Blog 64: Attractive Service Economic Index by Xavier Lo, FIA, FRM, MBA

A bit of a light-hearted post today. The Attractive Service Economic Index [侍應吸引力經濟指數]is a somewhat offensive [有攻擊性] and inaccurate [唔準確] way of measuring the state of the economy. However, I would like to use this example to show deliver a message.

Firstly, what is this index? It basically states that the attractiveness [吸引力] of waiters and waitresses in restaurants is inversely correlated [負相關] to the state of the economy [經濟狀況]. In other words, if you see a lot of good looking servers, the economy is weak. The idea is that during a weak economy, good-looking people will struggle to find work in industries like filming or modelling [模特兒職業], hence they would need to work as servers. I’m sure most of you can see how this can be offensive, but also how this index is quite flawed. Besides the fact that attractiveness is subjective [主觀], it also assumes that serving in restaurants does not require any skill and that everyone can just go in and do it if they wanted to.

However, we can learn something from this index. I don’t think it would have been an intuitive thought to link the economy’s health and attractiveness of workers. In insurance, we can find hidden correlations between random things too. For example, you might see more accidents with cars that have a certain wheel axle [輪軸]. Or you might see that some parts of your country have higher insurance renewal rates [續訂率] than others. What you need to do is dig deeper and really find out what is really causing that correlation. As you remember, correlation does not equal to causation! It does not make sense to come up with a theory unless you really understand the root cause.

One more lesson is that every time you see a theory or hear about a new rule, just question the assumptions. Its not that we can’t have any assumptions in any model, but we just need to be fully aware of these, and know how they impact our calculations. I’m going to end here on a good note before I offend anyone!

Qualified fellow actuary (in UK and Hong Kong), Financial Risk Manager, and MBA graduate (listed on the Dean's List) with a passion for insurance, data science, and analytics. Experienced in a broad range of insurance roles (pricing, capital modelling, reserving, ERM), along with a touch of knowledge in banking. Member of the General Insurance Committee (2021), Actuarial Innovation Committee (2019 - 2021) in ASHK.

    1 Comment

  1. November 27, 2021

    Thank you, This was a good read!

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