by Andrew White | October 25, 2017 | Comments Off on Contrarian View of Centralization and Diversity?
When Centralization is Good
Despite the rhetoric that centralization is out of fashion, and decentralization is in, there are plenty examples for when centralization is a good thing, even critical. News today in my US print edition of the Financial Times, titled “Brexit unleashes a three-way battle over clearing.” The clearing in question is the work the City of London does every day when it clears, or reconciles, all the losses and gains, for globally traded equities, swaps and repos (financial instruments). “(the city) has cleared a notional €174tn of euro-denominated derivatives this year, around 75 per cent of that market.
Financial regulation, as an outcome of the debacle of the financial industry collapse some years go, notes that this clearing work is very important and needs to be protected. Centrally collating all the pluses and negatives and then charging the net reduces the risks between agents. The more these trades are centralized the more the net exposure is reduced. If it were distributed, the number of net balances that then need further netting, somewhere, would increase thus increasing risk and complexity.
Brexit has created an opportunity whereby the EU is holding in abeyance the desire and demand to take over some of this clearing work. Politically for some in the EU it’s not good to see such an important job still led out of London. The US, also interested in taking a slice of the pie it can get it, is actually more concerned with the EU creating a fracture in how and where the work gets executed. So we have three agents all vying for control when it would be in our collective interests to centralize the lot. It just happens to be in London currently.
When Diversity is Bad
Diversity is popular. Diversity is valuable. This is true today and very popular. In our world of data and analytics we also talk about diversity of data and it’s sources as a superior approach to powering analytics; and diversity in analytic an algorithmic approach when it comes to application of AI. But there are examples, interesting, where diversity may create unintended and negative consequences.
In Thirteen Facts About Wage Growth (Brookings, 2017) there is an analysis for how economic dynamism has declined in the US over the last 30 years and how this is negatively impacted wages. One aspect of this is the declining mobility of workers. In the 1940s and 1950s it was much more likely that people would migrate across the US to go live near companies that were hiring. This was a time when there was a lot more affordable housing and employment was seen as a longer term relationship. This mobility was much more prevalent in the past and it helped keep the economy growing and nimble. So what happened?
It turns out that as our economy grew, the kinds of work that were undertaken started to appear in cities all across the country. Economic diversification took place as regions became more alike, more and more of them offering the same kinds of work. So when an industry grew, it grew everywhere and as such the need or opportunity for unmet employment driving migration fell away. In years past there had been more specialization that could be observed at the city or even state level.
Now that as cities have become more homogeneous and alike in terms of the kinds of work undertaken in them, we now see much less mobility. The research shows that this mobility is what helped drive wages. So reduced mobility is seen as one of the causes of lower wages. And that is not a good thing.
Interesting if contrarian perspective, perhaps? Or just proof that we don’t live in an all-or-nothing world?
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