At this point, I would like to pinpoint several examples that clearly demonstrate the value added of data granularity from policy perspective. How can we explain the phenomenon of slow credit growth despite the massive monetary policy accommodation? Obviously we need to dive into individual/ more granular data to understand the causes of the impaired transmission – is it demand or supply side? In our case, scrutinizing the individual balance sheet pointed to different obstacles for different  2  BIS central bankers’ speeches  banks – although in some cases it was the liquidity issue, mostly it was capital constraints in the context of deleveraging, new Basel regulation, as well as relatively high level of NPLs. It raised the question of the effectiveness of monetary policy transmission and factors which hinder it. In this respect, the aggregate data was not informative enough, but micro bank-bybank data were needed to detect constraints in a more proper manner. The scrutinization of the balance sheets of individual banks revealed that the main obstacles varied for different banks. For some of them the limited capital was the main constraining factor for expansion of their activities. Given that at that point in time the space for increasing the capital through profit retention or injection of additional capital was limited, the banks were not in a position to expand more, without jeopardizing their capital adequacy ratios. In other banks, the quality of the current credit portfolio and the large amount of NPLs were obstacle for providing more credit to the economy.
In general, innovations in monetary frameworks and instruments go hand in hand with innovations in statistics. The non-standard measures underline a need to devise a specific dataset, produced on a regular basis, for monitoring the effectiveness of the measures undertaken. The financial crisis has underpinned the importance of the financial stability, which forced many central banks to place much higher priority to the financial stability concerns. The focus has shifted from the stability and risks pertaining to individual financial institution to the systemic risk concept. In other words, what matters evenly is the potential instability of the financial system as a whole, and the risk of adverse loops with the real sector. More importantly, the crisis has stressed the time dimension of the systemic risk that is the procyclicality of the financial behavior and the structural dimension, which arises from the interconnectedness among the financial institutions and the possible contagion effects. Hence, the macro prudential analysis and measures are gaining more and more weight, and so are the new datasets requirements for pursuing the macro prudential role. I think that even prior to global crisis, central banks were well equipped with a wide range of macroeconomic, monetary and financial data. What was probably missing was a wellestablished platform for pursuing strong macro-financial analysis, higher frequency of some of the indicators, and uniformity in the way indicators were calculated.