BOX 1.2.1 | DECOMPOSING CREDIT GROWTH ON THE BASIS OF THE
BANK LENDING SURVEY
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that continues to characterise many advanced countries in the wake of the 2008 financial crisis. However,
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identifying the causes underlying these developments is particularly difficult.
Recent Developments
Weak credit developments are among the factors pointed out as causes for the weak economic growth
The purpose of this box is to decompose credit growth in terms of the contribution of credit supply and
demand, i.e., trying to understand whether weak credit growth is due to banks’ decisions to tighten
loans or to a retraction by households and firms. The methodology follows closely the one presented in
the second chapter of the latest IMF’s Global Financial Stability Report (GFSR). The time horizon of the
analysis spans from the first quarter of 2003 to the first quarter of 2013.
Data are taken from Bank Lending Surveys (BLS) of eight euro area countries.1 These quarterly surveys to
major banks in each country contain questions that try to capture bank officials’ opinions on developments
in the credit market in the previous quarter. In their responses to the survey, banks must inform whether
their credit standards have tightened or loosened in the previous quarter, as well as on their perception
regarding developments in demand for credit over the same period. Although responses are qualitative,
it is possible to assign them numeric values in order to create a quantitative index. These indices, one
for supply and one for demand, may subsequently be used as explanatory variables of credit behaviour.
However, many of the factors that may tighten credit standards do not depend directly on banks’
financial condition, but rather on exogenous factors, such as rising uncertainty or a deterioration of the
country’s economic growth prospects. This is why the supply index should be first “cleansed” of this
type of noise, so that an adjusted supply index may be obtained. For this purpose, it is possible to use
another series of questions included in the BLS aimed at assessing the contribution of a set of factors
to changes in credit standards. These factors may be classified as relating to the bank’s position (capital
position, liquidity and access to market financing), competition (from banks, markets and other) or the
economic environment (growth prospects, uncertainty and collateral risk). Based on these data, it is
possible to estimate what the change in credit standards would have been if subject only to changes in
the first type of factors.2 This adjusted supply index, combined with the demand index obtained directly
from the survey, is used in this box to explain credit growth.3
After obtaining the estimates for the impact of both indices on credit growth, it is possible to obtain a
decomposition of contributions from supply and demand, by resorting to a methodology similar to the
one used to adjust the supply index for demand effects.4 In this case, the purpose is to estimate credit
growth if it were conditional on changes on one side of the market alone. For this effect, a forecast is
made, based on the estimated model, where it is exogenously established that regressors associated
with the market side that is not intended to be studied are equal to zero.
The results obtained are similar to those presented in the recent IMF’s GFSR, using similar methodology
and data (Table 1, full panel). For Portugal, in particular, the results show a strong contribution of supply
factors to the decline in credit growth, chiefly as of the start of the Financial Assistance Programme.
1 Austria, France, Germany, Italy, Luxembourg, Netherlands, Portugal and Spain.
2 This calculation was based on the estimation of a fixed-effects regression with robust standard deviations.
3 It would be preferable to also “cleanse” the demand index of exogenous effects such as, for instance, the reluctance of households and firms to apply for loans, because of the anticipation of rejection. The surveys, however,
do not include questions that allow this analysis.
4 This time using an Arellano-Bond regression with robust pattern deviations and a credit-growth variable lagging
by one quarter.