Aware of how we generalize


I wonder whether I can develop sort of a general pattern on the basis of the case studies I presented in my recent updates: « Let’s Netflix a bit », « Brique par brique », and « Dans la tête d’un non-éléphant ». I mean, what I can do, as a social scientist, in a cognitive sequence that starts with finding the key metrics for the situation, in order to discover anything, then unfolds into finding the sources of information on the actual values of those metrics, just to use that information to identify the key resources, the core processes, and the fundamental ethical values of the social pattern studied.

Key metrics are observable, empirical variables, which I can use to assess the situation in a social context. Finding those key metrics and nailing down their actual values is the essence of what can be deemed as ‘economic method’. This is very largely the essential discovery that Adam Smith made: social systems can be observed mathematically, as sets of equations. Thus, the first step in that method I am unfolding in front of myself, and in front of you, my readers, is to find the key numbers in my social environment. How many people are there in my immediate social circle? With how many of them I should interact daily in order to build for myself a position in the local hierarchy?

Yes, I know, it sounds a bit artificial. People don’t intuitively think like this. I know I intuitively don’t think like this. First conclusion: this method I am unfolding is largely made into formalized research, not really the first cognitive reflex in a new social situation. I think that the other branch of the same path, which I have just published in French, in that update entitled « Dans la tête d’un non-éléphant », is a bit more intuitive. It spells: find the key rules of conduct in your social environment, try to nail down their alternative formulations, and find the meta-rules that serve to select the actual rules of conduct among all the available alternatives. In other words, figure out the game which is being played, get the hang of its rules, and then you have better grounds for enquiring about the numbers.

Good, let’s practice. I start exercising with the topic of my current research: renewable energies and my EneFin concept, that quasi-cooperative scheme where small consumers of energy buy, in the form of complex contracts, both energy and capital shares in the local suppliers of that energy. See, for example, that update entitled ‘The Tribal Equilibrium of the Joule’, in order to have a relatively fresh idea of that concept. When I step, as a newcomer, into any local market of energy, how can I identify the basic rules of the game that is being played in the whereabouts?

As it regards energy, the basic game is about how much energy do I need to occupy a given place in the local social hierarchy, and how much do I have to pay for that amount of energy? As you can notice, I do not really care, as a social Robinson Crusoe, about the natural environment. Yes, it sounds and looks primitive and short-sighted. Still, as I am trying to deconstruct honestly the course of social discovery, this is what I observe in my own thinking as for the market of energy: reference to natural environment and its well-being comes only secondarily, after I have put in place my essential bearings in the social reality strictly spoken.

Anyway, in this particular case – the market of energy – the rules of the game I am playing are very much quantitative. They are prices and quantities, essentially, but not exclusively. The contracts habitually practiced in that market come immediately after, or even ex aequo with prices and quantities. Contracts give an idea of the market power that individual market players can really deploy when negotiating the modalities of their mutual transactions.

If I had to present this path of discovery in a teachable form, like ‘Getting to know a local market of energy, in five easy steps’, what would it look like? Lesson #1 would probably start with a general advice: take some statistics about the local market of energy, for example from the website of the International Energy Agency, or from the World Bank, and check how much energy you are likely to consume per your own capita. Yes, that data is in kilograms of oil equivalent or in tons thereof, and your energy bill will be most likely in kilowatt hours, and thus it is useful to remember: 1 kg of oil equivalent = 11,63 kWh. Try to think, how much energy, above the strictly personal use, does a person need, in this particular market, when they want to start a small business, or when they want to turn from an individual into an organisation?

Lesson #2: get to know the prices of energy in your local market. Is there any reliable source of information in this respect, or do you have to sign, first, a contract with the local supplier of energy, and buy some, and receive a bill, in order to know the actual price? The transparency of pricing is an important institutional trait in energy markets, especially as it comes to the relative market power in small users, like households or really small businesses. As – or if – you become informed about the prices of energy, you can calculate the typical budget spent on energy, or simply the average annual energy bill, in typical social actors.

Lesson #3: get to know the typical contracts, in that local market of energy. First of all, is there any source of information about the contents of a typical contract for the supply of energy, or, as it is sometimes, and sadly, the case for the prices of energy, do you have to sign the contract first, and only then you are entitled to receive all those appendixes in small print, which fully explain what you have just signed? Yes, I know some of you can laugh, at this point, but I remember signing my first contract for the supply of electricity, for my first fully owned apartment in Poland, back in 1992. I had to sign a summary form, which essentially stated that I agree to the terms which will be delivered to me in written form once I sign that particular form. Kafka, you say? Yes, happens sometimes.

Anyway, in that lesson #3, the interesting path to follow in your own discovery is to observe the diversity of contracts. I am connecting, here, to my last update in French, entitled « Dans la tête d’un non-éléphant ». In this particular phase of research, it is interesting to discover how many different and clearly distinct contractual patterns are there in the given local market. Is it a ‘mono-contract’ environment, or is there some flexibility? The former suggests a typical market structure from textbooks on microeconomics: monopoly or oligopoly. The latter suggests something more competitive.

Basically, lessons #1 – #3 should tell us what room for institutional innovation is there in this precise market, i.e. what are the odds that a new institutional scheme will work and gain participants.

Good, lesson #4. Once we know the quantities, the prices, and the contracts, it is time to try something practical: a business concept. Not even a fully blown business plan, just a business concept. As you see that local market, can you think of a new, promising business? Logically, what you supply in the market of energy is, well, energy. There is not much room for product innovation in that respect. Still, as you think of it, what we consciously purchase is not the strictly spoken energy, as we do not decide about each individual electron flowing through the plug, but rather the access to energy. You can think about many different forms of that access.

A quick idea, just like that. Imagine a city with many, publicly available charging points for electronic devices. At some of them you can pedal to generate electricity, but just at some. Imagine that you have something like a unique login ID, or codename, which you use to plug your electronics into those publicly available sockets. Every time you use that form of energy outside your household (or the headquarters of your company), the corresponding intake of kilowatt hours charges your account. That would be a market of energy, where consumption is as individualized as technologically possible.

In that lesson #4, you can play with assessing this business concept. What are the odds that it catches on anywhere on Earth? What is the SWOT map, i.e. what are the required competitive strengths, the weaknesses to avoid, as well as opportunities and threats generated by the market?

I have that intuition that you reach the summit of scientific understanding about anything when you can design and control an experiment pertaining to that anything. This is the path to follow in your lesson #5 about the market of energy. Design and control an experiment, related or unrelated to the business concept from lesson #4. How can people experiment with energy? What types of behaviour are important to observe experimentally? How can you achieve, in your experimental environment, the usual attributes of a good experiment: isolation of precise phenomena, acceleration of their occurrence (as compared to real life), observability?

Why do I put experimentation in the last lesson? This is an old principle known to all engineers: if you can experiment with something, and survive, and have some fun, and, on the top of all that, have some new knowledge, it means you’ve got the hang of the thing.

I am taking on another particular, the teaching of management, a teaching I deliver to the 1st year Undergraduate students. If, hypothetically, I try to manage any type of organisational structure, from any hierarchical position that allows any management whatsoever, what are my first steps into an unknown territory? How can I know the rules of the game and which rules are a priority to figure out? Intuitively, I would look for the things that hold the surrounding organisation together. Are those people working together, although, let’s face it, they sometimes hate each other, because they refer and report to a common leader, or rather because they have common goals?

Thus, my lesson #1 in management would consist in observing patterns of behaviour in people around me. What exactly do they do together? How do they cooperate? How do they compete against each other? It is important, in that first lesson, out of the five (allegedly) easy steps, to observe rather than speculate. Just find patterns in human behaviour. The easiest way to do it is sequencing. Any pattern, in any part of observable reality, is a sequence of events. As you observe human behaviour around you, look for recurrent sequences. There are bound to be some. Mr A holds a meeting, every three of four days, with persons B, C, D, and E. The meeting usually lasts about one hour. The person D is usually pissed off, after those meetings.

Another one; when a customer complains about poor quality of the product, those complaints usually trigger a row. Who is arguing with whom?

Lesson #2 means jumping to another source of information: financial statements. Here, a remark. I know many people have a profound disgust of numbers and mathematics, usually because of shitty teaching thereof at the level of elementary school. Still, the outcomes of shitty teaching can be reverted, simply by triggering our own curiosity into action. The financials of an organisation are like the health metrics of a human. If you want to know somebody’s health, you need to understand the meaning of numbers like pulse, body temperature, the average length of sleep time during one night etc. Same thing with financials. They are pertinent metrics of an organism, period.

So, you go to those financials, and you take all of them, like the balance sheet, the income statement, the cash flow statement, and you simply look for the greatest numerical values. You figure out what is sticking out, quite simply. You select the categories attached to those numbers, and you connect them, as if you were connecting the dots in one of those graphical quizzes. This is an almost painfully basic, practical application of the scientific principle known as ‘the Ockham’s razor’. The principle states that the most obvious answer is usually the right one, where the most obvious means the one which requires the least assumptions. In this case, the greatest financial values are supposed to be the most important.

You can also get more sophisticated, during lesson #2, and take financial statements from two distinct periods, in the same organisation. You match the financial categories from two periods, and you calculate the relative magnitude of change, like value from the period T1 (later), divided by the value observed in the same category in period T0 (earlier). If you move along this tangent, you will pay attention to those categories, where the relative magnitude of change x(T1)/x(T0) is the greatest, in plus or in minus.

Lesson #2 teaches you basic empirical observation of quantitative variables, and now, in lesson #3, you are going to combine those empirical observations with the patterned human behaviour from lesson #1. Whatever type of measurement you chose in lesson #2 – the greatest absolute financial values or those displaying the greatest magnitude of change – in lesson #3 you assume that people do things about money. The patterns of behaviour you nailed down in lesson #1, they have a function, and that function is most likely connected to those big, or those quickly changing, financial amounts you observed in lesson #2. In lesson #3, therefore, you are pinning down the actual strategy – or strategies – in the organisation you are studying.

Here, one important distinction is due. The commonly used definitions of strategy, in management science, usually refer to the goals of the organisation, and the tasks planned in order to achieve those goals. Me, in my own little scientific garden, I cultivate the beautiful, behavioural flower of no-bullshit. I deeply agree with Bernard Bosanquet who used to say that it is bloody hard to know for sure what people want, and it is much more sensible to watch what they do. I also cherish John Nash’s point of view, namely that a strategy needs to have reasonably proven payoffs if it is to be seriously used in the future. To me, a strategy is a recurrently repeated pattern of action, with recurrently occurring results. A strategy can be something that people – or organisations – do even without being aware of doing it.

Anyway, in lesson #3, you define those connections between money and behaviour, as the typical strategies in the given organisation. Time has come for lesson #4, the lesson of what-if, the lesson of change. You know what people usually do in an organisation, you know what they are after, in terms of financial payoffs, and now you can imagine what will happen to this organisation if some of those parameters change. For example, what kind of change will this business – if this is a business, of course – undergo if they have the opportunity to attract an extra 40% of equity? (i.e. an addition of equity capital equal to 40% of what they already have as equity; search for the definition of ‘equity’, just to make sure you know what I am talking about). What would happen if they had to cut their equity down by 40%? What kind of strategies would they apply if there is a new opening, in their target market, which allows to pump their gross margin up by 20%, through higher prices? What if a new tax cuts their gross margin down by 20%?

Time for lesson #5, which is of the same kind as lesson #5 about the market of energy: design and control an experiment. Take the organisation you have studied in lessons #1 – #4. You can use the hypothetical changes you traced in lesson #4, or something else that comes to your mind as intriguing, like what-happens-if-I-press-this-button-oops-I-am-sorry but now transform those paths of change into experimental sequences. You give people some input – a task, a piece of information etc. – and you design a detailed sequence of how they should be responding to that input. You design that sequence so as the response, observed in the participants of your experiment, brings you the most valuable information possible.

You know what? I start liking that approach ‘learn Whatever The Hell You Want in 5 Lessons’. I know, I know: liking my own ideas is a slippery path. It is easy to misstep and fall into the abyss of hypocrisy. Still, I like the thing. Those five lessons about the market of energy seem to cover pretty much the basics of Microeconomics, one of my main teaching curriculums, and so, having covered microeconomics and management, I attempt a graceful jump towards another of my teaching paths, that of Political Systems and Economic Policy.

In order to make my jump look more graceful, i.e. in order to mask the possible awkwardness of my movements, I am doing something I like doing: I revert. I like reverting. This time, when teaching something about Political Systems, I will start, in lesson #1, by asking my students to design an experiment. Yes, this time, they start at the point where the students of management would be asked to finish. Let’s take a practical case: the constitution of The United Republic of Tanzania. The one from 1977.

Click this link, download the constitution and ask yourself the following question: how could you possibly stress-test the system? I mean, where can you see the weakest spots in the constitutional order? What sort of phenomena can hypothetically turn this order into disorder, and into what kind of disorder? At this stage, as this is your lesson #1, you can advance pretty intuitively. I am giving an example. In Part II, Article (47), points (1) and (2) of this constitution you can find the following rule: « 47.-(1) There shall be a Vice-President, who shall be the principal assistant to the President in respect of all the matters in the United Republic generally and, in particular shall assist the President in making a follow-up on the day-to-day implementation of Union Matters, perform all duties assigned to him by the President, and perform all duties and functions of the office of President when the President is out of office or out of the country. Without prejudice to the provisions of Article 37(5), the Vice-President shall be elected in the same election together with the President, after being nominated by his party at the same time as the Presidential candidate and being voted for together on the same ticket. When the Presidential candidate is elected the Vice-President shall have been elected. »

Now, imagine that for some reasons, the Vice-President has not been elected, or has been elected but he or she has resigned right after having been elected, and there is no one willing to take the office. In short, no Vice-President. What happens to the political system of Tanzania in such a case? Is it like that block of domino, which, once knocked down, drags the entire constitutional order into deep s**t (spell, as usually, s-asterisk-asterisk-t)? Or, maybe it is just a minor inconvenience?

Take another constitution, that of Australia. Do the same scanning as for this particular case. Look for really soft spots in the system: the institutions, political actors, or mutual checks of power between political actors, which, once disabled or out of control, can knock the whole system out of balance. The question is quite important, by the way. The Australians have the tenth Prime Minister appointed, over the last 10 years. This is a lot of change. Some kind of deep imbalance might be at work. Maybe you can put your finger on it?

Time for lesson #2: generalize the experiments from lesson #1. Take the same countries, those from lesson #1, Tanzania and Australia in the occurrence, and try to sketch the alternative avenues their respective political systems could possibly take from the present moment, into the future. Like three alternative paths of change for each country.

Lesson #3: generalize the observable idiosyncrasies from lessons #1 and #2. What structural (i.e. durable) differences can you notice between the two cases, Tanzania and Australia? What sort of difference between them can you pin down, as for the relative solidity of their constitutional orders, as well as regarding their possible paths of change? How would you describe the unique features observable in each of those political systems?

Lesson #4: figure out the rules of the game. If you had to give a piece of advice to your friend, like how to make a political career in Tanzania, what would you recommend? What does it mean to make political career in Tanzania? What are the most likely stages and pit stops? How long could it take to make the career in question? What strategies should your friend use to cover that path?

Move your (imaginary?) friend to Australia and try to repeat the process of designing their career path in politics. How is it different from Tanzania?

Lesson #5: nail down general metrics for political systems. Sum up your experience from lessons #1 – #4. Now, imagine that somebody asks you: ‘What are the most important facts and numbers to look upon if we want to understand how a given political system works? Which stones should we lift and turn in order to discover the fundamental mechanics of a political system?’. Now, I know that you might feel slightly ill at ease at this point. How can I make general rules on the grounds of two case studies? Well, firstly, this is how science works: brick by f***ing brick, you build that house. You observe one thing, you observe another thing, and you draw your conclusions even if you are not aware of drawing them. That whole piece of intellectual gymnastics, in 5 lessons, serves to make you aware of how you generalize.

Besides, as it comes to political systems, you do not have like a huge sample of cases; it is barely 150 more or less observable entities on the entire planet.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

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Something to exploit subsequently

In my last three updates, I’ve been turning around one specific topic, namely the technology of wind turbines with vertical axis. Like three updates ago, in Ma petite turbine éolienne à l’axe vertical, I opened up on to the topic by studying the case of a particular invention, filed for patenting, with the European Patent Office, by a group of Slovakian inventors. Just in order to place this one in a broader context, I did some semantic rummaging, with the help of I basically wanted to count how many such inventions had been filed for patenting in different regions of the world. In my research I have been using, for years, the number of patent applications as a metric of aggregate effort in invention, and so I did regarding those wind turbines with vertical axis.

This is when it started to turn weird. Apparently, invention in this specific field follows a stunningly regular trend, and is just as stunningly correlated with the metrics of renewable energies: the share of renewables in the overall output of energy (see Time to come to the ad rem) and the aggregate output of said renewables, in metric tons of oil equivalent (see Je corrèle). When I say ‘stunningly correlated’, I really mean it. In social sciences, coefficients of correlation around r = 0,95happen in truly rare cases, and when they happen, the first reflex of a serious social scientist is to assume that something is messed up in the source data. This is one of those cases. I am still trying to wrap my mind around the fact that the semantic incidence of some logical constructs in patent applications can coincide so strongly with the fundamental metrics of energy consumption.

In this update, I want to return to that business concept of mine, the EneFinproject. I am preparing a business plan for this one. Actually I have been preparing it for weeks, which you can find the track of in the past posts on this blog. Long story short, EneFinis the concept of a FinTech utility, which would allow the creators of new projects in the field of renewable energies to acquire capital, via a scheme combining the sales of futures contracts, on the future output of the business, with the issuance of equity. You can find more explanation in Traps and loopholes, for example.

I want to study this particular case, that wind turbine described in the patent application no. EP 3 214 303 A1, under the EneFinangle. How can a FinTech scheme like the one I am coming up with work for a business based on this particular invention? I start with figuring out the kind of business structure to build around this invention. Wind turbines with vertical axis are generally small stuff, distinctive from their bulky cousins with horizontal axis by the fact they can work in close proximity to human habitat. A wind turbine with vertical axis is something you can essentially install in your yard, and it you will be just fine together, provided there is enough wind in your yard. As for this particular aspect, the quick technological research that I documented in Ma petite turbine éolienne à l’axe vertical, showed that the really interesting places for using wind turbines with vertical axis are, for example, the coastal regions of Europe, with the average wind speed like 12 to 13 metres per second. With that amount of Aeol, this particular turbine starts being serious, at more than 1 MW of electrical capacity. Mind you, it doesn’t have to be coastal, that place where you install it. The upper storeys of a skyscraper, hilltops – in general all the places where you cannot expect your straw hat to hold on your head without a ribbon tied under your chin – are the right place to use that device shaped like a DNA helix.

This particular technology is unlikely to breed power plants in the traditional sense of the term. The whole idea of wind turbines with vertical axis is to make it more apt to being installed in the immediate vicinity of human habitat. You can install them completely scattered or a bit clustered, for example on the roof of a building. I am wrapping my mind around the practical idea, and I start the wrapping by doing two things: maths and pictures. As for maths, PW = ½ * Cp* p * A * v3is the general name of the game. ‘PW’ stands for electric power of a wind turbine with vertical axis, and said power stands on air, which has a density p = 1,225 kg/m3divided by half, so basically that air is dense, in the equation, at sort of p = 0,6125 kg/m3. Whatever speed of wind ‘v’ that air blows at, in this particular equation it blows at the third power of that speed, or v3. That half the density of air, multiplied by the cubic expression of wind speed, is the exogenous force that Mother Nature supplies here and now.

What Mother Nature supplies is being taken on the blades on the turbine, with a working surface of ‘A’, and that surface works with an average efficiency of Cp. That efficiency is technically comprised between 0 and 1, and actually, for this specific type of machine, between 59% and 72% (consult Bhutta et al.2012[1]), which I average at 65,5%. All in all, with that density of air cut by half and efficiency being what it is, my average wind turbine with vertical axis can take like 40,1% of the arithmetical product ‘working surface of the blades times wind speed power three’. Reminder, from school: power first, multiplication next. I mean, don’t raise to cubic power the product of wind speed and blade surface. Wind speed cubic power first, then multiply by the blades.

I pass to pictures, now. A picture is mostly a picture of something, even if that something is just in my mind. My first something is a place I like very much: Lisbon, Portugal, and more specifically the district of Belem, a good kick westwards from the Praca de Comercio. It is beautiful, and really windy. Here below, I am giving a graphical idea of how those small wind turbines with vertical axis could be located. Reminder: each of them, according to the prototype in the patent application no. EP 3 214 303 A1, needs like 5 m2of space to work. Let’s make it 20 m2, just to allow the wind to pass between those wind turbines.


In Lisbon, the average speed of wind is 10 mph, or 4,47 m/s, and that gives an exogenous energy of the wind like 54,72 kilowatts, to take whoever can take it. That prototype has real working surface of its blades like A = 1,334 m2, which gives, at the end of the day, an electric power of PW = 47,81 kW. In Portugal, the average consumption of energy at the level of households (so transport and industry excluded) seems to be like 4 214,55 kWh a year per person. I divide it by 8760 in your basic year (the odd ones make 8784 hours), which yields 0,48 kW required per person. My wind turbine could power 99 people in their household needs. If they start using that juice for transport, like charging their electric cars, or the batteries of their electric bicycles, that 99 could drop to 50 – 60, probably not less.

Hence, what my mind is wrapping around, right now, is a business that would manage the installation and exploitation of wind turbines with vertical axis, in groups of a few dozens of people, so like 20 – 50 households. Good, let’s try to move on: Lyon, France. Not very coastal, as the nearest sea is more than 300 km away, but: a) it is quite windy, due to the specific circulation of air along the valleys of two rivers, Rhône and Saône b) they are reconstructing a whole district, namely the Confluenceone, as a smart city c) I f*****g love the place. Average wind speed over the year: 4,6 m/s, which allows Mother Nature to supply around 52,25 kWto my prototype. The prototype is supposed to serve a population, where the average person needs 7 291,18 kWh for household use, whence 63 people being servedby my prototype, which could drop like to 20 – 30 people, if said people power their transportation devices with their household juice.


Good, last shot: Amsterdam. Never been there, mind you, but they are coastal, statistically speaking quite energy consuming, and apparently keen on innovation. The average wind speed there is 5,14 m/s, which makes my prototype generate a power of 72,72 kilowatts. With the average Dutch consuming around 8 369,15 kWh for household use, 76 such average Dutch could use one such turbine.

 Amsterdam with text

Maths and pictures made me clarify a business concept, or rather two business concepts. Concept #1is simple manufacturing of those wind turbines. Here, EneFin(see Traps and loopholesand the subsequent ones) does not really fit. I remind you that the EneFin concept is based on the observable discrepancy between two categories of final prices for electricity: those for big institutional users (low), and those for households and small businesses (high). Long story short, EneFin takes its appeal from the coincidence of very different prices for the same good (i.e. electricity), and from the juicy margin of value added hidden behind that coincidence. That Concept #1 is essentially industrial, and the value added to expect does not really blow one’s hat off. Neither should we expect any significant price discrepancy between categories of customers. Besides, whilst futures contracts on electricity are already widely practiced in the wholesale market, and the EneFin concept just attempts to transfer the idea to the retail market, I haven’t seen much use of futures contracts in the market of typical industrial products.

Concept #2, for exploiting this particular invention, would be complex, combining the engineering of those turbines so as to make the best version for the given location, their installation, then maintenance and management. The business entity in question would combine manufacturing, management of a value chain, site management, design and engineering, and maintenance. Here, that essentially cooperative version of the EneFinconcept would have more space to breathe. We can imagine a site, made of 200 households, who commission an independent company to engineer a local power system, based on wind turbines with vertical axis, to install, manage, and maintain that facility. In the price paid for particular components of that complex business scheme, those customers could progressively buy into that business entity.

Now, I am following another one of my research routines: I am deconstructing the business model. As truly enlightened a social thinker, I am searching online for the phrase ‘wind turbine investor relations’. To the mildly initiated: publicly listed companies have to maintain a special type of website, called, precisely ‘Investor Relations’, where they publish information about their business cuisine. This is where you can find annual reports, for example. The advantage of following this specific track is the easy access to information I am looking for, like the basic financials. The caveat is that I am browsing through relatively big businesses, big enough to be listed publicly, at least. Hence, I am skipping all the stories of small businesses.

Thus, the data my internal curious ape can find by peeling those ‘investor relations’ bananas is representative for relatively big, somehow established business structures. It can serve to build something like a target vision of what is likely to be created, in a particular field of business, after the early childhood of a project is over. And so I asked dr Google, and, just to make sure, I cross-asked dr Yandex, what they can tell me if I ask around for ‘wind turbine investor relations’. Both yielded more or less the same list of top hits: Nordex,VestasSiemens Gamesa, Senvion,LM Wind Power,  SkyWolf, and Arise. I collected their annual reports, with the exception of SkyWolf, which, for some reason, does not publish any on their ‘investor relations’ page. I followed this particular suspect home, I asked around who are they hanging with, and so I came to visiting their page at Nasdaq, and I finally got it. They are at the stage of their IPO (Initial Public Offering), so they are still sort of timid in annual reporting. Still, I could download their preliminary prospectus for that IPO, dated April 20th2018.

There is that thing about annual reports and prospectuses: they are both disclosure and public relations. Technically, an annual report should, essentially, be reporting about the things material to the business in question. Still, this type of document is also used for, well… for the show. Reading an annual report is good training at reading between the lines, and, more generally, at figuring out how to figure out when people are lying.

Truth has patterns, and lies have patterns as well, although the patterns of truth are somehow more salient. The truth that I look for in annual reports is mostly in the financials. Here is a first glimpse of these:

Revenues Net profit (loss) Assets Equity Ratio assets to revenue
Nordex 2017 EUR mlns 3 127,40 0,30 2 807,60 919,00 0,90
Vestas 2017 EUR mlns 9 953,00 894,00 10 871,00 3 112,00 1,09
Siemens Gamesa 2017 EUR mlns 6 538,20 (135,00) 16 467,13 6 449,87 2,52
Senvion 2017 EUR mlns 1 889,90 (121,10) 1 808,10 230,10 0,96
LM Group 2016 EUR mlns 1 059,00 52,00 1 198,00 445,00 1,13
SkyWolf 2017 USD (!) 49 000 (592 600) 139 730 (673 500) 2,85

As I see it, the business of doing business on installing and managing local power installations can go in truly divergent directions. You can start as SkyWolf is starting, with a ‘debt to assets’ ratio akin to the best (worst?) years of General Motors, or you can have that comfy financial cushion supplied by a big mother ship, as it is the case for Siemens Gamesa. One pattern seems to emerge: the ‘assets to revenue’ ratio seems to oscillate around 1,00. In other words, each dollar invoiced on our customers needs to be backed up by one dollar in our balance sheet. Something to exploit subsequently.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Support this blog


[1]Muhammad Mahmood Aslam Bhutta, Nasir Hayat, Ahmed Uzair Farooq, Zain Ali, Sh. Rehan Jamil, Zahid Hussain (2012) Vertical axis wind turbine – A review of various configurations and design techniques, Renewable and Sustainable Energy Reviews 16 (2012) 1926–1939

At the frontier, with my numbers

And so I am working on two business concepts in parallel. One of them is EneFin, my own idea of a FinTech utility in the market of energy, with a special focus on promoting the development of new, local providers in renewable energies. The other is MedUs, a concept I am developing together with a former student of mine, and this one consists in creating an online platform for managing healthcare services, as well as patients’ medical records, in the out-of-pocket market of medical services.

The basic concept of EneFinis to combine trade in futures contracts on retail supply of electricity, with trade in participatory deeds in the providers of said electricity. My sort of idée fixeis to create a FinTech utility that allows, in turn, creating local networks of energy production and distribution as cooperative structures, where the end-users of energy are, in the same time, shareholders in the local power installations. I want to use FinTech tools in order to extract all the advantages of a cooperative structure (low barriers to entry for new projects and investors, low prices of energy) with those of a typically capitalist one (high liquidity and adaptability).

After a cursory review of the available options in terms of legal and financial schemes (see Traps and loopholesas well as Les séquences, ça me pousse à poser cette sorte des questions), I came up with two provisional conclusions. Firstly, a crypto-currency, internal to EneFin looks like the best way of organising smooth trade in both the futures contracts on energy and the participatory shares in the energy providers. Secondly, the whole business has better chances to survive and thrive if the essential concept of EneFin is being offered to users as a set of specific options in an otherwise much broader trading platform.

EneFin as a business in itself can make profits on trading fees strictly spoken, like a percentage on every transaction, still, if the underlying technological platform develops really well, EneFin could grow an engineering branch, supplying that technology in itself to other organizations. This is an option to take into account in any business with ‘tech’ in its description.

MedUs, on the other hand, is based on the idea that the strictly spoken medical services, I mean the out-of-pocket paid ones, tend to be quite chaotic, at least in the context of European markets. In Europe, most healthcare is being financed via public pooled funds, accompanied by private pooled funds (or via network structures that operate de facto as pooled funds). The out-of-pocket paid healthcare is frequently an emergency or a luxury, usually not the bulk of medical care we use. Medical records generated in the out-of-pocket healthcare are technically there (each doctor has to create a file for a patient, even for one visit), and yet they have sort of a nebular structure: it is bloody hell of a nightmare to recreate your personal, medical history out of these.

The basic concept of MedUs consists in using Blockchain technology in order to create a dynamic ledger medical records. Blockchain acts as an archive in itself, very resilient to unlawful modifications. If my otherwise a bit accidental, dispersed medical visits, paid in the out-of-pocket system, are being arranged and paid via a Blockchain-based platform, it is possible to attach a ledger of medical records to the strictly spoken ledger of transactions. I say ‘possible’ because in that nascent business we still don’t have a clear idea of technological feasibility: Blockchain is cool in simple semantic structures, like cryptocurrencies, but becomes really consuming, in terms of energy and disk-space, if we want to handle large, complex sets of data.

MedUs, as we see it now, is supposed to earn money in three essential ways: a) through trading visit-coupons for private healthcare (i.e. coupons that serve to pay for medical care), in the form of coupons strictly spoken or of a cryptocurrency b) through running a closed platform accessible to medical providers after they pay for the initial software package and a monthly, participatory fee, and c) as a provider of the technology of creating local structures in (a) and (b). I can also see a possible carryover from the EneFin concept to MedUs: new, local providers of healthcare could sell their participatory shares to patients together with those visit-coupons, and thus create cooperative structures in local markets.

In this update I am focusing on one specific issue regarding both concepts, namely on the basic, quantitative market research, which I understand as the study of prices and quantities. My point is that you have two fundamental strategies of developing a new business. Your business can grow as your market grows, for one. That’s the classical approach, to find, for example, with Adam Smith. Still, there are businesses which flourish in slowly dying markets. The market of oil is a good example: there is no prospects for big growth, this is certain, and yet there are companies that still make profits in oil.

In a few past updates, I took something like a cursory set of 13 European countries and I calculated their various, quantitative attributes regarding EneFinand the European market of energy. These countries are: Austria, Switzerland, Czech Republic, Germany, Spain, Estonia, Finland, France, United Kingdom, Netherlands, Norway, Poland, Portugal. I am going to keep my focus on this set of countries and run a comparative market research, in terms of basic prices and quantities, for both concepts (i.e. EneFin and MedUS) together.

Now, I will try to move forward along that narrow crest that separates educational content from strictly spoken market research for business purposes. I want this blog to be educational, so I am going to give some methodological explanations as I run my quantitative analysis, and yet, in the same time, I want material, analytical progress for both business plans. Thus, here we go.

Both concepts address a similar relation suppliers and their customers. Households are the target customers in both cases. As for EneFin, the category of ‘households’ is a bit more flexible: it can encompass small businesses, small local NGOs, and farms as well. Still, in both of those business concepts populationis the most fundamental metric for measuring quantities. I usually reach to the demographics published by the World Bank: this source is quick to dig info out of it (I mean the interface is handy), and, as far as I know, it is reliable. I am a big fan of using demographics in market research, by the way: they can tell us much more than it superficially appears.

Demographic data from the World Bank covers the window since 1960 through 2016. Quantitative market research is about dynamics in time, as well as about cross-sectional differences. Here below, in Table 1, there is a bit of demographic info about my 13 countries:

Table 1 – Demographic analysis

Country Population headcount in 2016 Demographic growth since 1960 through 2016
Austria 8 747 358 24,1%
Switzerland 8 372 098 57,1%
Czech Republic 10 561 633 10,0%
Germany 82 667 685 13,5%
Spain 46 443 959 52,5%
Estonia 1 316 481 8,7%
Finland 5 495 096 24,1%
France 66 896 109 42,9%
United Kingdom 65 637 239 25,3%
Netherlands 17 018 408 48,2%
Norway 5 232 929 46,1%
Poland 37 948 016 28,0%
Portugal 10 324 611 16,6%
Total 366 661 622 29,3%

Good, now what do those demographics tell? In am interested in growth rates in the first place. Anyone who knows at least a little about the demographics of Europe can intuitively grasp the difference between, let’s say, the headcount of Switzerland as compared to that of Germany. On the other hand, growth rates are less intuitive. I start from the bottom line, i.e. from that compound rate of demographic growth in all the 13 countries taken together. It is 29,3% since 1960 through 2016, which makes a CAGR (Compound Annual Growth Rate) equal to CAGR = 29,9% / (2016 – 1959) = 0,51%. Nothing to write home about, really. The whole sample of 13 countries makes quite a placid demographic environment. Yet, the overall placidity is subject to strong cross-sectional disparities. Some countries, like Switzerland, or Spain, display strong demographic growth, whilst others are like really placid in that respect, e.g. Germany.

How does it matter? Good question. If each consecutive generation has a bigger headcount than the preceding one, in each such consecutive generations new social roles are likely to form. The faster the headcount grows, the more pronounced is that aspect of social change. On the other hand, we are talking about populations that grow (or not really) in constant territories. More people in a constant space means greater a density of population, which, in turn, means more social interactions and more learning in one unit of time. Summing up, the rate of demographic growth is one of those (rare) quantitative indicators that reflect true structural change.

Now, we can go a bit wild in our thinking and do something I call ‘social physics’. An elephant running at 10 km per hour represents greater a kinetic energy than a dog running at the same speed. Size matters, and speed matters. The size of the population, combined with its growth rate, makes something like a social force. Below, I am presenting a graph, which, I hope, expresses this line of thinking. In that graph, you can see a structure, where a core of 5 countries (Austria, Finland, Estonia, Czech Republic, and Portugal) sort of huddles against the origin of the manifold, whilst another set of countries sort of maxes out along some kind of frontier, enveloping the edges of the distribution. These max-outs are France and Spain, in the first place, followed by Switzerland and Netherlands on the side of growth, as well as by Germany and UK on the side of numerical size.

Some social phenomena behave like that, i.e. like a subset of frontier cases, clearly differentiating themselves from the subset of core cases. Usually, the best business is to be made at the frontier. Mind you, the entities of such a frontier analysis do not need to be countries: they can be products, business concepts, regions, segments of customers. Whatever differs by absolute size and its rate of change can be observed like that.


My little demographic analysis shows me that whichever of the two projects I think about – EneFin or MedUs – sheer demographics make some countries (the frontier cases) in my set of 13 clearly better markets than others. After demographics, I turn towards metrics pertinent to energy in general, renewable energies, and to the out-of-pocket market in healthcare. I am going to apply consistently that frontier-of-size-versus-growth-rate approach you could see at work in the case of demographic data. Let’s see where it leads me.

As for energy, I start with a classic, namely the final consumption of energy per capita, as published by the World Bank. This metric is given in kg of oil equivalent per person per year. You want to convert it into kilowatt hours, like in electricity? Just multiply it by 11,63. Anyway, I take a pinch of that metric, just enough for those 13 countries, and I multiply it by another one, i.e. by the percentage share of renewable energies in that final consumption, also from the website of the World Bank. I stir both of these with the already measured population, and I have like: final consumption of energy per capita * share of renewable energies * population headcount = total final consumption of renewable energies [tons of oil equivalent per year].

Table 2, below, summarizes the results of that little arithmetical rummaging. Is there another frontier? Hell, yes. Germany and United Kingdom are the clear frontier cases. Looks like whatever anyone would like to do with renewable energies, in that set of 13 countries, Germany and UK are THE markets to go.

Table 2 – National markets of renewable energies

Country Final consumption of renewable energies in 2015, tons of oil equivalent Final consumption of renewable energies, compound growth rate 1990 – 2015
Austria 11 296 981,38 80,7%
Switzerland 6 200 709,18 48,6%
Czech Republic 6 036 384,16 241,0%
Germany 44 301 158,29 501,2%
Spain 19 412 734,75 104,4%
Estonia 1 508 374,57 359,5%
Finland 14 036 145,55 101,8%
France 33 167 337,48 42,3%
United Kingdom 15 682 329,72 1069,6%
Netherlands 4 223 183,03 434,9%
Norway 17 433 243,73 39,8%
Poland 11 267 553,99 336,8%
Portugal 5 996 364,89 32,6%

 Good, time to turn my focus to the other project: MedUs. I take a metric available with the World Health Organization, namely ‘Out-of-Pocket Expenditure (OOPS) per Capita in PPP Int$ constant 2010’.  Before I introduce the data, a bit of my beloved lecturing about what it means. So, ‘PPP’ stands for purchasing power parity. You take a standard basket of goods that most people buy, in the amounts they buy it per year, and you measure the value of that basket, in local currencies of each country, at local prices. You take the coefficient of national income per capita in the given country, and you divide it by the monetary value of that basket. It tells you how many such baskets can your average caput(Latin singular from the plural ‘capita’) purchase for an average chunk of national income. That ratio, or purchasing power, makes two ‘Ps’ out of the three. Now, you take the PP of United States as PP = 1,00 and you measure the PP of each other country against the US one. This is how you get the parity of PPs, or PPP.

PPP is handy for converting monetary aggregates from different countries into a common denominator made of US dollars. When we compare national markets, PPP dollars are better than those calculated with the exchange rates, as the former very largely get rid of local inflation, as well as local idiosyncrasies in pricing. With those international dollars being constant for 2010, inflation is basically kicked out of the model. The final point is that measuring national markets in PPP dollars is almost like measuring quantities, sort of standard units of medical services in this case.

So, I take the OOPS and I multiply it by the headcount of the national population, and I get the aggregate OOPS, for all the national capita taken together, in millions of PPP dollars, constant 2010. You can see the results in Table 3, below, once again approached in terms of the latest size on record (2015 in this case) vs. the compound growth rate (2000 – 2015 for this specific metric, as it is available with WHO). Once again, is there a frontier? Yes, it is made of: United Kingdom, Germany and Spain, followed respectfully by Netherlands, Switzerland and Poland. The others are the core.

Question: how can I identify a frontier without making a graph? Answer: you can once again refer to that concept of social physics. You take the size of the market in each country, or its aggregate OOPS. You compute the share of this national OOPS in the total OOPS of all the 13 countries taken together. This is the relative weight of that country in the sample. Next, you multiply the compound growth rate of the national OOPS by its relative weight and you get the metric in the third numerical column, namely ‘Size-weighted growth rate’. The greater value you obtain in that one, the further from the centre of the manifold, the two variables combined, you would find the given country.

Table 3 – Aggregate Out-Of-Pocket Expenditure on Healthcare

Country Aggregate OOPS in millions of PPP dollars in 2015 Compound growth rate in the aggregate OOPS, 2000 -2015 Size-weighted growth rate
Austria 7 951 105,8% 3,8%
Switzerland 17 802 124,9% 9,9%
Czech Republic 3 862 300,2% 5,2%
Germany 54 822 104,9% 25,7%
Spain 35 816 146,9% 23,5%
Estonia 565 308,6% 0,8%
Finland 4 356 98,5% 1,9%
France 20 569 84,7% 7,8%
United Kingdom 39 935 275,5% 49,1%
Netherlands 11 027 227,7% 11,2%
Norway 4 607 100,3% 2,1%
Poland 15 049 124,1% 8,3%
Portugal 7 622 86,8% 3,0%

 Time to wrap up the writing and serious thinking for today. You had an example of quantitative market analysis, in the form of ‘frontier vs. core’ method. When we talk about the relative attractiveness of different markets, that method, i.e. looking  for frontier markets, is quite logical and straightforward.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Crossbreeds, once they survive the crossbreeding process


As a bit of a surprise, I presently have two business plans on the board, instead of just one. A former student of mine asked me to mentor a business project he is starting up with his friend. The basic concept is that of an online platform for managing medical visits, and the innovation consists in using the Blockchain technology to create, for each patient using that functionality, a digital, trusted ledger of all their medical documentation, i.e. their medical visits, diagnoses, treatments received etc. all in one set of data, properly secured and available from any place on Earth.

Additionally, an educational project – a book on the FinTech industry accompanied by an educational toolkit – which I am running with a friend of mine, has gained in maturity and we will be giving it a definitive form. All in all, ideas and projects abound, and I decided to use my blog for conveying as accurate an account of my intellectual journey into all of these three realms. From now on, I am doing my best to weave an interesting story of scientific research out of three distinct stories, namely: a) my EneFinproject b) that medical ledger project, which I provisionally name MedUs, and c) the FinTech educationalpackage.

As for the EneFinproject, in my last update in French, namely in Les séquences, ça me pousse à poser cette sorte des questions, I came to the conclusion that the best way of starting with the EneFin concept is to create or to join an existing generalist trading platform, possibly using a cryptocurrency, such as Katipult, and include in its general features some options, which, in turn, are likely to spur the emergence of new suppliers in renewable energies.

A little pill of update for those who didn’t follow that update in French: I used a technique that data scientists frequently use, and which consists in expressing something we want as a sequence of events, actions and decisions. When I did this with the general concept, to be found in Traps and loopholes, I discovered that at least some potential users of the EneFinfunctionality are likely to have and want a bit more choice and freedom of movement in their financial decisions. I came to the (provisional) conclusion that the strictly spoken EneFinscheme, i.e. promoting the development of new suppliers in renewable energies, will sell better when expressed as a set of financial incentives, placed in the environment of an otherwise general, well-running platform of exchange, rather than as a closed system.

Right now, I am working through the issue of contracts and the legal rules that accompany them. I am deconstructing the typical contracts signed for the supply of energy, in order to have a very precise idea of what should the smart, crypto-coined contracts at EneFinlook like. Contracts are about securing a precise pattern of behaviour from the part of the other contracting party. I want to understand thoroughly the patterns of behaviour, those wanted as well as those unwanted ones, in the relation between a supplier of energy and his customers.

The business plan I am preparing form the MedUsconcept, I am at the phase of evaluating the size and the value of the market, together with defining, progressively, the core business process. Let me present a bit of the initial idea, and a few openings that it creates. The idea has its roots in the observation of the Polish healthcare market, which is a maze of mutually interweaving public funding and private schemes. An average Polish patient seldom can rely exclusively on the public provision of medical care. Frequent blood diagnostics, dental care, post-surgery rehabilitation – sooner or later, you just need to stop waiting for the public funding of these, and pay privately, either in the out-of-pocket formula, or in some kind of pooled funding scheme.

Those entangled, disparate funding patterns results in the dissipation of the patients’ medical records. The initial ides of MedUsis to take the already known functionality of online arrangement of medical appointments, and combine it with the aggregation and proper handling of digitalized medical records. You make an appointment with one doctor via MedUs, you are being diagnosed and treated, then you make an appointment with another doctor, another diagnosis and treatment ensue, and the record of all that is being stored with MedUs.

This is where the Blockchain technology becomes interesting. Blockchain is basically a ledger, and in handling medical records we need, precisely, a ledger. Medical records contain legally sensitive data, and improper handling can lead to a lot of legal trouble. Every single action taken regarding that data has to be properly documented, and secured against fraud. The basic digital architecture of medical records is that of a database, with the identity of the patient as the leading variable.

In those databases, well, s**t happens, let’s face it. I had a good example of that in my own recent experience. As some of you could have read in ‘The dashing drip of Ketonal, or my fundamental questions for the New Year’, due to a complicated chain of events, involving me, some herrings, and the New Year’s party, I spent the New Year’s night in an emergency ward of the district hospital, with the symptoms of acute food poisoning. As I was being released, on the New Year’s day, I had my official discharging documents. In those documents, space and time warped a little. It started with my data, and then I could read that I had been taken in charge three days earlier, in Berlin, with acute cardiac symptoms, and subsequently transferred to the very same hospital, and then, all of a sudden, my own (real) description followed.

As for me, I wouldn’t care, but my wife said: ‘Look, if you have any complications, or if you need any follow up in treatment, that official discharge will matter. Go to that hospital and make them get your records straight’. So I did, and you would really like to see the faces of people, in the hospital’s administration, when I showed them what I am coming with and for. It was that specific ‘Oh, f**k, not again!’ look. They got it straight, and so I stopped being that cardiac patient hospitalized in Berlin, but as far as I know, it all required a little bit of IT acrobatics.

As I described the situation to a friend of mine, an IT engineer, he explained me that this sort of things happen all the time. Our sensitive data is being stored in a lot of databases, and errors happen recurrently. Technically, once they happen, they should be bound to stay happened. Still, what do we have those IT engineers for? What you do, in such a case, is either to run ‘a minor reloading of the database, just to remove some holes in the security systems’, or you deliberately put the system to failure, and reboot it. Both manoeuvres allow miraculous disappearance of embarrassing data. A lot of institutions do it, like hospitals, banks, even ministries, apparently on a recurrent basis. This is, for example, the way that banks hush up the traces of hacking attacks on their customers’ accounts.

Databases with medical records are basically proprietary, i.e. each database has to have a moral entity clearly owning it and being responsible for it. That’s the law. If I use the services of many different medical providers, each of them runs their own database of medical records, and each such database is proprietary, which, in turn, means that my personal medical data is being owned by many entities in the same time. Each of these entities holds one piece of the puzzle, and the law prohibits any sharing between them, basically, unless a chain of official requests for information is being put in motion. As strange as it seems, such a request cannot be issued by the patient, whose medical records are in question. Only doctors can put my dispersed medical records into one whole, and I have no leverage upon that process.

Strange? Absurd? Well, yes, still no more than the promises, which some politicians make during elections. Anyway, that student of mine came up with the idea of using Blockchain to revolutionize the system. There is that digital platform, MedUs, which starts innocently, as a simple device to make appointments for private medical care. Now, revolution begins: each action taken by the patient, and about the patient, via MedUs, is considered as a transaction, to be stored in a ledger powered by the Blockchain technology. The system allows the patient to be effectively in charge of his own medical record, pertaining to all the medical visits, tests, diagnoses and treatments arranged via MedUs.

A sequence comes to my mind. A patient joins the MedUsplatform, and buys a certain number of tokens in its internal cryptocurrency. Let’s call them ‘Celz’. Each Celzcan buy medical services from providers who have joined MedUs. As it is a token of cryptocurrency, each Celzis being followed closely in all its visits and acquaintances: the medical history of the patient is being written in the hash codes of the Celzeshe or she is using in the MedUsplatform.

Crossbreeds, once they survive the crossbreeding process strictly spoken, are the strongest, the meanest, and the toughest players in the game of existence, and so I am crossbreeding my business concepts. The genes (memes?) of EneFingently make their way inside MedUs, and the latter sends small parcels of its intellectual substance into EneFin. Yes, I know, the process of crossbreeding could be a shade more fun, but I am running a respectable scientific blog here. Anyway, strange, cross-bred ideas are burgeoning in my mind. Each subscriber of the EneFinplatform could have all the history of their transactions written into the hash codes of the cryptocurrency used there, and thus the EneFinutility could become something like a CRM system (Customer Relationship Management), where each token held is informative about the past transactions it changed hands in. How would the reading of such data, out of the hash code, work in the (legal) light of General Data Protection Regulation (GDPR)?

On the other hand, why couldn’t patients, who join the MedUsplatform, use their Celzesto buy participation in the balance sheet of those medical providers who wish such a complex deal? Celzes used to buy equity in medical providers could generate extra purchasing power – more Celzes – to pay for medical services.

In both projects, which I am currently preparing business plans for, namely in EneFin, and in MedUs, the Blockchain technology comes as a simplifying solution, for transforming complex sets of transactions, functionally interconnected, into a smooth flow of financial deeds. When I find a common denominator, I tend to look for common patterns. I am asking myself, what do these two ideas have in common. What jumps to my eye is that both pertain to that special zone of social interactions, when an otherwise infrastructural sector of the social system gently turns into something more incidental and mercantile. It is about giving some spin to those portions of the essential energy and healthcare systems, which can tolerate, or even welcome, some movement and liquidity, without compromising social stability.

As I see that similarity, my mind wanders towards that third project I am working on, the book about FinTech. One of the essential questions I have been turning and returning in my head spells: ‘What is FinTech, at the bottom line? What part of FinTech is just digital technology, versus financial innovation in general?’. Those fundamental questions popped in my head some time ago, after some apparently unconnected readings: the Fernand Braudel’s masterpiece book: ‘Civilisation and Capitalism’, ‘The Expression of The Emotions in Man and Animals’ by Charles Darwin, and finally ‘Traité de la circulation et du crédit’ by Isaac da Pinto. It all pushed me towards perceiving financial deeds, and especially money, as some kind of hormones, i.e. systemic conveyors of information about what is currently the best opportunity to jump on.

A hormone is information in solid form, basically, just obtrusive enough to provoke into action, and light enough to be conveyed a long way from the gland it originates from. OK, here I come: gently and quietly, I have drifted towards thinking about the nature and origins of money. Apparently, you cannot be a serious social thinker if you don’t think about it. Mind you, if you just think about the local (i.e. your own) lack of money, you are but a loser. It is only when you ascend beyond your own, personal balance sheet that you become a respectable economist. Karmic economics, sort of.

Being a respectable social thinker does not preclude practical thinking, I hope, and so I am drifting back to business planning, and to the MedUsconcept. My idea is that whatever will be the final span of customers with that online platform, it is going to start in the market of private healthcare, or, as I think about it, peri-healthcare as well (beauty clinics, spa centres, detox facilities etc.). Whatever the exact transactional concept will be finally developed, any payment made by the customers of MedUswill be one of these: a) a margin, paid by the patient over the strictly spoken price of the healthcare purchased b) a margin, paid by the provider of healthcare out of the price they receive from the patient, or, finally, c) a capital expense of the healthcare provider, to be reflected in some assets in their balance sheet. Hence, I need to evaluate the aggregate value of payments made by patients, the distribution of the corresponding expenditure per capita, and the capital investments in the sector. Studying a few cases of healthcare businesses, just to get the hang of their strategies, would do no harm either.

As I browsed through the website of the World Health Organization, I selected 17 indicators which seem relevant to studying the market for MedUs. I list them in Table 1, below. They are given either as straight aggregates (indicators #11 – 17), as per capita coefficients, or as shares in the GDP. When something is per capita, I need to find out about the number of capita, for example with the World Bankand from then on, it is easy: I multiply that thing per capita by the amount of capita in the given country, and I fall on the aggregate. When, on the other hand, I have data in percentages of the GDP, I need the GDP in absolute numbers, and the World Economic Outlook database, by the International Monetary Fund, comes handy in such instances. Once again, simple multiplication follows: % of GDP times GDP equals aggregate.

Table 1 – Selected indicators about national healthcare systems, as provided by the World Health Organization

Indicator #1 Current Health Expenditure (CHE) as % Gross Domestic Product (GDP)
Indicator #2 Health Capital Expenditure (HK) % Gross Domestic Product (GDP)
Indicator #3 Current Health Expenditure (CHE) per Capita in US$
Indicator #4 Domestic Private Health Expenditure (PVT-D) as % Current Health Expenditure (CHE)
Indicator #5 Domestic Private Health Expenditure (PVT-D) per Capita in US$
Indicator #6 Voluntary Financing Arrangements (VFA) as % of Current Health Expenditure (CHE)
Indicator #7 Voluntary Health Insurance (VHI) as % of Current Health Expenditure (CHE)
Indicator #8 Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE)
Indicator #9 Voluntary Financing Arrangements (VFA) per Capita in US$
Indicator #10 Out-of-Pocket Expenditure (OOPS) per Capita in US$
Indicator #11 Voluntary prepayment, in million current US$
Indicator #12 Other domestic revenues n.e.c., in million current US$
Indicator #13 Voluntary health insurance schemes, in million current US$
Indicator #14 NPISH financing schemes (including development agencies), in million current US$
Indicator #15 Enterprise financing schemes, in million current US$
Indicator #16 Household out-of-pocket payment, in million current US$
Indicator #17 Capital health expenditure, in million current US$

 I am wrapping up writing, for today. I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Traps and loopholes


My editorial via You Tube

I am focusing on one particular aspect of my EneFinconcept, namely on what exactly will the consumers of electricity acquire under the label of ‘participatory deeds in the supplier of energy’. For those, who have not followed my blog so far, or just haven’t followed along this particular path of my research, I am summing the thing up. In practically all the European countries I have studied, the retail sales of energy, i.e. to its final users, take place at two, very different prices. There is the retail price for households PH, much higher than the retail price for PIpracticed with big institutional consumers. The basic EneFinconcept aims at making energy accessible to households at a price just as low as or close to the PIlevel, and, in the same time, at promoting small, local suppliers of renewable energy. The basic concept is that of complex contracts, which combine a futures contract on the supplies of electricity with the acquisition of participatory deeds in the supplier of that electricity. For a given, small user who consumes QHkilowatt hours, we have QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI), where ‘t’ is the present moment in time, ‘t+z’ is a moment in the future, distant from the present by ‘z’ periods, and K(t)is investment capital supplied today, to the provider of electricity, by the means of this complex contract.

EneFin Concept

Now, the issue of those participatory deeds purchased together with the futures contracts on electricity. I am advancing step by step, just to keep an eye on details. So, I need something freely tradable, endowed with high liquidity. EneFinis supposed to be a FinTech business, and FinTech means finance, and finance means giving liquidity, i.e. movement, to the otherwise lazy and stationary capital goods. The imperative of liquid, unimpeded tradability almost automatically kicks out of the concept the non-securitized participatory deeds: cooperative shares in equity, and corporate shares in partnerships. These are tradable, indeed, but at a very slow pace. If you have cooperative shares or those in a partnership, selling them requires a whole procedure of formally expressed consent from the part of other members (in a cooperative) or partners (in a partnership). Can take months, believe me. Problems with selling those types of participatory deeds find their mirroring image in problems with buying them.

Securitized shares in a joint stock company give some hope regarding my concept: they are freely tradable and can be highly liquid if we only want them to. As the aim of the EneFinproject is to promote new suppliers of renewable energies, or the creation of new capacity in the existing suppliers, the first issuance of those complex contracts (futures on energy + capital participation) would be like an Initial Offering of corporate stock. I see an opening here, yet with some limitations. As soon as I offer my stock to a sufficiently large number of prospective buyers, my initial offering becomes an Initial Public Offering, and my stock falls under the regulations pertaining to the public exchange of corporate stock. The ‘sufficiently large number’ depends on the exact legal regime we are talking about, but is does not need to be that large. The relevant regulations of my home country, Poland, assume a public offering as soon as more than 300 buyers are being addressed. The targeted size of the customer population in the EneFinproject depends on the country of operations, but even for a really small, 1 MW local power installation, it takes certainly more than 300 (see This is how I got the first numerical column).

The thing is that in the legally understood public exchange of corporate stock I can trade only that stock. A complex contract in my line of thinking – futures on energy plus participatory deeds – would require, in such a case, to carry out two separate transactions in two separate markets: one transaction in the market of futures contracts, and another one in the public stock exchange. Maybe it is feasible, but looks sort of clumsy. Mind you, what looks clumsy when handled simultaneously can gain in gracefulness when turned into a sequence. First, I buy futures on energy, and then I present them to my provider, and they give me their corporate stock. Or another way round: first, I buy the stock of that provider, in an IPO, and then, with that stock in hand, I claim my futures on energy. That looks better. I’ll keep that avenue in mind.

Another caveat that comes together with the public exchange of corporate stock is that only licensed brokerage houses can do it. In the EneFinproject, that would mean the necessity of signing a contract with such a licensed entity. Right, if I have professional stock brokers in the game, I can entertain another option, that of offering that stock in secondary exchange, not in an IPO. A provider of energy does an ordinary IPO in the stock market, their stock comes into the system. Then, they offer the following deal: they buy their stock back and they redeem it, and they pay for it with those futures on energy. With good pricing, could be worth some further thinking.

Everything I have passed in review so far pertains to the equity of the energy provider. I might venture myself into the realm of debt, now. Customers can participate in the balance sheet of their provider via what the French call ‘the bottom part’, namely via liabilities. Along with the futures on energy, customers can acquire bonds or bills of exchange of some kind. Fixed interest rate, no headache about future profits in that energy provider, only some headache left about future liquidity. Debt has the reputation of being more disciplining for the corporate executives than equity.

F**k (spell ‘f-asterisk-asterisk-k’), my mind starts racing. I imagine a transactional platform, where customers buy futures contracts on energy, accompanied by a capital deed of their choice. I buy some kilowatt hours for my future Christmas cooking (serious business over here, in Poland, trust me), and the platform offers me choice. ‘Maybe sir would dare to have a look at those wonderful corporate shares, quite fresh, issued only two months ago, or maybe sir wants to consider choosing that basket with half corporate bonds, half government bonds inside, very solid, sir. Holds money well, sir. If sir is in a genuinely adventurous mood, sir could contemplate to mix Bitcoins with some corporate stock, peppered with a pinch of corporate options, and some futures on gold’.

Right, now I understand the deep logic of the business concept introduced by that Canadian company: Katipult. They have created a financial structure made of an investment fund, whose participatory shares are being converted into a cryptocurrency traded at their internal transactional platform. I understand, too, why they pride themselves with the number of distinct legal regimes they have adapted their scheme to. I see that I should follow the legal regime of my market very closely, in order to find traps and loopholes.

My mind keeps racing. There are three, internally structured and mutually connected sets of financial deeds: a) A set of futures contracts on energy, priced at the retail, non-household rate b) A set of capital deeds issued by the providers of energy, and c) A set of tokens, in some cryptocurrency, which can be purchased for the price of energy at the retail, household rate, and give a claim on both the energy futures and the capital deeds.

A new customer enters that transactional platform and buys a certain number of tokens. Each token can be converted, at a given exchange rate, against the futures on energy and/or the capital deeds. The customer can present the basket of tokens they are holding to any provider of energy registered with that platform, and make a choice of futures on energy and capital deeds.

I think I am progressively coming up with the core process for the EneFin project. Here, below, I am giving its first graphical representation.

EneFin Core Process First Approach

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Lean and adaptable

My editorial

Things of life make me circle around the core of my present work, namely around that business plan for the EneFinproject. Last Monday, a friend of mine, with whom I am doing a lot of work on FinTech things, asked me a general question: ‘If you had like to explain your students what is a financial innovation and how it is being done, what would you tell them?’. The question fits my current interests, so I will try to kill two birds with one stone. I will try to develop an intellectually satisfactory answer to that general question, and, in the same time, I will try to move one step forward in the writing of my business plan.

‘Financial’ means something about money. It is about money moving in transactions, or about money staying calmly and nicely in one place, in some cosy balance sheet. Innovating about money means figuring out some new ways of paying with money, or some new manner of piling money up. The essential job that money does for people is to assure liquidity, i.e. money makes it possible to move economic utilities in space and over time, with a minimum of transaction costs. If you have to move a mountain, like in one go, from spot A to spot B, you need either a small particle of really solid faith or a really big piece of civil engineering. Still, if you can move the mountain in question piece by piece, e.g. bucket by bucket, things become easier. You can move those buckets around with a lot less of civil engineering, and your faith is being put to significantly lesser a strain, as well. This is what ‘financial’ means in the first place: transforming a mountain of economic utility into a set of small buckets of financial equivalence, and moving those buckets around smoothly and efficiently.

Any movement happens with a given velocity, along a given vector, and our control over that movement is observable as the capacity to modify both the vector and the velocity. Inventing something new about money and its use, thus making a financial innovation, is equivalent to finding out how to give more velocity in and/or more control over the movement of money. This little elaboration in the lines of Newtonian physics serves me to introduce a secondary effect to the primary financial function. When you create financial instruments, i.e. standardized legal deeds representing small parcels of economic utility, those instruments, although being technically something derivative from something else, start having an economic life of their own. Having an economic life means being tradable, and actually traded, and thus having a market price.

Sounds nice, so far. I am trying to apply those fancy generalities to my EneFinconcept. The mountain of economic utility comes, logically, in the first place. I can distinguish three big masses in the geography around me: the capital immobilised in power generation (power installations), accompanied by the capital immobilised in power grids (systems of distribution), and finally the population of users. They all move, and so, logically, they are not mountains, but rather the tectonic plates that mountains rest on. The mountains per se are made of long-term contracts between power plants and power grids, for one, as well as contracts between power grids (distributors) and their end-users. Ah, there are, in some places, the contracts between power plants (those rather small, local ones, like solar farms) and the end users, directly.

Now, it looks weird. I have mountains rising sort of at the junction of tectonic plates they are supposed to stand on. These are intercontinental mountains. See? I was sure I can invent something unparalleled. That’s the thing with metaphors: you push too far and they become meaningless. Anyway, I have those big masses of long-term contracts, moving very slowly, or hardly at all. Those long-term contracts have economic utility in them. Anything financial one could possibly do about them would consist in creating a complex set of financial instruments, i.e. tradable legal deeds equipped with intrinsic value expressed in the units of currency. I have come up with a few ideas as for those financial instruments, and I am listing them below.

Idea #1are standardized coupons for purchasing electricity, like for 1 kWh each, tradable either like coupons strictly spoken, or, in a technologically fancier manner, as tokens of a cryptocurrency. Those coupons are financialized commitments from the long-term contracts between the consumers of electricity and their direct suppliers, this the distributors who operate power grids. In such a contract, the consumer commits to purchase electricity from a distributor, like for 2 years in a row, and said distributor commits to supply electricity, in a given amount. This type of tradable, standardized deeds for purchasing electricity have been used for decades in the wholesale market of electricity, i.e. between power plants and distributors. I am thinking about developing the same concept in the retail market. It would allow the distributors to cash today their future expected deliveries of power to consumers, and consumers could have a more diversified portfolio of suppliers.

As I think about it now, that type of financial instrument, as conceptualized in my Idea #1, would be workable mostly for new suppliers in the market, for example for new solar farms or new, local hydraulic turbines of moderate size. They could sell to the neighbouring consumers their future output of electricity, in the form of those coupons, instead of signing long-term contracts. Lower and more liquid a commitment from the part of consumers could convince the latter to try and buy that new, fancy power from the new solar farm, sort of.

The more I think about that Idea #1, the more I am convinced that it would require to operate power grids similarly to railways. In the railway business, at least in Europe, you frequently have a separation between the operator of the physical, actual railway (infrastructure), and the operators of trains, on the other hand.

Idea #2is based on the same contracts, and consists in making the consumers’ financial commitments into tradable deeds, like into bills of exchange. In a long-term contract I sign with a supplier of electricity, I am supposed to pay standardized amounts of money every month, and those monthly instalments are based on a smoothed prediction of my future consumption of power. Idea #2 sums up to singling each such future, promised instalment out of the long term contract and trading it as a security, or, once again, as a token of value in a cryptocurrency. As a financial utility, it gives, once again, more liquidity to the distributors. Of course, those financial claims on consumers would be conditional on supplying them a given amount of electricity. As I look at it now, it would essentially amount to have something like options or future contracts, i.e. financial instruments that guarantee a fixed future price of 1 kWh.

As for Ideas #1 and #2, you can look up Les marchés possibles à développer à partir d’une facture d’électricité, A first approach on the financial sideor Une plantation des clients qui portent fruit.

Idea #3is an app coupled with the network of electric sockets, in a smart city. You use the app to pay for electricity as you actually use it, sort of smoothly. Initially, I developed that idea for a network of publicly available electric sockets, made for sort of causal charging of smartphones (see Je recalcule ça en épisodes de chargement des smartphones), but, as I think of it, the idea could extend to all the usage of electricity in a smart city. I could, for example, use the app to drive the usage of electricity in my apartment to a strict minimum when I leave for the weekend, and I would pay just for that minimum. On the other hand, when I use a lot of electric tools in my garage, e.g. when I build a DIY intercontinental ballistic missile, I would pay on the spot for that extra juice.

In legal and financial terms, Idea #3 would amount, once again, to turn the long-term financial commitments on the part of consumers into short-term, spot payments. As I think of it, it wouldn’t even require much change in the distribution system. Large populations, and in the case of a typical, European project of smart city, we are talking like 1,5 million people or more, have a predictable consumption of energy. I managed to provide some scientific proof of that in my article: Settlement by energy. The amount, and, I dare say, the structure of final consumption in energy, remain fairly predictable in such a large human settlement. What is being financialized, i.e. made more liquid, is just the payment for energy.

Good, I have listed my so-far ideas. Now, I keep inventing. We are in a smart city, and there are many local projects of setting local power sources from renewable energies. They are competing for accessing the market of energy in that smart city. They are selling futures contracts on their future, expected output of power. Normally, when you sell your future expected output in the form of futures contracts, you need to give a substantial discount on the present price. If you buy futures on coffee, for example, and you agree to pay today for the coffee beans from the next harvest, like in 6 months, you can negotiate even 40% lower a price, as compared to the coffee beans actually available in the warehouse. Here, the same, future expected power, sold today, is bound to be noticeably cheaper than the presently available juice. Still, we can add to the fun. I imagine a financial instrument which embodies a compound contract: a futures contract on future expected supply of electricity paired with a participation in the equity of the supplier.

That would be my Idea #4, in the framework of the EneFinproject, and here comes a little calculation. In my home country, Poland, 1 kilowatt hour, in the retail market, costs like €0,15. In the semi-retail market, i.e. power for institutional consumers, costs about €0,09 per kWh. I want to start a local station of hydraulic turbines. Nothing big, let’s say two turbines of 1 megawatt each, thus 2 MW in total. That makes an annual output of 2000 kW times 8760 hours in a year, and equals 17 520 000 kWh in a year. In standard retail prices it would make a revenue of 17 520 000 * €0,15 = €2 628 000 a year. I am offering futures on my output, paired with shares in my equity. The buyer pays €0,09 per 1 kWh of future power supply, and €0,06 of small participation in the equity of my power plant, so €0,15 in total, which is just the same as the price of presently available, retailed power. That makes, from the point of view of the supplier, an annual revenue of 17 520 000 * €0,09 =  €1 576 800, plus 17 520 000 * €0,06 =  €1 051 200 in equity.

As business structures come, what comes out of my Idea $4 is an actual cooperative structure. Even if I make it into the legal vehicle of a company, the basic concept is cooperative: the buyers of my output become my shareholders. We can go even further down this path and create a FinTech platform for any market, where barriers to entry, in terms of physical investment, are relatively low, and there is a big fork between retail prices, and the wholesale ones. I think about transportation services, food supply, maybe construction services as well. A start-up sells futures on its future expected output, paired with small shares in its equity. This could make an excellent financial tool for building local cooperative structures.

I used to belong to a few cooperatives in the past. They were housing cooperatives. The thing about those structures is that they are functional as long as membership remains limited to a small number of people. A classical cooperative with 15 members is just fine, with 150 it becomes really clumsy as for decision making, and with 1500 it turns into a feral bureaucracy. My idea – for the moment I name it Coop EneFin– offers all the advantages of the cooperative scheme, whilst giving the whole thing the smooth liquidity of a corporate structure. If I do it the FinTech way, with some smart app facilitating the buying and selling, as well as reselling, of those cooperative futures contracts, it looks the way a good financial innovation should look: lean and adaptable.

There are two levels of testing that business concept. Firstly, the financial soundness: the transactional FinTech platform for trading those cooperative contracts should generate profits sufficient to give a good return on the equity invested in its creation. As a wannabe mad scientist (I don’t have any old castle in the mountains, so I have to wait a bit before graduating into a full mad scientist), I can use the same cooperative scheme to raise money for that technological platform. Anyway, the FinTech utility needs to earn its living somehow. There are three essential ways (look up Plus ou moins les facteurs associés). At the most basic level, Coop EneFin can be just a transactional platform, collecting a commission on each transaction in those ‘futures + equity’ contracts. Secondly, it can be a closed club, with an entry lump fee to pay for the corresponding software package, and then a monthly membership. Thirdly, and finally, the Coop EneFinproject can specialize just in the development of the technology, which, in turn, allows the creation of local cooperative networks around local suppliers of energy.

The realistically appraised cost of technological development, regarding that FinTech utility, comes as the key factor in the business planning. I already had that intuition after browsing the financials of Square Inc., one of the big players in the FinTech business (look up The smaller more and more in FinTech). I can intuitively, and provisionally, nail down the cost of product development at some 15% of revenues in the maturity phase of the business. Liaising with the preliminary calculations I presented a few paragraphs ago, that would make like €236 520 for maintaining and developing the FinTech technology for supporting the Coop Enefin scheme for one local power plant of 2 megawatts. The trick is to minimize the cost of current upgrading in that technology, or, in other words, to minimize its pace of moral obsolescence.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund (and you can access the French versionas well). You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

A first approach on the financial side

My editorial

Here is the deal: I officially authorise my provider of electricity to transfer on third parties the nominal value of my future liabilities resulting from the contract of energy supply. The transfer can be done in the form of IOUs, or promissory notes. In exchange of such an authorisation I get a discount on the price of electricity. I get a lower price, and my provider gains more liquidity as they can cash now my future liabilities to them. I mean, this is the deal I could possibly make at the exchange platform I am just inventing for my project EneFin, i.e. a FinTech functionality for the market of energy.

As I am working on the EneFin project, I have become aware how important legal analysis is. I started doing it, step by step, in my last update in French (seeLes marchés possibles à développer à partir d’une facture d’électricité). The deal I began this update with can be technically presented as present discounting of a future expected stream of payments. I (and you too, by the way) have a very predictable consumption of electricity. Most of us are highly predictable as for how many kilowatt hours we will consume, monthly, over like the next two years or even more. In many cases – as a matter of fact in most cases, I think – the electricity bill we pay monthly corresponds to a smoothed prediction of our future consumption of energy, priced according to the contract we have signed with the provider, rather than to our real, actual consumption of kilowatt hours over the last 30 days.

To my provider of electricity, the contract they have signed with me, together with such a smoothed prediction of my consumption, represent a chunk of liquid capital, to be cashed progressively over time, by instalments. What if my provider wanted to cash this capital right now? What if other providers, for example those who want additional liquidity in the future and have more than enough liquidity today, thank you very much, we have all the cash we need now, wanted to by that future liquidity from my current provider?

Whatifs, which are the neologism I invented to name the outcomes of ‘what if…?’ questions, have their contribution to the development of science. Most breakthrough experiments in science start with a whatif. In the preceding paragraph, I named two whatifs, and it is an intellectual duty on my part to study them. I quickly sketch two markets: that of the strictly spoken household consumption of electricity, and a second one, made of electricity consumed in transport if, hypothetically, all transport went electric. Hence, I have one actually existing market (households) and another one a bit futuristic.

Now, in those two markets, I imagine a FinTech utility, in the framework of my EneFinproject, where both the suppliers and the users of electricity can auction their future. Suppliers auction their future capacity (this is already being done on a routine basis), and users auction their future liquidity regarding energy bills (this is precisely the novelty with EneFin). I have that intellectual habit, when I come up with a business idea, to test it very quickly on any empirical data available. Here, I want to have some idea about their size and value. I start with getting data about individual consumption of energy. I turn to the World Bank and I download their data about energy use (kg of oil equivalent per capita), as well as indicative information about the structure of final consumption in energy, by the European Environment Agency, and, finally some data about electricity prices, by Eurostat. To all that, I add a pinch of data about population, by country, as provided by the World Bank.

I have my ingredients in front of me, and I start cooking. There is that thing about me and cooking: as a true scientist, I never know exactly what the final outcome will be. Some people complain, but you know what? Some people always complain. Anyway, I am cooking and here comes the first dish: an estimation of my two markets in a few European countries, presented in tables 1 – 3, further below. Consult this data at your pleasure and leisure, and I am developing on the business model. I assume that the auctioning I plan has any point if the participants can derive any substantial utility from it. The utility for suppliers is that of acquiring future liquidity, under two forms. Selling future electrical capacity assures them smooth sales in said future, and buying the users’ future liquidity gives them smooth financing of those future sales.

Now, it is important to understand what ‘smooth’ means regarding financial goods. It means ‘freely tradable’. In order to make future capacity in suppliers, and future liquidity in customers freely tradable, you need to have them securitized. No, it doesn’t hurt. I mean, it shouldn’t if done properly. Securitizing an obligatory contract means that individual claims are being singled out of that contract, and transformed into tradable deeds, like notes or tokens of cryptocurrency. The utility of securitizing, thus of making claims freely tradable, is in the fact that once securitized, those claims become abstract carriers of value, detached from causal links to their initial socio-economic context. That detachment has a legal side. In most legal systems, you can find an institution called ‘transfer of claims’. If you have a claim on me, e.g. to be paid $200 for something, you can transfer that claim on a third party, upon giving me proper notice, and I can transfer my liability, vis à vis you, to a third party, upon giving you notice and receiving explicit consent from your part.

Transfer of claims is possible, but in practical terms it is quite burdensome. Once we enter into the maze of questions like ‘Was the notice of transfer sufficiently clear and effective to assume that the addressee of the notice really knew what they are doing?’, it is not soon that we will see light at the end of the tunnel. On the other hand, once we securitize a claim, it can be traded (thus, de facto, claims can be transferred) without the whole ‘notice-and-consent’ circus.

When we securitize claims and make them tradable, the primary and essential function is precisely this: making those claims tradable, i.e. liquid. We cannot say anything a priori about the resulting economic equilibrium, or the lack thereof, just as we cannot preclude on justice and fairness, or, once again, the lack thereof, in the corresponding markets and contracts. Securitization gives higher liquidity to contractual claims, which, in turn, makes it easier to come and go in and out of deals at lower a risk. In that sense, securitization can reduce transaction costs, as long as it does not create its own, excessive risk. That’s the tricky question about options, for example. Technically, trading options on the price of a security should allow you to hedge the risk incurred in trading that security properly spoken. Still, prices of options tend to swing much wider than the prices of their base instruments, and you have more risk instead of less risk.

Thus, when, in my EneFinidea, suppliers auction their future capacity, and users auction their future liquidity, it is supposed to give additional liquidity to the market of energy, thus to reduce transaction costs. I haven’t the faintest idea if it is going to make a better market, whatever the denotation of ‘better’ could be, but it is likely to produce faster change in the market. This is what financial markets are for: facilitating change.

On the grounds of the three cases I studied for as business models– Square Inc., FinTech Group AGand Katipult – I think that the Katipult-like pattern looks the most workable on the suppliers’ side of the market. It implies the creation of a Blockchain-based transactional universe, where participants buy their initial software licence and then pay a monthly fee for staying in the game. It can be combined with marketing a FinTech technology designed for other platforms. On the demand side, i.e. that of electricity consumers, I think it is unrealistic to expect them paying for accessing the transactional platform, thus the price the consumers can pay to EneFin is a transactional margin.

I can make a first outline of revenues in the EneFin project by now: Revenue (EneFin) = Access fee to the transactional platform (Energy suppliers) + Monthly fee (Energy suppliers) + Gross margin on transactions (Future liquidity in consumers). Now, I take the estimate of annual household consumption of electricity, from Table 3, further below. I take two cases: Poland and France, thus, respectively, € 28 548 329 421,59 and € 84 037 923 404,69. To simplify: Poland – €28,5 million and France – €84 million. I assume that any economic agent can increase their liquidity by simply borrowing money from a bank. Further down this path of thinking comes the idea that EneFin’sfees should be lower than the real interest rate, which is like 7% a year for households, and using EneFinin order to assure liquidity should stay competitive in comparison to borrowing. I make the average fee of EneFinlike 3,5% on the typical transaction. That makes a potential market for EneFin, in this particular segment (electricity bills for households) worth a total of 3,5%*€ 28 548 329 421,59 =  € 999 191 529,76 in Poland and 3,5%*€ 84 037 923 404,69 =  € 2 941 327 319,16 in France.

Good, I have a first approach on the financial side. From this point on, the challenge consists in tracing a realistic path of development inside those markets.

As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund(and you can access the French versionas well).

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Table 1

Country  Current consumption of energy, 2015, in kg of oil equivalent per capita Current consumption of energy, 2015, in kWh Estimated household use, kWh Estimated use in transport (hypothesis of 100% electric transport)
Austria  3 804,49  44 246,27  7 654,60  9 512,95
Switzerland  2 960,07  34 425,65  5 955,64  7 401,52
Czech Republic  3 860,00  44 891,83  7 766,29  9 651,74
Germany  3 817,55  44 398,10  7 680,87  9 545,59
Spain  2 571,34  29 904,70  5 173,51  6 429,51
Estonia  4 173,33  48 535,79  8 396,69  10 435,19
Finland  5 924,70  68 904,26  11 920,44  14 814,42
France  3 687,82  42 889,32  7 419,85  9 221,20
United Kingdom  2 763,98  32 145,09  5 561,10  6 911,19
Netherlands  4 233,04  49 230,30  8 516,84  10 584,51
Norway  5 815,81  67 637,84  11 701,35  14 542,14
Poland  2 490,21  28 961,10  5 010,27  6 226,64
Portugal  2 131,68  24 791,46  4 288,92  5 330,16


Table 2

Country Price of electricity for households, € per kWh Non-household price of electricity, € per kWh Estimated value of household electricity market per capita Estimated value of transport electricity market per capita
Austria € 0,20 € 0,09 € 1 530,92 € 856,17
Switzerland € 0,19 € 0,10 € 1 131,57 € 759,18
Czech Republic € 0,14 € 0,07 € 1 087,28 € 675,62
Germany € 0,35 € 0,15 € 2 688,30 € 1 431,84
Spain € 0,23 € 0,11 € 1 189,91 € 707,25
Estonia € 0,12 € 0,09 € 1 007,60 € 939,17
Finland € 0,16 € 0,07 € 1 907,27 € 1 037,01
France € 0,17 € 0,10 € 1 261,37 € 922,12
United Kingdom € 0,18 € 0,13 € 1 001,00 € 898,46
Netherlands € 0,16 € 0,08 € 1 362,69 € 846,76
Norway € 0,17 € 0,07 € 1 989,23 € 1 017,95
Poland € 0,15 € 0,09 € 751,54 € 560,40
Portugal € 0,23 € 0,12 € 986,45 € 639,62


Table 3

Country Population Estimated aggregate value of household electricity market Estimated aggregate value of transport electricity market
Austria 8 633 169 € 13 216 699 535,91 € 7 391 420 116,18
Switzerland 8 282 396 € 9 372 121 038,65 € 6 287 807 395,97
Czech Republic 10 546 059 € 11 466 521 464,97 € 7 125 150 621,30
Germany 81 686 611 € 219 598 519 581,44 € 116 962 052 130,50
Spain 46 447 697 € 55 268 478 324,52 € 32 849 950 047,12
Estonia 1 315 407 € 1 325 407 989,57 € 1 235 387 504,73
Finland 5 479 531 € 10 450 945 006,44 € 5 682 323 784,21
France 66 624 068 € 84 037 923 404,69 € 61 435 408 133,32
United Kingdom 65 128 861 € 65 193 862 378,91 € 58 515 364 595,07
Netherlands 16 939 923 € 23 083 943 387,14 € 14 344 068 867,73
Norway 5 188 607 € 10 321 326 740,48 € 5 281 739 797,49
Poland 37 986 412 € 28 548 329 421,59 € 21 287 482 632,28
Portugal 10 358 076 € 10 217 746 935,20 € 6 625 229 226,64