We, the average national economy. Research and case study in finance.

 

My editorial on You Tube

 

I am returning to a long-followed path of research, that on financial solutions for promoting renewable energies, and I am making it into educational content for my course of « Fundamentals of Finance ». I am developing on artificial intelligence as well. I think that artificial intelligence is just made for finance. Financial markets, and the contractual patterns they use, are akin endocrine systems. They generate signals, more or less complex, and those signals essentially say: ‘you lazy f**ks, you need to move and do something, and what that something is supposed to be you can read from between the lines of those financial instruments in circulation’. Anyway, what I am thinking about is to use artificial intelligence for simulating the social change that a financial scheme, i.e. a set of financial instruments, can possibly induce in the ways we produce and use energy. This update is at the frontier of scientific research, business planning, and education strictly spoken. I know that some students can find it hard to follow, but I just want to show real science at work, 100% pure beef.

 

I took a database which I have already used in my research on the so-called energy efficiency, i.e. on the amount of Gross Domestic Product we can derive on the basis of 1 kilogram of oil equivalent. It is a complex indicator of how efficient a given social system is as regards using energy for making things turn on the economic side. We take the total consumption of energy in a given country, and we convert it into standardized units equivalent to the amount of energy we can have out of one kilogram of natural oil. This standardized consumption of energy becomes the denominator of a coefficient, where the nominator consists in the Gross Domestic Product. Thus, it goes like “GDP / Energy consumed”. The greater the value of that coefficient, i.e. the more dollars we derive from one unit of energy, the greater is the energy efficiency of our economic system.

 

Since 2012, the global economy has been going through an unprecedentedly long period of expansion in real output[1]. Whilst the obvious question is “When will it crash?”, it is interesting to investigate the correlates of this phenomenon in the sector of energy. In other terms, are we, as a civilisation more energy-efficient as we get (temporarily) much more predictable in terms of economic growth? The very roots of this question are to find in the fundamental mechanics of our civilisation. We, humans, are generally good at transforming energy. There is a body of historical and paleontological evidence that accurate adjustment of energy balance was one of the key factors in the evolutionary success of humans, both at the level of individual organisms and whole communities (Leonard, Robertson 1997[2]; Robson, Wood 2008[3]; Russon 2010[4])

When we talk about energy efficiency of the human civilisation, it is useful to investigate the way we consume energy. In this article, the question is being tackled by observing the pace of growth in energy efficiency, defined as GDP per unit of energy use (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD?view=chart ). The amount of value added we can generate out of a given set of production factors, when using one unit of energy, is an interesting metric. It shows energy efficiency as such, and, in the same time, the relative complexity of the technological basket we use. As stressed, for example, by Moreau and Vuille (2018[5]), when studying energy intensity, we need to keep in mind the threefold distinction between: a) direct consumption of energy b) transport c) energy embodied in goods and services.

One of the really deep questions one can ask about the energy intensity of our culture is to what extent it is being shaped by short-term economic fluctuations. Ziaei (2018[6]) proved empirically that observable changes in energy intensity of the U.S. economy are substantial, in response to changes in monetary policy. There is a correlation between the way that financial markets work and the consumption of energy. If the relative increase in energy consumption is greater than the pace of economic growth, GDP created with one unit of energy decreases, and vice versa. There is also a mechanism of reaction of the energy sector to public policies. In other words, some public policies have significant impact on the energy efficiency of the whole economy. Different sectors of the economy respond with different intensity, as for their consumption of energy, to public policies and to changes in financial markets. We can assume that a distinct sector of the economy corresponds to a distinct basket of technologies, and a distinct institutional outset.

Faisal et al. (2017[7]) found a long-run correlation between the consumption of energy and real output of the economy, studying the case of Belgium. Moreover, the same authors found significant causality from real output to energy consumption, and that causality seems to be uni-directional, without any significant, reciprocal loop.

Energy efficiency of national economies, as measured with the coefficient of GDP per unit of energy (e.g. per kg of oil equivalent), should take into account that any given market is a mix of goods – products and services – which generate aggregate output. Any combination “GDP <> energy use” is a combination of product markets, as well as technologies (Heun et al. 2018[8]).

There is quite a fruitful path of research, which assumes that aggregate use of energy in an economy can be approached in a biological way, as a metabolic process. The MuSIASEM methodological framework seems to be promising in this respect (e.g. Andreoni 2017[9]). This leads to a further question: can changes in the aggregate use of energy be considered as adaptive changes in an organism, or in generations of organisms? In another development regarding the MuSIASEM framework, Velasco-Fernández et al (2018[10]) remind that real output per unit of energy consumption can increase, on a given basis of energy supply, through factors other than technological change towards greater efficiency in energy use. This leads to investigating the very nature of technological change at the aggregate level. Is aggregate technological change made only of engineering improvements at the microeconomic level, or maybe the financial reshuffling of the economic system counts, too, as adaptive technological change?

The MuSIASEM methodology stresses the fact that international trade, and its accompanying financial institutions, allow some countries to externalise industrial production, thus, apparently, to decarbonise their economies. Still, the industrial output they need takes place, just somewhere else.

From the methodological point of view, the MuSIASEM approach explores the compound nature of energy efficiency measured as GDP per unit of energy consumption. Energy intensity can be understood at least at two distinct levels: aggregate and sectoral. At the aggregate level, all the methodological caveats make the « GDP per kg of oil equivalent » just a comparative metric, devoid of much technological meaning. At the sectoral level, we get closer to technology strictly spoken.

There is empirical evidence that at the sectoral level, the consumption of energy per unit of aggregate output tends to: a) converge across different entities (regions, entrepreneurs etc.) b) tends to decrease (see for example: Yu et al. 2012[11]).

There is also empirical evidence that general aging of the population is associated with a lower energy intensity, and urbanization has an opposite effect, i.e. it is positively correlated with energy intensity (Liu et al. 2017[12])

It is important to understand, how and to what extent public policies can influence the energy efficiency at the macroeconomic scale. These policies can either address directly the issue of thermodynamic efficiency of the economy, or just aim at offshoring the most energy – intensive activities. Hardt et al. (2018[13]) study, in this respect, the case of United Kingdom, where each percentage of growth in real output has been accompanied, those last years, by a 0,57% reduction in energy consumption per capita.

There is grounds for claiming that increasing energy efficiency of national economies matters more for combatting climate change that the strictly spoken transition towards renewable energies (Weng, Zhang 2017[14]). Still, other research suggest that the transition towards renewable energies has an indirectly positive impact upon the overall energy efficiency: economies that make a relatively quick transition towards renewables seem to associate that shift with better efficiency in using energy for creating real output (Akalpler, Shingil 2017[15]).

It is to keep in mind that the energy efficiency of national economies has two layers, namely the efficiency of producing energy in itself, as distinct from the usage we make of the so-obtained net energy. This is the concept of Energy Return on Energy Invested (EROI), (see: Odum 1971[16]; Hall 1972[17]). Changes in energy efficiency can occur on both levels, and in this respect, the transition towards renewable sources of energy seems to bring more energy efficiency in that first layer, i.e. in the extraction of energy strictly spoken, as compared with fossil fuels. The problematically slow growth in energy efficiency could be coming precisely from the de-facto decreasing efficiency of transformation in fossil fuels (Sole et al. 2018[18]).

 

Technology and social structures are mutually entangled (Mumford 1964[19], McKenzie 1984[20], Kline and Pinch 1996[21]; David 1990[22], Vincenti 1994[23]; Mahoney 1988[24]; Ceruzzi 2005[25]). An excellent, recent piece of research by Taalbi (2017[26]) attempts a systematic, quantitative investigation of that entanglement.

The data published by the World Bank regarding energy use per capita in kg of oil equivalent (OEPC) (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ) allows an interesting insight, when combined with structural information provided by the International Energy Agency (https://www.iea.org). As one ranks countries regarding their energy use per capita, the resulting hierarchy is, in the same time, a hierarchy in the broadly spoken socio-economic development. Countries displaying less than 200 kg of oil equivalent per capita are, in the same time, barely structured as economies, with little or no industry and transport infrastructure, with quasi-inexistent institutional orders, and with very limited access to electricity at the level of households and small businesses.  In the class comprised between 200 kg OEPC and approximately 600 ÷ 650 kg OEPC, one can observe countries displaying progressively more and more development in their markets and infrastructures, whilst remaining quite imbalanced in their institutional sphere. Past the mark of 650 OEPC, stable institutions are observable. Interestingly, the officially recognised threshold of « middle income », as macroeconomic attribute of whole nations, seems corresponding to a threshold in energy use around 1 500 kg OEPC. The neighbourhood of those 1 500 kg OEPC looks like the transition zone between developing economies, and the emerging ones. This is the transition towards really stable markets, accompanied by well-structured industrial networks, as well as truly stable public sectors. Finally, as income per capita starts qualifying a country into the class of « developed economies », that country is most likely to pass another mark of energy consumption, that of 3000 kg OEPC. This stylized observation of how energy consumption is linked to social structures is partly corroborated by other research, e.g. that regarding social equality in the access to energy (see for example: Luan, Chen 2018[27])

The nexus of energy use per capita, on the one hand, and institutions on the other hand, has even found a general designation in recent literature: “energy justice”. A cursory review of that literature demonstrates the depth of emotional entanglement between energy and social structures: it seems to be more about the connection between energy and self-awareness of societies than about anything else (see for example: Fuller, McCauley 2016[28]; Broto et al. 2018[29]). The difficulty in getting rid of emotionally grounded stereotypes in this path of research might have its roots in the fact that we can hardly understand what energy really is, and attempts at this understanding send us to the very foundations of our understanding as for what reality is (Coelho 2009[30]; McKagan et al. 2012[31]; Frontali 2014[32]). Recent research, conducted from the point of view of management science reveal just as recent an emergence of new, virtually unprecedented, institutional patterns in the sourcing and the use of energy. A good example of that institutional change is to find in the new role of cities as active players in the design and implementation of technologies and infrastructures critical for energy efficiency (see for example: Geels et al. 2016[33]; Heiskanen et al. 2018[34]; Matschoss, Heiskanen 2018[35]).

 

Changes observable in the global economy, with respect to energy efficiency measured as GDP per unit of energy consumed, are interestingly accompanied by those in the supply of money, urbanization, as well as the shift towards renewable energies. Years 2008 – 2010, which marked, with a deep global recession, the passage towards currently experienced, record-long and record-calm period of economic growth, displayed a few other interesting transitions. In 2008, the supply of broad money in the global economy exceeded, for the first documented time, 100% of the global GDP, and that coefficient of monetization (i.e. the opposite of the velocity of money) has been growing ever since (World Bank 2018[36]). Similarly, the coefficient of urbanization, i.e. the share of urban population in the global total, exceeded 50% in 2008, and has kept growing since (World Bank 2018[37]). Even more intriguingly, the global financial crisis of 2007 – 2009 took place exactly when the global share of renewable energies in the total consumption of energy was hitting a trough, below 17%, and as the global recovery started in 2010, that coefficient started swelling as well, and has been displaying good growth since then[38]. Besides, empirical data indicates that since 2008, the share of aggregate amortization (of fixed assets) in the global GDP has been consistently growing, after having passed the cap of 15% (Feenstra et al. 2015[39]). Some sort of para-organic pattern emerges out of those observations, where energy efficiency of the global economy is being achieved through more intense a pace of technological change, in the presence of money acting as a hormone, catabolizing real output and fixed assets, whilst anabolizing new generations of technologies.

 

Thus, I have that database, which you can download precisely by clicking this link. One remark: this is an Excel file, and when you click on the link, it downloads without further notice. There is no opening on the screen. In this set, we have 12 variables: i) GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) ii) Fixed assets per 1 resident patent application iii) Share of aggregate depreciation in the GDP – speed of technological obsolescence iv) Resident patent applications per 1 mln people v) Supply of broad money as % of GDP vi)

Energy use per capita (kg of oil equivalent) vii) Depth of the food deficit (kilocalories per person per day) viii) Renewable energy consumption (% of total final energy consumption) ix) Urban population as % of total population x) GDP (demand side) xi) GDP per capita, and finally xii) Population. My general, intuitive idea is to place energy efficiency in a broad socio-economic context, and to see what role in that context is being played by financial liquidity. In simpler words, I want to discover how can the energy efficiency of our civilization be modified by a possible change in financial liquidity.

 

My database is a mix-up of 59 countries and years of observation ranging from 1960 to 2014, 1228 records in total. Each record is the state of things, regarding the above-named variables, in a given year. In quantitative research we call it a data panel. You have bits of information inside and you try to make sense out of it. I like pictures. Thus, I made some. These are the two graphs below. One of them shows the energy efficiency of national economies, the other one focuses on the consumption of energy per capita, and both variables are being shown as a function of supply of broad money as % of GDP. I consider the latter to be a crude measure of financial liquidity in the given place and time. The more money is being supplied per unit of Gross Domestic Product, the more financial liquidity people have as for doing something with them units of GDP. As you can see, the thing goes really all over the place. You can really say: ‘that is a cloud of points’. As it is usually the case with clouds, you can see any pattern in it, except anything mathematically regular. I can see a dung beetle in the first one, and a goose flapping its wings in the second. Many possible connections exist between the basic financial liquidity of the economic system, on the one hand, and the way we use energy, on the other hand.

 

I am testing my database for general coherence. In the table below, I am showing the arithmetical average of each variable. As you hopefully know, since Abraham de Moivre we tend to assume that arithmetical average of a large sample of something is the expected value of that something. Thus, the table below shows what we can reasonably expect from the database. We can see a bit of incoherence. Mean energy efficiency is $8,72 per kg of oil equivalent in energy. Good. Now, I check. I take the energy consumption per capita and I multiply in by the number of capitae, thus I go 3 007,28 * 89 965 651 =  270 551 748,43 tons of oil equivalent. This is the amount of energy consumed in one year by the average expected national society of homo sapiens in my database. Now, I divide the average expected GDP in the sample, i.e. $1 120 874,23 mln, by that expected total consumption of energy, and I hit just $1 120 874,23 mln / 270 551 748,43 tons = $4,14 per kilogram.

 

It is a bit low, given that a few sentences ago the same variable was supposed to be$8,72 per kg. This is just a minor discrepancy as compared to the GDP per capita, which is the central measure of wealth in a population. The average calculated straight from the database is $22 285,63. Cool. This is quite a lot, you know. Now, I check. I take the aggregate average GDP per country, i.e.  $1 120 874,23 mln, and I divide it by the average headcount of population, i.e. I go $1 120 874 230 000 / 89 965 651 =  $12 458,91. What? $12 458,91 ? But it was supposed to be is $22 285,63! Who took those 10 thousand dollars away from me? I mean, $12 458,91 is quite respectable, it is just a bit below my home country, Poland, presently, but still… Ten thousand dollars of difference? How is it possible?

 

It is so embarrassing when numbers are not what we expect them to be. As a matter of fact, they usually aren’t. It is just our good will that makes them look so well fitting to each other. Still, this is what numbers do, when they are well accounted for: they embarrass. As they do so, they force us to think, and to dig meaning out from underneath the numbers. This is what quantitative analysis in social sciences is supposed to do: give us the meaning that we expect when we measure things about our own civilisation.

 

Table 1 – Average values from the pooled database of N = 1228 country-year observations

Variable Average expected value from empirical data, N = 1228 records
GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent) 8,72
Fixed assets per 1 resident patent application (constant 2011 PPP $) 3 534,80
Share of aggregate depreciation in the GDP – speed of technological obsolescence 14%
Resident patent applications per 1 mln people – speed of invention 158,90
Supply of broad money % of GDP – observed financial liquidity 74,60%
Energy use (kg of oil equivalent per capita) 3 007,28 kg
Depth of the food deficit (kilocalories per person per day) 26,40
Renewable energy consumption (% of total final energy consumption) 16,05%
Urban population as % of total population 69,70%
GDP (demand side; millions of constant 2011 PPP $) 1 120 874,23
GDP per capita (constant 2011 PPP $) $22 285,63
Population 89 965 651

 

Let’s get back to the point, i.e. to finance. As I explain over and over again to my students, when we say ‘finance’, we almost immediately need to say: ‘balance sheet’. We need to think in terms of a capital account. Those expected average values from the table can help us to reconstruct at least the active side of that representative, expected, average economy in my database. There are three variables which sort of overlap: a) fixed assets per 1 resident patent application b) resident patent applications per 1 mln people and c) population. I divide the nominal headcount of population by 1 000 000, and thus I get population denominated in millions. I multiply the so-denominated population by the coefficient of resident patent applications per 1 mln people, which gives me, for each country and each year of observation, the absolute number of patent applications in the set. In my next step, I take the coefficient of fixed assets per 1 patent application, and I multiply it by the freshly-calculated-still-warm absolute number of patent applications.

 

Now, just to make it arithmetically transparent, when I do (« Fixed assets » / « Patent applications ») * « Patent applications », I take a fraction and I multiply it by its own denominator. It is de-factorisation. I stay with just the nominator of that initial fraction, thus with the absolute amount of fixed assets. For my representative, average, expected country in the database, I get Fixed Assets = $50 532 175,96 mln.

 

I do slightly the same with money. I take “Supply of money as % of the GDP”, and I multiply it by the incriminated GDP, which makes Money Supplied = 74,60% * $1 120 874,23 mln =  $836 213,98 mln. We have a fragment in the broader balance sheet of our average expected economy: Fixed Assets $50 532 175,96 mln and Monetary Balances $836 213,98 mln. Interesting. How does it unfold over time? Let’s zeee… A bit of rummaging, and I get the contents of Table 2, below. There are two interesting things about that table.

 

 

Table 2 – Changes over time in the capital account of the average national economy

Year Average fixed assets per national economy, $ mln constant 2011 PPP GDP per unit of energy use (constant 2011 PPP $ per kg of oil equivalent), in the average national economy Supply of broad money in average national economy, $ mln constant 2011 PPP Money to fixed assets
1990 2 036 831,928 8,08 61,526 0,0030%
1991 1 955 283,198 8,198 58,654 0,0030%
1992 2 338 609,511 8,001 61,407 0,0026%
1993 2 267 728,024 7,857 60,162 0,0027%
1994 2 399 075,082 7,992 60,945 0,0025%
1995 2 277 869,991 7,556 60,079 0,0026%
1996 2 409 816,67 7,784 64,268 0,0027%
1997 2 466 046,108 7,707 71,853 0,0029%
1998 2 539 482,259 7,76 77,44 0,0030%
1999 2 634 454,042 8,085 82,987 0,0032%
2000 2 623 451,217 8,422 84,558 0,0032%
2001 2 658 255,842 8,266 88,335 0,0033%
2002 2 734 170,979 8,416 92,739 0,0034%
2003 2 885 480,779 8,473 97,477 0,0034%
2004 3 088 417,325 8,638 100,914 0,0033%
2005 3 346 005,071 8,877 106,836 0,0032%
2006 3 781 802,623 9,106 119,617 0,0032%
2007 4 144 895,314 9,506 130,494 0,0031%
2008 4 372 927,883 9,57 140,04 0,0032%
2009 5 166 422,174 9,656 171,191 0,0033%
2010 5 073 697,622 9,62 164,804 0,0032%
2011 5 702 948,813 9,983 178,381 0,0031%
2012 6 039 017,049 10,112 195,487 0,0032%
2013 6 568 280,779 10,368 205,159 0,0031%
2014 5 559 781,782 10,755 161,435 0,0029%

 

This is becoming really interesting. Both components in the capital account of the representative, averaged economy had been growing until 2013, then it fell. Energy efficiency has been growing quite consistently, as well. The ratio of money to assets, thus a crude measure of financial liquidity in this capital account, remains sort of steady, with a slight oscillation. You can see it in the graph below. I represented all the variables as fixed-base indexes: the value recorded for the year 2000 is 1,00, and any other value is indexed over that one. We do that thing all the time, in social sciences, when we want to study apparently incompatible magnitudes. A little test of Pearson correlation, and… Yesss! Energy efficiency is Pearson correlated with the amount of fixed assets at r = 0,953096394, and with the amount of money supplied at r = 0,947606073. All that in the presence of more or less steady a liquidity.

 

Provisional conclusion: the more capital we accumulate, we, the average national economy, the more energy efficient we are, and we sort of dynamically adjust to keep the liquidity of that capital, at least the strictly monetary liquidity, at a constant level.

 

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

 

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Locally smart. Case study in finance.

 

My editorial on You Tube

 

Here I go, at the frontier between research and education. This is how I earn my living, basically, combining research and education. I am presenting and idea I am currently working on, in a team, regarding a financial scheme for local governments. I am going to develop it here as a piece of educational material for my course « Fundamentals of Finance ». I am combining educational explanation with specific techniques of scientific research.

 

Here is the deal: creating a financial scheme, combining pooled funds, crowdfunding, securities, and cryptocurriences, for facilitating smart urban development through the creation of local start-up businesses. A lot of ideas in one concept, but this is science, for one, and thus anything is possible, and this is education, for two, hence we need to go through as many basic concepts as possible. It goes more or less as follows: a local government creates two financial instruments, a local investment fund, and a local crowdfunding platform. Both serve to facilitate the creation and growth of local start-ups, which, in turn, facilitate smart urban development.

 

We need a universe in order to do anything sensible. Good. Let’s make a universe, out of local governments, local start-up businesses, and local projects in smart urban development. Projects are groups of people with a purpose and a commitment to achieve it together. Yes, wars are projects, just as musical concerts and public fundraising campaigns for saving the grey wolf. Projects in smart urban development are groups of people with a purpose and a commitment to do something interesting about implementing new technologies into the urban infrastructures and this improving the quality, and the sustainability of urban life.

 

A project is like a demon. It needs a physical body, a vessel to carry out the mission at hand. Projects need a physical doorstep to put a clear sign over it. It is called ‘headquarters’, it has an official address, and we usually need it if we want to do something collective and social. This is where letters from the bank should be addressed to. I have the idea to embody local projects of smart urban development in physical bodies of local start-up businesses. This, in turn, implies turning those projects into profitable ventures. What is the point? A business has assets and it has equity. Assets can back equity, and liabilities. Both equity and liabilities can be represented with financial instruments, namely tradable securities. With that, we can do finance.

 

Why securities? The capital I need, and which I don’t have, is the capital somebody is supposed to entrust with me. Thus, by acquiring capital to finance my project, I give other people claims on the assets I am operating with. Those people will be much more willing to entrust me with their capital if those claims are tradable, i.e. when they can back off out of the business really quickly. That’s the idea of financial instruments: making those claims flow and float around, a bit like water.

 

Question: couldn’t we just make securities for projects, without embodying them in businesses? Problematic. Any financial instrument needs some assets to back it up, on the active side of the balance sheet. Projects, as long as they have no such back up in assets, are not really in a position to issue any securities. Another question: can we embody those projects in institutional forms other than businesses, e.g. foundations, trusts, cooperatives, associations? Yes, we can. Each institutional form has its pluses and its minuses. Business structures have one peculiar trait, however: they have at their disposal probably the broadest range of clearly defined financial instruments, as compared to other institutional forms.

 

Still, we can think out of the box. We can take some financial instruments peculiar to business, and try to transplant them onto another institutional body, like that of an association. Let’s just try and see what happens. I am a project in smart urban development. I go to a notary, and I write the following note: “Whoever hands this note on December 31st of any calendar year from now until 2030, will be entitled to receive 20% of net profits after tax from the business identified as LHKLSHFKSDHF”. Signature, date of signature, stamp by the notary. Looks like a security? Mmmwweeelll, maybe. Let’s try and put it in circulation. Who wants my note? What? What do I want in exchange? Let’s zeeee… The modest sum of $2 000 000? You good with that offer?

 

Some of you will say: you, project, you stop right there and you explain a few things. First of all, what if you really have those profits, and 20% of them really make it worth to hand you $2 000 000 now? How exactly can anyone claim those 20%? How will they know the exact sum they are entitled to? Right, say I (project), we need to write some kind of contract with those rules inside. It can be called corporate bylaw, and we need to write it all down. For example, what if somebody has this note on December 31st, 2025, and then they sell it to someone else on January 2nd, 2026, and the profits for 2025 will be really accounted for like in February 2026 at best, and then, who is entitled to have those 20% of profits: the person who had the note on December 31st, 2025, or the one presenting it in 2026, when all is said and done about profits? Sort of tricky, isn’t it? The note says: ‘Whoever hands this note on December 31st… etc.’, only the act of handing is now separated from the actual disclosure of profits. We keep that in mind: the whole point of making a claim into a security is to make it apt for circulation. If the circulation in itself becomes too troublesome, the security loses a lot of its appeal.

 

See? This note contains a conditional claim. Someone needs to hand the note at the right moment and in the right place, there need to be any profit to share etc. That’s the thing about conditional claims: you need to know exactly how to apprehend those conditions, which the claim is enforceable upon.

 

As I think about the exact contents of that contract, it looks like me and anyone holds that note are partners in business. We are supposed to share profits. Profits come from the exploitation of some assets, and they become real only after all the current liabilities have been paid. Hence, we actually share equity in those assets. The note is an equity-based security, a bit primitive, yes, certainly, still an equity-based security.

 

Another question from the audience: “Project, with all the due respect, I don’t really want to be partners in business with you. Do you have an alternative solution to propose?”. Maybe I have… What do you say about a slightly different note, like “Whoever hands this note on December 31st of any calendar year from now until 2030, will be entitled to receive $500 000 from the bank POIUYTR not later than until January 15th of the next calendar year”. Looks good? You remember what is that type of note? This is a draft, or routed note, a debt-based security. It embodies an unconditional claim, routed on that bank with an interesting name, a bit hard to spell aloud. No conditions attached, thus less paperwork with a contract. Worth how much? Maybe $2 000 000, again?

 

No conditions, yet a suggestion. If, on the one hand, I grant you a claim on 20% of my net profit after tax, and, on the other hand, I am ready to give an unconditional claim on $500 000, you could search some mathematical connection between the 20% and the $500 000. Oh, yes, and there are those $2 000 000. You are connecting the dots. Same window in time, i.e. from 2019 through 2030, which makes 11 occasions to hand the note and claim the money. I multiply occasions by unconditional claims, and I go 11*$500 000 = $5 500 000. An unconditional claim on $5 000 000 spread over 11 annual periods is being sold for $2 000 000. Looks like a ton of good business to do, still let’s do the maths properly. You could invest your $2 000 000 in some comfy sovereign bonds, for example the federal German ones. Rock solid, those ones, and they can yield like 2% a year. I simulate: $2 000 000*(1+0,02)11 =  $2 486 748,62. You pay me $2 000 000, you forego the opportunity to earn $486 748,62, and, in exchange, you receive an unconditional claim on $5 500 000. Looks good, at least at the first sight. Gives you a positive discount rate of ($5 500 000 – $2 486 748,62)/ $2 486 748,62 = 121,2% on the whole 11 years of the deal, thus 121,2%/11 = 11% a year. Not bad.

 

When you have done the maths from the preceding paragraph, you can assume that I expect, in that project of smart urban development, a future stream of net profit after tax, over the 11 fiscal periods to come, somewhere around those $5 500 000. Somewhere around could be somewhere above or somewhere below.  Now, we enter the world of behavioural finance. I have laid my cards on the table, with those two notes. Now, you try to figure out my future behaviour, as well as the behaviour to expect in third parties. When you hold a claim, on whatever and whomever you want, this claim has two financial characteristics: enforceability and risk on the one hand, and liquidity on the other hand. You ask yourself, what exactly can the current holder of the note enforce in terms of payback from my part, and what kind of business you can do by selling those notes to someone else.

 

In a sense, we are playing a game. You face a choice between different moves. Move #1: buy the equity-based paper and hold. Move #2: buy the equity-based one and sell it to third parties. Move #3: buy the debt-based routed note and hold. Move #4: buy the routed note and sell it shortly after. You can go just for one of those moves, or make a basket thereof, if you have enough money to invest more than one lump injection of $2 000 000 into my project of smart urban development.

 

You make your move, and you might wonder what kind of move will I make, and what will other people do. Down that avenue of thinking, madness lies. Finance means, very largely, domesticated madness, and thus, when you are a financial player, instead of wondering what other people will do, you look for reliable benchmarks in the existing markets. This is an important principle of finance: quantities and prices are informative about the human behaviour to expect. When you face the choice between moves #1 ÷ #4, you will look, in the first place, for and upon the existing markets. If I grant you 20% of my profits in exchange of $2 000 000, which, in fact, seem corresponding to at least $500 000 of future annual cash flow. If 20% of something is $500 000, the whole something makes $500 000/ 20% = $2 500 000. How much equity does it correspond to? Here it comes to benchmarking. Aswath Damodaran, from NYU Stern Undergraduate College, publishes average ROE (return on equity) in different industries. Let’s suppose that my project of smart urban development is focused on Environmental & Waste Services. It is urban, it claims being smart, hence it could be about waste management. That makes 17,95% of average ROE, i.e. net profit/equity = 17,95%. Logically, equity = net profit/17,95%, thus I go $2 500 000/17,95% = $13 927 576,60 and this is the equity you can reasonably expect I expect to accumulate in that project of smart urban development.

 

Among the numerous datasets published by Aswath Damodaran, there is one containing the so-called ROIC, or return on invested capital, thus on the total equity and debt invested in the business. In the same industry, i.e. Environmental & Waste Services, it is 13,58%. It spells analogously to ROE, thus it is net profit divided by the total capital invested, and, logically, total capital invested = net profit / ROIC = $2 500 000 / 13,58% = $18 409 425,63. Equity alone makes $13 927 576,60, equity plus debt makes $18 409 425,63, therefore debt = $18 409 425,63 – $13 927 576,60 =  $4 481 849,02.

 

With those rates of return on, respectively, equity and capital invested, those 11% of annual discount, benchmarked against German sovereign bonds, look acceptable. If I take a look at the financial instruments listed in the AIM market of London Stock Exchange, and I dig a bit, I can find corporate bonds, i.e. debt-based securities issued by incorporated business structures. Here come, for example, the bonds issued by 3i Group, an investment fund. They are identified with ISIN (International Securities Identification Number) XS0104440986, they were issued in 1999, and their maturity date is December 3rd, 2032. They are endowed with an interest rate of 5,75% a year, payable in two semi-annual instalments every year. Once again, the 11% discount offered on those imaginary routed notes of my project look interesting in comparison.

 

Before I go further, I am once again going to play at anticipating your questions. What is the connection between the interest rate and the discount rate, in this case? I am explaining numerically. Imagine you buy corporate bonds, like those 3i Group bonds, with an interest rate 5,75% a year. You spend $2 000 000 on them. You hold them for 5 years, and then you sell them to third persons. Just for the sake of simplifying, I suppose you sell them for the same face value you bought them, i.e. for $2 000 000. What happened arithmetically, from your point of view, can be represented as follows: – $2 000 000 + 5*5,75%*$2 000 000 + $2 000 000 = $575 000. Now, imagine that instead of those bonds, you bought, for an amount of $2 000 000,  debt-based routed notes of my project, phrased as follows: “Whoever hands this note on December 31st of any calendar year from now until Year +5, will be entitled to receive $515 000 from the bank POIUYTR not later than until January 15th of the next calendar year”. With such a draft (remember: another name for a routed note), you will total – $2 000 000 + 5*$515 000 = $575 000.

 

Same result at the end of the day, just phrased differently. With those routed notes of mine, I earn a a discount of $575 000, and with the 3i bonds, you earn an interest of $575 000. You understand? Whatever you do with financial instruments, it sums up to a cash flow. You spend your capital on buying those instruments in the first place, and you write that initial expenditure with a ‘-’ sign in your cash flow. Then you receive some ‘+’ cash flows, under various forms, and variously described. At the end of the day, you sum up the initial outflow (minus) of cash with the subsequent inflows (pluses).

 

Now, I look back, I mean back to the beginning of this update on my blog, and I realize how far have I ventured myself from the initial strand of ideas. I was about to discuss a financial scheme, combining pooled funds, crowdfunding, securities, and cryptocurriences, for facilitating smart urban development through the creation of local start-up businesses. Good. I go back to it. My fundamental concept is that of public-private partnership, just peppered with a bit of finance. Local governments do services connected to waste and environmental care. The basic way they finance it is through budgetary spending, and sometimes they create or take interest in local companies specialized in doing it. My idea is to go one step further, and make local governments create and run investment funds specialized in taking interest in such businesses.

 

One of the basic ideas when running an investment fund is to make a portfolio of participations in various businesses, with various temporal horizons attached. We combine the long term with the short one. In some companies we invest for like 10 years, and in some others just for 2 years, and then we sell those shares, bonds, or whatever. When I was working on the business plan for the BeFund project, I had a look at the shape those investment portfolios take. You can sort of follow back that research of mine in « Sort of a classical move » from March 15th, 2018. I had quite a bit of an exploration into the concept of smart cities. See « My individual square of land, 9 meters on 9 », from January 11, 2018, or « Smart cities, or rummaging in the waste heap of culture » from January 31, 2018, as for this topic. What comes out of my research is that the combination of digital technologies with the objectively growing importance of urban structures in our civilisation brings new investment opportunities. Thus, I have this idea of local governments, like city councils, becoming active investors in local businesses, and that local investment would combine the big, steady ventures – like local waste management companies – with a lot of small startup companies.

 

This basic structure in the portfolio of a local investment fund reflects my intuitive take on the way a city works. There is the fundamental, big, heavy stuff that just needs to work – waste management, again, but also water supply, energy supply etc. – and there is the highly experimental part, where the city attempts to implement radically new solutions on the grounds of radically new technologies. The usual policy that I can observe in local governments, now, is to create big local companies for the former category, and to let private businesses take over entirely the second one. Now, imagine that when you pay taxes to the local government, part of your tax money goes into an investment fund, which takes participations in local startups, active in the domain on those experimental solutions and new technologies. Your tax money goes into a portfolio of investments.

 

Imagine even more. There is local crowdfunding platform, similar to Kickstarter or StartEngine, where you can put your money directly into those local ventures, without passing by the local investment fund as a middleman. On that crowdfunding platform, the same local investment fund can compete for funding with other ventures. A cryptocurrency, internal to that crowdfunding platform, could be used to make clearer financial rules in the investment game.

 

When I filed that idea for review, in the form of an article, with a Polish scientific journal, I received back an interestingly critical review. There were two main lines of criticism. Firstly, where is the advantage of my proposed solution over the presently applied institutional schemes? How could my solution improve smart urban development, as compared to what local governments currently do? Secondly, doesn’t it go too far from the mission of local governments? Doesn’t my scheme push public goods too far into private hands and doesn’t it make local governments too capitalistic?

 

I need to address those questions, both for revising my article, and for giving a nice closure to this particular, educational story in the fundamentals of finance. Functionality first, thus: what is the point? What can be possibly improved with that financial scheme I propose? Finance has two essential functions: it meets the need for liquidity, and, through the mechanism of financial markets. Liquidity is the capacity to enter in transactions. For any given situation there is a total set T of transactions that an entity, finding themselves in this situation, could be willing to enter into. Usually, we can’t enter it all, I mean we, entities. Individuals, businesses, governments: we are limited in our capacity to enter transactions. For the given total set T of transactions, there is just a subset Ti that i-th entity can participate in. The fraction « Ti/T » is a measure of liquidity this entity has.

 

Question: if, instead of doing something administratively, or granting a simple subsidy to a private agent, local governments act as investment funds in local projects, how does it change their liquidity, and the liquidity of local communities they are the governments of? I went to the website of the Polish Central Statistical Office, there I took slightly North-East and landed in their Local Data Bank. I asked around for data regarding the financial stance of big cities in Poland, and I found out some about: Wroclaw, Lodz, Krakow, Gdansk, Kielce, and Poznan. I focused on the investment outlays of local governments, the number of new business entities registered every year, per 10 000 residents, and on population. Here below, you can find three summary tables regarding these metrics. You will see by yourself, but in a bird’s eye view, we have more or less stationary populations, and local governments spending a shrinking part of their total budgets on fixed local assets. Local governments back off from financing those assets. In the same time, there is growing stir in business. There are more and more new business entities registered every year, in relation to population. Those local governments look as if they were out of ideas as for how to work with that local business. Can my idea change the situation? I develop on this one further below those two tables.

 

 

The share of investment outlays in the total expenditures of the city council, in major Polish cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 31,8% 21,0% 19,7% 22,6% 15,3% 27,9% 19,8%
2009 34,6% 23,5% 20,4% 20,6% 18,6% 28,4% 17,8%
2010 24,2% 15,2% 16,7% 24,5% 21,2% 29,6% 21,4%
2011 20,3% 12,5% 14,5% 33,9% 26,9% 30,1% 17,1%
2012 21,5% 15,3% 12,6% 38,2% 21,9% 20,8% 16,8%
2013 15,0% 19,3% 11,0% 28,4% 18,5% 18,1% 15,0%
2014 15,6% 24,4% 16,4% 27,0% 18,6% 11,8% 17,5%
2015 18,4% 26,8% 13,7% 21,3% 23,8% 24,1% 10,2%
2016 13,3% 14,3% 11,5% 15,2% 10,7% 17,5% 9,0%
2017 11,7% 10,2% 11,5% 12,2% 14,1% 12,3% 12,0%
               
Delta 2017 – 2008 -20,1% -10,8% -8,2% -10,4% -1,2% -15,6% -7,8%

 

 

Population of major cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 632 162 747 152 754 624 455 581 205 094 557 264 1 709 781
2009 632 146 742 387 755 000 456 591 204 835 554 221 1 714 446
2010 630 691 730 633 757 740 460 509 202 450 555 614 1 700 112
2011 631 235 725 055 759 137 460 517 201 815 553 564 1 708 491
2012 631 188 718 960 758 334 460 427 200 938 550 742 1 715 517
2013 632 067 711 332 758 992 461 531 199 870 548 028 1 724 404
2014 634 487 706 004 761 873 461 489 198 857 545 680 1 735 442
2015 635 759 700 982 761 069 462 249 198 046 542 348 1 744 351
2016 637 683 696 503 765 320 463 754 197 704 540 372 1 753 977
2017 638 586 690 422 767 348 464 254 196 804 538 633 1 764 615
               
Delta 2017 – 2008 6 424 (56 730) 12 724 8 673 (8 290) (18 631) 54 834

 

Number of newly registered business entities per 10 000 residents, in major Polish cities
  City
Year Wroclaw Lodz Krakow Gdansk Kielce Poznan Warsaw
2008 190 160 200 190 140 210 200
2009 195 167 205 196 149 216 207
2010 219 193 241 213 182 238 274
2011 221 169 204 195 168 244 249
2012 228 187 230 201 168 255 274
2013 237 187 224 211 175 262 307
2014 236 189 216 217 157 267 303
2015 252 183 248 236 185 283 348
2016 265 186 251 238 176 270 364
2017 272 189 257 255 175 267 345
               
Delta 2017 – 2008 82,00 29,00 57,00 65,00 35,00 57,00 145,00

 

Let’s take two cases from the table: my hometown Krakow, and my capital Warsaw. In the former case, the negative gap in the investment outlays of the local government is – 44 mlns of zlotys – some €10 mln – and in the latter case it is minus 248,46 millions of zlotys, thus about €56,5 mln. If we want to really get after new technologies in cities, we need to top up those gaps, possibly with a surplus. How can my idea help to save the day?

 

When I try to spend €10 mln euro more on the urban fixed assets, I need to have all those €10 mln. I need to own them directly, in my balance sheet, before spending them. On the other hand, when I want to create an investment fund, which would take part in local startups, and by their intermediary would make those €10 mln worth of assets to happen in real life, I need much less. I start with the balance sheet directly attached to those assets: €10 mln in fixed assets = equity of the startup(s) + liabilities of the startup(s). Now, equity of the startup(s) = shares of our investment fund + shares of other partners. At the end of the day, the local government could finance assets of €10 mln with 1 or 2 millions of euro of own equity, maybe even less.

 

From there on, it went sort of out of hand. I have that mental fixation on things connected to artificial intelligence and neural networks. You can find the latest account in English in the update entitled « What are the practical outcomes of those hypotheses being true or false? ». If you speak French, there is a bit more, and more recent, in « Surpopulation sauvage ou compétition aux États-Unis ». Anyway, I did it. I made a neural network in order to simulate the behaviour of my financial concept. Below, I am presenting a graphical idea of that network. It combines a strictly spoken multilayer perceptron with components of deep learning: observation of the fitness function, and the feeding back of it, as well as selection and preference regarding different neural outputs of the network. I am using that neural network as a simulator of collective intelligence.

 

So, as I am assuming that we are collectively intelligent in our local communities, I make the following logical structure. Step 1: I take four input variables, as listed below. They are taken from real statistics about those 7 big Polish cities, named above – Wroclaw, Lodz, Krakow, Gdansk, Kielce, Poznan, Warsaw – over the period from 2008 through 2017.

 

Input variable 1: Investment outlays of the local government [mln]

Input variable 2: Overall expenses of the local government [mln]

Input variable 3: Population [headcount]

Input variable 4: Number of new business entities registered annually [coefficient]

 

In step 2, I attach to those real input variables an Output variable – Hypothetical variable: capital engaged in the local governments investment fund, initially calculated as if 5% of new business entities were financed with €100 000 each. I calculate the average value of that variable across the whole sample of 7 cities, and it makes €87 mln as expected value. This is the amount of money the average city among those seven could put in that local investment fund to support local startups and their projects of smart urban development.

 

In step 3, I run my neural network through the empirical data, and then I make it do additional 5000 experimental rounds, just to make it look for a match between the input variables – which can change as they want – and the output variable, which I have almost pegged at €87 mln. I say ‘almost’, as in practice the network will generate a bit of wobbling around those €87 mln. I want to see what possible configurations of the input variables can arise, through different patterns of collective learning, around that virtually pegged value of the output variable.

 

I hypothesise 5 different ways of learning, or 5 different selections in that Neuron 4 you can see in the picture above. Learning pattern #1 consists in systematically preferring the neural output of the sigmoid neural function. It is a type of function, which systematically calms down any shocks and sudden swings in input phenomena. It is like a collective pretention that whatever kind of s**t is really going on, everything is just fine. Learning pattern #2 prefers the output of the hyperbolic tangent function. This one tends to be honest, and when there is a shock, it yields a shock, without any f**kery about it. It is like a market with clear rules of competition. Learning pattern #3 takes the least error of the two functions. It is a most classical approach in neural networks. The closer I get to the expected value, the better I am learning, that sort of things. Learning pattern #4 makes an average of those two functions. The greatest value among those being averaged has the greatest impact on the resulting average. Thus, the average of two functions is like hierarchy of importance, expressed in one number. Finally, learning pattern #5 takes that average, just as #3, but it adds the component of growing resistance to new information. At each experimental round, it divides the value of the error fed back into the network by the consecutive number of the round. Error generated in round 2 gets divided by 2, and that generated in round 4000 is being divided by 4000 etc. This is like a person who, as they process new information, develops a growing sentiment of being fully schooled on the topic, and is more and more resistant to new input.

 

In the table below, I present the results of those simulations. Learning patterns #2 and #4 develop structures somehow more modest than the actual reality, expressed as empirical averages in the first numerical line of the table. These are urban communities, where that investment fund I am thinking about slightly grows in importance, in relation to the whole municipal budget. Learning patterns #1 and #3 develop crazy magnitudes in those input variables. Populations grow 9 or 10 times bigger than the present ones, the probability of having new businesses in each 10 000 people grows 6 or 7 times, and municipal budgets swell by 14 ÷ 15 times. The urban investment fund becomes close to insignificant. Learning pattern #5 goes sort of in the middle between those extremes.

 

 

  Input variable 1 Input variable 2 Input variable 3 Input variable 4 Output variable
Initial averages of empirical values  €177 mln  €996 mln                     721 083                               223  €87 mln
Type of selection in neural output Sample results of simulation with the neural network
Sigmoid preferred €2 440 mln €14 377 mln 7 093 526,21 1 328,83 €87 mln
Hyperbolic Tangent preferred €145 mln €908 mln 501 150,03 237,78 €87 mln
Least error preferred €2 213 mln €13 128 mln 6 573 058,50 1 490,28 €87 mln
Average of the two errors €122 mln €770 mln 432 702,57 223,66 €87 mln
Average of the two errors, with growing resistance to learning €845 mln €5 043 mln 2 555 800,36 661,61 €87 mln

 

What is the moral of the fairy tale? As I see it now, it means that for any given initial situation as for that financial scheme I have in mind for cities and their local governments, future development can go two opposite ways. The city can get sort of slightly smaller and smarter, with more or less the same occurrence of new businesses emerging every year. It happens when the local community learns, as a collective intelligence, with little shielding from external shocks. This is like a market-oriented city. In terms of quantitative dynamics, it makes me think about cities like Vienna (Austria), Lyon (France), or my home city, Krakow (Poland). On the other hand, the city can shield itself somehow against socio-economic shocks, for example with heavy subsidies, and then it gets out of control. It grows big like hell, and business starts just to pop around.

 

At the first sight, it seems counterintuitive. We associate market-based, open-to-shocks solutions with uncontrolled growth, and interventionist, counter-cyclical policies with sort of a tame status quo. Still, cities are strange beasts. They are like crocodiles. When you make them compete for food and territory, they grow just to a certain size, ‘cause when they grow bigger than that, they die. Yet, when you allow a crocodile to live in a place without much competition, and plenty of food around, it grows to enormous proportions.

 

My temporary conclusion is that my idea of a local investment fund to boost smart change in cities is workable, i.e. has the chances to thrive as a financial mechanism, when the whole city is open to market-based solutions and receives little shielding from economic shocks.

 

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

The painful occurrence of sometimes. Educational about insurance and financial risk.

 

My editorial on You Tube

 

Things happen sort of sometimes. You are never sure. Take a universe. Technically, there are so many conditions to meet if you want to have a decent universe that is seems a real blessing we have one. You need them electric charges, for example. We call them negative and positive, fault of a better description, but the fact is that in reality, we have two kinds of elementary particles, A and B, I mean protons and electrons, and each A repels any other A but is irresistibly attracted to any B. Same for B’s. Imagine that 50% of B’s start behaving like A’s, i.e. they are attracted by other B’s and repel A’s. You would have 50% of matter devoid of atoms, as you need A and B to behave properly, i.e. to cross-mate A+B, and avoid any A+A or B+B indecency, in order to have an atom.

Kind of stressful. You could think about an insurance. An insurance contract stipulates that the Insured pays to the Insurer a Premium, and in exchange the Insurer gives to the Insured the guarantee of paying them damages in case a specific unpleasant event happens in the future. We insure our cars against physical accident and theft, same for our houses and apartments. You can insure yourself when you travel, and you are afraid of medical bills in case something happens to your health when on the road.

I learnt, with a big surprise, when reading the magnificent Fernand Braudel’s “Civilisation and Capitalism”  , that insurance was the core business of the first really big financial institutions in Europe. Yes, we used to do banking. Yes, we did all that circulation in debt-based securities. Still, it was all sort of featherweight business. Apparently, the real heavyweights of finance appeared with the emergence of maritime insurance. When small, local bankers started offering to the owners of commercial ships those new contracts, guaranteeing to pay for their damages in case there are any, and they gave those guarantees in exchange of relatively small insurance premiums, it was, apparently, like the release of a new iPhone in the world of gadget-lovers: a craze. By offering such contracts to captains and ship owners, those local bankers had rapidly swollen to the size of really big financial institutions.

Financial instruments always have an underlying behavioural pattern. Financial instruments are what they are because we, humans, do what we do. One of the things we do is selective individuation. There is a hurricane coming your way. You board your windows, you attach your garden furniture, you lock yourself in your house, and you check the insurance of your house. You brace against the calamity as an individual. That hurricane is going to do damage. We know it. We know it is going to do damage to the whole neighbourhood. Technically, the entire local community in threatened. Still, we prepare as individuals.

As I check the insurance of my house, I can read that in exchange of a premium, which I have already paid, I can possibly receive a coverage of my damages. Do I really want things to take such a turn, which would make those damages material? With rare exceptions, not really. Yes, I have that insurance but no, I don’t want to use it. I just want to have it, and I want to avoid whatever event might make those damages payable.

I imagine other people in a similar position. All bracing for an imminent hurricane, all having their individual insurances, and all sincerely expecting not to suffer any damage covered by those insurance contracts.

This is selective individuation as it comes to the foresight of future events. I know some bad s**t is heading my way, I know it is going to hit all around, and I just expect it is not going to hit me. As it is bound to hit all around, there is bound to be some aggregate damage. The hurricane is bound to destroy property for an amount of $5 000 000. There are 500 000 people under threat. Let’s say that 24% of them think about insuring their property. How will an insurer approach the situation?

First of all, there is bound to be those $5 000 000 of damages. Seen from a financial point of view, it is a future, certain capital expenditure. I stress it very strongly: certain. What is just a probability at the individual level becomes a certainty at a given level of aggregation. What is the “given level”? Let’s suppose there is a 1% likelihood that I step on a banana skin, when walking down the street. With 100 of me, the 1% becomes 1%*100 = 100%. Sure as taxes.

You have (I hope) already studied my lectures on equity-based securities, the debt-based ones, and on monetary systems. Thus, you already know three manners of securing capital for a future, certain outlay. Way #1: create an entity, endowed with equity in some assets, and then split that equity into tradable shares, which you sell to third parties. This way is good for securing capital in the view of slightly risky ventures, with a lot of open questions as for the strategy to adopt. Way #2: borrow, possibly through issuance of promissory notes (oldie), or bonds, if you really mean business. This path is to follow when you can reasonably expect some cash flows in the future, with relatively low risk. Way #3: get hold of some highly liquid assets, somebody else’s bonds, for example, and then create a system of tokens for payment, backed with the value of those highly liquid assets. This manner of securing capital is good for large communities, endowed with a pool of recurrent needs, and recurrent, yet diffuse fears as for the future.

With insurance, we walk down a fourth avenue. There are some future capital outlays that will compensate a clear, measurable, future loss that we know is bound to happen at a certain level of social aggregation. This aggregate loss decomposes into a set of individual s**t happening to individual people, in a heterogenous manner. It is important, once again: what you can predict quite accurately is the aggregate amount of trouble, but it is much harder to predict individual occurrences inside this aggregate. What you need is a floating mass of capital, ready to rush into whatever individual situation it is needed to compensate for. We call this type of capital a pooled fund. Insurance is sort of opposite of equity or debt. With the latter two, we expect something positive to happen. With the former, we know something bad is going to occur.

According to the basic logic of finance, you look for people who will put money in this pooled fund. Let’s take those 500 000 people threatened by a hurricane and the resulting aggregate loss of $5 000 000. Let’s say that 24% of them think about insuring their property, which makes 24%*500 000 = 120 000. In order to secure the $5 000 000 we need, the basic scheme is to make those people contribute an average of $5 000 000/ 120 000 = $41,67 of insurance premium each. Now, if you take a really simplistic path of thinking, you will say: wait, $5 000 000 divided by 500 000 folks exposed makes $10 per capita, which is clearly less than the $41,67 of insurance premium to pay. Where is my gain? Rightful question, indeed. Tangible gains appear where the possible, individual loss is clearly greater than the insurance premium to pay. Those $5 000 000 of aggregate loss in property are not made as $10 times 500 000 people. It is rather made as 0,005% likelihood in each of those people to incur an individual loss of $200 000 in property. That makes 0,005%*500 000 (remember? the banana skin) = 25. Thus, we have 25 people who will certainly lose property in that hurricane. We just don’t know which 25 out of the general population 500 000 will they be. If you are likely, just likely, to lose $200 000, will you not pay $41,67 of insurance premium? Sounds more reasonable, doesn’t it?

You are not quite following my path of thinking? Calm down, I do not always do, either. Still, this time, I can explain. There are 500 000 people, right? There is a hurricane coming, and according to all the science we have, it is going to hit badly 0,005% of that population, i.e. 25 households, and the individual loss will be $200 000 on average. That makes 25*$200 000 = $5 000 000. In the total population of 500 000, some people acknowledge this logic, some others not really. Those who do are 120 000. Each of them is aware they could be among the 25 really harmed.  They want to sign an insurance contract. Their contracts taken together need to secure the $5 000 000. Thus, each of them has to contribute $41,67 of insurance premium.

At this very basic level, the whole business of insurance is sort of a balance between the real, actual risk we are exposed to, and our behavioural take on that risk. Insurance works in populations where the subset of people who really need capital to compensate damages is much smaller than the population of those who are keen to contribute to the pooled fund.

Interestingly, people are not really keen on insuring things that happen quite frequently. There is high likelihood, although lower that absolute certainty, that someone in the street will stick an old chewing gum on the seat, on a bus, and then you sit on that chewing gum and have your brand-new woollen pants badly stained. Will you insure against it? Probably not. Sort of not exactly the kind of catastrophe one should insure against. There is a certain type of risks we insure against. They need to be spectacular and measurable, and, in the same time, they need to be sufficiently uncertain so as to give us a sense of false security. That kind of trouble is certainly not going to happen to me, still, just in case, I buy that insurance.

We can behaviourally narrow the catalogue of insurable risks, by comparing insurance to hedging, which is an alternative way to shield against risk. When I hedge against a risk, I need to know what amount of capital, in some assets of mine, is exposed to possible loss. When I know that, I acquire other assets, devoid of the same risk, for a similar amount of capital. I have that house, worth $200 000, in a zone exposed to hurricanes. I face the risk of seeing my house destroyed. I buy sovereign bonds of the Federal Republic of Germany, for another $200 000. Rock solid, these ones. They will hold value for years, and can even bring me some profits. My portfolio of German bonds hedges the risk of having my house destroyed by a hurricane.

Thus, here is my choice as for shielding my $200 000 house against hurricane-related risks. Option #1: I hedge with equity in valuable assets worth $200 000 or so. Option #2: I insure, i.e. I buy a conditional claim on an insurer, for the price of $41,67. Hedging looks sort of more solid, and indeed it is. Yet, you need a lot of capital to hedge efficiently. For every penny exposed to a definite risk, you need to hedge with another penny free of that risk. Every penny doubles, sort of. Besides, the assets you hedge with can have their own, intrinsic risk, or, if they haven’t, like German sovereign bonds, you need to pay a juicy discount (price, in financial tongue) for acquiring them. Insurance is cheaper than hedging.

My intuitive perception of the financial market tells me that if somebody has enough capital to hedge efficiently against major risks, and there are assets in view, to hedge with, people will hedge rather than insure. They insure when they either have no big capital reserves at all, when they have run out of such reserves with the hedging they have already done, or when they have no assets to hedge with. When I run a pharmaceutical company and I am launching a new drug at high risk, I will probably hedge with another factory that makes plain, low risk aspirin. That makes sense. It is a sensible allocation of my capital. On the other hand, when I have a fleet of company cars worth $5 000 000, I would rather insure than hedge.

This is what people do in the presence of risk: they insure, and they hedge. They create pooled capital funds for insurance, and they make differentiated portfolios of investments for hedging. Once again: this is what people do, like really. This is how financial markets work, and this is a big reason why they exist.

As I talk about how it works, let’s have a closer look. It is finance and it is business, so what we need is a balance sheet. When, as an insurer, I collect $5 000 000 in insurance premiums to cover $5 000 000 of future damages, I have a potential liability. Here, it becomes a little tricky. Those damages have not taken place yet, and I do not need to pay them now. I am not liable yet to people I signed those insurance contracts with. Still, the hurricane is going to hit, and it is going to destroy property for $5 000 000, and then I will be liable. All in all, the principles of accounting specifically made for the insurance business impose an obligation, upon insurers, to account the insured value as a liability.

Now, a big thing. I mean, really big. Economically, most of what we call ‘public sector’ or ‘political organisations’ are pooled funds. The constitutional state can be seen as a huge pooled fund. We pay our taxes into it, and in exchange we receive security, healthcare, infrastructure, bullshit, enlightened guidance of our leaders etc. Just some of us can really experience that payoff, and, strangely enough, we don’t always want to. Yes, we all want to drive on good roads, but we don’t want to be in a situation when the assistance of a police officer is needed. Most of us wants to have nothing to do with prisons, which are also part of what this pooled fund finances.

There is a pattern in the way that pooled funds work. That pattern sums up to the residual difference between the insurance premiums actually collected, and the actual damages to be paid. A successful insurer manages to market his contracts in sufficiently big an amount so as to have a surplus of premiums collected over the damages to be paid. The part of premiums collected, which is supposed to pay for damages, is the technical reserve of the pooled fund. The residual surplus of premiums collected, over the technical reserve for damages, is de facto an investment fund, which brings a financial yield.

Most types of insurance are based on the scarcity of occurrence across space. Hurricanes do damage to just some of us, but many are willing to insure against.

There is a special type of insurance, usually observable as those special contracts called ‘life insurance’. Life insurance contracts are about death and severe illness rather than about life. When you think about it, those contracts insure a type of events, which are certainly going to happen. In ‘ordinary’ insurance we pool funds for events scarce across space: we don’t know where the hurricane is going to hit. In life insurance, we pool funds for financing occurrences 100% bound to happen to everyone, we just don’t know when.

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

More and more money just in case. Educational about money and monetary systems

 

My editorial on You Tube

 

Here comes the next, hopefully educational piece in Fundamentals of Finance. This time it is about money. Money strictly speaking. This is probably one of the hardest. Money is all around us, whether we have it or not. How to explain something so pervasive? I think the best way is to stick to facts, in the first place. I take my wallet. What’s inside? There is some cash, there is a debit card, and two credit cards. Oh, yes, and there is that payment app, SkyCash, on my phone. All that, i.e. cash + credit cards + debit card + payment app, is the money I am walking around with.

How to explain things which seem really hard to explain? One possible way is to ask THOSE questions. I mean those stupid, out of place questions. One such question is just nocking at the door of my consciousness. Are all these forms of money in my wallet just different forms of essentially the same thing, or are they rather essentially different things which just take a similar form? I mean, if this is all money, why is there not just one form of money? Why are there many forms? Why don’t I use just cash, or just a payment app? See? If anyone was in any doubt as for whether I can ask a really stupid question, here is the answer. Yes, I can.

Now, I need the really hard answer, I mean the answer to that stupid question. I observe things and try to figure something out. I observe my credit card, for example. What is that? It is a technology that allows me to tap into a credit account that a bank has allowed me. Which means that the bank studied me, and compared me to a bunch of other people, and they decided that I have a certain borrowing capacity, i.e. I am able to generate sufficient a stream of income over time to pay back a certain amount of credit. When I use a credit card, I use my future income. If this is a technology, there must have been need for its massive use. We usually make technologies for things that happen recurrently. Banks recurrently assess the amount of credit they can extend to non-bank people, and they take care of securing some kind of technology to do so. Here comes an important distinction in plastic, namely that between a credit card and a debit card. A debit card is a technology that allows me to tap into my own current bank account, which is different from my credit card account. I trust the bank with recording a certain type of transactions I make. These transactions are transfers to and from my current account. The bank is my book keeper, and, as far as a current account strictly spoken is concerned, it is a smart book keeper. I cannot make more transfers from my current account than I receive onto it. It is book keeping with a safety valve. Banks recurrently keep the record of financial transactions that people enter into, they take care of preventing negative balance on those transactions, and the temporary bottom line of such transactions is the current balance on the same people’s current accounts.

 

Good, now comes cash money. Those notes and coins I have in my wallet are any good for payment because a special bank, the Central Bank of my country, printed and minted them, put them in circulation, and guarantees their nominal (face) value. Guaranteeing means that the Central Bank can be held liable of the total nominal value of all the notes and coins in circulation. This means, in turn, that the Central Bank needs to hold assets of similar liquidity, just to balance the value of cash guaranteed. When I use cash, I indirectly use a fraction of those liquid assets held by the central bank. What kind of assets has a similar liquidity to money? Well, money, of course. The Central Bank can extend credit to commercial banks, and thus holding claims on the money those banks hold. The Central Bank can also buy the cash money guaranteed by other central banks, mostly those reliable ones. We have another behavioural pattern: governments form central banks, and those central banks hold some highly liquid assets, and they use those highly liquid assets to back a certain amount of cash they put in circulation.

Now, there is that beast called « FinTech » and all them Payment Apps we can use, like Apple Wallet. I can use a payment app in two ways: I can connect a credit card to it, or I can directly hold a monetary balance in it. Anyway, I need to register an account, and give it some liquidity. When I pay through connection with my credit card, the Payment App is just an extension of the same technology as the one in the card. On the other hand, when I hold a monetary balance with a payment app, that balance is a claim of mine on the operator of the app. That means the operator has a liability to me, and they need to hold liquid assets to balance that liability. By the way, when a bank holds my current account, the temporary balance on that account is also my claim on the bank, and the bank needs to hold some highly liquid assets to balance my current balance with them. Here comes an even more general behavioural pattern. Some institutions, called financial institutions, like commercial banks, central banks, and operators of FinTech utilities, are good at assessing the future liquidity in other agents, and hold highly liquid assets that allow them to be liable to third persons as for holding, and keeping operational, specific accounts of liabilities: current accounts and cash in circulation.

Those highly liquid assets held by financial institutions need to be similar in their transactional pattern to the liabilities served. They need to be various forms of money. A bank can extend me a credit card, because they have another bank extends them an even bigger credit card. A central bank can maintain cash in circulation because it can trust in the value of other currencies in circulation. Looks like a loop? Well, yes, ‘cause it is a loop. Monetary systems are made of trusted agents who are trusted precisely as for their capacity to maintain a reliable balance between what they owe and what they have claims on. Historically, financial institutions emerged as agents who always pay their debts.

 

Good, this is what them financial institutions do about money. What do I do about money? I hold it and I spend it. When I think about it, I hold much more than I spend. Even if I count just my current wallet, i.e. all those forms of liquidity I walk around with, it is much more than I need for my current expenses. Why do I hold something I don’t immediately need? Perhaps because I think I might need it. There is some sort of uncertainty ahead of me, and I more or less consciously assume that holding more money than I immediately need can help me facing those contingencies. It might be positive or negative. I might have to pay for sudden medical care, or I might be willing to enter into some sudden business deals. Some of the money I hold corresponds to a quantity of goods and services I am going to purchase immediately, and another part of my money is there just to assure I might be able to buy more if I need.

When I focus on the money I hold just in case, I can see another distinction. I just walk around with some extra money, and I hold a different balance of extra money in the form of savings, i.e. I have it stored somewhere, and I assume I don’t spend it now. When I use money to meet uncertainty, the latter is scalable and differentiated. There are future expenditures, usually in a more distant future, which I attempt to provide for by saving. There are others, sort of more diffuse and seemingly more immediate, which I just hold some money for in my current wallet. We use money to meet uncertainty and risk, and we adapt our use of money to our perception of that uncertainty and risk.

Let’s see how Polish people use money. To that end, I use the statistics available with the National Bank of Poland as well as those published by the World Bank. You can see a synthetic picture in the two graphs below. In the first one, you can see the so-called broad money (all the money we hold) in relation to the GDP, or to Gross Domestic Product. The GDP is supposed to represent the real amount of goods and services supplied in the country over 1 year. Incidentally, the way we compute GDP implies that it reflects the real amount of all final goods and services purchased over one year. Hence, that proportion between money supplied and GDP is that between the money we hold, and the things we buy. You can see, in the graph, that in Poland (it is the same a bit all around the world, by the way) we tend to hold more and more money in relation to the things we buy. Conclusion: we hold more and more money just in case.

In the second graph below, you can see the structure of broad money supplied in Poland, split into the so-called monetary aggregates: cash in circulation, current account money, and term deposits in money. You can see current account money gently taking over the system, with the cash money receding, and deposits sort of receding as well, still holding a larger position in the system. It looks as if we were adapting our way of using money to a more and more intense perception of diffuse, hardly predictable risks.

 

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

Unconditional claim, remember? Educational about debt-based securities

My editorial on You Tube

 

Here comes another piece of educational content, regarding the fundamentals of finance, namely a short presentation of debt-based securities. As I will be discussing that topic,  here below, I will compare those financial instruments to equity-based securities, which I already discussed in « Finding the right spot in that flow: educational about equity-based securities ».

In short, debt-based securities are financial instruments which transform a big chunk of debt, thus a big obligatory contract, into a set of small, tradable pieces, and give to that debt more liquidity.

In order to understand how debt-based securities work in finance, it is a good thing to put a few clichés on their head and make them hold that stance. First of all, we normally associate debt with a relation of power: the CREDITOR, or the person who lends to somebody else, has a dominant position over the DEBTOR, who borrows. Whilst being sometimes true, it is true just sometimes, and it is just one point of view. Debt can be considered as a way of transferring capital from entity A to entity B. Entity A has more cash than they currently need, whilst B has less. Entity A can transfer the excess of cash to B, only they need a contractual base to do it in a civilized way. In my last educational, regarding equity-based securities, I presented a way of transferring capital in exchange of a conditional claim on B’s assets, and of a corresponding decisional power: that would be investing in B’s equity. Another way is to acquire an unconditional claim on B’s future cash flows, and this is debt. Historically, both ways have been used and developed into specific financial instruments.

Anyway, the essential concept of debt-based securities is to transform one big, obligatory claim of one entity onto another entity into many small pieces, each expressed as a tradable deed (document). How the hell is it possible to transform a debt – thus future money that is not there yet – into securities? Here come two important, general concepts of finance: liquidity, and security. Liquidity, in financial terms, is something that we spontaneously associate with being able to pay whatever we need to pay in the immediate. The boss of a company can say they have financial liquidity when they have enough cash in their balance sheet to pay the bills currently on the desk. If some of those bills cannot be paid (not enough cash), the boss can say ‘Sorry, not enough liquidity’.

You can generalize from there: liquidity is the capacity to enter into new economic transactions, and to fulfil obligations resulting from such transactions. In markets that we, humans, put in place, there is a peculiar phenomenon to notice: we swing between various levels of required liquidity. In some periods, people in that market will be like immerged in routine. They will repeat the same transactions over and over again, in recurrent amounts. It is like an average Kowalski (the Polish equivalent of the English average Smith, or the French average Dupont) paying their electricity bills. Your electricity bill comes in the form of a six-month plan of instalments. Each month you will have to pay the same, fixed amount, which results from the last reading of your electricity counter. That amount is most likely to be similar to the amounts from previous six-month periods, unless you have just decided to grow some marijuana and you need extra electricity for those greenhouse lamps. If you manage to keep your head above the water, in day-to-day financial terms, you have probably incorporated those payments for electricity into your monthly budget, more or less consciously. You don’t need extra liquidity to meet those obligations. This is the state of a market, when it runs on routine transactions.

Still, there are times when a lot of new business is to be done. New technologies are elbowing their way into our industry, or a new trade agreement has been signed with another country, or the government had the excellent idea of forcing every entity in the market to equip themselves with that absolutely-necessary-thingy-which-absolutely-incidentally-is-being-marketed-by-the-minister’s-cousin. When we need to enter into new transactions, or when we just need to be ready for entering them, we need a reserve of liquidity, i.e. we need additional capacity to transact. Our market has entered into a period of heightened need for liquidity.

When I lend to someone a substantial amount of money in a period of low need for liquidity, I can just sit and wait until they pay me back. No hurry. On the other hand, when I lend during a period of increased need for liquidity, my approach is different: I want to recoup my capital as soon as possible. My debtor, i.e. the person which I have lent to, cannot pay me back immediately. If they could, they would not need to borrow from me. Stands to reason. What I can do is to express that lending-borrowing transaction as an exchange of securities against money.

You can find an accurate description of that link between actual business, its required liquidity, and all the lending business in: Adam Smith – “An Inquiry Into The Nature And Causes Of The Wealth of Nations”, Book II: Of The Nature, Accumulation, and Employment of Stock, Chapter IV: Of Stock Lent At Interest: “Almost all loans at interest are made in money, either of paper, or of gold and silver; but what the borrower really wants, and what the lender readily supplies him with, is not the money, but the money’s worth, or the goods which it can purchase. If he wants it as a stock for immediate consumption, it is those goods only which he can place in that stock. If he wants it as a capital for employing industry, it is from those goods only that the industrious can be furnished with the tools, materials, and maintenance necessary for carrying on their work. By means of the loan, the lender, as it were, assigns to the borrower his right to a certain portion of the annual produce of the land and labour of the country, to be employed as the borrower pleases.”

Here, we come to the concept of financial security. Anything in the future is subject to uncertainty and risk. We don’t know how exactly things are going to happen. This generates risk. Future events can meet my expectations, or they can do me harm. If I can sort of divide both my expectations, and the possible harm, into small pieces, and make each such small piece sort of independent from other pieces, I create a state of dispersed expectations, and dispersed harm. This is the fundamental idea of a security. How can I create mutual autonomy between small pieces of my future luck or lack thereof? By allowing people to trade those pieces independently from each other.

It is time to explain how the hell can we give more liquidity to debt by transforming it into securities. First things first, let’s see the typical ways of doing it: a note, and a bond. A note, AKA promissory note, or bill of exchange, in its most basic appearance is a written, unconditional promise to pay a certain amount of money to whoever presents the note on a given date. You can see it in the graphic below.

Now, those of you, who, hopefully, paid attention in the course of microeconomics, might ask: “Whaaait a minute, doc! Where is the interest on that loan? You told us: there ain’t free money…”. Indeed, there ain’t. Notes were invented long ago. The oldest ones we have in European museums date back to the 12th century A.D. Still, given what we know about the ways of doing business in the past, they had been used even further back. As you might know, it was frequently forbidden by the law to lend money at interest. It was called usury, it was considered at least as a misdemeanour, if not a crime, and you could even be hanged for that. In the world of Islamic Finance, lending at interest is forbidden even today.

One of the ways to bypass the ban on interest-based lending is to calculate who much money will that precise interest make on that precise loan. I lend €9000 at 12%, for one year, and it makes €9000 *12% = €1 080. I lend €9000, for one year, and I make my debtor liable for €10 080. Interest? Who’s talking about interest? It is ordinary discount!

Discount is the difference between the nominal value of a financial instrument (AKA face value), and its actual price in exchange, thus the amount of money you can have in exchange of that instrument.

A few years ago, I found that same pattern in an innocently-looking contract, which was underpinning a loan that me and my wife were taking for 50% of a new car. The person who negotiated the deal at the car dealer’s announced joyfully: ‘This is a zero-interest loan. No interest!’. Great news, isn’t it? Still, as I was going through the contract, I found that we have to pay, at the signature, a ‘contractual fee’. The fee was strangely precise, I mean there were grosze (Polish equivalent of cents) after the decimal point. I did my maths: that ‘contractual fee’ was exactly and rigorously equal to the interest we would have to pay on that loan, should it be officially interest-bearing at ordinary, market rates.

The usage of discount instead of interest points at an important correlate of notes, and debt-based securities in general: risk. That scheme with pre-calculated interest included into the face value of the note is any good when I can reliably predict when exactly will the debtor pay back (buy the note back). Moreover, as the discount is supposed to reflect pre-calculated interest, it also reflects that part of the interest rate, which accounts for credit risk.

There are 1000 borrowers, who borrow from a nondescript number of lenders. Each loan bears a principal (i.e. nominal amount) of €3000, which makes a total market of €3 000 000 lent and borrowed. Out of those 1000, a certain number is bound to default on paying back. Let it be 4%. It makes 4% * 1000 * €3000 = €120 000, which, spread over the whole population of borrowers makes €120 000/ 1000 = €120, or €120/€3000 = 4%. Looks like a logical loop, and for a good reason: you cannot escape it. In a large set of people, some will default on their obligations. This is a fact. Their collective default is an aggregate financial risk – credit risk – which has to be absorbed by the market, somehow. The simplest way to absorb it is to make each borrower pay a small part of it. When I take a loan, in a bank, the interest rate I pay always reflects the credit risk in the whole population of borrowers. When I issue a note, the discount I have to give to my lender will always include the financial risk that recurrently happens in the given market.

The discount rate is a price of debt, just as the interest rate. Both can be used, and the prevalence of one or the other depends on the market. Whenever debt gets massively securitized, i.e. transformed into tradable securities, discount becomes somehow handier and smoother to use. Another quote from invaluable Adam Smith sheds some light on this issue (

Adam Smith – “An Inquiry Into The Nature And Causes Of The Wealth of Nations”, Book II: Of The Nature, Accumulation, and Employment of Stock, Chapter IV: Of Stock Lent At Interest): “As the quantity of stock to be lent at interest increases, the interest, or the price which must be paid for the use of that stock, necessarily diminishes, not only from those general causes which make the market price of things commonly diminish as their quantity increases, but from other causes which are peculiar to this particular case. As capitals increase in any country, the profits which can be made by employing them necessarily diminish. It becomes gradually more and more difficult to find within the country a profitable method of employing any new capital. There arises, in consequence, a competition between different capitals, the owner of one endeavouring to get possession of that employment which is occupied by another; but, upon most occasions, he can hope to justle that other out of this employment by no other means but by dealing upon more reasonable terms.”

The presence of financial risk, and the necessity to account for it whilst maintaining proper liquidity in the market, brought two financial inventions: endorsement, and routed notes. Notes used to be (and still are) issued for a relatively short time, usually not longer than 1 year. If the lender needs to have their money back before the due date of the note, they can do something called endorsement: they can present that note as their own to a third party, who will advance them money in exchange. Presenting a note as my own means making myself liable for up to 100% of the original, i.e signing the note, with a date. You can find an example in the graphic below.

Endorsement used to be a normal way of assuring liquidity in the market financed with notes. Endorsers’ signatures made a chain of liability, ordered by dates. The same scheme is used today in cryptocurrencies, as the chain of hash-tagged digital signatures. Another solution was to put in the system someone super-reliable, like a banker. Such a trusted payer, who, on their part, had tons of reserve money to provide liquidity, made the whole game calmer and less risky, and thus the price of credit (the discount rate) was lower. The way of putting a banker in the game was to write them in the note as the entity liable for payment. Such a note was designated as a routed one, or as a draft. Below, I am presenting an example.

As banks entered the game of securitized debt, it opened the gates of hell, i.e. the way to paper money. Adam Smith was very apprehensive about it (Adam Smith – “Wealth of Nations”, Book II: Of The Nature, Accumulation, and Employment of Stock, Chapter II: Of Money, Considered As A Particular Branch Of The General Stock Of The Society, Or Of The Expense Of Maintaining The National Capital”): “The trader A in Edinburgh, we shall suppose, draws a bill upon B in London, payable two months after date. In reality B in Lon- don owes nothing to A in Edinburgh; but he agrees to accept of A ‘s bill, upon condition, that before the term of payment he shall redraw upon A in Edinburgh for the same sum, together with the interest and a commission, another bill, payable likewise two months after date. B accordingly, before the expiration of the first two months, redraws this bill upon A in Edinburgh; who, again before the expiration of the second two months, draws a second bill upon B in London, payable likewise two months after date; and before the expiration of the third two months, B in London redraws upon A in Edinburgh another bill payable also two months after date. This practice has sometimes gone on, not only for several months, but for several years together, the bill always returning upon A in Edinburgh with the accumulated interest and com- mission of all the former bills. The interest was five per cent. in the year, and the commission was never less than one half per cent. on each draught. This commission being repeated more than six times in the year, whatever money A might raise by this expedient might necessarily have cost him something more than eight per cent. in the year and sometimes a great deal more, when either the price of the commission happened to rise, or when he was obliged to pay compound interest upon the interest and commission of former bills. This practice was called raising money by circulation”

Notes were quick to issue, but a bit clumsy when it came to financing really big ventures, like governments. When you are a king, and you need cash for waging war on another king, issuing a few notes can be tricky. Same in the corporate sector. When we are talking about really big money, making the debt tradable is just one part, and another part is to make it nicely spread over the landscape. This is how bonds came into being, as financial instruments. The idea of bonds was to make the market of debt a bit steadier across space and over time. Notes worked well for short-term borrowing, but long-term projects, which required financing for 5 or 6 years, encountered a problem of price, i.e. discount rate. If I issue a note to back a loan for 5 years, the receiver of the note, i.e. the lender, knows they will have to wait really long to see their money back. Below, in the graphic, you have the idea explained sort of in capital letters.

The first thing is the face value. The note presented earlier proudly displayed €10 000 of face value. The bond is just €100. You divide €10 000 into 100 separate bonds, each tradable independently, at you have something like a moving, living mass of things, flowing, coming and going. Yep, babe. Liquidity, liquidity, and once again liquidity. A lot of small debts flows much more smoothly than one big.

The next thing is the interest. You can see it here designated as “5%, annuity”, with the word ‘coupon’ added. If we have the interest rate written explicitly, it means the whole thing was invented when lending at interest became a normal thing, probably in the late 1700ies. The term ‘annuity’ means that every year, those 5% are being paid to the holder of the bond, like a fixed annual income. This is where the ‘word’ coupon comes from. Back in the day, when bonds were paper documents (they are not anymore), they had detachable strips, as in a cinema ticket, one strip per year. When the issuer of the bond paid annuities to the holders, those strips were being cut off.

The maturity date of the bond is the moment, when the issuer is supposed to buy it back. It is a general convention that bonds are issued for many years. This is when the manner of counting and compound the interest plays a role, and this is when we need to remind one fundamental thing – bonds are made for big borrowers. Anyone can make a note, and many different anyones can make it circulate, by endorsement or else. Only big entities can issue bonds, and because they are big, bonds are usually considered as safe placements, endowed with low risk. Low risk means low price of debt. When I can convince many small lenders that I, the big borrower, am rock solid in my future solvency, I can play on that interest rate. When I guarantee an annuity, it can be lower than the interest paid only at the very end of maturity, i.e. in 2022 as regards this case. When all around us all of them loans are given at 10% or 12%, an annuity backed with the authority of a big institution can be just 5%, and no one bothers.

Over time, bonds have dominated the market of debt. They are more flexible, and thus assure more liquidity. They offer interesting possibilities as for risk management and discount. When big entities issue bonds, it is the possibility for other big entities to invest large amounts of capital at fixed, guaranteed rate of return, i.e. the interest rates. Think about it: you have an investment the size of a big, incorporated business, and yet you have a risk-free return. Unconditional claim, remember? Hence, over time, what professional investors started doing was building a portfolio of investment with equity-based securities for high yield and high risk, plain lending contracts for moderate yield (high interest rate) and moderate risk, and, finally, bonds for low yield and low risk. Creating a highly liquid market of debt, by putting a lot of bonds into circulation, was like creating a safe harbour for investors. Whatever crazy s**t they were after, they could compensate the resulting risk through the inclusion of bonds in their portfolios.

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

Finding the right spot in that flow: educational about equity-based securities

 

My editorial on You Tube

 

I am returning to educational content, and more specifically to finance. Incidentally, it is quite connected to my current research – crowdfunding in the market of renewable energies – and I feel like returning to the roots of financial theory. In this update, I am taking on a classical topic in finance: equity-based securities.

First things first, a short revision of what is equity. We have things, and we can have them in two ways. We can sort of have them, or have them actually. When I have something, like a house worth $1 mln, and, in the same time, I owe to somebody $1,2 mln, what is really mine, at the end of the day, is a debt of $1 mln – $1,2 mln = – $0,2 mln. As a matter of fact, I have no equity in this house. I just sort of have it. In the opposite case, when the house is worth $1,2 mln and my debt is just $1 mln, I really have $1,2 – $1 mln = $0,2 mln in equity.

There is a pattern in doing business: when we do a lot of it, we most frequently do it in a relatively closed circle of recurrent business partners. Developing durable business relations is even taught in business studies as one of the fundamental skills. When we recurrently do business with the same people, we have claims on each other. Some people owe me something, I owe something to others. The capital account, which we call « balance sheet », expresses the balance between those two types of claims: those of other people on me, against my claims on other people. The art of doing business consists very largely in having more claims on others than others have on us. That “more” is precisely our equity.

When we do business, people expect us to have and maintain positive equity in it. A business person is expected to have that basic skill of keeping a positive balance between claims they have on other people, and the claims that other people have on them.

There are two types of business people, and, correspondingly, two types of strategies regarding equity in business. Type A is mono-business. We do one business, and have one equity. Type B is multi-business. Type B is a bit ADHDish: those are people who would like to participate in oil drilling, manufacturing of solar modules, space travel to Mars, launching a new smartphone, and growing some marijuana, all in the same or nearly the same time. This is a fact of life that the wealthiest people in any social group are to be found in the second category. There is a recurrent pattern of climbing the ladder of social hierarchy: being restless, or at least open in the pursuit of different business opportunities rather than being consistent in pursuing just one. If you think about it, it is something more general: being open to many opportunities in life offers a special path of personal development. Yes, consistency and perseverance matter, but they matter even more when we can be open to novelty, and consistent in the same time.

We tend to do things together. This is how we survived, over millennia, all kinds of s**t: famine, epidemies, them sabretooth tigers and whatnot. Same for business: over time, we have developed institutions for doing business together.

When we do something again and again, we figure out a way of optimizing the doing of that something. In business law, we (i.e. homo sapiens) have therefore invented institutions for both type A, and type B. You look for doing the same business for a long time, and doing it together with other people, type A just like you? You will look for something like a limited liability partnership. If, on the other hand, you are rather the restless B type, you will need something like a joint stock company, and you will need equity-based securities.

The essential idea of an equity-based security is… well, there is more than one idea inside. This is a good example of what finance is: we invent something akin to a social screwdriver, i.e. a tool which unfolds its many utilities as it is being used. Hence, I start with the initial idea rather than with the essential one, and the initial one is to do business with, or between, those B-type people: restless, open-minded, constantly rearranging their horizon of new ventures. Such people need a predictable way to swing between different businesses and/or to build a complex portfolio thereof.

Thus, we have the basic deal presented graphically above: we set a company, we endow it with an equity of €3 000 000, we divide that equity into 10 000 shares of €300 each, and we distribute those shares among some initial group of shareholders. Question: why anyone should bother to be our shareholder, i.e. to pay those €300 for one share? What do they have in exchange? Well, each shareholder who pays €300, receives in exchange one share, nominally worth €300, a bundle of intangible rights, and the opportunity to trade that share in the so-called « stock market », i.e. the market of shares. Let’s discuss these one by one.

Apparently the most unequivocal thing, i.e. the share in itself, nominally worth €300, is, in itself, the least valuable part. It is important to know: the fact of holding shares in an incorporated company does not give to the shareholder any pre-defined, unconditional claim on the company. This is the big difference between a share, and a corporate bond. The fact of holding one €300 share does not entitle to payback of €300 from the company. You have decided to invest in our equity, bro? That’s great, but investment means risk. There is no refund possible. Well, almost no refund. There are contracts called « buyback schemes », which I discuss further.

The intangible rights attached to an equity-based security (share) fall into two categories: voting power on the one hand, and conditional claims on assets on the other hand.

Joint stock companies have official, decision-making bodies: the General Assembly, the Board of Directors, the Executive Management, and they can have additional committees, defined by the statute of the company. As a shareholder, I can directly execute my voting power at the General Assembly of Shareholders. Normally, one share means one vote. There are privileged shares, with more than one vote attached to them. These are usually reserved to the founders of a company. There can also be shares with a reduced voting power, when the company wants to reward someone, with its own shares, but does not want to give them influence on the course of the business.

The General Assembly is the corporate equivalent of Parliament. It is the source of all decisional power in the company. General Assembly appoints the Board of Directors, and, depending on the exact phrasing of the company’s statute, has various competences in appointing the Executive Management. The Board of Directors directs, i.e. it makes the strategic, long-term decisions, whilst the Executive Management is for current things. Now, long story short: the voting power attached to equity-based securities, in a company, is any good only if it is decisive in the appointment of Directors. This is what much of corporate law sums up to. If my shares give me direct leverage upon who will be in the Board of Directors, then I really have voting power.

Sometimes, when holding a small parcel of shares in a company, you can be approached by nice people, who will offer you money (not much, really) in exchange of granting them the power of attorney in the General Assembly, i.e. to vote there in your name. In corporate language it is called power of proxy, and those people, after having collected a lot of such small, individual powers of attorney, can run the so-called proxy votes. Believe me or not, but proxy powers are sort of tradable, too. If you have accumulated enough proxy power in the General Assembly of a company, you, in turn, might be approached by even nicer people, who will propose you (even more) money in exchange of having that conglomerate, proxy voting power of yours on their side when appointing a good friend of theirs to the Board of Directors.

Here you have a glimpse of what equity-based securities are in essence: they are tradable, abstract building blocks of an incorporated business structure. Knowing that, let’s have a look at the conditional claims on assets that come with a corporate share. The company makes some net profit at the end of the year, and happens even to have free cash corresponding to that profit, and the General Assembly decides to have 50% of net profit paid to shareholders, as dividend. Still, voting in a company is based on majority, and, as I already said, majority is there when it can back someone to be member of the Board of Directors. In practical terms it means that decisions about dividend are taken by a majority in the Board of Directors, who, in turn, represent a majority in the General Assembly.

The claim on dividend that you can have, as a shareholder, is conditional on: a) the fact of the company having any profit after tax, b) the company having any free cash in the balance sheet, corresponding to that profit after tax, and c) the majority of voting power in the General Assembly backing the idea of paying a dividend to shareholders. Summing up, the dividend is your conditional claim on the liquid assets of the company. Why do I say it is a conditional claim on assets, and not on net profit? Well, profit is a result. It is an abstract value. What is really there, to distribute, is some cash. That cash can come from many sources. It is just its arithmetical value that must correspond to a voted percentage of net profit after tax. Your dividend might be actually paid with cash that comes from the selling of some used equipment, previously owned by the company.

Another typical case of conditional claim on assets is that of liquidation and dissolvence. When business goes really bad, the company might be forced to sell out its fixed assets in order to pay its debts. When really a lot of debt is there to pay, the shareholders of the company might decide to sell out everything, and to dissolve the incorporation. In such case, should any assets be left at the moment of dissolvence, free of other claims, the proceeds from their sales can be distributed among the incumbent shareholders.

Right, but voting, giving or receiving proxy power, claiming the dividend or proceeds from dissolvence, it is all about staying in a company, and we were talking about the utility of equity-based securities for those B-type capitalists, who would rather trade their shares than hold them. These people can use the stock market.

It is a historical fact that whenever and wherever it became a common practice to incorporate business in the form of companies, and to issue equity-based securities corresponding to shares, a market for those securities arose. Military legions in Ancient Rome were incorporated businesses, which would issue (something akin to) equity-based securities, and there were special places, called ‘counters’, where those securities would be traded. This is a peculiar pattern in human civilisation: when we practice some kind of repetitive deals, whose structure can be standardized, we tend to single out some claims out of those contracts, and turn those claims into tradable financial instruments. We call them ‘financial instruments’, because they are traded as goods, whilst not having any intrinsic utility, besides the fact of representing some claims.

Probably the first modern stock exchange in Europe was founded in Angers, France, somehow in the 15th century. At the time, there were (virtually) no incorporated companies. Still, there was another type of equity. Goods used to be transported slowly. A cargo of wheat could take weeks to sail from port A to port B, and then to be transported inland by barges or carts pulled by oxen. If you were the restless type of capitalist, you could eat your fingernails out of restlessness when waiting for your money, invested in that wheat, to come back to you. Thus, merchants invented securities, which represented abstract arithmetical fraction of the market value ascribed to such a stock of wheat. They were called different names, and usually fell under the general category of warrants, i.e. securities that give the right to pick up something from somewhere. Those warrants were massively traded in that stock exchange in Angers, and in other similar places, like Cadiz, in Spain. Thus, I bought a stock of wheat in Poland (excellent quality and good price), and I had it shipped (horribly slowly) to Italy, and as soon as I had that stock, I made a series of warrants on it, like one warrant per 100 pounds of wheat, and I started trading those warrants.

By the way, this is where the name ‘stock market’ comes from. The word ‘stock’ initially meant, and still means, a large quantity of some tradable goods. Places, such as Angers o Cadiz, where warrants on such goods were being traded, were commonly called ‘stock markets’. When you think of it, those warrants on corn, cotton, wool, wine etc. were equity-based securities. As long as the issuer of warrants had any equity in that stock, i.e. as long as their debt was not exceeding the value of that stock, said value was equity and warrants on those goods were securities backed with equity.

That little historical sketch gives an idea of what finance is. This is a set of institutionalized, behavioural patterns and rituals, which allow faster reaction to changing conditions, by creating something like a social hormone: symbols subject to exchange, and markets of those symbols.

Here comes an important behavioural pattern, observable in the capital market. There are companies, which are recommended by analysts and brokers as ‘dividend companies’ or ‘dividend stock’. It is recommended to hold their stock for a long time, as a long-term investment. The fact of recommending them comes from another fact: in these companies, a substantial percentage of shares stays, for years, in the hands of the same people. This is how they can have their dividend. We can observe relatively low liquidity in their stock. Here is a typical loop, peculiar for financial markets. Some people like holding the stock of some companies for a long time. That creates little liquidity in that stock, and, indirectly, little variation in the market price of that stock. Little variation in price means that whatever you can expect to gain on that stock, you will not really make those gains overnight. Thus, you hold. As you hold, and as other people do the same, there is little liquidity on that stock, and little variation in its price, and analysts recommend it as ‘dividend stock’. And so the loop spins.

I generalize. You have some equity-based securities, whose market value comes mostly from the fact that we have a market for them. People do something specific about those securities, and their behavioural pattern creates a pattern in prices and quantities of trade in that stock. Other people watch those prices and quantities, and conclude that the best thing to do regarding those securities is to clone the behavioural pattern, which made those prices and quantities. The financial market works as a market for strategies. Prices and quantities become signals as for what strategy is recommended.

On the other hand, there are shares just made for being traded. Holding them for more than two weeks seems like preventing a race horse from having a run on the track. People buy and sell them quickly, there is a lot of turnover and liquidity, we are having fun with trade, and the price swings madly. Other people are having a look at the market, and they conclude that with those swings in price, they should buy and sell that stock really quickly. Another loop spins. The stock market gives two types of signals, for two distinct strategies. And thus, two types of capitalists are in the game: the calm and consistent A type, and the restless B type. The financial market and the behavioural patterns observable in business people mutually reinforce and sharpen each other.

Sort of in the shade of those ‘big’ strategies, there is another one. We have ambitions, but we have no capital. We convince other people to finance the equity of a company, where we become Directors or Executive Management. With time, we attribute ourselves so-called ‘management packages’, i.e. parcels of the company’s stock, paid to us as additional compensation. We reasonably assume that the value of those management packages is defined by the price we can sell this stock in. The best price is the price we make: this is one of the basic lessons in the course of macroeconomics. Hence, we make a price for our stock. As Board of Directors, we officially decide to buy some stock from shareholders, at a price which accidentally hits the market maximums or even higher. The company buys some stock from its own shareholders. That stock is usually specified. Just some stock is being bought back, in what we call a buyback scheme. Accidentally, that ‘just some stock’ is the stock contained in the management packages we hold as Directors. Pure coincidence. In some legal orders, an incorporated company cannot hold its own stock, and the shares purchased back must be nullified and terminated. Thus, the company makes some shares, issues them, gives them to selected people, who later vote to sell them back to the company, with a juicy surplus, and ultimately those shares disappear. In other countries, the shares acquired back by the company pass into the category of ‘treasury shares’, i.e. they become assets, without voting power or claim on dividend. This is the Dark Side of the stock market. When there is a lot of hormones flowing, you can have a position of power just by finding the right spot in that flow. Brains know it better than anyone else.

Now, some macroeconomics, thus the bird’s eye view. The bird is lazy, and it prefers having a look at the website of the World Bank, and there it picks two metrics: a) Gross Capital Formation as % of GDP and b) Stock traded as % of GDP. The former measures the value of new fixed assets that pop up in the economic system, the latter estimates the value of all corporate stock traded in capital markets. Both are denominated in units of real output, i.e. as % of GDP, and both have a line labelled ‘World’, i.e. the value estimated for the whole planet taken as an economic system. Here comes a table, and a graph. The latter calculates the liquidity of capital formation, measured as the value of stock traded divided by the gross value of fixed capital formed. Some sort of ascending cycle emerges, just as if we, humans, were experimenting with more and more financial liquidity in new fixed assets, and as if, from time to time, we had to back off a bit on that liquidity.

 

Year Gross capital formation (% of GDP), World Stocks traded, total value (% of GDP), World Year Gross capital formation (% of GDP), World Stocks traded, total value (% of GDP), World
1984 25,4% 17,7% 2001 24,0% 104,8%
1985 25,4% 23,7% 2002 23,4% 82,8%
1986 25,1% 32,4% 2003 23,9% 76,0%
1987 25,4% 46,8% 2004 24,7% 83,8%
1988 26,2% 38,1% 2005 25,0% 99,8%
1989 26,6% 44,5% 2006 25,4% 118,5%
1990 26,0% 31,9% 2007 25,8% 161,9%
1991 25,4% 24,1% 2008 25,6% 140,3%
1992 25,2% 22,5% 2009 23,4% 117,3%
1993 25,0% 30,7% 2010 24,2% 112,5%
1994 25,0% 34,0% 2011 24,5% 104,8%
1995 24,8% 34,1% 2012 24,3% 82,4%
1996 24,7% 41,2% 2013 24,2% 87,7%
1997 24,7% 58,9% 2014 24,4% 101,2%
1998 24,5% 73,1% 2015 24,2% 163,4%
1999 24,1% 103,5% 2016 23,8% 124,5%
2000 24,5% 145,7%

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

Rummaging inside Tesla: my latest exam in Microeconomics

 

My editorial on You Tube

 

One more educational update on my blog. This time, it is the interpretation of exam in microeconomics, which took place on February 1st, 2019, in two distinct majors of studies, i.e. International Relations, and Management. First, right below, I am presenting the contents of the exam sheet, such as it was distributed to students. Then, further below, I develop an interpretation of possible answers to the questions asked. One preliminary remark is due: the entire exam refers to Tesla Inc. as business case. In my classes of Microeconomics, as well as in those of Management, I usually base the whole semester of teaching on 4 – 6 comprehensive business cases. This time, during the winter semester 2018/2019, one of those cases was Tesla, and the main source material was Tesla’s Annual Report for 2017. The students who attended this precise exam were notified one week earlier that Tesla was the case to revise.

This said, let’s rock. Here comes the exam sheet:

 

Exam in Microeconomics February 1st, 2019

 

Below, you will find a table with selected financial data of Tesla Inc. Use that data, and your knowledge as regards the business model of this firm, to answer the two open questions below the table. Your answer to each of the questions will be graded on a scale from 0 to 3 points. No answer at all, or major mistakes, give you 0 points. Short descriptive answer, not supported logically with calculations, gives 1 point. Elaborate explanation, logically supported with calculations, gives 2 or 3 points, depending on the exhaustiveness of your answer. Points translate into your overall grade as follows: 6 points – 5,0 (very good); 5 points – 4,5 (+good); 4 points – 4,0 (good); 3 points – 3,5 (+pass); 2 points – 3,0 (pass); 0 ÷ 1 points – 2,0 (fail). 

 

 

Values in thousands of USD
Revenues 2017 2016 2015
Automotive sales    8 534 752       5 589 007       3 431 587    
Automotive leasing    1 106 548         761 759         309 386    
Energy generation and storage    1 116 266         181 394          14 477    
Services and other    1 001 185         467 972         290 575    
Total revenues   11 758 751       7 000 132       4 046 025    
Cost of revenues      
Automotive sales    6 724 480       4 268 087       2 639 926    
Automotive leasing      708 224         481 994         183 376    
Energy generation and storage      874 538         178 332          12 287    
Services and other    1 229 022         472 462         286 933    
Total cost of revenues    9 536 264       5 400 875       3 122 522    
Overall total gross profit    2 222 487       1 599 257         923 503    
Gross profit by segments      
Automotive sales 1 810 272 1 320 920 791 661
Automotive leasing 398 324 279 765 126 010
Energy generation and storage 241 728 3 062 2 190
Services and other (227 837) (4 490) 3 642
       
Operating expenses      
Research and development    1 378 073         834 408         717 900    
Selling, general and administrative    2 476 500       1 432 189         922 232    
Total operating expenses    3 854 573       2 266 597       1 640 132    
Loss from operations   (1 632 086)       (667 340)       (716 629)   

 

Question 1 (open): Which operating segment of Tesla generates the greatest value added in absolute terms? Which segment has the greatest margin of value added? How does it change over time? Are differences across operating segments greater or smaller than changes over time in each operating segment separately? How can you possibly explain those phenomena? Suggestion: refer to the theory of Marshallian equilibrium vs the theory of monopoly.

 

Question 2 (open): Calculate the marginal cost of revenue from 2015 to 2017 (i.e. ∆ cost of revenue / ∆ revenue), for the whole business of Tesla, and for each operating segment separately. Use those calculations explicitly to provide a balanced judgment on the following claim: “The ‘Energy and storage’ operating segment at Tesla presents the greatest opportunities for future profit”.  

 

Interpretation

 

Question 1 (open): Which operating segment of Tesla generates the greatest value added in absolute terms? Which segment has the greatest margin of value added? How does it change over time? Are differences across operating segments greater or smaller than changes over time in each operating segment separately? How can you possibly explain those phenomena? Suggestion: refer to the theory of Marshallian equilibrium vs the theory of monopoly.

 

The answer to that question starts with the correct understanding of categories in the source table. Value added can be approximated as gross profit. The latter is the difference between revenues and variable cost, thus between the selling price, and the price of key intermediate goods. This was one of the first theoretical explanations the students were supposed do start their answer with. As I keep repeating in my classes, good science starts with communicable, empirical observation, and thus you need to say specifically how the facts at hand correspond to the theoretical distinctions we hold.

 

As I could see from some of the exam papers that some of my students handed me back, this was the first checkpoint for the understanding of the business model of Tesla. The expression ‘operating segment’ refers to the following four categories from the initial table: automotive sales, automotive leasing, energy generation and storage, and services and other. To my sincere surprise, some of my students thought that component categories of operational costs, namely ‘Research and development’, and ‘Selling, general and administrative’ were those operational segments to study. If, in an exam paper, I saw someone calculating laboriously some kind of margin for those two, I had no other solution but marking the answer with a remark ‘Demonstrable lack of understanding regarding the business model of Tesla’, and that was one of those major mistakes, which disqualified the answer to Question 1, and gave 0 points.

 

In a next step, i.e. after matching the concept of value added with the category of gross profit, and explaining why they do so, students had to calculate the margin of value added. Of course, we are talking the margin of gross profit, or: ‘Gross Profit / Revenues’. Here below, I am presenting a table with the margin of gross profit at Tesla Inc.

 

 

Margin of gross profit 2017 2016 2015
Overall 18,9% 22,8% 22,8%
Automotive sales 21,2% 23,6% 23,1%
Automotive leasing 36,0% 36,7% 40,7%
Energy generation and storage 21,7% 1,7% 15,1%
Services and other -22,8% -1,0% 1,3%

 

There was a little analytical challenge in the phrasing of the question. When I ask whether  ‘differences across operating segments greater or smaller than changes over time in each operating segment separately‘, it is essentially a test for analytical flexibility. The best expected approach that a student could have developed was to use coefficients, like gross margin for automotive sales in 2017 divided by that in 2015, and, alternatively, divided by the gross margin on energy generation and storage etc. Thus, what I expected the most in this part of the answer, was demonstrable understanding that changes over time could be compared to cross-sectional differences with the use of a universal, analytical tool, namely that of proportions expressed as coefficients, like ‘A / B’.

As this particular angle of approach involved a lot of calculations (students could use calculators or smartphones in that exam), one was welcome to take some shortcuts based on empirical observation. Students could write, for example, that ‘The greatest gross profit in absolute terms is generated on automotive sales, thus is seems logical to compare the margin of value added in this segment with other segments…’. Something in those lines. This type of answer gave a clear indication of demonstrable understanding as regards the source data.

As for the theoretical interpretation of those numbers, I openly suggested my students to refer to the theory of Marshallian equilibrium vs the theory of monopoly. Here is how it goes. The margin of value added has two interpretations as regards the market structure. Value added can be what the supplier charges his customers, just because they are willing to accept it, and this is the monopolistic view. As the Austrian school of economics used to state, any market is a monopoly before being a competitive structure. It means that any relations a business can develop with its customers is, first of all, a one on one relation. In most businesses there is at least a small window of price, within which the supplier can charge their customers whatever he wants, and still stay in balance with demand. In clearly monopolistic markets that window can be quite wide.

On the other hand, value added is what the exogenous market equilibriums allow a firm to gain as a margin between the market of their final goods, and that of intermediate goods. This is value added understood as price constraint. Below, I present those two ideas graphically, and I expected my students to force their pens into drawing something similar.

 

Question 2 (open): Calculate the marginal cost of revenue from 2015 to 2017 (i.e. ∆ cost of revenue / ∆ revenue), for the whole business of Tesla, and for each operating segment separately. Use those calculations explicitly to provide a balanced judgment on the following claim: “The ‘Energy and storage’ operating segment at Tesla presents the greatest opportunities for future profit”.  

 

As I reviewed those exam papers, I could see that the concept of marginal change is enormously hard to grasp. It is a pity, as: a) the whole teaching of calculus, at high school, is essentially about marginal change b) the concept of marginal change is one of the theoretical pillars of modern science in general, and it comes straight from grandpa Isaac Newton.

Anyway, what we need, in the first place, is the marginal cost of revenue, from 2015 to 2017, calculated as ‘∆ cost of revenue / ∆ revenue’. The ∆ is, in this case, the difference between values reported in 2017, and those from 2015. The marginal cost of revenue is simply the cost of having one more thousand of dollars in revenue. The corresponding values of marginal cost are given in the table below.

 

Operating segment at Tesla Inc. Marginal cost of revenue from 2015 through 2017
Overall                             0,83
Automotive sales                             0,80
Automotive leasing                             0,66
Energy generation and storage                             0,78
Services and other                             1,33

 

Most of the students who took this exam, on the 1st of February, failed to address the claim phrased in the question, and it was mostly because they apparently did not understand what is the meaning of what they have calculated. Many had those numbers right, although some were overly zealous and calculated the marginal cost for two windows in time separately: 2015 – 2016, and then 2016 – 2017. I asked specifically to jump from 2015 straight into 2017. Still, the real struggle was the unit of measurement. I saw many papers, whose authors transformed those numbers – correctly calculated – into percentages. Now, look people. In the source table, you have data in thousands of dollars, right? A delta of $000 is given in $000, right? A coefficient made of two such deltas is still in $000. Those numbers mean that if you want to have one more thousand of them US dollars in revenues, at Tesla Inc., you need to spend $830 in cost of revenue, and correspondingly for particular operating segments.

Thus, when anyone wrote those marginal values as percentages, I was very sorry to give that answer a mention ‘Demonstrable lack of understanding regarding the concept of marginal cost’.

When considering the marginal cost of revenue as an estimation of future profits, the lower it is, the greater profit we can generate. With a given price, the lower the cost, the greater the profit margin. The operating segment labelled ‘Energy generation and storage’ doesn’t look bad at all, in that respect, certainly better than them ‘Services and other’, still it is the segment of ‘Automotive leasing’ that yields the lowest marginal cost of revenues. Thus, the claim “The ‘Energy and storage’ operating segment at Tesla presents the greatest opportunities for future profit” is false, as seen from this perspective.

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

 

The mathematics of whatever you want: some educational content regarding political systems

My editorial on You Tube

This time, I go educational, and I go educational about political systems, and more specifically about electoral regimes. I generally avoid talking politics with my friends, as I want them to keep being my friends. Really, politics have become so divisive a topic, those last years. I remember, like 20 years ago, talking politics was like talking about the way to organize a business, or to design a machine. Now, it has become more like an ideological choice. Personally, I find it deplorable. There are always people who have more power than other people. Democracy allows us to have some control over those people in power, and if we want to exercise effective control, we need to get your own s**t together, emotionally too. If we become so emotional about politics that we stop thinking rationally, there is something wrong with us.

OK, enough ranting and moaning. Let’s get into facts and method. So, I start as I frequently do: I make a structure, and I drop numbers casually into it, just like that. Later on, I will work through the meaning of those numbers. My structure is a simple political system made of a juxtaposition of threes. There are 3 constituencies, equal in terms of incumbent voters: each constituency has 200 000 of them incumbent voters. Three political parties – Party A, Party B, and Party C – rival for votes in those 3 constituencies. Each political party presents three candidates in the electoral race. Party A presents its candidate A.1. in Constituency 1, candidate A.2. runs in Constituency 2, and Candidate A.3 in Constituency 3. Party B goes sort of the opposite way, and makes its candidates run like: B.1. in Constituency 3, B.2. in Constituency 2, and B.3. in Constituency 1. Party C wants to be original and makes like a triangle: its candidate C.1. runs in Constituency 2, C.2. tries their luck in Constituency 3, and C.3. is in the race in Constituency 1.

Just to recapitulate that distribution of candidates as a choice presented to voters, those in Constituency 1 choose between candidates A.1., B.3., and C.3., voters in Constituency 2 split their votes among A.2., B.2., and C.1.; finally, voters in Constituency 3 have a choice between A.3., B.1., and C.2. It all looks a bit complicated, I know, and, in a moment, you will read a table with the electoral scores, as number of votes obtained. I am just signalling the assumption I made when I was making those scores up: as we have 3 candidates in each constituency, voters do not give, under any circumstance, more than 50% of their votes (or more than 100 000 in absolute numbers) to one candidate. Implicitly, I assume that candidates already represent, somehow, their local populations. It can be achieved through some kind of de facto primary elections, e.g. when you need a certain number of officially collected voters’ signatures in order to register a candidate as running in a given constituency. Anyway, you have those imaginary electoral scores in Table 1, below. Save for the assumption about ‘≤ 50%’, those numbers are random.

 

  Table 1 – Example of electoral score in the case studied (numbers are fictional)

Number of votes obtained
Party Candidate Constituency 1 Constituency 2 Constituency 3
Party A Candidate A.1 23 000
total score [votes]

              174 101    

Candidate A.2 99 274
Candidate A.3 51 827
Party B Candidate B.1 6 389
total score [votes]

              111 118    

Candidate B.2 40 762
Candidate B.3 63 967
Party C Candidate C.1 13 580
total score [votes]

              134 691    

Candidate C.2 33 287
Candidate C.3 87 824
Total 174 791 153 616 91 503

 

On the whole, those random numbers had given quite a nice electoral attendance. In a total population of 600 000 voters, 419 910 had gone to the ballot, which makes 70%. In that general landscape, the three constituencies present different shades. People in the 1 and the 2 are nicely dutiful, they turned up to that ballot at the respective rates of 87,4%, and 76,8%. On the other hand, people in Constituency 3 seem to be somehow disenchanted: their electoral attendance was 45,8%. Bad citizens. Or maybe just bloody pissed.

Now, I apply various electoral regimes to that same distribution of votes. Scenario 1 is a simple one. It is a strictly proportional electoral regime, where votes from all three constituencies are pooled together, to allocate 5 seats among parties. The number of seats going to each party are calculated as: “Total score of the party/ Total number of votes cast”. Inside each party, seats go specific candidates according to their individual scores. The result is a bit messy. Party A gets 2 seats, for its candidates A.2. and A.3., party B passes its B.3. man, and Party C gets C.3. into the Parliament. The first, tiny, little problem is that we had 5 seats to assign, and just 4 got assigned. Why? Simple: the parties acquired fractions of seats. In the strictly proportional count, Party A got 2,073075183 seats, Party B had 1,323116858, and Party’s C score was 1,603807959. I agree that we could conceivably give 0,32 of one seat to a party. People can share, after all. Still, I can barely conceive assigning like 0,000000058 of one seat. Could be tricky for sharing. That is a typical problem with strictly proportional regimes: they look nice and fair at the first sight, but in real life they have the practical utility of an inflatable dartboard.

Scenario 2 is once again a strictly proportional regime, with 6 seats to distribute, only this time,  in each constituency, 2 seats are to be distributed among the candidates with the best scores. The result is a bit of an opposite of Scenario 1: it looks suspiciously neat. Each party gets an equal number of seats, i.e. 2. Candidates A.2., A.3., B2., B.3., C.2., and C.3. are unfolding their political wings. I mean, I have nothing against wings, but it was supposed to be proportional, wasn’t it? Each party got a different electoral score, and each gets the same number of seats. Looks a bit too neat, doesn’t it? Once again, that’s the thing with proportional: growing your proportions does not always translate into actual outcomes.

Good. I go for the 3rd scenario: a strictly plural regime, 3 seats to allocate, in each constituency just one candidate, the one with the best score, gets the seat. This is what the British people call ‘one past the post’, in their political jargon. Down this avenue, Party A pushes it’s A.2. and A.3. people through the gate, and Party C does so with C.3. That looks sort of fair, still there is something… In Constituency 1, 87 000 of votes, with a small change, got the voters one representative in the legislative body. In constituencies 2 and 3, the same representation – 1 person in the probably right place – has been acquired with, respectively, 99 274, and 33 287 votes. Those guys from constituencies 1 and 2 could feel a bit disappointed. If they were voting in constituency 3, they would need much less mobilisation to get their man past the post.

Scenario 4 unfolds as a mixed, plural-proportional regime, with 5 seats to allocate; 3 seats go to the single best candidate in each constituency, as in Scenario 3, and 2 seats go to the party with the greatest overall score across all the 3 constituencies. Inside that party, the 2 seats in question go to candidates with the highest electoral scores. The results leave me a bit perplex: they are identical to those in Scenario 3. The same people got elected, namely A.2., A.3., and C.3., only this time we are left with 2 vacancies. Only 3 seats have been allocated, out of the 5 available. How could it have happened? Well, we had a bit of a loop, here. The party with the highest overall score is Party A, and they should get the 2 seats in the proportional part of the regime. Yet, their two best horses, A.2. and A.3. are already past the post, and the only remaining is A.1. with the worst score inside their party. Can a parliamentary seat, reserved for the best runner in the winning party, be attributed to actually the worst one? Problematic. Makes bad publicity.

Scenarios 5 and 6 are both variations on the d’Hondt system. This is a special approach to mixing plural with proportional, and more specifically, to avoiding those fractional seats as in Scenario 1. Generally, the total number of votes cast for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate. We get a grid, out of which we pick up as many greatest values as there are seats to allocate. In Scenario 5, I apply the d’Hondt logic to votes from all the 3 constituencies pooled together, and I allocate 6 seats. Scenario 6 goes with the d’Hondt logic down to the level of each constituency separately, 2 seats to allocate in each constituency. The total number of votes casted for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate (2 in this case). The two greatest values in such a grid get the seats. Inside each party, the attribution of seats to candidates is proportional to their individual scores.

Scenario 5 seems to work almost perfectly. Party A gets 3 seats, thus they get all their three candidates past the post, Party C acquires 2 seats for C.2. and C.3., whilst Party B has one seat for candidate B.3. In a sense, this particular mix of plural and proportional seems even more fairly proportional that Scenario 1. The detailed results, which explain the attribution of seats, are given in Table 2, below.

 

Table 2 – Example of application of the d’Hondt system, Scenario 5

Number of votes obtained divided by consecutive denominators
Denominator of seats Party A Party B Party C
1        174 101            111 118            134 691    
2          87 051              55 559          67 346     
3          58 034              37 039          44 897
4          43 525          27 780          33 673
5          34 820          22 224          26 938
6          29 017          18 520          22 449

 

On the other hand, Scenario 6 seems to be losing the proportional component. Table 3, below, shows how exactly it is dysfunctional. As there are 2 seats to assign in each constituency, electoral scores of each party are being divided by, respectively, 1 and 2. In Constituency 1, the two best denominated scores befall to parties C and B, thus to their candidates C.3. and B.3. In Constituency 2, both of the two best denominated scores are attributed to Party A. The trouble is that Party A has just one candidate in this constituency, the A.2. guy, and he (she?) gets the seat. The second seat in this constituency must logically befall to the next best party with any people in the game, and it happens to be Party B and its candidate B.2. Constituency 3, in this particular scenario, gives two best denominated scores to parties A and C, thus to candidates A.3. and C.2. All in all, each party gets 2 seats out of the 6. Uneven scores, even distribution of rewards.

 

Table 3 – Application of the d’Hondt logic at the level of separate constituencies: Scenario 6.

Party A Party B Party C
Denominator of seats Constituency 1
1        23 000        63 967            87 824    
2        11 500        31 984        43 912
Constituency 2
1        99 274            40 762  (?)        13 580
2        49 637            20 381          6 790
Constituency 3
1        51 827              6 389        33 287    
2        25 914          3 195        16 644

 

Any mechanism can be observed under two angles: how it works, and how it doesn’t. It applies to electoral regimes, too. An electoral regime doesn’t work in two respects. First of all, it does not work if it does not lead to electing anyone. Second of all, it does not work if it fails to represent the votes cast in the people actually elected. There is a term, in the science of electoral systems: the wasted votes. They are votes, which do not elect anyone. They have been cast on candidates who lost the elections. Maybe some of you know that unpleasant feeling, when you learn that the person you voted for has not been elected. This is something like frustration, and yet, in my own experience, there is a shade of relief, as well. The person I voted for lost their electoral race, hence they will not do anything stupid, once in charge. If they were in charge, and did something stupid, I could be kind of held accountable. ‘Look, you voted for those idiots. You are indirectly responsible for the bad things they did’, someone could say. If they don’t get elected, I cannot be possibly held accountable for anything they do, ‘cause they are not in a position to do anything.

Wasted votes happen in all elections. Still, an efficient electoral regime should minimize their amount. Let’s compare those six alternative electoral regimes regarding their leakiness, i.e. their tendency to waste people’s voting power. You can see the corresponding analysis in Table 4 below. The method is simple. Numbers in the table correspond to votes from Table 1, cast on candidates who did not get elected in the given constituency, under the given electoral regime. You can see that the range of waste is quite broad, from 4,8% of votes cast, all the way up to 43% with a small change. It is exactly how real electoral regimes work, and this is, in the long run, the soft spot of any representative democracy. In whatever possible way you turn those numbers, you bump on a dilemma: either the race is fair for the candidates, or the ballot is fair for voters. A fair race means that essentially the best wins. There is no point in making an electoral regime, where inefficient contenders have big chances to get elected. On the other hand, those who lose the race represent people who voted for them. If we want all the voters to be accurately represented in the government, no candidate should be eliminated from the electoral contest, only then it would not be a contest.

 

Table 4

Number of votes, which do not elect any candidate
Constituency 1 Constituency 2 Constituency 3 Total elections
Scenario 1 23 000 40 762 33 287 97 049
Scenario 2 0 13 580 6 389 19 969
Scenario 3 23 000 54 342 33 287 110 629
Scenario 4 63 967 54 342 39 676 157 985
Scenario 5 (d’Hondt method, pooled) 0 54 342 6 389 60 731
Scenario 6 (d’Hondt method, separately by constituency) 23 000 54 342 39 676 117 018
Percentage of votes cast, which do not elect any candidate
Constituency 1 Constituency 2 Constituency 3 Total elections
Scenario 1 13,2% 26,5% 36,4% 23,1%
Scenario 2 0,0% 8,8% 7,0% 4,8%
Scenario 3 13,2% 35,4% 36,4% 26,3%
Scenario 4 36,6% 35,4% 43,4% 37,6%
Scenario 5 (d’Hondt method, pooled) 0,0% 35,4% 7,0% 14,5%
Scenario 6 (d’Hondt method, separately by constituency) 13,2% 35,4% 43,4% 27,9%
Average 12,7% 29,5% 28,9% 22,4%

 

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

Educational: microeconomics and management, the market and the business model

My editorial

This time, in the educational stream of my blog, I am addressing the students of 1st year undergraduate. This update is about microeconomic and management. Regarding your overall educational curriculum, these two courses are very much complementary. I am introducing you now into the theory of markets, and, in the same time, into the managerial concept of business model. We are going to consider a business of vital importance for our everyday life, although very much unnoticed: energy, and, more specifically, electricity. We are going to have a look at the energy business from two points of view: that of the consumer, and that of the supplier. If you have a look at your energy bill, you can basically see two lines: a fixed amount you pay to your supplier of energy, just for being connected to the grid, and a variable amount, which is, roughly speaking, the mathematical product: [Price of 1 kWh * Quantity of kWh consumed]. Of course, ‘kWh’ stands for kilowatt-hour. On the whole, your expenditure on electricity is computed as:

E = Fixed price for connection to grid + [Price of 1 kWh * Quantity of kWh consumed]

                             P1                                                                 P2                                 Q   

From the point of view of the supplier of energy, their market is made of N consumers of energy. We can represent this market as a set made of N elements, for example as N = {k1, k2, …, kn}, where each i-th consumer ki pays the same fixed price P1 for the connection to the grid, the same price P2 for each kWh consumed, and consumes an individually specific amount Q(ki) of energy measured in kWh. In that set of N = {k1, k2, …, kn} consumers, the total volume Q of the market is computed as:

Q = Q(k1) + Q(k2) + …+ Q(kn) [kWh]

…whilst the total value of the market is more complex a construct, and you compute it as:

Value of the market = N*P1 + Q*P2

  Most consumers have a more or less fixed budget to spend on electricity. If you take 1000 people and you check their housing expenses every month, you will see that their expenditures on electric power are pretty constant, unless some of them are building spaceships in their basements. So we introduce in our model of the market a budget on electricity, or Be, specific to each individual customer ki. Hence, that budget can be noted as Be(ki). Actually, that budget is the same as what we have introduced earlier as expenditure E, so:

Be(ki) = E = P1 + P2*Q(ki)

This mathematical construct allows reverse engineering of individual power consumption. Each consumer uses the amount Q(ki) of kilowatt-hours, which satisfies the condition:

Q(ki) = [Be(ki) – P1] / P2

In other words, each of us has a budget to spend on electricity bills, from this budget we subtract the fixed amount of money P1, to pay for being connected to the power grid, and we use the remaining sum so as to buy as many kilowatt-hours as possible, given the price P2. This condition is a first approach to what is called the demand function, on the part of the consumers. Although this function is still pretty sketchy, we can notice one pattern. The total amount of electricity Q(ki) that I consume depends on three parameters: my budget Be(ki), and the two prices P1 and P2. In economics, we call this an elasticity. We say that the quantity Q(ki) is elastic on: Be(ki), P1, P2. How elastic is it? We can calculate it, if we now the magnitudes of change in particular factors. If I know that my consumption of electricity has changed from like 40 000 kWh a year to 42 000 kWh, and I know that in the meantime the price P2 of one kilowatt-hour has moved from 0,1 euro to 0,12 euro, I can calculate something called deltas:

delta [Q(ki)] = ∆ Q(ki) = 42 000 40 000 = 2 000 kWh

delta (P2) = ∆P2 = €0,12 €0,1 = €0,02

The local (i.e. specific to this precise situation) elasticity of my consumption Q(ki) to the price P2 can be estimated, in a first approximation, as

e = ∆ Q(ki) / ∆P2 = 100 000 kWh per €1

The first thing to notice about this elasticity is that it is exactly contrary to what you see in my lectures, and what you can read in textbooks, about the demand function. The basic law of demand says something like: the greater the price, the lower the consumers’ willingness to buy. Here, we have something contrary to that law: greater consumption of energy is associated with a higher price, through a positive elasticity. I am behaving contrarily to the law of demand. In science, we call such a situation a paradox. Yet, notice that it is a local paradox: I cannot keep on increasing my personal consumption of electricity ad infinitum, even in the presence of a constant price. At some point, I have to start saving energy and increase my consumption just as much, as the prices possibly fall. So, generally, as opposed to locally, I am likely to behave in conformity with the law of demand. Still, keep in mind that in real life, paradoxes abound. It is not obvious at all to peg down a market equilibrium exactly as shown in textbooks. Most real-life markets are imperfect markets.

Now, if you look at this demand function, you can find it a bit distant from how you consume electricity. I mean, personally I don’t purposefully maximize the quantity of kilowatt-hours consumed. I just buy stuff powered by electricity, like a computer or a refrigerator, I plug it in, I turn it on, and I use it. Sometimes, I vaguely practice energy saving, like turning off the light in rooms where I am not currently staying. Anyway, my consumption of electricity Q(ki) is determined by the technology T I have at my disposal, which, in turn, consists of a set M = {g1, g2, …, gm} of goods powered by electricity: fridge, computer, TV set etc. We say that each j-th good gj, in the set M, is a complementary good to electricity. I can more or less accurately assume that an average refrigerator consumes x1(fridge) kWh, whilst an average set of house lighting burns x2(lighting) kWh. We can slice subsets out of the set N of consumers: N1 people with fridges, N2 people with air conditioners etc. With Q(gj) standing for the consumption of electricity in a given item powered with it, I can write:

 Q(ki) = N1*Q(g1) + N2*Q(g2) + …+ Nm*Q(gm) = [Be(ki) – P1] / P2

It means that, besides being elastic on my budget and the prices of electricity, my individual demand for a given amount of kilowatt-hours is elastic on the range of electricity-powered items I possess, and this, in turn, means that it is elastic on the budget I spend on those pieces of equipment, as well as on the prices of those goods (with a given budget to spend on houseware, I am more likely to buy a cheaper fridge rather than a more expensive one).

Now, business planning and management. Imagine that you are an entrepreneur, and you want to build a solar farm, and sell electricity to the people living around it. Your market works as shown above. You know that whatever you want to do, your organisation will have to satisfy the needs of those N customers, with their individual budgets and their individual elasticities in expenditures. The size of your organization, and its structure, will be significantly determined by the necessity to maintain profitable relations with N customers. Two questions emerge: what such organizational structure (i.e. the one serving to build and maintain those customer relations) would look like, and how could it be connected to other functional structures in the business, like building the solar farm, maintaining it in good technical state, purchasing components for construction and maintenance, hiring and firing people etc. You certainly know one thing: you have a given value of the market = N*P1 + Q*P2 and you have to adapt your costs (e.g. the sum total of salaries paid to your people) to this value of the market. Thus, you know that:

Average salary in my business = [(N*P1 + Q*P2) – The profit I want – Other costs] / the number of employees

In other words, the size of my business, e.g. in terms of the number of people employed, as well as my profit and the wages I can pay, will be determined by the value of my market. Now, let’s go along a path at the frontier of economics and management. I want to know how much capital I should invest in my business. I posit a condition: that capital should return to me, in the form of profits from business, in 7 years. Thus, I know that:

My initial investment = 7* My annual profit = 7*(N*P1 + Q*P2 – Current costs) = N*Be(ki) current costs = N*E current costs = N*[P1 + P2*Q(ki)] current costs

This is how the size of my business, both in terms of capital invested, and in terms of the number of people employed, is determined by, or is elastic on, the prices I can practice with my customers, the sheer number of those customers, as well as on their individual budgets.

Educational: International Economic Transactions, Analysis of the GATT 1994

My editorial

In this update, I am mostly addressing my Graduate Master’s students in their curriculum entitled ‘International Economic Transactions’. Still, I will be delighted to provide meaningful insight to any of my readers. We take on analysing the GATT 1994. It is on purpose that I am not starting with GATT 1947. That mother-treaty of the World Trade Organization was signed in very special circumstances, when the Western world was shaking off after World War II. The political and economic climate was somehow unique. The GATT 1994 is much closer to the present reality, and the road covered between its signature and that of the Trade Facilitation Agreement (2013 – 2017) is easier to trace than if we were starting in 1947. And so we start with GATT 1994. I am starting with decrypting the acronym: GATT means ‘General Agreement on Tariffs and Trade’. Nothing to write home about, basically, and still one thing is interesting. If there is a general agreement, it logically implies the existence of specific agreements. This is very much the reality of international trade: wherever you look, you see complicated, multi-level, intricate structures made of bilateral agreements, multilateral ones, letters of understanding, memoranda and whatnot (e.g. protocols).

If now, you care to read GATT 1994, there is not much reading to do, indeed: it is just two pages. It is a strange logical structure. On those two pages, you have just two sections. Section 1 says what specific documents does the GATT 1994 cover, and section 2 provides ‘Explanatory Notes’. The questions pops up: why the hell should anyone put any effort in negotiating that looks like two pages of minutes from a management meeting? As you read through section 1, you notice that the member countries of the World Trade Organization (WTO) have hatched quite a lot of various documents concerning trade, between 1947 (some of them even before the entry into force of the GATT 1947) and 1994. The most cryptic category is to be found under section 1(a)(iv), namely ‘Other decisions of the Contracting Parties’. Thus, many governments had had signed the GATT 1947, and then they had been doing things that stretched the original agreement in so dire a way that a new agreement had to be signed, recognizing the legal validity of those things that governments had been doing.

This is a good moment to exemplify the relation between an international agreement pertaining to trade, and its economic context. The GATT 1947 had been signed with a general purpose of avoiding so-called ‘trade wars’, i.e. a spiral of aggressive pro-export policies in individual countries, when a government deliberately depreciates its own currency in order to make its exported goods more price-competitive in foreign markets. Thus, the GATT 1947 had been originally combined with an international financial architecture, where the currencies of major economies had been tied nominally to the price of gold, and de facto to the price of the US dollar. On the other hand, the general principle of free trade, in GATT 1947, was strongly supported by economic sciences, either on the grounds of the so-called Ricardian paradigm – countries benefit from free trade by the development of their most competitive categories of businesses – as well as based on the the Heckscher – Ohlin model , which, in turn, stated that free trade compensates the negative effects of the otherwise imperfect geographical distribution of production factors. Still, since 1947, things kept happening, and they did so in a way which very much contradicted the fundamental principles of free trade. The biggest economies in the world, led by the biggest of the biggest, the United States of America, kept enforcing protectionist policies regarding trade. The clause of domestic components has been really the fashion since 1947. It says that you can import any goods inside a given country, but if you do not include in those goods a given percentage of components made domestically in this country, you pay additional tariffs, or, for example, your goods are excluded from public procurement (i.e. the government cannot buy them). In the 1970ies, most economies started departing from the gold standard, and even the United States detached their dollar from the price of the gold. As the system of tied monetary exchange rates faded progressively, the idea of free trade supporting said system became obsolete as well. New economic research showed that whilst the Ricardian paradigm, and the Heckscher – Ohlin one generally hold, other forces are at work, which can actually increase economic inequalities . The idea of aggressive depreciation in domestic currencies returned, with the energetic showing around from the part of Asian economies (Japan, China etc.), and with the governments of developed countries rediscovering an old truth that manipulating their own currencies could help in alleviating the burden of public debt. Regional zones of free trade, like the European communities or the ASEAN in the Asia and Pacific region, made the provisions of GATT 1947 look a bit pale. All in all, by the beginning of the 1990ies, it became obvious that the GATT 1947 has to be changed somehow, only in the meantime, i.e. since 1947, a whole bureaucratic structure had emerged under the label of World Trade Organization, and this structure was not keen to give up their position. It is funny how an otherwise quite substantial bureaucratic structure can call for ‘minimizing bureaucracy in trade’.

Now, here is the first big lesson in understanding international economic transactions. When countries transact ‘economically’, it means they do so in a way that affects whole economic orders. In fact, countries do not transact at this level: governments do. In international economic exchanges, there is a business level, and a government level. The latter expresses itself in policies, and some of these policies find an expression in international agreements and treaties. Second lesson: those international agreements and treaties are usually at least one step behind the business level of economic exchange. When governments claim they ‘signed a forward looking agreement’, it is to be understood as ‘we sincerely hope that no bloody business people will think about something new and unexpected, which would force us to renegotiate this document’. Trade has been going on for millennia, and it will keep going on. When governments claim they ‘stimulate’ trade with their policies, it means that at best they don’t get in the way.

The third lesson in more complex: if you want to understand how a given regulation works, trade agreements included, try and build various antithetic alternatives for it, i.e. regulations built with provisions logically opposing those actually studied ones. Section 1 of the GATT 1994 starts with a general provision that “The General Agreement on Tariffs and Trade 1994 (“GATT 1994”) shall consist of: […]’, and the […] takes the remaining of the A4 first page of the document, listing all those things that governments had done since 1947. Imagine an agreement starting with “This agreement SHALL NOT consist of […]’, with the […] being rigorously the same as in the original. The first option means that the agreement being signed explicitly incorporates all those past, particular polices. It is usually practiced when the agreement being signed has to deal with, and de facto recognize, deep disagreement between the contracting parties. This is the type of agreement we sign just in order to give some flesh to further negotiations that we know inevitable. The second, antithetic a version means a sharp divide: we do not recognize the validity of those policies. It is being used when a real agreement has been reached, and the new regulations can safely supplant the old ones.

Thus, when a new agreement is being signed in the place of an old one, two big strategies can be followed: the new one can build on the predecessor, or it can completely supplant it. The GATT 1994 is an example of the former, but, for example, consecutive treaties of the European Union bend more towards the latter.