Feeding America acts as a remote clearinghouse for food donations from big businesses such as Tyson, Kraft, ConAgra, and Wal-Mart, which donate surplus food. When Tyson has a truckload of chicken on offer in Little Rock, Arkansas, Feeding America contacts a food bank to go get it—or at least that’s how it used to work.
Traditionally, Feeding America assigned these truckloads, without much consideration of content, on a sort of rotation system. The food banks could accept the food and pay transport costs to receive it, or they could decline it. In an attempt to get the biggest share to places with the biggest needs, the organization set allocation amounts for each food bank based on the bank’s size and the poverty level of the population it served. It also took into consideration proximity. The organization tried to figure out who needed what, in part by guessing.
But Feeding America recognized it had several problems with this system. Food banks often accepted food that they did not want. Idaho banks were offered potatoes they did not need, while others took in sodas, which were both expensive to transport and low in nutritional value. If a food bank declined a donation, the food it declined counted against its future allocations, plus it created potentially awkward situations with donors that Feeding America wanted to keep happy. Meanwhile, a food bank desperate for protein, or hitting a dry spell with local donors, had no efficient way of finding out which nearby sources had excess. Feeding America wanted to send the most food to the neediest places, but it didn’t know where those needs lay on any given day.
A traditional market wouldn’t have worked—taking the food banks’ money would have run counter to Feeding America’s mission. The task force proposed a different system, the fake-money market. Feeding America gave each food bank an allocation of shares (the fake money) based on the same size and poverty-level criteria. Descriptions and locations of every truckload of food the organization sourced were entered into a website, where auctions went live twice a day. At each auction, food banks used their fake money to bid on what they wanted.
Feeding America found that it could quickly match corporate donations to the food banks that needed them most. Because it knew placement had become more efficient, it accepted more donations, thereby increasing food supply. Food banks in turn added their own overstocks of locally sourced goods to the market.
The fake-money market turned out to be a tool that improved both efficiency and fairness relative to Feeding America’s old system of assigning donations. The food banks reported greater satisfaction with the Choice System, even though a quarter of them collected half as many pounds of food as they would have under the old allocations. In a demonstration of efficiency, they saved their fake money to buy the higher-quality, more-expensive goods that they needed most.
The market accurately priced goods to reflect demand from its consumers, a group whose concerns for spoilage and nutrition make them distinct from most grocery shoppers. In Feeding America’s market, peanut butter and noodles were the most desired foods. Truckloads of pasta and rice were expensive, while milk and cheese were relatively cheap. A single pound of cereal sold for more shares than 120 pounds of produce. In fact produce often traded at negative values. If Feeding America received a donation of broccoli, say, and the demand for that was low, Feeding America gave food banks points to take the broccoli.
Feeding America's supply
Prices and amount of items, 2005–2011
How to make fake-money markets work
The consistency of the Feeding America auction is crucial, says Prendergast. Fake-money markets require busy and ongoing trading that participants trust will continue day in and day out, with more good spending options consistently coming to auction. Consumers and food banks must have that faith, he explains, or they would have every incentive to immediately spend their entire account, regardless of need. When that happens, market efficiencies are lost as bidders dump soon-to-be worthless points, or attempt to game the system. Feeding America’s Choice System works because members have opportunities to spend their shares on 50 or so truckloads of food twice daily—today, tomorrow, and for pretty much every workday to come.
These markets typically break down quickly, undercut by myriad unpredictable, situation-specific issues of efficiency and fairness. In fact, creating an efficient fake-money market for a real-world problem is so difficult that there are few exemplary models to follow.
Chicago Booth’s Eric Budish and Wharton’s Judd B. Kessler touch on some of these design issues in a study of one of the more successful fake-money auctions, a 20-year-old course matching system at the Wharton School of the University of Pennsylvania.
Traditionally, some top schools, including Harvard, Wharton, and Chicago Booth, used fake money in what’s called a bidding points auction. Each school followed the same basic system: a multiround auction gave seats to the highest bidders, ostensibly ensuring that students who needed the course most got spots. But students could easily find themselves outbid on every course they selected, and some found ways to game the system.
But in research published in 2011, Budish proposed that schools could improve their ability to give students the schedules they wanted with several tweaks to the auction process and the sorting that followed. A school would give all students similar budgets of artificial currency, and instead of “bidding” on courses in an auction, students could instead report their preferences for courses. A computer would find the market cost of each course based on the bids and determine the most preferred schedule each student could afford.
Budish says the outcome is efficient, because each student gets the schedule she likes best out of the affordable options—and fair, because each student starts with approximately the same budget. Those budgets are similar rather than exactly equal in order to break ties. Say every student were originally given 10,000 points, and they all wanted to take the same course. In that case, it wouldn’t be possible to establish a market price for that class. But when the school varies budgets by even a handful of points, some students spend all their points on that one in-demand class, and others who can’t afford to do so spend their budgets on other classes they still want to take. “So the outcome is as fair as you can get,” Budish says. “That’s the beauty of the market.
“Also it aligns incentives,” says Budish, who claims that students have an incentive to honestly report the courses they want to take, rather than bid strategically as they did with a bidding points auction. Computers evaluate millions of possibilities when doing the tough computational work that students used to have to do as part of strategizing.
Budish and Kessler tested the updated market design in a lab, and Wharton decided to implement the new system in 2013. In a study of the results, the researchers find evidence that suggests the changes are working as intended. The Financial Times and Bloomberg Businessweek reported that Wharton’s new Course Match was a success that vastly increased the chances that a second-year student could enroll in a popular course. Other schools, including Booth, continue to use a bidding points auction.
But attention to detail in the market’s set-up—in Feeding America’s case, dozens of details specific to the project—is particularly critical. “It may be that it is not the broad match of the concept of fake currency to the problem that generated its likely success, but rather the myriad of small details that got it over the line,” Prendergast says.
Budish came to the same conclusion when revising Wharton’s course-allocation market. There’s no one-size template for establishing a fake-money market that will work. “In market design,” he says, “tiny details make a huge difference.”
For Feeding America, an important wrinkle was a clause that redistributed to all members the sales proceeds at the end of each day. At midnight, any fake money spent on a given day was split up and returned to food banks. That went a long way toward assuaging disappointment a food bank might have felt after losing out to a higher bidder, as everyone benefited from the higher price paid.
Also crucial were provisions for splitting bids, buying on credit, and collecting shares for hard-to-move goods. The details included a fairness committee, set up to handle any grievances with the system. So far the committee has not been tapped, but it nevertheless helped gain member trust, which in part led to the success of the system.
What else fake money could do
Nonprofits and similar institutions tend to determine need and then use centralized systems to allocate the goods and services they offer. Prendergast’s work offers a way to judge if market forces could better serve this group’s goals. His paper includes the first model for empirically evaluating welfare implications of moving from central assignment to a market system, and it can be adapted for research beyond food banks.
He has been mulling over several situations that might benefit from fake-money trading. For example, he says, the US Department of Defense takes thousands of satellite pictures a day that might be useful to many government agencies. There are far too many images to study in a day, and almost infinite possibilities for directing the cameras. Defense needs a way of judging where it should be looking, and where to assign employees for any specific project. A suggestion made to Prendergast was that a market in which other departments that want the images are assigned a fake currency to bid for control—perhaps time slots for use of the technical and human resources—could help the department prioritize analyses. The highest bids would represent the most-urgent demand.
Similarly, scientists vying for time with coveted pieces of equipment, perhaps a high-powered telescope, might be required to bid for time slots to use it. The institution or department holding it would issue points to each potential user, either in equal amounts or weighted to favor certain users. The scientists decide how to spend the points: perhaps all at once if a time slot is critical to research, or in smaller allocations to allow multiple uses.
Employers can use fake-money markets to allocate popular vacation days, particularly in places such as hospitals that require 24-hour coverage through holidays. Each employee is assigned a certain number of points for the year to use in holiday auctions. A worker who bids high for Christmas Eve off will get it, but has fewer points left to compete for Thanksgiving and New Year’s.
The common factor in all of these examples is the lack of a monetary profit motive. The institutions that hold the goods or vacation days simply want to get them into the places where they are most needed, but determining that on any given day is difficult. A market, even with fake money, prices products accurately for the moment, and then changes those prices quickly as needs change. The highest price is a good reflection of the highest need, even when values fluctuate constantly. The goal is to find an allocation that is efficient and fair—it’s not to profit from the scarce resources.
Prendergast says fake-money markets could even be further applied to the broader food-bank universe. Fake-money credit cards could help mitigate discrepancies in the quality of food offerings around a city. For example, the Chicago Food Depository, which sources food for area food banks much as Feeding America does nationally, would load the most points on cards for members that don’t attract high-quality donations from wealthy neighbors. The food banks would use the cards to shop at the depository’s warehouses. And even if the money involved is fake, the impact would be real.
- Eric Budish and Judd B. Kessler, “Bringing Real Market Participants’ Real Preferences into the Lab: An Experiment That Changed the Course Allocation Mechanism at Wharton,” NBER working paper, July 2016.
- Canice Prendergast, “The Allocation of Food to Food Banks,” Working paper, May 2016.
- Eric Budish, “The Combinatorial Assignment Problem: Approximate Competitive Equilibrium from Equal Incomes,” Journal of Political Economy, December 2011.
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