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Cash transfers in the developing world - 2012 version

We have published a more recent review of this intervention. See our most recent report on cash transfers.


In a nutshell

  • The Program: Giving unconditional 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. There is very strong evidence indicating that cash transfers lead to large increases in consumption, especially of food, and more limited evidence that they can be invested at high rates of return, leading to sustainable increases in income. Studies have mostly examined conditional cash transfer programs (in which cash is given on condition that children attend school, visit clinics, etc.); GiveDirectly's program differs in that it is unconditional and that it is structured as a wealth transfer (large transfer available one-time only) rather than an income transfer (smaller transfers over time).
  • Cost-effectiveness: We would guess that cash transfers are less cost-effective than the health interventions conducted by our two other top-recommended charities. However, these calculations are extremely sensitive to many assumptions, and there are plausible assumptions under which cash transfers appear more cost-effective.
  • Bottom line: Cash transfers have the strongest track records we've seen for a non-health intervention. We continue to guess that the interventions conducted by our top-rated health charities are stronger in terms of cost-effectiveness, but believe that the gap may be small.


Published: December 2012

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.1
  • 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, our #2-rated charity, 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 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.

The appeal of cash transfers

Cash transfers are potentially attractive for individual donors because they allow the recipients of charity to choose how to spend funds allocated for them. Provided that local markets can supply it, if the recipients feel that they need food, they can use their cash to purchase it; if they need medical care, they can buy it; if they have a business, they can invest in it. As we wrote in 2009:

Which would you bet on to get water to people in Kenya: an organization funded by wealthy Americans (motivated by guilt and the wish to display generosity, among other things), or an organization funded by Kenyan customers (motivated by a need for water)?

Why do cash handouts seem to be so rare in the charity world? Perhaps it’s because extensive experience and study have shown this approach to be inferior to others. Or perhaps it has more to do with the fact that giving out cash fundamentally puts the people, rather than the charity, in control.

Program Track Record

Below is a full list of the cash transfer programs evaluated by randomized controlled trials (RCTs) that we have found.2 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.

Program CCT, UCT, Business grant? Conditions Size and frequency Key Findings
Oportunidades (formerly PROGRESA) (1997-), Mexico3 CCT Health: checkups for all in household, lectures for 15+; Education: 80% attendance, complete middle school, complete grade 12 before 22.4 20% of PCE;5 bimonthly6 10-20% increase in food consumption (more); 6% increase in long-term consumption (more)
Programa de Asignacion Familiar (PRAF)(1998- ), Honduras7 CCT Health visits and 85% school enrollment8 9% of PCE;9 every 6 months10 N/A (all outcomes measured were conditioned outcomes)
Red De Proteccion Social (RPS) (2000-), Nicaragua11 CCT Health: workshops, regular health care visits, up-to-date vaccinations, adequate weight gain; Education: enrollment, 85% attendance, grade promotion.12 27% of PCE;13 bimonthly14 ~15% increase in household expenditures; ~25% increase in food expenditures; no increase in investment (more)
Atencion a Crisis (2005), Nicaragua15 CCT Education: enrollment, 85% attendance; occupational training course; business grant plan.16 18% of PCE;17 bimonthly18 ~30% higher food consumption; more use of health services; improved self-reported health but unimproved on anthropometric measures (more)
Bono de Desarrollo Humano (2003-), Equador19 CCT but not monitored No monitoring. Without being monitored: Health check-ups (0-5), Education: enrollment, 90% attendance.20 10% of PCE;21 monthly22 Mixed impact on school enrollment, child labor and cognitive development (more)
Programa Apoyo Alimentario, Mexico23 CCT but not monitored No monitored conditions24 11.5% of pre-transfer consumption;25 bimonthly26 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-)27 CCT & UCT Unconditional group and conditional group (80% or better school attendance)28 15% of household consumption;29 monthly30 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-)31 UCT Unconditional 21% of household spending;32 monthly33 Increase food consumption by 17% (more); may increase school enrollment amongst older students
Micro-enterprise RCT, Sri Lanka (2005)34 Grant Unconditional35 32.5% of annual profits;36 once37 >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)38 Grant Unconditional 12% of annual profits;39 once40 No statistically significant impact on business profits for cash grants; ~20% monthly returns for in-kind grants (more)
Micro-enterprise RCT, Mexico (2005-2006)41 Grant Unconditional42 4% of annual profits;43 once44 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 the cash transfer programs that have previously been studied. Accordingly, it is not clear that evidence from 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.

Despite the questions about the applicability of the evidence from other cash transfer programs to GiveDirectly's programs, we review the evidence about the use of cash transfers from academic studies below.

Food

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"45 - such as milk, meat, fruits, vegetables, and eggs.46

Across the four randomized control 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.47
  • 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.48
  • A randomized study of the RPS conditional cash transfer program in Nicaragua estimated that roughly three quarters of the transfer was spent on food.
    49
  • In a randomized control trial of the CT-OVC unconditional cash transfer program in Kenya, roughly half of transfers are spent on food.50

Alcohol and tobacco

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

The 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.51 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."52
  • 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.53 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."54
  • A randomized evaluation of Kenya's CT-OVC unconditional cash transfer program found a small and statistically insignificant decrease in alcohol consumption.55

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).56

Taking into account the three studies 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

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.57 The authors estimate that 74% of transfers are consumed and 26% are invested, an estimate that we discuss in more depth below.58 They go on to say:59
    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.60 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.”61
  • 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.62 The remaining 13% is not accounted for by the study, but the authors speculate that it may be invested.63 It could also have been saved (without investment), transferred to other individuals, or simply mis-measured.64
  • 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.65 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.66
  • 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.67

    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.68

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?

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.69 This implies, by our calculation, a 1.7% monthly return, and a 21% annual return, on the transfers,70 which further implies a 3.6% monthly, and 42.6% annual, rate of return on investment.71

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.72 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.73

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 have received had their children had perfect attendance.74 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.75 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).76 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 $1.60.77

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.78 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.79 This translates to a 6-12% monthly real return amongst male-owned businesses (with no measured benefits amongst businesses owned by women).80 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.81 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,82 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).83
  • 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.84 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.85 The biggest effects (~100% return per month) were concentrated within the 38% of micro-enterprises that were very credit-constrained.86 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.87

Note that the absolute level of investment in these studies is substantially smaller than our estimate of the level of investments stimulated by GiveDirectly's cash transfers, so the possibility of declining marginal returns to investment would suggest that GiveDirectly recipients may obtain lower average returns on investment than those observed in these studies.

Cost-effectiveness of cash transfers

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 two 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 based on limited evidence; and
  • the subjective assessment of the relative value of averting child mortality and improving incomes for adults.

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 the three programs.

Details of our cost-effectiveness analysis are discussed in a 2012 blog post. The general picture is that deworming appears to be between 2 and 5 times as cost-effective as cash-transfers, in financial terms. 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.

More, including links to our spreadsheets, at our 2012 discussion of the cost-effectiveness of cash transfers and other interventions.

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 have seen two randomized controlled trials investigating this issue, both in Mexico:
    • 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.88 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.89 We do not have a clear understanding of how the authors picked the prices they reported from the larger universe of prices they collected.90
    • 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.91 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).92
  • 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.93

    Note that there is substantially more evidence suggesting that conditional cash transfer programs lead to reductions in child labor,94 which may help explain the gap between transfer sizes and observed increases in consumption.95

  • Giving cash to some and not others could possibly cause social unrest. We haven’t seen 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.

What versions of the intervention are best?

There has been one RCT comparing physical cash transfers with electronic transfers to a recipient's cell phone.96 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.97 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).98 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.99 The study did not find any adverse effects of using cell phone transfers relative to handing out physical cash.

Our process

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 review100 and a Cochrane review.101 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 review.

Sources

Document Source
Aker et al. 2011 Source
Angelucci and De Giorgi 2009 Source (archive)
Attanasio, Kugler, and Meghir 2008 Source (archive)
Baird, McIntosh, and Ozler 2011 Source
Baird et al. 2009 Source
Cunha 2011 Source
Cunha, De Giorgi and Jayachandran 2011 Source
de Mel, McKenzie, and Woodruff 2008 Source (archive)
de Mel, McKenzie, and Woodruff 2012 Unpublished
Edmonds and Schady 2011 Source
Fafchamps et al. 2011 Source (archive)
Fiszbein and Schady 2009 Source
Gertler, Martinez and Rubio-Codina 2012 Unpublished
The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending Source (archive)
The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on human capital Source (archive)
Lagarde, Haines, and Palmer. 2009 Source (archive)
Macours, Schady, and Vakis 2008 Source
Maluccio and Flores 2005 Source
Maluccio 2010 Unpublished
McKenzie and Woodruff 2008 Source (archive)
Skoufias, Unar, and González-Cossío 2008 Source
Yablonski and O’Donnell 2009 Source

Supplemental sources

Document Source
Adato, Michelle and Lucy Bassett. 2008. What is the potential of cash transfers to strengthen families affected by HIV and AIDS? A review of the evidence on impacts and key policy debates Source
Aguero, Jorge M., Michael R. Carter, and Ingrid Woolard. 2009. The impact of unconditional cash transfers on nutrition: The South African child support grant Source
Baird, Sarah, Jacobus de Hoop, and Berk Ozler. 2011. Income shocks and adolescent mental health Source
Baird, Sarah et al. 2012. Effect of a cash transfer programme for schooling on prevalence of HIV and herpes simplex type 2 in Malawi: a cluster randomised trial Unpublished
Department for International Development. 2011. Cash transfers Source
Duflo, Esther. 2003. Grandmothers and granddaughters: Old age pensions and intrahousehold allocation in South Africa Unpublished
Jaspars, Susanne and Paul Harvey. 2007. A review of UNICEF’s role in cash transfers to emergency affected populations Source
Oosterbeek, Hessel, Juan Ponce, and Norbert Schady. 2008. The impact of cash transfers on school enrollment: Evidence from Ecuador Source
Paxson, Christina and Norbert Schady. 2007. Does money matter? The effects of cash transfers on child health and development in rural Ecuador Source
Schady, Norbert and Jose Rosero. 2007. Are cash transfers made to women spent like other sources of income? Source
Woolard, Ingrid and Murray Leibbrandt. 2010. The evolution and impact of unconditional cash transfers in South Africa Source
  • 1.

    See Bono de Desarrollo Humano (BDH) and Programa Apoyo Alimentario in the programs table below.

  • 2.

    The only program with an RCT that we know about which we left out is a program which gives cash to recipients for going to get the results of HIV tests. See Lagarde, Haines, and Palmer. 2009, Pg 17. The amounts involved were small ($1-$3), so we think this is better understood as “payment for picking up results” than as a kind of cash transfer program.

  • 3. Fiszbein and Schady 2009, Pg 268.
  • 4. “Conditions:
    • Health
      • Compliance by all household members with the required number of preventive medical checkups.
      • Attendance of family member older than 15 years at health and nutrition lectures.
    • Education
      • School enrollment and minimum attendance rate of 80% monthly and 93% annually.
      • Completion of middle school.
      • Completion of grade 12 before age 22.”

    Fiszbein and Schady 2009, Pg 268.

  • 5.

    Transfers represent 20% of baseline per capita expenditures (a measure of consumption) amongst beneficiaries.
    Fiszbein and Schady 2009 2009, Pg 19. Elsewhere, Fiszbein and Schady report figures for Oportunidades as high as 33% and notes, "The transfer amounts as a proportion of per capita expenditures (or consumption) are not the same across all tables in the report because of differences in the surveys used, including their coverage and year." Fiszbein and Schady 2009, Pg 110.

  • 6.

    Fiszbein and Schady 2009, Pg 212.

  • 7. Fiszbein and Schady 2009, Pg 264.
  • 8. “Conditions:
    • Health: Compliance with required frequency of health center visits; compliance enforced only in the 4 departments where PRAF is supported by the IDB; in the remaining 13 departments, households are encouraged only to send children to school/take them for health visit.
    • Education
      • School enrollment
      • Regular school attendance of at least 85%.”

    Fiszbein and Schady 2009, Pg 264

  • 9. Transfers represent 9% of baseline per capita expenditures (a measure of consumption) amongst beneficiaries. Fiszbein and Schady 2009, Pg 19.
  • 10. Fiszbein and Schady 2009, Pg 212.
  • 11. Fiszbein and Schady 2009, Pg 272.
  • 12. “Conditions:
    • Health
      • "Bimonthly health education workshops (all households).
      • Attendance at prescheduled health care visits every month (aged 0–2) or bimonthly (aged 3–5), adequate weight gain and up-to-date vaccinations (aged 0–5) for all households with children aged 0–5.
    • Education
      • Enrollment in grades 1–4 for children aged 7–13
      • Regular attendance of 85% (that is, no more than 5 absences without valid excuse every 2 months)
      • Grade promotion at end of school year.”

    Fiszbein and Schady 2009, Pg 272.

  • 13. Transfers represent 27% of baseline per capita expenditures (a measure of consumption) amongst beneficiaries. Fiszbein and Schady 2009, Pg 20.
  • 14. Fiszbein and Schady 2009, Pg 212.
  • 15. Fiszbein and Schady 2009, Pg 270.
  • 16. “Conditions:
    • Education
      • Enrollment in grades 1–6 for children aged 7–15
      • Regular attendance of 85%, (that is, no more than 5 absences without valid excuse every 2 months)
      • Deliver teacher transfer to teacher.
    • Other
      • For occupational training: household needed to decide on member who takes course, and payment is conditional on attendance at course.
      • For the business grant: business plan approved by technical team in the Ministry of Family”

    Fiszbein and Schady 2009, Pg 270.

  • 17. Transfers represent 18% of baseline per capita expenditures (a measure of consumption) amongst beneficiaries. Fiszbein and Schady 2009, Pg 20.
  • 18. Fiszbein and Schady 2009, Pg 212.
  • 19. Fiszbein and Schady 2009, Pg 258.
  • 20. “Conditions:
    • Health
      • Children aged 0–5: bimonthly visits to health posts for growth and development checkups and immunizations.
    • Education
      • School enrollment for children aged 6–15
      • School attendance at least 90% of school days
      • Must be enrolled in school and have attendance at basic education classes of at least 80% (including both justified and unjustified absences).”

    Fiszbein and Schady 2009, Pg 258.

  • 21. Transfers represent 10% of baseline per capita expenditures (a measure of consumption) amongst beneficiaries. Fiszbein and Schady 2009, Pg 19.
  • 22. Fiszbein and Schady 2009, Pg 212.
  • 23. Dates not listed in study. Skoufias, Unar, and González-Cossío 2008
  • 24.

    "Localities were randomly assigned into three treatment groups and one control group. Two of the treatment groups were assigned to receive food transfers with and without receiving a health and nutrition education package, and a third to a cash transfer of equal value to the food basket plus the education package...The PAL program offers nutrition and health education sessions (platicas), as well as participation in program-related logistic activities. However, given that attendance of the platicas is not a requirement for the receipt of the benefits, the PAL program is essentially an unconditional transfer program...” Skoufias, Unar, and González-Cossío 2008, Pgs 8-9.

  • 25.

    Skoufias, Unar, and González-Cossío 2008, Pg 15.

  • 26. “Both the in-kind and cash transfers were, in practice, delivered bimonthly, two monthly allotments at a time per household. The transfer size was the same for every eligible house- hold regardless of family size. Resale of in-kind food transfers was not prohibited, nor were there purchase requirements attached to the cash transfers. As mentioned above, the monthly box of food had a market value of about 206 pesos in the program villages, and the cash transfer was 150 pesos per month, based on the government’s wholesale cost of procuring the in-kind items.” Cunha, De Giorgi and Jayachandran 2011. Pg 13.
  • 27.

    Baird, McIntosh, and Ozler 2011.

  • 28.
    • “Monthly school attendance for all girls in the CCT arm was checked and payment for the following month was withheld for any student whose attendance was below 80% of the number of days school was in session for the previous month.” Baird, McIntosh, and Ozler 2011, Pg 9.
    • “In the UCT EAs, the offers were identical with one crucial difference: there was no requirement to attend school to receive the monthly cash transfers.” Baird, McIntosh, and Ozler 2011, Pg 9.
  • 29.

    “The average offer to the households consisted of $10/month – for a total of $100 for the school year transferred in equal amounts for 10 months. $10/month represents roughly 15% of total monthly household consumption in our sample households at baseline, which places this program in the middle-to-high end of the range of relative transfer sizes for conditional cash transfer programs elsewhere.” Baird et al. 2009, Pg 12.

  • 30.

    “The cash payments take place monthly at centrally located and well-known places, such as churches and schools.” Baird et al. 2009, Pg 13.

  • 31.

    The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on human capital.

  • 32.

    “Total adjusted mean monthly spending is Ksh 1442 at baseline among T households, or approximately US$18 per month and 60 US cents per day per adult equivalent. Although total adjusted expenditures are similar across T and C households at baseline, the value among T households at follow-up is about Ksh 253 greater – this is consistent with the size of the transfer, which averages Ksh 300 per household in 2007 Ksh.” The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending, pg 14. 300/1442 = 20.8%

  • 33. “Eligible households, those who are ultra-poor and contain an OVC, receive a flat monthly transfer of US$21 (this was increased in the 2011/12 budget from Ksh 1500 to Ksh 2000).” The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending Pg 11.
  • 34.
  • 35.

    “Cash treatments were given without restrictions. Those receiving cash were told that they could purchase anything they wanted, whether for their business or for other purposes. In reality, the grant was destined to be unrestricted because we lacked the ability to monitor what recipients did with the funds, and because cash is fungible. Being explicit about this was intended to produce more honest reporting regarding use of the funds.” de Mel, McKenzie, and Woodruff 2008 Pg 1337.

  • 36.

    Mean grant as a percentage of mean annual business profit. Mean grant was 15,000 LKR; mean real profits in March 2005 were 3,851 LKR (de Mel, McKenzie, and Woodruff 2008, Table 1). 15,000/(12*3851) = 32.46%.

  • 37.

    “Firms were told before the initial survey that we would survey them quarterly for five periods, and that after the first wave of the survey, we would conduct a random prize drawing, with prizes of equipment for the business or cash. The random drawing was framed as compensation for participating in the survey. We indicated to the owners that they would receive at most one grant. For logistical reasons, we distributed just over half the prizes awarded after the first wave of the survey, and the remaining prizes after the third wave; enterprises not given a prize after the first wave were not told whether or not they had won one of the prizes to be awarded in the second distribution until after the third wave. The prize consisted of one of four grants: 10,000 LKR (∼US$100) of equipment or inventories for their business, 20,000 LKR in equip- ment/inventories, 10,000 LKR in cash, or 20,000 LKR in cash. In the case of the in-kind grants, the equipment was selected by the enterprise owner and purchased by our research assistants.5 Subsequently, we received funding to extend the panel to nine waves. Because this represented an extension of the survey rela- tive to what firms were told before the baseline survey, we granted each of the untreated firms 2,500 LKR (~US$25) after the fifth wave. The randomization was stratified within district (Kalutara, Galle, and Matara) and zone (unaffected and indirectly affected by the tsunami). Allocation to treatment was done ex ante among the 408 firms kept in the sample after the baseline survey.6 A total of 124 firms received a treatment after wave 1, with 84 receiving a 10,000-LKR treatment and 40 receiving a 20,000-LKR treatment. Another 104 firms were selected at random to receive a treatment after the third survey wave: 62 receiving the 10,000-LKR treat- ment and 42 the 20,000-LKR treatment. In each case, half the firms receiving a treatment amount received cash, and the other half equipment.” de Mel, McKenzie, and Woodruff 2008.. Pgs 1335-1336.

  • 38.

    Fafchamps et al. 2011.

  • 39.

    Mean grant as a percentage of mean annual business profit. Mean grant was 150 cedis; mean profits in the trimmed sample at baseline are 106 cedis per month (= (103+99+115)/3) (Fafchamps et al. 2011 Table 1). 150/(12*106) = 11.83%.

  • 40.

    “We also randomly selected when firms would receive their grant, staggering the timing of the grants, so that 198 firms were assigned to receive the grants after the second round, a further 181 firms assigned to receive the grants after the third round, and 18 firms were assigned to receive the grants after the fourth round. This staggering was done both for the purpose of managing the logistics of making these grants, and to provide incentives for firms to remain in the study for multiple rounds since they were told more grants would be given out after rounds 3 and 4. These grants were framed to firms as prizes to thank firms for participating in the survey. Participants in the survey were told that we were undertaking a study of small firms in Ghana, and that some of the firms would be randomly chosen to receive prizes as a token of our appreciation for their participation in the survey. Firms which were selected in either treatment group were not told they had been selected for a prize until the time their prize was being given out.” Fafchamps et al. 2011. Pg 16.

  • 41.

    McKenzie and Woodruff 2008.

  • 42.

    “Cash was given without restrictions on its use. Owners were allowed to contribute funds of their own to purchase items costing more than 1,500 pesos (in practice none did).” McKenzie and Woodruff 2008. Pg 467.

  • 43.

    Mean grant as a percentage of mean annual business profit. Grants are 1,500 pesos; mean baseline profits are 3,373 pesos per month (= (3433+3312)/2) (McKenzie and Woodruff 2008 Table 2). 1500/(12*3373) = 3.71%

  • 44.

    “Before the first round of the survey, firms were told that the only compensation that they would receive for participating was a chance of receiving either cash or capital through prizes to be given after each survey round.6 The prize was a grant of 1,500 pesos (about $140). After the first round of the survey, a single draw from a computerized random number generator was used to randomly assign firms to treatment and control groups.7 Among the firms assigned to treatment status, the random draw also determined the round in which they would be treated and whether they would receive their grant as cash or capital for their enterprise. The results of the initial random draw were not revealed to either the survey company or the firms in the sample. After each round, the survey company was given a list of firms to which to distribute the grants. Each firm could receive a prize at most once, although this was not made explicit to the firms.” McKenzie and Woodruff 2008. Pg 466.

  • 45.

    Fiszbein and Schady 2009, Pg 16.

  • 46.

    "The increase in expenditures on food generally is directed toward increasing quality. Households that benefi ted from Familias en Acción in Colombia signifi cantly increased items rich in protein, such as milk, meat, and eggs (Attanasio and Mesnard 2006); and the increases in food expenditures in Mexico and Nicaragua were driven largely by increased consumption of meat, fruits, and vegetables (Hoddinott, Skoufias, and Washburn 2000; [Maluccio and Flores 2005]). Oportunidades also increased caloric diversity as measured by the number of different food-stuffs consumed. At similar overall food expenditure levels in Nicaragua, [Macours, Schady, and Vakis 2008] shows that households that receive transfers from the Atención a Crisis program spend significantly less on staples (primarily rice, beans, and tortillas) and significantly more on animal protein (chicken, meat, milk, and eggs), as well as on fruits and vegetables [Angelucci and De Giorgi 2009] and [Attanasio, Kugler, and Meghir 2008] report similar results using data for urban Oportunidades in Mexico. Not only did households diversify their diets; they also shifted toward higher-quality sources of calories." Fiszbein and Schady 2009, Pg 113.

  • 47.

    Angelucci and De Giorgi 2009. Table 1, Pg 30. May 1999 treatment effect is 24 pesos of additional food per adult equivalent. This amounts to roughly three quarters of the mean transfer: “The actual monthly grants up to November 1999 are sizeable, averaging 200 pesos per household, or 32.5 pesos per adult equivalent.” Pg 6.

  • 48.

    Mean 4.07 adult equivalents per household (Cunha 2011, Table 2. Pg 37). Cash transfers 150 pesos per month (Cunha 2011. Pg 10). Cash transfers are estimated to increase food consumption by 34.72 pesos per adult equivalent per month (Cunha 2011 Table 4. Pg 39). 4.07*34.72/(150)= 94%.

  • 49.

    “Over the 2 years, the actual average monetary transfer (excluding the teacher transfer) was approximately C$3,500 (US$272 or 17 percent of total annual household expenditures).” Pg 8. Estimated impacts on total spending at the household level are 2,817 Cordoba (Table 4.1, Pg 27), or 80% of the transfer. The mean per capita increase in total household expenditures is 686 Cordoba (Table 4.2, Pg 29), and the mean per capita increase in total food expenditure is 640 Cordoba (Table 4.3, Pg 30), or 93% of the increase in spending. 93%*80% = 75%. Maluccio and Flores 2005

  • 50.

    The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending,

    • “[T]he size of the transfer... averages Ksh 300 per household in 2007 Ksh.” Pg 14.
    • “Results from this estimation of programme impacts, without con- trolling for the change in total expenditure induced by the programme, are shown in the odd-numbered columns in Table 2, Panel A. These show statistically significant impacts of the programme expenditures for food (Ksh 145), health (Ksh 39), and clothing (Ksh 25).” Pg 16.
    • 145/300 = 48%
  • 51.
    • “For alcohol consumption, while both [in-kind and cash transfer] treatments induced statistically significant increases (1.73 pesos per [adult equivalent] under in-kind transfers and 2.89 under cash) they are also indistinguishable from each other.” Cunha 2011, Pg 22.
    • For tobacco, see Cunha 2011, Table 5.
    • The total cash transfer equivalent is 193 pesos, so to estimate the increase in alcohol consumption as a portion of the transfer we take 2.89/193 = 1.5%. See Cunha 2011, Pgs 14-15.
  • 52.

    Cunha 2011, Pg 22. In particular, as indicated above, food consumption was reported to increase by 94% of the total transfer amount.

  • 53.

    Maluccio and Flores 2005, Pg 32. Table 4.5.

  • 54.

    Maluccio and Flores 2005, Pg 32.

  • 55.

    The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending, Pg 13. Alcohol and tobacco consumption goes from .4% of consumption at baseline to .2% at follow-up in the treatment group, while going from .3% to .2% in the control group so the cash transfers are actually estimated to reduce alcohol and tobacco consumption, though the estimate is not statistically significant. (This is not due to an overall increase in consumption; the same pattern holds in absolute currency terms.)

  • 56.

    Three possible forms of misreporting would have different effects:

    • Simple linear underestimates: if everyone under-reported alcohol consumption by a fixed amount (e.g. $1), then the estimated effect of transfers on alcohol consumption would be unbiased, though they would be upward-biased as a proportion of baseline alcohol consumption.
    • Simple geometric underestimates: if everyone underreported alcohol consumption by a certain proportion (e.g. half), then absolute estimates of the impact of cash transfers on alcohol consumption (including the estimate that 1.5% of transfers were spent on alcohol in the Programa de Apoyo Alimentario food security program in Mexico) would be biased downwards. The estimated proportional increase (e.g. recipients of transfers spent twice as much on alcohol as the control group) would likely continue to be unbiased. This strikes us as the most likely form of measurement error.
    • Transfer recipients underestimate asymmetrically: if transfer recipients recognize that a study is attempting to measure the impact of their transfers on alcohol consumption and therefore lie about alcohol consumption while the control group is truthful, then the estimates would be biased downward in unpredictable ways.
  • 57.

    "We test this hypothesis using data from a controlled randomized experiment of the Oportunidades CCT program in Mexico. We find that beneficiary households increased ownership of productive farm assets, such as farm animals and land for agricultural production, significantly faster than nonbeneficiary households; that agri -cultural production in terms of both crops and animal products increased faster for beneficiary households than nonbeneficiary households; and that this resulted in sig -nificantly higher agricultural income. In fact, we estimate that an 18-month exposure to the program resulted in a 9.6 percent increase in agricultural income. Beneficiary households also started substantially more nonagricultural microenterprises, mainly production of handcrafts for sale, compared to nonbeneficiary households … We then explore whether returns on these investments persist over time and raise long-term living standards as measured by consumption. We find that even 4 years after households in the control group were incorporated into the program, consumption levels for the original treatment households were 5.6 percent higher than for the original control households. This result suggests that returns on investments made by treatment households during the initial 18-month experimental period did in fact translate into improvements in long-term living standards." Gertler, Martinez and Rubio-Codina 2012, Pg 2.

  • 58.

    In this case, investment is defined as anything that is not consumed in the current period. Gertler, Martinez and Rubio-Codina 2012. Pg 183. “In this section, we estimate how much of the transfer is consumed versus invested—i.e., the marginal propensity to consume (MPC), and the return on transfers in terms of long-term consumption via the investment pathway, which we call the marginal investment effect (MIE). The MPC and the MIE characterize two principal pathways through which transfers affect living standards. First, households can increase living standards in the short run by spending part of the current cash transfer. This amount is just the marginal propensity to consume transfers. Transfers not consumed are saved or invested, so that the marginal propensity to invest transfers is equivalent to (1–MPC).” It is not clear from the paper whether investment in household improvement, such a purchasing a new roof, is considered consumption or investment.

  • 59.

    Gertler, Martinez and Rubio-Codina 2012, pg 189.

  • 60.

    Investment in this context is defined as both in terms of specific spending activites (e.g. related to agriculture, home business, and durable goods) and as anything not consumed in the current period; both find limited to no effects:

    • “The study also asked about other forms of expenditures related to investments at the household level, such as on household improvements, durable goods, and so forth; none of these showed significant changes. Finally, we examine expenditures on other specific non-food items including books, furniture, child clothing, remittances sent, lotteries, and parties, and find no significant program effects. Naturally, since total expenditures were flat while the percentage spent on food remained the same, it was unlikely that investments or expenditures like these would have changed very much. It is important to emphasize that the evidence indicates 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. (35) This finding is somewhat weaker in the second year, however, where increased expenditures appear to be slightly smaller than the transfers. It is possible that any differences are relected in increased savings (or increased leisure, discussed later), although we do not have the information to verify this.” The footnote says, “(35) Information on savings is not available so it is not possible to assess whether there was increased savings. Given the evidence on expenditures relative to the transfer size, however, any such increase was likely to be small.” Maluccio and Flores 2005 pgs 32-33.
    • “In years when transfers were being given, the programme increased expendi- tures, and the lion’s share of the increase was on food and educational expenditures. These findings are consistent with the programme’s orientation toward increasing current expenditures (one of its three key objectives) and the required conditions, as well as with evidence from a variety of settings that resources in the hands of women are more likely to be directed to these types of expenditures. With those findings, I turned to an assessment of the programme on investment of various types. Overall, there was only limited evidence that the programme led to increases in the agricultural and non-agricultural types of investment considered. These results are corroborated by a separate analysis estimating a consumption equation, which demonstrated that even though the transfers were to last only for three years and thus were transitory, the average MPC out of transfers was approximately one. Moreover, cumulative past transfers had no effect on current expenditures.
      The findings do not imply that the programme had no long-term effects – it very likely did via increased investment in child health and education, which should continue to lead to benefits for many years to come. In contrast to PROGRESA in Mexico, where there seems to have been substantial agricultural investment and returns from it (Gertler et al., 2007; Todd et al., 2010), there is only weak evidence that RPS increased these other investments in the rural localities in which it operated.” Maluccio 2010, Pgs 34-35.
  • 61.

    Maluccio and Flores 2005 Pg 33.

  • 62.
  • 63.

    “The last column in Table 4, Panel A sums up the ex-ante and ex-post effects; the ex-ante effects suggest that about Ksh 7 out of the total transfer is spent on non-consumption expenditures, possibly investments, while the actual impacts suggest a much larger Ksh 40 (or 13 per cent of the transfer value) goes to non-consumption uses.” The Kenya CT-OVC Evaluation Team. The impact of the Kenya Cash Transfer Program for Orphans and Vulnerable Children on household spending Pg 23.

  • 64.

    If, for instance, the treatment and control group systematically underreported spending by a uniform proportion, there would be a gap between the estimated consumption increase and the transfer size of at least that proportion.

  • 65.

    “Cash treatments were given without restrictions. Those receiving cash were told that they could purchase anything they wanted, whether for their business or for other purposes. In reality, the grant was destined to be unrestricted because we lacked the ability to monitor what recipients did with the funds, and be- cause cash is fungible. Being explicit about this was intended to produce more honest reporting regarding use of the funds. In the survey subsequent to the treatment, we asked how they had used the treatment. On average, 58% of the cash treatments was invested in the business between the time of the treatment and the subsequent survey. An additional 12% was saved, 6% was used to repay loans, 5% was spent on household consumption, 4% was spent on repairs to the house, 3% was spent on equipment or inventories for another business, and the remaining 12% was spent on “other items.” Of the amount invested in the enterprise, about two-thirds was invested in inventories and the rest in equipment.” McKenzie and Woodruff 2008 Pg 1337.

  • 66.

    de Mel, McKenzie, and Woodruff 2008,online appendix, Table A4. With no trimming of capital stock, grants are found to increase capital stock by more than 100% of their value (107-115%); after trimming the top and bottom 1% of capital stocks, grants are estimated to increase capital stock by 62-87% of their value. “The first column of the table verifies that the treatment did increase capital stock as intended. All four treatments are significantly associated with higher levels of capital stock. The measured impact of the cash treatments is somewhat higher than the impact of the in-kind treatments, though the large standard errors on the individual treatments mean that the differences between cash and in-kind treatments are not significant. Trimming the top and bottom 1% of capital stock reduces these differences.11” Pgs 1341-42. Footnote 11 says: “The treatment effects after trimming capital stock are 5,780 (6,227) for the 10,000 LKR in-kind (cash) treatment and 13,443 (17,325) for the 20,000 LKR in-kind (cash) treatment.”

  • 67.

    Fafchamps et al. 2011. “The remaining columns report the estimated impacts on household expenditure, which was collected each wave. Point estimates suggest higher positive impacts on expenditure for those receiving the cash treatments than those getting the in-kind treatment or the control group, especially for women with low initial profits. We see a large and highly significant effect of the cash treatment on total quarterly spending for women as a whole, and for the subgroup of women with low initial profits. The coefficients are huge: women who were given a 150 cedis cash grant are estimated to be spending 120 cedis more a quarter after the grant. The magnitude of this coefficient appears to be driven by a few firm owners reporting very large spending levels — truncating at the 99th percentile of total expenditure lowers this coefficient to 95, and at the 95th percentile lowers it to 76 cedis (which is still significant at the 5% level). For males receiving the cash treatment, the point estimates also suggest large increases in total quarterly spending (with a coefficient of 50 to 73 cedis depending on the level of truncation), but the standard error is so large that we can never reject equality with zero.” Pgs 28-29.

  • 68.

    Fafchamps et al. 2011. Table 5, columns 1 and 2, Pg 53. Capital stock for women increased by 49.17-82.61 cedis, while capital stock for men increased by 2.21-31.36 cedis, both on a grant of 150 cedis.

  • 69.

    “We find that household per capita consumption in 2003 is 10.84 pesos higher for original treatment households, and this difference is statistically significant (first column in Table 5). This impact amounts to a 5.6 percent increase in consumption for treatment households, even 4 years after controls started receiving program benefits. While we do not have agricultural production for the 2003 survey round, we do have home-produced consumption. We also find a significant increase in home- produced consumption (significantly different from 0 at the 10 percent level), which is consistent with a sustained increase in agricultural productivity (second column in Table 5).” Gertler, Martinez and Rubio-Codina 2012, pg 179.

  • 70.

    “Eligible households in treatment communities began receiving benefits starting in March/April of 1998, while eligible house- holds in control communities were incorporated in November/December of 1999. In order to minimize anticipation effects, households in control communities were not informed that Oportunidades would provide benefits to them until two months before incorporation. Behrman and Todd (1999) confirm that the original randomization balanced the control and treatment communities; and Attanasio, Meghir, and Santiago (forthcoming) explicitly test, but find no evidence of, anticipation effects amongst control households.” Pgs 168-169. This implies that treatment communities received an additional 20 months of transfers relative to control communities.

    Table 8 reports that mean actual transfer per adult equivalent in treatment households in October 1998 was 24.196, in May 1999 was 38.691, and in November 1999 was 31.141. The mean of these values is 31.34. Multiplying by 20, for the number of months that the treatment communities received treatment and the control communities did not, we estimate that treatment caused an average transfer of 627 pesos per adult equivalent in the treatment group before the control group began to be treated.

    Four years later, consumption was 10.84 pesos per adult equivalent per month higher in the treatment group, implying a 1.7% (10.84/627) monthly return. 10.84/627*12 = 20.746%

  • 71.

    Table 8 reports that the difference between monthly transfer and increase in consumption was:

    • October 1998: 24.196 – 17.613= 6.583
    • May 1999: 38.691 – 16.033 = 22.658
    • November 1999: 31.141 – 14.596 = 16.545

    This implies a mean savings from transfers of 15.262 per month, or 305.24 over the period of treatment prior to the control group receiving treatment.

    Four years later, consumption was 10.84 pesos per adult equivalent per month higher in the treatment group, implying a 3.6% (10.84/305) monthly return and 42.6% annual return.

  • 72.

    Gertler, Martinez and Rubio-Codina 2012:

    • “The second coefficient, ρg. 12 , represents the effect on consumption of transfers made in the second 12-month period prior to the current transfers. This coefficient is positive and significantly different from zero. We estimate that for every peso transferred during this period, current consumption increased by 1.8 cents. The third coefficient represents the effect on consumption of transfers made in the period prior to 24 months before the current transfer period. This coefficient is positive and significantly different from zero, and implies that for every peso transferred during this period, current consumption increased by 1.6 cents. While this coefficient is slightly lower than the second lag, the two are not significantly different from one another, as reported in the sixth row of Table 10 ( p-value = 0.805). This suggests at best a slight drop off in the MIE due to depreciation.
      Using these estimates of MPC and MIE, we run a back-of-the-envelope calculation to predict the long-term effects of the cash transfers on living standards, simply multiplying the MIE by total cumulative transfers. By November 2003, after 51/2 years on the program, households in the treatment group had received a total of 2,624 pesos per capita, on average.28 Using our more conservative estimate of the MIE of 0.016, this implies an increase in consumption of 41.9 pesos per capita per month through the investment pathway. If these increases in consumption are, as argued here, derived from productive investments made by beneficiary households thanks to the cash transfers, then to the extent that the investments are long term, these increases in living standards are expected to be sustained over time even after the household is no longer receiving cash transfers from Oportunidades.” Pg 190.
    • “We estimate the MPC to be 0.74, implying that approximately three-quarters of the transfers are directly consumed and one-quarter are invested. The estimated MPC is significantly different from 1 at conventional significance levels, as the test in the fifth row in Table 6 reports ( p-value = 0.008). We also tested, and could not reject ( p-value of 0.956), the hypothesis that the MPC is the same for all four rounds of the data used (results available upon request).” Pg 189.
  • 73.

    19.2% per year / 26% invested = 73.8% annual return on invested funds.
    1.6% per month/ 26% invested = 6.2% monthly return on invested funds.

  • 74.

    Gertler, Martinez and Rubio-Codina 2012. “We solve this problem by instrumenting current transfers and past cumulative transfers with the maximum potential current and maximum potential past cumulative transfers, respectively, that a family could achieve if the maximum number of eligible children in the household were enrolled in school.27 At each time t, we compute a family’s maximum potential transfer using a modified version of the formula in (9) and assuming that all eligible children that were enrolled at baseline have advanced a grade per year. Because of the cap on total benefits and because the transfers are zero for the first three years of school, potential transfers are a nonlinear function of the number of children at baseline who could be enrolled in school in period t.
    Maximum potential transfers and the three lagged maximum potential cumulative transfers are likely to be valid instruments for three reasons. First, they are strong predictors of the actual transfers and the three actual cumulative lagged transfer variables. Indeed, and as expected, the distribution of potential transfers follows that of the actual transfers very closely, albeit potential transfers are an overestimate of the actual transfers (given noncompliance, administrative delays in payments, etc.). The simple correlation amongst both variables is 0.89. After controlling for time effects and baseline covariates, 55.7 and 65.9 percent of the variation in current and cumulative lagged transfers are explained by their potential counterparts.
    Second, they are unlikely to be correlated with consumption via other pathways, such as other income sources. Indeed, they are uncorrelated with changes in children’s labor supply due to the program as they are computed assuming that all eligible children enrolled at baseline are still in school and have advanced one school grade per school year. Nonetheless, the transfers could also be taken in leisure by reducing adult labor supply, which would reduce household income and therefore household consumption. Everything else held constant, this would imply a down- ward biased estimate of the MPC. Parker and Skoufias (2000) show that there is no effect of the program on adult labor supply, and we can thus safely assume that the transfer variables are not correlated with other earned sources of income.
    Regarding program impacts on unearned income, the crowding out effect of pri- vate transfers found in Albarran and Attanasio (2005) and discussed earlier, would suggest that our estimated MPC is underestimated. However, private transfers are unlikely to explain our results as the crowding out effects are small in size and, on average, approximately 7.3 percent of eligible households report receiving private transfers over the experimental period (fourth column in Table 6). This proportion doubles to 15.4 percent in November 2003 (see the fifth column in Table 5) suggest- ing that the crowding effects are not sustained over time.
    Finally, there is no bias from omitted family demographic structure as we directly control for family structure in the regression models. In fact, maximum potential transfers are not strongly correlated with the number of children in the household because of the nonlinear allocation rule. Let’s imagine the following extreme situations: a household with three girls in grade 2 of primary school, and a household with three girls in grade 2 of junior high school. Both households have three female children, but while the first household will receive no school transfers, the latter household will receive a large monthly transfer. In addition, families with four or more children in junior high school would receive the same transfer amount as the latter household because the cap on total benefits would be binding. Indeed, the data shows low correlations between transfers and the number of children under 17 (r = 0.14), or with the number of siblings 15–17 years old (r = 0.42), 12–14 years old (r = 0.22), 6–11 years old (r = −0.12), and 0–5 years old (r = −0.10). Thus, we are able to explicitly control for household size and the number of children in the household in the empirical specification, which allows for identification of the potential transfer variable.” Pgs 187-188.

  • 75.

    Gertler, Martinez and Rubio-Codina 2012. Table 10, column 2. Pg 189. This implies an 18% annual return on transfers (12*1.5%=18%) and a 35% annual return on investment (12*1.5%/(1-.487)=35.1%).

  • 76.

    “One potential concern with this specification is that the current and cumulative transfer amounts that the household actually receives are determined in part by whether children attend school. If a household sends their children to work instead of going to school, then the family would have lower transfers but higher income from the child’s work. This would imply a downward biased estimate of the MPC and an upward biased estimate of the MPI. In reality, this is a concern for our estimates given that Parker and Skoufias (2000) and Skoufias and Parker (2001) find that the program reduces child labor and increases enrollment in junior high (secondary) schools as the opportunity cost of these children being in the labor force is now higher. Schultz (2004) also finds positive effects for primary school and, more notably, junior high school enrollment for boys and girls.” Gertler, Martinez and Rubio-Codina 2012, pg 187.

  • 77.

    Gertler, Martinez and Rubio-Codina 2012, table 10, column 1.

  • 78.

    Both papers focus on the two-thirds of the initial sample that were not directly affected by the 2004 Indian Ocean Tsunami; including those firms that were affected would increase the estimated returns. The heterogeneity across tsunami-exposed areas is described in column 6 of Table 3 and discussion on page 1347 of de Mel, McKenzie, and Woodruff 2008.

      In general, the authors selected their sample by picking regions with high-percentage of self-employed workers and low education levels. The authors describe the remainder of the selection process: "The full survey was given to 659 enterprises meeting these criteria. After reviewing the baseline survey data, we eliminated 41 enterprises either because they exceeded the 100,000 LKR maximum size or because a follow-up visit could not verify the existence of an enterprise. The remaining 618 firms constitute the baseline sample. We present results later in the paper indicating that returns to capital were higher among firms directly affected by the tsunami, but we exclude these firms for most of the analysis because the tsunami recovery process might affect returns to capital. We leave the full analysis of the impact of the capital shocks on enterprise recovery to another paper. Excluding the directly affected firms leaves us with a baseline sample of 408 enterprises. The 408 firms are almost evenly split across two broad industry categories, with 203 firms in retail sales and 205 in manufacturing/services. Firms in retail sales are typically small grocery stores. The manufacturing/services firms cover a range of common occupations of microenterprises in Sri Lanka, including sewing clothing, making lace products, making bamboo products, repairing bicycles, and making food products such as hoppers and string hoppers" (de Mel, McKenzie, and Woodruff 2008, Pgs 1334-1335)

    • 79.

      de Mel, McKenzie, and Woodruff 2012. “[W]e found long-lasting impacts from one-time grants given in a randomized experiment to subsistence firms. Five years after we gave $100 or $200 to 115 of 197 male and 100 of 190 female Sri Lankan microenterprise owners, we found 10-percentage-point-higher enterprise survival rates, and $8-to-$12-per-month-higher profits for male-owned businesses that received the grants. Female-owned businesses showed no long-term (or short-term) impacts. Our follow-up investigation interviewed 94% of the original sample and collected survivorship data
      from the remaining 6%, demonstrating that tracking long-term outcomes is both feasible and worthwhile.” Abstract.

    • 80.

      de Mel, McKenzie, and Woodruff 2012. “For males, a 10,000 LKR grant increased monthly profits by 600 to 1200 LKR, a 6 to 12% monthly real return. This persists throughout the time period and does not narrow dramatically (as would be the case with a temporary effect) or increase dramatically (as would be the case if returns compounded). This effect is robust, and strengthened, when we look at labor income and include the labor income for those businesses which have closed, and are shown in SOM text 5 to be robust to any selective attrition.” pg 965.

    • 81.

      de Mel, McKenzie, and Woodruff 2008. “[W]e find that the measured effect of the cash treatment is larger than the effect of the in-kind treatment (a 6.7% vs. 4.2% monthly return), but the difference is not significant at conventional levels ( p = .45). Column (5) shows that we cannot rule out linearity of the returns measured by the two treatment levels. Profits increase by 760 LKR per month with the smaller treatment, 7.6% of the treatment amount, whereas they increase by 900 LKR per month, or 4.5% of the larger treatment. The difference in returns is not significant.” pg 1347.

    • 82.

      de Mel, McKenzie, and Woodruff 2008. Table V.

    • 83.

      de Mel, McKenzie, and Woodruff 2012. Table 3.

    • 84.

      Fafchamps et al. 2011:

      • “The first four columns of Table 3 show the treatment effects for the pooled sample. All four specifications show a large positive impact of the in-kind treatment on firm profits. Monthly firm profits are estimated to be 31-43 cedis higher as a result of the 150 cedis in-kind treatment. The cash treatment is significant at the 10 percent level in the untrimmed OLS specification, but becomes insignificant when trimming or using fixed effects. The coefficients are always much smaller than for the in-kind treatment, and we can reject equality of cash and in-kind grants at the 5 percent significant level for three out of four specifications and at the 10 percent level for the other. That is, cash grants have less impact on business profits than in-kind grants.” Pg 23.
      • Study participants were selected from two cities in Ghana during an initial screen. The authors describe the rest of the selection process: “The gender and business sector of all individuals passing this screen were then recorded. This resulted in screening 7,567 households to identify 3,907 individuals who passed the screen. Only 19.4 percent of these individuals were male, confirming the predominance of women among small enterprise operations in urban Ghana. We classified business sector into male-dominated industries, identified as construction, repair services, manufacturing, and shoe making and repair; female-dominated industries, identified as hair and beauty care, and food and restaurant sales; and mixed industries, identified as trade and retail, and sewing and tailoring. This classification into male-dominated, female-dominated, or mixed was based on the gender mix of selfemployed in these industries in the 2000 Census. These industries cover the vast majority of the industries in which the self-employed work in Ghana. The 4.6 percent of those screened who worked in other industries such as communication services, pharmacy, photography, fishing, and agriculture were not included in the sample. Our aim was then to arrive at a sample of roughly 900 baseline firms stratified by gender and sector. In order to minimize the spillovers from the treatments to be carried out, we did not want to select too many individuals from any given EA who were in the same line of business. We therefore randomly selected up to 5 males in male-dominated and up to 5 males in mixed industries from each EA, and up to 3 females in female-dominated and up to 3 females in mixed industries from each EA to survey, in the process ensuring that only one individual was chosen from any given household. This resulted in an initial sample of 907 firms, consisting of 538 females and 369 males. A baseline survey of these firms was conducted in October and November 2008. The firm owners were asked for details of both their firm and their household.” Pgs 14-15.
    • 85.

      McKenzie and Woodruff 2008:

      • “The results from table 5 show the treatment effects that are significant for two-stage least squares and instrumental variables random-effects estimation and marginally significant for instrumental variables fixed-effects estimation after 5 percent trimming. The estimated treatment effect ranges from 28.8 to 45.6 percent. It is well identified only for the subset of firms without very noisy profit data that take up the treatment when assigned.” Pg 473.
      • “The initial survey was conducted in November 2005, reflecting data from October 2005. Subsequent surveys were administered quarterly, with the fifth and last survey conducted in November 2006.” Pg 460.
      • “Before the first round of the survey, firms were told that the only compen- sation that they would receive for participating was a chance of receiving either cash or capital through prizes to be given after each survey round.6 The prize was a grant of 1,500 pesos (about $140). After the first round of the survey, a single draw from a computerized random number generator was used to ran- domly assign firms to treatment and control groups.7 Among the firms assigned to treatment status, the random draw also determined the round in which they would be treated and whether they would receive their grant as cash or capital for their enterprise. The results of the initial random draw were not revealed to either the survey company or the firms in the sample. After each round, the survey company was given a list of firms to which to distribute the grants. Each firm could receive a prize at most once, although this was not made explicit to the firms.” Pg 466.
    • 86.

      “Estimates of the treatment effects allowing for interactions between treatment and different measures of lack of financial constraints are reported after again eliminating firms with percentage changes in profits below the 5th per- centile or above the 95th percentile. Columns 1 and 2 of table 7 show a large and strongly significant interaction effect between treatment and whether a firm owner reports that finance is not a constraint to business growth. One cannot reject the possibility that firms that report that finance is not a constraint have no increase in profits from the treatment (the point estimate actually shows a decrease in profits). The treatment effect is much stronger for the 64 percent of firms that report that finance is a constraint: monthly profits increase 1,051–1,192 pesos for these firms, a 70–79 percent return. Similar but less significant interaction effects are found for the measures of previous use of credit. One cannot reject the possibility that there is no treatment effect for firms that previously had formal loans or sup- plier credit; the treatment effect for financially constrained firms is always positive, and it is significant in all but one case (firms that have not had a formal loan).
      The different measures are combined to create a set of firms that report that finance is a constraint to business growth and that have never had a formal loan or supplier credit. The 38 percent of firms that fall into this category are referred to as “financially superconstrained.” Interacting this variable with the treatment increases the profits among these firms by 1,430–1,515 pesos—an incredible 100 percent return.” McKenzie and Woodruff 2008, Pg 479.

    • 87.

      McKenzie and Woodruff 2008. “One potential concern is whether the process of trimming combined with attrition could be biasing the results. Attrition rates are similar for firms assigned to the control and treatment groups. However, after 5 percent trimming, attrition after five rounds is 58 percent for the control group and 55 percent for the group assigned to treatment.” McKenzie and Woodruff 2008 Pg 476.

    • 88.

      "The data for our analysis come from surveys of stores and households conducted in the experimental villages by the Mexican National Institute of Health both before and after the program was introduced. Baseline data were collected in the final quarter of 2003 and the first quarter of 2004, before villagers knew they would be receiving the program. Follow-up data were collected two years later in the final quarter of 2005, about one year after PAL transfers began in these villages. Our measure of post-program prices comes from a survey of local food stores. Enumerators collected prices for fixed quantities of 66 individual food items, from a maximum of three stores per village, though typically data were collected from one or two stores per village." Cunha, De Giorgi and Jayachandran 2011, Pg 14-15.

    • 89.

      Cunha, De Giorgi and Jayachandran 2011, Table 2, Pg 35.

    • 90.

      "Our final data set contains 6 basic PAL goods (corn flour, rice, beans, pasta, oil, fortified milk), 3 supplementary PAL goods (canned fish, packaged breakfast cereal, and lentils), and 51 non-PAL goods" Cunha, De Giorgi and Jayachandran 2011, Pg 15. However, the authors report only the change in prices for the PAL goods.

    • 91.

      Angelucci and De Giorgi 2009 “To test for effects on the goods market, we first compare prices in treatment and control localities. To do so, we consider village prices by good over time. We provide details on the creation of the price variables in the Appendix, as well as estimates of the price differences between treatment and control villages (Tables A3 and A4). While we find a small positive effect on 5 out of 36 food prices in November 1998, prices of staples such as rice, beans, corn, and chicken do not change. Therefore, we do not expect any substantial increase in the cost of the food basket. Moreover, we find no food price change in the later waves, nor evidence of changes for non-food prices. The evidence presented here is consistent with earlier work by Hoddinott et al. (2000).” Pg 21.

      However, the increases occur in only a small portion of a typical family's basket of goods and only in the first of three follow-up surveys, leading the authors to conclude “that, perhaps with the exception of a minor price increase for some goods in the end of 1998, [Oportunidades] does not significantly change prices in treatment areas.”

      Angelucci and De Giorgi 2009 Online appendix, pg 7. Full quote: “We find a small positive effect on some food prices in November 1998. Prices of onions (p2), lemons (p8), eggs (p26), and coffee (p34) are significantly higher in treatment than in control areas. At the same time, though, the price of fish (p23) is significantly lower. Despite the fact that onions, eggs, and coffee are commonly consumed foods (Hoddinott et al., (2000)), we do not expect these price changes to increase the cost of the food basket substantially, because prices of staples such as rice, beans, corn, and chicken do not change. Second, there is no price change in the later waves. Third, if we consider the pooled waves, the prices of 6 items
      increase, while the prices of 3 goods decrease in the observed time, out of a total of 36 items by 3 waves. This amounts to roughly 8% of good prices changing. We believe that, perhaps with the exception of a minor price increase for some goods in the end of 1998, Progresa does not significantly change prices in treatment areas.”

      Fiszbein and Schady 2009 adds that "The lack of impact on wages and prices of consumer goods is not surprising. In most countries in which CCTs have been evaluated, labor and goods markets are sufficiently developed so that both labor and goods are largely tradable. CCTs may induce larger local demand for goods and lower local supply of labor, and, in the short run, prices may change to reflect these imbalances; in the long run, however, prices should return to their initial equilibrium" (Pg 122).

    • 92.

      Angelucci and De Giorgi 2009, Table 1.

    • 93.

      “In practice, CCTs appear to have had, at most, modest disincentives for adult work. Two studies (Parker and Skoufias 2000; Skoufias and di Maro 2006) examine the effects of Oportunidades on adult labor supply; neither finds evidence of disincentive effects. The data used by Edmonds and Schady 2011 suggest that the BDH program in Ecuador had no effects on adult labor supply; in a similar vein, Filmer and Schady (2009c) report that adult labor supply was largely unaffected by the CESSP program in Cambodia. Only in Nicaragua is there some evidence of significant negative effects on adult work: [Maluccio and Flores 2005] show that the RPS resulted in a significant reduction in hours worked by adult men in the preceding week (by about 6 hours), with no effect among adult women.” Fiszbein and Schady 2009, Pgs 117-119.

    • 94.

      The evidence we have seen cannot distinguish between:

      • income effects: families using income from conditional cash transfers to offset income from child labor, and therefore reducing the amount of hours worked by children; and
      • conditionality effects: since many conditional cash transfer programs require school attendance in order to qualify for transfers, families may reduce the number of hours worked by children in order to qualify for the transfers.

      These effects differ in that they have disparate predictions for the effects of unconditional cash transfers on child labor.

      Summarizing the evidence, Fiszbein and Schady (2009) states, “several CCTs have been successful in reducing child work. Frequently, these impacts have been concentrated among older children. Table 4.5 shows that Oportunidades reduced child work among older children, aged 12–17, especially among boys (for whom baseline levels of child work also were substantially higher). Skoufias and Parker (2001) also show that domestic work decreased substantially, especially for girls.
      In Ecuador, Edmonds and Schady (2008) shows that the Bono de Desarrollo Humano program had very large effects on child work among those children most vulnerable to transitioning from schooling to work. Those effects are concentrated in work for pay away from the child’s home. BDH transfers, on the other hand, had small effects on child time allocation at peak school attendance ages and among children already out of school at baseline. In Cambodia, the CESSP program, which gives transfers to children in transition from primary to lower-secondary school, reduced work for pay by 11 percentage points (Filmer and Schady 2009c).
      Other CCT programs also appear to have reduced child work. In Nicaragua, the Red de Proteción Social program reduced child work by 3–5 percentage points among children aged 7–13 (Maluccio, John A., and Rafael Flores. 2005). Furthermore, the fraction of children who only studied (as opposed to worked and studied, only worked, or neither worked nor studied) increased significantly (from 59 percent to 84 percent) as a result of the RPS (Maluccio 2005). Yap, Sedlacek, and Orazem (2008) estimate the effects of the Brazilian Programa de Erradicação do Trabalho Infantil (PETI) program, another precursor of the Bolsa Família program. PETI gave out conditional transfers to secondary school-age children enrolled in school. Stipends were given directly to students, not to the families and were conditional on school attendance and participation in special training workshops. PETI beneficiaries reduced substantially their probability of working. Attanasio et al. (2006), however, finds no effect of the Familias en Acción program on child work in Colombia (although the program does appear to have reduced the amount of time dedicated to domestic chores). Glewwe and Olinto (2004) find no effect of the PRAF program on child work in Honduras.

      Two recent papers consider the impact of CCTs on child work when the transfer is conditional on school attendance for only one child in the household and that child has siblings. Programs of this nature could have positive or negative spillovers for other siblings. Positive outcomes include if the income effect reduces child work for all children, if transfers increase the bargaining capacity of women within the household, or if the social marketing by the program leads parents to reduce child work even for children whose school attendance is not monitored. Negative impact could include parents compensating for the reduction in work of one child by increasing the work of other siblings. Barrera-Osorio et al. (2008) analyze Subsidios Condicionados a la Asistencia Escolar, a pilot CCT program in Bogotá, Colombia. This program randomized assignment to individual children rather than households, and made transfers directly to students rather than to their parents. Barrera-Osorio et al. show that, within the same household, a student selected into the program is 2 percentage points more likely to attend school and works about 1 hour less than a sibling who has not been selected; however, the beneficiary’s sibling – particularly if this sibling is a girl – is less likely to attend school than are children in households that received no cash transfer at all. In contast, Filmer and Schady (2009c) find that the CESSP program in Cambodia had no effect on the school enrollment of a beneficiary’s ineligible siblings.” pgs 115-116.

    • 95.

      Fiszbein and Schady 2009, pg 114, “[T]able 4.1 shows that, for most countries, the impact of the transfer is generally somewhat smaller than the magnitude of the transfer (when both are normalized as a fraction of the consumption or income of households in the control group). The difference between these two values may be a result of behavioral changes by CCT beneficiaries, which partly offset the value of the transfer itself. We now turn to a discussion of the evidence on these possible offsetting effects, focusing on impacts on child labor, adult labor, remittances, fertility, and spillovers and other general equilibrium effects.”

      Other potential explanations might include:

      • reductions in adult labor in favor of increased leisure time
      • increased savings or investment in productive assets
      • errors in measuring consumption, leading to attenuation in the estimated increase associated with receiving cash transfers
      • misappropriation of transfers.
    • 96.

      Aker et al. 2011

    • 97.

      "Excluding the cost of the mobile phones, the per-recipient cost of the zap intervention falls to $8.80 per recipient. Thus, while the initial costs of the zap program were significantly higher, variable costs were 30 percent higher in the manual cash distribution villages." Aker et al. 2011, Pg 12.

    • 98.

      Aker et al. 2011, Pg 10.

    • 99.

      Aker et al. 2011, Tables 4 and 5.

    • 100. Fiszbein and Schady 2009.
    • 101.

      Lagarde, Haines, and Palmer. 2009