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 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

The first scientific puzzlement addressed in this book refers to the most recent research by the author. The research in question was oriented on explaining the role of renewable energies in the sustenance of our civilisation, and it was very much inspired by a piece of information the author had read in Fernand Braudel’s masterpiece ‘Civilisation and Capitalism’ (Braudel 1981[2]). According to historical accounts, based on the official documents of the Habsburg Empire, in the author’s home region, Lesser Poland, known as Austrian Galicia under the Habsburg rule, at the end of the eighteenth century, there was one water mill, on average, per 382 people. The author’s home town, Krakow, Poland, sustains a population of 800 000, which would correspond to 2094 water mills. Said watermills are significant by their absence. Since I had learnt about this little fact, reading Fernard Braudel’s monumental work in summer 2015, I have gradually become quasi-obsessed with the ‘what if?’ question: what if today we had those 2094 water mills in my home city? What would our life look like? How different would it be from the world we are actually living? This gentle obsession crystallized into a general theoretical question: can renewable energies sustain the present human population? This generality found a spur in the reading of statistics pertaining to renewable energies. In 2007 – 2008, the rate of growth in the market of renewable energies changed, and became higher than the rate of growth in the overall, final consumption of energy. This change in trends is observable on the grounds of data published by the World Bank, regarding the consumption of energy per capita ( ), and the share of renewable energies in that overall consumption ( ). This change of slope was something of a historical precedent since 1990. In 2007 – 2008, something important happened, and still, to the author’s knowledge, there is no research explaining what that something could possibly have been. Some kind of threshold has been overcome in the absorption of technologies connected to renewable energies.

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

Ma formule magique marche dans certains cas, et pas tout à fait dans des cas autres que certains

Mon éditorial

Hier, dans ma mise à jour en anglais (consultez “Core and periphery” ), j’ai creusé un peu le modèle de différentiation spatiale d’une économie, plume Paul Krugman (Krugman 1991[1]). J’avais pris l’équation (1) de son modèle original – U = CMµ*CA1-µ – où U est l’utilité agrégée, CM est la production manufacturière, CA correspond à la production agriculturale et µ est la part prise par la production manufacturière dans la demande finale. Sur cette base, j’ai développé ma propre équation U(AE) = Wµ*F1-µ  µ < 1, où U(AE) est l’utilité agrégée dérivée de la consommation de l’énergie sous toutes ses formes possibles, W correspond à la consommation finale de l’énergie, F est la consommation de nourriture et µ est la part de la demande finale dépensée sur l’énergie. Cette transmutation de ma part avait été très intuitive et en y regardant de près, après fait, j’avais remarqué que les deux équations – l’originale de Paul Krugman et la transformée façon Wasniewski – suivent la même logique de base, celle de la fonction de production de Charles W. Cobb et Paul H. Douglas[2]. J’ai revu leur article et j’ai essayé d’appliquer leur méthode originale pour donner un peu de fond et de gravitas à ma transformation.

Vu l’hypothèse que je suis en train de vérifier – « la structure spatiale de la civilisation humaine s’adapte et se regroupe en vue de l’absorption maximale d’énergie » – je me suis dit que l’utilité agrégée de la consommation de l’énergie c’est tout simplement qu’il y ait du monde en un endroit donné, donc qu’il y ait une population sur un territoire. J’ai donc mis la variable de population sur le côté gauche de l’équation en posant formellement U(AE) = Pop. Ensuite, j’ai commencé à expérimenter avec le côté droit de l’équation : je prenais de différentes variables pertinentes à la consommation de l’énergie ainsi que celles qui correspondent à l’alimentation et je les testais façon Cobb – Douglas, donc « Population = a * (Energie, pouvoir µ) * (absorption alimentaire, pouvoir 1 – µ». Après maints essais, j’ai commencé à trouver une logique qui consiste, tout d’abord à utiliser, sur le côté gauche, la population en millions (donc 36 millions était juste 36). Sur le côté droit j’avais mis la consommation finale d’énergie par tête d’habitant, par an, mesurée tonnes d’équivalent pétrole, comme ma variable « Energie ». Je la symbolise, dans ce qui suit, comme « W/Pop ». Je l’avais élevée au pouvoir 0,75, donc je l’avais traitée exactement de la même façon que Charles W. Cobb et Paul H. Douglas eût traitée leur variable dominante. Comme variable correspondante à l’absorption alimentaire, donc la variable secondaire élevée au pouvoir 1 – 0,75 = 0.25,  j’ai utilisé une métrique publiée par FAO : l’absorption annuelle de nourriture en mégacalories par personne par an, moyenne sur la période 1990 – 2008, ou « A/Pop » dans ma notation de travail. Dans Table 1, ci-dessous, je présente les résultats du test de cette fonction « Pop = (W/Pop)0,75 * (A/Pop)0,25 » dans le cas de l’Argentine. Pourquoi Argentine ? Je n’en sais rien. Pourquoi pas ? Probablement c’est juste parce que l’Argentine est au tout début des listes alphabétiques.

Table 1 – Modèle « Pop = (W/Pop)0,75 * (A/Pop)0,25 » testé pour Argentine

Année Population modèle Population réelle Population réelle divisée par population modèle
1990 16,94515987 32,72974 1,931509662
1991 17,15400412 33,19392 1,93505375
1992 17,63123882 33,655149 1,908836319
1993 17,49698756 34,110912 1,949530563
1994 18,1568104 34,558114 1,903314142
1995 18,17673406 34,994818 1,925253343
1996 18,50330199 35,419683 1,914235795
1997 18,77977444 35,833965 1,908114771
1998 19,09688338 36,241578 1,897774484
1999 19,18104255 36,648054 1,910639315
2000 19,19068218 37,057453 1,931012803
2001 18,33116134 37,471535 2,044144084
2002 17,79285682 37,889443 2,129474957
2003 18,56300544 38,309475 2,063753907
2004 19,67676644 38,728778 1,968249108
2005 19,61545345 39,145491 1,995645479
2006 20,73285834 39,55875 1,908022008
2007 20,77463916 39,969903 1,9239758
2008 21,42453224 40,38186 1,884842084
2009 20,82006283 40,798641 1,959582991
2010 21,30211457 41,222875 1,935154131
2011 21,37553072 41,655616 1,948752363
2012 21,27620936 42,095224 1,978511458
2013 21,17559846 42,538304 2,008835976

Alors, vous demanderez, qu’est-ce qu’il y a de si spécial au sujet de Table 1 ? Si vous regardez la dernière colonne, donc celle où je présente le quotient de la population réelle de l’Argentine, divisée par celle modelée avec l’équation, vous pouvez voir un quotient étonnamment stable : avec une moyenne de 1.952675804, cette proportion a une variance de 0,003343958, donc trois fois rien avec cette moyenne. Je suis donc arrivé, dans le cas de l’Argentine, à une proportion très stable entre le produit (W/Pop)0,75 * (A/Pop)0,25 et la population réelle. C’est exactement de cette façon que Charles W. Cobb et Paul H. Douglas avaient démontré la robustesse de leur fonction de production : ils avaient trouvé une proportion stable (a = 1,01) entre le produit K0,25 * L0,75 et le PIB des Etats-Unis.

Bon, alors si ça a marché pour Argentine, je teste pour un autre pays. Pour devancer des reproches d’alphabétisme ou de continentalisme, je saute jusqu’à la République Tchèque. Je présente les résultats dans Table 2, ci-dessous. Il y a deux trucs qui frappent. Premièrement, le quotient « population réelle divisée par la population modèle » est d’un ordre de grandeur plus petit que celui calculé pour Argentine, mais tout aussi stable. Avec une moyenne de 0.254871929, ce quotient présente une variance de      9,54914E-05 : presque rien.

Table 2 – Modèle « Pop = (W/Pop)0,75 * (A/Pop)0,25 » testé pour la République Tchèque

Année Population modèle Population réelle Population réelle divisée par population modèle
1990 42,01901303 10,32844 0,245803965
1991 41,09669593 10,33393 0,251454035
1992 40,12189819 10,338381 0,257674274
1993 39,19042449 10,339439 0,263825645
1994 39,60863068 10,335556 0,260942018
1995 40,58847225 10,326682 0,254424013
1996 40,82241752 10,313836 0,252651279
1997 39,88617912 10,297977 0,258184094
1998 37,84459093 10,280525 0,271651106
1999 39,37364352 10,26301 0,260656853
2000 40,28179246 10,244261 0,254314924
2001 40,66550053 10,225198 0,251446505
2002 42,07940371 10,211846 0,242680388
2003 42,82094284 10,212088 0,238483492
2004 42,39265377 10,230877 0,241336083
2005 43,04580379 10,271476 0,238617359
2006 42,88357419 10,330487 0,240896129
2007 41,88719031 10,397984 0,248237801
2008 39,71897639 10,460022 0,263350744
2009 41,20964533 10,506617 0,254955288
2010 40,03317891 10,533985 0,263131365
2011 39,8616928 10,545161 0,264543733
2012 39,38759924 10,545314 0,267731829
2013 39,0565498 10,542666 0,269933367

J’ai donc trouvé une fonction que, faute de pouvoir trouver mieux sur le champ, je peux appeler « fonction de population-énergie », produit un agrégat que je peux interpréter comme population potentielle possible sur la base de l’absorption agrégée de l’énergie. Je l’ai testé un peu au hasard, pour un pays-ci, un pays-là. D’une manière générale, la population modèle sur la base de l’absorption de l’énergie est plus grande que la population réelle, plutôt type République Tchèque, avec ce quotient « population réelle divisée par la population modèle » solide comme du béton armé. Encore, il y a des exceptions intéressantes. Tenez l’Indonésie. Je présente son cas dans Table 3, ci-dessous. Voilà une population réelle plusieurs fois plus élevée que la population modelée sur la base de l’absorption locale d’énergie. En plus, le quotient « population réelle divisée par la population modèle » dans le cas Indonésien est beaucoup moins stable : avec une valeur moyenne de 25.18571988, il présente une variance de 0.390129622, donc beaucoup plus respectable que chez les Tchèques et les Argentins. Conclusion : ma formule magique marche dans certains cas, et pas tout à fait dans des cas autres que certains. Chouette ! Je vois une bonne recherche à l’horizon.

Table 3 – Modèle « Pop = (W/Pop)0,75 * (A/Pop)0,25 » testé pour l’Indonésie

Année Population modèle Population réelle Population réelle divisée par population modèle
1990 6,910456897 182,177052 26,36251911
1991 7,07024969 185,379624 26,21967146
1992 7,20208569 188,554943 26,18060255
1993 7,632690336 191,693719 25,11482984
1994 7,549393699 194,782664 25,80110029
1995 8,031643459 197,814284 24,62936571
1996 8,157250449 200,786111 24,61443501
1997 8,271624937 203,707717 24,62729132
1998 8,049718606 206,598599 25,66531939
1999 8,248410897 208,644079 25,29506369
2000 8,672234316 211,540428 24,39284045
2001 8,734987683 214,448301 24,55049838
2002 8,88687902 217,369087 24,45955284
2003 8,816998424 220,307809 24,98671298
2004 9,158320367 223,268606 24,3787722
2005 9,188094507 226,254703 24,62476881
2006 9,257872076 229,26398 24,76421991
2007 9,135126017 232,29683 25,42896831
2008 9,180058336 235,360765 25,63826464
2009 9,567339213 238,465165 24,92492005
2010 9,806815912 241,613126 24,63726537
2011 9,56672306 244,808254 25,58956212
2012 9,698388916 248,037853 25,57516049
2013 9,665810563 251,268276 25,99557216

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

[2] 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

Core and periphery

My editorial

And so I am officially starting to prepare a manuscript for my book, which I provisionally give the title ‘Good at Energy’.  I am exploring the same general hypothesis I have already turned and returned many times on this blog, namely that technological change in our human civilisation is functionally oriented on maximising the absorption of energy from the environment. I articulate this general line of thinking into four more specific hypotheses, which are supposed to drive the writing of four distinct sections in my book. One, the spatial structure of the human civilisation adapts and rearranges so as to maximise the absorption of energy. Two, the pace of technological change is functionally connected to the food deficit the given society experiences, and reaches its peak in societies with a food deficit between zero and 90 kilocalories per day per person. Three, technological change follows an evolutionary function of selection and hierarchizing, where social entities specialise, respectively, in the male function of transmission and conception, or in the female function of recombination and reproduction, which creates a hierarchy of male entities according to their capacity of meeting the expectations of the female entities. Four, technological change in the absorption of energy is functionally connected to the development of communication systems, with the supply of money acting as a communication system among others and the velocity of money being inversely proportional to the pace of technological change.

Yesterday, as I formulated those hypotheses in my update in French (see Les implications de ce que je viens d’écrire), I started reviewing the literature regarding the first specific hypothesis, the one about the spatial structure of the human civilisation. Quite naturally, being an economist, I called by Paul Krugman and the so-called ‘new economic geography’ (Krugman 1991[1];  Krugman 1998[2]). The basic logic I could derive from these readings is that of differentiation inside a territory: geographical structures differentiate internally into specialized parts, and this differentiation follows a pattern of core different from periphery. I think I can take on the model proposed by Paul Krugman, and replace the maximisation of utility, in the original version, by the maximisation of energy absorbed. As I think about it, with this precise orientation in my hypothesis, I can take any economic model that implies the maximisation of utility, transform it so as it maximises the absorption of energy, and see what happens.

Now, the tricky part is the ‘I can’. Can I? Let’s see. I take on the equations from the original model by Paul Krugman (Krugman 1991[3]). I start with equation (1). With CM standing, for the consumption of the manufacturing aggregate, and CA corresponding to the consumption of agricultural goods, the former receives always a share µ of the aggregate expenditure, the given society maximizes its aggregate utility ‘U’ so as to satisfy U = CMµ*CA1-µ. Here comes the first big question from my point of view: whilst it is simple to replace aggregate utility by the aggregate absorption of energy – let’s call it ‘AE’ (could also stand for ‘Attractive Expectations’, mind you) – it is more delicate to rephrase the right side of my equation. In economics, utility is a blissful category, as it has no definite unit of measurement. Utility can be cardinal or ordinal, can be expressed in money or in equivalent units of any economic good. Utility is cool and relax, even when it maximizes itself. Now, the absorption of energy is stricter a category: there are always joules under the bottom line. They can gang up into kilojoules or mega joules, or even dress into calories or watts, but at the end of the day, I have to sum my calculations up with a unit of energy. Logically, on the right side of the equation, I have to put aggregates that sum up into joules, watts or related.

We absorb energy in two ways: we eat it in kilocalories and we use it in various units. All that stuff is convertible into watt-hours, fortunately. I assign the symbol ‘F’ to the aggregate absorption of energy through eating (comes from ‘food’, but you have probably guessed this one already), and I designate the aggregate use of energy as ‘W’, or something measured directly in watts. As I am having my first go at transforming the original equation by Paul Krugman, I’m saying AE = Fp*Wq. The next stop is by that ‘p’ and that ‘q’. What are they? They can be anything, but as I look at it, I have to transform this transformation a bit. I mean, if I literally take the absorption of food and the final use of energy, express them both in an aggregate of watts, I get straight to the left side, namely to the aggregate absorption of energy, without any powers. I know, I could make it look like AE = F1*W1, but: a) it looks stupid b) it does not make sense. The final absorption of energy is the sum total of food eaten and energy used in other forms, not their product, whatever power I raise them to. Thus, I should say AE = F + W, but this is an accounting identity, not a functional model. Master Paul, I humbly apologize for having doubted in your insightfulness, when you used that aggregate utility thingy. Now I can see the depth of your wisdom, and I humbly return to the path of enlightenment, and I know it is better to use U(AE), so the utility derived from aggregate absorption of energy, than the plain AE.

Still I have a question: in your initial model, Master Paul, you raised the manufacturing output to the power ‘µ’, and agricultural goods to ‘1 – µ’. I guess it means that first we spend money on manufactures, and only after having done that, we scratch the bottom of our purse and get the last ‘1 – µ’ pennies to buy them pork loins and tomatoes. If you say so, Master… But what should I do? Should I assume that we spend money on food first, and only then we pour fuel into our cars (if we have any), or the opposite way round, namely petrol tank first, stomach next? Master? What? I have to think by myself, as I am a university professor? If you say so, Master… I am giving a try at thinking by myself, and I recollect my earlier research, and I remember that sharp difference between societies with officially recorded food deficit, on the one hand, and the satiate ones, on the other hand. I guess I should assume both options as possible, and say:

Equation (1), Class #1: U(AE) = Fµ*W1-µ        >> these people eat first, and turn their TV on next. Expenditures on energy are residual regarding expenditures on food. Roughly speaking, this class covers all the cases of societies with the food deficit being kind of official.

Equation (1) Class #2: U(AE) = Wµ*F1-µ            >> of course, those people eat, too, and they have to, and probably they eat better and more than Class #1, and yet, as they don’t have any official food deficit displayed on their doorstep, they mostly forget that food can go scarce. They spend most of their revenue on other forms of energy use, and leave a reasonable residual for caviar and Champagne.

According to Paul Krugman, that ‘µ’ parameter is one of the main bearings in his original model. It determines whether regions converge or diverge. Anyway, I am skipping to equation (2) in the original model, which basically details the way we compute the consumption CM of manufactures, and which I can generalize as the way of computing the aggregate endowed raised to power µ in equation (1). Before I go further, an old reminder: I am writing this precise content for my blog, and neither of my blogging environments, namely neither Blogger nor Word Press, is at home with equations. Hence, I do my best to express the original scientific equations as text. So I say {F; W} = [∑(i = 1 -> N; ci(π-1)/π)]π/(π-1) , where ∑(i = 1 -> N) means sum total over the interval from i = 1 to N, ‘ci’is the i-th consumable in the lot, and π > 1 is the elasticity of substitution between those consumables. That ‘π’ parameter is the second anchor of the equilibrium in the model.

As I am quickly wrapping my mind around equation (2), I think that substitution between various foods is an abyssal topic, especially if I want to treat global data, with all the local specificities in alimentary regimes. In class #1, with food coming first, the aggregate F = [∑(i = 1 -> N; ci(π-1)/π)]π/(π-1) would be quite foggy. Conversely, there are sharp distinctions as for the use of energy. I can sharply divide electricity used in houseware from fuel burnt in cars etc. Intuitively, I would go for class #2 when applying this Paul Krugman’s model. I could even invent some kind of intellectual parkour in order to jump over the food deficit. Actually, I don’t even need parkour: common observation comes handy. This summer, in China, I had the occasion to observe people who have s***load of technology to their disposition and still are officially starving, by some 74 kilocalories per day per person, on average. In other words, a paradigm where money is spent on the use of energy first, and only then on the energy consumed via food, is not really confined to the wealthy and satiate societies. After reflection, I go for class #1, and so I state my rephrased model as follows:

Equation (1) U(AE) = Wµ*F1-µ      µ < 1

Equation (2) W = [∑(i = 1 -> N; ci(π-1)/π)]π/(π-1)     π > 1

As I understand the original reasoning by Paul Krugman, the internal, spatial differentiation of a territory into a core and a periphery depends, among others, on those two parameters: µ and π. I guess that the greater are the values of µ and π, the greater the potential for such differentiation. I will slowly drift towards rephrasing that original model so as to show, how does the working of equations (1) and (2) impact the density of population.

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

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

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

Les implications de ce que je viens d’écrire

Mon éditorial

Je suis en train de penser à plusieurs trucs à la fois, ce qui arrive parfois à tout le monde. Je continue mon apprentissage de Python 3.6.2 et de documenter ce processus. Je suis arrivé à ce stade d’ignorance heureuse où je prends simplement une commande de Python et je joue avec, très intuitivement, sans en espérer trop, juste pour voir ce que ça donne. J’ai donc arrêté d’essayer d’utiliser Python comme une version moins confortable d’Excel et j’explore sans idées prédéterminées. Je me demande si des changements technologiques à l’échelle des sociétés ne marcheraient mieux si on faisait ça de façon organisée, comme « jours d’apprentissage spontané ». L’autre truc auquel je pense c’est ma recherche en cours, celle qui concerne l’innovation, le changement technologique et la transition vers les énergies renouvelables. Je commence à mettre en place la structure d’un livre sur ce sujet. Le titre de travail c’est quelque chose comme « Bons en énergie », puisque je veux y développer cette idée centrale que nous, c’est-à-dire la civilisation humaine, nous excellons à l’absorption d’énergie de notre environnement et à sa transformation et que la transition vers les énergies renouvelables peut être mieux comprise et peut-être même mieux organisée si on la base sur cette auto-compréhension.

Il y a beaucoup de choses que je voudrais mettre dans ce livre. Tout d’abord, le fait central contemporain serait ce changement qui s’est effectué en 2007 – 2008, lorsque le marché d’énergies renouvelables avait tout à coup commencé à croître beaucoup plus vite qu’avant. Je veux trouver et exposer une explication de ce fait. La revue de littérature sur l’histoire de la technologie m’a fait découvrir l’hypothèse générale de déterminisme technologique, avec toutes ses nuances et contre-arguments et quand j’y pense, elle ferait un joli paysage théorique pour l’étude de la transition énergétique. Attention, ça arrive ! Voilà une hypothèse générale qui vient de se former dans mon esprit. Vite, avant qu’elle refroidisse : « Les changements sociaux et technologiques de la civilisation humaine sont fonctionnellement orientés sur la maximisation d’absorption d’énergie de l’environnement ». Ouais, ce vrai que ça fait un joli fond pour le contenu de ce livre. Sur ce fond, avec des coups légers (et bien incertains encore, soyons francs) de clavier de mon MacBook Air, j’esquisse quelques hypothèses plus spécifiques. Un, la structure spatiale de la civilisation humaine s’adapte et se regroupe en vue de cette absorption maximale d’énergie. Deux, la cadence de changement technologique est fonctionnellement liée au déficit alimentaire éprouvé par la société donnée et atteint son maximum dans les sociétés où ce déficit, tout en étant observable, n’excède pas 90 kilocalories par jour par personne. Trois, le changement technologique suit une fonction évolutive de sélection et hiérarchisation, où des entités sociales se spécialisent, respectivement, en la fonction mâle de conception et la fonction femelle de recombinaison et reproduction, ce qui crée une hiérarchie entre les entités mâles en fonction de leur aptitude à satisfaire les exigences des entités femelles. Quatre, le changement technologique au niveau de l’énergie est fonctionnellement lié au développement des systèmes de communication, avec la masse monétaire jouant le rôle d’un système de communication parmi autres et la vélocité de l’argent étant inversement proportionnelle à la cadence du changement technologique. Eh bien, voilà, ça n’a pas été si dur que ça. Une hypothèse générale et quatre hypothèses spécifiques, chacune correspondant à un chapitre du livre.

L’autre truc auquel je pense c’est le début des cours à la fac. Je suis prof d’université et mon année civile se structure en fonction de l’année académique. J’aime bien ce travail et c’est en fait avec un peu d’impatience que j’attends le premier Octobre chaque année. En ce qui concerne ce blog, l’avènement de l’année académique veut dire que je placerais, outre mes mises à jour genre recherche, des mises à jour éducatives. Comme j’enseigne en anglais et en polonais, je vais utiliser ces deux blogs jumeaux – et – pour placer du matériel éducatif en anglais et à part ça, je démarre avec un blog en polonais pour faire le même en ma langue natale. Je ne sais pas si j’aurai le temps et l’énergie pour jumeler en français le matériel éducatif publié en anglais mais enfin, on va bien voir. Je me dis, quand j’y pense, que ce serait judicieux de combiner d’une certaine façon le matériel éducatif avec l’écriture de mon livre. Après tout, je suppose que ce n’est pas interdit de partager mes intérêts de recherche avec mes étudiants.

Je m’en prends donc à la première hypothèse de mon livre : « la structure spatiale de la civilisation humaine s’adapte et se regroupe en vue de l’absorption maximale d’énergie ». Je pense qu’il est utile que j’explique, une fois de plus et certainement pas la dernière, à l’adresse des pas-tout-à-fait-initiés, à quoi ça sert, une hypothèse. Vous pouvez imaginer la réalité telle que nous la percevons comme du sacré bordel. L’une des premières choses à faire avec la réalité perçue consiste donc à y mettre de l’ordre. Une hypothèse est comme un classeur ou un carton de rangement : j’y mets des choses qui semblent y avoir leur place plutôt que dans un autre classeur (carton). Nous pouvons formuler un nombre indéfiniment grand d’hypothèses à propos de chaque morceau de réalité observable, même si vous venez d’extraire ledit morceau de l’une de vos narines. Dans ce domaine vaste de tout ce que je peux dire à propos de quelque chose, il y a un sous-ensemble d’hypothèses qui sont raisonnablement vérifiables, et il y a tout le reste, intéressant, certes, mais peu utile. La technique scientifique de base consiste donc à prendre une boîte de rangement et d’y mettre certains trucs, tout en laissant tout le reste de la réalité à ranger par d’autres esprits hantés comme le mien. Lorsque je formule cette hypothèse au sujet de la structure spatiale de la civilisation humaine, je collecte des faits et des théories à propos de la structure spatiale de l’habitat humain. Je ne sais pas, en ce moment précis, quand j’écris ces mots, si cette hypothèse est suffisamment robuste pour être admise comme vraie sous des conditions raisonnables. Je n’en sais rien et je veux le découvrir. L’hypothèse m’aide à diriger mes efforts. Elle est donc comme un classeur croisé avec un viseur optique.

C’est ainsi donc que je me dirige vers le Grand Maître de la géographie économique : Paul Krugman. Je fourre dans le passé du Grand Maître. Je vais suffisamment loin en arrière pour découvrir ce que le Grand Maître écrivait, lorsqu’il n’était pas encore tout à fait le Grand Maître : le début des années 1990. A l’époque, Paul Krugman était encore le Luke Skywalker de l’économie : main sûre, esprit alerte, du talent reconnu, mais pas encore de lettres de noblesse. En 1991, il a publié un article intitulé « Increasing Returns and Economic Geography » (Krugman 1991[1]). Dans cet article, Paul Krugman présente un modèle de différentiation interne d’un pays en un centre industrialisé et une périphérie agriculturale. Pour réaliser des économies d’échelle tout en minimisant le coût de transport, les entreprises manufacturières se situent dans la région avec la demande la plus significative, seulement la localisation de la demande elle-même dépend de la localisation de production. L’émergence d’un modèle « centre – périphérie » dépend des coûts de transport, d’économies d’échelle, ainsi que de la part relative de l’industrie manufacturière dans le revenu national.

En 1998, Paul Krugman avait donné une sorte de résumé de sa théorie de géographie économique (consultez : Krugman 1998[2]). Sa conclusion d’alors était que la soi-disant nouvelle géographie économique se démarque par l’utilisation systématique de la fonction d’utilité maximale dans le contexte de l’équilibre général, en dérivant le comportement agrégé de la maximalisation individuelle. L’avantage principal de cette théorie, selon Krugman, est de démontrer comment des accidents historiques peuvent donner une forme géographique à l’activité économique et comment des changements graduels dans les paramètres économiques peuvent produire des changements discontinus dans la structure spatiale. De cette façon, la géographie économique est placée droit dans le créneau central de la recherche économique. A ce point-là, j’ai comme un pressentiment qu’au moins certains d’entre vous vont avoir besoin d’une exégèse de ma part. Eh bien, à la source, c’est tout la faute à Léon Walras , un économiste français qui a inventé ce truc d’équilibre général. En gros, sa théorie, la voilà : lorsqu’on fait du business, même si on s’imagine d’en faire d’une manière absolument géniale, genre « plus ingénieux que moi, tu meurs », en fait, on en fait d’une façon terriblement standardisée à travers la structure sociale. Tout le monde pense qu’ils sont des génies de l’industrie mais ils convergent tous vers un nombre très limité de stratégies qui marchent vraiment. Si tout ce petit monde avait une information parfaite et pouvait transférer les moyens de production librement entre des différents emplois, on pourrait vite atteindre un état de productivité parfaite avec ce qu’on a en termes de capital et travail et ce serait précisément cet état d’équilibre général. Seulement voilà, en l’absence poignante de conditions parfaites, on doit se satisfaire d’un état voisin de l’équilibre général.

A quoi bon, vous demanderez, se donner de la peine pour étudier un état qui n’a aucune chance d’exister dans la vie réelle ? Eh bien, voilà le truc et la grosse découverte : les économistes ont découvert que la société peut changer au rythme des petits pas ou à celui des bonds de sept lieues. Tant que l’état de l’économie peut être interprété comme voisin du même état d’équilibre général, le changement prend place à petits pas. Lorsqu’on fiche vraiment du bordel autour de nous et lorsque le voisinage de l’équilibre général donné (donc avec des paramètres donnés) devient tellement distant qu’on ne peut même plus le voir à l’horizon, cet équilibre, et lorsque bon gré mal gré il faut se construire un nouvel état d’équilibre général pour l’avoisiner, alors c’est du changement social profond, comme un tsunami économique. Bref, tout état d’une société peut être étudié, du point de vue économique, comme voisin d’un équilibre général bien défini par un ensemble de paramètres.

Voilà donc que j’ai une piste Krugmanienne pour développer sur mon hypothèse. Je vais chercher un état d’équilibre général qui est plausiblement corrélé avec l’absorption de l’énergie. Ensuite, je vais l’utiliser comme un échafaudage pour bâtir un modèle de différentiation spatiale en fonction de l’absorption de l’énergie. Vous ne comprenez pas tout à fait ce que je veux dire ? Vous n’êtes pas les seuls : moi non plus je ne comprends pas tout à fait les implications de ce que je viens d’écrire. Pas encore. Ça va venir.

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

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