GiveDirectly, GW scratch sheet

• 227.
• 228.

GiveWell, GiveDirectly financials 2014 update Sheet: 20141017 RFMF scenarios

• 229.

GiveWell, GiveDirectly financials 2014 update Sheet: "Revenue and transfers"

• 230.
• 231.

GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses"

• 232.

GiveDirectly, 201410 Monthly Operations report

• 233.

One reason to pause new enrollments in Uganda is for GiveDirectly to direct all transfers to Kenya to increase the sample size for [the macroeconomic effects] study. Conversation with GiveDirectly, July 24, 2014

• 234.

GiveWell, GiveDirectly financials 2014 update Sheet: "20141017 RFMF scenarios"

• 235.

GiveWell, GiveDirectly financials 2014 update Sheet: "Revenue and transfers"

• 236.

GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses"

• 237.

GiveWell, GiveDirectly financials 2014 update Sheet: "Campaign efficiency FY 2014"

• 238.

GiveWell, GiveDirectly financials 2014 update Sheet: "Total expenses"

• 239.

GiveDirectly, GW scratch sheet

• 240.

GiveDirectly, GW scratch sheet

• 241.

GiveDirectly, GW scratch sheet

• 242.

Using the pace of $900,000 of transfer commitments/month, GiveDirectly would commit its remaining funds in less than three months starting from December 2014. • 243. For FY 2014, GiveDirectly projected that it had capacity to use$10 million for transfer campaigns (GiveDirectly, GD – GiveWell update, October 16, 2013, Pg 3). It actually enrolled approximately 5,600 new households (~$6.2 million total campaign costs). This is calculated based on total enrolled households from GiveDirectly, 201410 Monthly Operations report (10,298) minus households enrolled by the end of FY 2013, from GiveWell, GiveDirectly financials 2014 update Sheet: "Campaign efficiency FY 2013" (4,672). Including the months of September and October 2014, which come just after the fiscal year, brings the total of new households enrolled to approximately 10,300 (~$11.3 million in campaign costs). This is calculated using the total number of enrolled households listed in GiveWell, GiveDirectly financials 2014 update Sheet: "Campaign efficiency FY 2014," which includes future expenses committed during the months of September and October 2014 (15,006).

For FY 2013, GiveDirectly projected that it had capacity to use $5.25 million for transfer campaigns (GiveDirectly, Room for more funding summary). It actually sent and committed new transfers totaling approximately$2.3 million, or $4.3 million if you add on the months of September and October 2013, which comes just after the fiscal year. These numbers were calculated using the transfer amounts sent and committed in FY 2013 minus the transfer amounts committed in FY 2012 from GiveDirectly, 2013 Annual Report, Pg 7. • 244. GiveWell, GiveDirectly financials 2014 update Sheet: "20141017 RFMF scenarios" • 245. Conversation with Paul Niehaus, November 14, 2014 • 246. GiveDirectly is also meeting with senior or retired government officials who can provide guidance on navigating the government and connect GiveDirectly to allies on the public sector side. Conversation with GiveDirectly, April 8, 2014, Pg 11. • 247. By now, GiveDirectly understands well the process for seeking government approvals in Kenya and does not see acquiring approvals as a major risk. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014, Pg 3. • 248. "Obtained permissions for ~$26 M more" GiveDirectly, 20140929 GW - GD annual update, Pg 16.
GiveDirectly just received permission to work in Ukwala district in Kenya. Conversation with Piali Mukhopadhyay, GiveDirectly, October 20-21, 2014, Pg 3.

Ukwala district in Kenya has the capacity for about \$4 million of GiveDirectly transfers. GiveWell, GiveDirectly financials 2014 update Sheet: "20141017 RFMF scenarios" See note in A3 and figure in C22.

• 249.
• Political violence and terrorism are both risks in Kenya. Western Kenya has not been impacted since 2008 election violence
• Operations in Uganda provide an alternative, and funds could be shifted more heavily toward UG
• 250.

"Dependency on a single provider: low risk. In Uganda, we have Ezee Money and Airtel as a back-up, and in Kenya could use Equity Bank."
GiveDirectly, 20140723 GW GD quarterly update Pg 12

"Selected MTN as preferred provider in Uganda after assessing performance of Ezee/MTN (building relationship with Airtel so as to have an additional hedge)"
GiveDirectly, 20140723 GW GD quarterly update Pg 9

GiveDirectly continues to seek other mobile money providers who it might want to partner with for future campaigns in Uganda (e.g., Airtel).
Conversation with GiveDirectly, April 8, 2014 Pg 5

• 251. GiveDirectly, Give now
• 252.
• 253.