Economic Models for Turbulent Times Part 2

A new research study has already received unusual attention. The Network of Global Corporate Control [PDF] discovers a relatively small group of multinational companies with disproportionate influence over the global economy. The authors, a trio of complex systems theorists at the Swiss Federal Institute of Technology in Zürich, are supposedly the first to empirically identify such a network.

The problem is approached using mathematical models designed for capturing behavior of complex natural systems. The study applies this methodology to a large data set of corporate information, to map ownership among the world’s transnational corporations (TNCs). Previous studies reported that a few TNCs drive much of the global economy. However, they analyzed fewer companies. Due to limited data availability and computing resources, past studies did not consider the effect of indirect ownership.

Methodology

Orbis 2007, a repository of over 30 million private and public companies, published by Bureau van Dijk, was the data source. The study sample used the 43,060 largest TNCs, and derived the associated ownership linkages. The network structure was based on the relationships between shareholding interests, then weighted by each company’s operating revenue. This yielded a directional map of global economic power.

Quantitative results

The model revealed a core group with networked ownership, see image below. These 1,318 companies:

• all had ties to at least two other companies in the core group
• on average, were each connected to 20 other core group companies
• represented 20% of all global operating revenues,
• collectively owned the majority of the world’s largest blue chip and manufacturing firms:
• in total, generated 60% of all global revenues.

Results were represented using a data visualization, via New Scientist: The global capitalist network, revealed. The core group of 1,318 TNCs was further concentrated in a “super-entity” of 147 even more tightly knit companies, all of whom had ownership interests exclusively in each other.

Global network model of corporate connectivity

Super-connected companies are red dots. Highly connected companies are yellow dots. The green lines show ownership. The size of each dot represents relative amount of revenue for that TNC. The super-entity, comprising less than 1% of all companies in the study, controlled 40% of the total wealth in the global network of 43,060 TNC’s. The study classified most of these 147 firms as financial institutions.

Newcomers to any network prefer highly connected members

TNCs buy shares in each other for business reasons, not for world domination. If connectedness clusters, so does wealth. In similar models, money flows towards the most highly connected members. The Zürich study is strong evidence that simple rules governing TNCs give rise spontaneously to highly connected groups.

Does Occupy Wall Street’s claim that 1% of the people own most of the world’s wealth merely reflect a self-organizing economy? I’m not certain of that, as it would imply that the 99% vs 1% situation is structural and inevitable.

Yet the super-entity is unlikely to be the intentional result of a conspiracy to rule the world. Such structures are common in nature. So then, the super-entity may not result from conspiracy. Can it exert political power? 147 entities seems like too many to sustain collusion. The researchers suggest that the 147-member super-entity might compete in the market but behave constructively to realize common interests. A particularly unsettling possibility: Resisting changes to the network structure may be a common interest.

Critique of assumptions and results

The authors, and the media coverage of the study, emphasized that the majority of the 147 companies in the “super entity” were financial institutions. In fact, some of the largest companies of the 147 have revenue sources that are outside of the financial and insurance services sector, see Top 50, excerpt including Walton Enterprises LLC, Natixis,  Aviva plc, Dodge & Cox and China Petrochemical Group Company.

Largest financial institution TNCs

A far more significant flaw is that the analysis assumes ownership is equal to control. This is not always true. Equity in many public companies is held as shares of common stock in investment or pension funds. Portfolio managers for the funds may or may not control what the companies actually do. This effect on the system’s behavior is difficult to measure. and was not included in the study.

Additional data issues affecting robustness of the 147 super-entity result

1. The research assigned ownership of investments to banks not individuals. There is nothing sinister about that, nothing implying that financial services companies have geopolitical hegemony. While there is certainly outsized concentrations of wealth in closely held financial conglomerates, much of the world population’s wealth is held by banks and investment groups, through pension plans, 401k and 403b retirement plans. This is normal, they don’t “own the economy”, as those assets ultimately belong to individuals. The most obvious are mutual insurance companies. Several are on the Top 50 list. Mutual insurance companies are actually 100% owned by their policy holders, who are individuals!
2. China Petrochemical is number 50 on the list. Who owns this company: Individuals or the People’s Republic of China?
3. Why is The Depository Trust Company (DTCC) included in this study? DTCC is a clearing house, an intermediary and my former employer, so I know what I am talking about here. All large, self-clearing firms participate in the DTCC. DTCC is an industry-owned, not-for-profit organization. Securities held in brokerage accounts in “street name” (which are negotiable, but still owned by individuals) have book entry notations with the DTCC, but are neither owned nor controlled by the DTCC.
4. Similarly, the list includes custodian banks. Custodian banks hold securities belonging to asset managers, but only to ensure timely processing of foreign dividend and bond interest payments, name changes e.g. due to mergers, corporate actions and foreign currency conversion. Again, they neither own nor control assets, they merely hold the assets for a third-party.
5. I read the New Scientist article, I read the paper itself. I was unable to make sense of the numerical values e.g. 20% of global operating revenue versus 60% of global revenue. I could not find a distinction based on a clearly stated definition of “operating revenue” versus “revenue” in the paper. Perhaps the results are correct, but I couldn’t confirm them using basic arithmetic.

A more accurate input data set would be real asset owners, whether corporate or individual. Accomplishing that is not straightforward.

Conclusions

By identifying the architecture of global economic power, the analysis could encourage greater geopolitical stability. By finding the vulnerable aspects of the system, economists might suggest measures to prevent future collapses spreading through the world economy. Perhaps we need global anti-trust rules, which now exist only at national levels, to limit over-connection among TNCs?

The analysis suggests a possible solution: TNCs could be taxed for excess inter-connectivity to discourage this risk. This is NOT a transaction tax, like a Tobin Tax!

The true value of an analysis such as this is not just to see if a small number of people control the global economy, but to gain insights into economic stability. Concentration of power is not good or bad, but the core’s tight interconnections could be. As the world learned in 2008, with the sub-prime mortgage mess in the USA and now sovereign debt crises in Euro zone countries, such networks are often unstable.

Which is better?

Scenario 1: World government  National governments do not harmonize on tax, accounting and other regulatory standards. Transnational companies can exploit the differences, and evade part of their tax share by transferring to others instead. Free trade and supra-national agencies mitigate these effects.

Scenario 2: Good fences make good neighbors Control system engineers know the hazards of a large, tightly coupled system. Decouple as much as possible. In the context of current events, that suggests one or more of the following: Break up the EU. Eliminate multinational trade organizations such as the WTO. Allow sovereign nations to arrange bilateral trade as each sees fit. Cross-border flows of goods, capital and jobs occur only if beneficial to the people of both nations.

Trade-off between unification and isolation

Unify when there is a compelling reason, such as geography or symbiotic relationship. Work toward maintaining the balance between total unity versus total separatism. Inefficiency of separate accounting and regulatory frameworks must be evaluated and compared with the risk of instability due to tightly coupled systems.

Macroeconomic theory proves that fewer impediments to trade and flow of capital are a net benefit. Issues such as intellectual property theft, corrupt regimes, industrial espionage through information and data security breaches are probably considered externalities by economic models. At times, that is reasonable. At the moment, it seems less so. However, the framework is not necessarily flawed, economic theory remains relevant, markets aren’t “broken”. Adaptability is key. But more is required.

My thoughts

Capitalism should encourage prudent behaviour because the penalties for poor choices should be borne by those making them. Any political system that has an adequate system of checks and balances is likely to reinforce this. Additional requirements for global realization of liberty and pursuit of happiness are a society of moderation (which is not equal to mediocrity!) instead of excess; unhesitating willingness to co-operate when appropriate, while striving for independence and self-determination as the status quo. I have no easy answers to suggest.

* As usual I pondered this for a such a lengthy period of time that the article HAS been published, see “The Network of Global Corporate Control” Stefania Vitali, James B. Glattfelder, Stefano Battiston, Public Library of Science http://dx.doi.org/10.1371/journal.pone.0025995