# Educational: power and money, or short introduction to fiscal policy

### My editorial

Anyway, you can know a government by its expenditures, and this is the nominator I used in that Excel file. I put this nominator (the expenditures of the central government) over two distinct denominators in order to understand its relative magnitude. I have prepared, on the grounds of my own, compound database, made of Penn Tables 9.0 (Feenstra et al. 2015[1]), enriched with data from the World Bank, a selective comparison of countries, regarding the share of expenditures, made annually by their central governments, in the stock of fixed capital available in the national economy (machines, buildings etc.), as well as in the stock of money being supplied to said economy, and all that in 2014. Click this link to access the corresponding Excel file. As you do so (cmon, click!), you can see, for example, the central government of Switzerland (the Swiss are a federation, keep that in mind, this is just the central government, not the cantons), with its expenditures making 1,9% of the local stock of fixed capital and just 4% of the money supply. You move up in the ranking, like to France, and you have the French government with its annual expenditures that make 4,9% of the available fixed capital and 16,5% of the national money supply. You move really up the scale, and you get to Armenia, and here, brothers and sisters, you have a nice piece of a government. Its expenditures make 13,2% of the national stock of fixed capital, and 64,8% in the supply of money. You can also use another denominator to measure the relative importance of public expenditures: the Gross Domestic Product. You can find this proportion in different countries by clicking here. Once again, you can see strong differentiation across countries. The first piece of science we have, thus, about the topic of fiscal policy, is that national governments form local, strongly idiosyncratic, institutional environments for the appropriation of capital.

As we have quickly overviewed differences across space, let’s do the same over time. By clicking this particular link you will open an Excel file, which shows the share of the expenditures, made annually by the central, federal government of the United States of America, denominated over three different aggregates: GDP, supply of money, and the stock of fixed capital. You can observe that the latter, namely the share of government spending in the capital stock, changes very little over time. The one in the middle – the proportion between annual expenditures of the central government and the supply of money – varies a little bit more. It is the first column, the share of government spending in the Gross Domestic Product, which displays the greater variance over time. Yet, as I repeat the same analytical procedure in the case of Uganda (you know, click), we can see that, obviously, all countries are not like United States. In Uganda, the share of government spending in the GDP looks quite steady over time, whilst the proportions between said spending and both fixed capital, and money supply, tend to swing wildly. Here, we have the second piece of science: the local, national mechanisms of appropriating capital, on the part of the government, can take the form of participating rather in the current output of the economy (case United States), or, on the other hand, the form of tapping directly into the assets of the economic system (case Uganda). A government can behave like a steady rentier, just sipping the cream from over the milk of current output in the economy, or like an aggressive investor, who comes and goes in the balance sheet of the productive sector.

Now, as we know the possible changes over time and differences between countries, let’s focus on the process of appropriating capital in the government. You can go to the website of the International Monetary Fund and there you can look for the title ‘World Economic Outlook’ . After you have localised it, you can download the latest version of the database, which accompanies that report. As you open the database, you can see each country described with truly broad a range of metrics, and, among them, you can see those:

• General government revenue, consisting of taxes, social contributions, grants receivable, and other revenue. It is being assumed that a financial inflow to the government is really revenue, when it increases the government’s net worth, which is the difference between its assets and liabilities.
• General government total expenditure, which corresponds to the total expense and the net acquisition of nonfinancial assets.
• General government net lending/borrowing, which is simply revenue minus total expenditure. This metric says that the government can either put financial resources at the disposal of other sectors in the economy and non-residents (net lending), or utilize the financial resources generated by other sectors and non-residents (net borrowing).
• General government primary net lending/borrowing Primary net lending/borrowing is net lending (+)/borrowing (?) plus net interest payable/paid (interest expense minus interest revenue).
• General government structural balance, which refers to the general government cyclically adjusted balance. In other words, we can statistically distinguish, in the net lending or borrowing of the government, a slice corresponding to the impact of cyclical phenomena, mostly inflation and real economic growth, although unemployment has two words to say as well.
• General government gross debt, which covers all liabilities of the public sector, which require payment or payments of interest and/or principal by the debtor to the creditor at a date or dates in the future.
• General government net debt, calculated as gross debt minus financial assets corresponding to debt instruments.

So far, I have used the metrics pertaining to central governments. At the International Monetary Fund, they use aggregates corresponding to general governments.  Political systems host many distinct pockets of political power, and said pockets can be found in many places outside the central government. You have local governments (like cantons in Switzerland or states in the US), you have peripheral agencies (Finland or Israel have a cartload of these), and you have public funds, like the really big Social Security Fund in Poland. In any country, any government is a heterogeneous structure, which combines four types of fiscal entities, namely: budgetary units, executive agencies, targeted funds, and public-private partnerships. Budgetary units are the building blocks of the strictly spoken administrative structure in the public sector. They are fully financed through the current budget of the government, and fully accountable within one fiscal year. If they have any freedom in spending cash, this freedom is of short range. Public executive agencies follow specific missions ascribed by specific laws distinct from the budget, and from the regulations of fiscal governance. These laws form the legal basis of their existence. The mission of executive agencies usually consists in carrying out long-term tasks connected to large non-wage expenditures, e.g. the distribution of targeted subsidies, or the maintenance of strategic reserves. Executive agencies have more fiscal autonomy than budgetary units: they usually govern some kind of circulating capital untrusted with them by the government. Whilst they receive subsidies from the current budget, they usually do not make the full financial basis of their expenditures. In the same manner, they can retain their current financial surpluses over many fiscal years. The financial link of executive agencies with the current fiscal flows is fluid and changing from one budgetary cycle to another. Targeted public funds are separate public entities in charge of managing specific masses of capital paired with specific public missions to carry out. Just as executive agencies, targeted funds have a separate legal basis of their own. Their specificity consists in quite a strict distinction in their accounts: all the current costs of governance should be covered out of the financial rent of the capital managed, and the possible budgetary subsidies should serve only to back up the financial disbursements directly linked to the mission of the given fund. The distinction between executive agencies and targeted funds may be fluid: some agencies are de facto funds, and some funds are actually agencies. Public-private partnerships are joint ventures, through which private agents are commissioned to carry out specific public missions, in exchange of subsidies, direct payments or specific rights. One of the most obvious examples are contract-based healthcare systems, in which private providers of healthcare services are commissioned to fulfil the constitutional mission of the state to provide for citizens’ health. More subtle schemes are possible, of course. Private agents may provide, with their own financial means, for the creation of some infrastructure commissioned by the government, and their payment is the right to exploit said infrastructure.

The point of all that structural specification is to demonstrate that the broad category of fiscal flows that we use to call “public expenditures” is actually a financial compound. It covers both the expenditures strictly spoken (i.e. current payments for goods and services), and capital outlays that accrue to many different pockets of capital appropriated by public agents in many different ways. Capital accruals have different cycles, ranging from the ultra-short (days or weeks) cycle of consolidated accounting in budgetary units, passing through the mid-range cycle of appropriation in executive agencies and public-private partnerships, up to the frequently many-decade long cycle of capital appropriation in targeted public funds. Each of those pockets of capital makes a unit of economic power, in the hands of some public agents. Each accrual to or from such a capital pocket means a shift up or down in the actual economic power of those agents. Thus, it can be argued that the total stream of financial inflows to public treasury, through current revenues and current borrowing, is congruent with the sum of the strictly spoken public expenditures, and capital accruals in the public sector. Each such accrual corresponds to a pocket of political power in the structure of government.

[1] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at http://www.ggdc.net/pwt

# Anyway, the two equations, or the remaining part of Chapter I

### My editorial

And so I continue my novel in short episodes, i.e. I am blogging the on-going progress in the writing of my book about renewable technologies and technological change. Today, I am updating my blog with the remaining part of the first Chapter, which I started yesterday. Just for those who try to keep up, a little reminder about notations that you are going to encounter in what follows below: N stands for population, E represents the non-edible energy that we consume, and F is the intake of food. For the moment, I do not have enough theoretical space in my model to represent other vital things, like dreams, pot, beauty, friendship etc.

Anyway, the two equations, namely ‘N = A*Eµ*F1-µ’ and ‘N = A*(E/N)µ*(F/N)1-µ’ can both be seen as mathematical expressions of two hypotheses, which seems perfectly congruent at the first sight, and yet they can be divergent. Firstly, each of these equations can be translated into the claim that the size of human population in a given place at a given time depends on the availability of food and non-edible energy in said place and time. In a next step, one is tempted to claim that incremental change in population depends on the incremental change in the availability of food and non-edible energies. Whilst the logical link between the two hypotheses seems rock-solid, the mathematical one is not as obvious, and this is what Charles Cobb and Paul Douglas discovered as they presented their original research in 1928 (Cobb, Douglas 1928[1]). Their method can be summarised as follows. We have three temporal series of three variables: the output utility on the left side of the equation, and the two input factors on the right side. In the original production function by Cobb and Douglas had aggregate output of the economy (Gross Domestic Product) on the output side, whilst input was made of investment in productive assets and the amount of labour supplied. We return, now, to the most general equation (1), namely U = A*F1µ*F21-µ, and we focus on the ‘F1µ*F21-µ’ part, so on the strictly spoken impact of input factors. The temporal series of output U can be expressed as a linear trend with a general slope, just as the modelled series of values obtained through ‘F1µ*F21-µ’. The empirical observation that any reader can make on their own is that the scale factor A can be narrowed down to that value slightly above 1 only if the slope of the ‘F1µ*F21-µ’ on the right side is significantly smaller than the slope of U. This is a peculiar property of that function: the modelled trend of the compound value ‘F1µ*F21-µ’ is always above the trend of U at the beginning of the period studied, and visibly below U by the end of the same period. The factor of scale ‘A’ is an averaged proportion between reality and the modelled value. It corresponds to a sequence of quotients, which starts with local A noticeably below 1, then closing by 1 at the central part of the period considered, to rise visibly above 1 by the end of this period. This is what made Charles Cobb and Paul Douglas claim that at the beginning of the historical period they studied the real output of the US economy was below its potential and by the end of their window of observation it became overshot. The same property of this function made it a tool for defining general equilibriums rather than local ones. As regards my research on renewable energies, that peculiar property of the compound input of food and energy calculated with ‘Eµ*F1-µ’ or with ‘(E/N)µ*(F/N)1-µ’ means that I can assess, over a definite window in time, whether available food and energy stay in general equilibrium with population. They do so, if my general factor of scale ‘A’, averaged over that window in time, stays very slightly over 1, with relatively low a variance. Relatively low, for a parameter equal more or less to one, means a variance, in A, staying around 0,1 or lower. If these mathematical conditions are fulfilled, I can claim that yes, over this definite window in time, population depends on the available food and energy. Still, as my parameter A has been averaged between trends of different slopes, I cannot directly infer that at any given incremental point in time, like from t0 to t1, my N(t1) – N(t0) = A*{[E(t1)µ*F(t1)1-µ] – [E(t0)µ*F(t0)1-µ]}. If we take that incremental point of view, the local A will be always different than the general one.

Bearing those theoretical limitations in mind, the author undertook testing the above equations on empirical data, in a compound dataset, made of Penn Tables 9.0 (Feenstra et al. 2015[2]), enriched with data published by the World Bank (regarding the consumption of energy and its structure regarding ‘renewable <> non–renewable’), as well as with data published by FAO with respect to the overall nutritive intake in particular countries. Data regarding energy, and that pertaining to the intake of food, is limited, in both cases, to the period 1990 – 2014, and the initial, temporal extension of Penn Tables 9.0 (from 1950 to 2014) has been truncated accordingly. For the same reasons, i.e. the availability of empirical data, the original, geographical scope of the sample has been reduced from 188 countries to just 116. Each country has been treated as a local equilibrium, as the initial intuition of the whole research was to find out the role of renewable energies for local populations, as well as local idiosyncrasies regarding that role. Preliminary tests aimed at finding workable combinations of empirical variables. This is another specificity of the Cobb – Douglas production function: in its original spirit, it is supposed to work with absolute quantities observable in real life. These real-life quantities are supposed to fit into the equation, without being transformed into logarithms, or into standardized values. Once again, this is a consequence of the mathematical path chosen, combined with the hypotheses possible to test with that mathematical tool: we are looking for a general equilibrium between aggregates. Of course, an equilibrium between logarithms can be searched for just as well, similarly to an equilibrium between standardized positions, but these are distinct equilibriums.

After preliminary tests, equation ‘N = A*Eµ*F1-µ’, thus operating with absolute amounts of food and energy, proved not being workable at all. The resulting scale factors were far below 1, i.e. the modelled compound inputs of food and energy produced modelled populations much overshot above the actual ones. On the other hand, the mutated equation ‘N = A*(E/N)µ*(F/N)1-µ’ proved operational. The empirical variables able to yield plausibly robust scale factors A were: final use of energy per capita, in tons of oil equivalent (factor E/N), and alimentary intake of energy per capita, measured annually in mega-calories (thousands of kcal), and averaged over the period studied. Thus, the empirical mutation of produced reasonably robust results was the one, where a relatively volatile (i.e. changing every year) consumption of energy is accompanied by a long-term, de facto constant over time, alimentary status of the given national population. Thus, robust results could be obtained with an implicit assumption that alimentary conditions in each population studied change much more slowly than the technological context, which, in turn, determines the consumption of energy per capita. On the left side of the equation, those two explanatory variables matched with population measured in millions. Wrapping up the results of those preliminary tests, the theoretical tool used for this research had been narrowed down to an empirical situation, where, over the period 1990 – 2014, each million of people in a given country in a given year was being tested for sustainability, regarding the currently available quantity of tons of oil equivalent per capita per year, in non-edible energies, as well as regarding the long-term, annual amount of mega calories per capita, in alimentary intake.

The author is well aware that all this theoretical path-clearing could have been truly boring for the reader, but it seemed necessary, as this is the point, when real surprises started emerging. I was ambitious and impatient in my research, and thus I immediately jumped to testing equation N = A*(E/N)µ*(F/N)1-µ’ with just the renewable energies in the game, after having eliminated all the non-renewable part of final consumption in energy. The initial expectation was to find some plausible local equilibriums, with the scale factor A close to 1 and displaying sufficiently low a variance, in just some local populations. Denmark, Britain, Germany – these were the places where I expected to find those equilibriums, Stable demographics, well-developed energy base, no official food deficit: this was the type of social environment, which I expected to produce that theoretical equilibrium, and yet, I expected to find a lot of variance in the local factors A of scale. Denmark seemed to behave according to expectations: it yielded an empirical equation N = (Renewable energy per capita)0,68*(Alimentary intake per capita)1 0,68 = 0,32. The scale factor A hit a surprising robustness: its average value over 1990 – 2014 was 1,008202138, with a variance var (A) = 0,059873591. I quickly tested its Scandinavian neighbours: Norway, Sweden, and Finland. Finland yielded higher a logarithm in renewable energy per capita, namely µ = 0,85, but the scale factor A was similarly robust, making 1,065855419 on average and displaying a variance equal to 0,021967408. With Norway, results started puzzling me: µ = 0,95, average A = 1,019025526 with a variance 0,002937442. Those results would roughly mean that whilst in Denmark the availability of renewable energies has a predominant role in producing a viable general equilibrium in population, in Norway it has a quasi-monopole in shaping the same equilibrium. Cultural clichés started working at this moment, in my mind. Norway? That cold country with low density of population, where people, over centuries, just had to eat a lot in order to survive winters, and the population of this country is almost exclusively in equilibrium with available renewable energies? Sweden marked some kind of a return to the expected state of nature: µ = 0,77, average A = 1,012941105 with a variance of 0,003898173. Once again, surprisingly robust, but fitting into some kind of predicted state.

What I could already see at this point was that my model produced robust results, but they were not quite what I expected. If one takes a look at the map of the world, Scandinavia is relatively small a region, with quite similar, natural conditions for human settlement across all the four countries. Similar climate, similar geology, similar access to wind power and water power, similar social structures as well. Still, my model yielded surprisingly marked, local idiosyncrasies across just this small region, and all those local idiosyncrasies were mathematically solid, regarding the variance observable in their scale factors A. This was just the beginning of my puzzlement. I moved South in my testing, to countries like Germany, France and Britain. Germany: µ = 0,31, average A = 1,008843147 with a variance of 0,0363637. One second, µ = 0,31? But just next door North, in Denmark, µ = 0,63, doesn’t it? How is it possible? France yielded a robust equilibrium, with average A = 1,021262046 and its variance at 0,002151713, with µ = 0,38. Britain: µ = 0,3, whilst average A = 1,028817158 and variance in A making 0,017810219.  In science, you are generally expected to discover things, but when you discover too much, it causes a sense of discomfort. I had that ‘No, no way, there must be some mistake’ approach to the results I have just presented. The degree of disparity in those nationally observed functions of general equilibrium between population, food, and energy, strongly suggested the presence of some purely arithmetical disturbance. Of course, there was that little voice in the back of my head, saying that absolute aggregates (i.e. not the ratios of intensity per capita) did not yield any acceptable equilibrium, and, consequently, there could be something real about the results I obtained, but I had a lot of doubts.

I thought, for a day or two, that the statistics supplied by the Word Bank, regarding the share of renewable energies in the overall final consumption of energy might be somehow inaccurate. It could be something about the mutual compatibility of data collected from national statistical offices. Fortunately, methods of quantitative analysis of economic phenomena supply a reliable method of checking the robustness of both the model, and the empirical data I am testing it with. You supplant one empirical variable with another one, possibly similar in its logical meaning, and you retest. This is what I did. I assumed that the gross, final consumption of energy, in tons of oil equivalent per capita, might be more reliable than the estimated shares of renewable sources in that total. Thus, I tested the same equations, for the same set of countries, this time with the total consumption of energy per capita. It is worth quoting the results of that second test regarding the same countries. Denmark: average scale factor A = 1,007673381 with an observable variance of 0,006893499, and all that in an equation where µ = 0,93. At this point, I felt, once again, as if I were discovering too much at once. Denmark yielded virtually the same scale factor A, and the same variance in A, with two different metrics of energy consumed per capita (total and just the renewable one), with two different values in the logarithm µ. Two different equilibriums with two different bases, each as robust as the other. Logically, it meant the existence of a clearly cut substitution between renewable energies and the non-renewable ones. Why? I will try to explain it with a metaphor. If I manage to stabilize a car, when changing its tyres, with two hydraulic lifters, and then I take away one of the lifters and the car remains stable, it means that the remaining lifter can do the work of the two. This one tool is the substitute of two tools, at a rate of 2 to 1. In this case, I had the population of Denmark stabilized both on the overall consumption of energy per capita (two lifters), and on just the consumption of renewable energies (one lifter). Total consumption of energy stabilizes population at µ = 0,93 and renewable energies do the same at µ = 0,68. Logically, renewable energies are substitutes to non-renewables with a rate of substitution equal to 0,93/0,68 = 1,367647059. Each ton of oil equivalent in renewable energies consumed per capita, in Denmark, can do the job of some 1,37 tons of non-renewable energies.

Finland was another source of puzzlement: A = 0,788769669, variance of A equal to 0,002606412, and µ = 0,99. Ascribing to the logarithm µ the highest possible value at the second decimal point, i.e. µ = 0,99, I could not get a model population lower than the real one. The model yielded some kind of demographic aggregate much higher than the real population, and the most interesting thing was that this model population seemed correlated with the real one. I could know it by the very low variance in the scale factor A. It meant that Finland, as an environment for human settlement, can perfectly sustain its present headcount with just renewable energies, and if the non-renewables are being dropped into the model, the same territory has a significant, unexploited potential for demographic growth. The rate of substitution between renewable energies and the non-renewable ones, this time, seemed to be 0,99/0,85 = 1,164705882. Norway yielded similar results, with the total consumption of energy per capita on the right side of the equation: A = 0,760631741, variance in A equal to 0,001570101, µ = 0,99, substitution rate 1,042105263. Sweden turned out to be similar to Denmark: A = 1,018026405 with a variance of 0,004626486, µ = 0,91, substitution rate 1,181818182. The four Scandinavian countries seem to form an environment, where energy plays a decisive role in stabilizing the local populations, and renewable energies seem to be able to do the job perfectly. The retesting of Germany, France, and Britain brought interesting results, too. Germany: A = 1,009335161 with a variance of 0,000335601, at µ = 0,48, with a substitution rate of renewables to non-renewables equal to 1,548387097. France: A = 1,019371541, variance of A at 0,001953865, µ = 0,53, substitution at 1,394736842. Finally, Britain: A = 1,028560563 with a variance of 0,006711585, µ = 0,52, substitution rate 1,733333333. Some kind of pattern seems to emerge: the greater the relative weight of energy in producing general equilibrium in population, the greater the substitution rate between renewable energies and the non-renewable ones.

At this point I was pretty certain that I am using a robust model. So many local equilibriums, produced with different empirical variables, was not the result of a mistake. Table 1, in the Appendix to Chapter I, gives the results of testing equation (3), with the above mentioned empirical variables, in 116 countries. The first numerical column of the table gives the arithmetical average of the scale factor ‘A’, calculated over the period studied, i.e. 1990 – 2014. The second column provides the variance of ‘A’ over the same period of time (thus the variance between the annual values of A), and the third specifies the value in the parameter ‘µ’ – or the logarithm ascribed to energy use per capita – at which the given values in A have been obtained. In other words, the mean A, and the variance of A specify how close to equilibrium assumed in equation (3) has it been possible to come in the case of a given country, and the value of µ is the one that produces that neighbourhood of equilibrium. The results from Table 1 seem to confirm that equation (3), with these precise empirical variables, is robust in the great majority of cases.

Most countries studied satisfying the conditions stated earlier: variances in the scale factor ‘A’ are really low, and the average value of ‘A’ is possible to bring just above 1. Still, exceptions abound regarding the theoretical assumption of energy use being the dominant factor that shapes the size of the population. In many cases, the value of the exponent µ that allows a neighbourhood of equilibrium is far below µ = 0,5. According to the underlying logic of the model, the magnitude of µ is informative about how strong an impact does the differentiation and substitution (between renewable energies, and the non-renewable ones), have on the size of the population in a given time and place. In countries with µ > 0.5, population is being built mostly through access to energy, and through substitution between various forms of energy. Conversely, in countries displaying µ < 0,5, access to food, and internal substitution between various forms of food becomes more important regarding demographic change. United States of America come as one of those big surprises. In this respect, empirical check brings a lot of idiosyncrasies to the initial lines of the theoretical model.

Countries accompanied with a (!) are exceptions with respect to the magnitude of the scale factor ‘A’. They are: China, India, Cyprus, Estonia, Gabon, Iceland, Luxembourg, New Zealand, Norway, Slovenia, as well as Trinidad and Tobago. They present a common trait of satisfactorily low a variance in scale factor ‘A’, in conformity with condition (6), but a mean ‘A’ either unusually high (China A = 1.32, India A = 1.40), or unusually low (e.g. Iceland A = 0.02), whatever the value of exponent ‘µ’. It could be just a technical limitation of the model: when operating on absolute, non-transformed values, the actual magnitudes of variance on both sides of the equation matter. Motor traffic is an example: if the number of engine-powered vehicles in a country grows spectacularly, in the presence of a demographic standstill, variance on the right side is much greater than on the left side, and this can affect the scale factor. Yet, variances observable in the scale factor ‘A’, with respect to those exceptional cases, are quite low, and a fundamental explanation is possible. Those countries could be the cases, where the available amounts of food and energy either cannot really produce as big a population as there really is (China, India), or, conversely, they could produce much bigger a population than the current one (Iceland is the most striking example). From this point of view, the model could be able to identify territories with no room left for further demographic growth, and those with comfortable pockets of food and energy to sustain much bigger populations. An interpretation in terms of economic geography is also plausible: these could be situations, where official, national borders cut through human habitats, such as determined by energy and food, rather than circling them.

Partially wrapping it up, results in Table 1 demonstrate that equation (3) of the model is both robust and apt to identify local idiosyncrasies. The blade having been sharpened, the next step of empirical check consisted in replacing the overall consumption of energy per capita with just the consumption of renewable energies, as calculated on the grounds of data published by the World Bank, and in retesting equation (3) on the same countries. Table 2, in the Appendix to Chapter I, shows the results of those 116 tests. The presentational convention is the same (just to keep in mind that values in A and in µ correspond to renewable energy in the equation), and the last column of the table supplies a quotient, which, fault of a better expression, is named ‘rate of substitution between renewable and non-renewable energies’. The meaning of that substitution quotient appears as one studies values observed in the scale factor ‘A’. In the great majority of countries, save for exceptions marked with (!), it was possible to define a neighbourhood of equilibrium regarding equation (3) and condition (6). Exceptions are treated as such, this time, mostly due to unusually (and unacceptably) high a variance in scale factor ‘A’. They are countries where deriving population from access to food and renewable energies is a bit dubious, regarding the robustness of prediction with equation (3).

The provisional bottom line is that for most countries, it is possible to derive, plausibly, the size of population in the given place and time from both the overall consumption of energy, and from the use of just the renewable energies, in the presence of relatively constant an alimentary intake. Similar, national idiosyncrasies appear as in Table 1, but this time, another idiosyncrasy pops up: the gap between µ exponents in the two empirical mutations of equation (3). The µ ascribed to renewable energy per capita is always lower than the µ corresponding to the total use of energy – for the sake of presentational convenience they are further being addressed as, respectively, µ(R/N), and µ(E/N) –  but the proportions between those two exponents vary greatly between countries. It is useful to go once again through the logic of µ. It is the exponent, which has to be ascribed to the consumption of energy per capita in order to produce a neighbourhood of equilibrium in population, in the presence of relatively constant an alimentary regime. For each individual country, both µ(R/N) and µ(E/N) correspond to virtually the same mean and variance in the scale factor ‘A’. If both the total use of energy, and just the consumption of renewable energies can produce such a neighbourhood of equilibrium, the quotient ‘µ(E/N)/µ(R/N)’ reflects the amount of total energy use, in tons of oil equivalent per capita, which can be replaced by one ton of oil equivalent per capita in renewable energies, whilst keeping that neighbourhood of equilibrium. Thus, the quotient µ(E/N)/µ(R/N) can be considered as a levelled, long-term rate of substitution between renewable energies and the non-renewable ones.

One possible objection is to be dealt with at this point. In practically all countries studied, populations use a mix of energies: renewable plus non-renewable. The amount of renewable energies used per capita is always lower than the total use of energy. Mathematically, the magnitude of µ(R/N) is always smaller than the one observable in µ(E/N). Hence, the quotient µ(E/N)/µ(R/N) is bound to be greater than one, and the resulting substitution ratio could be considered as just a mathematical trick. Still, the key issue here is that both ‘E/Nµ’ and ‘R/Nµ’ can produce a neighbourhood of equilibrium with a robust scale factor. Translating maths into the facts of life, the combined results of tables 1 and 2 (see Appendix) strongly suggest that renewable energies can reliably produce a general equilibrium in, and sustain, any population on the planet, with a given supply of food. If a given factor A is supplied in relatively smaller an amount than the factor B, and, other things held constant, the supply of A can produce the same general equilibrium than the supply of B, A is a natural substitute of B at a rate greater than one. Thus, µ(E/N)/µ(R/N) > 1 is far more than just a mathematical accident: it seems to be the structural property of our human civilisation.

Still, it is interesting how far does µ(E/N)/µ(R/N) reach beyond the 1:1 substitution. In this respect, probably the most interesting insight is offered by the exceptions, i.e. countries marked with (!), where the model fails to supply a 100%-robust scale factor in any of the two empirical mutations performed on equation (3). Interestingly, in those cases the rate of substitution is exactly µ(E/N)/µ(R/N) = 1. Populations either too big, or too small, regarding their endowment in energy, do not really have obvious gains in sustainability when switching to renewables.  Such a µ(E/N)/µ(R/N) > 1 substitution occurs only when the actual population is very close to what can be modelled with equation (3). Two countries – Saudi Arabia and Turkmenistan – offer an interesting insight into the underlying logic of the µ(E/N)/µ(R/N) quotient. They both present µ(E/N)/µ(R/N) > 2. Coherently with the explanation supplied above, it means that substituting renewable energies for the non-renewable ones, in those two countries, can fundamentally change their social structures and sustain much bigger populations. Intriguingly, they are both ‘resource-cursed’ economies, with oil and gas taking so big a chunk in economic activity that there is hardly room left for anything else.

Most countries on the planet, with just an exception in the cases of China and India, seem being able to sustain significantly bigger populations than their present ones, through shifting to 100% renewable energies. In two ‘resource-cursed’ cases, namely Saudi Arabia and Turkmenistan, this demographic shift, possible with renewable energies, seems not less than dramatic. As I was progressively wrapping my mind around it, a fundamental question formed: what exactly am I measuring with that logarithm µ? I returned to the source of my inspiration, namely to the model presented by Paul Krugman in 1991 (Krugman 1991 op. cit.). That of the two factors on the right side of the equation, which is endowed with the dominant power is, in the same time, the motor force behind the spatial structuring of human settlement. I have, as a matter of fact, three factors in my model: non-edible renewable energy, substitutable to non-edible and non-renewable energy, and the consumption of food per capita. As I contemplate these three factors, a realisation dawns: none of the three can be maximized or even optimized directly. When I use more electricity than I did five years earlier, it is not because I plug my fingers more frequently into the electric socket: I shape my consumption of energy through a bundle of technologies that I use. As for the availability of food, the same occurs: with the rare exception of top-level athletes, the caloric intake is the by-product of a life style (office clerk vs construction site worker) rather than a fully conscious, purposeful action. Each of the three factors is being absorbed through a set of technologies. Here, some readers may ask: if I grow vegetables in my own garden, isn’t it far-fetched to call it a technology? If we were living in a civilisation who feeds itself exclusively with home-grown vegetables, that could be an exaggeration, I agree. Yet, we are a civilisation, which has developed a huge range of technologies in industrial farming. Vegetables grown in my garden are substitutes to foodstuffs supplied from industrially run farms, as well as to industrially processed food. If something is functionally a substitute to a technology, it is a technology, too. The exponents obtained, according to my model, for particular factors, in individual countries, reflect the relative pace of technological change in three fundamental fields of technology, namely:

1. a) Everything that makes us use non-edible energies, ranging from a refrigerator to a smartphone; here, we are mostly talking about two broad types of technologies, namely engines of all kind, and electronic devices.
2. b) Technologies that create choice between the renewable, and the non-renewable sources of energy, thus first and foremost the technologies of generating electricity: windmills, watermills, photovoltaic installations, solar-thermal plants etc. They are, for the most part, one step earlier in the chain of energy than technologies mentioned in (a).
3. c) Technologies connected to the production and consumption of food, composed into a long chain, with side-branches, starting from farming, through the processing of food, ending with packaging, distribution, vending and gastronomy.

As I tested the theoretical equation N = A*(E/N)µ*(F/N)1-µ’, most countries yielded a plausible, robust equilibrium between the local (national) headcount, and the specific, local mix of technologies grouped in those three categories. A question emerges, as a hypothesis to explore: is it possible that our collective intelligence expresses itself in creating such, local technological mixes of engines, electronics, power generation, and alimentary technologies, which, in turn would allow us to optimize our population? Can technological change be interpreted as an intelligent, energy-maximizing adaptation?

## Appendix to Chapter I

Table 1 Parameters of the function:  Population = (Energy use per capita[3])µ*(Food intake per capita[4])(1-µ)

 Country name Average daily intake of food, in kcal per capita Mean scale factor ‘A’ over 1990 – 2014 Variance in the scale factor ‘A’ over 1990 – 2014 The exponent ‘µ’ of the ‘energy per capita’ factor Albania 2787,5 1,028719088 0,048263309 0,78 Algeria 2962,5 1,00792777 0,003115684 0,5 Angola 1747,5 1,042983003 0,034821077 0,52 Argentina 3085 1,05449632 0,001338937 0,53 Armenia 2087,5 1,027874602 0,083587662 0,8 Australia 3120 1,053845754 0,005038742 0,77 Austria 3685 1,021793945 0,002591508 0,87 Azerbaijan 2465 1,006243759 0,044217939 0,74 Bangladesh 2082,5 1,045244854 0,007102476 0,21 Belarus 3142,5 1,041609177 0,016347323 0,8 Belgium 3655 1,004454515 0,003480147 0,88 Benin 2372,5 1,030339133 0,034533869 0,61 Bolivia (Plurinational State of) 2097,5 1,019990919 0,003429637 0,62 Bosnia and Herzegovina (!) 2862,5 1,037385012 0,214843872 0,81 Botswana 2222,5 1,068786155 0,009163141 0,92 Brazil 2907,5 1,013624942 0,003643215 0,26 Bulgaria 2847,5 1,058220643 0,005405994 0,82 Cameroon 2110 1,021629875 0,051074111 0,5 Canada 3345 1,036202396 0,007687519 0,73 Chile 2785 1,027291576 0,003554446 0,65 China (!) 2832,5 1,328918607 0,002814054 0,01 Colombia 2582,5 1,074031013 0,013875766 0,44 Congo 2222,5 1,078933108 0,024472619 0,71 Costa Rica 2802,5 1,050377494 0,005668136 0,78 Côte d’Ivoire 2460 1,004959783 0,007587564 0,52 Croatia 2655 1,072976483 0,009344081 0,72 Cyprus (!) 3185 0,325015959 0,00212915 0,99 Czech Republic 3192,5 1,004089056 0,002061036 0,84 Denmark 3335 1,007673381 0,006893499 0,93 Dominican Republic 2217,5 1,062919767 0,006550924 0,65 Ecuador 2225 1,072013967 0,00294547 0,6 Egypt 3172,5 1,036345512 0,004306619 0,38 El Salvador 2510 1,013036366 0,004187964 0,7 Estonia (!) 2980 0,329425185 0,001662589 0,99 Ethiopia 1747,5 1,073625398 0,039032523 0,31 Finland (!) 3147,5 0,788769669 0,002606412 0,99 France 3557,5 1,019371541 0,001953865 0,53 Gabon (!) 2622,5 0,961643759 0,016248519 0,99 Georgia 2350 1,044229266 0,059636113 0,76 Germany 3440 1,009335161 0,000335601 0,48 Ghana 2532,5 1,000098029 0,047085907 0,48 Greece 3610 1,063074 0,003756555 0,77 Haiti 1815 1,038427773 0,004246483 0,56 Honduras 2457,5 1,030624938 0,005692923 0,67 Hungary 3440 1,024235523 0,001350114 0,78 Iceland (!) 3150 0,025191922 2,57214E-05 0,99 India (!) 2307,5 1,403800869 0,024395268 0,01 Indonesia 2497,5 1,001768442 0,004578895 0,2 Iran (Islamic Republic of) 3030 1,034945678 0,001105326 0,45 Ireland 3622,5 1,007003095 0,017135706 0,96 Israel 3490 1,008446182 0,013265865 0,87 Italy 3615 1,007727182 0,001245927 0,51 Jamaica 2712,5 1,056188543 0,01979275 0,9 Japan 2875 1,0094237 0,000359135 0,38 Jordan 2820 1,015861129 0,031905756 0,77 Kazakhstan 3135 1,01095925 0,021868381 0,74 Kenya 2010 1,018667155 0,02914075 0,42 Kyrgyzstan 2502,5 1,009443502 0,053751489 0,71 Latvia 3015 1,010440502 0,023191031 0,98 Lebanon 3045 1,036073511 0,054610186 0,85 Lithuania 3152,5 1,008092894 0,025234007 0,96 Luxembourg (!) 3632,5 0,052543325 6,62285E-05 0,99 Malaysia 2855 1,017853322 0,001002682 0,61 Mauritius 2847,5 1,070576731 0,019964794 0,96 Mexico 3165 1,01483014 0,009376118 0,36 Mongolia 2147,5 1,061731985 0,030246541 0,9 Morocco 3095 1,07892333 0,000418636 0,47 Mozambique 1922,5 1,023422366 0,041833717 0,48 Nepal 2250 1,059720031 0,006741455 0,46 Netherlands 2925 1,040887411 0,000689576 0,78 New Zealand (!) 2785 0,913678062 0,003946867 0,99 Nicaragua 2102,5 1,045412214 0,007065561 0,69 Nigeria 2527,5 1,069148598 0,032086946 0,28 Norway (!) 3340 0,760631741 0,001570101 0,99 Pakistan 2275 1,062522698 0,020995863 0,24 Panama 2347,5 1,007449033 0,00243433 0,81 Paraguay 2570 1,07179452 0,021405906 0,73 Peru 2280 1,050166142 0,00327043 0,47 Philippines 2387,5 1,0478458 0,022165841 0,32 Poland 3365 1,004848541 0,000688294 0,56 Portugal 3512,5 1,036215564 0,006604633 0,76 Republic of Korea 3027,5 1,01734341 0,011440406 0,56 Republic of Moldova 2762,5 1,002387234 0,038541243 0,8 Romania 3207,5 1,003204035 0,003181708 0,62 Russian Federation 3032,5 1,050934925 0,001953049 0,38 Saudi Arabia 2980 1,026310231 0,007502008 0,72 Senegal 2187,5 1,05981161 0,021382472 0,54 Serbia and Montenegro 2787,5 1,0392151 0,012416926 0,8 Slovakia 2875 1,011063497 0,002657276 0,92 Slovenia (!) 3042,5 0,583332004 0,003458657 0,99 South Africa 2882,5 1,053438343 0,009139913 0,53 Spain 3322,5 1,061083277 0,004844361 0,56 Sri Lanka 2287,5 1,029495671 0,001531167 0,5 Sudan 2122,5 1,028532781 0,044393335 0,4 Sweden 3072,5 1,018026405 0,004626486 0,91 Switzerland 3385 1,047790357 0,007713383 0,88 Syrian Arab Republic 2970 1,010909679 0,017849377 0,59 Tajikistan 2012,5 1,004745997 0,078394669 0,62 Thailand 2420 1,05305435 0,004200173 0,41 The former Yugoslav Republic of Macedonia 2755 1,064764097 0,003242024 0,95 Togo 2020 1,007094875 0,014424982 0,66 Trinidad and Tobago (!) 2645 0,152994618 0,003781236 0,99 Tunisia 3230 1,053626454 0,001201886 0,66 Turkey 3510 1,02188909 0,001740729 0,43 Turkmenistan 2620 1,003674668 0,024196536 0,96 Ukraine 3040 1,044110717 0,005180992 0,54 United Kingdom 3340 1,028560563 0,006711585 0,52 United Republic of Tanzania 1987,5 1,074441381 0,031503549 0,41 United States of America 3637,5 1,023273537 0,006401009 0,3 Uruguay 2760 1,014226024 0,019409309 0,82 Uzbekistan 2550 1,056807711 0,031469698 0,59 Venezuela (Bolivarian Republic of) 2480 1,048332115 0,012077362 0,6 Viet Nam 2425 1,050131152 0,000866138 0,31 Yemen 2005 1,076332698 0,029772287 0,47 Zambia 1937,5 1,0479534 0,044241343 0,59 Zimbabwe 2035 1,063047787 0,022242317 0,6

Source: author’s

Table 2 Parameters of the function:  Population = (Renewable energy use per capita[5])µ*(Food intake per capita[6])(1-µ)

 Country name Mean scale factor ‘A’ over 1990 – 2014 Variance in the scale factor ‘A’ over 1990 – 2014 The exponent ‘µ’ of the ‘renewable energy per capita’ factor The rate of substitution between renewable and non-renewable energies[7] Albania 1,063726823 0,015575246 0,7 1,114285714 Algeria 1,058584384 0,044309122 0,44 1,136363636 Angola 1,044147837 0,063942546 0,49 1,06122449 Argentina 1,039249286 0,005115111 0,39 1,358974359 Armenia 1,082452967 0,023421839 0,59 1,355932203 Australia 1,036777388 0,009700331 0,52 1,480769231 Austria 1,017958672 0,007854467 0,71 1,225352113 Azerbaijan 1,07623299 0,009740098 0,47 1,574468085 Bangladesh 1,088818696 0,017086232 0,2 1,05 Belarus (!) 1,017676486 0,142728478 0,51 1,568627451 Belgium 1,06314732 0,095474709 0,52 1,692307692 Benin (!) 1,045986178 0,101094528 0,58 1,051724138 Bolivia (Plurinational State of) 1,078219551 0,034143037 0,53 1,169811321 Bosnia and Herzegovina 1,077445974 0,084400986 0,66 1,227272727 Botswana 1,022264687 0,056890261 0,79 1,164556962 Brazil 1,066438509 0,005012883 0,24 1,083333333 Bulgaria (!) 1,022253185 0,190476288 0,55 1,490909091 Cameroon 1,040548202 0,059668736 0,5 1 Canada 1,02539319 0,005170473 0,56 1,303571429 Chile 1,006307911 0,001159941 0,55 1,181818182 China 1,347729029 0,003248871 0,01 1 Colombia 1,016164864 0,019413193 0,37 1,189189189 Congo 1,041474959 0,030195913 0,67 1,059701493 Costa Rica 1,008081248 0,01876342 0,68 1,147058824 Côte d’Ivoire 1,013057174 0,009833628 0,5 1,04 Croatia 1,072976483 0,009344081 0,72 1 Cyprus (!) 1,042370253 0,838872562 0,72 1,375 Czech Republic 1,036681212 0,044847525 0,56 1,5 Denmark 1,008202138 0,059873591 0,68 1,367647059 Dominican Republic 1,069124974 0,020305242 0,53 1,226415094 Ecuador 1,008104202 0,025383593 0,47 1,276595745 Egypt 1,03122058 0,016484947 0,28 1,357142857 El Salvador 1,078008598 0,028182822 0,64 1,09375 Estonia (!) 1,062618744 0,418196957 0,88 1,125 Ethiopia 1,01313572 0,036192629 0,3 1,033333333 Finland 1,065855419 0,021967408 0,85 1,164705882 France 1,021262046 0,002151713 0,38 1,394736842 Gabon 1,065944525 0,011751745 0,97 1,020618557 Georgia 1,011709194 0,012808503 0,66 1,151515152 Germany 1,008843147 0,03636378 0,31 1,548387097 Ghana (!) 1,065885579 0,106721005 0,46 1,043478261 Greece 1,033613511 0,009328533 0,55 1,4 Haiti 1,009030442 0,005061414 0,54 1,037037037 Honduras 1,028253048 0,022719417 0,62 1,080645161 Hungary 1,086698434 0,022955955 0,54 1,444444444 Iceland 0,041518305 0,000158837 0,99 1 India 1,414055357 0,025335408 0,01 1 Indonesia 1,003393135 0,008680379 0,18 1,111111111 Iran (Islamic Republic of) 1,06172763 0,011215001 0,26 1,730769231 Ireland 1,075982896 0,02796979 0,61 1,573770492 Israel 1,06421352 0,004086618 0,61 1,426229508 Italy 1,072302127 0,020049639 0,36 1,416666667 Jamaica 1,002749054 0,010620317 0,67 1,343283582 Japan 1,082461225 0,000372112 0,25 1,52 Jordan 1,025652757 0,024889809 0,5 1,54 Kazakhstan 1,078500526 0,007887364 0,44 1,681818182 Kenya 1,039952786 0,031445338 0,41 1,024390244 Kyrgyzstan 1,036451717 0,011487047 0,6 1,183333333 Latvia 1,02535782 0,044807273 0,83 1,180722892 Lebanon 1,050444418 0,053181784 0,6 1,416666667 Lithuania (!) 1,076146779 0,241465686 0,72 1,333333333 Luxembourg (!) 1,080780192 0,197582319 0,93 1,064516129 Malaysia 1,018207799 0,034303031 0,42 1,452380952 Mauritius 1,081652351 0,082673843 0,79 1,215189873 Mexico 1,01253558 0,019098478 0,27 1,333333333 Mongolia 1,073924505 0,017542414 0,6 1,5 Morocco 1,054779512 0,005553697 0,38 1,236842105 Mozambique 1,062086076 0,047101957 0,48 1 Nepal 1,02819587 0,008319264 0,45 1,022222222 Netherlands 1,079123029 0,043322084 0,46 1,695652174 New Zealand 1,046855187 0,004522505 0,83 1,192771084 Nicaragua 1,034941617 0,021798159 0,64 1,078125 Nigeria 1,03609124 0,030236501 0,27 1,037037037 Norway 1,019025526 0,002937442 0,95 1,042105263 Pakistan 1,068995505 0,026598749 0,22 1,090909091 Panama 1,001556162 0,038760767 0,69 1,173913043 Paraguay 1,049861415 0,030603983 0,69 1,057971014 Peru 1,06820116 0,008122931 0,41 1,146341463 Philippines 1,045289953 0,035957042 0,28 1,142857143 Poland 1,035431925 0,035915212 0,39 1,435897436 Portugal 1,044901969 0,003371242 0,62 1,225806452 Republic of Korea 1,06776762 0,017697832 0,31 1,806451613 Republic of Moldova 1,009542233 0,033772795 0,55 1,454545455 Romania 1,011030974 0,079875735 0,47 1,319148936 Russian Federation 1,083901796 0,000876184 0,24 1,583333333 Saudi Arabia 1,099133179 0,080054524 0,27 2,666666667 Senegal 1,019171218 0,032304226 0,49 1,102040816 Serbia and Montenegro 1,042141223 0,00377058 0,63 1,26984127 Slovakia 1,062546838 0,08862799 0,61 1,508196721 Slovenia 1,00512965 0,039266211 0,81 1,222222222 South Africa 1,056957556 0,012656394 0,41 1,292682927 Spain 1,017435095 0,002522983 0,4 1,4 Sri Lanka 1,003117252 0,000607856 0,47 1,063829787 Sudan 1,00209188 0,060026529 0,38 1,052631579 Sweden 1,012941105 0,003898173 0,77 1,181818182 Switzerland 1,07331184 0,000878485 0,69 1,275362319 Syrian Arab Republic 1,048889583 0,03494333 0,38 1,552631579 Tajikistan 1,03533923 0,055646586 0,58 1,068965517 Thailand 1,012034765 0,002131649 0,33 1,242424242 The former Yugoslav Republic of Macedonia (!) 1,021262823 0,379532891 0,72 1,319444444 Togo 1,030339186 0,024874996 0,64 1,03125 Trinidad and Tobago 1,086840331 0,014786844 0,69 1,434782609 Tunisia 1,042654904 0,000806403 0,52 1,269230769 Turkey 1,0821418 0,019688124 0,35 1,228571429 Turkmenistan (!) 1,037854925 0,614587094 0,38 2,526315789 Ukraine 1,022041527 0,026351574 0,31 1,741935484 United Kingdom 1,028817158 0,017810219 0,3 1,733333333 United Republic of Tanzania 1,0319973 0,033120507 0,4 1,025 United States of America 1,001298132 0,001300399 0,19 1,578947368 Uruguay 1,025162405 0,027221297 0,73 1,123287671 Uzbekistan 1,105591195 0,008303345 0,36 1,638888889 Venezuela (Bolivarian Republic of) 1,044353155 0,012830255 0,45 1,333333333 Viet Nam 1,005825608 0,003779368 0,28 1,107142857 Yemen 1,072879389 0,058580323 0,3 1,566666667 Zambia 1,045147143 0,038548336 0,58 1,017241379 Zimbabwe 1,030974989 0,008692551 0,57 1,052631579

Source: author’s

[1] Charles W. Cobb, Paul H. Douglas, 1928, A Theory of Production, The American Economic Review, Volume 18, Issue 1, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association (March 1928), pp. 139 – 165

[2] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at http://www.ggdc.net/pwt

[3] Current annual use per capita, in tons of oil equivalent

[4] Annual caloric intake in mega-calories (1000 kcal) per capita, averaged over 1990 – 2014.

[5] Current annual use per capita, in tons of oil equivalent

[6] Annual caloric intake in mega-calories (1000 kcal) per capita, averaged over 1990 – 2014.

[7] This is the ratio of two logarithms, namely: µ(renewable energy per capita) / µ(total energy use per capita)

# Something like a potential to exploit

### My editorial

I have become quite accidental in my blogging. I mean, I do not have more accidents than I used to, I am just less regular in posting new content. This is academic life: giving lectures, it just drains you out of energy. Not only do you have to talk to people who mostly assume that what you tell them is utterly useless, but also you had to talk meaningfully so as to prove them wrong. On the top of that, I am writing that book, and it additionally taxes my poor brain. Still, I can see a light at the end of the tunnel, and this is not a train coming from the opposite sense. It is probably nothing mystical, as well. When I was a kid (shortly after the invention of the wheel, before the fall of the Warsaw Pact), there was a literary form called ‘novel in short episodes’. People wrote novels, but the socialist economy was constantly short of paper, and short of trust as for its proper use. Expecting to get printed in hard cover could be more hazardous an expectation than alien contact. What was getting printed were newspapers and magazines, as the government needed some vehicle for its propaganda. The caveat in the scheme was that most people didn’t want to pay for being served propaganda. We were astonishingly pragmatic in this respect, as I think of it now. The way to make people buy newspapers was to put inside something more than propaganda. Here, the printless writers, and the contentless newspapers could meet and shake their hands. Novels were being published in short episodes, carefully inserted at the last page of the newspapers, so as the interested reader has the temptation to browse through the account of Herculean efforts, on the part of the government, to build a better world, whilst fighting against the devils from the West.

As for me, I am running that blog at https://discoversocialsciences.com and it is now becoming endangered species in the absence of new, meaningful content being posted regularly. I mean, when you don’t breed, you become endangered species. On the other hand, I have that book in process, which might very well become the next bestseller, but it as well might not. Thus, I shake by blog hand with my book hand, and I decided to post on my blog, the content of the book, as it is being written. Every update will be, from now for the next five weeks or so, an account of my wrestling with my inner writer. I have one tiny little problem to solve, though. Over the last months, I used to blog in English and in French, kind of alternately. Now, I am writing my book in English, and the current account of my writing is, logically, in the beautiful language of Shakespeare and Boris Johnson. I haven’t figured out yet how the hell am I going to insert French in the process. Oh, well, I will make it up as I will be going. The show must go on, anyway.

And so I start.

(Provisional) Introduction (to my book)

This book is the account of the author’s research concerning technological change, especially in the context of observable shift towards renewable energies. This is an account of puzzlement, as well. As I developed my research on innovation, I remember being intrigued by the discrepancy between the reality of technological change at the firm and business level, on the one hand, and the dominant discourse about innovation at the macroeconomic level. The latter keeps measuring something called ‘technological progress’, with coefficients taken from the Cobb – Douglas production function, whose creators, Prof Charles W. Cobb and Prof Paul H. Douglas, in their common work from 1928[1], very strongly emphasized that their model is not really made for measuring changes over time. The so defined technological progress, measured with Total Factor Productivity, has not happened at the global scale since the 1970ies. In the same time, technological change and innovation keep happening. The human civilisation has reached a stage, when virtually any new business needs to be innovative in order to be interesting for investors. Is it really a change? Haven’t we, humans, been always like that, inventive, curious and bold in exploring new paths? The answer is ambiguous. Yes, we are and have been an inventive species. Still, for centuries, innovation has been happening at the fringe of society and then used to take over the whole society. This pattern of innovation is to find in business practices not so long ago, by the end of the 17th century. Since then, innovation, as a pattern of doing business, has progressively passed from the fringe to the centre stage of socio-economic change. Over the last 300 years or so, as a civilisation, we have passed, and keep passing, from being innovative occasionally to being essentially innovators. The question is: what happened in us?

In the author’s opinion, what happened is first and most of all, an unprecedented demographic growth. According to the best historical knowledge we have, right now we are more humans on this planet than we have ever been. More people being around in an otherwise constant space means, inevitably, more human interaction per unit of time and space, and more interaction means faster a learning. This is what technological change and innovation seem to be, in the first place: learning. This is learning by experimentation, where each distinct technology is a distinct experiment. What are we experimenting with? First of all, we keep experimenting with the absorption and transformation of energy. As a species, we are champions of acquiring energy from our environment and transforming it. Secondly, we are experimenting with monetary systems. In the 12th and 13th century, we harnessed the power of wind and water, and, as if by accident, the first documented use of bills of exchange dates back precisely to this period. When Europe started being really serious about the use of steam power, and about the extraction of coal, standardized monetary systems, based on serially issued bank notes, made their appearance during the late 18th century. At the end of the 19th century, as natural oil and gas entered the scene, their ascent closely coincided with final developments in the establishment of corporate structures in business. Once again, as if by accident, said developments consisted very largely in standardizing the financial instruments serving to trade shares in the equity of industrial companies. Presently, as we face the growth of electronics, the first technology ever to grow in complexity at an exponential pace, we can observe both an unprecedented supply of official currencies money – the velocity of money in the global economy has descended to V < 1 and it becomes problematic to call it a velocity – and nothing less than an explosion of virtual currencies, based on the Blockchain technology. Interestingly, each of those historical moments marked by the emergence of both new technologies, and new financial patterns, was associated with new political structures as well. The constitutional state that we know seems to have grown by big leaps, which, in turn, took place at the same historical moments: 12th – 13th century, 18th century, 19th century, and right now, as we are facing something that looks like a shifting paradigm of public governance.

Thus, historically, it is possible to associate these four streams of phenomena: demographic growth, deep technological changes as regards the absorption and use of energy, new patterns of using financial markets, and new types of political structures. Against this background of long duration, the latest developments are quite interesting, too. In 2007 – 2008, the market of renewable energies displayed – and this seems to be a historical precedent since 1992 – a rate of growth superior to that observable in the final consumption of energy as a whole. Something changed, which triggered much faster a quantitative change in the exploitation of renewables. Exactly the same moment, during the years 2007 – 2008, a few other phenomena coincided with this sudden surge in renewable energies. The supply of money in the global economy exceeded the global gross output, for the first time in recorded statistics. Apparently, for the first time in history, one average monetary unit, in the global economy, finances less than one unit of gross output per year. On the side of demography, the years 2007 – 2008 marked a historical threshold in urbanisation: the urban population on our planet exceeded, for the first time, 50% of the total human headcount. At the same moment, the average food deficit, i.e. the average deficit of kilocalories per day per capita, in our civilisation, started to fall sharply below the long-maintained threshold of 131 kcal, and presently we are at a historical minimum of 88,4 kcal. Those years 2007 – 2008, besides being the moment when the global financial crisis erupted, marked a significant turn in many aspects of our collective, global life.

Thus, there is the secular perspective of change, and the recent breakthrough. As a scientist, I mostly ask two questions, namely ‘how?’ and ‘what happens next?’. I am trying to predict future developments, which is the ultimate purpose of any theory. In order to form a reliable prediction, I do my best to understand the mechanics of the predicted change.

Chapter I (or wherever it lands in the final manuscript) The first puzzlement: energy and population

As the author connected those two dots – the historical facts and the recent ones – the theoretical coin started dropping. If we want to understand the importance of renewable energies in our civilisation, we need to understand how renewable energies can sustain local populations. That general intuition connected with the theoretical contribution of the so-called ‘new economic geography’. In 1998, Paul Krugman referred to models, which allow construing spatial structures of the economy as general equilibriums (Krugman 1998[3]). Earlier work by Paul Krugman, dating from 1991 (Krugman 1991[4]) supplied a first, coherent, theoretical vehicle for the author’s own investigation. The role of renewable energies in any local, human community is possible to express as aggregate utility derived from said energies. Further reflexion led to a simple observation: the most fundamental utility we derive from any form of energy is the simple fact of us being here around. The aggregate amount of utility that renewable energies can possibly create is the sustenance of a given headcount in population. In this reasoning, a subtle tension appeared, namely between ‘any form of energy’ and ‘renewable energies’. An equation started to form in the author’s mind. On the left side, the size of the population, thus the most fundamental, aggregate utility that any resource can provide. On the right side, the general construct to follow was that suggested by Paul Krugman, which deserves some explanation at this point. We divide the whole plethora of human activity, as well as that of available resources into two factors: the principal, differentiating one, and the secondary, which is being differentiated across space. When we have a human population differentiated into countries, the differentiating factor is the political structure of a country, and the differentiated one is all the rest of human activity. When we walk along a busy commercial street, the factor that creates observable differentiation in space is the institutional separation between distinct businesses, whilst labour, capital, and the available urban space are the differentiated ones. In the original model by Paul Krugman, the final demand for manufactured goods – or rather the spatial pattern of said demand – is the differentiating factor, which sets the geographical frame for the development of agriculture. The fundamental mathematical construct to support this reasoning is as in equation (1):

• ### (1)         U = A*F1µ*F21-µ        µ < 1

…where ‘U’ stands for the aggregate utility derived from whatever pair of factors F1 and F2 we choose, whilst ‘A’ is the scale factor, or the proportion between aggregate utility, on the one hand, and the product of input factors, on the other hand. This mathematical structure rests on foundations laid 63 years earlier, by the seminal work by Prof Charles W. Cobb and Prof Paul H. Douglas (Cobb, Douglas 1928[5]), which generations of economists have learnt as the Cobb-Douglas production function, and which sheds some foundational light on the author’s own intellectual path in this book. When Charles Cobb and Paul Douglas presented their model, the current economic discourse turned very much around the distinction between nominal economic change and the real one. The beginning of the 20th century, besides being the theatre of World War I, was also the period of truly booming industrial markets, accompanied by significant changes in prices. The market value of any given aggregate of economic goods could swing really wildly, whilst its real value, in terms of utility, remained fairly constant. The intuition behind the research by Charles Cobb and Paul Douglas was precisely to find a way of deriving some kind of equilibrium product, at the macroeconomic scale, out of the observable changes in industrial investment, and in the labour market. This general intuition leads to find such a balance in this type of equation, which yields a scale factor slightly above 1. In other words, the product of the input factors, proportioned in the recipe with the help of logarithms construed as, respectively, µ < 1, and 1-µ, should yield an aggregate utility slightly higher than the actual one, something like a potential to exploit. In the original function presented by Cobb and Douglas, the scale factor A was equal to 1,01.

Investigating the role of renewable energies in the sustenance of human populations led the author to experiment with various input variables on the right side of the equation, so as to have the consumption of renewable energies as input no. 1, something else (we are coming to it) as input no.2. The exploratory challenge was, firstly, to find the right variables, and then the right logarithms to raise them to, in order to obtain a scale factor A slightly above one. The basic path of thinking was that we absorb energy from environment in two essential forms: food, and everything else, which, whilst non-edible, remains useful. Thus, it has been assumed that any human community derives an aggregate utility, in the form of its own headcount, to be subsequently represented as ‘N’, out of the use ‘E’ of non-edible energies (e.g. fuel burnt in vehicles or electricity used in house appliances), and out of the absorption as food, further symbolized as ‘F’.

Thus, we have two consumables – energy and food – and one of the theoretical choices to make is to assign them logarithms: µ < 1, and 1-µ. According to the fundamental intuitions of Paul Krugman’s model from 1991, there are two paths to follow in order to find the dominant factor in the equation, i.e. the differentiating one, endowed with the logarithm µ <  1. The first path is the actual, observable change. Paul Krugman suggested that the factor, whose amount of input changes faster than the other one, is the differentiator, whilst the one displaying slower a pace of change is being differentiated. The second path pertains to the internal substitution between various goods (sub-inputs) inside each of the two big input factors. The new economic geography suggests that the capacity of industrial facilities to shape the spatial structure of human settlements comes, to a great extent, from the fact that manufactured goods have, between them, much neater a set of uses and mutual substitution rates than agricultural goods. Both of these road signs pointed at the use of non-edible energies as the main, differentiating factor. Non-edible energies are used through technologies, and these have clearly cut frontiers between them. A gasoline-based combustion engine is something different from a diesel, which, in turn, is fundamentally different from a power plant. The output of one technology can be substituted, to some extent, to the output of another technology, with relatively predictable a rate of substitution. In comparison, foodstuffs have much foggier borderlines between them. Rice is rice, and is part of risotto, as well as of rice cakes, rice pasta etc., and, in the same time, you can feed your chicken with rice, and thus turn the alimentary value of rice into the alimentary value of meat. This intricate scheme of foods combining with each other is made even more complicated due to idiosyncratic culinary cultures. One pound of herring trades against one pound of pork meat differently in Alaska and in Lebanon. As for the rate of change, technologies of producing food seem changing at slower a pace than technologies connected to the generation of electricity, or those embodied in combustion engines.

Thus, both paths suggested in the geographic model by Paul Krugman pointed at non-edible energies as the factor to be endowed with the dominant logarithm µ < 1, leaving the intake of food with the residual logarithm ‘1 – µ’. Hence, the next step of research consisted in testing empirically the equation (2):

• ### (2)         N = A*Eµ*F1-µ        µ < 1; A > 1

At this point, the theoretical model had to detach itself slightly from its Cobb-Douglas-Krugman roots. People cluster around abundance and avoid scarcity. These, in turn, can be understood in two different ways: as the absolute amount of something, like lots of food, or as the amount of something per person. That distinction is particularly important as we consider established human settlements with lots of history in their belt. Whilst early colons in a virgin territory can be attracted by the perceived, absolute amount of available resources, their distant ancestors will care much more about the availability of those resources to particular members of the established community, thus about the amount of resources per inhabitant. This principle pertains to food as well as to non-edible energies. In their early days of exploration, entrepreneurs in the oil & gas industry went wherever they could find oil and gas. As the industry matured, the daily yield from a given exploitation, measured in barrels of oil, or cubic meters of gas, became more important. This reasoning leads to assuming that quantities of input on the right side in equation (2) are actually intensities per capita in, respectively, energy use and absorption of food, rather than their absolute volumes. Thus, a mutation of equation (2) is being posited, as equation (3), where:

### (3)                        N =A*[(E/N)µ]*[(F/N)1-µ]          µ < 1; A > 1

[1] Charles W. Cobb, Paul H. Douglas, 1928, A Theory of Production, The American Economic Review, Volume 18, Issue 1, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association (March 1928), pp. 139 – 165

[2] Braudel, F., 1981, Civilization and Capitalism, Vol. I: The Structures of Everyday Life, rev.ed., English Translation, William Collins Sons & Co London and Harper & Row New York, ISBN 00216303 9, pp. 341 – 358

[3] Krugman, P., 1998, What’s New About The New Economic Geography?, Oxford Review of Economic Policy, vol. 14, no. 2, pp. 7 – 17

[4] Krugman, P., 1991, Increasing Returns and Economic Geography, The Journal of Political Economy, Volume 99, Issue 3 (Jun. 1991), pp. 483 – 499

[5] Charles W. Cobb, Paul H. Douglas, 1928, A Theory of Production, The American Economic Review, Volume 18, Issue 1, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association (March 1928), pp. 139 – 165

# Educational (and more): The TFA, or negotiating about standards

### My editorial

I am going to practice my favourite method of analysis, which goes from the end towards the beginning. It means I start studying a legal document from its last provisions (bottom of the last page). This is a place, where many people don’t even bother to look, and, kind of by acceptance, those last provisions turn into the proverbial ‘small print’ in a contract. The TFA ends with Annex 3, which specifies the Code of Good Practice for The Preparation, Adoption and Application of Standards. Annex 3, in turn, ends up with substantive provision (Q), which states: ‘The standardizing body shall afford sympathetic consideration to, and adequate opportunity for, consultation regarding representations with respect to the operation of this Code presented by standardizing bodies that have accepted this Code of Good Practice. It shall make an objective effort to solve any complaints’. Good. So now, we know that whatever grand goals and intentions are declared in the preamble of the TFA, we have a network of the so-called standardizing bodies, which interact with other, local standardizing bodies, and they do so with sympathetic consideration, offering adequate opportunities, and making objective effort.

This little technical provision at the very end of the TFA opens up on two important aspects of international relations in general, as well as of trade specifically. If you look at the phrasing, it essentially means that standardizing bodies commit to being nice to each other, which in nice in general, but could be seen as odd in a contract. After all, the TFA is a contract between countries. Have you seen many private contracts, where the contracting parties engage into being nice? Probably you haven’t seen that many. So, what’s the point? The point is one of the most fundamental principles in the international public law, namely the principle of national sovereignty. Each country is sovereign in their national territory, just as each government is. It means that technically countries cannot force each other into doing anything, and they cannot prevent each other from doing things. Every single country is a sovereign entity. Sometimes, sovereign countries are in disagreement about things important to them. Actually, this type of sometimes happens quite often, and even between countries signatories of the same treaty or agreement.

Good, now we pass to the second technique of blunting sharp disputes: the legal precedent. Maybe you remember those times, when you refuse to do someone wants you to do, and then the someone in question gently reminds you ‘But you promised…’. Past promises make a frame, made of precedents, and that frame limits the freedom of future action. Notions like ‘objective effort’ or ‘sympathetic consideration’ have their force of precedent, as well. If a national standardizing body addresses another standardizing body with some complaint, or is the addressee of a complaint, someone in the interaction could, technically, stick their middle finger up in a big, juicy f*** you. They could, but they probably are not going to, as all the countries agreed that it should ‘give sympathetic consideration’ and ‘make objective effort’. These, in turn, translate into procedures. When a complaint is being filed, it should be handled according to procedures, which reflect sympathetic consideration and objective effort. Moreover, those procedures can be reviewed and evaluated according to how sympathetically considerate and objectively effortful they are. Legal precedents form something called ‘customary international law’, which is really important in international relations (see for example Goldsmith & Posner 1999 [2]).

When we discussed the GATT 1994, I explained that it was being signed in a climate of tension between countries as it regards trade. This is why the GATT 1994 is so strange an agreement, stating that whatever the signatory governments have done so far is just fine, provided it can be presented as a rational policy. Right now, tension builds up in international trade as well. The geography of trade is changing, progressively. The so-called ‘resource curse’, which is the Ricardian paradigm of comparative advantage gone bad, and on steroids, is really kicking many asses. The world is changing and that creates tensions.

Let’s scroll further back to the beginning. Substantive provisions (O) and (P) in Annex 3 state, respectively, that: ‘(O) Once the standard has been adopted, it shall be promptly published (P) On the request of any interested party within the territory of a Member of the WTO, the standardizing body shall promptly provide, or arrange to provide, a copy of its most recent work programme or of a standard which it produced. Any fees charged for this service shall, apart from the real cost of delivery, be the same for foreign and domestic parties’. We can see that the TFA, whilst technically being an agreement between governments is, in fact, a base for technical cooperation between standardizing bodies. The core issue if is smooth communication: whatever we agree on, as well as whatever any of us has come up with individually, should be made known to the others. This is the deep sense of standardization: quick communication with quick feedback.

I scroll further back towards the beginning, and in the same Annex 3 there is the substantive provision (F): ‘Where international standards exist or their completion is imminent, the standardizing body shall use them, or the relevant parts of them, as a basis for the standards it develops, except where such international standards or relevant parts would be ineffective or inappropriate, for instance, because of an insufficient level of protection or fundamental climatic or geographical factors or fundamental technological problems’. It shows an interesting aspect of customary international law: the intentional ambiguity. What does it mean ‘imminent completion’? In a week or in a year? Who assesses whether standards are effective or not? How can anyone say, which technological problems are fundamental? These ambiguities make this provision hardly applicable directly, as such, to any standards, but it creates the frame for negotiating about those standards.

[1] Mahoney, M.S., 1988, The History of Computing in the History of Technology, Princeton, NJ, Annals of the History of Computing 10(1988), pp. 113-125

[2] Goldsmith J.L., Posner, E.A., 1999, A Theory of Customary International Law, JOHN M. OLIN LAW & ECONOMICS WORKING PAPER NO. 63 (2D SERIES), THE LAW SCHOOL, THE UNIVERSITY OF CHICAGO, This paper can be downloaded without charge at: The Chicago Working Paper Series Index: http://www.law.uchicago.edu/Publications/W orking/index.html or at The Social Science Research Network Electronic Paper Collection: http://papers.ssrn.com/paper.taf?abstract_id=145972

# Stratégie fin 17ème , stratégie fin 18ème

### Mon éditorial

Je suis en train de revoir mes notes de recherche (donc ce que j’avais écrit sur mon blog) et de les compiler en un livre. Voilà donc que je revoie deux mises à jour récentes (Quite abundant a walk of life et Countries never behave as they should ) et voilà (seconde fois) que je tombe sur quelque chose d’intéressant : la connexion entre la croissance du marché et l’opportunité pour innover. Je compare deux traités : « Le parfait négociant » de Jacques Savary, de 1675, et « La richesse des nations » d’Adam Smith, datant d’un siècle plus tard. Adam Smith, au milieu de la seconde moitié du 18ème siècle, dit fermement que les meilleures opportunités pour ce qu’il appelait « la division du travail » – et qui aujourd’hui voudrait dire l’innovation – se présentent dans les marchés qui croissent à une cadence relativement rapide. En revanche, Maître Savary, au milieu de la seconde moitié du 17ème siècle, était beaucoup plus enclin à voir des bonnes opportunités dans des marchés bien stables. Qu’est-ce qui eût changé le contexte de l’innovation si profondément in l’espace d’un siècle ?

Trois facteurs de différence viennent à mon esprit : la croissance démographique, la standardisation des systèmes monétaires, et la diversification des technologies. Les années 1670, c’était le temps quand une récession démographique profonde commençait à se faire remarquer un peu partout en Europe. Il avait fallu attendre les années 1760 pour voir un rebond dans la population. Vous pouvez trouver une description fascinante de ce processus de plusieurs décennies – quoi que c’est une histoire froide comme la finance dont elle parle – dans « La théorie de l’impôt » (1760) par Victor Riqueti, marquis de Mirabeau (oui, le même Mirabeau).

Donc, lorsque Jacques Savary écrivait, en 1675, que « cest une chose bien importante que dentreprendre des Manufactures, car il ny va pas moins que de la ruine des entrepreneurs, si elle nest pas conduite avec prudence et jugement », il faisait un croquis sur un fond de teint fait d’une population en déclin. Cette différence cardinale de contexte démographique se mariait d’une façon intéressante avec la diversité technologique. Durant la seconde moitié du 17ème siècle, l’industrie textile semble avoir été le secteur dominant, et de loin, en ce qui concerne l’innovation. Apparemment, à l’époque, inventer un tissu nouveau était l’équivalent de l’invention informatique aujourd’hui : un vrai cerveau, ça inventait un nouveau draguet pour les gens pauvres ou un nouveau ruban décoratif pour les riches. Notre guide dans les aléas de cette époque, Maître Savary, se vante lui-même d’avoir inventé, ainsi qu’avoir mis en marché, trois produits textiles différents, dont un – et voilà un petit bijou historique – était un ruban tissé en fil d’or et d’argent. En revanche, à la fin du 18ème , l’innovation prenait place un peu partout et, ce qui est un phénomène intéressant, elle prenait place dans le secteur financier, tout en contribuant à la standardisation financière. A l’époque, la finance, c’était en train d’inventer sa Ford Modèle T.

Voilà donc que j’arrive à ce troisième facteur : le pognon. Lorsque Maître Savary décrit les différentes stratégies de ce qu’il appelait « La Manufacture » – donc l’industrie – il préconise très clairement de se concentrer sur les étoffes bien établies dans le marché, qui ont « un cours ordinaire ». Cette notion de cours ordinaire reflète bien le fonctionnement des systèmes monétaires de l’époque : extrêmement diversifiés, basés très largement sur la circulation, par l’endossage, de la dette privée décentralisée. La plupart du monde d’affaires était basée sur un système des prix qui se croisait constamment avec le système des taux de change très fluide, y compris les taux de change des dettes privées provenant des sources diverses. Aussi bien dans le marché de vente que dans le marché d’achat, ce qu’on appelle aujourd’hui la politique de prix, dans le marketing mix, ressemblait plutôt au marché Forex moderne, mais avec plus de risque et avec une absence quasi-totale de ce que nous appelons, de nos jours, les valeurs-refuge (le franc suisse, tiens). La seconde moitié du 18ème siècle – les temps d’Adam Smith – c’était presque ennuyeux, par comparaison.

Voilà donc que nous arrivons à deux types de stratégies différentes en ce qui concerne l’innovation et le changement technologique. La stratégie « Fin 17ème » est celle qu’on pratique dans des marchés en déclin démographique, où la perte de vitesse en termes de population se traduit par un rétrécissement dramatique de la palette d’innovations possibles, ainsi que par un système monétaire où personne n’a vraiment d’intérêt à créer une circulation prévisible et à réduire le risque financier. D’autre part, je définis la stratégie « Fin 18ème », où une croissance démographique marquée, une innovation florissante et des systèmes monétaires qui croissent par standardisation. Bon, maintenant j’applique ça à ma petite obsession : les énergies renouvelables. Dans cet article que je viens de terminer , j’ai découvert un équilibre entre la population et la quantité d’énergies renouvelables par tête d’habitant. Il y a des pays, où l’importance des renouvelables pour l’équilibre démographique est extrêmement importante. Ce sont des cas aussi divers que l’Arabie Saoudite, Turkménistan, Botswana, Finlande ou la Lettonie. Là-bas, la population, ça semble être étroitement lié au marché d’énergies renouvelables. Par coïncidence, ce sont des pays avec des populations relativement stables et pas vraiment les plus grandes du monde. Intuitivement, j’associe leurs marchés d’énergies renouvelables avec la stratégie « Fin 17ème ». A l’extrémité opposée de l’échelle vous trouverez des pays comme la Chine ou l’Inde (mais aussi l’Ethiopie ou le Japon), où le marché des renouvelables semble avoir relativement peu de connexion avec le facteur population. Je pourrais être tenté de les associer avec la stratégie type « Fin 18ème » et en plus ça pourrait tenir pour les pays comme la Chine ou l’Inde, mais l’Ethiopie… Pas évident du tout. Là, je me sens comme dans un cul de sac. On va bien voir.

# Educational (very educational): embarrassing questions about monetary systems

### My editorial

This particular update on my blog is both a piece of educational content, and a piece of general research methodology in social sciences. It regards monetary systems. In terms of education, it mostly addresses those 3rd year students, Undergraduate, whom I am currently lecturing about Economic Policy. Still, the graduate Master’s students in the curriculum of International Economic Transactions can have some benefits out of it. I start with an old and classical one: the quantitative monetary equilibrium, or, in fancy economic writing:

P*T = M*V

P – the index of prices

T – the volume of transactions in the economy

M – the supply of money (monetary mass) in the economy

V – the velocity of money

Now, when you switch from differences in time to those across space, and you take a snapshot from 2016, you have, for example: Argentina M = 28,9% * Q, Australia M = 118,8% * Q, Hong Kong M = 363% * Q, China M = 208,3%*Q, United Kingdom M = 144%*Q, United States M = 90,6%*Q. You can see that money works very differently across space. Each country seems to be a highly idiosyncratic monetary system. Good, so we keep on asking embarrassing questions. In textbooks, and in my lectures in the first year, you could have learnt that the supply of money is practically equal to the supply of credit from the banking system. It is generally true to the extent, that when banks get profuse on lending money, you can immediately see prices rise in the economy, and one of the best ways to slow down inflation is to make credit more expensive in terms of interest rates. Still, let’s check. In 2016, the global supply of credit (you know, click), from banks to the real side of the global economy, made 177,421% of the global GDP. Simple arithmetic indicate that we had [177,421/116,411] = 1,52 times more credit than money supplied. Back in 1990, the credit supplied from all banks in the world made 126,138% of the global GDP. Once again, we check credit for its attendance to the money being supplied, and we get [Credit/Money] = [126,138/88,01] = 1,433. Interesting: there seems to be more and more credit who lost its way from banks to purses (happens usually on a late hour at night), and there seems to be more and more credit in that awkward situation. Let’s snapshot across space in 2016. Argentina, credit = 38,8%*Q, credit/M = 38,8/28,9 = 1,34; Australia, credit = 183,4%*Q, credit/M = 183,4/118,8 = 1,544; Hong Kong, credit = 212%*Q, credit/M = 212/363 = 0,584; China, credit = 215%*Q, credit/M = 215/208,3 = 1,032; United Kingdom, credit = 167,8%*Q, credit/M = 167,8/144 = 1,1653; United States, credit = 242,6%*Q, credit/M = 242,6/90,6 = 2,677. Each country has a different system of transmission from credit lent to money supplied.

Now, if you are a government, you want two things on the left, P*T side of monetary equilibrium. You want to see your real output, or the volume of transactions T, gallop joyfully forward, i.e. grow like hell, whilst controlling the level of prices P. In order to do that, you need to control, somehow, the way your national monetary system works. As you can see from the numbers presented above, this is not obvious at all. The basic leverages you have are (check them at Wikipedia or elsewhere): the supply of currency through the central bank, the interest rates on credit, the ratio of mandatory reserves (the % of deposits held from customers that commercial banks have to hold, in turn, at the central bank), open market operations by the central bank and sometimes by the national Treasury (Minister of Finance in continental Europe), and the so-called quantitative ease (this is when the government buys financial assets in the domestic market; it acts on financial markets like a toilet plunger, you know, that big rubber sucker that you use to make your plumbing cooperative again).

# Des choix faits sous incertitude

### Mon éditorial

J’ai remarqué que ce début d’année académique, ça a sacrément déstabilisé mon cours de travail intellectuel. Par « travail intellectuel » je comprends le fait que j’écrive quelque chose sur mon blog ou dans un article scientifique. Depuis l’avènement de la psychologie behavioriste il n’est pas tout à fait clair si le fait d’utiliser le langage, même dans le haut registre grammatical, est une preuve d’intelligence. Il y a des cas pour et il y a des cas contre. J’espère être un cas pour. Bon, assez de psychanalyse, faut faire ce travail intellectuel dont je parle. Depuis que j’avais pondu ce dernier article, je réfléchis comment je pourrais bien développer cette idée sous la forme d’un livre et, en même temps, comment je peux inclure les résultats de ma recherche dans mon enseignement à la fac. Procédons par ordre : il serait bon de faire un petit sommaire de ce que j’avais fait en termes de recherche, cette année.

Tout d’abord, l’idée que j’avais nourri depuis printemps, cette année, et qui pour le moment n’a abouti à aucune conclusion : le Wasun ou la monnaie virtuelle attachée au marché d’énergies renouvelables. Mon idée de base était que la création d’une telle monnaie – « création » semble être un terme plus approprié que l’émission, dans ce cas précis – pourrait faciliter la transition des communautés locales vers une base énergétique verte à 100%. Bien que j’avais tourné et retourné cette idée sous – comme je pense – tous les angles possibles, rien ne semblait coller. Après, comme je me suis fait une base empirique à propos d’énergies vertes, j’ai un peu compris pourquoi ça ne collait pas. Un, la transition vers les énergies renouvelables, ça se fait à une cadence de plus en plus accélérée, un peu partout dans le monde, et cette accélération est peut-être le fait le plus important dans toute ma recherche cette année. Deux, je viens de prouver que – ou, comme on dit dans le langage élégant et barbant de la science, de contribuer à clarifier les présomptions qui laissent poser l’hypothèse que – la grande majorité des populations locales sur Terre peut se stabiliser et même croître significativement autour d’énergies renouvelables. Pas vraiment besoin de fouetter ces chevaux. Ils sont déjà en plein galop. Trois, j’avais produit une preuve scientifique convaincante que le changement technologique accéléré, ça produit notoirement un surplus de masse monétaire. Là aussi, il n’est pas vraiment impératif de pousser plus : ça roule tout seul.

Par contre, un truc qui semble avoir marché d’une façon très intéressante, c’est l’équivalent de cette astuce où on tire la nappe d’une table, d’un coup sec, sans renverser les couverts. Les couverts sont les faits empiriques. La vie, quoi, juste exprimée en nombres. La nappe que j’avais tirée d’en-dessous ces couverts c’est l’assomption que notre civilisation devrait économiser l’énergie. Je suis fermement convaincu et j’ai une méthode scientifique de prouver que le comportement collectif de notre espèce – y compris la transition vers les énergies vertes – s’explique d’une façon beaucoup plus raisonnable avec  l’assomption contraire, c’est-à-dire que nous maximisons, systématiquement, l’absorption de l’énergie de notre environnement. Nous demander d’économiser l’énergie c’est comme demander à un tigre de se convertir au véganisme.

Bon, tout ça, ci-dessus, c’est ce que j’avais plus ou moins prouvé ou présenté sous forme d’une preuve scientifique. Ensuite, il y a mes idées : ces trucs importuns dans ma tête dont je ne sais pas comment les présenter d’une façon 100% scientifique et donc je ne sais pas s’ils sont vrais ou faux. Je pourrais les appeler hypothèses, seulement voilà, là, il y a comme un petit problème : une hypothèse scientifique, ça devrait être vérifiable, et pour ces trucs-là, je ne sais même pas comment les vérifier. Alors, première idée : le changement technologique s’effectue par expérimentation qui, à son tour, est un processus évolutif dans une structure sociale où des entités femelles – des gens avec du pognon qui en connaissent d’autres avec du pognon – recombinent des technologies initialement crées par des entités mâles (des gens avec des idées). Ce processus crée des hiérarchies des technologies, ou plutôt des hiérarchies des entités mâles, suivant les préférences des entités femelles. Idée no. 2 est que la hiérarchisation due aux mécanismes évolutifs s’effectue à travers trois processus de base : définition (et distribution) des rôles sociaux, définition d’identités de groupe, et enfin le gain d’accès aux ressources. Enfin, la troisième idée est qu’à présent nous traversons, comme espèce, une période d’expérimentation sociale accélérée et ceci pour deux raisons. Premièrement, plus on est du monde sur la Terre, plus on a d’interactions mutuelles. C’est comme une rue de grande ville : plus il y a du monde dans le quartier, plus il est probable qu’on croise quelqu’un dans la rue. Plus on a d’interactions, plus vite on apprend et plus on expérimente. Par ailleurs, cette période de 2007 – 2008, quand le marché d’énergies renouvelables avait tout à coup accéléré sa croissance, c’était précisément le moment quand la population urbaine mondiale avait franchi le cap des 50% de l’humanité. Deuxièmement, dans ma recherche j’ai découvert que l’intensité de l’innovation est la plus grande dans les pays où le déficit alimentaire est entre zéro et 88 kilocalories par jour par personne. Eh bien, il se fait que le déficit alimentaire moyen de la population globale vient de franchir ces 88 kilocalories par jour par personne. Nous sommes cette bête qui est déjà acceptablement nourrie mais pas encore tout à fait à sa faim.

Côté enseignement, j’ai déjà commencé à inclure l’étude du marché de l’énergie dans l’enseignement de la microéconomie, mais le truc le plus intéressant est comment enseigner à mes étudiants les façons d’étudier le phénomène d’intelligence collective et d’apprentissage collectif. J’avoue que je suis conscient de mes propres limites dans le domaine : l’intelligence collective c’est plutôt le truc d’informaticiens, mais j’ai quelques idées en tête. Je pense utiliser des fondements de la théorie des jeux pour montrer le mécanisme des choix faits sous incertitude. Peut-être j’utiliserai le rectangle Bayésien . Ce dernier truc, ça peut captiver l’attention des étudiants, avec toute cette histoire du philosophe (Thomas Bayes) mort plus d’un an avant la publication de son article. C’est que je veux c’est d’aller un peu à travers les disciplines. Ceci peut consister, par exemple, à montrer comment la définition des rôles sociaux peut induire du changement dans l’équilibre local d’un marché.

# 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

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.

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.

# Quite abundant a walk of life

### My editorial

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

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