The role of the central bank is to create and implement monetary policy in the state or (more precisely) inside the independent monetary area.
Central banks use different decision support systems based on different statistical information systems and forecast models. In our experience, the ECB is trying to establish a similar statistical information system,
comparable to that developed by the Fed (Teplin, 2001, p. 432). For its financial accounts data collection and compilation system, the ECB is using the national central bank (NCB) members of the Eurosystem.
Furthermore, statistics is a function of the European System of Cen- tral Banks (ESCB), so all other NCBs of Member States outside the EMU are also involved in the process. Slovenia entered the monetary union relatively well prepared concerning the quality of financial accounts statistics.
Following the financial and economic crisis, and as part of economic and monetary analysis, the ECB (see Winkler, 2010, p. 355) highlighted further development in four areas:
1. a dynamic stochastic general equilibrium model;
2. analysis of the transmission mechanism (especially the economic effects);
3. early indicators of economic crisis; and
4. development of financial or broader sectoral accounts.
The subject of our interest is the fourth of these development areas, and proposed solutions will be directed towards a consistency on this aggre- gate level of information in support of decision-making. This, in turn, is geared towards cross-checking the findings of economic and monetary analysis in support of decision-making by the ECB for monetary pol- icy purposes. This may also benefit decision-making by the European Supervisory Authorities (ESAs) and the European Systemic Risk Board (ESRB) for their micro and macroprudential oversight, respectively.
The financial sector of the economyis at the forefront or centre of the concept.
Since 2010 we have been witness to processes of centralisation of the supervision of financial corporations on the national level, including under the aegis of national central banks, which reflects the need for standardisation or harmonisation of information systems for supervi- sory purposes. There were three supervisors operating in Slovenia in 2011: the Bank of Slovenia, responsible for the supervision of banks and savings banks; the Securities Market Agency, responsible for super- vising investment and pension funds; and the Insurance Supervision Agency, responsible for supervising insurance companies and pension firms. In the financial corporations sector no one is supervising leas- ing firms, for instance, while, pursuant to its constitutive act (Bank of Slovenia Act), the Bank of Slovenia is indirectly responsible for financial stability. Therefore, it must have detailed and consistent information on
the entire financial corporations sector (S.12). Moreover, this last facet must be systematically incorporated into the information on the entire economy orother sectorsunder the European System of Accounts: S.11 – non-financial corporations; S.13 – general government; S.14 – house- holds; S.15. – non-profit institutions; S.2 – rest of the world (Bank of Slovenia, Financial Accounts, 2003–10).
From the aspect of financial instruments, there is an understandable need for harmonisation of statistical measurement in the context of understanding complex transactions, which is in the interest of super- visory cooperation and effective financial supervision. The complexity of financial instruments is also a trigger for accelerated growth of net financial transactions that are not associated with financial brokering for the needs of the real sector, especially of non-financial corporations and households.
We develop a statistical information system that, supported by eco- nomic policy, at the same time isa proposal of further development of the statistical information function in the ESCB and will be consistent with its needs.
As thestarting point of the concept we have placed a two-pillar sys- tem, as derives from the two-pillar concept of economic and monetary analysis that the ECB uses in monetary policy decisions (Winkler, 2010, p. 360). As in the quoted source, we focus on non-financial and finan- cial accounts in economic or monetary analysis, as that part of sectoral accounts in which we believe individual ‘pillar analysis’ is founded in practice within the Eurosystem. Even before the crisis, the ECB (Keun- ing, 2008)1 in both cases of analysis emphasised the completeness of sectoral accounts, and at the end of 2011 (ECB, 2011b) in the economic part of the analysis it highlights financial accounts as a limitation of models for predicting economic growth. This last feature was especially important upon the onset of restrictions on fiscal borrowing by periph- eral eurozone countries in the second half of 2011 and on possibilities for external financing. As we will see in this chapter, Slovenia also serves as an example.
As a rule (Ireland is the exception in the EMU), non-financial accounts are drawn up by the national statistical offices under the aegis of Euro- stat, and financial accounts by the national central banks under the aegis of the ECB. Cooperation on national levels is arranged in various ways, and on the EU level a special committee, common Eurostat work- ing groups and the ECB perform expert work with the representatives of national statistical offices and statistical departments of the national central banks of the Member States.
The hypothetical question is: should a two-pillar system of analysis also be implemented at the level of national central banks? The financial and economic crisis that broke out in 2008 provides affirmative answers and demands a further intensification and perfection of the methods of cross-checking the consistency of real (non-financial accounts) and financial (financial accounts) variables on various levels of granulation in studying the multidimensional space of sectoral accounts and key economic indicators on the national level.2Coming to the forefront are demands for high-quality national macroeconomic statistics as a con- dition for high-quality aggregates on the EMU level (Schubert, 2011).
Abandoning the methodological qualities of these statistics upon entry into monetary union turned out to be a mistake for individual Mem- ber States (for example, the national balance of payments, supervision of the quality of public financial statistics, and so forth). Slovenia did not make that mistake. Through appropriate further development of the statistical information system it is possible to acquire a basis for high-quality implementation of a two-pillar analysis on the national level, and thereby high-quality support for decision-making in the con- ditions of single monetary policy. This need is underpinned even more by the diverse nature of the exit from the crisis for individual members of the monetary union, which indicates the importance of adequate information support for individual economic policies, especially fiscal and income, which are ‘still’ within national competence.
The Bank of Slovenia (2011) developed quarterly financial accounts in accordance with the European System of Accounts methodology (ESA, 1995). Together with the Statistical Office of the Republic of Slovenia, which covers non-financial accounts, it is building the consistency of comprehensive quarterly sectoral accounts.
Financial accounts themselves do not ensure the detection of rea- sons for individual financial transactions or processes (Winkler, 2010, p. 356). We wonder whether it is possible to set up such a statistical information system in support of decision-making that will be based significantly more on facts and evidence and thereby enable the rapid detection of reasons for change in individual phenomena, while at the same time it will provide a better data basis for prediction models. In this development it is essential to take account of the possibilities of mod- ern information technology for managing databases. In cross-checking, the financial and non-financial part of sectoral accounts on the indi- vidual sector level needs to be linked to the possibility of analysing important dimensions (activity, region or even firm) on lower hierarchi- cal levels. In analysing the consistency of financial and non-financial
accounts on a different hierarchical level of the multidimensional space of the data store, one must be aware of the differences in business accounting in respect of the methodology of keeping national accounts.
For example, consumption of fixed assets in national accounts is not equal to amortisation in company accounts (Lequiller and Blades, 2006, p. 197).
The diversity, especially in the competitiveness of EMU national economies, is very large. The Treaty on the Functioning of the EU requires Member States to cooperate closely in implementing economic policies as part of the principles of the open market and free competition (Article 119). For this reason, countries, especially EMU members, must ensure the proper conditions: price stability, sound public finances and stable monetary conditions, as well as a sustainable balance of payments situation. This last factor was still far from reality in 2011.
Identifying ‘pure’ financial instruments and transactions means at the same time developing and implementing an information system that will support decision-making primarily as part of three targets:
1. in measuring the effects of monetary policy at the euro area level and the level of the national economies;
2. financial stability; micro and macroprudential supervision (require- ments of ESAs and ESRB respectively);
3. economic policy on the national and EU levels.
Separating pure financial transactions from those associated with the real sector and creating added value signifies in particular ensur- ing a level of systematic granulation of data or information. This should enable high-quality interpretation of the causal consequences of changes within non-financial and financial accounts and between them, both on the sources and expenditures side and on the liabilities and assets side. If we try to compare national accounts with business finances, this means in practice ensuring sectoral balances and financial statements on a standardised or comparable basis.
Due to the above, as well as to the experiences from the financial crisis, financial accounts represent a natural basis (platform) for inten- sifying and expanding monetary and economic analysis. With the use of modern IT, in our opinion, it also opens up further possibilities for developing economic theory and practice in the area of monetary policy and finance (for example, empirically through methods of data mining).
Financial accounts enable cross-checking in the two-pillar ECB decision-making process, taking into account the availability of
consistent quarterly sectoral accounts. In practical terms, cross-checking means the activity of assessing the credibility of individual portions of information from various points of view, although in a coherent framework of sectoral accounts (ESA 1995), or, in the technical and tech- nological sense, in a regulated and structured multidimensional space.
Here we always ask ourselves: what level of data granulation do we need or is practically possible, given various factors in an individual seg- ment (dimension) of a multidimensional space (for example, economic sector)?
The highest data granulation in support of financial stability and availability of data in identifying clean financial transactions is, in the sense of ensuring the objectives of the proposed concept, possible pri- marily in the area of the financial intermediation sector. In their role, a financial intermediator enables the transfer of funds from surplus to deficit economic subjects. In this business they must have all the infor- mation on clients in order to manage risk. The concept of capturing highly granulated data available at financial intermediators and used for their own purposes is, in essence, consistent with the need for data and methodologies of the system of comprehensive quarterly sectoral accounts. Financial intermediators enable us to obtain data analysed on the basic dimensions of sector – financial instrument, and to define financial relations between economic sectors.
Below we provide a conceptual framework for a new model of data capture on the micro level, which in our opinion can enable high- quality decision-making on the economic policy of the individual Member State within the framework of the ECB’s single monetary pol- icy, and an appropriate micro–macro model for fulfilling the functions of financial stability and supervision in the financial sector of the economy.
In essence, the model also tends towards a clearer analysis of the link between the real and financial sectors of the economy. From the aspect of monetary policy, we must construct an information system that provides comparable measurement of the effects of decisions on the individual national economy, while at the same time ensuring the functioning of the IS–LM mechanism (which combines the invest- ment/saving equilibrium with the liquidity preference/money supply equilibrium), in terms of the interaction of national economic policy and the ECB’s single monetary policy decisions.
The nucleus of the model has systematically analysed data from the financial sector (S.12) of the economy. The systematic nature signifies in information terms ensuring a regulation of input data records that
enables the design of a data model in the sense of a data warehouse.
Such a concept of demands for data sources is being adopted in gen- eral, but the Bank of Slovenia obtains more aggregated data from other sectors of the economy, since it is not also carrying out a supervisory function.
The concept of a matrix enables the capture of data for several pur- poses at once, especially for supervision and economic and financial statistics and analysis. It is essential that individual financial instru- ments are analysed in terms of the numerous variables or regulated dimensions. In this way the Bank of Slovenia obtains individual loans approved by individual banks with all the key characteristics of a loan agreement. These characteristics are, in essence, regulated and har- monised through the measurement of phenomena within the EMU, for instance for the needs of calculating the effective interest rate of loans given to households for housing purposes. Similarly, granulated data are obtained for the instrument of securities, and so on. It is important to identify the opposite party in the transaction, and a link to registers enables the Bank to see its sectoral characteristics, activities and owner- ship. Via the sector one can compare the party with the aggregate data for the relevant sector.
The matrix method of reporting for financial intermediaries is at the same time a source of data for macro calculations of the financial sec- tor – financial and non-financial accounts. In this context, it enables the Bank to indirectly measure the banking sector’s financial services or the contribution of financial intermediaries to economic growth or the change in GDP. The ‘excessive contribution’ of the financial sector enables us to hint at the relatively low support for the real sector on the part of financial intermediaries. Aggregate data from matrix reporting for the non-financial sectors can be compared with original aggregate data for non-financial corporations and general government, reported via the Central Balance Sheet Office (AJPES), and for the rest of the world using data obtained from direct reporting for the balance of payments and through survey assessments for the households and non-profit insti- tutions sectors (S.14 and S.15). For the latter two sectors, the matrix is also a basic data source in terms of their financial operations within the economy.
The Statistical Office prepares non-financial quarterly sectoral accounts, while the Bank of Slovenia makes up the financial part of quarterly accounts for the individual sector. Theoretically, from the consistency aspect, the item net lending/borrowing for each sector of the economy and the whole must be identical in terms of preparation
(statistical process) on the part of financial accounts and non-financial accounts (B.9f=B.9), while in practice the Bank is trying to come close to quantitatively and qualitatively acceptable and explainable devia- tions. Such quality of accounts ensures the qualitative correctness of the equal value of investments and savings at the level of the national economy (I=S).
The financial part of the economy should provide as far as possible for the optimum allocation of excess funds for investment, bringing new added value and prosperity or socially responsible behaviour by owners, taking into account the principles of economic justification. Transmis- sion of the single monetary policy should enable the best possible such functioning of the financial system for Member States. In the theoretical aspect, we encounter here an IS–LM equilibrium. Due to the diversity of Member States, alongside a non-real convergence of EMU formulation, in the initial period of EMU existence this was not the case. We may
Economic Sector
S. 121, S. 122 S. 123, S. 125
S.13 S. 14 S. 15
S.2 High granularity of data – matrix reporting
F.4 – loan by loan, F.3., F.5 – security by security, … Who to whom approach – counterpart sector,
financial instrument
Basic Data Sources
Financial Statements CBO
Matrix Supervisors
Counterpart sector + Questionnaires
Direct Reporting, BoP
Macro level Registers, CBO, NSI
IS – LM, real - financial
part of economy
Economic Policy and Financial Stability
Fully consistent quarterly sectoral accounts and indicators: I = S, B9 = B9f by sector and instrument
Consistent data
Financial Statements CBO, MF S. 11
Figure 5.1 Conceptual framework of statistical information system Notes: CBO – Central Balance Sheet Office, named AJPES in Slovenia, NSI – National Statistical Institute, MF – Ministry of Finance, BoP – Balance of Payments, S.11 – Non-financial corporations, S.12 – Financial Corporations, and so on. (according to ESA 1995 classification).
ask ourselves, therefore, what statistical information system is needed by the individual Member State in order to take correct decisions on the micro and macro levels and to arrive at a relatively optimal allocation of excess sources for productive investment.
We propose, therefore, the construction of an information system from the nucleus – the financial sector – outwards, with a gradu- ally lower granulation of demands; from the financial sector directed towards other sectors of the economy. The nature of financial inter- mediation is, indeed, managing risky transactions, and therefore the best possible knowledge of customers or clients. From the aspect of the supervisory, financial stability and statistical function, in this knowledge we suggest a fundamental harmonisation of the dimensions of studying clients, and indirectly also we contribute to the interest in building up integrated accounting information systems in banks.
On the macro level of Figure 5.1, we propose in Slovenia the priority construction of comprehensive quarterly financial accounts, and only then comprehensive non-financial accounts: partly due to the method- ologically ‘easier’ implementation or lower complexity in the financial part of the accounts, partly due to the actual state of development of individual segments of comprehensive and consistent quarterly sectoral accounts.