The effect on related-party lending

In line with the econometric methodology described in section 1.3, it is more appropri-ate to apply a Tobit model rather than an OLS model in equation 1.7 at the second stage to analyze the relationship between the related-party transaction (RPT) and bank-firm relationship (BFR) measurements after correcting for selection and endogeneity bias.

Equation 1.12 illustrates the specific regression model:

RP Ti,j,[t+1,t+n] =α+β₁BF Rˆ _i,j,t+β₂X_{i,j,[t−n,t−1]}

+β₃RP T_{i,j,[t−n,t−1]}+σLambda_i,t+ui,j,[t+1,t+n] (1.12) Since the analysis focuses on the loan contract level, the impact of each loanj on firm ishould reasonably last through the loan tenor periodn. Thus, for each deal observation, the dependent variable,RP Ti,j,[t+1,t+n], is averaged overn years after the yeartin which a commercial loan is issued, i.e., from year t+ 1 to year t+n. Correspondingly, the control variables X_{i,j,[t−n,t−1]} and the lagged preloan dependent variable RP T_{i,j,[t−n,t−1]} are also averaged over the same n years but before the loan is contracted, i.e., from year t−n to year t−1. For instance, concerning a 5-year loan, the dependent variable is the average value of the RPT value over the years [t+ 1, t+ 5], and control variables are averaged over the years [t−5, t−1], wheret is the year in which the loan initiates. The fact that sampling is event-based eliminates the issue of autocorrelation.

The regression model 1.12 applies the classical Tobit model (Tobin, 1958, Greene, 2003) assuming a normal distribution for the dependent variable with left-censoring at 0 where the listed firm does not have RPT during the activation years of the loan contract.

In the Tobit model, the dependent variableRP Ti,j,[t+1,t+n]and the corresponding pre-loan variable RP T_{i,j,[t−n,t−1]} define as the RPT amount over the total asset at the beginning of the year and then average over the contract tenor period as described above, which makes the relative comparison to eliminate the size impact of listed firms. BF Rˆ _i,j,t are the fitted value derived from table 1.2 following the regression model of equation 1.9. Lambda_i,t, derived from column 3 of table1.1by equation 1.8, is estimated at the firm level to reflect selection bias. The definitions of the control variables X_{i,j,[t−n,t−1]} and year and industry fixed effects are the same as in section 1.4.

Table 1.3 shows the Tobit model’s estimation and compares the impact of BFR on three RPT categories in panels A and B. Panel A focuses on the related party direct capital transaction and operational category RPT. Panel B examines the impact on related party guarantees with the same bank and with a different bank. Under each RPT category, the impacts from proximity, equity exposure, and the combination of the two proxies illustrate in separate columns.

In table 1.3 panel A, columns 1 to 3 suggest that the bank lending relationship signif-icantly impacts related-party direct capital transactions. By making commercial loans, banks can acquire external soft information (through proximity) and internal information (through equity exposure), thereby monitoring the borrowing firm’s tunneling activities.

Column 1 suggests that a bank with more external information (closer proximity) can reduce the borrowing firm’s relative level in tunneling capital participation. In column 2, when a bank possesses inside information via equity exposure, it can also significantly reduce RPT loans granted relative to the firm size. The combination of both BFR mea-sures, in column 3, does not change the significance and level of their coefficients, which implies the independence of the two information sources. Not only are the results statis-tically significant, but they are also economically relevant. A 1-percent increase in equity exposure can reduce RPT loan amounts by 0.4 percent relative to the firm’s size, and a 1-unit increase in proximity can reduce RPT direct lending by 0.2 percent relative to the firm’s total asset.

From columns 4 to 6 in panel A, the bank-firm relationship measures suggest a similar negative impact on operational RPTs that are not related to direct lending nor guaranteed lending. With 1 unit increase of proximity, banks can decrease the tunneling amounts on

the operation by 0.25 percent relative to the firm’s size. Banks that lend to the borrowing firm and possess inside information on the firm via equity exposure reduce the operation amount of related-party transactions by 0.2 percent over the total asset.

In table 1.3 panel B, this study separates the RPT in guarantee further into two categories. The related firm belonging to the same business group as the listed firm borrows from a commercial bank and uses the listed firm to guarantee that the listed firm would pay back the loan on behalf of the related party if it defaults. The commercial bank involving in this lending contract has two possible characters: 1) it is the same bank that already builds up a relationship with the listed firm (guarantor) via an existing loan contract; 2) it is a different bank without any existing contract connection with the guarantor. The existence of the bank-firm relationship causes the opposite impact on the RPT guarantee lending as the sorting-by-private-information theory suggests.

From columns 1 to 3 in panel B, the same bank of the existing lending contract with the listed firm (guarantor) encourages the guaranteed lending to the related firms in the same business group by 0.82 percent with 1 unit increase of proximity and 0.71 percent with 1 percent increase of equity exposure, respect to the firm’s size, as long as the lending bank participates both contracts.

From columns 4 to 6, the lending bank persists the negative impact on the related lending via guarantee as on the direct lending and operational RPT, when the lending bank does not participate in the contract with the related borrowing firms. One percent extra equity exposure reduces 0.45 percent of RPT in guarantee relative to the listed firm’s total asset.

Both effects align with hypothesis 3 that a stronger bank-firm relationship facilitates the guaranteed lending to the related parties with the same bank lender but reduces the guaranteed lending with a different bank lender.

Overall, these findings align with the literature’s theories that closer proximity im-proves banks’ capability to monitor firm managers and that equity exposure favors the acquisition of more inside information (Dass and Massa, 2011). Table 1.3 provides sub-stantial evidence that a strong bank-firm relationship reduces tunneling activities in all perspectives except the guaranteed lending with the same bank. For explaining this result, this chapter further investigates the channels connecting bank-firm relationships with firm tunneling activities, which can help us interpret how banks use the information on the borrowing firm and decide whether to monitor the firm closely and under what conditions.

Table 1.3: Impact of bank-firm relationship (BFR) on related party transactions (RPT)

RPT Loan RPT Operation

(1) (2) (3) (4) (5) (6)

Proximity −0.204^∗∗∗ −0.197^∗∗∗ −0.250^∗∗∗ −0.248^∗∗∗

(0.047) (0.047) (0.043) (0.043)

Equity Exposure −0.625^∗∗∗ −0.609^∗∗∗ −0.217^∗∗ −0.197^∗

(0.124) (0.124) (0.109) (0.109)

Preloan RPT Loan 0.258^∗∗∗ 0.258^∗∗∗ 0.258^∗∗∗

(0.009) (0.009) (0.009)

Preloan RPT Operation 0.158^∗∗∗ 0.156^∗∗∗ 0.158^∗∗∗

(0.007) (0.007) (0.007)

Lambda −3.028^∗∗∗ −3.162^∗∗∗ −3.089^∗∗∗ 7.202^∗∗∗ 7.212^∗∗∗ 7.188^∗∗∗

(0.443) (0.443) (0.443) (0.237) (0.238) (0.237)

Size 0.009 0.035 0.020 −0.513^∗∗∗ −0.492^∗∗∗ −0.510^∗∗∗

(0.071) (0.071) (0.071) (0.061) (0.061) (0.061)

Leverage −0.359 −0.562 −0.447 2.895^∗∗∗ 2.783^∗∗∗ 2.867^∗∗∗

(0.555) (0.555) (0.555) (0.485) (0.484) (0.485)

Cash −0.409 −0.299 −0.393 −3.663^∗∗∗ −3.611^∗∗∗ −3.675^∗∗∗

(0.684) (0.684) (0.684) (0.589) (0.588) (0.589)

ROA −10.783^∗∗∗ −10.432^∗∗∗ −10.340^∗∗∗ 5.240^∗∗∗ 5.176^∗∗∗ 5.364^∗∗∗

(1.405) (1.407) (1.407) (1.250) (1.250) (1.252)

Capital Expenditure 5.364^∗∗∗ 5.677^∗∗∗ 5.529^∗∗∗ −2.692^∗∗∗ −2.324^∗∗∗ −2.595^∗∗∗

(0.980) (0.980) (0.980) (0.884) (0.884) (0.886)

Market-to-Book −0.010 −0.010 −0.010 −0.006 −0.006 −0.006

(0.007) (0.007) (0.007) (0.006) (0.006) (0.006)

SOE 0.214 0.166 0.237 0.805^∗∗∗ 0.710^∗∗∗ 0.812^∗∗∗

(0.163) (0.162) (0.163) (0.147) (0.146) (0.147)

Age −0.015 −0.017 −0.013 −0.113^∗∗∗ −0.117^∗∗∗ −0.113^∗∗∗

(0.012) (0.012) (0.012) (0.010) (0.010) (0.010)

Constant 20.463^∗∗∗ 20.541^∗∗∗ 20.275^∗∗∗ 2.097 2.299^∗ 2.065

(1.596) (1.594) (1.595) (1.379) (1.377) (1.379)

Year Fixed Effects YES YES YES YES YES YES

Industry Fixed Effects YES YES YES YES YES YES

Observations 22,053 22,053 22,053 22,053 22,053 22,053

Note: ^∗p<0.1;^∗∗p<0.05;^∗∗∗p<0.01

(a) Panel A: Bank-firm relationship impacts on RPT in direct lending and operation

RPT Guarantee

(same or different banks) (0.016) (0.016) (0.016) (0.012) (0.012) (0.012)

Lambda −1.701 −1.425 −1.643 −9.127^∗∗∗ −9.198^∗∗∗ −9.170^∗∗∗

Capital Expenditure 15.470^∗∗∗ 14.225^∗∗∗ 15.174^∗∗∗ −2.041 −1.771 −1.852

(3.145) (3.147) (3.150) (1.783) (1.783) (1.785)

Year Fixed Effects YES YES YES YES YES YES

Industry Fixed Effects YES YES YES YES YES YES

Observations 22,053 22,053 22,053 22,053 22,053 22,053

Note: ^∗p<0.1;^∗∗p<0.05;^∗∗∗p<0.01

(b) Panel B:Bank-firm relationship impacts on RPT in guarantees with the same and different banks