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The data set on credit to the non-financial sector captures borrowing activity by the government sector and the private non-financial sector in more than 40 economies.

Quarterly data on credit to the government sector cover on average 20 years, while those on credit to the private non-financial sector on average more than 45 years. The statistics follow the framework of the System of National Accounts.

On the lending side, two credit data series are provided. On the one hand, total credit comprises financing from all sources, including domestic banks, other domestic financial corporations, non-financial corporations and non-residents. On the other, bank credit includes credit extended by domestic banks to the private non-financial sector.

On the borrowing side, total credit to the non-financial sector is broken down into credit to the government sector and the private non-financial sector, and the latter is further split between non-financial corporations and households (including non-profit institutions serving households).

The financial instruments covered comprise currency and deposits (which are mostly zero in the case of credit to the private non-financial sector), loans and debt securities. The sum of these three instruments is defined here as “core debt”. For the government sector, core debt generally represents the bulk of total debt.

The statistics follow the framework of the System of National Accounts 2008, which stipulates that outstanding credit instruments should be valued at market values. For credit to the government, data are also provided in nominal (face) values, since these can be useful in some forms of debt sustainability analysis.

Metadata

Seasonal adjustment
Non seasonally adjusted
Frequencies
Quarterly
Contact
BIS statistics
Units
Domestic currency (incl. conv. to current ccy made using a fix parity)
Percentage of GDP
Percentage of GDP (using PPP exchange rates)
US dollar
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Methodology

How much does the private sector really borrow - a new database for total credit to the private non-financial sector

Christian Dembiermont, Mathias Drehmann and Siriporn Muksakunratana
Despite their importance, data capturing total credit to the private non-financial sector are scarce. This article introduces a new BIS database that provides this information for 40 economies with, on average, more than 45 years of quarterly data, reaching back to the 1940s and 1950s in some cases. It explains the key concepts underlying the compilation of the new series, including a description of the high-level statistical criteria applied, the characteristics of the underlying series used and the statistical techniques employed. For illustration purposes, some facets of the historical evolution of total credit are explored, revealing interesting similarities and differences across countries.

A new database on general government debt

Christian Dembiermont, Michela Scatigna, Robert Szemere and Bruno Tissot
We present a new data set on credit to the general government sector for 26 advanced and 14 emerging market economies. The main benefit of these new BIS series for 'public debt' is that they provide data with similar characteristics from across the globe, facilitating cross-country comparisons. Another distinctive feature is that the data set contains series expressed in both nominal and market value terms, allowing for a wide range of analyses. Lastly, the statistical concepts are identical to those underlying the BIS data set on credit to the private non-financial sector, published since 2013. Taken together, the data sets can thus provide a useful picture of the aggregated indebtedness of all non-financial sectors.

Research and publications

Emerging markets' reliance on foreign bank credit

This article examines the importance of foreign banks in the provision of credit to emerging market borrowers. It documents this along two dimensions: the share of total credit provided and the concentration of claims from different foreign banking systems. The share of credit from foreign banks in total credit to emerging market economies has fallen since the Great Financial Crisis, but still stands at 15-20% on average, with the remainder provided by domestic banks or non-bank creditors. ...

How much income is used for debt payments? A new database for debt service ratios

Debt service ratios (DSRs) provide important information about the interactions between debt and the real economy, as they measure the amount of income used for interest payments and amortisations. Given this pivotal role, the BIS has started to produce and release aggregate DSRs for the total private non-financial sector for 32 countries from ...

How much does the private sector really borrow - a new database for total credit to the private non-financial sector

Despite their importance, data capturing total credit to the private non-financial sector are scarce. This article introduces a new BIS database that provides this information for 40 economies with, on average, more than 45 years of quarterly data, reaching back to the 1940s and 1950s in some cases. It explains the key concepts underlying the compilation of the new series, including a description of the high-level statistical criteria applied, the characteristics of the underlying series used and the statistical techniques employed. For illustration purposes, some facets of the historical evolution of total credit are explored, revealing interesting similarities and differences across countries.

Do debt service costs affect macroeconomic and financial stability?

Excessive private sector debt can undermine economic stability. In this special feature, we propose the debt service ratio (DSR) as a measure of the financial constraints imposed by private sector indebtedness, and investigate its association with recessions and financial crises. We find that the DSR prior to conomic slumps is related to the size of the subsequent output losses. Moreover, the DSR provides a very accurate early warning signal of impending systemic banking crises at horizons of up to one to two years in advance. We conclude that the DSR can serve as a useful supplementary indicator for the build-up of vulnerabilities in the real economy and financial sector.

Anchoring countercyclical capital buffers: the role of credit aggregates

We investigate the performance of different variables as anchors for setting the level of the countercyclical regulatory capital buffer requirements for banks. The gap between the ratio of credit-to-GDP and its long-term backward-looking trend performs best as an indicator for the accumulation of capital as this variable captures the build-up of system-wide vulnerabilities that typically lead to banking crises. Other indicators, such as credit spreads, are better in indicating the release phase as they are contemporaneous signals of banking sector distress that can precede a credit crunch.

FAQs

Data are released every quarter. The publication date and the latest reference period are shown in the statistics release calendar.
The credit statistics are from national data sources.

The credit to the non-financial sector data set captures the indebtedness of the major non-financial sectors. It shows the outstanding amount of debt by (ie liabilities of) the non-financial sector, broken down into the government sector and the private non-financial sector, where the latter is further split between non-financial corporations and households (including non-profit institutions serving households).

The debt instruments covered are debt securities, loans and currency and deposits (the sum of which is defined as “core debt”). These sectors and debt instruments are as defined in the System of National Accounts. For more details, see BIS Quarterly Review articles on private and government credit.

Two different lender categories are available but only for the borrower sector "private non-financial sector" (PNFS). "Total credit" comprises financing from all sources, including domestic banks, other domestic financial corporations, non-financial corporations and non-residents. "Bank credit" includes credit extended by domestic banks to the PNFS.

The NFC sector is as defined in the System of National Accounts (SNA sector S.11) and includes public and private corporations. PNFS is the sum of NFC and households. The “household” sector in the data set in turn comprises the households (SNA sector S.14) plus non-profit institutions serving households (SNA sector S.15).

Data on the private non-financial sector, non-financial corporations (NFC) and households are unconsolidated, eg credit from one NFC to another NFC is not netted out, ie included in the debt figures. However, for the general government sector, data are consolidated, eg credit from the central government to the local government is netted out, ie excluded from the debt figures.

Data on the debt of these sectors are shown on a gross basis, ie their own assets are not subtracted.

The total credit to the non-financial sector aggregate is the sum of the debt of the private and government sectors. For some countries, government sector debt is available in two valuations: market and nominal. Whenever available, the government data on market value are used to calculate the NFS aggregate.

General information on the data set and details on the source of each country's data are provided in these documents: private and government credit metadata.

Data are expressed in the following units: in local currency, in US dollars and as a percentage of GDP. Credit-to-GDP is calculated as the outstanding amount of debt at the end of the quarter compared with the sum of the last four quarters of GDP. The BIS does not publish the underlying GDP data. Data in local currency are converted to US dollars using the prevailing exchange rates at the end of the quarter. These US dollar exchange rates are published on the BIS Data Portal.

The data set provides two measures: aggregates based on conversion to US dollars at market exchange rates and at purchasing power parity (PPP) exchange rates. PPP rates data are taken from IMF, World Economic Outlook.

The G20 comprises Argentina, Australia, Brazil, Canada, China, the euro area, India, Indonesia, Japan, Korea, Mexico, Russia, Saudi Arabia, South Africa, Türkiye, the United Kingdom and the United States. The advanced economies comprise Australia, Canada, Denmark, the euro area, Japan, New Zealand, Norway, Sweden, Switzerland, the United Kingdom and the United States. The emerging market economies comprise Argentina, Brazil, Chile, China, Colombia, Czechia, Hong Kong SAR, Hungary, India, Indonesia, Israel, Korea, Malaysia, Mexico, Poland, Russia, Saudi Arabia, Singapore, South Africa, Thailand and Türkiye.

Since the release of this data set in 2013, the coverage of this data set has changed and expanded. The "Changes to the data set" lists changes since then, including the addition of new countries, new starting date and major revisions.