Meanwhile, PIs are the adjectives of this new vocabulary, describing the elements of each financial transaction. Together, LEI-nouns and PI-adjectives are key building blocks of a new common financial language. LEIs and PIs have natural counterparts in the world of product supply chains. For LEIs read GLNs – the Global Location Numbers that uniquely define nodes in the global supply chain. GLNs are ultra-flexible. They can denote something as macro as a Wal-Mart warehouse or as micro as a specific shelf in a Wal-Mart store. They provide the co-ordinates of a high-definition map of the global supply chain. For PIs read GTINs (Global Trade Identification Numbers) and the GDSN (Global Data Synchronisation Network). Together, these identifiers define not only products (such as toothpaste) but their attributes (the dimensions of the box the toothpaste is stored in). They too are ultra-flexible. These identifiers define the DNA string for everything from chocolate wafers to collateral swaps. LEIs and PIs are being cast from a similar mould to GLNs and GTINs. International efforts so far have focussed on global LEIs. These are means of capturing, in a common data string, the old banking maxim of “know your counterparty”. In today’s financially interconnected world, knowing your counterparty may be insufficient. So global LEIs aim to barcode counterparty linkages at any order of dimensionality. In November 2010, the OFR issued a policy statement on LEIs.29 This kicked-off an industry process, led by the Securities Industry and Financial Markets Association (SIFMA), aimed at settling on a single, industry-wide entity identification standard.

Our model suggests that the desire to signal ability,  17  We also conducted some probit regressions using lagged real credit growth to predict the probability of banking crisis, following Schularick and Taylor (op.cit.). We replicated their results: lagged real credit growth (up to five or six years) was jointly significant in positively contributing to the probability of there being a subsequent banking crisis, and robust across sub-samples pre and post WWII. The joint significance of fivesix annual lags of real credit growth is consistent with our depiction of the credit cycles as a medium term phenomenon in which sustained booms are statistically significantly related to banking crises. 18  See also Bordo and Haubrich (2010), who show that more severe financial events are associated with more severe real effects in the US 1875–2007. 19  There are, of course, alternative explanations. See, for example, Gomes, Kogan and Zhang (2003), whose model predicts counter cyclical dispersion driven by productivity shocks. Our explanation differs both in that (a) we relate dispersion to lower frequency fluctuations in fundamentals than the business cycle and (b) we stress imperfect information, and in particular the incentive to convey type, which is absent in their model. 16  BIS Review 168/2010  rooted in imperfect information and short horizons, may be a cause of counter-cyclical dispersion in banking returns. 20 Charts 14 and 15 look at the same metrics for UK banks. We have also examined return patterns among the largest global banking and non-financial firms.