Alois in the middle


I am returning to my syllabuses for the next academic year. I am focusing more specifically on microeconomics. Next year, I am supposed to give lectures in Microeconomics at both the Undergraduate, and the Master’s level. I feel like asking fundamental questions. My fundamental question, as it comes to teaching any curriculum, is the same: what can my students do with it? What is the function and the purpose of microeconomics? Please, notice that I am not asking that frequently stated, rhetorical question ‘What are microeconomics about?’. Well, buddy, microeconomics are about the things you are going to lecture about. Stands to reason. I want to know, and communicate, what is the practical utility, in one’s life, of those things that microeconomics are about.

The basic claim I am focusing on is the following: microeconomics are the accountancy of social structures. They serve exactly the same purpose that any kind of bookkeeping has ever served: to find and exploit patterns in human behaviour, by the means of accurately applied measures. Them ancients, who built those impressive pyramids (who builds a structure without windows and so little free space inside?), very quickly gathered that in order to have one decent pyramid, you need an army of clerks who do the accounting. They used to count stone, people, food, water etc. This is microeconomics, basically.

Thus, you can do with microeconomics if you want to build an ancient pyramid. Now, I am dividing the construction of said ancient pyramid in two stages: Undergraduate, and Master’s. An Undergraduate ancient pyramid requires the understanding of what do you need to keep the accounts of if you don’t want to be thrown to crocodiles. At the Master’s level, you will want to know what are the odds that you find yourself in a social structure, where inaccurate accounting, in connection with a pyramid, will have you thrown to crocodiles.

Good, now some literature, and a little turn by my current scientific work on the EneFin concept (see « Which salesman am I? » and « Sans une once d’utopisme » for sort of a current account of that research). I have just read that sort of transitional form of science, between an article and a book, basically a report, by Bleich and Guimaraes 2016[1]. It regards investment in renewable energies, mostly from the strictly spoken view of investment logic. Return on investment, net present value – that kind of thing. As I was making my notes out of that reading, my mind made a jump, and it landed on the cover of the quite-well-known book by Joseph Schumpeter: ‘Business Cycles’.

Joseph Schumpeter is an intriguing classic, so to say. Born in 1883, he published ‘Business Cycles’ in 1939, being 56 year-old, after the hell of a ride both for him and for the world, and right at the beginning of another ride (for the world). He was studying economics in Austria, in the early 1900, when social sciences in general were sort of different from their today’s version. They were the living account of a world that used to be changing at a breath-taking pace. Young Joseph (well, Alois in the middle) Schumpeter witnessed the rise of Marxism, World War I, the dissolution of his homeland, the Austro-Hungarian Empire, the rise of the German Reich. He moved from academia to banking, and from European banking to American academia.

I deeply believe that whatever kind of story I am telling, whether I am lecturing about economics, discussing a business concept, or chatting about philosophy, at the bottom line I am telling the story of my own existence. I also deeply believe that the same is true for anyone who goes to any lengths in telling a story. We tell stories in order to rationalize that crazy, exciting, unique and deadly something called ‘life’. To me, those ‘Business Cycles’ by Joseph Schumpeter look very much like a rationalized story of quite turbulent a life.

So, here come a few insights I have out of re-reading ‘Business Cycles’ for the n-th time, in the context of research on my EneFin business concept. Any technological change takes place in a chain of value added. Innovation in one tier of the chain needs to overcome the status quo both upstream and downstream of the chain, but once this happens, the whole chain of technologies and goods changes. I wonder how it can apply specifically to EneFin, which is essentially an institutional scheme. In terms of value added, this scheme is situated somewhere between the classical financial markets, and typical social entrepreneurship. It is social to the extent that it creates that quasi-cooperative connexion between the consumers of energy, and its suppliers. Still, as my idea assumes a financial market for those complex contracts « energy + shares in the supplier’s equity », there is a strong capitalist component.

I guess that the resistance this innovation would have to overcome would consist, on one end, in distrust from the part of those hardcore activists of social entrepreneurship, like ‘Anything that has anything to do with money is bad!’, and, on the other hand, there can be resistance from the classical financial market, namely the willingness to forcibly squeeze the EneFin scheme into some kind of established structure, like the stock market.

The second insight that Joseph has just given me is the following: there is a special type of business model and business action, the entrepreneurial one, centred on innovation rather than on capitalizing on the status quo. This is deep, really. What I could notice, so far, in my research, is that in every industry there are business models which just work, and others which just don’t. However innovative you think you are, most of the times either you follow the field-tested patterns or you simply fail. The real, deep technological change starts when this established order gets a wedge stuffed up its ass, and the wedge is, precisely, that entrepreneurial business model. I wonder how entrepreneurial is the business model of EneFin. Is it really as innovative as I think it is?

In the broad theoretical picture, which comes handy as it comes to science, the incidence of that entrepreneurial business model can be measured and assessed as a probability, and that probability, in turn, is a factor of change. My favourite mathematical approach to structural change is that particular mutation that Paul Krugman[2] made out of the classical production function, as initially formulated by Prof Charles W. Cobb and Prof Paul H. Douglas, in their common work from 1928[3]. We have some output generated by two factors, one of which changes slowly, whilst the other changes quickly. In other words, we have one quite conservative factor, and another one that takes on the crazy ride of creative destruction.

That second factor is innovation, or, if you want, the entrepreneurial business model. If it is to be powerful, then, mathematically, incremental change in that innovative factor should bring much greater a result on the side of output than numerically identical an increment in the conservative factor. The classical notation by Cobb and Douglas fits the bill. We have Y = A*F1a*F21-a and a > 0,5. Any change in F1 automatically brings more Y than the identical change in F2. Now, the big claim by Paul Krugman is that if F1 changes functionally, i.e. if its changes really increase the overall Y, resources will flow from F2 to F1, and a self-reinforcing spiral of change forms: F1 induces faster a change than F2, therefore resources are being transferred to F1, and it induces even more incremental change in F1, which, in turn, makes the Y jump even higher etc.

I can apply this logic to my scientific approach of the EneFin concept. I assume that introducing the institutional scheme of EneFin can improve the access to electricity in remote, rural locations, in the developing countries, and, consequently, it can contribute to creating whole new markets and social structures. Those local power systems organized in the lines of EneFin are the factor of innovation, the one with the a > 0,5 exponent in the Y = A*F1a*F21-a function. The empirical application of this logic requires to approximate the value of ‘a’, somehow. In my research on the fundamental link between population and access to energy, I had those exponents nailed down pretty accurately for many countries in the world. I wonder to what extent I can recycle them intellectually for the purposes of my present research.

As I am thinking on this issue, I will keep talking on something else, and the something else in question is the creation of new markets. I go back to the Venerable Man of microeconomics, the Source of All Wisdom, who used to live with his mother when writing the wisdom which he is so reputed for, today. In other words, I am referring to Adam Smith. Still, just to look original, I will quote his ‘Lectures on Justice’ first, rather than going directly to his staple book, namely ‘The Inquiry Into The Nature And Causes of The Wealth of Nations’.

So, in the ‘Lectures on Justice’, Adam Smith presents his basic considerations about contracts (page 130 and on): « That obligation to performance which arises from contract is founded on the reasonable expectation produced by a promise, which considerably differs from a mere declaration of intention. Though I say I have a mind to do such thing for you, yet on account of some occurrences I do not do it, I am not guilty of breach of promise. A promise is a declaration of your desire that the person for whom you promise should depend on you for the performance of it. Of consequence the promise produces an obligation, and the breach of it is an injury. Breach of contract is naturally the slightest of all injuries, because we naturally depend more on what we possess that what is in the hands of others. A man robbed of five pounds thinks himself much more injured than if he had lost five pounds by a contract ».

People make markets, and markets are made of contracts. A contract implies that two or more people want to do some exchange of value, and they want to perform the exchange without coercion. A contract contains a value that one party engages to transfer on the other party, and, possibly, in the case of mutual contracts, another value will be transferred the other way round. There is one thing about contracts and markets, a paradox as for the role of the state. Private contracts don’t like the government to meddle, but they need the government in order to have any actual force and enforceability. This is one of the central thoughts by another classic, Jean-Jacques Rousseau, in his ‘Social Contract’: if we want enforceable contracts, which can make the intervention of the government superfluous, we need a strong government to back up the enforceability of contracts.

If I want my EneFin scheme to be a game-changer in developing countries, it can work only in countries with relatively well-functioning legal systems. I am thinking about using the metric published by the World Bank, the CPIA property rights and rule-based governance rating.

Still another insight that I have found in Joseph Schumpeter’s ‘Business Cycles’ is that when the entrepreneur, introducing a new technology, struggles against the first inertia of the market, that struggle in itself is a sequence of adaptation, and the strategy(ies) applied in the phases of growth and maturity in the new technology, later on, are the outcome of patterns developed during that early struggle. There is some sort of paradox in that struggle. When the early entrepreneur is progressively building his or her presence in the market, they operate under high uncertainty, and, almost inevitably, do a lot of trial and error, i.e. a lot of adjustments to the initially inaccurate prediction of the future. The developed, more mature version of the newly introduced technology is the outcome of that somehow unique sequence of trials, errors, and adjustments.

Scientifically, that insight means a fundamental uncertainty: once the actual implementation of an entrepreneurial business model, such as EneFin, gets inside that tunnel of learning and struggle, it can take on so many different mutations, and the response of the social environment to those mutations can be so idiosyncratic that we get into really serious economic modelling here.

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

Support this blog


[1] Bleich, K., & Guimaraes, R. D. (2016). Renewable Infrastructure Investment Handbook: A Guide for Institutional Investors. In World Economic Forum, Geneva.

[2] Krugman, P. (1991). Increasing returns and economic geography. Journal of political economy, 99(3), 483-499.

[3] 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

Which salesman am I?


I am working on a specific aspect of the scientific presentation regarding my EneFin concept, namely on transposing the initial idea – a quasi-cooperative scheme between a local supplier of renewable energies and his local customers, in an essentially urban environment (I was thinking about smart cities) – into the context of poor, rural communities in developing countries. Basically, it was worth approaching the topic from the scientific angle, instead of the purely business-planning one. When I do science, I need to show that I have read what other scientists have written and published on a given topic. So I did, and a few articles have given me this precise idea of expanding the initial concept: Muller et al. 2018[1], Du et al. 2016[2], Wang et al. 2017[3], and Moallemi, Malekpour 2018[4].

I like feeling that the things I do are useful to somebody. I mean, not just interesting, but like really useful. When I write on this blog, I like the thought that some students in social sciences could use the methods presented in their own learning, or that some teachers in social sciences could get inspired. I’m OK with inspiring negatively. If some academic in social sciences, after reading some of my writing, says ‘This Wasniewski guy is one of the dumbest and most annoying people I have ever read anything written by, and I want to prove it by my own research!’, I’m fine with that. This is inspiration, too.

Science is like a screwdriver: the more different contexts you can use your screwdriver in, the more useful it is. This is, by the way, a very scientific approach to economic utility. The more functions a thing can perform, the greater its aggregate utility. So I want those things I write to be useful, and making them functional in more contexts increases their utility. That’s why applying the initial, essentially urban idea of EneFin to the context of alleviating poverty in developing countries is an interesting challenge.

Here is my general method. I imagine a rural community in some remote location, without regular access to electricity at all. All they have are diesel generators. According to Breyer et al. 2010[5], even in the most favourable conditions, the LCOE (Levelized Cost Of Electricity) for energy generated out of diesel is like 0,16 – 0,34 €/kWh. Those most favourable conditions are made of a relatively low price of crude oil, and, last but not least, the virtual absence of transportation costs as regards the diesel oil itself. In other words, that 0,16 – 0,34 €/kWh is essentially relevant for a diesel generator located right by the commercial port where diesel oil is being unloaded from a tanker ship. Still, we are talking about a remote rural location, and that means far from commercial ports. Diesel has to come there by road, mostly. According to a blog post which I found (OK, Google found) at the blog of the Golden Valley Electric Association, that cost per 1 kWh of electricity could even go up to US$ 0,64 = €0,54.

Technological change brings alternatives to that, in the form of renewable energies. Photovoltaic installations come at really a low cost: their LCOE is already gravitating towards €0,05. Onshore wind and small hydro are quite close to that level. Switching from diesel generators to renewables equals the same type of transition that I already mentioned in « Couldn’t they have predicted that? », i.e. from a bracket of relatively high prices of energy, to that of much lower a price (IRENA 2018[6]).

Here comes the big difference between an urban environment in Europe, and a rural community in a developing country. In the former, shifting from higher prices of energy to lower ones means, in the first place, an aggregate saving on energy bills, which can be subsequently spent on other economic utilities. In the latter, lower price of energy means the possibility of doing things those people simply couldn’t afford before: reading at night, powering a computer 24/24, keeping food in a fridge, using electric tools in some small business etc. More social roles define themselves, more businesses start up; more jobs, crafts and professions develop. It is a quantum leap.

Analytically, the initially lonely price of energy from diesel generators, or PD(t), gets company in the form of energy from renewable sources, PRE(t). As I have already pointed out, PD(t) > PRE(t). The (t) symbol means a moment in time. It is a scientific habit to add moments to categories, like price. Things just need time in order to happen, man. A good price needs to have a (t), if it is to prove its value.

Now, I try to imagine the socio-economic context of PD(t) > PRE(t). If just the diesel generators are available, thus if PD(t) is on its own, a certain consumption of energy occurs. Some people are like 100% on the D (i.e. diesel) energy, and they consume QD(t) = QE(t) kilowatt hours. The aggregate QE(t) is their total use of energy. Some people are to some extent on diesel power, and yet, for various reasons (i.e. lack of money, lack of permanent physical access to a generator etc.), that QD(t) does not cover their QE(t) entirely. I write it as QD(t) = a*QE(t) and 0 < a < 1. Finally, there are people for whom the diesel power is completely out of reach, and, temporarily, their QE(t) = 0.

In a population of N people, I have, thus, three subsets, made, respectively, of ‘m’ people who QD(t) = QE(t), ‘p’ people who QD(t) = a*QE(t) and 0 < a < 1, and ‘q’ people on the strict QE(t) = 0 diet. When renewable energies are being introduced, at a PRE(t+1) < PD(t+1) price, what happens is a new market, balanced or monopolized at the price PRE(t+1), and at the QRE(t+1) aggregate quantity, and people start choosing. As they choose, they actually make that QRE(t+1) happen. Among those who were QE(t) = 0, an aggregate b*QE(t+1) flocks towards QRE(t+1), with 0 < b ≤ 1. In the subset of the QD(t) = a*QE(t), at least (1-a)*QE(t+1) go PRE(t+1) and QRE(t+1), just as some c*QD(t) out of the QD(t) = QE(t) users, with 0 ≤ c ≤ 1.

It makes a lot of different Qs. Time to put them sort of coherently together. What sticks its head through that multitude of Qs is the underlying assumption, which I have just figured out I had made before, that in developing countries there is a significant gap between that sort of full-swing-full-supply consumption of energy, which I can call ‘potential consumption’, or QE(t), on the one hand, and the real, actual consumption, or QA(t). Intuitively, QE(t) > QA(t), I mean way ‘>’.

I like checking my theory with facts. I know, might look not very scientific, but I can’t help it: I just like reality. I go to the website of the World Bank and I check their data on the average consumption of energy per capita. I try to find out a reference level for QE(t) > QA(t), i.e. I want to find a scale of magnitude in QA(t), and from that to infer something about QE(t). The last (t) that yields a more or less comprehensive review of QA(t) is 2014, and so I settle for QA(2014). In t = 2014, the country with the lowest consumption of energy per capita, in kilograms of oil equivalent, was technically South Sudan: QA(2014) = 60,73 kg of oil equivalent = 60,73*11,63 kWh = 706,25 kWh. Still, South Sudan started being present in this particular statistic only in 2012. Thus, if I decide to move my (t) back in ‘t’, there is not much moving to do in this case.

Long story short, I take the next least energy-consuming country on the list: Niger. Niger displays a QA(2014) = 150,73 kg of oil equivalent per person per year = 1753,04 kWh per person per year. I check the energy profile of Niger with the International Energy Agency. Niger is really a good case here. Their total QA(2014) = 2 649 ktoe (kilotons of oil equivalent), where 2 063 ktoe = 77,9% consists in waste and biofuel burnt directly for residential purposes, without even being transformed into electricity. Speaking of the wolf, electricity strictly spoken makes just 55 ktoe in the final consumption, thus 55/2649 = 2% of the total. The remaining part of the cocktail are oil products – 506 ktoe = 19,1% –  mostly made domestically from the prevalently domestic crude oil, and burnt principally in transport (388 ktoe), and then in industry (90 ktoe). Households burn just 20 ktoe of oil products per year.

That strange cocktail of energies reflects in the percentages that Niger displays in the World Bank data regarding the share of renewable energies in the overall consumption of energy, as well as in the generation of electricity. As for the former, Niger is, involuntarily, in the world’s vanguard of renewables, with 78,14% coming from renewables. Strange? Well, life is strange. Biofuels are technically renewable source of energy. When you burn the wood and straw that grows around, there will be some new growing around, whence renewability. Still, that biomass in Niger is being just burnt, without transformation of the resulting thermal energy into electric power. As we pass to data on the share of renewables in the output of electricity, Niger is at 0,58%. Not much.

From there, I have many possible paths to follow so as to answer the basic question: ‘What can Niger get out of enriching their energy base with renewables, possibly using an institutional scheme in the lines of the EneFin concept?’. My practical side tells me to look for a benchmark, i.e. for another country in Africa, where the share of renewable energy in the output of electricity is slightly higher than in Niger, without being lightyears away. Here, surprise awaits: there are not really a lot of African countries close to Niger’s rank, regarding this particular metric. There is South Africa, with 1,39% of their electricity coming from renewable sources. Then, after a long gap, comes Senegal, with 10,43% of electricity from renewables.

I quickly check those two countries with the International Energy Agency. South Africa, in terms of energy, is generally coal and oil-oriented, and looks like not the best benchmark in the world for what I what to study. They are thick in energy, by the way: QA(2014) = 2 695,73 kg of oil equivalent, more than 100 times the level of Niger. Very much the same with Senegal: it is like Niger topped with a large oil-based economy, and with a QA(2014) = 272,08 kg of oil equivalent. Sorry, I have to move further up the ranking of African countries in terms of renewables’ share in the output of electricity. Here comes Nigeria, 17,6% of electricity from renewables, and it is like a bigger brother of Niger: 86% of energy comes from the direct burning of biofuels and waste, only those biofuels are like 50 times more than in Niger. Their QA(2014) = 763,4 kg of oil equivalent per person per year.

I check Cote d’Ivoire, 23,93% of electricity from renewable sources, and I get the same, biofuels-dominated landscape. Gabon, Tanzania, Angola, Zimbabwe: all of them, however is their exact metric as for the share or renewables in the output of electricity, have mostly biofuels as renewable sources. Ghana, QA(2014) = 335.05, Mozambique, QA(2014) = 427.6, and Zambia, QA(2014) = 635.5, present slightly different a profile, with a noticeable share of hydro, but still heavily relying on biofuels.

In general, Africa seems to love biofuel, and to be largely ignoring the solar, the wind, and the hydro. This is a surprise. They have a lot of sunlight and sun heat, over there, for one. I started all my research on renewable energies, back in winter 2016, on the inspiration I had from the Ouarzazate-Noor Project in Morocco (see official updates: 2018, 2014, 2011). I imagined that Africa should be developing a huge capacity in renewable sources other than biofuels.

There is that anecdote, to find in textbooks of marketing. Two salesmen of a footwear company are sent to a remote province in a developing country, to research the local market. Everybody around walks barefoot. Salesman A calls his boss and says there are absolutely no market prospects whatsoever, as all the locals walk barefoot. Salesman B makes his call and, using the same premise – no shoes spotted locally at all – concludes there is a huge market to exploit.

Which salesman am I? Being A, I should conclude that schemes like EneFin, in African countries, should serve mostly to develop the usage of biofuels. Still, I am tempted to go B. As the solar, the hydro and the wind power tend to strike by their absence in Africa, this could be precisely the avenue to exploit.

What is there exactly to exploit, in terms of economic gains? The cursory study of African countries with respect to their energy use per capita show huge disparities. The most notable one is to notice between countries relying mostly on biofuels, on the one hand, and those with more complex energy bases. The difference in terms of the QA(2014) consumption of energy per capita is a multiple, not a percentage margin. Introducing a new source of energy into those economies looks like a huge game-changer.

There is that database I built, last year, out of Penn Tables 9.0, and from stuff published by the World Bank, and that database serves me to do like those big econometric tests. Cool stuff. Works well. Everybody should have one. You can see some examples of how I used it last year, if you care to read « Conversations between the dead and the living (no candles) » or « Core and periphery ». I decided to test my claim, namely that introducing more energy per capita into an economy will contribute to the capita in question having more of average Gross Domestic Product, per capita of course.

I made a simple linear equation with natural logarithms of, respectively, GDP per capita, expenditure side, and energy use per capita. It looks like ln(GDP per capita) = ln(Energy per capita) + constant. That’s all. No scale factors, no controlling variables. Just pure, sheer connection between energy and output. A beauty. I am having a first go at the whole sample in my database, with that most basic equation.

Table 1

Explained variable: ln(GDP per capita), N = 5498, R2 = 0,752
Explanatory variable Coefficient of regression


(Robust) Standard Error Significance level at t Student test
Ln(Energy per capita)


0,947 (0,007) p < 0,001


2,151 (0,053) p < 0,001

Looks promising. When driven down to natural logarithm, variance in consumption of energy per capita explains like 75% of variance in GDP per capita. In other words, generally speaking, if any institutional scheme allows enriching the energy base of a country – any country – it gives a high probability of going along with higher an aggregate output per capita.

A (partial) summing up is due. The idea of implementing a contractual scheme like EneFin in developing countries seems to make sense. The gains to expect are actually much higher than those I initially envisaged for this business concept in the urban environments of European countries. If I want to go after a scientific development of this idea, the avenue of developing countries and their rural regions seems definitely promising.

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

Support this blog


[1] Müller, M. F., Thompson, S. E., & Gadgil, A. J. (2018). Estimating the price (in) elasticity of off-grid electricity demand. Development Engineering, 3, 12-22.

[2] Du, F., Zhang, J., Li, H., Yan, J., Galloway, S., & Lo, K. L. (2016). Modelling the impact of social network on energy savings. Applied Energy, 178, 56-65.

[3] Wang, G., Zhang, Q., Li, H., Li, Y., & Chen, S. (2017). The impact of social network on the adoption of real-time electricity pricing mechanism. Energy Procedia, 142, 3154-3159.

[4] Moallemi, E. A., & Malekpour, S. (2018). A participatory exploratory modelling approach for long-term planning in energy transitions. Energy research & social science, 35, 205-216.

[5] Breyer, C., Gerlach, A., Schäfer, D., & Schmid, J. (2010, December). Fuel-parity: new very large and sustainable market segments for PV systems. In Energy Conference and Exhibition (EnergyCon), 2010 IEEE International (pp. 406-411). IEEE.

[6] IRENA (2018), Renewable Power Generation Costs in 2017, International Renewable Energy Agency, Abu Dhabi, ISBN 978-92-9260-040-2

Everything even remotely economic

My editorial

Back to my work on innovation, I am exploring a new, interesting point of view. What if we perceived technological change and innovation as collective experimentation under uncertainty, an experimentation that we, as a species, are becoming more and more proficient at?  Interesting path to follow. It has many branches into various fields of research, like games theory, for example. The curious ape in me likes branches. They allow it to dangle over problems and having an aerial view. The view involves my internal happy bulldog rummaging in the maths of the question at hand, and my internal monk, the one with the Ockham’s razor, fending the bulldog away from the most vulnerable assumptions.

One of the branches that my ape can see almost immediately is that of incentives. Why do people figure out things, at all? First, because they can, and then because they’d better, under the penalty of landing waist deep in shit. I think that both incentives, namely ‘I can’ and ‘I need to’ sum up very much to the same, on the long run. We can do things that we learn how to do it, and we learn things that we’d better learn if we want our DNA to stay in the game, and if such is our desire, we’d better not starve to death. One of the most essential things that we have historically developed the capacity of learning about is how to get our food. There is that quite cruel statistic published by the World Bank, the depth of food deficit. It indicates the amount of calories needed to lift the undernourished from their status, everything else being constant. As the definition of that variable states: ‘The average intensity of food deprivation of the undernourished, estimated as the difference between the average dietary energy requirement and the average dietary energy consumption of the undernourished population (food-deprived), is multiplied by the number of undernourished to provide an estimate of the total food deficit in the country, which is then normalized by the total population’.

I have already made reference to this statistic in one of my recent updates (see ). This time, I am coming back with the whole apparatus. I attach this variable, as reported by the World Bank, to my compound database made of Penn Tables 9.0 (Feenstra et al. 2015[1]), as well as of other data from the World Bank. My curious ape swings on a further branch and asks: ‘What does innovation and technological progress look like in countries where people still starve? How different is it from those wealthy enough for not worrying so much about food?’. Right you are, ape. This is a good question to ask, on this Thursday morning. Let’s check.

I made a pivot out of my compound database, summarizing the distribution of key variables pertaining to innovation, across the intervals defined regarding the depth of food deficit. You can grab the Excel file at this link: . A few words of explanation are due as for the contents. The intervals in the depth of food deficit have been defined automatically by my statistical software, namely Wizard for MacOS, version 1.9.9 (222), created by Evan Miller. Those thresholds of food deficit look somehow like sextiles (spelled together!) of the distribution: there is approximately the same number of observations in each interval, namely about 400. The category labelled ‘Missing’ stands for all those country – year observations, where there is no recorded food deficit. In other words, the ‘Missing’ category actually represents those well present in the sample, just eating to their will.

I took three variables, which I consider really pertinent regarding innovation: Total Factor Productivity, the share of the GDP going to the depreciation in fixed assets, and the ratio of resident patent applications per one million people. I start with having a closer look at the latter. In general, people have much more patentable ideas when they starve just slightly, no more than 28 kilocalories per day per person. Those people score over 312 resident patent applications per million inhabitants. Interestingly, those who don’t starve at all score much lower: 168,9 on average. The overall distribution of that variable looks really interesting. Baby, it swings. It swings across the intervals of food deficit, and it swings even more inside those intervals. As the food deficit gets less and less severe, the average number of patent applications per one million people grows, and the distances between those averages tend to grow, too, as well as the variance. In the worst off cases, namely people living in the presence of food deficit above 251 kilocalories a day, on average, that generation of patentable ideas is really low and really predictable. As the situation ameliorates, more ideas get generated and more variability gets into the equation. This kind of input factor to the overall technological change looks really unstable structurally, and, in the same time, highly relevant regarding the possible impact of innovation on food deficit.

I want this blog to have educational value, and so I explain how am I evaluating relevance in this particular case. If you dig into the theory of statistics, and you really mean business, you are likely to dig out something called ‘the law of large numbers’. In short, that law states that the arithmetical difference between averages is highly informative about real differences between populations these averages have been computed in. More arithmetical difference between averages spells more real difference between populations and vice versa. As I am having a look at the distribution in the average number of resident patent applications per capita, distances between different classes of food deficit are really large. The super-high average in the ‘least starving’ category, the one between 28 kilocalories a day and no deficit at all, together with the really wild variance, suggest me that this category could be sliced even finer.

Across all the three factors of innovation, the same interesting pattern sticks out: average values are the highest in the ‘least starving’ category, and not in the not starving at all. Unless I have some bloody malicious imp in my dataset, it gives strong evidence to my general assertion that some light discomfort is next to none in boosting our propensity to figure things out. There is an interesting thing to notice about the intensity of depreciation. I use the ratio of aggregate depreciation as a measure for speed in technological change. It shows, how quickly the established technologies age and what economic effort it requires to provide for their ageing. Interestingly, this variable is maybe the least differentiated of the three, between the classes of food deficit as well as inside those classes. It looks as if the depth of food deficit hardly mattered as for the pace of technological change.

Another interesting remark comes as I look at the distribution of total factor productivity. You remember that on the whole, we have that TFP consistently decreasing, in the global economy, since 1979. You remember, do you? If not, just have a look at this Excel file, here: . Anyway, whilst productivity falls over time, it certainly climbs as more food is around. There is a clear progression of Total Factor Productivity across the different classes of food deficit. Once again, those starving just a little score better than those, who do not starve at all.

Now, my internal ape has spotted another branch to swing its weight on. How does innovation contribute to alleviate that most abject poverty, measured with the amount of food you don’t get? Let’s model, baby. I am stating my most general hypothesis, namely that innovation helps people out of hunger. Mathematically, it means that innovation acts as the opposite of food deficit, or:

Food deficit = a*Innovation     , a < 0

 I have my three measures of innovation: patent applications per one million people (PattApp), the share of aggregate depreciation in the GDP (DeprGDP), and total factor productivity (TFP). I can fit them under that general category ‘Innovation’ in my equation. The next step consists in reminding that anything that happens, happens in a context, and leaves some amount of doubt as for what exactly happened. The context is made of scale and structure. Scale is essentially made of population (Pop), as well as its production function, or: aggregate output (GDP), aggregate amount of fixed capital available (CK), aggregate input of labour (hours worked, or L). Structure is given by: density of population (DensPop), share of government expenditures in the capital stock (Gov_in_CK), the supply of money as % of GDP (Money_in_GDP, or the opposite of velocity in money), and by energy intensity measured in kilograms of oil equivalent consumed annually per capita (Energy Use). The doubt about things that happen is expressed as residual component in the equation. The whole is driven down to natural logarithms, just in order to make those numbers more docile.

In the quite substantial database I start with, only n = 296 observations match all the criteria. On the one hand, this is not much, and still, it could mean they are really well chosen observations. The coefficient of determination is R2 = 0.908, and this is a really good score. My model, as I am testing it here, in front of your eyes, explains almost 91% of the observable variance in food deficit. Now, one remark before we go further. Intuitively, we tend to interpret positive regression coefficients as kind of morally good, and the negative ones as the bad ones. Here, our explained variable is expressed in positive numbers, and the more positive they are, the more fucked are people living in the given time and place. Thus, we have to flip our thinking: in this model, positive coefficients are the bad guys, sort of a team of Famine riders, and the good guys just don’t leave home without their minuses on.

Anyway, the regressed model looks like that:

variable coefficient std. error t-statistic p-value
ln(GDP) -5,892 0,485 -12,146 0,000
ln(Pop) -2,135 0,186 -11,452 0,000
ln(L) 4,265 0,245 17,434 0,000
ln(CK) 3,504 0,332 10,543 0,000
ln(TFP) 1,766 0,335 5,277 0,000
ln(DeprGDP) -1,775 0,206 -8,618 0,000
ln(Gov_in_CK) 0,367 0,11 3,324 0,001
ln(PatApp) -0,147 0,02 -7,406 0,000
ln(Money_in_GDP) 0,253 0,06 4,212 0,000
ln(Energy use) 0,079 0,1 0,796 0,427
ln(DensPop) -0,045 0,031 -1,441 0,151
Constant residual -6,884 1,364 -5,048 0,000

I start the interpretation of my results with the core factors in the game, namely with innovation. What really helps, is the pace of technological change. The heavier the burden of depreciation on the GDP, the lower food deficit we have. Ideas help, too, although not as much. In fact, they help less than one tenth of what depreciation helps. Total Factor Productivity is a bad guy in the model: it is positively correlated with food deficit. Now, the context of the scale, or does size matter? Yes, it does, and, interestingly, it kind of matters in opposite directions. Being a big nation with a big GDP certainly helps in alleviating the deficit of food, but, strangely, having a lot of production factors – capital and labour – acts in the opposite direction. WTH?

Does structure matter? Well, kind of, not really something to inform the government about. Density of population and energy use are hardly relevant, given their high t-statistic. To me, it means that I can have many different cases of food deficit inside a given class of energy use etc. Those two variables can be useful if I want to map the working of other variables: I can use density of population and energy use as independent variables, to construe finer a slicing of my sample. Velocity of money and the share of government spending in the capital stock certainly matter. The higher the velocity of money, the lower the deficit of food. The more government weighs in relation to the available capital stock, the more malnutrition.

Those results are complex, and a bit puzzling. Partially, they confirm my earlier intuitions, namely that quick technological change and high efficiency in the monetary system generally help in everything even remotely economic. Still, other results, as for example that internal contradiction between scale factors, need elucidation. I need some time to wrap my mind around it.

[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