Workshop on the Implementation of the 2008 SNA in EECCA
Countries and Linkages with BPM6 and GFSM 2014
6-8 May 2015, Istanbul, Turkey
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Outline of Presentation
• Introduction
• SebStat: Step Forward Towards Innovative
Solution
• How the data are structured?
• SebStat as an additional data source for SNA
and BOP compilers
• SebStat: How does it work?
• Lessons Learned and Way Forward
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Introduction
• Against the background of rapidly increasing statistical
standards and requirements National Bank of Georgia
(NBG) carries out a consistent strategy for the sustainable
development of statistics under its mandate.
• Moreover, expanding and improving our data sources and
statistical production, in general, we strongly believe that
we should think about other compilers of macroeconomic
statistics also.
• This task is quite solvable with the recently launched
completely new statistical business process model for
National Bank of Georgia, so called SebStat.
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SebStat: Step Forward Towards Innovative solution
• SebStat is an innovative statistical business process
model for NBG providing full range of possibilities to
satisfy requirements of monetary and financial statistics,
as well as the needs of various macroeconomic statistics,
directly or indirectly.
• This can be achieved by structuring of statistical and
financial data using standardized approach for all
statistical domain under the NBG’s mandate.
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How the data are structured?
Is it difficult to define data structure properly?
The answer is “Yes” and “No”.
In order to build the data structure several phases
shall be done:
– Identification of peer data groups to create proper
data families for Central Bank’s needs;
– Elaboration of the Code Lists for each data families;
– Development of appropriate methodology how the
financial instruments should be classified and
structured properly by financial institutions;
– The room for further development should be left.
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How the data are structured? (example for monthly
financial statement data structuring)
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Financial Statement Data Structure
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How the data are structured? (example for monthly
financial statement data structuring)
The structure of financial statement data (FIM_Data Family) consists of:
•
Data entries
– Data family
– Source
– Frequency
•
Data characteristics
–
–
–
–
–
•
Client’s characteristics
–
–
–
–
•
Financial/nonfinancial instruments
Assets/liabilities
Stock/flow
Maturity
Currency
Residency
Institutional sector
Type of economic activities
Region (if resident)
Additional info
– Additional info on loans
– Loans collateral
– Range (for loans&Deposits)
•
Attributes
– Interest rate
– Measure type
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How the data are structured: Other data families
FEX_data Family Foreign Currency
Transactions
• Data entries (data
family, source,
frequency)
• Type of transactions
• Buying prices
• Selling prices
• Counterpart
• Measure type
MTR_Data Family Money Transfer
Operations
• Data entries (data family,
source, frequency)
• Type of transactions
• Type of wire transfer
• Country (sender/receiver)
• Currency
• Measure type
BPC_Data Family Payment Cards’
Statistics
• Data entries (data
family, source,
frequency)
• Card type and category
• Type of transaction
• Type of service post
• Measure type
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SebStat as an additional data source for SNA and
BOP compilers
1. Data sources to calculate the output of financial
corporations
• Output of Financial Institutions
FIM_Data Family –
Monthly Financial
Statements
FEX_Data Family –
Foreign Currency
Transactions
• Financial Intermediation
• Central Bank
• Monetary Policy Services
• FISIM
• Deposit-taking Corporations
• Explicit fees charged in lieu of providing
services
• FISIM
• Transactions in foreign currencies
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SebStat as an additional data source for SNA and BOP
compilers
2. Data sources to calculate the part of international
transactions of Goods and Services Account of BOP
BPC_FamilyPayment
Cards’
Statistics
• Goods and Services Account
• Goods
• E-Commerce (to be added)
• Services
• Travel
• Additional information to financial
intermediation services, related with
acquiring of payment cards
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SebStat as an additional data source for SNA and BOP
compilers
3. Data sources for calculation of PIB & SIB items of BOP
MTR_Data
FamilyMoney
Transfer
Operations
• PIB - Primary Income Balance
• Compensation of Employees
• SIB - Secondary Income Balance
• Personal Transfers
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SebStat: How does it work?
Financial/Nonfinancial
Instruments
Monetary Gold&SDRs
Report description (example)
Currency
Indicator: Loans
Stock/Flow
Deposits
Assets
Maturity
Securities other than shares
Stock
Currency
Loans
Maturity: 10 year and more
Residency
Shares and other equity
Currency: GEL
Insurance technical reserves
Counterpart description:
Financial derivatives
Residency: Resident
Other accounts receivable/payable
Sector: Nonfinancial corporation
Nonfinancial assets
Economic activity: Trade
Assets/Liabilities
Institutional Sector
Type of economic activity
Region
Additional info on loans
Loan’s collateral
February
Range
Region: Kakheti
Interest rate
Additional info: SME loan
Measure type
Collateral: Real estate
Range: 5000-25000
Next
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Back
Interest rate:
Generate
12 value)
Measure type: BV (book
SebStat: How does it work? (example)
Table 1. Loans granted by commercial banks, Jan 2010-Feb 2015 (Mln GEL)
1.01.2010
1.02.2010
…
1.02.2015
…
…
…
…
Loans, total
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
01/01/15
01/10/14
01/07/14
01/04/14
01/01/14
01/10/13
01/07/13
01/04/13
01/01/13
01/10/12
01/07/12
01/04/12
01/01/12
01/10/11
01/07/11
01/04/11
01/01/11
01/10/10
01/07/10
01/04/10
01/01/10
0.0
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Lessons Learned
• Based on Georgian experience, it is obvious, that comprehensive
multifunctional statistical data model for Central Bank is best solution
in order to meet not only own statistical requirements, but also
needs of other macroeconomic statistical systems compilers;
• The right cooperation strategy with data providers is essential, to
ensure project success in terms of data relevancy and quality, and
readiness for boosting joint effort aimed at strengthening of
statistical capacity;
• Close cooperation with SNA, BOP and GFS compilers on the earlier
stage of project designing is important to ensure data model
comprehensiveness and methodological consistency.
• In addition to high level management support, it is very important to
have the backing of international partners to raising awareness of the
similar achievements on the national and international level, in order
to get more benefit from each other’s experience and knowledge.
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Thank you
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Contact information:
Nana Aslamazishvili
tel: (995 32) 2406 251,
e-mail: naslamazishvili@nbg.gov.ge
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