399639
/en/bank-og-finansmarked/statistikker/kredind/maaned
399639
statistikk
2020-01-31T08:00:00.000Z
Banking and financial markets
en
kredind, Credit indicatorFinancial indicators, Banking and financial markets
true

Credit indicator

Updated

Next update

Key figures

5.1 %

twelve-month growth in the general public’s domestic loan debt C2 December 2019

The general public's debt. 12-month growth. Per cent
Domestic loan debt (C2)
General publicHouseholds etc.Non-financial corporationsMunicipal government
June 20195.85.65.86.8
July 20195.65.35.87.0
August 20195.55.25.76.9
September 20195.85.17.07.0
October 20195.65.06.17.6
November 20195.65.25.77.8
December 20195.15.04.97.0
Total loan debt (C3)
General publicMainland NorwayMainland Norway, non-financial corporationsPetroleum activity and ocean transport
2nd quarter 20194.25.75.4-9.7
3rd quarter 20194.86.07.1-7.2

See selected tables from this statistics

Table 1 
C2, domestic debt to the general public

C2, domestic debt to the general public1
Unadjusted figuresSeasonally adjusted figures
NOK millionPer centNOK millionPer cent
StocksTransactions over past 12 months12-month growthStocksTransactions over past month1-month growth2
1All growth calculations based on stocks that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate changes not related to transactions. The growth calculations are also adjusted for statistical breaks that are not attributable to transactions or revaluation. This can cause deviation between stock changes and transactions.
2Converted to annual rate.
December 20185 728 887305 0905.65 735 46234 3597.5
January 20195 750 759314 8175.85 756 31728 0616.0
February 20195 783 122318 4555.85 790 40831 8686.8
March 20195 813 115322 4865.95 817 12026 2485.6
April 20195 842 106320 1985.85 844 17927 5015.8
May 20195 874 449318 7085.75 869 56021 7714.6
June 20195 906 464320 9035.85 890 21024 9335.2
July 20195 910 303313 9425.65 910 93616 9683.5
August 20195 936 650308 9995.55 941 75823 6194.9
September 20195 967 969327 1745.85 967 21026 8575.6
October 20196 007 684316 3435.66 002 97426 5415.5
November 20196 027 192318 4475.66 022 59826 4295.4
December 20196 021 294293 4705.16 028 00013 2402.7

Table 2 
C2 by debtor sector

C2 by debtor sector1
Stocks. NOK millionTransactions over past 12 months. NOK million12-month growth. Per cent
Municipal governmentNon-financial corporationsHouseholds etc.Municipal governmentNon-financial corporationsHouseholds etc.Municipal governmentNon-financial corporationsHouseholds etc.
1All growth calculations based on stocks that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate changes not related to transactions. The growth calculations are also adjusted for statistical breaks that are not attributable to transactions or revaluation. This can cause deviation between stock changes and transactions.
December 2018514 0731 758 1783 456 63631 88890 654182 5486.65.55.6
January 2019517 0881 762 3893 471 28231 30596 194187 3176.45.85.7
February 2019522 3411 780 7943 479 98835 73094 704188 0237.35.65.7
March 2019525 9471 791 7263 495 44237 20297 652187 6327.65.85.7
April 2019527 3891 803 8703 510 84734 55197 507188 1407.05.75.7
May 2019529 4811 812 4063 532 56333 88697 156187 6676.85.75.6
June 2019530 0671 821 6583 554 73933 77399 683187 4476.85.85.6
July 2019531 9231 819 3943 558 98635 01399 465179 4647.05.85.3
August 2019533 0361 828 6613 574 95334 33898 334176 3276.95.75.2
September 2019533 6001 841 3213 593 04834 797118 752173 6257.07.05.1
October 2019538 6721 855 6013 613 41138 056106 010172 2777.66.15.0
November 2019544 3721 857 1703 625 65139 264100 017179 1667.85.75.2
December 2019550 2161 842 7263 628 35336 14385 657171 6717.04.95.0

Table 3 
C3, total debt by selected industries. Foreign debt

C3, total debt by selected industries. Foreign debt1
Stocks. NOK millionTransactions over past 12 months. NOK million12-month growth. Per cent
Total loan debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign loan debtTotal loan debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign loan debtTotal loan debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign loan debt
1All growth calculations based on stocks that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate changes not related to transactions. The growth calculations are also adjusted for statistical breaks that are not attributable to transactions or revaluation. This can cause deviation between stock changes and transactions.
3rd quarter 20186 857 1346 224 091633 0421 229 392137 920226 046-88 128-155 2872.03.7-12.4-11.3
4th quarter 20186 961 4366 331 295630 1411 232 557181 199261 821-80 622-123 8832.74.3-11.6-9.2
1st quarter 20197 056 1416 434 899621 2421 243 026251 561328 607-77 046-70 9253.75.4-11.5-5.5
2nd quarter 20197 159 4896 550 551608 9381 253 025288 354351 712-63 359-32 5494.25.7-9.7-2.6
3rd quarter 20197 249 9766 623 475626 5011 282 007328 053373 707-45 6538554.86.0-7.20.1

About the statistics

The credit indicator measures the general public’s debt. The statistics differentiate between domestic debt C2 and total debt C3, which include external debt. Transaction and growth estimations are adjusted for changes in stocks that are not due to new borrowings or repayments of loans.

Definitions

Definitions of the main concepts and variables

C1 shows the development in the general public’s loan debt to Norwegian lenders in NOK.

C2 shows the development in the general public’s loan debt to Norwegian lenders in NOK and foreign currency.

C3 shows the development in the general public’s loan debt to domestic and foreign lenders in NOK and foreign currency.

The general public comprises the institutional sectors general government, non-financial corporations and households etc. Non-profit institutions serving households is included in the household sector in C2. In the credit indicator statistics, we measure the development in debt for these borrowing sectors.

The loan debt in C1 and C2 comprises loans from banks and other financial institutions as well as debt securities issued in Norway with a Norwegian lender. The external loan debt in C3 comprises the general public’s remaining debt securities and other loans with a foreign lender, including intercompany loans.

Standard classifications

The credit indicator statistics apply different combinations of borrowing sectors, lenders, industry classification and currency.

Borrowing sectors: the sector aggregate general public comprise of the institutional sectors general government (sector code 6500), non-financial corporations (sector code 1110-2500) and households etc. (sector code 7000-8500, as well as 0800 unspecified sector).

Industry classification: mainland Norway and oil activities and ocean transport are the industry aggregates we apply in the credit indicator statistics and follows the definition of the National accounts.

Oil activities comprise all enterprises in industry 22 (Services linked to extraction of crude petroleum and natural gas) and industry 23 (Extraction of crude petroleum and natural gas). The industry codes here are referring to the codes in the reporting ORBOF and are based on the European classification of industries, NACE.

Ocean transport comprises all enterprises classified in industry 49 (Sea transport abroad and transport via pipelines).

Currency: NOK and sum foreign currency.

Administrative information

Name and topic

Name: Credit indicator
Topic: Banking and financial markets

Next release

Responsible division

Division for Financial Markets Statistics

Regional level

Only at national level.

Frequency and timeliness

C1 and C2: Monthly. Released approximately 30 days after the reference period.

C3: Quarterly. Preliminary release of credit to non-financial corporations with the industry classification mainland Norway approximately 55 days after the reference period. The complete and final C3 statistics are released approximately 60 days after the reference period.

International reporting

C2 is included in IMF’s Special Data Dissemination Standard (SDDS). Data are posted on Statistics Norway’s website under “Economic Indicators".

Microdata

Collected and published data is stored in SSB's data base.

Background

Background and purpose

The credit indicator measures the debt of selected sectors in Norway. The indicator is one source of information when the authorities formulate the monetary policy of Norway. The statistics provide an overview of the development of credit at an early stage and is an important indicator of economic activity.

The central bank of Norway (Norges Bank) introduced the credit indicator statistics in the mid-1980s, and such data are available dating back to December 1985. After Statistics Norway took over most of the work involved in collecting and publishing financial statistics from Norges Bank in 2007, the credit indicator statistics, was also transferred to Statistics Norway.

Users and applications

Monetary authorities, i.e. Norges Bank and the Ministry of Finance. Other important users are the Financial Supervisory Authority of Norway, the financial markets, research institutions, international organisations, the media as well as students.

Equal treatment of users

No external users have access to the statistics and analyses before they are published and accessible simultaneously for all users on ssb.no at 8 am. Prior to this, a minimum of three months advance notice is given in the Statistics Release Calendar. For more information, see Principles for equal treatment of users in releasing statistics and analyses.

Coherence with other statistics

The statistics are based on the guidelines in the System of National Accounts (SNA 2008), the European System of Accounts (ESA 2010), the Monetary and Financial Statistics Manual and Compilation Guide (IMF 2016) and the Manual on MFI Balance Sheet Statistics (ECB 2019).

The source data for loans from banks, mortgage companies, state lending institutions and finance companies is the same as in the statistics Finance companies, balance sheet. Loans from life and non-life insurance companies is the same as found in the statistics Life and non-life insurance companies, accounts. Loans from pension funds comes from the statistics Pension funds. Finally, debt securities are obtained from the same source as used in the statistics for Securities registered with VPS.  

Legal authority

Not relevant.

EEA reference

The statistics is derived without direct Council Directives or Council Regulations from the EU.

Production

Population

Sources included in C2 are loans in NOK and foreign currency to the general public by banks, state lending institutions, finance companies, life and non-life insurance companies, mortgage companies, pension funds, the Norwegian Public Service Pension Fund, Export Credit Norway and Norges Bank. C2 also includes the general public's debt securities with domestic lenders. The owner of a debt security is the lender, while the issuer is the borrower.

C3 is comprised of the sum of C2 and the general public’s external loan debt. The external part of C3 comprises external debt statistics for the main institutional sectors general government, non-financial corporations and households etc. The debt figures comprise external long-term debt such as bond loans, loans from credit institutions, loans from companies within the same group, subordinated loans and short-term debt such as certificates, overdraft facilities, short-term debt to companies within the same group of companies, owners, employees etc. Foreign shareholders in Norwegian companies are not included.

Data sources and sampling

The C2 statistics are derived from the accounting statistics of ORBOF (Reporting of banks, mortgage companies, state lending institutions and finance companies accounts to the public authorities), FORT (Reporting of life and non-life insurance companies accounts for the public authorities) and PORT (Reporting of pension funds account to the public authorities). The data for the general public's debt securities are derived from statistics for securities registered in the Norwegian CSD (VPS). The Norwegian Public Service Pension Fund and Export Credit Norway also report data for these statistics.

The calculations of revaluations due to exchange rate fluctuations are based on stock data for the general public’s debt to the credit sources in the C2 statistics, official data for exchange rates and data for the composition of currencies of the bank’s receivables and debts from the quarterly BIS survey.

The data on the external loan debt are based on the Balance of Payments Statistics. A new system for collecting and producing data for the balance of payments was established in 2005 after the Norges Bank Foreign Exchange Statistics were discontinued. The most significant change in the data collection process is that Statistics Norway has established new sampling surveys for non-financial and private quasi-corporated public enterprises. The shareholdings of foreign shareholders in Norwegian enterprises, as mentioned above, and “other liabilities” are not included in the general public’s external debt. This is in accordance with the definitions of C1 and C2, neither of which including these financial objects.

Data from the Balance of Payments reporting is used for the external loan debt of non-financial corporations and quasi-corporated private enterprises. This survey is linked to the Standard Industrial Form (SIF) from the Directorate of Taxes, and data for trade in services, financial incomes and costs, foreign assets and liabilities etc. are collected quarterly and annually for use in the Balance of Payments survey. Information on securities from the Norwegian CSD is also used.

For the external loan debt, the sample of non-financial enterprises and quasi-corporated private enterprises are used for quarterly and annual surveys. The samples cover about 90 per cent of total foreign assets and liabilities on a quarterly basis, and approximately 95 per cent of the annual surveys on average. For the non-financial corporations and quasi-corporated private companies, the samples comprise approximately 650 enterprises on a quarterly basis and 2 700 enterprises annually.

Collection of data, editing and estimations

Statistics Norway have the responsibility of collecting accounting data for banks, mortgage companies, financial corporations, insurance companies and pensions funds. The collecting of data is done in collaboration with the Financial Supervisory Authority of Norway and Norges Bank. In addition, Statistics Norway obtains data from the Norwegian Public Service Pension Fund, Export Credit Norway and the Norwegian CSD.

For the external part of C3, data from a sample of non-financial corporations’ debt are collected from their reporting of balance of payments data. Information about the households’ external debt is collected from their tax return.  

The editing of the financial corporations’ accounting statements is undertaken by Statistics Norway and the Financial Supervisory Authority of Norway. The data from the Norwegian Public Service Pension Fund, Export Credit Norway and the Norwegian CSD are controlled by Statistics Norway. Manual controls are undertaken when the data on the general public’s external loan debt are received, and the database contains control routines for content and logical coherence.

The policy is to disseminate changes of the previous month’s data together with the current month’s data. With every release, the latest 25 periods of stock data and 13 periods of transaction and growth data are updated. Statistics Norway is fully prepared to edit in a timely manner, with appropriate notification to users and the media, should it be deemed necessary by the magnitude of a past error, or, owing to other exceptional circumstances. Some of the reported data may contain preliminary data that are subsequently corrected.

The figures for the external loan debt of the non-financial companies and quasi-corporated private companies are scaled up in the quarterly and annual surveys by means of statistical methods to represent figures for the total external loan debt.

All growth rate calculations based on holdings that include foreign currency loans are adjusted for exchange rate fluctuations in order to eliminate all changes not related to transactions. The growth rate calculations are also adjusted for structural breaks that are not attributable to transactions or valuation changes. Examples of this kind of break could be that a financial corporation moves from one sector to another or an introduction of a new financial source. Changes in accounting principles may also cause breaks in time series. This calculation method means that there will not be full accord between the transaction figures and the changes in volume figures.

Seasonal adjustment

The seasonal adjustment of the credit indicator C2 is carried out using the method X12-Arima. Seasonal components are calculated for each release of data, meaning that seasonal adjusted stocks as well as monthly transactions and growth rates are updated.  The seasonal adjusted series for domestic debt in NOK, C1, is indirectly adjusted. This imply that the seasonal adjusted C1 is calculated as the sum of the three seasonal adjusted series C1-households, C1-non-financial corporations and C1-general government. The seasonal adjusted series for domestic debt in NOK and foreign currency, C2, is calculated as the sum of the unadjusted series for C2-foreign currency and the seasonally adjusted series for C1. For more information, see the bullet point “About seasonal adjustment”, further down on this page.  

Confidentiality

Normally, the debt data will not be published if there is a risk of identification, i.e. that the figures can be traced back to the reporting unit. Exceptions here are Norges Bank and the Norwegian Public Service Pension Fund, who do not object to such identification.

Comparability over time and space

The revision of international standards and major changes in accountancy laws may result in a gap in the time series data. A change of sectors may have the same result, for instance if a financial corporation moves from one institutional sector to another. We try as far as possible to correct for structural breaks in our calculations of transactions (break corrections).

The external loan debt statistics in their present form were collected by Statistics Norway for the first time in March 2005 (figures for January 2005).

 

Change in the statistics on banks and mortgage companies in 2018

The adjustment of ORBOF to IFRS has led to a change in the statistics on banks and mortgage companies from January 2018. An important implication for the credit indicator statistics is that accrued interests and changes in value are included with the underlying financial object. In addition to this, gross loans have replaced loans deducted for loan loss provisions. Net debt securities have replaced gross debt securities, meaning that own holdings of debt securities are deducted from gross debt securities. Finally, the method for adjusting transactions for fluctuations in exchange rates is changed in accordance with IMF’s standard. As of January 2018, the stock time series are not comparable with previous periods. Transaction and growth series are corrected for this break.

 

Changed frequency for the C3 statistics in 2017

As of the reference period June 2017, the C3 statistics changed frequency from monthly to quarterly. The change in procedures imply a more automated approach to estimation of transactions in the external debt. This can cause some deviations in estimated changes in exchange rates compared to the transactions in the closed table nr. 07477 in the StatBank. Before, quarterly volume figures where used and these where reported by currency. As of the reference period June 2017, yearly data are utilised to estimate exchange rate revaluations that are incorporated into the transactions.

 

 New institutional sector classification in 2012

As from January 2012, the Norwegian institutional sector classification has been revised in line with the international classification. This change implies a break in stock time series between February and March 2012.

Accuracy and reliability

Sources of error and uncertainty

The C2 statistics are mainly derived from the financial markets statistics. Errors and inconsistencies in these statistics will also affect C2. In this context, we refer to the sections on sources of error and uncertainty from these statistics. The sources of inconsistencies for data from the Norwegian Public Service Pension Fund and Export Credit Norway will also be of the same type as the statistics mentioned above.

For the non-financial corporations used in the sample for C3, the quarterly surveys on external loan debt are based on a sample of companies. Furthermore, for these companies the surveys comprise parts of the companies' balance sheet according to the SIF, i.e. the items that provide information on the foreign debt. The interpretation of what constitutes a liability between a Norwegian company and a foreign counterpart, and how the debt should be distributed into the various debt items can lead to errors in the statistics.

The response rate:

The response rate for the C2 statistics are 100 per cent.

The response rate for the external loan debt in C3 usually amounts to 95-96 per cent for the part of the survey covering the non-financial corporations. Hence, the non-response figure is relatively low. There are, however, some non-response errors with regard to some of the debt items in the forms. This is corrected through contact with the respondents and estimation of data for some units in the sample in such a manner that the published data probably do not contain any notable errors with regard to the total level of debt or distributed by debt objects.

Sampling:

The sampling error for the external loan debt in C3 is the uncertainty caused by producing figures based on a selection of entities and not the total population. Hence, the sampling error measures the expected deviation between the result from using the sample and the expected result from running a total census for the entire population of companies with foreign assets and liabilities.

The sampling methods in C3 presuppose that the population itself covers the vast majority of all companies with foreign assets and liabilities. There is, however, no overview of companies with these balance sheet items, and hence it may be difficult to detect all relevant units that should be included in the surveys. The collection procedures are, however, modeled in such a way that it is unlikely that important units are not included.

Other errors:

There may be errors or omissions in the reporting that the credit indicator is based on. The most common mistakes are due to the fact that the interpretation of individual posts may differ from what is correct by definition.

For the external part of C3, the distinction between Norwegian and foreign entities may be unclear. In addition, the reporting of balance of payments can sometimes be insufficiently completed.

Revision

The statistics show preliminary figures. Data may be edited and included in the first possible future publication. With every release, the latest 25 periods of stock data and 13 periods of transaction and growth data are updated.

About seasonal adjustment

General information on seasonal adjustment

Monthly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

For more information on seasonal adjustment see metadata on methods: seasonal adjustment

Why seasonally adjust these statistics?

On the basis of public holidays and holiday period in July and December the intensity of the supply and demand of credit fluctuates through the year. This complicates a direct comparison of debt figures from one month to the next. To adjust for these relations the debt is seasonally adjusted for the actual levels, so that one can analyse the underlying credit indicator development.

Series that are seasonally adjusted

The credit indicator statistics publishes five seasonally adjusted series; C1, C2, C2 foreign currency, C2-households and C2-non-financial corporations. The seasonally adjusted figures for C2-foreign currency is not a result of own seasonal adjustment, but a residual from the difference of seasonally adjusted C2 and the seasonally adjusted C1.

Pre-treatment

Pre-treatment is an adjustment for variations caused by calendar effects and outliers.

No pre-treatment.

Calendar adjustment

Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another.

No calendar adjustment of any kind is performed.

Methods for trading/working day adjustment

No correction.

Correction for moving holidays

No correction.

National and EU/euro area calendars

Definition of series not requiring calendar adjustment.

Treatment of outliers

Outliers, or extreme values, are abnormal values of the series.

Outliers are detected automatically by the seasonal adjustment tool. The outliers are removed before seasonal adjustment is carried out, and then reintroduced into the seasonally adjusted data.

Model selection

Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.

Automatic model selection by established routines in the seasonal adjustment tool.

Decomposition scheme

The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.

Multiplicative decomposition is applied.

Seasonal adjustment

Choice of seasonal adjustment approach

X-12-ARIMA

Consistency between raw and seasonally adjusted data

In some series, consistency between raw and seasonally adjusted series is imposed.

Do not apply any constraint.

Consistency between aggregate/definition of seasonally adjusted data

In some series, consistency between seasonally adjusted totals and the aggregate is imposed. For some series, there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.

Do not apply any constraint.

Direct versus indirect approach

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

Mixed indirect approach where the seasonal adjustment of components possibly occurs using different approaches and software.

Comments: The total is computed independently of the components. The last component is computed as a residual of the difference between the total and the other components.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.

Stocks from the last ten years is used to estimate the correction factors.

Audit procedures

General revision policy

Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.

Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar.

Comments: The program for seasonal adjustment with new seasonal components is made once a year, but seasonally adjusted figures are audited in accordance with audited raw data.

Concurrent versus current adjustment

Controlled current adjustment: Forecasted calendar factors derived from a current adjustment are used to seasonally adjust the new or revised raw data. The numbers are revised when new, fixed factors are estimated once a year.

Horizon for published revisions

The entire time series is revised in the event of a re-estimation of the seasonal factors.

Comments: The whole series which enters into seasonal adjustment is audited once a year. Apart from this, the elderly seasonal adjustment figures are only audited when unadjusted figures have been audited.

Quality of seasonal adjustment

Evaluation of seasonally adjustment data

Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.

Quality measures for seasonal adjustment

No quality measures for seasonal adjustment assessment are used.

Special cases

Seasonal adjustment of short time series

All series are sufficiently long to perform an optimal seasonal adjustment.

Treatment of problematic series

Νο series are treated in a special way, irrespective of their characteristics.

Posting procedures

Data availability

Raw and seasonally adjusted data are available.

Press releases

In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.