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/en/bank-og-finansmarked/statistikker/k2/maaned
298287
statistikk
2017-08-08T08:00:00.000Z
Banking and financial markets
en
k2, The credit indicator C2, general public’s gross domestic debt, credit expansion, credit transactions, liabilities, creditFinancial indicators, Banking and financial markets
true

The credit indicator C2

As from 2017 the statistics is published with Credit indicator.

Updated

Key figures

5.7 %

twelve-month growth in the general public’s domestic debt

The general public’s gross domestic debt (C2). Percentage change
January 2017February 2017March 2017April 2017May 2017June 2017
1Annualised figure
12-month growth, total5.15.05.25.15.45.7
3-month moving average, total14.35.46.06.66.7..
12-month growth, households6.56.66.76.56.76.6
12-month growth, non-financial corporations2.21.92.32.33.14.0

See selected tables from this statistics

Table 1 
Domestic credit to general public. Stocks. NOK million

Domestic credit to general public. Stocks. NOK million
Actual figuresSeasonally adjusted figures
C2C1Foreign exchangeC2C1Foreign exchange
June 20165 041 4384 805 034236 4045 029 8194 792 172237 647
July 20165 053 4664 818 202235 2645 052 0064 818 159233 847
August 20165 066 9174 837 784229 1335 069 5984 843 510226 088
September 20165 086 6814 865 618221 0635 085 1454 865 029220 116
October 20165 117 0084 894 495222 5135 110 8654 888 975221 890
November 20165 137 2864 911 720225 5665 127 2624 904 163223 099
December 20165 139 6314 916 211223 4205 141 2354 922 255218 980
January 20175 160 9164 947 530213 3865 164 2174 955 000209 217
February 20175 179 8164 966 748213 0685 186 6744 975 607211 067
March 20175 216 2354 997 314218 9215 227 2185 004 827222 391
April 20175 240 2995 018 985221 3145 249 6815 021 666228 015
May 20175 282 7945 057 918224 8765 279 9225 047 173232 749
June 20175 328 5355 096 325232 2105 316 6635 082 627234 036

Table 2 
Domestic credit to general public. Transactions

Domestic credit to general public. Transactions1
C2
Actual figuresSeasonally adjusted figures
12-month growth, transactions12-month growth, percentage change1-month growthGrowth this yearAnnualised 1-month growthAnnualised growth this yearGrowth based on the 3-month moving average2
1All 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 statistical breaks that are not attributable to transactions or valuation changes.
2Annualised figure.
June 2016237 4405.020 990128 1455.15.35.1
July 2016239 2205.017 207145 3524.25.14.9
August 2016240 7205.023 824169 1765.85.24.9
September 2016246 7375.123 077192 2535.65.35.3
October 2016244 2625.022 447214 7005.45.35.2
November 2016252 7905.212 523227 2233.05.14.6
December 2016238 0984.811 001238 2242.64.84.2
January 2017248 7755.130 28630 2867.37.34.3
February 2017248 6485.021 00351 2895.06.15.4
March 2017258 7685.234 57285 8618.36.86.0
April 2017253 8575.121 666107 5275.16.46.6
May 2017270 0305.431 282138 8097.46.66.7
June 2017284 9655.735 824174 6338.56.9..

Table 3 
C2 credit sources. Stocks. NOK million

C2 credit sources. Stocks. NOK million
Domestic sources, totalLoans issued byBond debtCertificate debtOther sources
Loans from banksLoans from state lending institutionsMortgage companiesFinancing companiesLife insurance companiesNon-life insurance companiesPension funds
June 20165 041 4382 432 262316 2171 601 448141 22483 5552 0571 785271 26677 672113 952
July 20165 053 4662 447 455315 8201 598 773141 31483 9202 0591 767272 56877 866111 924
August 20165 066 9172 455 819319 0001 602 808142 48984 2842 0601 749270 45777 210111 041
September 20165 086 6812 454 173321 0441 618 073144 46184 6492 0621 722279 07372 171109 253
October 20165 117 0082 469 685322 5211 617 878146 70891 2121 9771 713282 15174 308108 855
November 20165 137 2862 486 696317 7961 620 724148 47197 7771 8911 695280 59773 372108 267
December 20165 139 6312 473 733318 7711 627 933148 235104 3401 8061 675283 67172 543106 924
January 20175 160 9162 492 679322 2411 636 750139 818105 2101 9511 675285 18271 220104 190
February 20175 179 8162 489 296323 2001 654 118141 777106 0832 0951 675286 67071 430103 472
March 20175 216 2352 514 139325 7241 662 136142 878106 9532 2401 675290 21967 394102 877
April 20175 240 2992 493 934328 4551 703 594144 125107 8172 1781 675286 88569 804101 832
May 20175 282 7942 519 357329 1011 706 436146 420108 6812 1141 675295 20073 684100 126
June 20175 328 5352 553 659328 9541 712 795149 129109 5452 0521 675297 70473 60499 418

Table 4 
C2 credit sources. Transactions over past 12 months

C2 credit sources. Transactions over past 12 months1
Per cent
Domestic sources, totalLoans issued byBond debtCertificate debtLoans from other sources
Loans from banksLoans from state lending institutionsMortgage companiesFinancing companiesLife insurance companiesNon-life insurance companiesPension funds
1The growth rates in banks and mortgage companies’ loans are revised in the period October 2015 to March 2016 due to changes in the method for statistical breaks caused by portfolio shifts between banks and mortgage companies.
June 20165.03.34.47.810.359.4-39.7-18.6-0.920.8-16.5
July 20165.03.54.37.610.659.1-39.1-17.5-0.117.0-18.0
August 20165.03.54.27.610.758.8-38.3-16.4-0.617.6-16.9
September 20165.13.34.38.011.258.4-37.7-15.72.110.7-16.6
October 20165.03.93.96.412.046.5-30.9-14.03.612.4-15.9
November 20165.23.83.97.412.237.4-21.8-12.83.17.7-14.5
December 20164.83.73.66.711.330.0-8.4-11.12.77.1-13.2
January 20175.14.43.86.411.030.2-0.9-10.73.04.2-14.4
February 20175.03.73.97.411.130.36.7-9.85.1-2.4-14.1
March 20175.24.44.36.610.830.514.3-8.99.2-13.9-13.6
April 20175.13.03.98.610.230.79.3-8.07.3-11.6-12.9
May 20175.43.53.97.710.530.94.4-7.110.8-3.8-13.9
June 20175.74.64.07.010.331.1-0.2-6.29.7-5.2-13.1

Table 5 
C2 by debtor sector. Stocks. NOK million

C2 by debtor sector. Stocks. NOK million
Municipal governmentNon-financial corporationsHouseholds etc.
June 2016446 4231 621 0022 974 013
July 2016447 0141 619 2912 987 161
August 2016448 0101 615 1203 003 787
September 2016451 4271 608 6833 026 571
October 2016452 8461 616 5893 047 573
November 2016454 8311 623 4433 059 012
December 2016457 3271 615 8063 066 498
January 2017461 5481 618 1693 081 199
February 2017465 4381 622 5423 091 836
March 2017466 5401 638 8343 110 861
April 2017469 0601 646 2983 124 941
May 2017467 5211 665 8703 149 403
June 2017470 3021 687 8913 170 342

Table 6 
C2 by debtor sector. Transactions

C2 by debtor sector. Transactions1
12-month growth, transactions. NOK million12-month growth, transactions. Per cent
Municipal governmentNon-financial corporationsHouseholds etc.Municipal governmentNon-financial corporationsHouseholds etc.
1All 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 statistical breaks that are not attributable to transactions or valuation changes.
June 201622 86147 273167 3065.43.06.0
July 201621 69951 424166 0975.13.35.9
August 201621 28746 755172 6785.03.06.1
September 201627 06740 641179 0296.42.66.3
October 201625 27942 715176 2695.92.76.1
November 201621 46043 286188 0465.02.76.5
December 201624 08030 897183 1225.61.96.3
January 201725 36034 679188 7375.82.26.5
February 201728 14329 994190 5126.41.96.6
March 201727 00536 382195 3826.12.36.7
April 201725 05037 388191 4195.62.36.5
May 201723 17550 319196 5365.23.16.7
June 201723 87964 875196 2105.34.06.6

About the statistics

The main focus of the credit indicator C2 is the growth in the general public’s domestic debt over the past twelve months (twelve-month growth). 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

The public comprises the institutional sectors general government, non-financial enterprises and households etc. Non-profit institutions serving households is included in the household sector in C2.

  • C1 stands for "Credit from domestic sources in NOK", i.e. the indicator of the general public’s gross domestic debt in NOK".
  • C2 stands for "Credit from domestic sources in NOK and foreign currency", i.e. the indicator of the general public’s gross domestic debt in NOK and foreign currency".

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 C2 statistics have three types of classifications; NOK/foreign currency, credit sources and borrowing sectors:

NOK/foreign currency: The C2 data is divided into NOK and foreign currency.

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.

Borrowing sector: The C2 data is classified according to the borrowing sector’s general government, non-financial enterprises and households etc.

Administrative information

Name and topic

Name: The credit indicator C2
Topic: Banking and financial markets

Responsible division

Division for Financial Markets Statistics

Regional level

Only at national level

Frequency and timeliness

Monthly

International reporting

No mandatory reporting, but 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

C2 is an approximate measure of the magnitude of the gross domestic debt of the public (households etc., non-financial enterprises and general government) in NOK and foreign currency.

The purpose is to contribute to the basis of information for the monetary policy. The statistics provide an overview of the development of credit at an early stage and is an important indicator of economic activity.

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 work with the credit indicator statistics, C2, 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 and research institutions. These statistics attract a great deal of media attention.

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 1993), European System of Accounts (ESA 1995), Manual on Monetary and Financial Statistics (IMF) and Balance of Payments Manual (IMF 1993).

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 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 public's bond loan debt to domestic lenders and the public's certificate debt in the domestic market. The public's debt, in the form of secured and non-secured domestic inter-company loans, is included up to the end of 1999.

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 public's bond and certificate debts are derived from statistics for securities registered in 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 public’s gross 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 C2 statistics, are in principle, based on total censuses.

Collection of data, editing and estimations

From 2007, Statistics Norway have had the responsibility for collecting accounting data for banks and financial corporations, while the Financial Supervisory Authority of Norway and Statistics Norway are jointly collecting accounting data from insurance companies and pensions funds. In addition, Statistics Norway obtains data from the Norwegian Public Service Pension Fund, Export Credit Norway and VPS.

The editing of the financial corporations’ accounting statements are undertaken by Statistics Norway and the Financial Supervisory Authority of Norway. The data from the Norwegian Public Service Pension Fund and VPS are controlled by Statistics Norway.

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.

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 statistical breaks that are not attributable to transactions or valuation changes. Examples of this kind of break could be that a financial enterprise moves from one sector to another or an introduction of a new financial source.

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 three-month period adjusted for exchange rate valuation changes and statistical breaks as an annualised figure. The calculation is centred, 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, se About seasonal adjustment.

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 statistical breaks in our calculations of transactions (break corrections).

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.

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.

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

Revision

The statistics show preliminary figures. Data may be revised in future publications.

About seasonal adjustment

General information on seasonal adjustment

Monthly and quarterly 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 X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.

For more information on seasonal adjustment: 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 gross debt figures from one month to the next. To adjust for these relations the gross debt is seasonally adjusted for the actual levels, so that one can analyse the underlying credit indicator development.

The credit indicator statistics publishes five seasonally adjusted series; K1, K2, K2 foreign currency, K2-households and K2-non financial corporations. The seasonally adjusted figures for K2-foreign currency is not a result of own seasonal adjustment, but a residual from the difference of seasonally adjusted K2 and the seasonally adjusted K1.

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.

Manual decomposition scheme selection after graphical inspection of the series.

Comments: We have chosen compulsory multiplicative decompositions. The program chose automatically this option until 2008, and it is now incorporated as a claim.

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.

Only part of the time series is used to estimate the correction factors and the model.

Comments: The data is used for K1 and K2 from January 1989 to the last observed December figure. For K2-households and K2-non financial corporations the data is used from December 1987 to the last observed December figure.

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 are 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.

Relevant documentation