The GiveWell Blog

Is fundraising stuck in the world of Mad Men?

(updated on: ) | by Elie

Advertising is based on one thing: Happiness. And you know what happiness is? Happiness is the smell of a new car… It’s freedom from fear. It’s a billboard on the side of the road that screams with reassurance that whatever you’re doing is okay. You are okay. — Don Draper on AMC’s Mad Men. (Video on YouTube.)

I sometimes wonder how Don Draper would react to someone telling him that in 2009, many consumers make their buying decisions based on a product’s quality rather than the feeling a product’s advertising instills in them, or the narrative in which the ad places them.

I picture Don saying, “User reviews? Restaurant ratings? Product testing? No way. That’s just not how consumers are hardwired to think.”

When I see Sasha Dichter and Nathaniel Whittemore saying the same thing, I wonder whether they’re right that the problem is donors as opposed to the information donors have access to.

Sure, it’s possible that donors are hardwired a certain way and may never care about the real impact their gift has. But it’s also possible that many donors would look past happiness and use information on impact – if only it were easier to find. That’s what we’re trying to bring about.

What’s different about Kiva

(updated on: ) | by Holden

Why has Kiva been singled out for so much criticism lately (see GiveWell Board member Tim Ogden’s summary)?

Part of the answer is that Kiva has arguably been misleading donors – but that can’t be the full answer. David Roodman’s original post could never have come about without the fact, as Mr. Roodman puts it, that “the way Kiva actually works is hidden in plain sight.” And our followup analysis on repayment rates was only possible because Kiva makes all its repayment data publicly and easily available. As Elie said in that post, “Similar analysis would be impossible for nearly every other charity I can think of.”

Contrast Kiva with, for example, UNICEF. Kiva makes it possible to trace the path of your donation, to the extent that such tracing is realistic (and it largely turns out to be more along the lines of “you funded a certain MFI” rather than “you funded a certain person”). UNICEF doesn’t even seem to have a breakdown of how much money is going to each continent. We definitely can’t find information on questions like (a) What specific projects are you funding? (b) What is your role in each? (c) What new projects are planned, and where? (d) How is each project going, whom is it affecting, and how?

There are no strange patterns in UNICEF’s numbers because there are no numbers. There are no contradictions because there is no concrete information. And the intent here isn’t to single out UNICEF – it’s merely one of the vast majority of international aid organizations about which we know essentially nothing.

Giving an impression to donors that’s undermined by the facts is a minor scandal. When will complete opacity – simply sharing no information at all – be a major one?

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.