11 September 2018 ~ 0 Comments

The Struggle for Existence in the World Market Ecosystem

The global trade market definitely seems red in tooth and claw. Competition is fierce and some claim it has to be that way: only the fittest should survive because the fittest, at least in theory, are the ones satisfying the customers’ needs the best. In a paper with Viviana Viña-Cervantes and Renaud Lambiotte, we explored this metaphor to see if we can say something about how world trade will evolve. The paper will soon appear in PLoS One and it is titled “The Struggle for Existence in the World Market Ecosystem“. In it we create competition networks and we show that positions in these networks are a predictor of future growth: a strong position in a product means that the country will increase its export share in that product in the medium term.

How do we create competition networks? In our view, each slice of the market is an ecological niche. For instance, the car market in the United States or the computer market in Germany. If you can sell a lot of cars in the US, it means you’re fit to occupy that niche. You’re meeting the needs of that set of customers, and thus you can make a profit there, cutting out other exporters who would oh so much like to make a buck there.

An example of data generating one edge in the competition network: Japan emerges beyond 1% of the car market in the US at the same time that Italy plunges below the 1% mark. With this data, we create an edge from Japan to Italy in the US-car 1960 competition network.

 

Niches are not stable: they change over time. As a consequence of evolution, animals can become fitter to fill their current niche — or a new one. Out of this observation, we create our competition networks. Suppose that you were doing well at selling cars to Americans, like Italy was in the 60s — who doesn’t love a vintage Alfa Romeo?  Then something happens: a mutation, an evolution. Japan’s cars start gaining appeal in North America. All of a sudden, the market share of Italy declines once Japan appears on the scene. In this case, we can create a directed edge going from Japan to Italy, because Japanese firms happened to be successful at the same time that Italian ones lost their, um, edge.* That’s our competition network. We built one per decade: 1960, 1970, 1980, 1990, and 2000.

In the vast majority of cases, when you study a network, the edges have a positive meaning: they imply connections, social relations, friendship. Competition networks tell a fundamentally negative story. The originator of the edge is displacing the receiver of the edge in a market, something definitely not nice. The out-degree tells us something about a country’s fitness: how many competitors it displaced. The in-degree is the other side of the coin: how many times the country’s entrepreneurs were just not good enough. So these two measures should tell us what we want, right? A high out-degree is a sign of strength and growth, a high in-degree a sign of weakness.

The correlation between in- and out-degree is pretty darn high.

Not really. The problem is that big countries produce a lot of stuff and export it everywhere. So they are constantly fighting a lot of battles. Winning some and losing some. The in- and out-degree are highly correlated, and thus they do not give much information. We decided to look at a generalization of in- and out-degree. When displacing a country from a market, quantity is not as important as quality. Displacing a fierce exporter like the US is not the same as tripping up a weaker economy. So we weight the out-degree by counting each displaced country as much as their out-degree. This is a higher-order degree, because it looks at a second hop beyond the displaced. The more this country was a displacer, the more it counts that we were able to displace it. Displacing the displacers is the way to go.

At this point interesting stuff starts emerging. We can use this normalized in- and out-degree to classify countries into three buckets: out-competing (high out-degree), displaced (high in-degree), and transitioning (roughly equivalent in- and out-degree). We do so per decade and per product. Then, we check whether belonging to one cluster has any relationship with how the country will evolve its market share in the decade following the one we used for the classification. If you were a strong out-competitor in the 60s in the car market, your position in the car market in the 70s will get stronger and stronger.

The growth rate the decade after the observation window used for classifying countries. Here: 1 = Displaced, 2 = Transitioning, and 3 = Out-competing countries.

We see these strong relationships for all products and for all decades, with a couple of exceptions. For instance, our method does not work for natural resources. Which is expected: we cannot use this system to predict whether you’re going to find oil in your territory or not. It also does not work in the last decade, the 2000s, because we have very little data for making the prediction: our data runs only until 2013. Thus, it means this method cannot work for short term predictions: it works well when looking at decade-long transitions, not year-long ones. The effect gets a bit weaker if we look at what happens two, three and even four decades after our classification, but it’s still significant.

We also checked the robustness of our results by creating a synthetic trade world. We broke all relationships between countries by generating random trade, maintaining the sparseness — most exporter-importer-product relationships never happen — and the skewed nature of our data — a few high-throughput links constitute most of world trade, and the vast majority of links are low-value ones. In this world with random competition, we see far fewer links in our networks. Using the ratio between in- and out-degree also doesn’t work: as predictor, it returns a much lower result quality.

The average growth rate for out-competing countries when the prediction period is one, two, three or four decades away from the observation one.

So, to wrap up, in the paper we show how to build competition networks, by connecting strong emerging economies to the ones they are out-competing for specific products. Analyzing the higher-order relationships in this networks — i.e. going beyond the simple degree — uncovered a way to estimate the real strength of these emerging countries. A lot of questions remain unanswered. Chief among them: what if we ensured that each edge in the competition networks is truly causal? This will be a quest for another time.


* We’re extremely aware of the fiendish operation we did: these competition networks are absolutely correlational and will never imply causation. However we believe there’s also value in looking at these serendipitous events. If nothing else, at least some econometrician might have had a stroke reading the original sentence.

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18 November 2015 ~ 0 Comments

Evaluating Prosperity Beyond GDP

When reporting on economics, news outlets very often refer to what happens to the GDP. How is policy X going to affect our GDP? Is the national debt too high compared to GDP? How does my GDP compare to yours? The concept lurking behind those three letters is the Gross Domestic Product, the measure of the gross value added by all domestic producers in a country. In principle, the idea of using GDP to take the pulse of an economy isn’t bad: we count how much we can produce, and this is more or less how well we are doing. In practice, today I am jumping on the huge bandwagon of people who despise GDP for its meaningless, oversimplified and frankly suspicious nature. I will talk about a paper in which my co-authors and I propose to use a different measure to evaluate a country’s prosperity. The title is “Going Beyond GDP to Nowcast Well-Being Using Retail Market Data“, my co-authors are Riccardo Guidotti, Dino Pedreschi and Diego Pennacchioli, and the paper will be presented at the Winter edition of the Network Science Conference.

GDP is gross for several reasons. What Simon Kuznets said resonates strongly with me, as already in the 30s he was talking like a complexity scientist:

The valuable capacity of the human mind to simplify a complex situation in a compact characterization becomes dangerous when not controlled in terms of definitely stated criteria. With quantitative measurements especially, the definiteness of the result suggests, often misleadingly, a precision and simplicity in the outlines of the object measured. Measurements of national income are subject to this type of illusion and resulting abuse, especially since they deal with matters that are the center of conflict of opposing social groups where the effectiveness of an argument is often contingent upon oversimplification.

cdp1

In short, GDP is an oversimplification, and as such it cannot capture something as complex as an economy, or the multifaceted needs of a society. In our paper, we focus on some of its specific aspects. Income inequality skews the richness distribution, so that GDP doesn’t describe how the majority of the population is doing. But more importantly, it is not possible to quantify well-being just with the number of dollars in someone’s pocket: she might have dreams, aspirations and sophisticated needs that bear little to no correlation with the status of her wallet. And even if GDP was a good measure, it’s very hard to calculate: it takes months to estimate it reliably. Nowcasting it would be great.

And so we tried to hack our way out of GDP. The measure we decided to use is the one of customer sophistication, that I presented several times in the past. In practice, the measure is a summary of the connectivity of a node in a bipartite network*. The bipartite network connects customers to the products they buy. The more variegated the set of products a customer buys, the more complex she is. Our idea was to create an aggregated version at the network level, and to see if this version was telling us something insightful. We could make a direct correlation with the national GDP of Italy, because the data we used to calculate it comes from around a half million customers from several Italian regions, which are representative of the country as a whole.

gdp2

The argument we made goes as follows. GDP stinks, but it is not 100% bad, otherwise nobody would use it. Our sophistication is better, because it is connected to the average degree with which a person can satisfy her needs**. Income inequality does not affect it either, at least not in trivial ways as it does it with GDP. Therefore, if sophistication correlates with GDP it is a good measure of well-being: it captures part of GDP and adds something to it. Finally, if the correlation happens with some anticipated temporal shift it is even better, because GDP pundits can just use it as instantaneous nowcasting of GDP.

We were pleased when our expectations met reality. We tested several versions of the measure at several temporal shifts — both anticipating and following the GDP estimate released by the Italian National Statistic Institute (ISTAT). When we applied the statistical correction to control for the multiple hypothesis testing, the only surviving significant and robust estimate was our customer sophistication measure calculated with a temporal shift of -2, i.e. two quarters before the corresponding GDP estimate was released. Before popping our champagne bottles, let me write an open letter to the elephant in the room.

gdp3

As you see from the above chart, there are some wild seasonal fluctuations. This is rather obvious, but controlling for them is not easy. There is a standard approach — the X-13-Arima method — which is more complicated than simply averaging out the fluctuations. It takes into account a parameter tuning procedure including information we simply do not have for our measure, besides requiring observation windows longer than what we have (2007-2014). It is well possible that our result could disappear. It is also possible that the way we calculated our sophistication index makes no sense economically: I am not an economist and I do not pretend for a moment that I can tell them how to do their job.

What we humbly report is a blip on the radar. It is that kind of thing that makes you think “Uh, that’s interesting, I wonder what it means”. I would like someone with a more solid skill set in economics to take a look at this sophistication measure and to do a proper stress-test with it. I’m completely fine with her coming back to tell me I’m a moron. But that’s the risk of doing research and to try out new things. I just think that it would be a waste not to give this promising insight a chance to shine.


* Even if hereafter I talk only about the final measure, it is important to remark that it is by no means a complete substitute of the analysis of the bipartite network. Meaning that I’m not simply advocating to substitute a number (GDP) for another (sophistication), rather to replace GDP with a fully-blown network analysis.

** Note that this is a revealed measure of sophistication as inferred by the products actually bought and postulating that each product satisfies one or a part of a “need”. If you feel that the quality of your life depends on you being able to bathe in the milk of a virgin unicorn, the measure will not take into account the misery of this tacit disappointment. Such are the perils of data mining.

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