Matching Credits: A Market Mechanism for Discovering Donor Preferences

By Brian Foerster @ 2026-01-18T20:06 (+9)

Considering its size and seriousness, the EA community lacks a great deal of information about itself, particularly with regards to the financial preferences of EA donors. This post proposes a novel[1] way to elicit marginal donor preferences and provide greater incentives for discovery of donor-relevant information without requiring factual validation.

Matching Credits

A matching credit is a financial instrument that provides $1 to a specified charity when the holder donates $1 of their own. The charity for the matching credit is specified on creation and cannot change. Matching credits are tradable and enter the market through an initial offering by their creator. I will assume the creator also operates the sole exchange on which credits trade. The creation of credits requires a subsidy on the part of the creator. This subsidy varies based on how much the credits can be initially sold for, but I believe this will always be at least slightly less than $1.

With these parameters, we can imagine a useful market in which prices reflect how much subsidy participants require to donate, and participants are rewarded for correctly anticipating price movements or uncovering information relevant to pricing.

For example, imagine a market with two participants, Alice and Bob, trading only Fivethought[2] matching credits. Fivethought is a new, small, and poorly understood charity. Alice believes donating to Fivethought is only half as valuable as her best option, so she values the credits at zero. Bob believes Fivethought is 80% as good as his best option and therefore values a $1 matching credit at $0.60[3]. If the market price settles below $0.60, Bob has an incentive to argue publicly for a higher valuation and thereby raise the price.

Several complications must be resolved for the market to function as described. First, participation would need to be restricted. For an EA funder, subsidizing credit creation and operating an exchange is only worthwhile if the market produces information about EA donors, which likely entails restricting participation to the EA community.

Second, limits are needed on how much capital each participant can deploy in the market. This is necessary for several reasons. In order for market outcomes to be reflective of EA funding at large and not the people most interested in the market, participants’ market budgets should be proportional to their historical EA donations[4]. Additionally, total participant capital must be lower than the total redeemable value of outstanding credits. Credits must also only be usable within the system. This ensures that participants are actually making decisions under scarcity, and that they cannot assume all credits will be redeemed.

Third, participants must face restrictions on when credits can be redeemed. The market is only informative if participants cannot immediately exit by redeeming credits. In order to prevent this, redemptions should be restricted to one month in the year, likely December. Relatedly, credits should either never expire or have very long durations. This would be more expensive for the credit provider but would allow participants to express a wider range of views about timing.

Because no market of this specific type has existed, several design parameters remain uncertain. They include the following:

Beyond the implementation details of the market, there are broader questions about its viability and usefulness.

While I think this idea is very intellectually interesting, I’m not sure it has much merit as a practical solution. There are likely cheaper ways to generate socially valuable information about funding decisions in EA, such as surveys or contests. I think better aggregation of already public information could also be cost effective and useful, and I hope to write a separate post about this.

  1. ^

     To my knowledge.

  2. ^

     25% better than Forethought

  3. ^

     The value of a credit is 2 x (relative effectiveness to best option in %) - 1. Below 50% relative effectiveness to best option, the credits have a value of $0.

  4. ^

     This is clearly inegalitarian but is necessary so that the market can reflect the EA funding landscape, which is also inegalitarian.

  5. ^

     Note that with the conditions already specified, prices are also downstream of the marginal buyer’s speculative belief in the future value of the credit. I think this is a positive, but it does complicate things.


SummaryBot @ 2026-01-19T17:24 (+2)

Executive summary: The author proposes “matching credits” as a novel, subsidized market mechanism to elicit marginal EA donor preferences and incentivize information discovery, while expressing uncertainty about whether this approach is more practical or cost-effective than simpler alternatives.

Key points:

  1. The author argues that the EA community lacks information about donors’ marginal funding preferences and suggests a market mechanism that does not rely on factual validation to generate such information.
  2. Matching credits are defined as tradable instruments that provide $1 to a fixed charity when the holder donates $1, requiring an upfront subsidy from the credit creator.
  3. Market prices are intended to reflect how much subsidy participants require to donate, with participants incentivized to surface information or arguments that shift prices.
  4. To make the market informative, the author argues participation should be restricted to EAs, participant capital should be capped and proportional to past donations, and total capital should be less than total redeemable credit value.
  5. Redemption timing should be restricted (e.g., to one month per year) and credits should have long or indefinite durations to prevent immediate exit and allow expression of timing preferences.
  6. The author lists many unresolved design and viability questions and concludes that, while intellectually interesting, the proposal may be less practical than cheaper methods like surveys, contests, or better aggregation of existing information.

 

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