dent variables, we estimate the same model using a random effects panel Probit specification and a Mundlak-type specification. Results are presented in columns 2 and 3 of Table 5. Results obtained with these specifications are in line with the fixed effects model estimations, although some of the coeffi cients vary in significance levels. The outcome of the Hausman test (p-value = 0.92) implies that the null hypothesis that the models yield similar coeffi cients is not rejected at any reasonable significance level, and that the random effect model yields a more effi cient estimator of the probability of credit-less recovery. We perform an additional test on the panel-level means of the Mundlak error-correction model presented in column 3 of Tables 5. The p-value of the test is 0.60 suggesting that the time-invariant unobservables are not related to the independent covariates, and that the model satisfies the random effects model assumptions.

In our sample, seven (or 33 percent) of the overall credit-less recoveries occurred after the global financial crisis erupted in 2007. This period has been characterized by a sharp contraction in credit in many countries worldwide, driven by both demand and supply factors. To assess how our results change when these observations are removed, we estimate our model excluding recovery episodes that occurred after 2010. Results are presented in Tables 7 and 8. Although some of our coeffi cients vary in significance, overall results are in line with our estimates and confirm that on average demand-side factors have a stronger predictive power on credit-less recoveries.

Table 6 presents estimation results obtained analyzing supply-side drivers of credit-less recoveries. Evidence from the real side of the economy confirms that negative GDP growth increases the probability of credit-less episodes. Recessionary episodes featuring real supply shocks increase the likelihood of credit-less episodes. When looking at the role played by financial sector drivers of credit growth, findings show that supply-side shocks in financial markets did not play on average a significant role in favoring the occurrence of credit-less recoveries. Although we cannot exclude the possibility that in many crisis episodes, especially those associated with banking crises, disruptions in the supply of credit may have played a role in keeping credit low during the recovery, our results suggest that on average credit-less episodes have been an outcome of subdued demand for liquidity by the private sector in the aftermath of the crisis.

Columns 2 and 3 of Table 6 present estimations of the random effects panel Probit specification and a Mundlak-type specification. Similarly to above, we perform a Hausman test for the hypothesis that the differences between the coeffi cients of the fixed and random effects models are not systemic, finding a p-value of 0.86. Testing at the 5 and 10 percent significance levels, the null hypothesis that both the fixed and random effects models yield similar coeffi cients is not rejected.

Estimation coeffi cients presented in Table 5 and Table 6 are not interpretable using standard inference methods. As the next step, we compute the marginal effects for changes in the explanatory 18