WhatsApp and Encryption

Encryption is in the news; last night you may have noticed WhatsApp inform you that your messages are now “end-to-end encrypted”, and it’s hard not to link this to other events such as repeated attempts by the FBI to get Apple to let them inside its products.

The issue is one of privacy – should your messages to others on your phone be subject to warrants by governments and law enforcement agencies to access them? By adding this level of encryption, WhatsApp cannot see the messages we send, and hence cannot comply with any such warrant. No “wire tap” can be put on your WhatsApp messages, and nobody can “overhear” them, or “eavesdrop” on them any more.

For many people, that’s a great thing – nobody wants others muscling in on private correspondence. Equally, of course, it comes at the same time as another vast tranche of private correspondences regarding tax avoidance and offshore tax havens, were made publicly available in the Panama Papers. Which illustrates the tension: having privacy is great provided we are all nice and law abiding citizens doing nothing wrong. The moment we start doing things that are wrong, then our private correspondences are where we will discuss that wrongdoing.

And of course, on a much more sinister level, it is alleged that terrorists use apps like WhatsApp to communicate in order to avoid the attentions of law enforcement, and hence the desire of the FBI to get hold of the iPhone of a US terrorist. Hence should we afford terrorists more protection by allowing WhatsApp to encrypt like this, or should we, as the UK government has spoken of doing (although not in light of the Panama papers, it might be noted), in the interests of national security?

What could economics shed on to this? One insight economics often affords is that there is an optimal level of everything. We might assert the optimal level of illegal activity is zero, but when there is a private benefit to engaging in illegal activity, and a cost to law enforcement, then there must be a trade off such that the optimal level is not zero. It would take a practically infinite amount of resources to stamp out every kind of illegal activity and as such is impractical. Hence we cannot expect to stamp out terrorism completely, and we must accept that it will always exist. If end-to-end encrypted messaging services exist, they will be used by those engaging in illegal activity. But by and large there will be other ways in which to catch such people in the act of carrying out illegal activities such that impinging on the civil liberties of the masses need not be a necessary cost of making the job of law enforcement much easier.

Equally, a little reasoning from statistical or econometric thinking might help here, too. With any decision in econometrics, there is the risk of a Type I or Type II Error. These are false positives, and false negatives, respectively: incorrectly rejecting something that is true, and incorrectly not rejecting something that is false. With easier ability to eavesdrop on people, will law enforcement agencies use this to pursue the wrong people, people who are simply going about their day-to-day activities without engaging in any kind of terrorist activities? Of course greater surveillance means that catching terrorists will be easier, but will it mean that innocent people are caught up in the machinery set up to catch terrorists?

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.

Macroeconomic Uncertainty

Today’s BBC headline is the Chancellor warning about a “‘dangerous cocktail’ of economic risks” facing the UK economy in 2016, suggesting this year will be the toughest since the financial crisis. It’s more politics than economics as one reads what the Chancellor actually said in a BBC Radio 4 interview this morning, representing expectations management: after all the positive talk about the economy by the Conservatives before the election and since, things have changed somewhat in recent months to challenge that outlook.

One of those things was that GDP growth for 2015Q3 was revised down – not by a huge amount, from 0.5% to 0.4% – but a downward revision nonetheless, and along with other downward revisions has meant that the UK grew significantly less in 2015 than was previously thought.

Another is the continued low oil price which, while great at the petrol pump for the paying customer, has mixed impacts on the UK economy which does export oil.

Perhaps most interest, however, is to also consider the Independent’s take on the Chancellor’s recent actions (as well as rhetoric): it talks about the role that economic forecasts played in the Chancellor’s budget giveaway in the Autumn Statement in November, noting that they might have been “potentially unreliable”. We noted here at the time that the budget giveaway did rely heavily on forecasts for GDP growth: governments need forecasts of economic growth in order to project the tax receipts they will get, and the amounts of benefits they’ll have to pay out, which heavily influences the level of the budget deficit/surplus. If growth now comes in lower than was forecast, this will almost certainly mean that the budget deficit will be worse than expected, casting doubt on the ability of the UK economy to meet the Chancellor’s new Fiscal Charter: to balance the budget in normal economic times.

As well as lecturing Introductory Macroeconomics this term and hence covering issues mentioned here in greater depth, I’ll also be lecturing a third year course on forecasting, where we will discuss the kinds of methods that are used to produce the kinds of forecasts that underly government budget decisions like these. Many of our undergraduate students take placements and graduate roles with the Government Economic Service, which could see them being placed in the Treasury, hence right in the centre of the process of generating those forecasts. What you are doing here as a student could play a crucial role in shaping the future of our country!

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.