There are few real-world economic transactions that do not involve an element of trust, yet in textbook economics trust is not prominently discussed. In that world, perfectly informed and computationally endowed agents reach optimal, enforceable decisions in continuously harmonising exchanges. Trust is therefore linked to deviations from the textbook ideal: incomplete information, costly enforcement, and computational limitations faced by agents. Trust can then be thought of as an algorithm, in other words, a way of resolving uncertainty in a complex world. In this sense trust may be seen as a form of expectation concerning the behaviour of other agents whose actions and intentions cannot be (fully) observed. This paper pursues this approach by “running the algorithm backwards” and trying to establish what factors led a 19th century provincial English bank to trust different loan applicants. Using a data-set of some 200 loan decisions, and knowing the size of collateral (if any) requested, we develop a method to estimate the probability that the bank attached to each borrower’s promise to repay (i.e., the trust the bank had towards the borrower), adjusting for stages in the business cycle. We then regress this estimated probability on a variety of observable borrower characteristics. We find that trust is not correlated with a priori expected variables, such as borrower’s assets or frequency of interaction. This suggests that trust was built up in other interactions, possibly through social or religious networks, and that the banking relationship reflected information available to bank directors other than what was purely pertinent to the borrowers’ economic conditions. This has strong implications for the allocation of credit to industry in 19th century England
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