In Diamond and Dybvig (1983) banks fulfill an explicit economic function. Banks transform illiquid assets into liquid liabilities.
Model demonstrates three points:
- Banks issuing demand deposits can improve on competitive market by providing better risk sharing among people who need to consume at different random times.
- Demand deposit contract that provides this improvement has a "bad" equilibrium (a bank run) in which all depositors panic and withdraw funds immediately.
- Bank runs have real economic consequences because even "healthy" banks can fail, causing the recall of loans and the termination of productive investment.
Diamond and Dybvig (1983) argue that demand deposit contracts can help achieve the same optimal risk sharing arrangements that can be made when liquidity needs are publicly observable. However, the ability to achieve optimal risk sharing via demand deposits comes at the cost of introducing the possibility of bank runs.
Demand deposit contracts can be improved upon if the bank has some information about the distribution of liquidity needs amongst its depositors. If the normal volume of withdrawals is known and not stochastic, then writing demand deposit contracts that call for the suspension of convertibility (i.e., a suspension of allowing withdrawal of deposits) in the event of a bank run can actually prevent a run from happening along the equilibrium path. However, the situation is very different in the event that the volume of withdrawals is stochastic. In this case, bank contracts cannot achieve optimal risk sharing (although deposit contracts with suspension are still an improvement over the basic demand deposit contracts).
Diamond and Dybvig (1983) conclude by demonstrating how government deposit insurance can provide improvements over both basic demand deposit contracts, and demand deposit contracts with suspension in the event that the volume of withdrawals is stochastic (i.e., liquidity needs are randomly distributed). In fact, demand deposits with government deposit insurance can achieve the full-information optimum even in this most general stochastic case.
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