Propensity score matched (PSM) sample

Prior literature in CDS stream (e.g., Subrahmanyam et al., 2014; Chang et al., 2019) documents

that firms selected to be referred for CDS trading are generally larger and more mature, more

informationally transparent, and possess better creditworthiness. This fact is consistent with lemon

effects. Comparing with CDS buyers, CDS sellers face an information disadvantage regarding the

referenced firms because CDS buyers are generally loan originators or bondholders, and thus hold

private information of the borrowers. Therefore, CDS sellers prefer to trade trustworthy companies

that are less opaque and larger in market value by way of reducing transaction risk. It is possible

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performance. To address those concerns, I follow the literature (Ashcraft and Santos, 2009;

Subrahmanyam et al., 2014; Martin and Roychowdhury, 2015; Chang et al. 2019) to form matched

control firms that have never been selected to trade CDS throughout the whole sample period.

I use the following probit model to capture the likelihood of CDS trading each year.

𝑃𝑟𝑜𝑏(𝐶𝐷𝑆𝐼𝑁𝐼𝑇_𝑖,𝑡 = 1) = ∅(𝛼 + 𝜃𝑋_{𝑖,𝑡−1}+ 𝜑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦_𝑗 + 𝛽𝐶𝑜𝑢𝑛𝑡𝑟𝑦_𝑘+ 𝜔𝑌𝑒𝑎𝑟_𝑡) (3)

in which ∅ is the cumulative distribution function of standard normal distribution. CDSINIT is an

indicator variable that has a value of one in and after CDS trading initiation, and zero otherwise.

𝑋 is a vector of firm-level characteristics that are used to predict the inception of CDS trading. Following Chang et al. (2019), I include all controls into vector 𝑋 to mitigate the concern that the

determinants of CSR performance may also be factors driving CDS trading initiation. Besides,

following Subrahmanyam et al. (2014), I include the ratio of working capital to total assets and

return on assets as well. Furthermore, I include Fama-French 48 industry classification to isolate

industry fixed effects on CSR performance. I also include country- and year-fixed effects into my

model to address country differential and aggregate time trends effects on CSR performance.

Following Ashcraft and Santos (2009), I use firm-year observations of CDS firms until the

beginning of CDS trading, i.e., excluding CDS firm-year observations in post-CDS-initiation

periods. I combine this traded CDS subsample with all firm-year observations in which firms are

never traded in CDS markets (i.e., non-CDS firms) to construct my whole probit sample and use

it to estimate equation (3). Table 3.4 reports the probit regression results. The model (3) predicts

the onset of CDS trading reasonably well, as evidenced by the high concordant percentage (92.2%)

and pseudo-R2 _{(35.7%). These statistics are comparable to previous studies. For example, the}

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in Martin and Roychowdhury (2015). The coefficients of predictors are in line with the extant

literature (Subrahmanyam et al., 2014; Martin and Roychowdhury, 2015). For instance, larger

firms, firms with high leverage, and more profitable firms attract more attention from CDS market

participants. The coefficient of Governance is positive and significant at the 10% level, suggesting

that firms with good governance are likely to have CDS trading originated in the sample period.

<Insert Table 3.4 about here>

Next, I use the estimates of equation (3) to generate control firms for each CDS firm by comparing

the computed propensity of CDS initiation. Specifically, I compare the estimated likelihood of

CDS initiation of non-CDS firms to that of CDS firms in the year prior to the CDS trading initiation.

I follow Subrahmanyam et al. (2014) to produce three matched samples using different matching

criteria to further verify my results and counter the limitations of propensity score matching

methodology39_{: (1) the single non-CDS firm has the same Fama-French 48 industry as the CDS}

firm and has the closest propensity score to the CDS firm, (2) besides conditions of (1), the non-

CDS firm and CDS firm come from the same country, (3) the two non-CDS firms have the same

CDS firm’s Fama-French 48 industry and have the closest propensity score to the CDS firms.

Moreover, I require the distance of mean propensity scores between CDS and non-CDS samples

not to be statistically significant at the 10% level. I employ Martin and Roychowdhury’s (2015)

approach to allow a non-CDS firm to match multiple CDS firms. However, the same non-CDS

firm can go into the control sample only once for each year. This way, I have unique firm-year

observations throughout my samples even though a non-CDS firm may serve as a control for

several CDS firms.

39 _{For example, the propensity score matching method is based on the premise that the control unit is independent of}

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I tabulate the firms’ characteristics of the control-treated sample before the year of CDS trading

initiation in Table 3.5. For brevity, I only present the statistical results based on the matching

criteria (1), which require the closest matched non-CDS firm to have the same Fama-French 48

industry classification as the one of CDS firm. Panel A compares the E&S performances of

matched non-CDS and CDS firms, while Panel B assesses the thirteen firm characteristics and

logit of propensity scores for both samples. In Panel A, I observe that all E&S scores are

statistically the same for CDS and matched non-CDS samples. In Panel B, as shown, CAPEX is

the only firm-level characteristic that is statistically significantly different between the control and

treated samples. The high CAPEX suggests that CDS firms have higher capital intensity than non-

CDS firms. Except for this firm characteristic, CDS firms statistically exhibit the same

characteristics with the non-CDS firms prior to the initiation of CDS trading. These results suggest

that these firm-level CSR determinants documented in extant CSR literature are unlikely the

sources of differences in CSR performance after the inception of CDS trading.

<Insert Table 3.5 about here>