The South Park Gnomes

It was another day, we were doing zapping, when passing by we came across an episode of the animated series South Park. As in previous days of laziness we had already found some economic theories mixed into the jokes - especially the apparently meaningless ones -, we decided to put ourselves in economist mode, explain to our roommates that we were now working, and we went on to look exhaustively at the episode that was starting titled “Gnomes.”

For those who did not have the opportunity to see it, or if you want to see the episode again, you can find it in the following link. If not, you can choose to access a summary of the chapter here: link

The program reminded us of some publications from economics and finance sites that, with a style similar to that of this section, used said chapter as an example to refer to some discussions of economic policy, and also to the behavior of microentrepreneurs. However, in this article we will use it to explain correlations.

In the episode in question, the four children who play the main characters of the series are forced to do an essay on current events, along with Tweek, a strange and nervous child. However, this guy suggests they make it about the mysterious gnomes who steal her underwear.

In the series, it is shown that the reason for this theft is to generate economic benefits, as one of the gnomes says “stealing underwear, big business.”

These dwarfs in a friendly manner reveal to the 4 children the business plan behind the operation, which consists of 3 phases:

• 1 phase: steal underwear
• 2 phase: ?
• 3 phase: Earnings

This is exactly what we wanted to get to. The joke that can be interpreted in various ways, serves as a kick to explain the measurements and y correlations.

Underpants as index

The sale of men's underwear is in fact a well-known indicator of the economic cycle, which is even consulted by economists such as Alan Greenspan, former president of the US Federal Reserve (see Men's Underwear Index on Wikipedia).

What is this about? Although it is a product whose sales remain relatively stable most of the time, when a major economic crisis occurs they suffer a very sharp drop: hence the importance of this indicator.

The above means that when one does not have income, one decides to adjust what cannot be seen, so to speak. In the same way, when a recovery begins, sales of underpants grow.

This indicator has the advantage that it is very simple to construct, therefore, the data of the Underpants Index They are usually available long before the main economic variables, which require further elaboration (not to mention GDP).

This advantage is what really gives importance to the indicator. Measurements of other variables will come a few months after the recovery has begun. Some will even begin to rebound a few months later. Underpants sales react and measure quickly. We could say that they measure the economy minute by minute.

Underpants for cement

In Argentina, several economists have stated in recent days that there are some signs of economic recovery. Indicators such as cement shipments or sales of some items show an improvement, but the reality is that this supposed recovery (or at least, deceleration of the decline) is very difficult to confirm. In fact, the latest activity data available continue to show declines in year-on-year terms.

Precisely, the difficulty lies in the lack of economic data in real time to be able to carry out such an evaluation. For example, in the case of industrial activity, at the date of writing this report, the last available measurement of the Industrial Monthly Estimator It dates back to the end of January.

So yes, in a hypothetical situation, a recovery occurs starting in February, we will have to wait until the end of March to be able to observe it.

This demonstrates the need for different indicators that can be quickly calculated and analyzed to draw conclusions. And it is not only important for the public sector, but also the private sector.

Imagine a private investor who manages to anticipate an economic recovery could, therefore, advance decisions and take advantage of his competitors - the example becomes extreme in the stock market world. The underpants index provides very partial information, that is not in dispute, but it has the advantage of immediacy.

Now, although the color data may be interesting, it is not the only topic that was sought to be discussed in this article. Let's go back to the episode of the animated series again: why do gnomes steal underwear? Apparently, because they interpreted the data incorrectly.

Let's imagine one of the gnomes behind a monitor observing the data on the performance of the North American economy. What would they observe? That every time underwear sales grow, the economy is on the verge of an economic recovery.

Or conversely - even clearer - that just before sales of everything begin to fall (which will then lead to a reduction in production), sales of underpants fall. Thus, they assume that more underwear generates a better performance of the economy, only because there is a correlation between both variables.

Causality?

This was where we wanted to get to, the problem is that gnomes cannot differentiate a simple correlation between two variables from a causal relationship between them. Two series or indicators are correlated when they vary in a similar way (or negatively correlated, if they vary inversely).

But from no point of view does that imply that one is necessarily the cause of the other. This distinction is fundamental, and can lead to many misinterpretations. The thing is that we encounter these types of statements every day.

In fact, many times ordinary people or even professional journalists assume that any correlated series can be presented as evidence of some type of argument that they defend.

In view of taking the argument to absurdity, it must be taken into account that some correlated series do not make the slightest sense.

E.g., the following graph shows a very tight correlation of the number of people who drowned after falling into a pool in the US against the number of Nicolas Cage appearances in films per year (to see more examples of spurious correlations, you can visit the following link : http://www.tylervigen.com/spurious-correlations).

Note also that the correlation between two variables refers to the degree of variability associated with each one, rather than the level that each of them reaches in the measurement.

What is even more important is that when there are two correlated series, in many cases it is difficult to establish which variable generates the others, that is, which causes which.

E.g., if we examine the data on crimes committed and police assigned in each neighborhood, we may see that more crimes are committed where more police are assigned. Can this be taken as evidence against the effectiveness of the police? Probably not. On the contrary, the government will most likely decide to send more police where more crimes are committed.

In this regard, one of the best-known anecdotes in the history of economic thought is the peculiar theory of William Stanley Jevons (1835 – 1882), who argued that economic crises do not occur by chance but by causality.

In his book “Trade crises and sunspots”, Jevons presented statistical data where he related sunspots to economic cycles, under the hypothesis that sunspots affect the climate, and therefore the crops.

Recently, a news article cited a study according to which thin people tend to consume more chocolate than those who are overweight. Don't get excited, it's probably not that chocolate makes you lose weight, but that those who are already obese try to avoid it.

In short, we have several options, it could be that variable A causes B, or that variable B causes A, or until there is a variable C that could be an intermediate variable or the cause of both A and B, and finally We also have chance (which is often very difficult to rule out).

When serious economic research is being carried out to estimate the effects of a given policy measure, we are faced with a problem of causal inference. That is, we want to evaluate the effect of the program on the results. It is essential then to try to isolate the effects, in order to establish to what extent that program alone has contributed to changing a result.

There are several techniques, but the fundamental thing is that, if you want to test, for example, whether police officers reduce crime, you must analyze an assignment of police officers that is not correlated with crime levels, because otherwise we would arrive at a highly biased result.

Another example in economics, widely used by the media, has to do with the most common explanations about the inflation. In general, one of them indicates that it is caused by changes in the amount of money in the economy.

By increasing the money supply, the price level increases; they claim. What's more, some economics textbooks actually begin by showing a strong correlation between the money supply and the inflation rate. But is this irrefutable evidence? Not necessarily, remember like a mantra that:

“Correlation does not imply causation”

Other theories, called endogenous moneyIn fact, they maintain that it is inflation that generates an increase in the supply of money, since it influences the nominal amount of loans granted by banks.[1].

In summary, from this delirious chapter of the animated series, we can extract two lessons very important for economic analysis. First, indicators that can be measured quickly are valuable. Secondly, the fact that two series show similar behavior does not imply that they are causally related.

Of course, the fact that this other theory is true does not invalidate the fact that the first one can be true as well. And in particular, the theory of endogenous money loses validity when the increase in the money supply is derived from a greater monetary base and not from a greater multiplier.