The GiveWell Blog

DonorsChoose vs. Kiva

(updated on: ) | by Holden

This Tactical Philanthropy post implies that the salient difference between DonorsChoose and Kiva is that DonorsChoose is more “authentic” in terms of connecting donors to projects. (Update: Sean points out in the comment below that most of the post I cited was a quote from a former DonorsChoose employee and doesn’t necessarily reflect TacticalPhilanthropy’s opinion.)

We think there’s another important difference. Kiva and DonorsChoose work on very different types of problems, that offer donors vastly different opportunities to cause significant impact. And if we had to pick one, we’d bet on Kiva as the better option if you want to change lives.

The problem of improving children’s education is far, far more difficult than most people suspect – for our most recent coverage, see our multi-part series on the recent Harlem Children’s Zone study, and how excited the academic community has been over a one-time and modest improvement. And DonorsChoose does not offer the chance to support any of the most promising and tested educational interventions, such as the creation of intensive charter schools. Funding DonorsChoose is making a bet that classroom materials are where it’s at, even when the teachers, school systems and children’s outside environments remain the same. As far as we know, there is zero evidence that improved school supplies have resulted in any measurable improvements in the U.S., and even in the developing world the proposition seems iffy.

Funding Kiva, even if it doesn’t mean funding individuals, means funding microfinance charities. We are very ambivalent about microfinance; recent posts on this topic include questions about whether there’s any empirical support for microfinance charities’ claims and an interview with David Roodman about microfinance charity in which many unresolved questions come up. But the bottom line is that when you fund microfinance charities, your money ends up in the poorest parts of the world, and often (though we don’t know how often or how much) helps people there manage their volatile financial lives with credit, savings and other assistance.

(In fact, the way in which your money is most helpful may be by doing exactly what it has been criticized for doing – effectively funding partners’ other projects, such as savings vehicles, rather than the loans you see on your screen.)

Fund financial services in the poorest parts of the world, or fund an untested and low-intensity approach to a problem that higher-intensity programs have struggled with for decades. $900 can be five digital cameras for a classroom or a year’s income for 3 people.

That’s the decision as it looks to us, as we ask not “How can I make sure that I can see the exact person who’s getting my money?” but “How can my money accomplish as much good as possible?”

And on that note, we feel that our top-rated charities offer far better opportunities to improve lives than either, even if they can’t deliver the same emotional experience. As a donor, which would you rather have?

Kiva repayment data

(updated on: ) | by Elie

GiveWell Board member Tim Ogden asks, is Kiva reporting phantom payments?

It’s hard to say for sure, but it seems that the answers is either (a) yes, they are or (b) no, but borrowers listed on Kiva’s website are not representative of the average borrowers visiting that MFI.

The table below shows default rates for Kiva’s largest field partners (in terms of total loans provided through Kiva) and compares the default rates reported on Kiva to default rates reported for those same institutions on MixMarket, a site devoted to sharing data on microfinance.

Data from the two sources aren’t perfectly compatible but the broad story is clear: Kiva’s partners have higher repayment rates on their Kiva-listed loans than on their portfolio overall, as listed on MixMarket. This could come about in a couple ways:

  • The borrowers that partners list on Kiva could be substantially more likely to repay than “other” borrowers. If Kiva loans are effectively fungible between partners (as the recent discussions imply), this would mean that donors are likely “effectively” funding clients who are less likely to repay than those in the profiles they’re scanning.
  • Kiva’s partners could be “covering for” their clients to keep their Kiva-listed repayment rates high.

(Kiva data comes from a superb site, Kiva Data, a site stating that it is unaffiliated with Kiva as well as the partners’ pages on the Kiva website itself. MixMarket data comes from the MixMarket website. For definitions of items in the table, see the notes section below it.

It’s worth noting that this analysis is only possible because Kiva makes its data available and due to the efforts of those at Kiva Data and MixMarket who have created extremely useful information resources. Similar analysis would be impossible for nearly every other charity I can think of.)

Loans and default rates for Kiva’s 10 largest partners

Partner Number of loans on Kiva Number of loans on Mix Start date on Kiva Kiva-reported default rate Mix-reported default rate over similar time period p
PRISMA 4,493 22,306 8/18/07 0.0% 0.4% <.001%
LAPO-NGR 3,180 329,384 12/3/06 0.0% 0.4% <.001%
Manuela Ramos 3,918 16,982 9/24/07 0.0% 0.0% 100%
CREDIT 2,172 52,013 6/3/06 0.0% 0.1% 15%
SAT 1,645 73,268 12/7/07 0.0% 0.0% 100%
FINCA – PER 2,704 13,084 8/24/07 0.0% 0.3% 0.115%
BRAC – TZA 1,174 69,502 1/22/08 0.0% 6.6% <.001%
SPBD 1,863 7,816 9/1/06 0.0% 0.8% <.001%
MLF MicroInvest 2,240 13,248 4/6/07 0.0% 0.7% <.001%
Fundación Paraguaya 1,956 34,222 6/23/07 0.0% 2.4% <.001%

Notes on the data:

  • The data in the table above comes from Kiva’s 10 largest partners (in terms of number of loans made to date) who provided full MixMarket data for 2008. 2 of Kiva’s 10 largest partners doesn’t have this data accessible at the moment. AMK, Kiva’s 7th largest partner, reported no outstanding loans for 2008 on MixMarket and ADMIC, Kiva’s 9th largest partner, has not reported any data for 2008 yet.
  • p refers to the probability that, if the MFI’s Kiva borrowers were equally likely to default as its borrowers in general, the MFI would happen to have a 0% default rate for its Kiva loans. (Note that this calculation assumes that the time periods and the two definitions of “default rate” are comparable.) Low values mean it is extremely unlikely that this phenomenon would arise by chance, implying instead that the Kiva borrowers are systematically better than the normal borrowers, or that defaults are simply not being reported to/by Kiva.
  • The data on MixMarket is self-reported. Nevertheless, Mix is a respected source, and we think it’s reasonable to rely on it.
  • The date-ranges used for the Kiva data and Mix data above don’t sync up perfectly. Mix reports data for each calendar year. Kiva reports all-time data for each of its partners, some of whom joined in the middle of a calendar year. We’ve roughly adjusted for this by ignoring a Kiva partner’s first partial year and weighting the Mix data for the remaining years by the number of loans outstanding each year.
  • MixMarket doesn’t use the term “default rate.” Instead, we’ve used “Write-off ratio %,” defined as Write Offs / Loan Portfolio, gross, average. We’d prefer to also look at Portfolio at Risk figures. However, Kiva only reports delinquencies for loans currently outstanding and therefore can’t be matched well to Mix data.
  • MixMarket reports data as a % of the portfolio’s dollar value. Kiva reports data as a % of loans made and value loaned. Given that Kiva reports 0% default rates and Mix reports >0% default rates, there’s clearly some inconsistency. Nevertheless, without information regarding the size of the loans defaulted on at Kiva, we can’t guess at the magnitude of the inconsistency.
  • All data used in this table is available in an Excel file. If you’re interested see Kiva major partners data (XLS). This file also contains links directly to Kiva’s partners page both on Kiva and MixMarket.

Maternal mortality: “Important” yes, “just needs funding” no

(updated on: ) | by Holden

Change.org global health blog:

we know how to avoid maternal mortality (it’s 1 in 47,600 in Ireland, but 1 in 7 in Niger), it’s just that “poor, uneducated women in Africa and Asia have never been a priority either in their own countries or to donor nations.” Kristof is demanding action and aid to help save lives, advocating tried and tested methods that just need funding in order to make a real difference.

We agree that maternal mortality is a vital problem, but we don’t agree that there are “tried and tested methods that just need funding in order to make a real difference.” Our review of maternal mortality programs concludes that even though the developed world has made great progress in making childbirth safer, there have been no consistently successful programs for extending these gains to the developing world, and some prominent failures.

The observed difference between Ireland and Niger (in the quote above) indicates that there is a problem, but does not indicate that we know how to solve it. After all, Ireland has over 50x the per-person income of Niger (based on 3 data sets collected by Wikipedia), yet there is no consensus on how to promote growth and reduce poverty overall.

We don’t have the same pessimism about all causes. There are many health programs that have had repeated success in the past and likely deserve the designation of “tried and tested methods that just need funding in order to make a real difference.”

If donors are to be well-informed and live up to their potential effectiveness, it is vital not just to identify important problems, but to distinguish between areas where funding is the primary constraint to impact and areas where it isn’t.

Kiva and fungibility

(updated on: ) | by Holden

David Roodman, whom we previously interviewed, has a very interesting post up about a specific microfinance vehicle, Kiva.org.

Our existing report argues that donations through this sort of vehicle are likely “fungible,” and therefore better thought of (for impact purposes) as general support of organizations rather than as support of specific projects or people. Mr. Roodman demonstrates this issue in a very concrete way:

Less [than] 5% of Kiva loans are disbursed after they are listed and funded on Kiva’s site. Just today, for example, Kiva listed a loan for Phong Mut in Cambodia and at this writing only $25 of the needed $800 has been raised. But you needn’t worry about whether Phong Mut will get the loan because it was disbursed last month. And if she defaults, you might not hear about it: the intermediating microlender MAXIMA may cover for her in order to keep its Kiva-listed repayment rate high.

Mr. Roodman goes on to argue that the way sites like Kiva are generally imagined to work would make little to no sense.

Imagine if Kiva actually worked the way people think it does. Phong Mut approaches a MAXIMA loan officer and clears all the approval hurdles, making the case that she has a good plan for the loan, has good references, etc. The MAXIMA officer says, “I think you deserve a loan, and MAXIMA has the capital to make it. But instead of giving you one, I’m going to take your picture, write down your story, get it translated and posted on an American web site, and then we’ll see over the next month whether the Americans think you should get a loan. Check back with me from time to time.” That would be inefficient, which is to say, immorally wasteful of charitable dollars. And it would be demeaning for Phong Mut. So instead MAXIMA took her picture and story, gave her the loan, and then uploaded the information to Kiva. MAXIMA will lend the money it gets from Kiva to someone else, who may never appear on kiva.org.

We agree. We don’t believe fungibility is a bad thing – but we believe that impact-focused donors should recognize it, and aim for the reasonable goal of supporting strong organizations rather than the unreasonable goal of tracing their dollars to a specific individual.

Cash as a benchmark?

(updated on: ) | by Holden

Franck Wiebe, guest-blogging at Aid Watch, proposes:

in the interest of aid effectiveness and as a starting point, donors could agree not to fund projects unless they can be demonstrated to be at least as good as a cash transfer. Is that too much to ask of aid?

In concept, we think it’s a great idea (and we proposed something similar for evaluations of U.S. charities). However, one major issue for individual donors is simply that we know of no charity that focuses on delivering cash directly to low-income people.

Women for Women and Village Enterprise Fund both are putting about 1/3 of their total expenses toward grants to individuals, a figure as high as we’ve seen anywhere.

If you have no practical vehicle for making cash gifts, it may unfortunately make sense to give to a charity that can’t demonstrate that it’s “beating cash” in terms of impact. This is another case where an individual donor is constrained by the choice of vehicles, not just the choice of programs.