by a credit boom, then credit dynamics during the recovery may look different than the case where the crisis was preceded by average credit growth. Similarly, credit dynamics during recoveries are influenced by policy responses affecting monetary aggregates. An expansionary monetary stance similar to the one that occurred in the Euro area following the global crisis contributes to increase liquidity levels and leads to more favorable funding conditions. To control for these conditions, we include two additional dummy variables, one signaling the occurrence of a credit boom prior to the crisis and another one controlling for the presence of a monetary expansion during the crisis.<sup>7</sup> Our empirical strategy allows to test the hypotheses on credit-less recoveries through a comprehensive framework of analysis featuring demand and supply side covariates, while at the same time controlling for country specific factors influencing the drivers of credit growth.

# 3.3 Main results

Our first step consists in estimating the fixed effects Probit model (30) on the entire sample of advanced and emerging countries. Table 5 presents probit estimations for demand-side drivers of credit-less recoveries. Countries with negative GDP growth have a higher probability of experiencing a credit-less recovery. During economic downturns financially constrained firms may be able to accumulate unused capacity, which can be used during the recovery phase to restore activity<sup>8</sup> . Because of this “bounce-back”effect, larger contractions of GDP during the recession increase the likelihood that production can recover without need of further lending, as firms have access to unused capacity limiting their need for lending during the recovery phase of the cycle. In a similar way, large GDP contractions can make households reluctant to resume borrowing during the recovery, especially in the presence of a weak growth outlook. In our estimates, the occurrence of a real demand-side shock does not significantly increases the probability of credit-less recovery episodes. When looking at credit growth dynamics, estimates indicate that the recovery in output during these episodes has a closer relationship with the change in the flow of credit (i.e. △dt−1 −△dt−2 or what is named credit impulse in our model) rather than with the change in the stock of credit.<sup>9</sup> In other words, our results show that a rebound in output can occur in the presence of negative credit growth (or when the economy is deleveraging). However, in order to have an economic recovery, credit must be

> 7Both of these variables are taken fom Laeven and Valencia’s (2008) database on banking crisis. Credit booms are identified as those during which the deviation of credit-to-GDP ratio relative to its trend is greater than 1.5 times its historical standard deviation and its annual growth rate exceeds 10 percent, or years during which the annual growth rate of the credit-to-GDP ratio exceeds 20 percent. Monetary expansions are computed as the change in the monetary base between its peak during the crisis and its level one year prior to the crisis.

> 8This result is also in line with the findings of Sugawara and Zalduendo (2013) and Bijsterbosch and Dahlhaus (2011).

> 9Biggs et. al. (2009) were the first authors to refer to a credit impulse effect deriving from the change in the pace of deleveraging and these results are in line with their findings.

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