Market design

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Market design is an economic discipline that deals with the design of market processes with regard to previously defined goals. The Alfred Nobel Memorial Prize for Economics was awarded to Alvin Roth and Lloyd Shapley in 2012 “for the theory of stable distributions and the practice of market design” . Mechanisms developed by market designers must endure under real conditions. Axel Ockenfels defines market design as follows:

“Market design is the art of designing institutions in such a way that the behavioral incentives for individual market participants are in harmony with the overarching goals of the market architect. Such goals can be maximizing revenues, efficiency or liquidity, minimizing costs, disclosing private information, etc. "

- Axel Ockenfels

To achieve these goals, Marktdesign takes an interdisciplinary approach. Methodologically, a distinction can be made between the design of two types of markets. Firstly, normal markets where prices can be used as coordination instruments. On the other hand, there are markets where this is not possible or desirable. This is also referred to as matching markets. Problems in both markets are explained below and market design solutions are presented.

Normal markets

In many markets, the price mechanism leads to a distribution / allocation of goods, which is desirable from a social point of view: A good is traded when the price is less than the buyer's willingness to pay for the good and greater than that of the seller. If there is asymmetrical information about the value of a good in the market , an auction can be used to determine prices. Auctions are used in many different contexts, such as when issuing government bonds , awarding public contracts or in private procurement . Auctions also play an important role in the organization of the electricity market, the award of mobile phone licenses or the award of take-off and landing rights for airlines. The task of the market designer is to design the central allocation mechanism in such a way that the overarching goal of achieving an efficient distribution of goods, keeping barriers to market entry low and preventing collusion between the participants is achieved.

Frequency auctions

Frequency auctions for mobile communications licenses represent an important field of application for auction design. A government uses an auction to auction licenses that allow the owner to transmit signals over certain frequency ranges of the electromagnetic spectrum . The complementarities between the licenses represent a particular challenge.

A bad auction design can lead to collusion between companies and make it difficult for new bidders to enter. The corresponding auction design of a simultaneous ascending auction led to inefficiencies in the first UMTS license auction in Germany in 2000. The most common goals in such state auctions are efficiency, transparency, simplicity and fairness. Frequency auctions are efficient when the spectrum is put to its best use. On the other hand, it is short-sighted when states want to generate high auction revenues by creating monopolies .

In contrast to the simultaneous ascending auction, a covert (first price) auction does not offer any possibility for signaling or punishment in order to support agreements. In addition, the entry of weaker bidders is encouraged as they know that they have a better chance of winning. But covert auctions can also have disadvantages. The ability of weaker, lower-valued bidders to outbid stronger, higher-valued bidders can be detrimental as it increases the likelihood of an inefficient outcome. This example has shown that there is no one-size-fits-all solution to good auction design. For this reason, market designers deal intensively with auction theory in order to design market-specific improvements.

Matching Markets

There are also markets in which it matters for market participants which partner they trade with. In these matching markets, a price mechanism alone does not lead to an efficient allocation. The labor market is a classic case: it is not enough for an employee to be willing to work in a company for a fixed wage. Only when the employer has also decided in favor of the employee can the employee take up his job. In some markets, the price is also socially undesirable as a coordination tool. In the health and education sectors, especially when allocating school and university places or allocating donor organs, allocation mechanisms without prices are mostly used as a coordination instrument.

Education markets

Allocation of school places

A classic matching situation is the allocation of children to school places. The aim here is to optimally distribute children to a limited number of school places , taking into account their preferences via schools. This requires an allocation mechanism that regulates which child is accepted at which school. Such a mechanism must guarantee that no school accepts more children than there are places and that all children are assigned to a school.

A proven solution to this problem is the student-proposing deferred acceptance mechanism developed by David Gale and Lloyd Shapley, which is also known as the Gale-Shapley algorithm . At the beginning of the process, parents indicate their preferred schools in the form of a ranking list. Each school in turn determines the priority of the individual children who have applied for a place at the school. Each school therefore has a ranking of the children in order of priority. The assignment implemented by the algorithm is stable and strategy-safe. In this context, stability means that there is no child-school pair ( k , s ′) in which a child k would rather attend school s ′ than the school s assigned to it and at the same time have a higher priority at school s ′ as one of the children admitted there. Therefore, stability also implies that the priorities set in advance are always taken into account. An algorithm is strategy-safe when it is optimal for parents to state their true preferences.

When it was first used in the USA, the matching algorithm was able to improve the school placement process in Boston and New York. In Boston, the allocation was already regulated by a central mechanism. However, this gave parents incentives to cover up their true preferences in order to improve their position through strategic behavior. After implementing the Gale-Shapley algorithm, parents were able to truthfully state their preferences without fear of being at a disadvantage. In the case of New York, the situation was exacerbated by the fact that in the allocation procedure originally used, up to 30,000 students could not be allocated to a school specified by them. Whereas 17,000 pupils received offers from several schools. This imbalance could be remedied by the introduction of a central clearing house using the Gale-Shapley algorithm.

In Europe, the Gale-Shapley algorithm is used in Hungary, among others. Since 2000, the places at secondary schools have been distributed in a centralized process via a government information center, the so-called KIR. The students have the opportunity to create a ranking of the various school programs according to their preferences. There are no restrictions on the length of this list. The schools, in turn, form their lists of preferences based on grades and the results of an entrance examination or an application interview. The assignment is stable and strategy-safe in accordance with the properties of the Gale-Shapley algorithm. The acceptance rates and detailed statistics are provided by the KIR. According to a central survey in 2011, the number of applicants was 91,580, 96.2% of them were admitted to the main process, 75.6% of them to their first choice and 95.1% of them to one of their first three elections.

Labor and entry-level labor markets

Practical training in medical studies

In the USA, medical students must complete a practical phase in order to successfully complete their medical studies. Before a market design solution was implemented, hospitals competed for medical students by making entry-level offers earlier and earlier. As a result, students received and were able to accept offers early on in their studies before they could consider all options. This resulted in an inefficient allocation. In this case, one also speaks of market failure .

In order to make the allocation more optimal for hospitals and students, the National Resident Matching Program (NRMP) , which coordinates the central allocation, has implemented a new allocation algorithm based on the Gale-Shapley algorithm. In the NRMP award procedure, medical students indicate their preferences via the hospitals in the form of a ranking list. The hospitals also create rankings with their preferences about the medical students. The algorithm uses these preference lists to create stable matchings for students and hospitals. That is, each student is assigned to a hospital that he prefers most over other hospitals and at the same time prefers him over other students. Each year the NRMP distributes more than 20,000 students using this algorithm. He is considered to be one of the most successful and well-known examples of market design in the field of matching markets.

The requirements for the algorithm have changed fundamentally as more and more women started studying medicine, and accordingly more couples were looking for apprenticeships in close proximity. The previous deferred acceptance algorithm returned matchings that were not stable with respect to pairs. Therefore, in 1995 the board of the NRMP commissioned the development of a new algorithm. The Roth-Peranson algorithm has been used since 1998. This hybrid algorithm takes into account the complementarities that can arise from pairs and selects from all stable allocations, if any, the one that is optimal for the students.

Kidney swap

Why good market design is important in matching markets can be illustrated particularly drastically using the example of organ donation , which is also a form of matching. Because although patients have the prospect of a living donation of an organ (usually a kidney) from their partner or family members / friends, there is a possibility of incompatibility.

The Nobel laureate in economics and matching pioneer Alvin Roth has therefore brought up so-called ring swaps for mutually incompatible donor / recipient pairs. A living donor who is part of an incompatible couple donates their kidney to the needy recipient of a second incompatible couple. In exchange for this, his incompatible partner also receives an organ donation from the donor of the second couple. At least two couples must participate in such an exchange. However, it is also possible to exchange between several mutually incompatible pairs. In 2003 a kidney transplant was performed for the first time with three couples. Meanwhile, chain deceptions are also taking place in the USA, which are initiated by an altruistic donor without a partner. With these chains it is possible that a single donor kidney could save more than 50 lives.

The algorithm behind this Top Trading Cycles and Chains (TTCC) exchange mechanism determines a ring or chain exchange, so that there is no other ring exchange for any group of donors and recipients that they find better. Roth found out that the possibility of cross and ring donations leads to significantly more live kidney donations than the pure pair donation. Especially when an altruistic donor is at the beginning of a chain, he can initiate an exchange that goes through mutually incompatible couples and can end in a donation for a patient on the waiting list. Thus, more people are being helped than would be the case without such an exchange mechanism. This also increases the degree of compatibility between the donor organ and the recipient. Even patients without an exchange partner benefit from ring fooling because the competition for post-mortem donor organs decreases.

Individual evidence

  1. ^ The Prize in Economic Sciences 2012 In: nobelprize.org , accessed February 14, 2018.
  2. ^ Axel Ockenfels: Market design . In: Springer Gabler Verlag (editor) . Gabler Wirtschaftslexikon, August.
  3. ^ Peter Cramton: Market Design: Harnessing Market Methods to Improve Resource Allocation . 2010.
  4. ↑ a b Paul Klemperer: How (not) to run auctions: The European 3G telecom auctions . In: European Economic Review . 46, No. 4-5, 2002, pp. 829-845.
  5. ^ Peter Cramton: Market Design in Energy and Communications . 2015.
  6. ^ Alvin E. Roth: The Theory and Practice of Market Design . In: Nobel Media AB . 2010.
  7. ^ David Gale, Lloyd S. Shapley: College admissions and the stability of marriage . In: American Mathematical Monthly . 69, nos. 9-15, 1962.
  8. ^ A. Abdulkadiroğlu, T. Sönmez: School choice: A mechanism design approach . In: American Economic Review . 93, No. 3, 2003, pp. 729-747.
  9. ^ A. Abdulkadiroğlu, PA Pathak, AE Roth, T. Sönmez: The Boston public school match . In: American Economic Review . 95, No. 2, 2005, pp. 368-371.
  10. ^ A. Abdulkadiroğlu, PA Pathak, AE Roth: The New York city high school match . In: American Economic Review . 95, No. 2, 2005, pp. 364-367.
  11. ^ Péter Biró: Matching Practices for Secondary Schools - Hungaryh . MiP Country Profile 6. 2012.
  12. ↑ Közoktatási Információs Iroda (Information Office on Public Education) . Retrieved February 12, 2018.
  13. ↑ Felvételi a középfokú iskolákban a 2010/2011 tanévben (Admissions to secondary schools) . Retrieved February 12, 2018.
  14. ↑ AE Roth, E. Peranson: The redesign of the matching market for American physicians: Some engineering aspects of economic design (No. w6963) . In: National bureau of economic research . 1999.
  15. ↑ RW Irving: Matching medical students to pairs of hospitals: a new variation on a well-known theme . In: ESA . 1461, 1998, pp. 381-392.
  16. ↑ a b A.E. Roth: Who Gets What-and Why: The New Economics of Matchmaking and Market Design . In: Houghton Mifflin Harcourt . 2015.
  17. ↑ An 8-person chain in Chicago, with news coverage . 2017. Retrieved February 12, 2018.
  18. ^ Another long kidney chain: 56 people, 28 transplants . 2013. Accessed February 12, 2018.
  19. ^ AE Roth, T. Sönmez, MU Ünver: Kidney exchange . In: The Quarterly Journal of Economics . 119, No. 2, 2004, pp. 457-488.