Los orígenes de la información de conflictos.

The main identifying assumption of the regression in equation 4.2 is that the differ- ence in loan terms between 2006-2007 and 2004-2005 is not driven by observed and unobserved factors that are unrelated to judicial efficiency but are correlated with

Exposured, i.e. the share of non-business bankruptcies in a district’s total weighted

caseload. In my setting, the potentially most important concern are differences across districts in their exposure to the housing bubble during that period, but also funda- mental differences in the pool of firm borrowers.

To investigate whether observable differences across bankruptcy districts are a cause for concern, I plot correlations between Exposured and a host of covariates in

table4.3. I obtain these correlations by averaging each characteristic on the district- level and regressing it on the exposure measure.17 _{All dependent variables andExposure}

d

are standardized to have mean 0 and a standard deviation of 1; this is to make mag- nitudes comparable across regressions. Reassuringly, borrower and loan characterist- ics in the pre-reform period seem to be largely orthogonal with exposure to BAPCPA (see column (1) in panel A and B); the only coefficients slightly passing the 10% sig- nificance threshold are a dummy for the existence of a credit rating and a dummy for an existing lender-borrower relationship. This suggests that, from a borrower’s per- spective, exposure to the drop in court caseload following BAPCPA was as good as random.

Column (3) in panel C shows that Exposuredis significantly correlated with a few

county characteristics, namely the share of manufacturing in total employment (pos- itive), the share of finance employees (negative), income per capita (negative), and the Republican vote share in 2000 (positive). There is a also negative correlation with the share of Asians and Hispanics as fraction of the total population. Crucially, measures of exposure to the intensity of the housing boom during the period do not appear to be positively correlated with the nonbusiness caseload share. The share of owner- occupied housing as well as changes in employment in the construction or real estate sector are not statistically significant. The exposure variable is furthernegativelycor- related with house price growth during the boom: if anything, this implies that there was less of a demand boom in areas with a higher share of individual bankruptcies.

While it is possible to control for the interaction of P ost−BAP CP At with these

county characteristics, the few statistically significant correlations raise the question

Table 4.3: Comparing Bankruptcy Districts Before the Reform

This table presents reports coefficients from regressing each firm, loan, and county characteristic on the treatment variables Exposure and High congestion dummy. I standardize all dependent vari- ables and the continuous Exposure treatment to have a mean of 0 and standard deviation of 1 to make the coefficients comparable. The sample is the main estimation sample; see column (1) and (4) in table 4.4. Exposure is a bankruptcy district’s share of non-business bankruptcy cases in 2003. High congestion dummy is a dummy equal to 1 for districts in the top quartile of a state’s share of non-business bankruptcy cases in 2003; and 0 for districts in the bottom quartile. Firm and loan characteristics refer to the pre-reform period (2004 and 2005). County characteristics are as of 2003, except for the education, ethnic composition, and owner-occupied share variables (which refer to 2000). All characteristics are averaged on the district-level; county characteristics are weighted by population in 2003. Standard errors reported in parentheses are clustered by district.

High High

Congestion Congestion Exposure Dummy Exposure Dummy

(1) (2) (3) (4)

Panel A: Borrower characteristics Panel C: County characteristics

Book leverage -0.098 0.096 Manufacturing employment share 0.230*** 0.031 (0.097) (0.236) (0.079) (0.288) Log(Total assets) -0.004 0.176 Finance employment share -0.423*** -0.259 (0.083) (0.246) (0.136) (0.256) ROA 0.068 -0.060 Income/capita (×1000) -0.486** -0.136 (0.065) (0.101) (0.222) (0.327) Negative debt-to-cash flow -0.073 0.099 Population density -0.487 -0.375 (0.068) (0.099) (0.407) (0.420) High debt-to-cash flow -0.112 0.152 Republican vote (2000) 0.428*** 0.208

(0.121) (0.258) (0.142) (0.262) Sales growth 0.032 -0.028 High school graduates/Pop. -0.129 -0.006 (0.034) (0.126) (0.104) (0.258) Rating dummy -0.142* -0.044 Asians/Pop. -0.272* -0.041 (0.085) (0.289) (0.148) (0.214) Credit rating -0.116 0.277 Hispanics/Pop. -0.285** -0.000 (0.096) (0.318) (0.133) (0.317) Tangibility -0.042 0.039 African-Americans/Pop. 0.261 0.215

(0.113) (0.259) (0.166) (0.265) Owner-occupied housing share 0.356 0.474

Panel B: Loan characteristics (0.273) (0.330)

Log(Loan size) 0.081 -0.016 ∆real estate employees (’01-’06) 0.001 0.118 (0.068) (0.228) (0.078) (0.260) Log(Maturity) 0.122 -0.125 ∆construction employees (’01-’06) -0.063 0.264

(0.109) (0.238) (0.114) (0.258) Interest rate spread -0.051 0.024 ∆house prices (’02-’06) -0.301*** -0.091 (0.078) (0.297) (0.095) (0.262) Collateral dummy -0.129 -0.006

(0.110) (0.261) Relationship dummy -0.192* -0.241 (0.113) (0.262) Term loan dummy -0.122 -0.050 (0.108) (0.244)

whether unobserved district factors might drive my results. I attempt to circumvent this issue twofold. First, I create a “High congestion” dummy variable that compares the share of non-business caseload within states with multiple bankruptcy districts. In particular, the dummy is 1 for districts in the top quartile of theRef orm Exposure

measure within a state; and 0 for districts in the bottom quartile. Effectively, this en- ables me to compare the impact of BAPCPA within the same state. Columns (2) and (4) of table4.3shows that the High exposure dummy is uncorrelated withallobservable borrower, loan, and county characteristics. In fact, the coefficient signs switch signs for 11 out of the 15 borrower and loan controls. This makes it even more plausible that any effect I find is indeed due to exposure to the reform, rather than other correlates.

Second, I exploit the fact that borrowers differ markedly in their refinancing needs, default risk, and expected recovery valuespriorto the implementation of BAPCPA. As I will outline in more detail below, this cross-sectional variation is plausibly exogenous to the legal changes which were ultimately passed in April 2005. By including an interaction term of the main effectP ost−BAP CP At×Ref orm Exposuredwith these

characteristics allows me to include a full vector of district×year dummies, which absorbs all time-varying district-level correlates unrelated to judicial efficiency. I apply this methodology in section4.5.2.