additional measure would indicate that learning was occurring more via country-level institutional transfer, rather than bank-level mechanisms. We find that although the financial reform variable _is_ significant (suggesting that some institutional learning may be at play), the coefficient on crisis experience remains significant. Perhaps more importantly, the magnitude of the coefficients are comparable to the baseline, which suggests that including a proxy for country-level knowledge transfer does not diminish the likelihood that learning is also occurring at the level of the bank.

The third test changes the dependent variable itself: instead of changes in the loan-to-asset ratio, we utilize the difference in gross loans directly. The total amount of loans is used as often as the loan-to-asset ratio in empirical studies studying banks lending behavior (see, _inter alia_ , Ivashina & Scharfstein (2010) and Puri _et al._ (2011)). Since we are no longer normalizing our loan measure by size, we are careful to always include our measure of size (total bank assets in 2006) as a control. These results are reported in columns (C5)–(C6). While the point estimate of the coefficients for crisis experience are no longer comparable, we note that its sign is consistent (negative), and that it is statistically significant. As might be expected given the close relationship between loans and total assets, the model’s goodness-of-fit (measured by the second-stage _F_ statistic) rises substantially, although this is more a statistical artifact than an actual increase in explanatory power.

In our final test, we replace the home-country covariates, which are taken at their 2006 values to minimize endogeneity, by their difference between 2006 and 2009. Intuitively, the evolution of home countries’ macroeconomic environments may well feature in explaining banks’ lending activity. For instance, entities established in a faster-growing country may have a greater propensity to expand their activities and influence across borders. A similar argument can be made for countries that have become richer over the time period, as captured by changes in GDP per capita. While we are more cautious about including the change in these variables in baseline due to endogeneity concerns, we nevertheless report this alternative as a robustness check in columns (C7) and (C8). Again, the baseline results remain largely unchanged; however, given the weaker performance of the home-country variables in these specifications, we regard our choice of variables taken at their initial 2006 as appropriate.

# **6.3 Unpacking the crisis experience**

Our third set of robustness checks considers decomposing the crisis experience variable into its constituent parts. Doing so achieves two objectives. First, it helps clarify the robustness of our results to an alternative construction methodology for our key variable. Second—and more importantly in our view—we can use this decomposition to peer into exactly what sorts of crises appear to be driving our results, and consequently whether we can make reasonable economic inferences regarding the types of crises that banks appear to learn from.

These decompositions are reported in Table 4. Columns (D1)–(D4) include, separately, the stock of experience corresponding to each of the four classes of crises in the Reinhart & Rogoff (2009) dataset that are represented in our aggregate crisis experience variable (as described in Subsection 4.1). The most striking result is that every single crisis type has a negative coefficient— 24