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  http://researchsocialsci.blogspot.com/2017/08/life-idea-research-some-facts-bloody.html ). 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:    https://drive.google.com/file/d/0B1QaBZlwGxxAQ1ZBR0Z1MU9oRTA/view?usp=sharing . 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: https://drive.google.com/file/d/0B1QaBZlwGxxAZ3MyZ00xcV9zZ1U/view?usp=sharing . 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 http://www.ggdc.net/pwt

What’s up, Joseph?


My editorial on You Tube >> https://youtu.be/XnlGWiliAwk

I am starting to mirror my research blog at a new site, namely https://discoversocialsciences.wordpress.com . My goal for the next year or so is to create a fully-blown, scientific and educational website devoted to social sciences. I thought that Word Press is a good tool in that view. Anyway, for the months to come, my readers from http://researchsocialsci.blogspot.com can find a copy of each post at https://discoversocialsciences.wordpress.com and vice versa.

This said, I am getting back to scientific writing. That science is not going to develop by itself: it needs me. Yesterday, in my update in French (see http://researchsocialsci.blogspot.com/2017/08/a-bientot-milton.html ), with the help of Milton Friedman (yes, I know he is no more among us since 2006, and still he wants to be of some help) I started to lay the foundations for that book I intend to write this year, to satisfy the terms of my research grant, and the terms of my curiosity. As I was doing it, some facts attracted my attention. This is usually how it starts with me. Some facts attract the attention of the curious ape in me, and then, man, God only knows what can happen next. As I am basically agnostic, I can even face a situation when no one knows what happens next.

Anyway, facts attracted my attention. Calm down, ape, we are going to play with those facts in a minute. Now, please, let me explain to the readers. Nice ape. So I am explaining. My basic field of interest in that research grant is innovation, which you might already know from my last two posts. In economic sciences, scientific invention is treated very much as exogenous to innovation in production. Probably it goes back to Joseph Schumpeter and his theory of business cycles (see Schumpeter 1939[1]). Schumpeter assumed that science is exciting, of course, but just some science does any stir in the world of business. Sometimes, a scientific invention hits the business so hard that the latter is being knocked off balance, or, in elaborate scientific terms, it is being pushed off the neighbourhood of general Walrasian equilibrium, where was dozing calmly just before the shock, and it goes into creative destruction.

Having made that observation, Joseph Schumpeter couldn’t but explain what is so special about those precise scientific inventions, which make the world of business rock and sway. His assertion was that science knocks business off balance when said science can significantly improve the efficiency of the production function in business. Economic sciences use the term ‘productivity’ to express this efficiency. It is an old intuition, going back to Adam Smith and David Ricardo, that productivity is the key to successful business practice. Still, for a long time since those first T-rexes of economics, it was assumed that business actions taken by business people simply display different levels of efficiency, full stop. If someone was really keen on moral philosophy, like John Stuart Mill, they could add that it is a good thing to develop efficient practices, and generally a bad habit to indulge in inefficient ones. Still, some kind of diversity in productive output was being implicitly assumed to exist in the social fabric around us.

Joseph Schumpeter took a different hand of cards to approach the problem. Born in 1883, his scientific mind had been bred both on the stupefying speed of development in industrial production, and on the great reshufflings in industrial structures, made of spectacular bankruptcies, mergers, and acquisitions. To Joseph Schumpeter, capitalism was by definition something similar to the battle for Gondor. It was supposed to be epic, turbulent, and spectacular, or it didn’t count as real capitalism. Schumpeter used to perceive technologies as something akin to tsunamis. His question was simple: when two or more tsunamis meet at some point, which one prevails? Answer: the most powerful one. The transformative power of new technologies was supposed to be observable as their capacity to increase efficiency in the use of production factors, or their productivity.

Look, Joseph, I fully agree with you that new technologies should be more productive than the old ones. Only you see, Joseph, after your death we started to have sort of a problem: they are not. I mean, new technologies do not seem to be definitely more productive that the old ones. I am sorry, Joseph. I know that any respectable scientist has the right to have a quiet after-life, but I just had to tell you. You take that database called Penn Tables 9.0 (Feenstra et al. 2015[2]). I know you liked data and statistics, Joseph. This was the basic for your critical stance towards Karl Marx, who did not really bother about real numbers. So you take that Penn Tables 9.0, Joseph, and you take out of it a variable called ‘total factor productivity’. They even have it, over at Penn Tables, in two different flavours.

I know you are an inquisitive mind, Joseph, so you can read about the exact recipes of those two flavours at http://www.rug.nl/ggdc/productivity/pwt/related-research-papers/capital_labor_and_tfp_in_pwt80.pdf . Anyway, the one labelled ‘ctfp’ measures total factor productivity at current Purchasing Power Parities, with your new home, USA, standing for the jauge (USA=1). The other one, called ‘cwtfp’, measures the welfare-relevant TFP levels at current PPPs (USA=1). I made a data pivot for you, Joseph. You can find it at my Google Disc, right here: https://drive.google.com/file/d/0B1QaBZlwGxxAZ3MyZ00xcV9zZ1U/view?usp=sharing

You can see by yourself, Joseph, that this productivity you used to be so keen about is not really keen to cooperate. Back in the day, until the late 1970ies, it had been growing gently and in conformity with the economic theory that you, Joseph, contributed to create. Only after 1979, something broke in the machinery, and total factor productivity started to fall. It is still falling, Joseph, and we don’t exactly know why. I mean, you have those General Electric, Tesla, Microsoft and l’Oreal guys launching another revolutionary technology every two or three years, but these revolutions kind of get bogged down, somewhere down the road to Total Factor Productivity.

Still, Joseph, there is light at the end of the tunnel, and this is not a train coming the opposite way. I like physics, Joseph, and I am kind of thinking that we can go a long way with physics. Them people in physics, they say we all need energy. On the top of that, Joseph, we have biology, and biology says we need to eat energy in order to have energy to spend. So I take two basic measures of our efficiency in the use of energy: the consumption of energy per capita, in kilograms of oil-equivalent, and the cereal yield in kilograms per hectare. You can find both of these metrics, as aggregate averages for the global economy, as published by the World Bank, right at this address here: https://drive.google.com/file/d/0B1QaBZlwGxxAZnJldTZDV0pHMWM/view?usp=sharing

So, Joseph, I’ll tell you what I think. We, as a species, are still quite young. We didn’t even have to fight off the dinosaurs: a bloody asteroid did the job. We came to the grand landscape of history with kind of a joker card up our sleeve. It is only now that we are realizing the true challenge of staying alive as a civilisation. The good thing is that we obviously learn to get more and more food from your average hectare. I know, not everybody eats cereals. I don’t, for example. Yet, once we have learnt how to get more cereals from one hectare, we can have some carryover to other types of food. I like bananas, for example. More bananas from one average hectare, it sounds optimistic to me. Could work nicely, Joseph, if nothing kills us in the meantime. We are still struggling to manage primary energy use, although we succeeded to press on the brake, those last decades. Still, I agree, Joseph: total factor productivity is a mess.

So what do we do, Joseph, with that book I am supposed to write until the end of this year, about innovation. I had that idea, Joseph, that I could kind of go a different way than you did. You represented innovation and technological progress as a way towards more efficient production. I am tempted to try a different approach. When we are around, we tend to gather around something: fire, temple, market place etc. As we gather around, there are more and more of us around, and then, there is that funny thing that happens: the more we are around per square kilometre, the more ideas we have per one thousand people. The more densely we live, the more things we can figure out. We do innovation simply because we can, not necessarily because we have precise gains in view. I mean, gains are important, but the process of figuring out things goes on kind of propelled by its own momentum. We invent things, we try them out, sometimes it works just smoothly (the wheel), sometimes we can even have fun with it (cognac and other distillates of fermented vegetable material), and sometimes it is kind of a failure.

So, Joseph, my view of technological change is that of adaptation going on in a loop. One of the most visible patterns in the historical development of mankind is that we create more and more densely populated social structures. Greater density of population creates new social structures, which impose upon us new challenges about how to sustain more people per square mile. This is how and why we invent and try new things. From this point of view, anything we do is a technology. The pattern of my average working day, combined with the working day of my neighbour, and all that combined with the way we feed ourselves and power our machines, it can all be perceived as a technology. Technologies that you defined, Joseph, like the process of making a car, could be just small building blocks in much broader and more intricate a process.

[1] Schumpeter, J.A. (1939). Business Cycles. A Theoretical, Historical and Statistical Analysis of the Capitalist Process. McGraw-Hill Book Company

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