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?