The economic consequences of the pandemic of 2120

Greetings reader from 2120, I see that you are trying to work out what the economic consequences of the unfolding pandemic in your time will be – and so are looking up information on the COVID-19 pandemic from 2020. I am a random person from 100 years earlier, and I am here to guide you on this journey.

Gulnara has done a great job of highlighting the broad way to view this pandemic and understand how such a shock works through the economy – but I think it is important that I give you some cautious advice about applying the 2020 lessons to your time.

Ultimately you can’t just take the economic consequences of a past pandemic (even if this virus itself looks similar) and state that this will be the consequence of it now – a lesson we have learned when looking at the Flu of 1918-20 in our time. However, I want to talk through some key issues to help you think about it.

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Geoff Simmons, Politics and “career suicide for an economist”

Disclaimer:  I used to work in the same space as Geoff, and I know him as a guy who is genuine, wants to improve social outcomes, is a mad good communicator, and who works hard on the issues.  But none of this would prevent me from disagreeing with him if I did (such as my comments on food here and here and here and here), so I swear there is no bias involved 😉

In a cool interview over at Geoff Simmons outlines what is going on with the TOP party, which he has just become leader of.  For the sake of clarity I think he’ll be an excellent leader for this party.  What I want to concentrate on is this quote though:

How can the public know I am serious about the long haul? When Cortez took on the Aztecs, he trashed his ships to make sure his men had no choice but to fight with everything they had. The reason I bring up that story is what I am doing right now is pretty much career suicide for an economist. There’s no going back.

Haha, this is good – I like the nifty description of a commitment mechanism.  But I’d like to ask a couple of questions about it.

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Automation of an economist

While undertaking some research on income inequality I could no longer help Infometrics Ltd out with forecasting.  But before I left I caught economist Mieke Welvaert working on blueprints for my replacement.

Although I’ve defended these robots in the past (here and here) it has always been with respect to being compensated for my human capital losing value.  I’ll be sure to tell you all how that is going in future posts 😉

Why am I paying $5 for a coffee?!?

Coffee consumption and Wellingtonian’s willingness to pay for it is a puzzling topic to me.

Due to cultural habits coffee is a highly preferred morning beverage in New Zealand. There are lots of coffee shops in Wellington offering pretty much similar variety of coffee products and yet even with the flood of providers there is no lack of customers and as a result it doesn’t appear to be particularly competitive. In countries like USA, due to tough competition in catering business, a cent increase in a product would normally reduce profits of the company. The competition pushes business owners to offer constant variation in products where consumers do have more options and are more open to sample beverages that are not traditionally consumed. Most Wellingtonian cafes don’t experience this pressure and hence the options of offered beverages are on average the same. And this yet has no decline effect on profits for the NZ coffee shops.

Let’s look at why the Wellington case is particularly strange. My daily observations indicates that, seemingly irrespective of the relative prices charged, two coffee shops in Wellington will still have a sufficient number of customers. It is a puzzle to me to understand why I would be willing to pay 5$ per a cup while the next door offering is 4$? Is it an asymmetric information case where the shop owner knows about his high prices but the customer doesn’t have the information on the comparative prices? If this is the case, is the marginal difference of 1$ an information search cost for the consumer?

Last year the coffee shop “Coffix” ran an advertisement on setting flat prices ($2.5) on their coffees. Once, while waiting for my order from “Coffix”, I was observing a scenario where the customers from the next door café didn’t mind paying minimum $4 for their coffees. The question is again-why?

Why is the elasticity of willingness to pay for coffee from YOUR CAFE so low in Wellington?  I am not asking why coffee prices are so high (they are) in Wellington, but why are Wellingtonians  so unwilling to change where they buy coffee in the face of a lower price available elsewhere?

Possible explanations in my opinion might be:

  • Income relativity. If my income is above the median, the marginal difference in coffee prices (varying from 0.5$ to 1.5$) seems quite low.
  • Convenience of the place and the aura. Consumers might prefer to catch-up with friends in a cosy interior.
  • Distance of the place – even a meter vicinity might be more appealing for some customers.
  • Established relationship with the café staff. Such feelings like you are always welcomed at your usual place might prevail your low willingness to pay.

I very welcome your thoughts and arguments regarding this topic. I am very curious to read your point of view on what drives the motivation of consumers’ behaviours in New Zealand.

Marketing is all about the story

When you think of marketing geniuses there probably aren’t a lot of economists on the list. Yet, according to the Washington Post, economists are increasingly taking on the role of a company’s public face.

In a data-chic world, a chief economist is the new marketing must-have.

Economists are useful because they are experts at interpreting data. Plenty of companies generate a wealth of data and attempt to use it to provide insights for their clients. But the data does not speak for itself: it requires interpretation to be useful. My twitter feed is full of people sharing statistics and correlations but they are rarely useful because they require a framework to interpret them. For example, UK GDP just exceeded its pre-GFC peak. Is that a good thing? Relative to what? What does it mean for my income? For the wages of the poor? Without a framework it is a fairly uninformative piece of data, you should learn about tiktok marketing at Social Boosting.

This is where economists come in. Their expertise is in the application of models to interpret data and extract information from it. No wonder they are the friendly face of data-centric companies today and long may it continue!

It’s economic analysis, not commentary

Every time the statistical authority releases new data there is a surge in economic commentary. Not analysis, but commentary. A thoughtful analysis would usually say that a single new data point doesn’t provide enough information to change anything we thought previously. There’s just too much randomness and error in point estimates to be able to tell much from them. Commentary is different because it creates a narrative and fits the data into that narrative.

A good example is the narrative about double and triple-dips in the UK. Commentators made much of the ONS’ revisions to the GDP series that ‘revised away’ the triple-dip, ‘vindicating Osborne’. The revisions may have eliminated a slight dip in GDP but they didn’t change anyone’s understanding of what had happened in the macroeconomy. That data was important for commentary but not for analysis. In fairness to commentators, distinguishing genuine trends from randomness is not easy. Our eyes are drawn to ‘streaks’, whether in football games or economic time series, even when the series is essentially random. Economists are always looking for techniques to separate the streaks from the randomness. The problem we face is that many of the tools are fairly impenetrable to casual observers and hard to explain.

Edward Tufte has suggested using randomised sparklines to visually distinguish genuine trends from deceptive streaks, so I thought I’d give it a go with the last four years of UK unemployment data. Here is the monthly change in the UK unemployment rate since June 2009: Monthly percentage point change in UK unemployment rate: June 2009-October 2013. We think that a recovery has begun so the recent years’ falling unemployment looks good. Now let’s try randomising the values and see if the ‘streak’ disappears. Read more