Imagine you are running a team of salespeople and, as a highly motivated manager, you are working on strategies to improve the performance of your team. After a close study of your team’s client call reports you realise that the high performers in the team consistently meet with their clients more frequently than the poor performers. Eureka! You now have a plan: you set targets for the number of times your team should meet with clients each week. Bonuses will depend upon performance against these targets. Confident that your new client call metric is highly correlated with sales performance, is objective and easily measurable, you sit back and wait.
Six months later, it is time to review the results. Initially you are pleased to discover that a number of your poor performers have achieved very good scores relative to your new targets. Most of the high performers have done well also, although you are a little disappointed that your best salesperson came nowhere near the “stretch target” you set. You then begin to review the sales results and find them very puzzling: despite the high number of client meetings, the results for most of your poor performers are worse than ever. Not only that, your top salesperson has had a record quarter. After you have worked out whether you can wriggle out of the commitment you made to link bonuses to your new metric, you would do well to reflect on the fact that you have fallen victim to Goodhart’s Law.
According to Goodhart’s Law, the very act of targeting a proxy (client meetings) to drive a desired outcome (sales performance) undermines the relationship between the proxy and the target. In the client meeting example, the relationship clearly broke down because your team immediately realised it was straightforward to “game” the metric, recording many meetings without actually doing a better job of selling. Your highest performer was probably too busy doing a good job to waste their clients’ time with unnecessary meetings.
The Law was first described in 1975 by Charles Goodhart in a paper delivered to the Reserve Bank of Australia. It had been observed that there was a close relationship between money supply and interest rates and, on this basis, the Bank of England began to target money supply levels by setting short-term interest rates. Almost immediately, the relationship between interest rates and money supply broke down. While the reason for the breakdown was loosening of controls on bank lending rather than salespeople gaming targets, the label “Goodhart’s Law” caught on.
Along with its close relatives Campbell’s Law and the Lucas Critique, Goodhart’s Law has been used to explain a broad range of phenomena, far removed from its origins in monetary policy. In 18th century Britain, a crude form of poll tax was levied based on the number of windows on every house. The idea was that the number of windows would be correlated with the number of people living in the house. It did not take long for householders to begin bricking up their windows. A more apocryphal example is the tale of the Soviet-era nail factory. Once central planners set targets for the weight of nail output, artful factory managers met their target by making just one nail, but an enormous and very heavy nail.
Much like the Law of Unintended Consequences, of which it is a special case, Goodhart’s Law is one of those phenomena that, once you learn about it, you cannot help seeing it at work everywhere.