andrew g haldane : turning the red tape tide remarks by mr andrew g haldane, executive director, financial stability, bank of england, at the international financial law review dinner, london, 10 april 2013. * * * regulatory and legal frameworks share common roots. both are complex, evolutionary systems, shaped by history. they are the result of a set of well - intentioned historical actions by technicians charged with filling cracks, creating certainty, shaping incentives for the common good. both legal and regulatory frameworks have many of the characteristics of a classic public good. but the cumulative consequences of even well - intentioned actions may not always deliver outcomes which necessarily serve society well. that is because such actions are typically a response to events and circumstance. the resulting frameworks have a history of pathdependence ( david ( 1985 ) ). this history - dependence may “ lock - in ” sub - optimal technologies, such as qwerty keyboards and vhs video - recorders. the financial regulatory framework may be another example of a locked - in technology. the events which have shaped the evolution of financial regulation include crises, scandals, innovation and liberalisation. each has elicited a response, typically the addition of a new regulatory layer. the cumulative consequence has been a regulatory tide which has tended to flow in only one direction – towards a lengthier, more complex rulebook. in this respect, financial regulation shares many evolutionary characteristics with elements of the legal framework – for example, the tax code. it too has been responsive to events and circumstance including crises, scandals, innovation and liberalisation. and for many of the same reasons, it too has resulted in a rising tide of rulebook complexity. the facts are striking. in the uk, the banking act ( 1979 ) covered 52 sections and 75 pages. by the mid - 1980s, the financial services act ( 1986 ) and an updated banking act ( 1987 ) had expanded primary legislation to 110 sections ( 106 pages ) and 212 sections ( 299 pages ) respectively. the financial services and markets act ( 2000 ) took this to 433 sections or 321 pages. and the new financial services act ( 2012 ) takes this to 695 sections or 534 pages – a tenfold increase in primary legislation in a generation. in the us, the picture is no less dramatic. contrast the legislative responses in the two largest financial crises of the past century – the great depression and the great recession. the great depression spawned the glass - steagall

great a role relative to theory and causality. in both cases, these mistakes might have been reduced had inductive and deductive approaches been used in a complementary, or iterative, fashion. this iterative approach has a strong pedigree in other disciplines. the history of advance in many scientific disciplines has involved a two - way process of learning between theory and empirics, with theory motivating measurement at some times, and measurement motivating theory at others, in a continuous feedback loop. one example of this approach, discussed by governor carney at the time the bank ’ s own data analytics programme was launched, concerns the dynamics of planetary motion. it was sir isaac newton ( a fellow money - printer, as former master of the royal mint ) who developed the physical theory of celestial motion. but this theory was built on the empirical shoulders of another scientific giant, johannes kepler. when it comes to planetary motion, empirics first led theory, the inductive led the deductive. the same has been true, at times, when understanding the motion of economies and financial markets. keynesian and monetarist theory were both built on empirical experience during the great depression. the phillips curve began life as a kepler - esque empirical regularity, which was only subsequently given a newtonian theoretical basis. many puzzles in finance, which have taxed theorists for decades, began as empirical anomalies in asset markets. in each case, empirics led theory, the inductive led the deductive. my lesson from all of this is clear. if that iterative learning process between empirics and theory is to continue to bear fruit in economics, then deductive and inductive approaches may need to share a broadly equal billing. if so, i think there are high returns to economics and finance making a further intellectual investment in big data and accompanying analytical techniques in the period ahead, the path less followed. the definition of big data if big data holds promise, it is probably useful to start by defining just what it is. this is not entirely straightforward. like beauty, what counts as big data lies in the eye of the beholder. it is also a fluid concept. for example, it is clear that data no longer means just numbers ; it means words too. indeed, growth in research on semantics has taken off over recent years, including in economics and finance. what is less contentious is that there has been the most extraordinary