### My editorial

I hope I am on the right track with that idea that the maturing of markets can be represented as incremental change in the density of population. This is what I came up with yesterday, in my research update in French (see ‘Le mûrissement progressif du marché, ça promet’). I am still trying to sort it out, intellectually. This is one of those things, which just seem to work but you don’t exactly know how they do it. I think I need some time and some writing in order to develop a nice, well-rounded, intellectual crystallization of that concept. It all started, I think, as I multiplied tests on different quantitative models to explain incremental changes in the value of those two variables I am currently interested in: the percentage of renewable energy in the primary production of electricity (https://data.worldbank.org/indicator/EG.ELC.RNEW.ZS ), and the percentage of renewables in the final consumption of energy (https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS ).

With the software I have, that Wizard for MacOS – and this is really not heavy artillery as statistical software comes – testing models sums up to quick clicking. Setting up and testing a model – or an equation – with that tool is much faster than my writing about it. This is both the blessing and the curse of modern technology: it does things much faster than we can wrap our mind around things. In order to understand fully this idea that I came up with yesterday, I need to reconstruct, more or less, the train of my clicking. That should help me in reconstructing the train of my thinking. So, yesterday, I was trying to develop, once again, on that idea of the Wasun, or virtual currency connected to the market of renewable energies. I assumed that empirical exploration of the question would consist in taking the same equations I have been serving you on my blog for the last few weeks, and inserting the supply of money as one more explanatory variable on the right side in those equations. It kind of worked, but just kind of: adding the supply of money, as a percentage of the GDP, to a model explaining the percentage of renewables in the final consumption of energy, for instance, added some explanatory power to that model, i.e. it pumped the R^{2} coefficient of determination up. Still, the correlation attached to the supply of money, in that model, did not seem very robust. With a p-value like 0,3 or 0,4 – depending on the exact version of the equation I was testing – it turned out that I have like 30 or 40% of probability that I can have any percentage of renewable energies with a given velocity of money. That p-value is the probability of the null hypothesis, i.e. of no correlation whatsoever between variables.

Interestingly, I had the same problem with a structural variable I was using as well: the density of population. I routinely use the density of population as a quantitative estimator of difference between social structures. I have that deeply rooted intuition that societies displaying noticeable differences in their densities of population are very different in other respects as well. Being around in a certain number in a given territory, and thus having, on average, a given surface of that territory per person, is, for me, a fundamental trait of any society. Fundamental or not, it behaved in those equations of mine in the same way the supply of money did: it added to the coefficient of determination R^{2}, but it refused to establish robust correlations. Just for you, my readers, to understand the position I was in, as a researcher: imagine that you discover some kind of super cool spice, which can radically improve the taste of a sauce. You know it does, but you have one tiny little problem: you don’t know how much of that spice, exactly, you should add to the sauce, and you know that if you add too much or too little, the sauce will taste much worse. Imagined that? Good. Now, imagine you have two such spices, in the same recipe. Bit of a cooking challenge, isn’t it?

What you can do, and what great cooks allegedly do, is to prepare a few alternative sauces, each with the same recipe, but with a different, and precisely defined amount of the spice under investigation. As you taste each of those alternative sauces, you can discover the right amount of spice to add. If you are really good at it, you can even discover the gradient of taste, i.e. the incremental change in taste that has been brought by a given incremental change in the quantity of one particular ingredient. In quantitative research, we call it ‘control variable’: instead of putting a variable right in the equation, we keep it out, we select different subsets of empirical data, each characterized by a different class of value in this particular variable, and we test the equation, without the variable in question, in those different subsets. The mathematical idea behind this approach is that we never know for sure whether our way of counting and measuring things is accurate and adequate to the changes and differences we can observe in those things. Take distance, for example: sometimes it is better to use kilometres, but sometimes even a centimetre it too much. Sometimes, small incremental changes in a measurable phenomenon induce too much complexity for us to crystallize any intelligible thought about it. In statistics, it manifests as a relatively high p-value, or the probability of the null hypothesis. Taking that complexity out of the equation and simplify it into a few big chunks of reality can help our understanding.

Anyway, I had two spices: the density of population, and the supply of money. I had to take one of them out of the equation and treat as control variable. As I am investigating the role of monetary systems in all that business of renewable energies, it seemed just stupid to take it out of the equation. Mind you: it seemed, which does not mean it was. There is a huge difference between seeming to be stupid and being really stupid. Anyway, I decided to keep the supply of money in, whilst taking the density of population out and just controlling for it, i.e. testing the equation in different classes of said density. For a reason that I ignore, when I ask my statistical software to define classes in a control variable, it makes sextiles (spelled jointly!), i.e. it divides the whole sample into six subsets of roughly the same size, 1577 or 1578 observations each in the case of the actual database I am using in that research. Why six? Dunno… Why not, after all?

So I had those sextiles in the density of population, and I had my equation, regarding the percentage of renewable energies in the final consumption of energy, and I had that velocity of money in it, and I tested inside each sextile. Interesting things happened. In the least dense populations, the equation barely had any explanatory power at all. As my equation was climbing the ladder of density in population, it gained explanatory power as well. Still, there is an interval of density, where that explanatory power fell again, just to soar in the densest populations. Those changes in the coefficient of determination R^{2} were accompanied by visible changes in the sign and the magnitude of the regression coefficient attached to the velocity of money. The same happened in other explanatory variables as well. My equation, as I was trying to wrap my mind around all that, works differently in different types of populations, regarding their density. It works the most logically, in economic terms, in the densest populations. The percentage of renewable energy in the final basket of consumption depends nicely and positively on the accumulation of production factors and on the supply of money. The more developed the local economic system, the better are the chances of going greener and greener in that energy mix.

In economics, demographic variables tend to be considered as a rich and weird cousin. The cousin is rich, so they cannot be completely ignored, but the cousin is kind of a weirdo as well, not really the kind you would invite risk-free to a wedding, so we don’t really invite them a lot. This nice metaphor sums up to saying that I tried to find a purely economic interpretation for those changes I observed when controlling for the density of population. My roughest guess was that money matters the most when we have really a lot of people around us and a lot of transactions to make (or avoid). With hardly any people around me (around is another simplification here, it can be around via Internet), money tends to have less importance. That’s logical. In other words, the velocity of money depends on the degree of development in the market we consider. The more developed a market is, the more transactions are there to finance, and the more money we need in the system to make that market work. Right, this works for any market, regardless whether we are talking about long-range missiles, refrigerators or spices. Now, how does it matter for this particular market, the market of energy? Please, notice: I used the ‘how?’ question instead of ‘why?’. Final consumption of energy is a lifestyle and a social structure doing its job. If the factors determining the percentage of renewable energies in said final consumption work differently in different classes of density in the population, those classes probably correspond to different lifestyles and different types of local social structures.

I imagined a local community, where people progressively transition towards the idea of renewable energies. In the beginning, there are just a few enthusiasts, who, with time, turn into a few hundred, then a few thousands and so on. From then on, I unhinged my mind a bit. I equalled the local community at the starting point, when nobody gives a s*** about green energy, as a virgin land. As new settlers come, new social relations emerge, and new opportunities to transact and pay turn up. Each person, who starts actively to use renewable energies, is like a pioneering settler coming to that virgin land. The emergence of a new market, like that of renewable energy, in an initially indifferent population, is akin to a growing density in a population of settlers. So, I further speculated, the nascence and development of a new market can be represented as a growing density in the population of customers. I know: at this point, it could be really hard to follow me. I even have trouble following myself. After all, if there are like 150 people per square kilometre in a population, according to my database, there are just them in that square kilometre, and no one else. It is not like they are here, those 150 pioneers, and a few hundred others, who are there, but remain kind of passive. Here, you have an example of the kind of mindfuck a researcher deals all the time. Data exploration is great, but data tends to have sharp edges. There is a difference, regarding the role of money in going green in our energies, between a population of 100 per km^{2} and a population of 5000 per km^{2}. The difference is there, it jumps to my eye, but what does it mean? How does it work? My general intuition is that the density of population, as control variable, controls for the intensity of social interactions (i.e. interactions per unit of time). The degree of maturity in a market is the closest economic meaning I can associate with that intensity of interactions, but there could be something else.