Cautiously bon-vivant

I keep developing on a few topics in parallel, with a special focus on two of them. Lessons in economics and management which I can derive for my students, out of my personal experience as a small investor in the stock market, for one, and a broader, scientific work on the civilizational role of cities and our human collective intelligence, for two.

I like starting with the observation of real life, and I like ending with it as well. What I see around gives me the initial incentive to do research and makes the last pitch for testing my findings and intuitions. In my personal experience as investor, I have simply confirmed an initial intuition that giving a written, consistent and public account thereof helps me nailing down efficient strategies as an investor. As regards cities and collective intelligence, the first part of that topic comes from observing changes in urban life since COVID-19 broke out, and the second part is just a generalized, though mild an intellectual obsession, which I started developing once I observed the way artificial neural networks work.

In this update, I want to develop on two specific points, connected to those two paths of research and writing. As far as my investment is concerned, I am seriously entertaining the idea of broadening my investment portfolio in the sector of renewable energies, more specifically in the photovoltaic. I can notice a rush on the solar business in the U.S. I am thinking about investing in some of those shares. I already have, and have made a nice profit on the stock of First Solar (https://investor.firstsolar.com/home/default.aspx ) as well as on that of SMA Solar (https://www.sma.de/en/investor-relations/overview.html ). Currently, I am observing three other companies: Vivint Solar (https://investors.vivintsolar.com/company/investors/investors-overview/default.aspx ),  Canadian Solar (http://investors.canadiansolar.com/investor-relations ), and SolarEdge Technologies (https://investors.solaredge.com/investor-overview ). Below, I am placing the graphs of stock price over the last year, as regards those solar businesses. There is something like a common trend in those stock prices. March and April 2020 were a moment of brief jump upwards, which subsequently turned into a shy lie-down, and since the beginning of August 2020 another journey into the realm of investors’ keen interest seems to be on the way.

Before you have a look at the graphs, here is a summary table with selected financials, approached as relative gradients of change, or d(x).

 Change from 01/01/2020 to 31/08/2020
Companyd(market cap)d(assets)d(operational cash-flow)
First Solar+23,9%-6%Deeper negative: – $80 million
SMA Solar+27,5%-10%Deeper negative: -€40 million
Vivint Solar+362%+11%Deeper negative: – $9 million
SolarEdge+98%0+ $50 million
Canadian Solar+41%+4%+ $90 million

There are two fundamental traits of business models which I am having a close look at. Firstly, it is the correlation between changes in market capitalization, and changes in assets. I am checking if the solar businesses I want to invest in have their capital base functionally connected to the financial market. Looks a bit wobbly, as for now. Secondly, I look at current operational efficiency, measured with operational cash flow. Here, I can see there is still a lot to do. Here is the link to You Tube video with all that topic developed: Business models in renewable energies #3 Solar business and investment opportunities [Renew BM 3 2020-09-06 09-20-30 ; https://youtu.be/wYkW5KHQlDg ].

Those business models seem to be in a phase of slow stabilization. The industry as a whole seems to be slowly figuring out the right way of running that PV show, however the truly efficient scheme is still to be nailed down. Investment in those companies is based on reasonable trust in the growth of their market, and in the positive impact of technological innovation. Question: is it a good move to invest now? Answer: it is risky, but acceptably rational; once those business models become really efficient, the industry will be in or close to the phase of maturity, which, in turn, does not really allow expecting abnormally high return on investment.  

This is a very ‘financial’, hands-off approach to business models. In this case, business models of those photovoltaic businesses matter to me just to the extent of being fundamentally predictable. I don’t want to run a solar business, I just want to have elementary understanding of what’s going on, business-wise, to make my investment better grounded. Looking from inside a business, such an approach is informative about the way that a business model should ‘speak’ to investors.

At the end of the day, I think I am most likely to invest in SolarEdge. It seems to have all the LEGO blocks in place for a good opening. Good cash flow, although a bit sluggish when it comes to real investment.

As regards COVID-19 and cities, I am formulating the following hypothesis: COVID-19 has awakened some deeply rooted cultural patterns, which date back to the times of high epidemic risk, long before vaccines, sanitation and widespread basic healthcare. Those patterns involve less spatial mobility in the population, and social interactions within relatively steady social circles of knowingly healthy people. As a result, the overall frequency of social interactions in cities is likely to decrease, and, as a contingent result, the formation of new social roles is likely to slow down. Then, either digital technologies take over the function of direct social interactions and new social roles will be shaping themselves via your average smartphone, with all the apps it is blessed (haunted?) with, or the formation of new social roles will slow down in general. In that last case, we could have hard times with keeping up our pace of technological change. Here is the link to You Tube video which summarizes what is written below: Urban Economics and City Management #4 COVID and social mobility in cities [ Cities 4 2020-09-06 09-43-06 ; https://youtu.be/m3FZvsscw7A  ].

I want to gain some insight into the epidemiological angle of that claim, and I am passing in review some recent literature. I start with: Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, 117(19), 10484-10491 (https://www.pnas.org/content/pnas/117/19/10484.full.pdf ). As it is usually the case, my internal curious ape starts paying attention to details which could come as secondary for other people, and my internal happy bulldog follows along and bites deep into those details. The little detail in this specific paper is a parameter: the number of people quarantined as a percentage of those positively diagnosed with Sars-Cov-2. In the model developed by Gatto et al., that parameter is kept constant at 40%, which is, apparently, the average level empirically observed in Italy during the Spring 2020 outbreak. Quarantine is strict isolation between carriers and (supposedly) non-carriers of the virus. Quarantine can be placed on the same scale as basic social distancing. It is just stricter, and, in quantitative terms, it drives much lower the likelihood of infectious social interaction. Gatto el al. insist that testing effort and quarantining are essential components of collective defence against the epidemic. I generalize: testing and quarantine are patterns of collective behaviour. I check whether people around me are carriers or not, and then I split them into two categories: those whom I strongly suspect to host and transmit Sars-Cov-2, and all the rest. I define two patterns of social interaction with those two groups: very restrictive with the former, and cautiously bon vivant with the others (still, no hugging). As the technologies of testing will be inevitably diffusing across the social landscape, that structured pattern is likely to spread as well.    

Now, I pay a short intellectual visit to Jiang, P., Fu, X., Van Fan, Y., Klemeš, J. J., Chen, P., Ma, S., & Zhang, W. (2020). Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. Journal of Cleaner Production, 123673 https://doi.org/10.1016/j.jclepro.2020.123673 . Their methodology is based on correlating spatial mobility of cars in residential areas of Singapore with the risk of infection with COVID-19. A 44,3% ÷ 55,4% decrease in the spatial mobility of cars is correlated with a 72% decrease in the risk of social transmission of the virus. I intuitively translate it into geometrical patterns. Lower mobility in cars means a shorter average radius of travel by the means of available urban transportation. In the presence of epidemic risk, people move across a smaller average territory.

In another paper (or rather in a commented dataset), namely in Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific data, 7(1), 1-7. https://www.nature.com/articles/s41597-020-00575-2.pdf?origin=ppub , I find an enlarged catalogue of metrics pertinent to spatial mobility. That paper, in turn, lead me to the functionality run by Google: https://www.google.com/covid19/mobility/ . I went through all of it a bit cursorily, and I noticed two things. First of all, countries are strongly idiosyncratic in their social response to the pandemic. Still, and second of all, there are common denominators across idiosyncrasies and the most visible one is cyclicality. Each society seems to have been experimenting with the spatial mobility they can afford and sustain in the presence of epidemic risk. There is a cycle experimentation, around 3 – 4 weeks. Experimentation means learning and learning usually leads to durable behavioural change. In other words, we (I mean, homo sapiens) are currently learning, with the pandemic, new ways of being together, and those ways are likely to incrust themselves into our social structures.    

The article by Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., … & Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497 (https://science.sciencemag.org/content/368/6490/493 ) shows that without any restrictions in place, the spatial distribution of COVID-19 cases is strongly correlated with spatial mobility of people. With restrictions in place, that correlation can be curbed, however it is impossible to drive down to zero. In plain human, it means that even as stringent lockdowns as we could see in China cannot reduce spatial mobility to a level which would completely prevent the spread of the virus. 

By the way, in Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12(1), 16-26 (https://arxiv.org/pdf/2004.04544.pdf ), I read that the whole idea of tracking spatial mobility with people’s personal smartphones largely backfired because the GDS transponders, installed in the average phone, have around 20 metres of horizontal error, on average, and are easily blurred when people gather in one place. Still, whilst the idea went down the drain as regards individual tracking of mobility, smartphone data seems to provide reliable data for observing entire clusters of people, and the way those clusters flow across space. You can consult Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020). Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 1-5.  (https://www.nature.com/articles/s41586-020-2284-y?sf233344559=1) .

Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., … & Pammolli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, 117(27), 15530-15535 (https://www.pnas.org/content/pnas/117/27/15530.full.pdf ) show an interesting economic aspect of the pandemic. Restrictions in mobility give the strongest economic blow to the poorest people and to local communities marked by relatively the greatest economic inequalities. Restrictions imposed by governments are one thing, and self-imposed limitations in spatial mobility are another. If my intuition is correct, namely that we will be spontaneously modifying and generally limiting our social interactions, in order to protect ourselves from COVID-19, those changes are likely to be the fastest and the deepest in high-income, low-inequality communities. As income decreases and inequality rises, those adaptive behavioural modifications are likely to weaken.

As I am drawing a provisional bottom line under that handful of scientific papers, my initial hypothesis seems to hold. We do modify, as a species, our social patterns, towards more encapsulated social circles. There is a process of learning taking place, and there is no mistake about it. That process of learning involves a downwards recalibration in the average territory of activity, and smart selection of people whom we hang out with, based on what we know about the epidemic risk they convey. This is a process of learning by trial and error, and it is locally idiosyncratic. Idiosyncrasies seem to be somehow correlated with differences in wealth. Income and accumulated capital visibly give local communities an additional edge in the adaptive learning. On the long run, economic resilience seems to be a key factor in successful adaptation to epidemic risk.

Just to end up with, here you have an educational piece as regards Business models in the Media Industry #4 The gaming business[ Media BM 4 2020-09-02 10-42-44; https://youtu.be/KCzCicDE8pc]. I study the case of CD Projekt (https://www.cdprojekt.com/en/investors/ ), a Polish gaming company, known mostly for ‘The Witcher’ game and currently working on the next one, Cyberpunk, with Keanu Reeves giving his face to the hero. I discover a strange business model, which obviously has hard times to connect with the creative process at the operational level. As strange as it might seem, the main investment activity, for the moment, consists in terminating and initiating cash bank deposits (!), and one of the most important operational activities is to push further in time the moment of officially charging customers with some economically due receivables. On the top of all that, those revenues deferred into the future are officially written in the balance sheet as short-term liabilities, which CD Projekt owes to…whom exactly?   

Time to come to the ad rem

This last update in French, namely Ma petite turbine éolienne à l’axe vertical, it stirred something interesting in my mind. As my internal happy bulldog is sniffing around that patent application no. EP 3 214 303 A1, questions take shape. How can this particular technology interact with its social environment?

How can any invention interact with its environment? Surprisingly enough, inventions behave very much akin to living organisms, in that respect: the main way they interact with their environment is breeding. Cross-breeding too, as I think of it. One invention seldom is a game changer. As soon as it starts multiplying, things start happening seriously. Let’s see, then, what’s up in the multiplying department. Any kind of multiplying results in a multiple, i.e. in a certain number of something. Mind you, if the incriminated multiplying goes on like really dynamically, it could be an uncertain number of something. Whatever. I am developing on that data you can already find in the Excel file you can see and download from the archive of my blog: I used https://patents.google.comonce again and I sifted out all those patent applications, which pertain to wind turbines with vertical axis, just as the one in that patent application no. EP 3 214 303 A1. I did for the same three big patent offices: the European Patent Office (EPO), the U.S. Patent and Trademark Office (USPTO), and finally for the patent office of the People’s Republic of China (just ‘China’).

Table 1, below, shows the results of that little rummaging I did. This is one of those rare times when I am really puzzled by the numbers I find. You can notice that in all the three patent offices under scrutiny, patent applications pertaining to wind turbines with vertical axis make a very consistent percentage in the total stream of inventions filed for patenting under the general category of ‘wind turbine’. Especially with the EPO and the USPTO, that percentage is solid like a tax rate. With the Chinese patent office, it is a clement, descending tax rate.

 

Table 1 – Patent applications pertaining to wind turbines with vertical axis

Year Number of patent applications with EPO % share in the total of EPO’s  ‘wind turbine’ patent applications Number of patent applications with USPTO % share in the total of USPTO’s  ‘wind turbine’ patent applications Number of patent applications in China % share in the total of Chinese ‘wind turbine’ patent applications
[a] [b] [c] [d] [e] [f] [g]
2001 616 41,5% 1266 38,5% 369 29,1%
2002 599 37,8% 1294 38,3% 478 27,0%
2003 645 37,6% 1491 40,0% 645 27,0%
2004 806 40,7% 1703 40,7% 961 29,9%
2005 821 41,7% 1744 38,8% 1047 25,7%
2006 937 44,1% 1999 39,4% 1553 27,6%
2007 960 40,0% 2150 38,4% 1844 27,3%
2008 1224 44,5% 2454 39,4% 2342 26,7%
2009 1445 45,4% 2813 40,2% 2497 22,8%
2010 1746 46,6% 3482 42,4% 3298 24,8%
2011 2006 44,9% 3622 39,3% 4139 23,1%
2012 1886 42,1% 3699 39,0% 4551 20,6%
2013 1781 41,8% 3829 39,2% 5307 20,2%
2014 1800 38,8% 4074 40,4% 5740 18,1%
2015 1867 42,5% 4013 40,2% 7870 19,6%
2016 1089 39,8% 3388 40,6% 9325 20,4%
2017 349 42,9% 2115 42,7% 9321 22,3%

 I am definitely surprised with those results. Let’s rephrase it, to understand better the phenomenon hiding behind the numbers: whatever the actual number of patent application filed under the general category of ‘wind turbine’, those pertaining to wind turbines with vertical axis make around 40% in Europe and in the United States, whilst consistently descending from around 30% to some 20% in China. Here, we can see one of those phenomena that remain structurally stable no matter what is their actual size.

This is the moment when the teacher in me awakens and wants to do some lecturing about the foundations of the scientific method. In my previous updates, I gave you a glimpse of two distinct types of logic in interpreting numerical data: the frontier plot (At the frontier, with my numbers), and that of an indifference curve (Good hypotheses are simple). Now, I am going to use the occasion – namely that of explaining how a technology of wind turbine can interact with its social environment – to expose the fundamentals of studying time series.

The data in Table 1, above, shows, in general, how frequently people apply for patenting technologies connected to wind turbines with vertical axis. The ‘how frequently?’ further decomposes into ‘how many times in a unit of time?’, and ‘how many times out of a broader number?’, and these two shades of ‘how frequently?’ have different meanings. When I wonder (and measure) how many times a given thing is being done in a unit of time, it is like the social size of that thing. Big social things are those done a lot of times, like over one year, and small social things are performed much lesser a number of times.

Columns [b], [d], and [f] in Table 1express this approach to the social phenomenon labelled ‘invention in wind turbines with vertical axis’. They inform about the size of the phenomenon. In Europe, and in the United States, the size in question had been growing since 2001 until 2014, when it reached a temporary peak, which seems to have become sort of less protruding in 2016 and 2017. In China, the size of the thing named ‘invention in wind turbines with vertical axis’ has been changing differently: it is continuous growth since 2001 all the way through 2017.

Now, I pass to studying the ‘how many times out of a broader number?’ shade of ‘how frequently?’. Columns [c], [e], and [g] in Table 1 give me some insight in that respect. Those percentages are proportions, and thus they are measures of structure rather than size. Values in columns [c], [e] are remarkably recurrent, as if pegged down by some invisible hand. Those structures, in Europe and in the United States, are really stable. Whatever the size of the phenomenon labelled ‘invention in wind turbines with vertical axis’, its proportion to the broader phenomenon named ‘invention in wind turbines’remains fairly constant.

What does it mean? Imagine a human body. When it grows in size, do its internal proportions remain constant? Sometimes they do, but really just sometimes. When a child grows into an adult, many morphological proportions change, like the proportion ‘waist circumference to the length of the torso’. When an adult grows into more corpulent an adult (happens frequently), it changes, too. If a proportion is to remain stable over many different sizes, it has to be really, bloody fundamental.

You could raise a legitimate objection, here. After all, those numbers I quote in Table 1 come from semantic filtering at https://patents.google.com. There can be a cartload of semantic coincidences, for example an invention pertaining to wind turbines with horizontal axis might be mentioning the vertical axis of rotation. Windmills with horizontal axis can do that, i.e. turn on their vertical axis to catch the best wind. Already those Dutch oldies from the 17thcentury were able to perform that trick. It is possible that some of the patent applications accounted for in Table 1 contain this semantic bias. Still, it would be a remarkably consistent bias, occurring over and over again in the space of many years.

China presents a different picture. As the size of the phenomenon labelled ‘invention in wind turbines with vertical axis’, its proportion to the broader phenomenon named ‘invention in wind turbines’shrinks. This particular structure changes as the size of the phenomenon changes. Still, the change is far from random: it follows a relatively smooth, downwards path.

We have a first approach of how this particular technology – wind turbines with vertical axis – can work with its social environment. It can stay in some sort of homeostasis with other, similar technologies, or it can sort of slowly retreat to the benefit of those other, similar ones. Let’s go one step further and connect it to the share of renewable energy in the overall, final consumption of energy, as published by the World Bank. In Table 2, below, you can find the data pertaining to our three markets under scrutiny.

Table 2

  Share of renewable energy in the total consumption of energy
Year European Union United States China
2001 7,9% 4,7% 28,5%
2002 7,9% 4,8% 27,1%
2003 8,2% 5,3% 23,9%
2004 8,4% 5,5% 20,2%
2005 8,8% 5,8% 18,2%
2006 9,4% 6,4% 17,1%
2007 10,3% 6,3% 15,3%
2008 11,0% 6,8% 14,6%
2009 12,2% 7,4% 13,9%
2010 13,0% 7,5% 12,9%
2011 13,3% 8,2% 11,7%
2012 14,5% 8,5% 12,0%
2013 15,3% 8,7% 11,8%
2014 16,2% 8,8% 12,2%
2015 16,6% 8,7% 12,4%

OK, now I have two sets of variables: one about those inventions pertaining to wind turbines with vertical axis, and another one about the share of renewables in the overall energy consumption. Both are presented in the form of time series. What I can do with them both is to check for their mutual correlation. Among the many possible coefficients of correlation, I go for a classic: the Pearson correlation coefficient. I start checking that correlation in pairs of time series, for each geographical region separately. Table 3, below, presents the results, which are a bit puzzling. Before discussing them, let me introduce to the little presentational trick I am doing. In that table, I ascribed symbols to lines – [A] and [B] – and to columns, as consecutive roman numbers from [I] to [III]. It is just for the sake of convenience. In order not to repeat, each time, that long name ‘correlation between … and …’, I can just say ‘correlation [I][A]’ and everybody knows I am talking about the coefficient in the left upper case of the matrix etc. So, armed with that little editorial subterfuge, I develop my interpretation further below, underneath Table 3.

 

Table 3 – Matrix of Pearson correlation coefficients between the incidence of patent applications pertaining to wind turbines with vertical axis, and the share of renewable energy in the total consumption of energy

    Share of renewable energy in the total consumption of energy
  European Union United States China
    [I] [II] [III]
Number of patent applications pertaining to wind turbine with vertical axis [A] 0,945423082 0,986363487 -0,776361916
% share in the total of ‘wind turbine’ patent applications [B] 0,288279238 0,321381613 0,722853177

 I start with correlations [I][A] and [II][A]. They are high, and, I you want my opinion, they are surprisingly high. I didn’t expect such high values. They mean that the respective pairs of variables determine each other’s variance like around 90%, and this is very nearly a perfect congruence. It is as if each kilowatt hour of renewable energy literally dragged an invention about wind turbines with vertical axis out of the void of wannabe ideas, and vice versa.

Now, correlations [III][A], [I][B], and [II][B] do not really make me gasp for air. Looking at the numbers being correlated, these coefficients come as sort of logical. On the other hand, the last one, the [III][B] once again surprises me with its high positive value.

Good, time to come to the ad rem, as one of my professors in the law studies used to say. I asked a question: how can this particular technology, namely those vertical wind rotors, interact with its social environment? My first conclusion is that it interacts differently, depending on where it is actually interacting. In Europe and in the United States it interacts in a really strange, extremely patterned manner, as if each invention pertaining to those vertical Aeolian rotors had strings attached to it, and as if those strings had their other extremity anchored in a different invention in the wind energy, and to a kilowatt hour of renewables. Once again, believe, such strong, stable, structural patterns happen really seldom, particularly between so different phenomena. It looks almost like a Cartesian mechanism, with cogwheels moving each other. In China, that interaction is different, sort of less rigid and less determinist. There is some play in the game, over there.

All that little research about wind turbines with vertical axis turns weird. This is another of those empirical observations, which look extremely interesting, whilst I wish I could phrase out what they mean. I can cautiously formulate a working hypothesis, that the technologies of wind turbines make systems of different coherence according to the geographical region of the world, and that in some regions, those systems can be extremely determinist. Still, as scientific standards come, this is more a sketch of a hypothesis, rather than truly rigorous stuff.

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?

 

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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.

Demogr13_1 

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?

This is how I got the first numerical column

 

My editorial on You Tube

And so I am developing the concept of Coop EneFin, which I hinted at inLean and adaptableand started developing more seriously in La morale de ce conte de fées. The whole idea comes from the observation that, in the European market of electricity, there is a strong differentiation in the retail price of 1 kWh, depending on the category of consumer. Small users, namely all the households plus small institutional ones, pay a price much higher than the big consumers of energy. I am designing the business concept of Coop EneFinas a way for small, local suppliers of renewable energies to attract capital and to find themselves a place in the market. 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.

If the price to pay by small users is PH, and the price for the big institutional ones is PI, and a representative small user consumes QHkilowatt hours, that basic concept can be expressed mathematically as QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI). In that mathematical expression, ‘t’ is the present moment in time, whilst ‘t+z’ is a moment in the future, with said future being distant from the present by ‘z’ periods. K(t)is investment capital supplied today, to the provider of electricity, by the means of this complex contract.

In other words, the Coop EneFinconcept assumes that households will buy their future supplies in electricity, and, in the same time, they will buy participations in the providers of that future electricity, and they will pay just the normal price they pay today for their average kilowatt hour. Coop EneFinis supposed to be a business on its own right, an essentially FinTech enterprise, partly or completely independent from the suppliers of electricity.

I need to check more thoroughly the components of this business concept. It is worth exploring what exactly should I expect to find, in real life, under the label of ‘small local supplier of renewable energies’, i.e. what do those entities really look like today, what are their ties with their markets, and what are the likely vectors of development for the future. I need to develop the concept of ‘participatory deeds’, and, in general, to blueprint the financial product to be marketed. A more in-depth study of the energy market could serve, too.

There is one thing I certainly need to work on for Coop EneFin: the name. I need to change it. I was so engrossed in the ‘cooperative’ meaning of ‘Coop’ that I completely forgot other connotations, such as ‘chicken coop’. We certainly don’t want any business to stay in a coop, unless it is money laundering. Coops are safe, but sort of limiting. Thus, I am trying to extract some other catchy word from that idea, and, in the meantime, I simply kick the ‘Coop’ out of the name, and I return to the initial ‘EneFin’.

Mind you, I have that curious ape inside me, and that happy bulldog. They love rummaging in anything that can be even remotely useful in my intellectual quest. Here are some sources those two helpful beasts have dug out of the Internet, just like that, on the spot. European Small Hydropower Association, Wind Europe, Solar Power Europe, and World Energy Councilare the ORG-type pages, just as IRENA. The latter (Irena) publishes a lot of useful stuff regarding renewable energies. Here are the links to some of their reports: Renewable technologies cost analysis – hydropower, Renewable Power Generation Costs in 2017, and Cost-competitive renewable power generation: Potential across South East Europe. Besides, I collected some stuff, here and there online: ‘The Economics of Hydroelectricity’ by Jean-Marie Martin-Amouroux, ‘Hydropower Costs. Renewable Energy Hydroelectricity Costs vs Other Renewable & Fossil Costs’ by Glenn Meyers, ‘Hydropower Baseline Cost Modeling’ by Patrick W. O’Connor et al. , and finally State of the Art on Small-Scale Concentrated Solar Power Plantsby A.Giovannelli.

Right, now it is time for the third inside me, my internal austere monk, the one armed against bullshit with the Ockham’s razor, to step into the game. Let’s nail it down: what is a small provider of renewable energy? As I rummage through the literature, being small has different denotations, depending on the exact type of renewable energy we have in mind. In the wind energy, being small is probably the hardest job. One windmill of average size generates about 1,5 MW of electrical power, and still there is one caveat: noise. Ever heard one of those buskers who perform with a two-person buck saw? That long, flexible blade played on with a fiddlestick? If the answer is ‘yes’, now imagine that performance by someone deprived both of musical ear, and of elementary skill with a fiddlestick. This is the type of noise that windmills make, partly in infrasound. Really nasty, I can tell you, and this is why they have to be located some distance from human habitats.

There seems to be some new generation of windmills coming to the market, though. As I can read with Vanessa Bates Ramirez at SingularityHub, a company named Semtiveis launching small windmills, like 1,6 kilowatt each, designed for being used in densely populated, urban habitats. Another one is the Dutch Archimedes, and those guys are doing really small as wind turbines come. Their designs range from 125 watts of power, up to 1 kW. This is really retail in wind energy. The two designs differ substantially from each other, yet both create an opening for reducing both the size of one windmill, and the distance it needs to be located from residential buildings. As a matter of fact, the distance shrinks to zero. That 125-watt thingy by Archimedes is something you can basically drag behind you on a bicycle. As I think of it, my EneFin concept QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI)could be a nice financial leverage for launching those technologies among the general public.

As for hydro, you can find all sizes: from a fancy-looking small one, 1 kW of capacity, from PowerSpout, all the way up to the 30-megawatt bulky ones by General Electric. Still, as I browsed through my notes from the last year, there are two thresholds as for the hydroelectric: 1 MW and 10 MW. Anything up to one megawatt is basically considered as DIY power generation, and between 1 MW and 10 MW the installation can be still eligible for public funding addressed to ‘small hydro’. All kinds of designs are burgeoning; it seems to be like the Golden Age of small hydro. You can even have embroideries on.

The photovoltaic is probably the most scalable, with a typical roof-of-my-garage installation going into something like 200 – 300 watts, and possible to expand according to the available surface. Yet, photovoltaic is not the only cat in the yard, as it comes to solar energy. There is that big comeback from the part of concentrated solar power. Do you remember those science-fictionish movies, mostly from the 1980ies, where solar energy was being captured with parabolic mirrors (occasionally turned by evil geniuses into deadly weapons)? Well, this is basically concentrated solar power. You capture the heat in the centre of the parabolic mirror, and then it becomes really hot, and it can give heat to water, which turns into steam and puts in motion the basic electric turbine you have in an ordinary, thermal power plant. Heat can be stored in molten salt during the night, so as not to turn the turbine off completely. In places with really a lot of heat from the sun, like from Marseille (France) southwards, you can have the most of your sun with that technology. The paper I have already linked to, namely State of the Art on Small-Scale Concentrated Solar Power Plantsby A.Giovannelli gives an idea of what is possible. Apparently, the possible is quite versatile, starting below 1 MW of power.

So, all in all, I have two classes of size, out of my research. One is around 1 MW of capacity, the second more like 10 MW. I call them, respectively, a small power installation, and a medium-sized one. Now, I go one step further and I follow Adam Smith: the size of a business is determined by the size of its market. I take my two model sizes: 1 MW and 10 MW, and I calculate the number of individual customers that such an installation could provide with electricity. Table 1, below, shows my calculations. What I did was to take the data about final consumption of energy, in kilograms of oil equivalent, as it is published by the World Bank. Then, I took 17,3% out of this final consumption, for selected European countries. That 17,3% roughly corresponds, according to what I found, to the strictly spoken household use of energy. Then I multiplied the number in kilograms of oil equivalent by 11,63 in order to have it in kilowatt hours. This is how I got the first numerical column in the table. Next, I divided the kilowatt hours by 8760, i.e. by the number of hours in an ordinary year, and so I got the capacity presented in the next column, measured in kilowatts. After having divided 1000 kW (or 1 megawatt) by that required capacity, I obtained the number of households that an installation of 1 MW could possibly supply in electricity, should they switch completely to the services of said installation.

Table 1 

Country Estimated household use of energy, kWh per annum per household Capacity needed for 1 household, in kW Number of households supplied by a 1 MW installation
Austria 7 654,60 0,87 1 144
Switzerland 5 955,64 0,68 1 471
Czech Republic 7 766,29 0,89 1 128
Germany 7 680,87 0,88 1 140
Spain 5 173,51 0,59 1 693
Estonia 8 396,69 0,96 1 043
Finland 11 920,44 1,36 735
France 7 419,85 0,85 1 181
United Kingdom 5 561,10 0,63 1 575
Netherlands 8 516,84 0,97 1 029
Norway 11 701,35 1,34 749
Poland 5 010,27 0,57 1 748
Portugal 4 288,92 0,49 2 042

 The size of the market nailed down, I turn to its value. I return to my QH(t+z)*PH= QH(t+z)*PI+ K(t)and K(t) = QH(t+z)*(PH– PI)golden recipe, and I consider the prices in question. Just to update those, who have not quite followed so far: the whole scheme consists in selling futures contracts on electricity to households, paid nominally at the ordinary household rate per 1 kW, only that ordinary rate buys them electricity at non-household prices, much lower, and, additionally, participatory deeds in the balance sheet of the supplier.

Anyway, I take prices of energy as they are, and I calculate the things you can find in Table 2, below. What I call ‘Revenue from the local market of a 1 MW installation, at non-household prices’ is the market value of electricity sold, and non-household prices to the population of households calculated in the last column of Table 1. The so-called capital contribution from the same population is the amount paid in as the surplus of the household price over the non-household price of energy. Now, I take a value which I found online – €2 445 – which apparently corresponds to the cost of physical investment in 1 kW of capacity in small hydro. It makes €2 445 000 for 1 MW, and I divide my ‘Capital contribution’ by that sum. What I get is the estimated contribution of said capital contribution to the physical setting up of the installation. Why hydro? I am a bit obsessed with it, I admit. You can find an explanation with Impakter.

Germany looks like the best market for my EneFin scheme, hands down, once again. Spain, Austria, Poland, and Portugal follow at a respectable distance.

Table 2

Country Price of electricity for households, per 1 kWh Non-household price of electricity, per 1 kWh Revenue from the local market of a 1 MW installation, at non-household prices Capital contribution from the local market of a 1 MW installation Estimated percentage of the physical investment needed in small hydro
Austria € 0,20 € 0,09 € 788 400,00 € 963 600,00 39%
Switzerland € 0,19 € 0,10 € 898 517,81 € 765 882,19 31%
Czech Republic € 0,14 € 0,07 € 613 200,00 € 613 200,00 25%
Germany € 0,35 € 0,15 € 1 314 000,00 € 1 752 000,00 72%
Spain € 0,23 € 0,11 € 963 600,00 € 1 051 200,00 43%
Estonia € 0,12 € 0,09 € 788 400,00 € 262 800,00 11%
Finland € 0,16 € 0,07 € 613 200,00 € 788 400,00 32%
France € 0,17 € 0,10 € 876 000,00 € 613 200,00 25%
United Kingdom € 0,18 € 0,13 € 1 138 800,00 € 438 000,00 18%
Netherlands € 0,16 € 0,08 € 700 800,00 € 700 800,00 29%
Norway € 0,17 € 0,07 € 613 200,00 € 876 000,00 36%
Poland € 0,15 € 0,09 € 788 400,00 € 525 600,00 21%
Portugal € 0,23 € 0,12 € 1 051 200,00 € 963 600,00 39%

 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?

The other cheek of business

My editorial

I am turning towards my educational project. I want to create a step-by-step teaching method, where I guide a student in their learning of social sciences, and this learning is by doing research in social sciences. I have a choice between imposing some predefined topics for research, or invite each student to propose their own. The latter seems certainly more exciting. As a teacher, I know what a brain storm is, and believe: a dozen dedicated and bright individuals, giving their ideas about what they think it is important to do research about, can completely uproot your (my own?) ideas as what it is important to do research about. Still, I can hardly imagine me, individually, handling efficiently all that bloody blissful diversity of ideas. Thus, the first option, namely imposing some predefined topics for research, seems just workable, whilst still being interesting. People can get creative about methods of research, after all, not just about topics for it. Most of the great scientific inventions was actually methodology, and what was really breakthrough about it consisted in the universal applicability of those newly invented methods.

Thus, what I want to put together is a step-by-step path of research, communicable and teachable, regarding my own topics for research. Whilst I still admit the possibility of student-generated topics coming my way, I will consider them as a luxurious delicacy I can indulge in under the condition I can cope with those main topics. Anyway, my research topics for 2018 are:

  1. Smart cities, their emergence, development, and the practical ways of actually doing business there
  2. Fintech, and mostly cryptocurrencies, and even more mostly those hybrid structures, where cryptocurrencies are combined with the “traditional” financial assets
  • Renewable energies
  1. Social and technological change as a manifestation of collective intelligence

Intuitively, I can wrap (I), (II), and (III) into a fancy parcel, decorated with (IV). The first three items actually coincide in time and space. The fourth one is that kind of decorative cherry you can put on a cake to make it look really scientific.

As I start doing research about anything, hypotheses come handy. If you investigate a criminal case, assuming that anyone could have done anything anyhow gives you certainly the biggest possible picture, but the picture is blurred. Contours fade and dance in front on your eyes, idiocies pop up, and it is really hard to stay reasonable. On the other hand, if you make some hypotheses as for who did what and how, your investigation gathers both speed and sense. This is what I strongly advocate for: make some hypotheses at the starting point of your research. Before I go further with hypothesising on my topics for research, a few preliminary remarks can be useful. First of all, we always hypothesise about anything we experience and think. Yes, I am claiming this very strongly: anything we think is a hypothesis or contains a hypothesis. How come? Well, we always generalise, i.e. we simplify and hope the simplification will hold. We very nearly always have less data than we actually need to make the judgments we make with absolute certainty. Actually, everything we pretend to claim with certainty is an approximation.

Thus, we hypothesise intuitively, all the time. Now, I summon the spirit of Milton Friedman from the abyss of pre-Facebook history, and he reminds us the four basic levels of hypothesising. Level one: regarding any given state of nature, we can formulate an indefinitely great number of hypotheses. In practice, there is infinitely many of them. Level two: just some of those infinitely many hypotheses are checkable at all, with the actual access to data I have. Level three: among all the checkable hypotheses, with the data at hand, there are just some, regarding which I can say with reasonable certainty whether they are true or false. Level four: it is much easier to falsify a hypothesis, i.e. to say under what conditions it does not hold at all, than to verify it, i.e. claiming under what conditions it is true. This comes from level one: each hypothesis has cousins, who sound almost exactly the same, but just almost, so under given conditions many mutually non-exclusive hypotheses can be true.

Now, some of you could legitimately ask ‘Good, so I need to start with formulating infinitely many hypotheses, then check which of them are checkable, then identify those allowing more or less rigorous scientific proof? Great. It means that at the very start I get entangled for eternity into checking how checkable is each of the infinitely many hypotheses I can think of. Not very promising as for results’. This is legit to say that, and this is the reason why, in science, we use that tool known as the Ockham’s razor. It serves to give a cognitive shave to badly kept realities. In its traditional form it consists in assuming that the most obvious answer is usually the correct one. Still, as you have a closer look at this precise phrasing, you can see a lot of hidden assumptions. It assumes you can distinguish the obvious from the dubious, which, in turn, means that you have already applied the razor beforehand. Bit of a loop. The practical way of wielding that razor is to assume that the most obvious thing is observable reality. I start with finding my bearings in reality. Recently, I gave an example of that: check ‘My individual square of land, 9 meters on 9’  . I look around and I assess what kind of phenomena, which, at this stage of research, I can intuitively connect to the general topic of my research, and which I can observe, measure, and communicate intelligibly about. These are my anchors in reality.

I look at those things, I measure them, and I do my best to communicate by observations to other people. This is when the Ockham’s razor is put to an ex post test: if the shave has been really neat, other people can easily understand what I am communicating. If I and a bunch of other looneys (oops! sorry, I wanted to say ‘scientists’) can agree on the current reading of the density of population, and not really on the reading of unemployment (‘those people could very well get a job! they are just lazy!), then the density of population is our Ockham’s razor, and unemployment not really (I love the ‘not really’ expression: it can cover any amount of error and bullshit). This is the right moment for distinguishing the obvious from the dubious, and to formulate my first hypotheses, and then I move backwards the long of the Milton Friedman’s four levels of hypothesising. The empirical application of the Ockham’s razor has allowed me to define what I can actually check in real life, and this, in turn, allows distinguishing between two big bags, each with hypotheses inside. One bag contains the verifiable hypotheses, the other one is for the speculative ones, i.e. those non-verifiable.

Anyway, I want my students to follow a path of research together with me. My first step is to organize the first step on this path, namely to find the fundamental, empirical bearings as for those four topics: smart cities, Fintech, renewable energies and collective intelligence. The topic of smart cities certainly can find its empirical anchors in the prices of real estate, and in the density of population, as well as in the local rate of demographic growth. When these three dance together – once again, you can check ‘My individual square of land, 9 meters on 9’ – the business of building smart cities suddenly gets some nice, healthy, reddish glow on its cheeks. Businesses have cheeks, didn’t you know? Well, to be quite precise, businesses have other cheeks. The other cheek, in a business, is what you don’t want to expose when you already get hit somewhere else. Yes, you could call it crown jewels as well, but other cheek sounds just more elegantly.

As for Fintech, the first and most obvious observation, from my point of view, is diversity. The development of Fintech calls into existence many different frameworks for financial transactions in times and places when and where, just recently, we had just one such framework. Observing Fintech means, in the first place, observing diversity in alternative financial frameworks – such as official currencies, cryptocurrencies, securities, corporations, payment platforms – in the given country or industry. In terms of formal analytical tools, diversity refers to a cross-sectional distribution and its general shape. I start with I taking a convenient denominator. The Gross Domestic Product seems a good one, yet you can choose something else, like the aggregate value of intellectual property embodied in selfies posted on Instagram. Once you have chosen your denominator, you measure the outstanding balances, and the current flows, in each of those alternative, financial frameworks, in the units of your denominator. You get things like market capitalization of Ethereum as % of GDP vs. the supply of US dollar as % of its national GDP etc.

I pass to renewable energies, now. When I think about what is the most obviously observable in renewable energies, it is a dual pattern of development. We can have renewable sources of energy supplanting fossil fuels: this is the case in the developed countries. On the other hand, there are places on Earth where electricity from renewable sources is the first source of electricity ever: those people simply didn’t have juice to power their freezer before that wind farm started up in the whereabouts. This is the pattern observable in the developing countries. In the zone of overlapping, between those two patterns, we have emerging markets: there is a bit of shifting from fossils to green, and there is another bit of renewables popping up where nothing had dared to pop up in the past. Those patterns are observable as, essentially, two metrics, which can possibly be combined: the final consumption of energy per capita, and the share of renewable sources in the final consumption of energy. Crude as they are, they allow observing a lot, especially when combined with other variables.

And so I come to collective intelligence. This is seemingly the hardest part. How can I say that any social entity is kind of smart? It is even hard to say in humans. I mean, virtually everybody claims they are smart, and I claim I’m smart, but when it comes to actual choices in real life, I sometimes feel so bloody stupid… Good, I am getting a grip. Anyway, intelligence for me is the capacity to figure out new, useful things on the grounds of memory about old things. There is one aspect of that figuring out, which is really intriguing my internal curious ape: the phenomenon called ultra-socialisation, or supersocialisation. I am inspired, as for this one, by the work of a group of historians: see ‘War, space, and the evolution of Old World complex societies’ (Turchin et al. 2013[1]). As a matter of fact, Jean Jacques Rousseau, in his “Social Contract”, was chasing very much the same rabbit. The general point is that any group of dumb assholes can get social on the level of immediate gains. This is how small, local societies emerge: I am better at running after woolly mammoths, you are better at making spears, which come handy when the mammoth stops running and starts arguing, and he is better at healing wounds. Together, we can gang up and each of us can experience immediate benefits of such socialisation. Still, what makes societies, according to Jean Jacques Rousseau, as well as according to Turchin et al., is the capacity to form institutions of large geographical scope, which require getting over the obsession of immediate gains and provide long-term, developmental a kick. What is observable, then, are precisely those institutions: law, state, money, universally enforceable contracts etc.

Institutions – and this is the really nourishing takeaway from that research by Turchin et al. (2013[2]) – are observable as a genetic code. I can decompose institutions into a finite number of observable characteristics, and each of them can be observable as switched on, or switched off. Complex institutional frameworks can be denoted as sequences of 1’s and 0’s, depending on whether the given characteristics is, respectively, present or absent. Somewhere between the Fintech, and collective intelligence, I have that metric, which I found really meaningful in my research: the share of aggregate depreciation in the GDP. This is the relative burden, imposed on the current economic activity, due to the phenomenon of technologies getting old and replaced by younger ones. When technologies get old, accountants accounts for that fact by depreciating them, i.e. by writing off the book a fraction of their initial value. All that writing off, done by accountants active in a given place and time, makes aggregate depreciation. When denominated in the units of current output (GDP), it tends to get into interesting correlations (the way variables can socialize) with other phenomena.

And so I come with my observables: density of population, demographic growth, prices of real estate, balances and flows of alternative financial platforms expressed as percentages of the GDP, final consumption of energy per capita, share of renewable energies in said final consumption, aggregate depreciation as % of the GDP, and the genetic code of institutions. What I can do with those observables, is to measure their levels, growth rates, cross-sectional distributions, and, at a more elaborate level, their correlations, cointegrations, and their memory. The latter can be observed, among other methods, as their Gaussian vector autoregression, as well as their geometric Brownian motion. This is the first big part of my educational product. This is what I want to teach my students: collecting that data, observing and analysing it, and finally to hypothesise on the grounds of basic observation.

[1] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

[2] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

Quite abundant a walk of life

My editorial

I have just finished writing an article about the link between energy and human settlement. You could have noticed that I have been kind of absent from scientific blogging for a few days. I had my classes starting, at the university, and this was the first reason, but the second one was precisely that article. On Wednesday, I started doing some calculations, well in the lines of that latest line of my research (you can look up ‘Core and periphery’ ). Nothing very serious, just some casual dabbling with numbers. You know, when you are an economist, you start having cold turkey symptoms when you are parted with an Excel spreadsheet. From time to time, you just need to do some calculations, and so I was doing when, suddenly, those numbers started making sense. It is a peculiar feeling when numbers start making sense, because usually, you just kind of feel that sense but you don’t exactly know what it actually is. That was exactly my case, on Wednesday. I started playing with the parameters of that general equilibrium, with population size on the left side of the equation, and energy use, as well as food intake, on the other side. All of a sudden, that theoretical equilibrium started yielding real, robust, local equilibria in individual countries. Then, something just fired off in my mind. My internal happy bulldog, you know, that little beast who just loves biting into big, juicy loafs of data, really bit in. My internal ape, that curious and slightly impolite part of me, went to force the bulldog’s jaws open, but it got fascinated. My internal austere monk, that really-frontal-cortex guy inside of me, who walks around with the Ockham’s razor ready to slash into bullshit, had to settle the matters. He said: ‘Good, folks, as you are, we need to hatch an article, and we do it know’. You don’t discuss with a guy who has a big razor, and so all of me wrote this article. Literally all of me. It was the first time, since I was 22 (bloody long ago), that I spent a night awake, writing. The result, for the moment in the pre-editorial form, is entitled ‘Settlement by energy – can renewable energies sustain our civilisation?’  and you can read it just by clicking this link.

Anyway, now I am in a post-article frame of mind, which means I need to shake it off a bit. What I usually do in terms of shaking off is having conversations with dead people. No, I don’t need candles. One of my favourite and not-quite-alive-anymore interlocutors is Jacques Savary, a merchant and public officer, who, in 1675, two years after both the real and the fictional d’Artagnan had been dead, published, with the privilege of the King, and through the industrious efforts of the publishing house run by Louis Billaine, located at the Second Pillar of the Grand Salle of the Palace, at Grand Cesar, a book entitled, originally, ‘Le Parfait Négociant ou Instruction Générale Pour Ce Qui Regarde Le Commerce’. In English, that would be ‘The Perfect Merchant or General Instructions as Regards Commerce’. And so I am summoning Master Savary from the after world of social sciences, and we start chatting about what he wrote regarding manufactures (Book II, Chapter XLV and XLVI). First, a light stroke of brush to paint the general landscape. Back in the days, in the second half of the 17th century, manufactures meant mostly textile and garments. There was some industrial activity in other goods (glass, tapestry), but the bulk of industry was about cloth, in many forms. People at the time were really inventive as it came to new types of cloth: they experimented with mixing cotton, wool and silk, in various proportions, and they experimented with dyeing (I mean, they experimented with dying, as well, but we do it all the time), and they had fashions. Anyway, textile and garment was THE industry.

As Master Savary starts his exposition about manufactures, he opens up with a warning: manufactures can lead you to ruin. Interesting opening for an instruction. The question is why? Or rather, how? I mean, how could a manufacturing business lead to ruin? Well, back in the day, in 17th century, in Europe, manufacturing activities used to be quite separated institutionally from the circulation of big money. Really big business was being done mostly in trade, and large-scale manufacturing was seen as kind of odd. In trade, merchants of the time devised various legal tools to speed up the circulation of capital. Bills of exchange, maritime insurance, tax farming – it all allowed, with just the right people to know, a really smooth flow of money, even in the presence of many-year-long maritime commercial trips. In manufacturing, many of those clever tricks didn’t work, or at least didn’t work yet. They had to wait, those people, some 200 years before manufacturing would become really smooth a way of circulating capital. Anyway, putting money in manufacturing meant that you could not recover it as quickly as you could in trade. Basically, when you invested in manufactures, you were much more dependent on the actual marketability of your actual products than you were in trade. Thus, many merchants, Master Savary obviously included, perceived manufacturing as terribly risky.

What did he recommend in the presence of such dire risk? First of all, he advised to distinguish between three strategies. One, imitate a foreign manufacture. Second, invent something new and set a new manufacture. Third, invest in ‘an already established Manufacture, whose merchandise has an ordinary course in the Kingdom as well as in foreign Countries, by the general consent of all the people who had recognized its goodness, in the use of fabric which have been manufactured there’. I tried to translate literally the phrasing of the last strategy, in order to highlight the key points of the corresponding business plan. An established manufacture meant, first of all, the one with ‘an ordinary course in the Kingdom as well as in foreign Countries’. Ordinary course meant a predictable final selling price. As a matter of fact, this is my problem with that translation. Master Savary originally used the French expression: ‘cours ordinaire’, which, in English, becomes ambiguous. First, it can mean ‘ordinary course’, i.e. something like an established channel of distribution. Still, it can also mean ‘ordinary rate of exchange’. Why ‘rate of exchange’? We are some 150 years before the development of modern, standardized monetary systems. We are even some 100 years before the appearance of paper money. There were coins, and there was a s***load of other things you could exchange your goods against. At Master Savary’s time, many things were currencies. In business, you traded your goods against various types of coins, you accepted bills of exchange instead of coins, you traded against gold and silver in ingots, as well, and finally, you did barter. Some young, rich, and spoilt marquis had lost some of its estates by playing cards, he signed some papers, and here you are, with the guy who wants to buy your entire stock of woollen garments and who wants to pay you precisely with those papers signed by the young marquis. If you were doing really big business, none of your goods has one price: instead, they all had complex exchange rates against other valuables. Trading goods with what Master Savary originally called ‘cours ordinaire’ meant that the goods in question were kind of predictable as for their exchange rate against anything else in that economic jungle of the late 17th century.

What worked on the selling side, had to work on the supply side as well. You had to buy your raw materials, your transport, your labour etc. at complex exchange rates, and not at those nice, tame, clearly cut prices in one definite currency. Making the right match between exchange rates achieved when purchasing things, and those practiced at the end of the value chain was an art, and frequently a pain in your ass. In other words, business in 17th century was very much like what we would have now if our banking and monetary systems collapsed. Yes, baby, them bankers are mean and abjectly rich, but they keep that wheel spinning smoothly, and you don’t have to deal with Somalian pirates in order to buy from them some drugs, which you are going to exchange against natural oil in Yemen, which, in turn, you will use to back some bills of exchange, which will allow you to buy cotton for your factory.

Now, let’s return to what Master Savary had to say about those three strategies for manufacturing. As he discusses the first one – imitating a foreign factory – he recommends five wise things to do. One, check if you can achieve exactly the same quality of fabric as those bloody foreigners do. If you cannot, there is no point in starting imitation. Two, make sure you can acquire your raw materials, in the necessary bracket of quality, in the place where you locate your manufacture. Three, make sure the place where you locate your operations will allow you to practice prices competitive as compared to those foreign goods you are imitating. Four, create for yourself conditions for experimenting with your product and your business. Launch some kind of test missiles in many directions, present your fabrics to many potential customers. In other words, take your time, bite your ambition, suck ass and make your way into the market step by step. Five, arrange for acquiring the same tools, and even the same people that work in those foreign manufactures. Today, we would say: acquire the technology, both the formal, and the informal one.

As he passes to discussing the second strategy, namely inventing something new, Master Savary recommends even more prudence, and, in the same time, he pulls open a bit the veil of discretion regarding his own life, and confesses that he, in person, had invented three new fabrics during his business career: a thick woollen ribbon made of camel wool, a thick drugget for making simple, coarse, work clothes, and finally a ribbon made of woven gold and silver. Interesting. Here is a guy, who started his professional life as a merchant, then he went into commercial arbitrage for some time, then he went into the service of a rich aristocrat ( see ‘Comes the time, comes the calm duke’ ), then he entered into a panel of experts commissioned by Louis XIV, the Sun King, to prepare new business law, and in the meantime he invented decorative ribbons for rich people, as well as coarse fabrics for poor people. Quite abundant a walk of life. As I am reading the account of his textile inventions, he seems to be the most attached to, and the most vocal about that last one, the gold and silver ribbon. He insists that nobody before him had ever succeeded in weaving gold and silver into something wearable. He describes in detail all the technological nuances, like for example preventing the chipping off of the very thinly pulled, thread size, golden wire. He concludes: ‘I have given my own example, in order to make those young people, who want to invent new Manufactures, understand they should take their precautions, not to engage imprudently and not to let themselves being carried away by the profits they will make on their first fabrics, and to have a great number of them fabricated, before being certain they will be pleasant to the public, as well as for their beauty as for quality; for it is really dangerous, and they will risk their fortune at it’.