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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
Collection indicators
End of period
Next release date
Last release date
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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

Which credit gap is better at predicting financial crises? A comparison of univariate filters

Mathias Drehmann
 and James Yetman
The credit gap, defined as the deviation of the credit-to-GDP ratio from a one-sided HP-filtered trend, is a useful indicator for predicting financial crises. Basel III therefore suggests that policymakers use it as part of their countercyclical capital buffer frameworks. Hamilton (2018), however, argues that you should never use an HP filter as it results in spurious dynamics, has end-point problems and its typical implementation is at odds with its statistical foundations. Instead he proposes ...

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Related topics

Credit-to-GDP gaps
Credit-to-GDP gaps

Corresponds to the difference between the credit-to-GDP ratio and its long-run trend

Debt service ratios
Debt service ratios

Measures the amount of income used for interest payments and amortisations