317834
/en/bank-og-finansmarked/statistikker/kredind/maaned
317834
statistikk
2017-11-30T08:00:00.000Z
Banking and financial markets
en
kredind, Credit indicatorFinancial indicators, Banking and financial markets
true

Credit indicator

Updated

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Key figures

5.7 %

twelve-month growth in the general public’s domestic loan debt C2 October 2017

The general public's debt. 12-month growth. Per cent
Domestic loan debt (C2)
General publicHouseholds etc.Non-financial corporationsMunicipal government
April 20175.16.52.35.6
May 20175.46.73.15.2
June 20175.76.64.05.3
July 20175.76.64.15.8
August 20175.66.63.76.0
September 20175.86.44.75.3
October 20175.76.34.65.3
Total loan debt (C3)
General publicMainland NorwayMainland Norway, non-financial corporationsPetroleum activity and ocean transport
2nd quarter 20171.15.02.6-21.3
3rd quarter 20171.24.92.7-21.6

See more tables on this subject

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 monthTransactions this year1-month growth2Growth this year23-month moving average2
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.
October 20165 117 272244 2245.05 111 12822 649214 7905.55.35.2
November 20165 137 813253 0275.25 127 78812 797227 5873.05.14.7
December 20165 140 429238 7174.95 142 03311 256238 8432.74.94.2
January 20175 161 448249 1955.15 164 75030 04630 0467.27.24.3
February 20175 180 078248 8195.05 186 93620 72450 7704.96.15.3
March 20175 216 232258 6355.25 227 21534 30585 0758.26.85.9
April 20175 240 316253 7605.15 249 69821 716106 7915.16.46.6
May 20175 282 840269 9615.45 279 96831 311138 1027.46.66.7
June 20175 328 608285 0145.75 316 73635 861173 9638.56.96.9
July 20175 328 673288 7385.75 326 87320 827194 7904.86.66.6
August 20175 341 830284 1835.65 344 24119 339214 1294.46.36.1
September 20175 381 405292 9405.85 379 49531 735245 8647.46.45.6
October 20175 409 222289 3815.75 402 17818 564264 4284.26.2..

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.
October 2016452 8471 616 8503 047 57525 28142 686176 2555.92.76.1
November 2016454 8301 623 9713 059 01221 46143 532188 0345.02.76.5
December 2016457 3261 616 6053 066 49724 07931 537183 1015.62.06.3
January 2017461 5491 618 7013 081 19825 36135 107188 7285.82.26.5
February 2017465 4381 622 8043 091 83528 14330 174190 5026.41.96.6
March 2017466 5401 638 8333 110 86027 00436 262195 3706.12.36.7
April 2017469 0581 646 3153 124 94325 04637 301191 4125.62.36.5
May 2017467 5201 665 9103 149 40923 17450 255196 5315.23.16.7
June 2017470 3031 687 9503 170 35523 88064 915196 2185.34.06.6
July 2017472 7311 672 9723 182 97025 71766 370196 6505.84.16.6
August 2017474 9991 666 7863 200 04426 98960 381196 8126.03.76.6
September 2017475 3441 686 4243 219 63723 91676 097192 9285.34.76.4
October 2017476 6431 693 8643 238 71523 79674 610190 9755.34.66.3

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 debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign debtTotal debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign debtTotal debt (C3)Mainland NorwayPetroleum activity and ocean transportForeign 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 20166 658 5625 733 453925 1071 571 874................
4th quarter 20166 582 6675 827 616755 0491 442 238151 411238 207-86 800-87 3062.34.3-9.7-5.6
1st quarter 20176 648 2485 939 134709 1151 432 01671 467271 016-199 551-187 1681.14.8-21.5-11.7
2nd quarter 20176 764 3966 023 379741 0181 435 78874 759282 266-207 509-210 2551.15.0-21.3-12.9
3rd quarter 20176 754 0916 052 006702 0861 372 68679 731279 242-199 510-213 2091.24.9-21.6-13.6

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 creditors in NOK.

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

C3 shows the development in the general public’s loan debt to domestic and foreign creditors 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

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.

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).

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

Standard classifications

The credit indicator statistics apply different combinations of borrowing sectors, credit sources, industry classification and currency:

Borrowing sectors: the sector aggregate general public comprise of the institutional sectors general government, non-financial corporations and households etc.

Credit sources: the C2 data is classified according to credit source, where a source could either be a combination of a lending sector and a finance object, for instance bank loan, or just a finance object, for instance bond debt.

Industry classification: mainland Norway and oil activities and ocean transport follows the definition of the National accounts.

Currency: NOK and foreign currency.

Administrative information

Name and topic

Name: Credit indicator
Topic: Banking and financial markets

Next release

Responsible division

Division for Financial Corporations

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

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 and the media.

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), European System of Accounts (ESA 2010), Manual on Monetary and Financial Statistics (IMF) and Balance of Payments Manual (IMF 2002).

Legal authority

Not relevant.

EEA reference

Derived statistics, 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 bond debt to domestic lenders and the general public's certificate debt in the domestic market.

As discussed, 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 of companies, 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 bond and certificate debt 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 and depot information for financial corporations is collected by Norges Bank.

For both non-financial enterprises and quasi-corporated private enterprises in the general public’s external loan debt samples 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. 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. This calculation method means that there will not be full accord between the transaction figures and the changes in volume figures.

Growth based on the three-month moving average is defined as growth in average outstanding credit (seasonally-adjusted figures) in the latest three-month period in relation to the previous non-overlapping three-month period. The growth is adjusted for exchange rate fluctuations and structural breaks and given as an annualised figure. The calculation is centered, i.e. the observation is set at the middle month of the latest three-month period.

Seasonal adjustment

The seasonal adjustment of the credit indicator C2 is carried out using the X12-Arima program. Seasonal components are calculated for one year ahead, when publishing data for January. Revisions of the components for previous periods are also carried out when publishing the January figures. Seasonally adjusted volume figures for previous periods and growth rates based on such figures are thus affected. For more information, see the bullet point “About seasonal adjustment”, further down on this page.  

Seasonally adjusted volume figures are not included in the estimation of twelve-month growth, thus seasonal adjustment is not relevant for C3.

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 make adjustments 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).

New institutional sector classification

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.

Changed frequency for the C3 statistics

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.

 

Accuracy and reliability

Sources of error and uncertainty

The C2 statistics are mainly derived from the financial 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.

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.

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