a common research methodology, the results are comparable across countries and can be analysed using meta - analyses ( buch and goldberg 2015 ). in each national jurisdiction, additional technical solutions are available that facilitate the use of micro data without compromising on data confidentiality. the bundesbank, for example, has recently established a research data and service centre ( rdsc ), which provides researchers inside and outside the bank with access to its micro data. in general, the more detailed the data are, the more restrictive is the data access offered by the rdsc. outside bundesbank premises ( off - site ), only anonymised data specially prepared for academic research purposes ( scientific use files ) from the panel on household finances study – a representative survey on the structure and composition of households ’ wealth – may be used. besides the panel on household finances, the rdsc provides anonymised datasets on banks, securities, investment funds, enterprises and households, all of which can be accessed at dedicated researcher workstations inside the bundesbank. for some datasets, remote execution is possible, too. external researchers have to send in their syntax files, which will be processed by the rdsc staff. for both of these means of access, the rdsc staff screen the output files, preserve anonymity, and send them to the external researcher. bis central bankers ’ speeches the range of methods for access to data satisfies researchers ’ need for the rapid provision of high - quality data, while at the same time keeping sensitive information confidential. the bundesbank is thus adhering to an established global standard : it is ensured that analytical findings that are to be published are anonymised such that no inferences may be drawn regarding particular individuals or enterprises. the international standard may be briefly summarised as follows : “ safe people, safe projects, safe settings, safe outputs, safe data ”. b ) investment in ( it ) infrastructure investment in it infrastructure is a part of the construction of every new statistics ( schubert 2015 ). the procedures for processing micro data, which are only collected once, can be standardised to a greater extent than is the case for the individual silo procedures used to date. accordingly, the multi - purpose approach will make it easier for both, data collecting authorities and reporting parties, to develop integrated systems. two initiatives of the escb should be mentioned in this context : 1. the banks ’ integrated reporting dictionary (

aggregate time series. an improved understanding of ( risks to ) financial stability and of the transmission of monetary policy is a clear benefit that the use of micro data brings to central banks. this enables central banks to better perform their core tasks – maintaining price stability and contributing to financial stability. in this sense, micro data benefit both, society as a whole as well as financial institutions that operate in a more stable environment. an additional advantage of an increased use of micro data is that these data can be used for multiple purposes. traditional statistics tend to serve specific purposes, such as the construction of aggregate indicators for statistics on the financial account. if new analytical questions arise, these ( aggregate ) data often do not fit the purpose. analysis then has to rely on information which is not really targeted, or additional surveys have to be conducted. in addition to being costly, these surveys often have the disadvantage that they do not allow developments to be tracked over time and that they survey only a sample of firms or banks and are thus not very representative. using micro data that have already been collected, it is comparatively simple to respond to a new request by carrying out an appropriate aggregation of the data. looking ahead, the microdata approach to compiling statistics may even replace the traditional one, leading to cost - savings on the side of the reporting agents. in the traditional system, data has to be aggregated and reported separately for each dataset. the cost of aggregation is borne by the reporting agent. according to the microdata approach, reporting agents have to send their data only once. everything else is done by the central bank. hence, the costs of aggregation are passed on to the central bank. that is a considerable advantage for the industry, but is that everything? is it not the case that the industry not only benefits from cost savings but can also make direct use of the micro data? can statistics be jointly compiled by the industry and the central bank on a voluntary basis in order to increase benefits and reduce costs? this is not a purely hypothetical question. the bundesbank ’ s corporate financial statement statistics, which contains data on german non - financial corporations, are based on several sources, a key one being a data pool with financial statements provided on a voluntary basis by credit institutions and financial service providers. the individual institutions cannot access the micro data of the other parties. however, they receive a very wide range of statistics on key ratios, broken down by sector and size category, which they can use as part of