MICROCONTROLADOR SECUNDARIO DE CONTROL DE RIEGO Y NEUTRALIZADOR DE PH

The baseline results, however, could also overstate the effect of subsidiary failure on capital structure decisions of parent BHCs if unobservable BHC characteristics are correlated with the failure of subsidiary banks and with the changes of BHC capital. There is evidence that subsidiary performance is related to the background financial characteristics of parent BHCs (Ashcraft, 2008). Financially fragile BHCs may be more prone to suffer subsidiary failure. The financial management and risk preferences of senior managers of parent BHCs could also simultaneously elevate the probability of capital adjustments and a subsidiary failure. All these imply that BHCs having subsidiary failure is endogenous to their underlying characteristics.

To address this bias in tests that seek to discover changes in BHCs’ capital structure around subsidiary failure time, we exploit the variation in the failure time of subsidiary banks among BHCs with subsidiary failure. We treat BHCs with later subsidiary failure as a control group for BHCs with earlier subsidiary failure. Specifically, we match the BHCs who have subsidiary failure in year (treatment group) with the BHCs that suffer subsidiary failure in the future year (placebo control group). Both groups of BHCs have subsidiary failure and hence would potentially share certain common characteristics. However, because the BHCs in the control group did not have subsidiary failure in year , their capital structure changes from to serve as a comparison with the treatment group. This approach allows us to control for potential common characteristics when we test the capital structure changes of BHCs around the year of subsidiary failure.

To this end, for each BHC in the control group, we create a placebo subsidiary failure date equal to the actual failure date of their subsidiary banks minus n years. We then compare the capital structure changes of the treatment and the control groups from to . We

T_i T_i+n

T_i

T_i-2 T_i+3

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want to ensure that, during this period (i.e., from T_i2 to T_i 3), while BHCs in the treatment group experience subsidiary bankruptcy, BHCs in the control group do not. This requires certain restrictions on our original sample data: the treatment group is comprised of all BHCs that had subsidiary failure during 1988 – 1999, and the control group consists of BHCs whose subsidiary failure occurred during 2005 – 2016. The five-year gap in between is to minimize the correlation between post-placebo subsidiary failure window and actual subsidiary failure event for each control BHC. Finally, there are 112 BHCs included into the treatment and control group, and we include all their yearly data from 1986 – 2016, which finally form the subsample for the use of this approach. In this subsample, both the treatment and control groups experience subsidiary failure, however, only the treatment group experiences a subsidiary failure in the estimation window.

Applying this placebo control strategy, we estimate the following specification:

3 3 , 0 , , 2, 0 2, 0 , 1 , i i i t i t T i i t T i i t i t i t Y D Treat D Treat                                    





whereTreat_i is a dummy variable that equals one if BHC i belongs to the treatment group defined in this section. The coefficients of interest are , which are difference-in-differences estimators, measuring the difference between treatment and placebo control BHCs with respect to the changes in capital structure from two years prior to subsidiary failure to three years after the failure.

Based on this identification strategy, if a BHC’s capital structure changes prior to or following its subsidiary failure, that change will be captured by the coefficients _ estimated before or after the failure. These coefficients indicate whether the capital structure of the BHCs in the treatment group (who experience a subsidiary failure at time T_i) deviates from

100

that of their counterparts in the placebo control group (who experience a subsidiary failure at a future time ) during the years prior to or following T_i.

Table 4.8 reports estimates from specification (4.2). The dummy variable Treat is dropped from the regression because of the inclusion of BHC fixed effects. We observe similar pre- failure and post-failure trends of capital structure changes compared with those reported in Table 4.5 and Table 4.7. For instance, the positive (negative) and significant coefficient on Year 1 Before  Treat (Year 1 After  Treat) in column (1) indicates that, relative to the placebo control group, the leverage of the BHCs in the treatment group increases (decreases) during the year prior to (following) the subsidiary failure. All the other columns are largely consistent with the event-study specification (4.1). The results confirm that “troubled” BHCs increase leverage ratio as early as one year prior to the failure of their subsidiaries, and that leverage increase is significantly reversed following the subsidiary failure.

4.5.5. Subsidiary failure effect by BHC size and capital ratio

The results in Table 4.7 show a marked pre-failure increase in “troubled” BHCs’ relative long-term debt as well as their subordinate debt issuance. This could reflect strategic behavior, or it could reflect an increased financing demand by BHCs who were already in financial distress. We address this concern by identifying subsets of BHCs with different sizes. Large banks may have greater flexibility to withstand a short-term liquidity shortage (Vazquez and Federico, 2015). If BHCs’ financial distress is the key driver of the leverage changes around subsidiary failure, we would expect that this change is more significant for small banks compared to large banks. In addition, the impact of subsidiary failure on bank capital structure could also be shaped by the regulatory frameworks. Banks may hold discretionary capital, above the regulatory minimum in order to avoid the costs of having to issue fresh equity at