Measuring ourselves

We take measuring ourselves incredibly importantly. This is true at the individual level (we want to know whether we fit in), but also at the national level – how well are we doing relative to, say, the Eurozone countries?

In order to be able to say anything at all about the latter, we have to have some measurements. The standard measurement we use when it comes to entire economies like the UK, or like France or Germany or the USA, is Gross Domestic Product, or GDP. It’s the value at market prices of all the goods and services produced in an economy over some period of time. It’s everything we make as a country, at the value we place upon it – very broadly speaking.

That it’s not a good measure of welfare is almost universally well known. This article from a very interesting blog (well worth a read if you’re feeling like procrastinating but want to feel like you’ve been at least a little bit productive) notes that by and large innovation doesn’t appear in the statistics either. Relative to even our parents, but certainly our grandparents’ eras, ours is one of mass variety, it’s argued, and this is the result of innovation – loads more great products for us to buy and enjoy.

The very basic economics says this should be good – we can find the products that most suit us in all areas of our lives, and be happier than if we were more restricted in the choices we could make. However, choices have opportunity costs, and opportunity costs may lead to regrets – if one makes one choice, one cannot have done the other similarly enticing thing. If innovations are sufficiently small (the difference between, say, the latest Android phone made by Samsung and the latest one may by LG), we as consumers cannot realistically be expected to be well informed about these kinds of differences and what they mean.

Anyhow, the bottom line is that we use GDP, and we use it in all sorts of ways (not least to determine how much the government should spend). It’s a vitally important statistic, and a huge amount of effort goes into producing it (effort that shows up in GDP) – and we should be aware of its shortcomings, without necessarily advocating its replacement. It’s not clear how any other measure of well-being could appropriately factor in the amount of choice we have, and how differently it affects each of us.

Hate quants? But it’s awesome!

If you’re the average first year undergraduate student (yes, I know, nobody’s really the average, but anyhow) you’ll either really hate quants (econometrics), or you’ll feign dislike in order to avoid seeming to be a geek.

My hope is that as you learn more about economics, you’ll learn to enjoy and even love the subject more, but also realise that data, and hence econometrics, is utterly central to all of it. All of the theories we teach you in micro and macro need to be verified out there in the real world, and the only way to do that properly is to collect data about the real world. Testing theories properly also requires that we learn appropriately what the data can, and is, telling us. This bit is econometrics. It’s absolutely essential if we’re going to determine which economic theories are worth taking seriously, and which we can safely discard.

Data can be pretty awesome at times, too. For example, in this day and age betting is ubiquitous on all kinds of events – see www.oddschecker.com/ if you want to get some sense of this. Data on the bets multiple bookmakers offer for events as diverse as the Premiership (Leicester City, really?!), and the next elimination on Strictly Come Dancing. These are predictions, or forecasts, about unknown as yet future events. Economic activity relies entirely on predictions about future events – how many sales will my company get with that new product, will that job be just right for me, should I take out insurance on my new phone, and so on…

If you’re concerned about more conventional data though, and the important messages we can learn from a proper and detailed look, here’s an example from yesterday on earnings. Hopefully it makes the point really clear: it’s vital for our good as a nation, and as a society, that we know about our statistics. Stagnant earnings growth that spawned the whole “cost of living crisis” (however real it felt for your dear lecturer over that period ;-)) may well have been bad statistics caused by a misleading calculation of the average that treats new entrants to the labour market, on low wages, equally to existing members of the workforce who are receiving more “normal” pay rises. Worth a read.

It makes the bigger point though: there’s an issue with how our statistics are calculated, and that needs to be investigated. Thankfully that is happening; I’m no fan of the Chancellor of the Exchequer, but this is one of his better moved by some distance: he has set up a review into how statistics like GDP are being calculated, particularly in this day and age of masses of data (think about how much data Tescos and Sainsburys must have on you). Dry stuff I’ll grant you, but this section is particularly relevant for the first week of term after Christmas:

The Review was prompted by the increasing difficulty of measuring output and productivity accurately in a modern, dynamic and increasingly technological economy. In addition, there was a perception that ONS were not making full use of the increasingly large volume of information that was becoming available about the evolution of the economy, often as a by-product of the activities of other agents in the public and private sectors. Finally, frequent revisions to past data, together with several recent instances where series have turned out to be deficient or misleading, have led to a perception by some users that official data are not as accurate and reliable as they could be.