Dos libros como espejos: una identificación personal

In this section, I estimate the average partial effects of access to debt markets on investment and leverage under conditional moment independence assumptions. I estimate the following model:

I/K=β0+β1Q+β2CF+β31(Access) +β41(Access)Q+β51(Access)CF+ε (1.3)

Here, I regress investment on an indicator variable1(Access)that marks the time when a firm gets access to credit markets, after controlling for Tobin’sQand cash flowC F(scaled by previous period’s PP&E). I also include interaction terms between firm characteristics (Tobin’sQand cash flowC F) and access to public debt markets:1(Access)Qand1(Access)CF respectively.

Tables 1.6 and 1.7 report the results of the analysis. Table 1.7 reports results for investment regression(I/K)using annual observations. All numbers are in %. The standard errors are

robust under heteroscedasticity. Column (2) shows that firms that gain access to public debt markets invest more in net Property, Plant and Equipment each year. However this estimate has a selection bias, that I will correct for next. Columns (3) and (4) estimate the average treatment effect of getting access to public debt market financing on investment for firms that previously only had access to bank financing for credit. The endogeneity problem in column (2) is that firms that use public debt differ from firms that use bank financing over many firm characteris- tics, and hence any treatment effect measurement needs to control for this difference. To solve this problem, I follow the propensity score method suggested for non-random sampling (See Horvitz and Thompson (1952), Rosenbaum and Rubin (1983) and Wooldridge (2004)). To do this, I estimate probability of treatmentp(x)given the covariatesx, which are the firm charac- teristics in this case:

p(x) =P(w=1|x),

wherew is an indicator for treatment andp(x)is the propensity score of accessing the public debt markets. Denis and Mihov (2003), examine the determinants of the source of new debt. Using their work, I use firm size (measured by log(assets)), Tobin’sQ and cash flow scaled by PP&E (to measure growth options), fixed asset ratio (Property, Plant and Equipment over To- tal Assets), Book Leverage (Debt/Total Assets), Profitability (Operating Income/Total Assets) to predict the source of debt financing using a probit model. Table 1.6 reports the estimation results of the probit regression. Column (4), that includes fixed asset ratio, profitability, book leverage, firm age, firm size, Tobin’sQ and cash flow as right hand side firm characteristics, is used for the estimation of the propensity score (propensity of having access to public debt mar- ket access). The predicted valuepˆ(x)is then used as a control in the regression:

I/K=β0+β1Q+β2CF+β3pˆ(x) +β41(Access) +

X

i

Rosenbaum and Rubin (1983) and Wooldridge (2004) show that the coefficient on the access indicator variable and interaction variables are consistent for the average treatment effect, ATE. Table 1.7 reports the results of the estimation of the average partial effects of access to debt markets on investment under conditional moment independence assumptions. I use annual observations for firms in DealScan Database that could be matched to Compustat. All numbers are in %. The standard errors are robust under heteroscedasticity. I obtain the historical ratings from S&P Credit Ratings database. In column (3), I find that after controlling for propensity score, Tobin’sQ andC F, and other interaction terms, firms with access to public debt markets invest 11% more PP&E per annum. This supports proposition (P1). Column (5) excludes the firms that have propensity scores above 90% or below 10%. This excludes firms that have no possibility of being in the other category (access or no access). The sample now contains firms that are closer in characteristics. This is confirmed by noting that the propensity score is not significant anymore in the estimation consistent with the premise that the selection bias has been controlled for. When looking at this set of firms that have a larger common support in the sample, I find that firms with access to debt markets grow faster by 11% of their PP&E per annum. Time and firm fixed effects are included for all columns. The reported standard errors are robust to heteroscedasticity and allow for firm level intra-cluster correlation of errors.

Table 1.7 also provides evidence in support of proposition (P4). As expected, investment is not as sensitive to innovations in cash flow when the firm has access to only bank debt, which comes with investment restrictions. I infer this by noting the coefficient of the inter- action term(Cash Flow)×Access. In column (6), one percentage increase in cash flow leads to 7% excess increase in investment for firms with limited access to public debt markets in column (4). Proposition (P4) also suggests that when the outside financing options of an unconstrained firm improve, its investment shows a higher sensitivity to Tobin’sQ than a firm that has more restrictions. This inference is corroborated by noting the coefficient of 0.6% for the interaction

termQ×Access, giving support to proposition (P4).

Table 1.8 estimates the average partial effects of access to debt markets on leverage under conditional moment independence assumptions. The model estimated is as follows:

Leverage = β0+β1Profitability+β2Fixed Asset Ratio+β3Market to Book

+β4Sales+β5pˆ(x) +β61(Access) +ε. (1.5)

In column (2), after controlling for propensity score, profitability, tangibility, size and growth options (which are commonly used explanatory variables for leverage) and interaction terms, I estimate that firms, when they get access to public debt markets, have an average of 35% more leverage. This supports proposition (P2). Faulkender and Petersen (2006) have also shown that firms with access to debt market take similar amount of additional leverage. Column (3) ex- cludes the firms that have propensity scores above 90% or below 10%. This excludes firms that have no possibility of being in the other category (access or no access). Even then, access to public debt markets has an average treatment effect of 24% Time and firm fixed effects are in- cluded. The reported standard errors are robust to heteroscedasticity and allow for firm level intra-cluster correlation of errors.