I needed that

It’s been quite a few days without me writing and posting anything new on my blog. This is one of those strange moments, when many different strands of action emerge, none is truly preponderant over the others, and I feel like having to walk down many divergent paths all at once. As such an exercise can end up in serious injuries, the smart way to go is to make those divergent paths converge at some point.

As usually in such situations of slight chaos in my head, I use the method of questions to put some order in it. Let’s do it. What do I want? I want to develop my theoretical concept of collectively intelligent social structure into a workable, communicable, and reproducible methodology of research. I want to use that methodology as intellectual core for a big project of research and development. The development part would be some kind of digital tool which, using an otherwise very simple version of artificial neural network, can run the diagnosis of a society (e.g. a city), regarding: a) the collective outcomes pursued by the collective intelligence of that society b) the patterns of collective learning, and more specifically the phenomena which are likely to knock that society out of balance as opposed to those which make it stabilize.

As I am writing these words, I intuitively guess that my investment in the stock market, such as I consistently do it, is successfully based on the hypothesis of collective intelligence in the stock market, and in the industries which I invest in. As I consistently oscillate around 50% of annual return on the cash invested in the stock market, that hypothesis of collective intelligence seems to be workable. When I think about my recipe for success, it strangely resembles the findings of my scientific research. In a paper published with the journal ‘Energy’, titled ‘Energy efficiency as manifestation of collective intelligence in human societies’, I found out that the coefficient of fixed assets per one patentable invention is a key variable that societies optimize, and prioritize over energy efficiency. When I look at my investment portfolio, and what seems to work in it, it is precisely about some kind of balance between innovation and assets. When that sweet spot is there, the company’s stock brings me nice return.

I want to develop my concept of collectively intelligent social structure into a method of teaching social sciences, and to interweave that teaching into the canonical subjects I teach: microeconomics, macroeconomics, international trade etc. I wonder how I can use that concept e.g. in business planning or in the analysis of contracts and legal acts.

What am I afraid of? What can possibly go wrong with my plans? Good question. My fears are essentially those of publicly acknowledged failure on my part. I am shit scared of being labelled as a loser, but also of being seen as someone who fails to take any challenge at all. There is another deep fear in me, and this is a strange fear, as it is interwoven with hope: it is both the fear and the hope of deep change in my existence, like changing my professional occupation for a radically new one, or moving to live in another place, that kind of thing. It looks like I dread two types of suffering: that coming from socially recognized failure in building my position in social hierarchy, and that coming from existential change. Yet, my apprehension vis a vis those two types of suffering is different. Socially recognized failure is something I simply want to avoid. Existential change is that strange case of love and hate, a bit like my practice of the Wim Hof method. As I think of it, overcoming the fear of change can lead me to discovering new, wonderful things in my life, and this is what I want.

As I connect the dots I have just written down, turns out that what I really need to do is to utilise my research on collective intelligence as a platform for deep existential change. What specific kind of change would both scare me and thrill me in the best possible combination? What kinds of change can I take into account at all? Change of job inside the same occupation, i.e. inside the academia, for one. Further reaching a change of occupation, thus going outside academia, is the next level of professional change. The slightly fantasque move in that department would be to transform my investment in the stock market into a small investment fund for innovative projects, like a start-up fund. Moving to another place – a different city or a different country – is another option. Change of environment can be enormously stimulating, I know it by experience. Besides, my home country, Poland, is progressively turning into a mix of a catholic version of Iran, i.e. a religious state, with what I remember from the times of communism. A big part of the Polish population seems to be delighted with the process, and I am not delighted at all. I intuitively feel that compulsive thinking about how much ours is what we have means heading towards a disaster, and we just serve ourselves a lot of tranquilizing pills to kill the otherwise quite legitimate fear. It is all becoming both scary and suffocating, and I feel like getting out of the swamp before I sink too deep. Still, I know that geographical move has to be backed with realistic assumptions as for my social role: job, family etc. I am the kind of big, steady animal, like a moose, and it is both physical and existential. Jumping from one rooftop to another, parkour-style, is something I like watching but I completely suck at. I need a path and a structure to achieve change. 

I am exploring my deeply hidden drivers, and I am trying to be honest with myself and my readers. Which of those existential moves looks the most tempting to me? I think that a progressive transition, or, I should rather say: expansion, of out the academia is the most thrilling to me. I want it to be a progressive expansion, with a path of progress and learning. What do I need to learn in that process? In order to answer that question, I need to define my endgame, i.e. the target state I am working up to. In other words, how will I know I have what I want? I know I have a method when it has been intersubjectively validated, either by publication or by practical use in a collective research project.  How will other people know I have what I want? How will other people know I have a valid method? They need to buy into its logic, and acknowledge it as fit for publication or for application in a collective research project.

Here comes a fortunate coincidence, which has just knocked me out of philosophizing and closer to actual life. A scientific journal, Applied Energy, has just rejected positively my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, and I am sort of happy about it. Why being happy about rejection? Well, in the world of science, there are two types of rejection: the ‘f**k you, man!’ type, and the maybe-if-you-improve-and-develop type. With that specific manuscript, I have already knocked at the doors of many scientific journals, and each time I received the former type of rejection letter. This time, with Applied Energy, it is the latter type. The editorial letter I have just received states ‘While your submission is of interest to Applied Energy, your manuscript does not meet the following criteria, we are returning the manuscript to you before the review:

*Lack of scientific originality/novelty:

The novelty/originality shall be justified by highlighting that the manuscript contains sufficient contributions to the new body of knowledge. The knowledge gap needs to be clearly addressed in Introduction.

*Literature survey is not sufficient to present the most updated R&D status for further justification of the originality of the manuscript. You should carry out a thorough literature survey of papers published in a range of top energy journals in the last three/four years so as to fully appreciate the latest findings and key challenges relating to the topic addressed in your manuscript and to allow you to more clearly present your contributions to the pool of existing knowledge. In the case the subject is really novel and few or no specific references are found, the novelty of the subject, the methodology used and the similarity to other older or newer subjects should be explicitly addressed.

At this time, your submission will be rejected from Applied Energy but please feel free to re-submit to the journal once the aforementioned comments have been addressed’.

The journal Applied Energy is top of the food chain in as journals about energy economics come. Such a nice and polite rejection from them is an invitation to dialogue. At last! I really needed that.

As I am preparing teaching material for the next semester, and I am interweaving that stream of work with my research on collective intelligence in human societies. I drop by some published science, just to chat with Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications. https://doi.org/10.1016/j.spa.2020.12.006 . There is a state of reality Xn = {x1, x2, …, xn}, which we cannot observe directly; {Xn} slips easily of our observational capacity. Thus, instead of chasing ghosts, we nail down a set of observables {Yn} such that Yn = π (Xn), the π being a coding map of Xn so as we can observe through the lens of Yn.

These are the basic assumptions expressed in the paper by Berghout & Verbitskiy, and this is an important building bloc in my research and in my teaching. If I want to teach my hypothesis of collective intelligence to undergraduate students, I need to make it simple, and to show immediate benefits of using an analytical method based on it. I want to focus, for a moment, on the latter component, thus on practical applications. The hypothesis of collective intelligence implies that human societies are intelligent structures, and they learn new stuff by experimenting with many alternative versions of themselves. That capacity of learning by experimenting with ourselves, whilst staying structurally coherent, is precisely the gain out of being collectively intelligent. Here, I go a bit far with my next claim: I think we can enhance our capacity of collective learning if we accurately grasp and communicate the exact way we learn collectively, i.e. the exact way we experiment with many alternative versions of us doing things together. That hypothesis comes from my observation about myself, and about some other people I know: when I narrate to myself the way I learn something, my learning speeds up. What if we, humans being together, can speed up the process of our collective learning by narrating to ourselves the exact way we learn?

Here, I stress the ‘exact way’ part. We have culture, which recently turns into outrage culture, with a lot of moralizing and little action. Here, I allow myself to quote one of my students. The guy comes from Rwanda, Africa, and in the class of management, when we were discussing different business concepts my students come up with, he gave the example of an actual business model which apparently grows like hell in Rwanda and in Africa as a whole. You buy a small fleet of electric cars, like 5 – 10, you rent them, and you assure full technical support to your clients, and you build a charging station for those cars, powered by a solar farm just next door. Investment goes into five types of assets: land, solar farm with full equipment (big batteries for storage included), electric cars, and equipment for their maintenance. You sell rental hours, additional maintenance services, and energy from the charging station. Simple, clean, workable, just the way I like it.

When I heard that story from my student, I had one of those ‘F**k!’ realizations. In Europe, and I think in North America as well, when we want to do something for the planet and the climate, we start by bashing each other about how bad we are at it and how necessary it is to turn vegan, then we burn thousands of tons of fuel to gather in one place and do a big march for the planet, then we do a strike for climate, and finally we claim that the government should do something about the climate, and, by the way, it would be a good thing if Jeff Bezos gave away some of his wealth. In Rwanda, when those people realize they should take care of the climate and the planet, they develop businesses which do. I think their way is somehow more promising.

I come back to the exact way we learn collectively. There is the Greta-Thunberg-way of caring about the planet, and there is the Rwandan way. Both exist, both are different experimental versions of ourselves, and both get reinforced by communication. One march for the planet, properly covered by the media, incites further marches for the planet, and, in the same way, disseminating that business model – involving a small fleet of electric vehicles, charging stations and solar farms – is likely to speed up its development. Narrating to ourselves the ways we develop new technologies can speed up their development.

The exact way we learn collectively is made, in the first place, of the specific, alternative versions of the social structure. When I want to know the exact way we learn collectively, I need to look at the alternative versions (of our collective) which we are experimenting with, thus at the actual degrees of freedom we have in that experimentation. Those alternative versions are described in terms of observables that Yn = π (Xn), which, in turn, are our best epistemological take on the otherwise unobservable reality {Xn}, through the coding map π.

I can see something promising here, I mean in that notion of actual experimental versions of ourselves. My scientific discipline, i.e. social sciences with a strong edge of economics and management, is plagued by claims that things ‘should be done’ in a given way just because it worked locally. Recently, I witnessed a heated debate between some acquaintances of mine, on Facebook, as for which economic model is better: the American one or the Scandinavian one. You know, the thing about education, healthcare, economic equality and stuff. As I was observing the ball of thoughts being played between those people, I had the impression of seeing an argument without common field. One camp argued that because something works in Sweden or Finland, it should be applied everywhere, whilst their opponents claimed exactly the same about the American economic model. In the middle of that, I was watching the protagonists flexing their respective intellects, and I couldn’t help thinking about my own research on economic models. I found empirical evidence that economic systems, across the board, aim for optimizing the average number of hours worked per person per year, and the amount of education one needs to get into the job market. All the rest is apparently instrumental.

F**k! I got distracted once again. I am supposed to show practical applications of my hypothesis regarding collective intelligence. Here comes an idea for a research project, with some potential for acquiring a research grant, which is as practical an application as there can be, in science. In my update titled ‘Out-of-the-lab monsters’, I hypothesised that economic recovery after the COVID-19 pandemic will be somehow slower than we expect, and certainly very different in terms of business models and institutions. The pandemic has triggered accelerated change as regards the use of digital technologies, the prevalence of biotechnology as business, and as regards social roles that people can endorse. Therefore, it would be a good thing to know which specific direction that change is going to take.

My idea is to take a large sample of business entities listed in public stock markets, which disclose their activity via the mechanism of investor relations, and to study their publicly disclosed information in order to discover the exact way they take in their business models. I am formulating the following hypothesis: in the economic conditions peculiar to the COVID-19 pandemic, business entities build up their reserves of cash and cash-equivalent securities in order to reinforce their strategic flexibility as regards technological change

Out-of-the-lab monsters

That period, end of January, beginning of February, is usually a moment of reassessment for me. This might be associated with my job – I am a scientist and an academic teacher – and right now, it is the turn of semesters in my country, Poland. I need to have some plan of teaching for the next semester, and, with the pandemic still around, I need to record some new video material for the courses of the Summer semester: Macroeconomics, International Trade, and International Management.

That being said, I think that formulating my current research on collective intelligence in terms of teachable material could help me to phrase out those thoughts of mine coherently and intelligibly enough to advance with the writing of my book on the same topic. I feel like translating a few distinct pieces of scientific research into teaching. The theoretical science of Markov chains is the first one. The empirically observed rise of two technologically advanced industry, namely biotechnology and electric vehicles comes as the second big thing. Thirdly, and finally, I want to develop on the general empirical observation that money tends to flow towards those new technologies even if they struggle to wrap themselves into operationally profitable business models. Next comes a whole set of empirical observations which I made à propos of the role of cities in our civilization. Finally, the way we collectively behave amidst the pandemic is, of course, the most obvious piece of empirical science I need connecting to in my teaching. 

In discussing those pieces of science in a teachable form, I feel like using the method I have been progressively forming in my research over the last 2 years or so. I use simple artificial neural networks as simulators of collectively intelligent behaviour. I have singled out a few epistemological regularities I feel like using in my teaching. Large datasets of socio-economic variables seem to have privileged orientations: they sort of wrap themselves around some specific variables rather than others. When disturbed with a random exogenous factor, the same datasets display different ways of learning, depending, precisely, on the exact variable I make them wrap themselves around. One and the same dataset, annoyingly disturbed by the buzz of a random disturbance, displays consistent learning when oriented on some variables, and goes haywire when oriented on others.

On the top of all that, I want to use in my teaching the experience I have collected when investing in the stock market. This is mostly auto-narrative experience, about my own behaviour and my own reactions when sailing in my tiny boat across the big ocean, filled with sharks, of the stock market.

What exactly do I want to teach my students? I mean, I know the labels: Macroeconomics, International Trade, International Management. These are cool labels. Yet, what do I want to teach in terms of real skills and understanding? I think that my core message is that science is f**king amazing, and when we combine scientific thinking with good, old-fashioned perseverance and grit, great things emerge. My students are young people, and having been their age, back in the day, I know that entering adulthood and developing personal independence is a lot about pretending, and a lot about finding one’s place in a fluid, essentially chaotic reality. That place is called a social role. I think I can deliver valuable teaching as for how to use the basic tools of social sciences in order to make ourselves good, functional social roles.

Concurrently to that purpose, I have another one, about mathematics. I can see many of my students the same kind of almost visceral, and yet visibly acquired abhorrence of mathematics, which I used to have in my mind. I think this is one of the failures in our educational system: early at school, we start learning mathematics as multiplication tables, which quite thoroughly kills the understanding that mathematics are a language. It is a language which speaks about the structure of reality, just a bit less convivially than spoken languages do. That language proves being bloody useful when talking about tough and controversial, such as ways of starting a new business from scratch (hence engaging people’s equity into something fundamentally risky), ways of getting out of an economic crisis, or ways of solving a political conflict.     

I think I can teach my students to perceive their existence as if they were travelling engineers in the small patch social reality around them, particularly engineers of their own social role. Look around you, across the surrounding social landscape. Find your bearings and figure out your coordinates on those bearings. Formulate a strategy: set your goals, assess your risks, make the best-case scenario and the worst-case scenario. What is your action? What can you do every day in order to implement that strategy? Therefore, what repetitive patterns of behaviour should you develop and become skilful at, in order to perform your action with the best possible outcomes? Let’s be clear: it is not about being world champion in anything (although it wouldn’t hurt), it is about being constructively optimistic, with a toolbox close at hand.  

What do I really know about macroeconomics, international trade, and international management? This is a fundamental question. Most of what I know, I know from the observation of secondary sources. Periodical financial reports of the companies, coupled with their stock prices, and with general economic reports, such as the World Economic Outlook, published by the International Monetary Fund, are my basic sources of information about what’s up in business and economics. What I know in those fields is descriptive knowledge.    

Where do I start? We, humans, form collectively intelligent structures which learn by experimenting with many alternative versions of themselves. Those versions are built around a fundamental balance between two institutional orders: the institutions of agriculture, which serve as a factory of food, and the institutions of cities, whose function consists in creating and sustaining social roles, whilst speeding up technological change. We collectively experiment with ourselves by creating demographic anomalies: abnormally dense populations in cities, next door to abnormally dispersed populations in the countryside. I think this is the fundamental distinction between the populations of hunters-gatherers, and the populations of settlers. Hunters-gatherers live in just one social density, whilst settlers live in two of them: the high urban density coexisting with low rural density.

I can put it in a different way. We, humans, interact with the natural environment, and interact with each other.  When we interact with each other a lot, in highly dense networks of social relations, we reinforce each other’s learning, and start spinning the wheel of innovation and technological change. Abundant interaction with each other gives us new ideas for interacting with the natural environment.

Cities have peculiar properties. Firstly, by creating new social roles through intense social interaction, they create new products and services, and therefore new markets, connected in chains of value added. This is how the real output of goods and services in a society becomes a complex, multi-layered network of technologies, and this is how social structures become self-propelling businesses. The more complexity in social roles is created, the more products and services emerge, which brings the development in greater a number of markets. That, in turn, gives greater a real output, greater income per person, which incentivizes to create new social roles etc. This how social complexity creates the phenomenon called economic growth.

The phenomenon of economic growth, thus the quantitative growth in complex, networked technologies which emerge in relatively dense human settlements, has a few peculiar properties. You can’t see it, you can’t touch it, and yet you can immediately feel when its pace changes. Economic growth is among the most abstract concepts of social sciences, and yet living in a society with real economic growth at 5% per annum is like a different galaxy when compared to living in a place where real economic growth is actually a recession of -5%. The arithmetical difference is just 10 percentage points, around the top of something underlying which makes the base of 1. Still, lives in those two contexts are completely different. At +5% in real economic growth, starting a new business is generally a sensible idea, provided you have it nailed down with a business plan. At – 5% a year, i.e. in recession, the same business plan can be an elaborate way of committing economic and financial suicide. At +5%, political elections are usually won by people who just sell you the standard political bullshit, like ‘I will make your lives better’ claimed by a heavily indebted alcoholic with no real career of their own. At -5%, politics start being haunted by those sinister characters, who look and sound like evil spirits from our dreams and claim they ‘will restore order and social justice’.

The society which we consider today as normal is a society of positive real economic growth. All the institutions we are used to, such as healthcare systems, internal security, public administration, education – all that stuff works at least acceptably smoothly when complex, networked technologies of our society have demonstrable capacity to increase their real economic output. That ‘normal’ state of society is closely connected to the factories of social roles which we commonly call ‘cities’. Real economic growth happens when the amount of new social roles – fabricated through intense interactions between densely packed humans – is enough for the new humans coming around. Being professionally active means having a social role solid enough to participate in the redistribution of value added created in complex technological networks. It is both formal science and sort of accumulated wisdom in governance that we’d better have most of the adult, able bodied people in that state of professional activity. A small fringe of professionally inactive people is somehow healthy a margin of human energy free to be professionally activated, and when I say ‘small’, it is like no more than 5% of the adult population. Anything above becomes both a burden and a disruption to social cohesion. Too big a percentage of people with no clear, working social roles makes it increasingly difficult to make social interactions sufficiently abundant and complex to create enough new social roles for new people. This is why governments of this world attach keen importance to the accurate measurement of the phenomenon quantified as ‘unemployment’.  

Those complex networks of technologies in our societies, which have the capacity to create social roles and generate economic growth, work their work properly when we can transact about them, i.e. when we have working markets for the final economic goods produced with those technologies, and for intermediate economic goods produced for them. It is as if the whole thing worked when we can buy and sell things. I was born in 1968, in a communist country, namely Poland, and I can tell you that in the absence of markets the whole mechanism just jams, progressively to a halt. Yes, markets are messy and capricious, and transactional prices can easily get out of hand, creating inflation, and yet markets give those little local incentives needed to get the most of human social roles. In the communist Poland, I remember people doing really strange things, like hoarding massive inventories of refrigerators or women’s underwear, just to create some speculative spin in an ad hoc, semi-legal or completely illegal market. It looks as if people needed to market and transact for real, amidst the theoretically perfectly planned society.   

Anyway, economic growth is observable through big sets of transactions in product markets, and those transactions have two attributes: quantities and prices AKA Q an P. It is like Q*P = ∑qi*pi. When I have – well, when we have – that complex network of technologies functionally connected to a factory of social roles for new humans, that thing makes ∑qi*pi, thus a lot of local transactions with quantities qi, at prices pi. The economic growth I have been so vocal about in the last few paragraphs is the real growth, i.e. in quantity Q = ∑qi. On the long run, what I am interested in, and my government is interested in, is to reasonably max out on ∆ Q = ∆∑qi. Quantities change slowly and quite predictably, whilst prices tend to change quickly and, mostly on the short term, chaotically. Measuring accurately real economic growth involving kicking the ‘*pi’ component out of the equation and extracting just ∆ Q = ∆∑qi. Question: why bothering with the observation of Q*P = ∑qi*pi when the real thing we need is just ∆ Q = ∆∑qi? Answer: because there is no other way. Complex networks of technologies produce economic growth by creating increasing diversity in social roles in concurrence with increasing diversity in products and their respective markets. No genius has come up, so far, with a method to add up, directly, the volume of visits in hairdresser’s salons with the volume of electric vehicles made, and all that with the volume of energy consumed.

Cities trade. Initially, they trade with the surrounding farms, out in the countryside, but, with time, the zone of trade relations tends to extend, and, interestingly enough, its extent is roughly proportional to the relative weight of the given city’s real output in the overall economic activity of the whole region. It is as if cities were developing some sort of gravitational field around them. The bigger the city as compared to other cities in the vicinity, the greater share of overall trade it takes, both in terms of exports and imports. Countries with many big cities trade a lot with other countries.     

There is an interesting relationship between exports and imports. Do I, as a person, import anything? Sure, I import plenty of goods. This software I am writing in is an imported good, to start with. Bananas which I ate for breakfast are imported. I drive a Honda, another imported good. My washing machine is a Samsung, my dish washer is a Siemens, and my phone and computer both come from Apple. I am a walking micro-hub of imports. Do I export anything? Almost nothing. One could argue that I export intellectual content with my blog. Still, as I am not being paid (yet) for my blog, it is rather voluntary cultural communication than exports. Well, there is one thing that creates a flow of export and import in me: my investment in the stock market. The money which I invested in the stock market is mostly placed in US-based companies, a few German and Dutch, and just a tiny bit is invested in Poland. Why? Because there is nothing happening in the Polish stock market, really. Boring. Anyway, I sort of export capital.

Cities and countries import a whole diversified basket of goods, but they usually export just a few, which they are really good at making and marketing. There is something like structural asymmetry between exports and imports. As soon as economic sciences started to burgeon, even before they were called economics and had been designated as ‘political economy’, social thinkers were trying to explain that phenomenon. Probably the best known is the explanation by David Ricardo, namely the notion of comparative advantage AKA productive specialization. There are exceptions, called ‘super exporters’, e.g. China or South Korea. These are countries which successfully export virtually any manufactured good, mostly due to low labour costs. However we label that phenomenon, here it is: whilst the global map of imports look like a very tight web, the map of exports is more like a few huge fountains of goods, pouring their output across the world. Practically every known imported good has its specialized big exporters. Thus, if my students ask me what international trade is, I am more and more prone to answer that trade is a structural pattern of the human civilization, where some places on Earth become super-efficient at making and marketing specific goods, and, consequently, the whole planetary civilization is a like team of people, with clearly assigned roles.

What is international management in that context? What is the difference between international management and domestic management, actually? What I can see, for example in the companies whose stock I invest my savings in, there is a special phase in the development of a business. It is when you have developed a product or service which you start marketing successfully at the international scale, thus you are exporting it, and there comes a moment when branching abroad with your organisational structure looks like a good idea. Mind you, there are plenty of business which, whilst growing nicely and exporting a lot, remain firmly domestic. If I run a diamond mine in Botswana – to take one of the most incongruous examples that come to my mind – I mind those diamonds in order to export them. There is no point in mining diamonds in Botswana just to keep those diamonds in Botswana. Export is the name of the game, here. Still, do I need to branch out internationally? My diamonds go to Paris, but is it a sensible idea to open a branch office in Paris? Not necessarily, rents for office space are killers over there. Still, when I run a manufacturing business in Ukraine, and I make equipment for power grids, e.g. electric transformers, and I export that equipment across Europe and to US, it could be a good idea to branch out. More specifically, it becomes a good idea when the value of my sales to a given country makes it profitable to be closer to the end user. Closer means two things. I can clone my original manufacturing technology in the target market, thus instead of making those transformers in Ukraine and shipping them to Texas, I can make them in Texas. On the other hand, closer means more direct human interaction, like customer support. 

Good. I got carried away a bit. I need to return to the things I want to teach my students, i.e. to skills I want to develop in them when teaching those three courses: Macroeconomics, International Trade, and International Management. Here is my take on the thing. These three courses represent three levels of work with quantitative data. Doing Macroeconomics in real life means reading actively macroeconomic reports and data, for the purposes of private business or those of public policy. It means being able to interpret changes in real output, inflation, unemployment, as well as in financial markets.

Doing International Trade for real might go two different ways: either you work in international trade, i.e. you do the technicalities of export and import, on the one hand, or you work about and around international trade, namely you need to nail down some kind of business plan or policy strongly related to export and import. That latter aspect involves working with data much more than the former, which, in turn, is more about documents, procedures and negotiation. I am much more at home with data analysis, contracts, and business planning than with the very technicalities of international trade. My teaching of international trade will go in that direction.

As for International Management, my only real experience is that of advising, doing market research and business planning for people who are about to decide about branching out abroad with their business. This is the only real experience I can communicate to my students.

I want to combine that general drift of my teaching with more specific a take on the current social reality, i.e. that of pandemic, economic recession and plans for recovery, and technological change combined with a modification of established business models. That last phenomenon, namely new technologies coming to the game and forcing a change in business structures is the main kind of understanding I want to provide my students with, as regards current events. Digital technologies, biotechnologies, and complex power systems increasingly reliant on both renewable energies and batteries of all kinds, are the thread of change. On and around that thread, cash is being hoarded, in unusually big cash-oriented corporate balance sheets. Cash is king, and science is the queen, so to say, in those newly developing business models. That’s logical: deep and quick technological change creates substantial risks, and increased financial liquidity is a normal response thereto.

Whatever will be happening over the months and years to come, in terms of economic recovery after the epidemic recession, will be happening through and in businesses which hoard important amounts of cash, and constantly look for the most competitive digital technologies. When governments say ‘We want to support the bouncing back of our domestic businesses’, those governments have to keep in mind that before investing in new property, plant, equipment, and in new intangible intellectual property, those businesses will be bouncing back by accumulating cash. This time, economic recovery will be probably very much non-Keynesian. Instead of unfreezing cash balances and investing them in new productive assets, microeconomic recovery of local business structures will involve them juicing themselves with cash. I think this is to take or to leave, as the French say. Bitching and moaning about ‘those capitalists who just hoard money with no regard for jobs and social gain’ seems as pointless as an inflatable dartboard.

Those cash-rich balance sheets are going to translate into strategies oriented on flexibility and adaptability more than anything else. Business entities are naturally flexible, and they are because they have the capacity to build, purposefully, a zone of proximal development around their daily routines. It is a zone of manageable risks, made of projects which the given business entity can jump into on demand, almost instantaneously. I think that businesses across the globe will be developing such zones of proximal development around themselves: zones of readiness for action rather than action itself. There is another aspect to that. I intuitively feel that we are entering a period of increasingly quick technological change. If you just think about the transformation of manufacturing processes and supply chains in the pharmaceutical industry, so as to supply the entire global population with vaccines, you can understand the magnitude of change. Technologies need to break even just as business models do. In a business model, breaking even means learning how to finance the fixed costs with the gross margin created and captured when transacting with customers and suppliers. In a technology, breaking even means to drive the occurrence of flukes and mistakes, unavoidable in large-scale applications, down to an acceptable level. This, in turn, means that the aggregate costs of said flukes and mistakes, which enters into the fixed costs of the business structure, is low enough to be covered by the gross margin generated from the technological process itself.

That technological breaking even applies to the digital world just as it applies to industrial processes. If you use MS Teams, just as I and many other people do, you probably know that polity enquiry which Teams address you after each video call or meeting: ‘What was the call quality?’. This is because that quality is really poor, with everybody using online connections much more than before the pandemic (much worse than with Zoom, for example), and Microsoft is working on it, as far as I know. Working on something means putting additional effort and expense into that thing, thus temporarily pumping up the fixed costs.

Now, suppose that you are starting up with a new technology, and you brace for the period of breaking even with it. You will need to build up a cushion of cash to finance the costs of flukes and mistakes, as well as the cost of adapting and streamlining your technology as the scale of application grows (hopefully).

We live in a period when a lot of science breaks free out of experimental labs much earlier and faster than it was intended to. Vaccines against COVID-19 are the best example. You probably know those sci fi movies, where some kind of strange experimental creature, claimed to be a super-specimen of a new super-species, and yet strangely ill-adapted to function in the normal world, breaks out of a lab. It wreaks havoc, it causes people to panic, and it unavoidably attracts the attention of an evil businessperson who wants to turn it into a weapon or into a slave. This is, metaphorically, what is happening now and what will keep happening for quite a while. Of course, the Sars-Cov-2 virus could very well be such an out-of-the-lab monster, still I think about all the technologies we deploy in response, vaccines included. They are such out-of-the-lab monsters as well. We have, and we will keep having, a lot of out-of-the-lab monsters running around, which, in turn, requires a lot of evil businesspeople to step in and deploy they demoniac plots.

All that means that the years to come are likely to be bracing, adapting and transforming much more than riding a rising wave crest of economic growth. Recovery will be slower than the most optimistic scenarios imply. We need to adapt to a world of fence-sitting business strategies, with a lot of preparation and build-up in capacity, rather than direct economic bounce-back. When preparing a business plan, we need to prepare for investors asking questions like ‘How quickly and how specifically can you adapt if the competitor A implements the technology X faster than predicted? How much cash do we need to shield against that risk? How do we hedge? How do we insure?’, rather than questions of the type ‘How quickly will I have my money back?’. In such an environment, substantial operational surplus in business is a rarity. Profits are much more likely to be speculative, based on trading corporate stock and other financial instruments, maybe on trading surpluses of inventories.

Educational (very educational): embarrassing questions about monetary systems

My editorial

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

P*T = M*V

P – the index of prices

T – the volume of transactions in the economy

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

V – the velocity of money

This equation is both a mindfuck and a useful tool to understand how money works at the macroeconomic level. As for being a mindfuck, it is simple: both sides of this equation are equal to Q, or nominal aggregate output, measured in current prices. So you essentially start with the not-too-risky assumption that Q = Q and then you unleash yourself on maths. The nominal output is equal to the real output (i.e. the physical volume of goods and services produced), and it gets nominal by being nominated in a currency, i.e. by being multiplied by the current prices of things inclusive in the real output. Thus, Q = P*T is pretty intuitive. Now, the right side of the equation is more based on the current empirical observation. If you care to have a look at a statistic published by the World Bank under the label of supply of broad money as % of the GDP (you know, you click on the underlined phrase), you will get the current proportion between nominal output and the monetary mass supplied to the economy. The first observation is that it is never equal. In other words, you have very little chances to hit Q = P*T = M, with velocity V =1. When the equation of quantitative monetary equilibrium was being formulated, back in the 1950ies and 1960ies, the supply of money to the economy was consistently lower than the nominal output of the economy, i.e. we recurrently had: Q = P*T > M. Intuitively, you could guess that money supplied to the economy serves more than one transaction a year, or, in other words, money was seen as something circulating pretty quickly across the economy. There were even people who claimed that it circulates at a constant velocity. The well-known master of economic methodology, Milton Friedman, used to be one of those people. Still, as time passes, things change. Since the 1950ies the proportion between nominal output and the supply of money (look up that statistic with the World Bank) has been consistently shrinking as for the global economy. The monetary mass supplied made 50,465% of the global GDP in 1960 (velocity V = 1/0,50465 = 1,981571386). In 1990, the proportion climbed to 88.01%, thus velocity fell to V = 1/0,8801 = 1,136234519. Around 2007 – 2008, the global economy passed the magical threshold of Q = M. Interestingly, the global financial crisis broke out just then. In 2016 the supply of money in the world made 116,411% of the global GDP, with the velocity sadly falling down to V = 1/1,16411 = 0,85902535. Thus, in real life, money works differently in the economy, depending on the period of time. Currently, it seems to slow down its circulation. If people have more goods than money, so if we have like M = 50%*Q, you accumulate real goods rather than money, and you make those coins spin quickly, just to have all your goods well financed. Yet, when you have more money than real goods (case of the present day), you accumulate money and you speed up the flow of goods, so as to have any grounds for having the money. Over the last 70 years, the global monetary system has shifted from one monetary paradigm to another one, and we still don’t understand completely what has actually happened.

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

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