In a nutshell The program: Giving cash grants to poor people in low-income countries.

Giving cash grants to poor people in low-income countries. Track record: Cash transfers are one of the most-studied development interventions, though evidence drawing a direct connection to particular humanitarian outcomes is sparse. We put the most weight on a randomized controlled trial of the short-term effects of a variant of GiveDirectly's program. This trial indicated that unconditional cash grants lead to large increases in recipients' consumption, assets, business investment, and revenue, but did not observe a short-term increase in profits. Studies of cash transfer programs that differ in meaningful ways from GiveDirectly's have suggested that transfers may be invested at high rates of return over the long term.

Cash transfers are one of the most-studied development interventions, though evidence drawing a direct connection to particular humanitarian outcomes is sparse. We put the most weight on a randomized controlled trial of the short-term effects of a variant of GiveDirectly's program. This trial indicated that unconditional cash grants lead to large increases in recipients' consumption, assets, business investment, and revenue, but did not observe a short-term increase in profits. Studies of cash transfer programs that differ in meaningful ways from GiveDirectly's have suggested that transfers may be invested at high rates of return over the long term. Cost-effectiveness: Cost-effectiveness calculations are extremely sensitive to many assumptions, and cash transfers are in the same range of cost-effectiveness of other priority programs we have considered.

Cost-effectiveness calculations are extremely sensitive to many assumptions, and cash transfers are in the same range of cost-effectiveness of other priority programs we have considered. Bottom line: Cash transfers have the strongest track record we've seen for a non-health intervention, and are a priority program of ours.



Published: December 2012; Last updated: November 2018

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Program description

There are three types of cash transfer programs that have been studied:

Conditional cash transfers (CCTs), in which recipients receive cash only if they fulfill various requirements such as rates of school attendance or visits to health centers. There is a subset of CCTs in which the conditions are announced but are not formally monitored, so all participants receive a transfer regardless of compliance with the announced conditions.

Unconditional cash transfers (UCTs), in which selected participants receive funds without a requirement to meet additional conditions.

Business grant programs, in which unconditional in-kind or cash grants are given to micro-enterprises that have no paid employees other than the owners.

The program conducted by GiveDirectly is different from most of the cash transfer programs that have previously been studied because it aims to transfer wealth rather than income, and not exclusively to business-owners. In practice, this means that participants receive large sums of money (~100% of per-capita annual consumption) over a relatively short period of time (~8 months), with no formal or informal restrictions on how the funds are used.

We review the results of all three different types of cash transfer programs that have been studied, including a randomized controlled trial of a variant of GiveDirectly's program, in order to address the likely effects of GiveDirectly's cash transfers. In the case of conditional cash transfers, we focus on impacts that do not seem relevant to the conditionality itself, e.g., impacts on consumption rather than on school attendance or other behaviors that are conditions of receiving the transfers.

Program Track Record

Below is a list of the cash transfer programs evaluated by randomized controlled trials (RCTs) that we reviewed for our initial report in 2012. This list includes basic information about the program and key findings from the RCT that studied the program. We discuss many of the studies in more detail on our old cash transfer review page. For a list of the studies we identified in our 2013 update of this report, see this footnote.

Program CCT, UCT, Business grant? Conditions Size and frequency Key Findings Oportunidades (formerly PROGRESA) (1997-), Mexico CCT Health: checkups for all in household, lectures for 15+; Education: 80% attendance, complete middle school, complete grade 12 before 22. 20% of PCE; bimonthly 10-20% increase in food consumption (more); 6% increase in long-term consumption (more) Programa de Asignacion Familiar (PRAF)(1998- ), Honduras CCT Health visits and 85% school enrollment 9% of PCE; every 6 months N/A (all outcomes measured were conditioned outcomes) Red De Proteccion Social (RPS) (2000-), Nicaragua CCT Health: workshops, regular health care visits, up-to-date vaccinations, adequate weight gain; Education: enrollment, 85% attendance, grade promotion. 27% of PCE; bimonthly ~15% increase in household expenditures; ~25% increase in food expenditures; no increase in investment (more) Atencion a Crisis (2005), Nicaragua CCT Education: enrollment, 85% attendance; occupational training course; business grant plan. 18% of PCE; bimonthly ~30% higher food consumption; more use of health services; improved self-reported health but unimproved on anthropometric measures (more) Bono de Desarrollo Humano (2003-), Ecuador CCT but not monitored No monitoring. Without being monitored: Health check-ups (0-5), Education: enrollment, 90% attendance. 10% of PCE; monthly Mixed impact on school enrollment, child labor and cognitive development (more) Programa Apoyo Alimentario, Mexico CCT but not monitored No monitored conditions 11.5% of pre-transfer consumption; bimonthly Slight improvements in weight for in-kind transfers (not for cash); slight decrease in self-reported sickness for both (more) Zomba Cash Transfer Program, Malawi (2008-) CCT & UCT Unconditional group and conditional group (80% or better school attendance) 15% of household consumption; monthly Improved school attendance & performance (more for conditional transfers); reduced psychological distress during but not after transfer period Cash Transfer for Orphans and Vulnerable Children (CT-OVC), Kenya (2007-) UCT Unconditional 21% of household spending; monthly Increase food consumption by 17% (more); may increase school enrollment amongst older students Micro-enterprise RCT, Sri Lanka (2005) Grant Unconditional 32.5% of annual profits; once >60% annual return on investment via increased business profits for two years, continuing for at least five years for men but not women, with no clear differences between cash and in-kind grants (more) Micro-enterprise RCT, Ghana (2009) Grant Unconditional 12% of annual profits; once No statistically significant impact on business profits for cash grants; ~20% monthly returns for in-kind grants (more) Micro-enterprise RCT, Mexico (2005-2006) Grant Unconditional 4% of annual profits; once Returns of 28 to 46% per month based on impact on business profits, with indistinguishable differences between cash and in-kind grants (more)

A more detailed version of this table is available here (XLS).

How do people spend the money they receive via cash transfers?

GiveDirectly's program is substantially different from nearly all cash transfer programs that have previously been studied. Accordingly, it is not clear that evidence from most academic studies (of very differently-structured cash transfer programs) will provide accurate estimates of the effect of GiveDirectly's cash transfers on their recipients; GiveDirectly's transfers might be either far more or far less effective than more conventionally-structured cash transfers:

receiving large lump sums might lead people to spend more frivolously; or

lump sums might be invested at higher rates than ongoing transfers would be, leading to higher long-term consumption.

For this reason, in assessing GiveDirectly's impact, we put the most weight on the results of an RCT of a variant of GiveDirectly's program, discussed below. Despite the questions about the applicability of the evidence from other cash transfer programs to GiveDirectly's programs, we review this evidence below.

Food

Note: the section below was published in December 2012. In the years since, we have searched for additional literature on cash transfers but have not found any studies that substantively changed our views (more on our research process here). Of all the recent literature, we put the most significant weight on an RCT of a variant of GiveDirectly's program. For more on how recipients spent funds in this study, see below.

As discussed at our old in-depth review of cash transfer impacts, most of the studies we have reviewed on cash transfers show meaningful impacts on food consumption (~20% increase over baseline/control group spending on food). In addition, the World Bank's review states, "There is a good deal of evidence that households that receive CCTs spend more on food and, within the food basket, on higher-quality sources of nutrients than do households that do not receive the transfer but have comparable overall income or consumption levels" - such as milk, meat, fruits, vegetables, and eggs.

Across the four randomized controlled trials where it is measured in comparable terms, increases in spending on food makes up more than half of the transfer amount:

In a randomized study of the Mexican Oportunidades conditional cash transfer program, approximately three quarters of transfers are estimated to be spent on food.

A second study from Mexico, of the Programa Apoyo Alimentario, an unmonitored conditional cash transfer, found that nearly all (94%) of cash transfers were spent on food.

A randomized study of the RPS conditional cash transfer program in Nicaragua estimated that roughly three quarters of the transfer was spent on food.



In a randomized controlled trial of the CT-OVC unconditional cash transfer program in Kenya, roughly half of transfers are spent on food.

Alcohol and tobacco

Note: the section below was published in December 2012. In the years since, we have searched for additional literature on this topic but have not found any studies that substantively changed our views (more on our research process here). Of all the recent literature, we put the most significant weight on an RCT of a variant of GiveDirectly's program, which did not find an increase in spending on alcohol or tobacco. For more on recipients' spending on alcohol and tobacco in this study, see below.

Cash transfers could be used for alcohol or tobacco, which may have adverse effects.

Three randomized controlled trials of cash transfers that report spending on alcohol or tobacco do not find large increases due to cash transfers:

A randomized study of the Programa de Apoyo Alimentario food security program in Mexico, which had formal conditions that were not enforced, found that cash transfers caused an increase in alcohol consumption equivalent to 1.5% of the value of the transfer, and no increase in tobacco consumption. The authors add, "Only 5% of households report consuming alcohol in any amount. This is most likely an underestimate as the survey was usually answered by the female head of the households who might not be aware of all alcohol purchases by other family members. Importantly, given the large increase in consumption of non-alcohol goods under both transfer types, there is little leeway for household members to purchase non-recorded alcohol."

In a randomized study of the Nicaraguan conditional cash transfer program Red de Protección Social, alcohol and tobacco made up 0.5% of food expenditures, and the effect of cash transfers was small (0.1% of food expenditures) and statistically insignificant. However, this study also states, "Information about alcohol and tobacco expenditures in these types of surveys is often unreliable; it is presented separately and we draw no conclusions from the reported information."

A randomized evaluation of Kenya's CT-OVC unconditional cash transfer program found a small and statistically insignificant decrease in alcohol consumption.

In all three studies, these results come from surveys of how people spend money in general, rather than specifically asking about the spending of cash transfer funds, and then comparing reported spending on alcohol and tobacco across the treatment and control groups. Different forms of misreporting of spending would have different effects on the validity of the estimates, but we would guess that the misreporting would lead the estimated effects to be biased downward (i.e. to underestimate the effect of transfers on alcohol consumption).

A study by the World Bank, which we haven't looked at closely, reviewed "19 studies with quantitative evidence on the impact of cash transfers on temptation goods [primarily alcohol and tobacco], as well as 11 studies that surveyed the number of respondents who reported they used transfers for temptation goods" and concluded that "Almost without exception, studies find either no significant impact or a significant negative impact of transfers on temptation goods [and in] the only (two, non-experimental) studies with positive significant impacts, the magnitude is small."

Taking into account the three studies, the review and the potential for bias, we would guess that any increases in consumption of alcohol or tobacco due to cash transfers would be small.

Investment

Note: the section below was published in December 2012. In the years since, we have searched for additional literature on this topic but have not found any studies that substantively changed our views (more on our research process here). Of all the recent literature, we put the most significant weight on an RCT of a variant of GiveDirectly's program. For more on how recipients spent funds in this study, see below.

We have seen five studies examining the extent to which people invest their cash transfers, leading to longer-term gains:

Gertler, Martinez and Rubio-Codina 2012 is a followup of a randomized rollout of the Oportunidades program in Mexico, in which the treatment group was randomly selected to receive conditional cash transfers a year and a half earlier than the control group. It finds that people in the treatment group saw faster increases in their ownership of farm assets like land, started more microenterprises--“mainly production of handcrafts for sale”--and ultimately saw ~5% higher consumption levels than those in the control group, even four years after the latter had been enrolled in the program. The authors estimate that 74% of transfers are consumed and 26% are invested, an estimate that we discuss in more depth below. They go on to say: Note that our estimate of the [marginal propensity to consume] MPC is consistent with other estimates in developing countries. For example, Musgrove (1979) estimates MPCs of 0.881 for urban Colombia, 0.896 for urban Ecuador, and, 0.776 for urban Peru; and Bhalla (1979) reports an MPC of 0.61 for rural India. Moreover, its value is relatively high, which is suggestive that beneficiaries are perceiving the program as a permanent—as opposed to transitory—source of income: Paxson (1992) reports MPCs out of permanent income from 0.56 to 0.84, and MPCs out of transitory income ranging from 0.17 to 0.27 for a sample of rice farmers in Thailand.

A randomized evaluation of the Nicaraguan Red de Protección Social, a conditional cash transfer program, did not find any change in investment (except in human capital) due to the cash transfers. However, the authors note that, “households are indeed following the recommendations of the program; that is, they are spending most of their income from the program on current (food and education) expenditures.”

A randomized evaluation of the Kenya Cash Transfer for Orphans and Vulnerable Children (CT-OVC), an ongoing unconditional cash transfer program, found that 87% of the transfer was consumed. The remaining 13% is not accounted for by the study, but the authors speculate that it may be invested. It could also have been saved (without investment), transferred to other individuals, or simply mis-measured.

In a randomized study of unconditional grants to micro-enterprises without any paid employees in Sri Lanka, recipients reported that 58% of their unconditional grants were immediately invested in the business, another 12% was saved, and the remaining 30% was spent on household consumption, investment, or other uses. Measures of the impact of the grants on capital stock show more dramatic effects, suggesting that cash grants increase capital stock for the recipient's micro-enterprise by as much as 100% of the grant amount.

A similar randomized study of unconditional grants to micro-enterprises without any paid employees in Ghana estimated that cash grants to women increased household spending by 50-80% of their value during the quarter following grant receipt and cash grants to men increased household spending by 33-50% of their value, though the estimate for men was not statistically significant. The variation in estimated spending, especially amongst women, arises from very high levels of household spending amongst relatively few individuals, though the results for women remain significant after truncating at various levels. The estimated effects of the cash grants on business capital stock were not statistically significant, but varied between roughly one third and one half of the transfer value for women and between roughly zero and 20% of the transfer value for men, depending on whether full or truncated values were used.

Note, though, that the cash transfers by GiveDirectly are both larger and made to poorer individuals than any of the transfers discussed above:

Economic theory and the citations quoted above from Gertler, Martinez, and Rubio-Codina (2012) suggest that larger, shorter term transfers are more likely to be invested than smaller, ongoing transfers, and comparing the results from the ongoing cash transfers and the business grants appears to bear this conclusion out, though the estimates overlap significantly.

However, there is also a potential tradeoff with beneficiary wealth: poorer recipients are typically expected to consume more of a cash transfer, relative to wealthier individuals. This coincides with our worry that GiveDirectly's focus on targeting the poorest may be systematically targeting individuals who are less likely to invest or, if they do invest, to reap large returns.

What return on investment do cash-transfer recipients earn?

Note: the section below was originally published in December 2012. In September-October 2013, we searched for additional literature on this topic (more on our research process here). Of all the recent literature, we put the most significant weight on an RCT of a variant of GiveDirectly's program in which recipients earned substantially smaller returns. Two other new studies found high returns from unconditional cash grants with features encouraging investment. In November 2013, we updated this section to include results from these three studies.

A variant of GiveDirectly

Haushofer and Shapiro 2013, an RCT of a variant of GiveDirectly's program, found that cash transfers increased the likelihood of owning an iron roof by 23 percentage points. The study estimated that iron roofs have annual investment returns of 19%, which includes both the savings from no longer having to repair thatched roofs and the savings from no longer having to replace thatched roofs. The cost estimates come from a survey of one respondent from each of 20 villages.

GiveDirectly also conducted a survey on roof costs. This survey found costs that imply an annual investment return of 48%. We have not been able to resolve this discrepancy.

In addition, Haushofer and Shapiro 2013 Policy Brief estimates an annual investment return of 7% or 14% depending on whether thatched roofs have to be replaced once every 2 years or once a year. It does not mention the repair costs associated with thatched roofs, while Haushofer and Shapiro 2013 includes both repair and replacement costs. It is possible that these differences account for the discrepancy between the policy brief and the paper, but we are not certain. In any case, the estimates of cost from the policy brief and paper also appear to conflict with GiveDirectly’s survey.

We have not been able to identify the reason for differences between these 3 different estimates. We currently do not have high confidence in the annual investment return calculations in the above sources.

Haushofer and Shapiro 2013 is discussed in much more detail below.

Unconditional wealth transfers with features encouraging investment

Two studies of unconditional wealth transfers in Uganda both found high annual returns of 30%-39% on the original grant. Both of these programs included many features designed to encourage investment and improve returns, which distinguish them from GiveDirectly's intervention. The studies are discussed in more detail below.

Conditional cash transfers

The only randomized controlled trial of ongoing cash transfers that discusses the returns that recipients earn on their investments is based on the Oportunidades conditional cash transfer program in Mexico, described above. Gertler, Martinez, and Rubio-Codina (2012) estimates that, four years after the control group began to receive treatment and five and a half years after the treatment group began to receive transfers, the treatment group continued to have consumption 5.6% higher than the control group. This implies, by our calculation, a 1.7% monthly return, and a 21% annual return, on the transfers, which further implies a 3.6% monthly, and 42.6% annual, rate of return on investment.

Using a different method, Gertler, Martinez, and Rubio-Codina (2012) estimates that for every hundred dollars transferred more than two years ago, recipients continue to earn $1.60 per month in additional income, for an annual return of 19.2%, even though they estimate that only 26% of transfers are invested. Since only 26% of transfers are invested, this implies an even higher rate of return on investments, of roughly 75% per year, or 6% per month.

In these calculations, the authors do not rely purely on the randomized comparison between the treatment and control group. Instead, they estimate current consumption as a result of current and past cumulative transfers, which depend both on the randomized roll-out of the program, and on the number, age, gender, and school attendance of the children in a family. While the number, age, and gender of children are plausibly exogenous (i.e. they are not influenced by current consumption), and thus can just be included in the regressions as controls, the school attendance of children in the family is endogenous: worse school attendance is likely to lead to both decreased transfers (since the conditions punish families for not sending children to school) and increased consumption, since children not in school may be more likely to work.

For exogenous variation in the transfers, the authors use instruments consisting of the maximum amount of transfers that the family could have received had their children had perfect attendance. This explains a large portion of the variation in actual transfers received, and is likely exogenous (i.e. maximum potential transfers are likely “as good as random” once family demographics are controlled for, and family demographics are likely not influenced by consumption).

Regressing current consumption on the current transfers and cumulative transfers from previous years, the authors estimate that every $100 in current transfers translates into an additional $48.70 of reported spending during the past month, and every $100 in transfers from more than 2 years ago translates into an additional $1.50 of reported spending during the past month. However, this is likely to be an underestimate of the total effect because many students worked, causing both a reduction in transfers for them relative to the maximum possible and an increase in consumption for their families (through their wages). By assuming that maximum potential transfers influence current consumption only through actually received transfers, which seems plausible, (i.e. by using maximum potential transfers as an instrument for actual transfers) the authors estimate that $100 of actually received transfers this month increases consumption by $74, and $100 of actually received transfers from more than two years ago increases consumption this month by $1.60.

The authors pursue this instrumental variables strategy, rather than just regressing current consumption on total realized transfers, because the realized transfers are negatively correlated with consumption because of the child labor effects of conditionality, which would lead to an underestimate of the effects of transfers.

The three different estimates discussed for long-term annual returns on cash transfers are quite similar (20.4%, 18%, 19.2%), though the estimated returns on investment differ significantly (42.6%, 35%, 73.8%), because of different estimates of how much of the transfers are consumed.

Business grants

These estimated returns seem quite high, but there is a separate literature on the returns to capital in micro-enterprises that is relevant to this issue. In a series of experiments in Sri Lanka, Mexico, and Ghana, researchers giving grants on the order of $100 to micro-enterprises without any paid employees have found high returns on investment, in the range of 6%-46% per month:

A series of papers by de Mel, McKenzie, and Woodruff based on a randomized controlled trial of one-time grants to micro-enterprises in Sri Lanka have found large positive effects on profits for male owners. Approximately five years after initially making grants of $100-$200, divided between cash and in-kind gifts, to microenterprises that did not have any non-owner employees, the authors found $8-$12 higher monthly profits in male-owned businesses that received grants. This translates to a 6-12% monthly real return amongst male-owned businesses (with no measured benefits amongst businesses owned by women). In earlier work covering the first two years after the grants were made, the authors found similar monthly rates of return, and could not reject the hypothesis that cash and in-kind grants had similar effects on profits. During the first two years after the grants were made, the combined effect of the cash on men and women was large and statistically significant, though the returns for men were substantially larger than for women, but after five years, the combined effect for men and women appears to no longer be statistically significant (this is our conjecture; we cannot confirm it without examining the raw data and calculations for the study, which we have not done).

In a similar randomized experiment conducted in Ghana, with a larger sample size and shorter follow-up period, Fafchamps et al. (2011) found comparable large effects on microenterprise profits for in-kind transfers (~20% return per month), but effects on business profits for cash were statistically indistinguishable from zero. As discussed above, this may be a result of recipients spending transfers in the household rather than investing in their businesses.

A similar randomized controlled trial in Mexico, which gave cash or in-kind grants of about $140 to retail micro-enterprises (all owned by men and without paid employees), found returns to capital of 28 to 46% per month over a 3-12 month follow-up period, with indistinguishable differences between cash and in-kind grants. The biggest effects (~100% return per month) were concentrated within the 38% of micro-enterprises that were very credit-constrained. Although the effects in the credit-constrained subgroup were large and precisely-estimated, the study suffered from substantial attrition (>50% in both treatment and control groups, with similar rates for the two), harming its power and calling the accuracy of the estimates into question.

RCT of GiveDirectly’s program

We place very high weight on the results of Haushofer and Shapiro 2013, a publicly pre-registered, randomized controlled trial of a variant of GiveDirectly’s program in Rarieda, Western Kenya, because the intervention it evaluated was the most similar to GiveDirectly’s standard program out of all of the RCTs we have seen and because it explicitly addressed issues related to selective reporting bias and multiple comparison problems. The trial differed from GiveDirectly’s standard program in one key way: while GiveDirectly currently transfers $1,000 to program recipients, 72% of treatment group members in the evaluation received just $287, less than 30% of the standard amount (one smaller treatment arm did receive $1,085 transfers). When interpreting the results, we keep this difference in mind because we believe that the size of a transfer probably has substantial effects on the magnitude of its impact and its effect on recipients’ behavior.

How the program worked

GiveDirectly is an organization that delivers unconditional cash transfers to poor households in Kenya through a mobile money system called M-Pesa. While it has altered its model for various experiments, GiveDirectly’s standard model involves transferring $1,000 to poor households, which are identified by their use of thatch roofs. Please see our review of GiveDirectly for much more detail on GiveDirectly’s program.

Haushofer and Shapiro 2013’s evaluation of GiveDirectly was randomized on two levels: eligible villages were randomly sorted into treatment and control groups and eligible households within treatment villages were randomized again into treatment households and controls. Researchers were therefore able to calculate treatment effects by comparing treatment households to control households within treatment villages. They could also calculate the spillover effects the transfers had on non-recipients by comparing control households in treatment villages to otherwise similar households in control villages.

Different variations of the program were evaluated. Treatment households were cross-randomized into three arms, which varied the gender of the recipient (when households had both male and female household heads), the frequency of the transfer (lump-sum v. monthly installment), and the size of the transfer ($287 v. $1,085). The study measured short-term effects of the transfer. The time between end-line survey and receipt of final transfer ranged from zero months (for households still receiving transfers when they were surveyed) to fifteen months.

Effects on GiveDirectly recipients

72% of treatment group households in the evaluation received just $287; the rest received $1,085. In the sections below, we use the outcomes from the larger transfer group unless otherwise specified, because GiveDirectly typically gives transfers of similar size. 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.

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. Their spending is broken down in more detail below.

Total non-land assets. Receipt of large transfers increased households’ non-land assets by an average of $463 (95% CI: $378 to $549). 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). 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. 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. 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. GiveDirectly ran a survey that sampled a respondent from each of 20 villages and found that iron roofs cost $418 USD PPP on average. We do not know what explains this discrepancy.

Receipt of large transfers increased households’ non-land assets by an average of $463 (95% CI: $378 to $549). 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). 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. 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. 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. GiveDirectly ran a survey that sampled a respondent from each of 20 villages and found that iron roofs cost $418 USD PPP on average. 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. Recipients of small transfers also spent about $13 more per month (95% CI: $4 to $22).

. 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. Recipients of small transfers also spent about $13 more per month (95% CI: $4 to $22). Health expenditures Recipients of large transfers spent about $3 (95% CI: -$1 to $6) per month more than control households on health expenditures. Recipients of small transfers also spent about $3 (95% CI: $1 to $5) more. This spending was also included within the estimate of spending on consumption, below.

Recipients of large transfers spent about $3 (95% CI: -$1 to $6) per month more than control households on health expenditures. Recipients of small transfers also spent about $3 (95% CI: $1 to $5) more. 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. 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. 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. Education expenditures were also included within the estimate of spending on consumption, below.

. 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. 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. 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. 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. About half of this additional consumption was on food. This additional consumption also included increased spending on social expenditures and various other expenditures.

. Treatment households consumed about $51 more per month (95% CI: $32 to $70) than control households. About half of this additional consumption was on food. This additional consumption also included increased spending on social expenditures and various other expenditures. Alcohol and tobacco. Treatment households did not increase their spending on alcohol or on tobacco.

Impacts of GiveDirectly transfers on recipients

Food security . At baseline, food security was low among participants. Program participants reported a 0.37 standard deviation (95% CI: 0.17 to 0.57) increase in a food security index over controls.

. At baseline, food security was low among participants. Program participants reported a 0.37 standard deviation (95% CI: 0.17 to 0.57) increase in a food security index over controls. Health and education . The study did not detect an effect on indices of health and educational outcomes.

. The study did not detect an effect on indices of health and educational outcomes. 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. 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). 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.

. Treatment improved an index of psychological wellbeing by 0.45 standard deviations (95% CI: 0.25 to 0.65). 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. 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. 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. Note that we report this result for the sake of comprehensiveness but would guess that it is more likely to be random than real.

Spillover effects and village-level effects

Update (November 2018): We provide a more recent and detailed discussion of these effects below.

Other than the potential positive effects on female empowerment, Haushofer and Shapiro 2013 found no statistically significant spillover effects (i.e., effects on non-recipient households in recipient villages). However, imprecise and statistically insignificant point estimates suggest the possibility of moderate negative effects on non-recipient households.

The study found no evidence of village-wide impacts on prices, wages, or crime but estimates are imprecise.

Program variations

The study did not find statistically discernible differences when it varied the gender of the recipient. There was some evidence suggesting that recipients of lump-sum transfers were more likely to purchase assets and recipients of monthly installments were more likely to spend their transfers on food.

Limitations

Spillover and village-level effects: differences from GiveDirectly’s standard model . Large transfers might have very different spillover effects and village-level effects from small transfers. Spillover effects and village-level effects also might be larger in GiveDirectly’s standard model, where all eligible households in treatment villages receive transfers, than in the RCT, where a control group within the village does not receive transfers.

. Large transfers might have very different spillover effects and village-level effects from small transfers. Spillover effects and village-level effects also might be larger in GiveDirectly’s standard model, where all eligible households in treatment villages receive transfers, than in the RCT, where a control group within the village does not receive transfers. Medium and long-term results . Haushofer and Shapiro 2013 is a short-term evaluation of GiveDirectly. We do not know whether the additional consumption enabled by the transfers will persist over the long-term and whether households’ investment in business expenditures might lead to long-term profits. The long-term effects of cash transfers are a key question that affects our estimate of the intervention’s cost effectiveness.

. Haushofer and Shapiro 2013 is a short-term evaluation of GiveDirectly. We do not know whether the additional consumption enabled by the transfers will persist over the long-term and whether households’ investment in business expenditures might lead to long-term profits. The long-term effects of cash transfers are a key question that affects our estimate of the intervention’s cost effectiveness. Dynamics of transfers . The dynamics of the program’s effects are particularly difficult to interpret because some households were still receiving payments at the time of the survey while others received their final transfers more than a year prior to the endline survey. Haushofer and Shapiro 2013 may have underestimated effects on investment spending and farm revenue by asking backwards looking questions about treatment households’ spending in time periods before some households received their transfers.

. The dynamics of the program’s effects are particularly difficult to interpret because some households were still receiving payments at the time of the survey while others received their final transfers more than a year prior to the endline survey. Haushofer and Shapiro 2013 may have underestimated effects on investment spending and farm revenue by asking backwards looking questions about treatment households’ spending in time periods before some households received their transfers. Data on roofs . Haushofer and Shapiro 2013 , an RCT of a variant of GiveDirectly's program, found that cash transfers increased the likelihood of owning an iron roof by 23 percentage points. The study estimated that iron roofs have annual investment returns of 19%, which includes both the savings from no longer having to repair thatched roofs and the savings from no longer having to replace thatched roofs. The cost estimates come from a survey of one respondent from each of 20 villages. GiveDirectly also conducted a survey on roof costs. This survey found costs that imply an annual investment return of 48%. We have not been able to resolve this discrepancy. In addition, Haushofer and Shapiro 2013 Policy Brief estimates an annual investment return of 7% or 14% depending on whether thatched roofs have to be replaced once every 2 years or once a year. It does not mention the repair costs associated with thatched roofs, while Haushofer and Shapiro 2013 includes both repair and replacement costs. It is possible that these differences account for the discrepancy between the policy brief and the paper, but we are not certain. In any case, the estimates of cost from the policy brief and paper also appear to conflict with GiveDirectly’s survey. We have not been able to identify the reason for differences between these 3 different estimates. We currently do not have high confidence in the annual investment return calculations in the above sources.

Lack of statistical power The study included 471 households cross-randomized among three treatment arms, which led to eight possible treatment groups. Some treatment arms therefore have fairly small sample sizes (for example, only 128 households received large transfers) and estimates of relative effects of treatment arms are imprecise.

The study included 471 households cross-randomized among three treatment arms, which led to eight possible treatment groups. Some treatment arms therefore have fairly small sample sizes (for example, only 128 households received large transfers) and estimates of relative effects of treatment arms are imprecise. Self-reporting bias . Outcome data in this study are self-reported, which could lead to positive bias if treatment households sought to please researchers with their use of the transfers. We are especially concerned that treatment households might under-report their use of alcohol and tobacco.

. Outcome data in this study are self-reported, which could lead to positive bias if treatment households sought to please researchers with their use of the transfers. We are especially concerned that treatment households might under-report their use of alcohol and tobacco. Positive bias due to negative spillovers. The study estimates treatment effects by comparing treatment households in treatment villages to control households in treatment villages. If transfers had negative spillover effects on control households in treatment villages, estimates of their effects on recipients would be overestimated. The researchers address this issue explicitly in their paper.

Our interpretation of the results

We place high weight on Haushofer and Shapiro 2013 because we believe it is a well-executed, pre-registered RCT and because it directly measures variations on GiveDirectly’s program. We believe that the RCT supports the notion that, in the short-run, participating in GiveDirectly is likely to increase recipients’ assets and consumption.

Because of the study’s short-term nature, we cannot confidently determine whether recipients’ increased spending on livestock, agriculture, non-agricultural businesses, and (possibly) education have positive returns but, as of the time of the end-line survey, recipients’ additional business spending had not yielded additional profits.

Conclusion

Haushofer and Shapiro 2013 supports the notion that GiveDirectly has meaningful impacts on asset holdings and consumption for recipients. However, it also provides initial evidence that GiveDirectly recipients are unlikely to invest their transfers in business activities at high returns, as seen in RCTs of programs that provide business training in addition to cash transfers and/or require recipients to propose business plans.

Evidence from two studies of unrestricted wealth transfers

Here we provide more detail on two studies which provided unrestricted cash grants to recipients in Uganda, the Youth Opportunities Program (YOP) (evaluated by Blattman, Fiala, and Martinez 2013) and the Women’s Income Generating Support (WINGS) Program (evaluated by Blattman et al 2013). We discuss these programs in detail because they are the best evidence, other than the RCT of GiveDirectly's program, of the effects of unrestricted wealth transfers to individuals. However, both program differ from GiveDirectly's in key ways including their features that encourage recipients to invest their transfers.

The Youth Opportunities Program in Northern Uganda

How the program worked

In YOP, groups of 10 to 40 young adults aged 16-35 applied for cash grants so that their members could enter skilled trades. In general, participants were “young, rural, poor, credit constrained, and underemployed.” Groups submitted written proposals and could request up to $1,000 for non-agricultural skills training and start up costs. Cash was transferred to bank accounts in the name of treatment groups’ management committees and no restrictions were placed on the money’s use. The size of the grants varied widely; the average grant was $382 per capita and 80% of grants were between $200 and $600 per capita.

How did YOP recipients spend the transfers?

Most groups chose one trade and chose to have all members enter that trade. Strong evidence suggests that participants invested a large majority of their funds. At the median, treatment group members estimated that their group and fellow members spent 11% on skills training, 52% on tools, 13% on materials, and 24% shared in cash or spent on other things. When asked about their own investments two years after the distribution, treatment group members reported 340 more hours of vocational training than controls, most of which was in tailoring, carpentry, metalwork, or hairstyling. On average, treatment group members also reported $219 more in business assets than controls two years after the disbursement, although this difference was reduced to $130 after four years.

What were the overall effects of YOP?

Labor supply. Program participants worked 4.1 more hours per week than controls in 2010 and 5.5 more hours per week in 2012. In general, participants increased the hours that they spent working in skilled trades to supplement their agricultural income but continued to spend the same amount of time working in agriculture as controls.

Program participants worked 4.1 more hours per week than controls in 2010 and 5.5 more hours per week in 2012. In general, participants increased the hours that they spent working in skilled trades to supplement their agricultural income but continued to spend the same amount of time working in agriculture as controls. Return on investment. There is fairly strong evidence that YOP participants received large returns on their investments, leading to higher income than controls in the long-run. Members of treatment groups earned $8.50 more per month than controls in 2010 and $10.50 more per month in 2012. Blattman, Fiala, and Martinez 2013 estimate that the increased earnings in 2010 and 2013 represent average annual returns on the original grant of 30% and 39% respectively. There is no evidence that treatment group members earned more per hour than control group members, so the return probably comes from increased opportunities to do profitable work.

There is fairly strong evidence that YOP participants received large returns on their investments, leading to higher income than controls in the long-run. Members of treatment groups earned $8.50 more per month than controls in 2010 and $10.50 more per month in 2012. Blattman, Fiala, and Martinez 2013 estimate that the increased earnings in 2010 and 2013 represent average annual returns on the original grant of 30% and 39% respectively. There is no evidence that treatment group members earned more per hour than control group members, so the return probably comes from increased opportunities to do profitable work. Other impacts. Participation in YOP also lead to increased durable assets, non-durable consumption, and subjective well-being. There was no evidence that YOP affected non-economic factors such as kin integration, community participation, community and public good contributions, anti-social behavior, and protest attitudes and participation.

Limitations

There are some limitations that lead us to exercise caution when applying results from YOP to GiveDirectly’s program.

Group structure. The group structure of YOP may have acted as a commitment device, leading recipients to invest a greater portion of their grants than recipients of unconditional transfers directly to individuals, like GiveDirectly.

The group structure of YOP may have acted as a commitment device, leading recipients to invest a greater portion of their grants than recipients of unconditional transfers directly to individuals, like GiveDirectly. Application process. Groups had to apply for grants with a proposal for investing in skilled trades. Motivated, patient, or talented individuals may have been more likely to apply and less likely to be screened out, which could have increased the propensity to invest or returns on investment. The application process could also have led to more investment through a mental accounting mechanism, by framing the grant in terms of business.

Groups had to apply for grants with a proposal for investing in skilled trades. Motivated, patient, or talented individuals may have been more likely to apply and less likely to be screened out, which could have increased the propensity to invest or returns on investment. The application process could also have led to more investment through a mental accounting mechanism, by framing the grant in terms of business. Size of grant. YOP grants were, on average, $382 per group member. Like GiveDirectly, YOP’s grants are one-time unconditional wealth transfers. However, YOP’s grants are less than 40% of the size of GiveDirectly’s. We do not have a strong intuition about whether this would lead GiveDirectly recipients to invest a larger or smaller portion of their transfers. However, GiveDirectly participants may have lower ROIs if recipients experience diminishing returns on investment.

YOP grants were, on average, $382 per group member. Like GiveDirectly, YOP’s grants are one-time unconditional wealth transfers. However, YOP’s grants are less than 40% of the size of GiveDirectly’s. We do not have a strong intuition about whether this would lead GiveDirectly recipients to invest a larger or smaller portion of their transfers. However, GiveDirectly participants may have lower ROIs if recipients experience diminishing returns on investment. Long-run effects and divestment. Treatment members' increased earnings can only be interpreted as return on investment if they are expected to be maintained in the long-run. There is evidence that some recipients divested from their grant over time, which could mean that earning gains were not permanent.

Treatment members' increased earnings can only be interpreted as return on investment if they are expected to be maintained in the long-run. There is evidence that some recipients divested from their grant over time, which could mean that earning gains were not permanent. Demographic limitations. Program participants were mostly young adults. The effects on business assets and earnings largely disappear if the data is reweighted to reflect the demographic distribution of the entire population.

The Women’s Income Generating Support (WINGS) Program in Northern Uganda

How the program worked

Villages in WINGS were randomly assigned to one of two phases. Participants assigned to phase one entered WINGS in mid-2009 and received five days of business skills training, a $150 startup grant (once they had a business plan approved), and follow up visits and advice from AVSI staff. Half of phase 1 participants were also randomly assigned to receive business networking training. An end-line survey in November 2010 allowed Blattman et al 2013 to estimate the medium-term effects of the full WINGS program by comparing phase 1 villages to phase 2 villages (who had not yet entered the program).

Phase 2 villages (which served as controls during phase 1) entered WINGS in early 2011. To disentangle the effects of the cash grants from the effects of follow-up, researchers randomly assigned one-third of these villages to receive no follow-up.

In all, 1,800 of the most vulnerable 14-30 year olds in 120 villages in two districts were selected by AVSI and community leaders to participate in WINGS’ two phases. “[T]he typical WINGS candidate was a young woman between the ages of 20 and 35, with little or no formal education, low income and limited access to credit.”

How did WINGS recipients spend the transfers?

Over half of all participants proposed selling mixed items in their AVSI-approved business plans. The vast majority of other business involved the selling of livestock, fish, and farm products.

When participants themselves estimated the proportion of the grant they spent on different categories, those recipients who did not receive follow up from AVSI staff reported that 27% of the grant was spent on business, 15% was spent on long-term consumption, 2% was spent on short-term consumption, and 54% was saved. However, when recipients were asked to estimate the cash amount that they spent on their businesses, they reported an average of just $14.50 of business expenditures, suggesting that they spent less than 10% of the grant on business expenditures.

What were the overall effects of WINGS 18 months after the grants were distributed?

Phase 1 and phase 2 villages were surveyed eighteen months after phase 1 villages entered WINGS (and before phase 2 villages entered).

Labor supply . Participating in WINGS caused a 61% increase in employment hours, which consisted of a 41% increase in hours spent on subsistence work and a 79% increase in hours spent on market activities.

. Participating in WINGS caused a 61% increase in employment hours, which consisted of a 41% increase in hours spent on subsistence work and a 79% increase in hours spent on market activities. Earnings and return on investment. After subtracting out the effects of follow-up visits by AVSI staff, we estimate that the other components of WINGS increased beneficiaries’ monthly net earnings by an average of about $4.49/month. From these earnings numbers we can estimate a mean monthly return on investment of about 3.0% and a mean annual return on investment of about 35.9% on the original $150 grant. These calculations are an overestimate of the return on the grant itself, however, because they include the benefits but not the (quite high) costs of services like business skills training, targeting and disbursement, and (for some recipients) group dynamics training. We cannot confidently disentangle the effects of the $150 grant from the effects of the add-on services.

After subtracting out the effects of follow-up visits by AVSI staff, we estimate that the other components of WINGS increased beneficiaries’ monthly net earnings by an average of about $4.49/month. From these earnings numbers we can estimate a mean monthly return on investment of about 3.0% and a mean annual return on investment of about 35.9% on the original $150 grant. These calculations are an overestimate of the return on the grant itself, however, because they include the benefits but not the (quite high) costs of services like business skills training, targeting and disbursement, and (for some recipients) group dynamics training. We cannot confidently disentangle the effects of the $150 grant from the effects of the add-on services. Other economic effects. Participation in WINGS increased short-term spending, wealth, and savings.

Participation in WINGS increased short-term spending, wealth, and savings. Inflation, economic externalities, and general equilibrium effects. Researchers surveyed randomly chosen non-participant households in treatment and control villages in order to measure WINGS’ effects on non-participants (who made up 75%-85% of households in treatment villages). By going into trade and increasing the supply of scarce goods, WINGS recipients appear to have created slightly lower prices for all village members. Blattman et al 2013 suggests that WINGS led to decreased profits for existing microentrepreneurs (through enhanced competition) and increased wages and income for non-participating agricultural workers (through reduced supply of agricultural labor as WINGS participants work less on others households’ plots). The published paper on WINGS (Blattman et al 2015) suggests that WINGS had little effect on the incomes or occupational choice of nonparticipating households. We have not attempted to reconcile these findings though it seems like the updated analysis makes a weaker claim about negative effects on nonparticipants than the policy report and it strikes us as reasonable to rely on the updated analysis.

Researchers surveyed randomly chosen non-participant households in treatment and control villages in order to measure WINGS’ effects on non-participants (who made up 75%-85% of households in treatment villages). By going into trade and increasing the supply of scarce goods, WINGS recipients appear to have created slightly lower prices for all village members. Blattman et al 2013 suggests that WINGS led to decreased profits for existing microentrepreneurs (through enhanced competition) and increased wages and income for non-participating agricultural workers (through reduced supply of agricultural labor as WINGS participants work less on others households’ plots). The published paper on WINGS (Blattman et al 2015) suggests that WINGS had little effect on the incomes or occupational choice of nonparticipating households. We have not attempted to reconcile these findings though it seems like the updated analysis makes a weaker claim about negative effects on nonparticipants than the policy report and it strikes us as reasonable to rely on the updated analysis. Hostility towards recipients. Overall, recipients report a low level of hostility from their community (such as serious conflicts, insults, harm, or unprovoked aggression), but they do report 38% more hostility than controls.

Overall, recipients report a low level of hostility from their community (such as serious conflicts, insults, harm, or unprovoked aggression), but they do report 38% more hostility than controls. Health and social impacts. Despite large economic returns, WINGS led to few observable medium-run health and social improvements for participants.

Limitations

There are some limitations that lead us to exercise caution when applying results from WINGS to GiveDirectly’s program.

Business plan requirement. We are unable to determine the extent to which the requirement to have a business plan approved encouraged participants to invest more money at higher returns than they would have in the absence of such a requirement.

We are unable to determine the extent to which the requirement to have a business plan approved encouraged participants to invest more money at higher returns than they would have in the absence of such a requirement. Disentangling program elements. We are unable to determine the extent to which WINGS’ business skills training and group dynamics training may have contributed to positive outcomes. While we were able to adjust estimated returns to subtract out the effects of follow-up by AVSI workers, doing so also increases our uncertainty about the magnitude of returns.

We are unable to determine the extent to which WINGS’ business skills training and group dynamics training may have contributed to positive outcomes. While we were able to adjust estimated returns to subtract out the effects of follow-up by AVSI workers, doing so also increases our uncertainty about the magnitude of returns. Demographics. WINGS participants were chosen as the most vulnerable members of their communities, while GiveDirectly transfers cash to about 40% of the households in a given village. This could lead WINGS to have higher returns than GiveDirectly (if the poorest of the poor have the greatest credit constraints and therefore the greatest returns to capital) or could lead GiveDirectly to have higher returns (if the poorest of the poor have worse spending opportunities or make worse spending decisions).

WINGS participants were chosen as the most vulnerable members of their communities, while GiveDirectly transfers cash to about 40% of the households in a given village. This could lead WINGS to have higher returns than GiveDirectly (if the poorest of the poor have the greatest credit constraints and therefore the greatest returns to capital) or could lead GiveDirectly to have higher returns (if the poorest of the poor have worse spending opportunities or make worse spending decisions). Grant size. WINGS’ grant of $150 is only 15% the size of GiveDirectly’s $1,000 transfers. This could lead to GiveDirectly recipients achieving a higher or lower return on their transfers than WINGS recipients. This difference could also lead to differing effects on inflation and on village-wide economies.

WINGS’ grant of $150 is only 15% the size of GiveDirectly’s $1,000 transfers. This could lead to GiveDirectly recipients achieving a higher or lower return on their transfers than WINGS recipients. This difference could also lead to differing effects on inflation and on village-wide economies. Long-run effects and divestment. The longest run effects measured by Blattman et al 2013 were eighteen months. Our return on investment calculations assume that earnings gains sustained over eighteen months are permanent and we would guess that these earnings gains are maintained in the long-term. However, households may gradually divest from their investments, causing temporary earnings gains.

The longest run effects measured by Blattman et al 2013 were eighteen months. Our return on investment calculations assume that earnings gains sustained over eighteen months are permanent and we would guess that these earnings gains are maintained in the long-term. However, households may gradually divest from their investments, causing temporary earnings gains. Conflicting data on investment spending. Blattman et al 2013 measures investment spending in two ways: 1) by asking grant recipients to estimate the portion of their grant that they spent on business expenditures; and 2) by surveying treatment and control households on their expenditures over the course of a month and comparing investment by treatment households against investment by controls. These methods result in very different estimates of investment spending, which leave us very uncertain about the proportion of grants that were invested and also decrease our certainty in the reliability of the study’s (self-reported) survey data on the whole.

Blattman et al 2013 measures investment spending in two ways: 1) by asking grant recipients to estimate the portion of their grant that they spent on business expenditures; and 2) by surveying treatment and control households on their expenditures over the course of a month and comparing investment by treatment households against investment by controls. These methods result in very different estimates of investment spending, which leave us very uncertain about the proportion of grants that were invested and also decrease our certainty in the reliability of the study’s (self-reported) survey data on the whole. Incomplete description of methodology. Blattman et al 2013 is a policy brief and does not contain the complete methodological description that we would ideally like to see in order to evaluate a study. For example, the study does not describe how data on participants’ net earnings or savings are measured. We believe that accurately measuring earnings and savings among the very poor is difficult so our uncertainty about this data somewhat reduces our confidence in our ROI estimates.

Our interpretation of studies of unconditional wealth transfers with features encouraging investment

Together, Blattman, Fiala, and Martinez 2013 and Blattman et al 2013 provide strong evidence that cash grants of about $150 to $600 to groups of poor applicants and to very poor, uneducated women with business training and business plans can lead to high returns in the medium-term. Blattman et al 2013 also provides some of the most relevant evidence we’ve seen suggesting that hostility from neighbors is not a major problem for most recipients of cash transfers and that cash transfers do not cause inflation.

However, Haushofer and Shapiro 2013’s evaluation of GiveDirectly itself found much smaller business returns. We believe that some combination of GiveDirectly’s lack of a business plan requirement, lack of business skills training, and broader age-range of participants is the best explanation for the apparently lower returns earned by its participants. However, it is also possible that the failure to find investment returns was caused by the shorter time horizon of Haushofer and Shapiro 2013 and GiveDirectly participants’ investments may still mature.

Cost-effectiveness of cash transfers

See the most recent version of our cash cost-effectiveness analysis on this page.

In practice, these calculations are sensitive to assumptions, especially regarding:

the investment returns to cash transfers;

the subjective assessment of the relative value of averting child mortality and improving incomes for adults.

We estimate that cash transfer programs are in the same range of cost-effectiveness as our other priority programs.

Note that our cost-effectiveness analyses are simplified models that do not take into account a number of factors. There are limitations to this kind of cost-effectiveness analysis, and we believe that cost-effectiveness estimates such as these should not be taken literally, due to the significant uncertainty around them. We provide these estimates (a) for comparative purposes and (b) because working on them helps us ensure that we are thinking through as many of the relevant issues as possible.

Recommendations and concerns

What are the potential downsides of the intervention?

There are a few potential adverse effects of cash transfers:

Inflation: a sudden injection of cash into an area may cause inflation. We reviewed four randomized controlled trials investigating this issue: In Programa de Apoyo Alimentario, an un-monitored conditional cash transfer program, no significant effect on inflation was found. The researchers used surveys of stores and households to measure prices of goods at baseline and one year after cash transfers began. The reported prices were 2.7% higher in villages receiving cash transfers than in control villages after one year, though the increases were not statistically significant. We do not have a clear understanding of how the authors picked the prices they reported from the larger universe of prices they collected. A randomized study of the Oportunidades conditional cash transfer program finds small increases in prices of 5 of 36 food items for sale in treatment villages immediately following deployment of the cash transfers. Although the authors do not observe meaningful increases in prices, they do find positive externalities of cash transfers on ineligible families in treatment villages, equivalent to ~10% of consumption (which is about 2/3 of the benefits experienced by the eligible families in the treatment villages). Blattman et al 2013, the study of WINGS in Uganda, found evidence of a slight decrease in village-wide prices. Haushofer and Shapiro 2013, the study of a variant of GiveDirectly's program, found no evidence of village-wide impacts on prices, but estimates are imprecise and may not rule out the possibility of substantial inflation.

a sudden injection of cash into an area may cause inflation. We reviewed four randomized controlled trials investigating this issue: Cash transfers could discourage wage-earning work by adults. If adults can control the distribution of their work and leisure time, cash transfers may lead them to substitute some leisure for work, leading to a decrease in wages earned (but most likely not a decrease in overall income). A World Bank review of the evidence on cash transfers (which we have not vetted) examines this question and concludes that transfers "appear to have had, at most, modest disincentives for adult work"; it discusses 5 studies, of which 4 found no impact along this dimension. Blattman, Fiala, and Martinez 2013 and Blattman et al 2013 both found that cash transfers with features encouraging investment increased hours worked by recipients. Haushofer and Shapiro 2013, the study of a variant of GiveDirectly's program, found evidence of a $17/month increase in recipients' total revenue, which suggests that did not decrease their hours worked. Note that there is substantially more evidence suggesting that conditional cash transfer programs lead to reductions in child labor, which may help explain the gap between transfer sizes and observed increases in consumption.

If adults can control the distribution of their work and leisure time, cash transfers may lead them to substitute some leisure for work, leading to a decrease in wages earned (but most likely not a decrease in overall income). A World Bank review of the evidence on cash transfers (which we have not vetted) examines this question and concludes that transfers "appear to have had, at most, modest disincentives for adult work"; it discusses 5 studies, of which 4 found no impact along this dimension. Giving cash to some and not others could possibly cause social unrest. Haushofer and Shapiro 2013, the RCT of a variant of GiveDirectly's program, found no significant effects of transfers on the rate of crime in treatment villages or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages. In Blattman et al 2013, recipients reported a low level of hostility from their community (such as serious conflicts, insults, harm, or unprovoked aggression), but they do report 38% more hostility than controls. We have not seen any other rigorous evidence discussing this issue.

Haushofer and Shapiro 2013, the RCT of a variant of GiveDirectly's program, found no significant effects of transfers on the rate of crime in treatment villages or on instances of physical, sexual, or emotional violence in treatment households as compared to control households in treatment villages. In Blattman et al 2013, recipients reported a low level of hostility from their community (such as serious conflicts, insults, harm, or unprovoked aggression), but they do report 38% more hostility than controls. We have not seen any other rigorous evidence discussing this issue. Diversion of transfers to wealthier individuals. It’s not clear to us whether this problem would be more or less of an issue in the case of cash transfers than in-kind transfers, and we would guess that the extent of the problem depends heavily on the method of making transfers. Our review of GiveDirectly discusses the extent to which this appears to have been a problem in their distributions.

Do cash transfers have negative effects on neighbors of recipients?

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

GiveDirectly, one of our top charities, provides cash transfers to extremely low-income households. We wrote in May 2018 about new research on potential “negative spillover” effects of cash transfers: i.e., negative effects that cash transfers might have on people who live nearby transfer recipients. At that time, we wrote that we would reassess this evidence when we had results from GiveDirectly’s “general equilibrium” (GE) study, which we expected to play a major role in our conclusions because it is the largest and highest quality study on spillover effects that we are aware of.

We have now seen private draft results from the GE study. In brief, it did not find negative spillover effects of cash transfers. Considering the GE study alongside other relevant studies of the spillover effects of cash transfers, it appears that the overall evidence base is mixed. Of the five randomised controlled trials (RCTs) which look at the spillover effects of unconditional cash transfers on consumption in sub-Saharan African countries, three RCTs find substantial negative spillover effects, one RCT finds no spillover effects, and the GE study finds no or even a small positive spillover effect.

We attempted to combine the results from these studies and create a model of the magnitude of possible spillover effects. However, we did not feel comfortable relying on this model because we lack basic information on a number of key parameters, such as how many non-recipient households may be affected by spillover effects for each treated household and how the magnitude of spillover effects changes with distance. We would revisit this explicit model if further academic analysis is able to shed light on these parameters.

In the meantime, our best guess is that negative or positive spillover effects of cash are minimal on net. We believe potential negative spillover effects of GiveDirectly’s program are likely to be minimal on net for a number of reasons, including: the largest and highest quality study (the GE study) found no evidence of negative spillovers, and we have not seen strong evidence on the mechanisms for large negative spillover effects. However, given that negative spillover effects via inflation are theoretically plausible, and given that three studies find evidence of negative spillovers, we do include a small negative discount in our cost-effectiveness analysis for this concern. We emphasize that our conclusion at this point is very tentative, and we hope to update our views next year if there is more public discussion or research on the areas of uncertainty highlighted in our analysis.

For more, see our full report on this question.

What versions of the intervention are best?

We have reviewed one RCT comparing physical cash transfers with electronic transfers to a recipient's cell phone. The study found that transferring money to cell phones was cheaper than transferring physical cash to individuals, though the initial cost of the cell phones made the cell phone transfer more expensive than handing out cash. Had the study continued longer, the cheaper ongoing costs of the cell phone transfer mechanism would have made up for the higher initial costs. The study also finds that recipients of the cell phone transfer recipients had to walk less than 25% as far, on average, as those who received physical cash in order to “cash out” their transfers (0.9 vs. 4.04 km). The cell phone transfers also appear to have increased the diversity of crops grown and consumed by people who received them, relative to the “placebo” group that just received physical transfers and a cell phone. The study did not find any adverse effects of using cell phone transfers relative to handing out physical cash.

Our process

2012

Initially, we conducted searches on JSTOR and Google Scholar for terms related to cash transfers, especially seeking out systematic reviews, and tracing citations in order to find randomized trials.

We relied particularly heavily on two major literature reviews in our research on CCTs: a World Bank review and a Cochrane review. Of the literature reviews that we found, we relied on these two because they included a high percentage of RCTs and they presented the data from the studies clearly.

We also searched the World Bank DIME database for relevant studies, discussed with GiveDirectly staff, and added studies as they arose in the process of drafting and updating this report.

2013

Many new studies on cash transfers have been published since December 2012. For this update we have not attempted to thoroughly read all new research published on cash transfers over the course of the last year. We focused on a recently released RCT of a variation on GiveDirectly’s program in Kenya.

We also looked for RCTs with evidence on programs that were unconditional, large, wealth transfers (the approach taken by GiveDirectly) and research that appeared most likely to affect our views of the evidence for this intervention.

We relied heavily on studies and commentary on cash transfers that were sent to us by people who follow GiveWell. We also looked at the abstracts of studies cited by two papers by Chris Blattman , which reported results from experiments involving large, unconditional cash transfers.

In addition, we searched Google Scholar for studies with “cash transfers” in the title from 2011-2013. We listed all randomized controlled trials (RCTs) of cash transfers in a spreadsheet and read the abstract for all studies to try to identify studies that might change our views on the intervention. We also looked for evidence on the propensity to invest cash transfers, returns to investment, inflation, economic effects on non-recipients, spending on alcohol and tobacco, and community resentment or hostility toward recipients.

Changes in our views

In general, the additional evidence we reviewed did not substantively change our assessment of the impact of cash transfers. We found little evidence that cash transfers caused inflation, negative economic effects on non-recipients, recipient spending on alcohol and tobacco, and community resentment or hostility toward recipients.

The most significant updates to the evidence are results from a randomized controlled trial of a variant of GiveDirectly's program in Western Kenya. These results are broadly consistent with results measured in other cash transfer programs, but do suggest that GiveDirectly recipients may earn lower investment returns on their transfers than recipients in previously studied cash transfer programs.

These results are particularly significant because they assess GiveDirectly's program itself. We continue to interpret other evidence for cash transfers cautiously, as most research examines programs that differ from GiveDirectly in that: 1) the transfers are conditional or framed heavily in terms of investment; 2) the transfers are 60% to 90% smaller than GiveDirectly’s; 3) trainings or other services are provided alongside the transfers. We are not aware of any other studies of the propensity to invest large wealth transfers that were not framed as business grants, restricted to participants with investment proposals and/or provided alongside some form of business training or technical assistance.

Studies we considered

A list of the studies reviewed for our initial report in 2012 is available above. A list of the research on cash transfers that we have seen between December 2012 and December 2013 is available at this footnote.

This list includes all new RCTs we have seen but does not attempt to comprehensively list every non-RCT that has been published, instead focusing on studies most likely to affect our views.

2014

We reviewed a list of studies and articles on cash transfers that we'd come across or were sent to us throughout the year and conducted an informal search for additional studies that might substantively change our view on cash transfers. We did not find any study that passed that threshold and continue to rely on the studies identified in our last review. We think that (a) studies of large, unrestricted cash transfers or (b) studies that rigorously examine the negative effects of cash transfers could potentially change our view.

2015

We primarily searched for new studies on (a) large, unconditional cash transfers and/or (b) the negative effects of cash transfers because we think these are the types of studies that would be most likely to affect our view of GiveDirectly’s program.

New studies on large, unconditional cash transfers

To find new studies on large, unconditional cash transfers, we (a) searched Google scholar for studies published since 2014 that contained the keywords “Haushofer and Shapiro 2013” (without the quotes) or “GiveDirectly”, (b) reviewed studies mentioned in a section of a World Bank blog post on education and cash transfers and (c) reviewed a list of studies sent to us by Paul Niehaus of GiveDirectly. We identified the following papers as warranting further investigation, but all of the studies differed enough from GiveDirectly’s program that they did not substantively change our view:

Harris 2015 is an observational study comparing households in Ethiopia that received very large cash transfers (5x annual consumption expenditure on average) as compensation for the government taking a substantial portion of their land (70% on average) to households that did not get their land taken away.

Blattman, Jamison and Sheridan 2015 randomly assigned $200 grants (about 3 months wages) to criminally engaged Liberian men (some also received therapy).

Emergency Economies 2014 examined a program that provided cash transfers ($575 USD per household over 5 months) to Syrian refugees in Lebanon during the winter.

We also found a published paper on WINGS (Blattman et al 2015) whereas we previously relied on the policy report (Blattman et al 2013). The updated analysis did not substantively change our view, but we have added a small update from the new analysis about the effects of WINGS on non-participating households in the relevant section above.

Finally, we examined a pilot study run by GiveDirectly, which they had previously sent to us, that randomized young women to receive large, unconditional transfers. This pilot study did not substantively change our view because it had a very small sample size, differed from GiveDirectly’s core program, and did not seem to analyze return on investment of transfers.

New studies on the negative effects of cash transfers

To find new studies on the negative effects of cash transfers, we searched Google scholar for studies published since 2014 with “cash randomized”, “cash randomised”, “cash experimental”, or “cash experiment” (all without the quotes) in the title. We culled the studies based on their titles. We skimmed the 5 remaining studies for discussion on any negative effects of the transfer programs. We identified one of the studies (White and Basu 2014) as warranting further investigation (based on a cursory look, none of the other studies reported negative effects). White and Basu 2014, which examined the effect of change in payment schedule for a cash transfer program in Peru on expenditures on temptation goods, did not substantively change our view because it did not, in our view, find meaningfully negative effects (see following footnote for details).

We also looked for discussion of negative effects in the new studies of large, unconditional transfers that we identified as warranting further investigation. None of the studies reported findings on negative effects that substantively changed our view because either (a) they did not find meaningfully negative effects of transfers, or (b) the potential negative effects seemed to be unlikely to apply to GiveDirectly’s program.

Haushofer, Reisinger and Shapiro 2015 was released after we finished the search described above. We wrote up our view of the study.

For more information on our search for studies, see this spreadsheet.

Other studies

We de-prioritized further investigation of two RCTs examining the effect of cash transfers on cognitive development (Barham, Macours and Maluccio 2013 and Gilligan and Roy 2014), because it seemed that the conditionality (or perceived conditionality) of the transfers in both studies played a key role in the outcomes, which makes these studies less relevant to GiveDirectly’s unconditional transfer program.

2017

Throughout the year we tracked new literature that might be relevant to assessing the impact of GiveDirectly's cash transfer program by:

Setting up alerts so that we were notified whenever new studies cited key studies such as Haushofer and Shapiro 2013.

Reviewing papers that were sent to us or that were discussed by various development economics blogs and social media accounts that we follow.

We did not find any studies that we thought warranted deeper investigation at this time. We would conduct a deeper investigation of a cash transfer study if, based on the abstract, it seemed that it might provide high-quality information about the humanitarian impacts of unconditional cash transfers in contexts similar to those in which GiveDirectly operates.

We did not deeply review studies of other kinds of cash transfer programs because we believed that if a study was not highly relevant to the core parameters driving our cost-effectiveness analysis of GiveDirectly then it was unlikely to affect our decision making in 2017. We also believe that our cost-effectiveness analysis of GiveDirectly is relatively robust since we estimate that a large portion of the humanitarian benefit of the intervention comes from temporarily raising recipients' consumption.

We expect to reevaluate our views on cash transfers when results from a large, new study of GiveDirectly's program are released; this study will assess the general equilibrium effects of cash transfers: see General Equilibrium Effects of Cash Transfers in Kenya, AEA RCT Registry.

We also edited the negative spillovers section of this report because we believe that the previous version of this section provided too many speculative caveats on the findings of potential negative effects. We plan to revisit this evidence in the future alongside the evidence from the general equilibrium study mentioned above to consider whether we should directly factor in negative effects to our cost-effectiveness analysis.

2018

In this May 2018 post, we explained that we were aware of recently released research which suggested that cash transfers may be less effective than we previously believed in two ways:

Cash may have negative spillovers on non-recipient households who live close to recipient households. The benefits of cash for recipients may fade quickly over time.

We conducted a more thorough literature review on the spillover effects of cash transfers and shared our updated conclusions in November 2018. We concluded that our best guess is that negative or positive spillover effects of cash are minimal on net. For more, see the full report.

We plan to address the concern about the duration of benefits at a later date because we believe it has less potential to substantially affect our cost-effectiveness analysis for cash transfers, for reasons that are explained in this post.

Sources

Supplemental sources