This document discusses a study analyzing the risk mitigation effect of ESG performance on corporate default risk over time and across sectors. It begins with an introduction discussing relevant regulatory frameworks. The literature review finds that ESG metrics can reduce default risk, though impacts may differ for individual ESG factors. Two hypotheses are presented: 1) ESG performance has a greater impact on default risk over longer time horizons. 2) The impact of ESG varies across sectors. The methodology section describes the dataset of European firms and regression models used to test the hypotheses. Preliminary results are then shown for one regression on 1-year probability of default.

1. ESG DEFAULT RISK
MITIGATION EFFECT: A
TIME-SECTORIAL
ANALYSIS.
E. Palmieri
1

2. INTRODUCTION THE
REGULATORY
FRAMEWORK
LITERATURE
REVIEW
THEORETICAL
FRAMEWORK
DATABASE AND
METHODOLOGY
RESULTS CONCLUSION
Agenda
2

3. Introduction
Paris Agreement - ONU
Agenda for Sustainable Development – ONU
Action Plan: Financing Sustainable Growth – European
Commission
Guidelines on Loan Origination and Monitoring - EBA
3

4. Literature Review
4
• ESG metrics can increase firms’ creditworthiness, in fact, both ESG individual components
and ESG overall score is able to reduce companies’ probability of default (Sassen et al., 2016;
Devalle et al., 2017; De Santis et al., 2020; Höck et al., 2020; Jang et al., 2020);
• Another research stream considers CDS spread as a proxy for credit risk (Hübel, 2020; Barth
et al., 2021);
• Multiple studies have found different contributions in reducing firms’ probability of default for
each ESG component:
o environmental (Sassen et al., 2016; Gibson, 2021) ;
o social (Stellner et al., 2015; Bhattacharya and Sharma, 2019);
o governance (Cash, 2018; Kiesel and Lücke, 2019).
• There is a problem inherent the lack of uniformity and transparency of the methods for
detecting ESG metrics (Stubbs, 2016; Avetisyan and Hockerts, 2017);

5. Literature Review
5
• An increased attention of national regulation expressed in terms of environment
safeguard, social and governance inequality reduction can considerably amplify the
effect of ESG performance on credit worthiness. Indeed, the risk mitigation effect
triggered by ESG performance improvement it is more evident in countries with a
consolidated and strong attention to environment protection, social and governance
issues. In fact, ESG metrics have a greater impact in the reduction of EU firms’
probability of default rather than American ones (Liang and Renneboog, 2017; Kim et al.,
2021; Barth et al., 2021);
• Early literature studied the effect of ESG metrics on credit worthiness considering only a
one-year perspective, ignoring long term implication. ESG criteria should represent a
managerial incentive to take long term choices (Henisz and McGlinch, 2019);

6. 6
• ESG criteria can be exploited in rating methods improving the predicting power of credit
rating algorithms (Klein, 2019; Michalski and Low, 2020);
• Exist a positive correlation among ESG performance and corporate profitability (Kim and Li,
2021).
• The problem of the lack of uniformity regarding ESG indicators has been faced in a paper
that observed three different types of divergence: (1) scope divergence; (2) measurement
divergence; (3) weight divergence (Berg et al., 2019; Christensen et al., 2021);
Literature Review

7. 7
• According to stakeholder theory the interest of the firm in environmental and social issues is
rewarded by the market in terms of performance and consequently increasing firm value
(Clarkson, 1995; Donaldson and Preston, 1995);
• Corporate performance cannot be split by risk dimensions, according to the paradigm of risk-
return, increases in risk levels are related to higher returns (Fama, 1971; Bettis and Mahajan,
1985; Campbell and Viceira, 2005);
• In accordance with stakeholder theory, an increased attention in social and governance issue
will lead to a better consolidation of company relationships with government and financial
community, improving firm reputation and brand value (McGuire et al., 1988; Brown and Dacin,
1997);
• The development of social and governance attention constitutes an indicator for excellent
management abilities and the availability of high-quality workforce (Turban and Greening 1997;
Waddock and Graves 1997; Greening and Turban, 2000);
• All these factors contribute to the reduction of financial risks and lowers the likelihood of
financial distress (Oikonomou et al., 2012).
Theoretical background

8. 8
HYP1: ESG performance variation has a major impact on firms’
probability of default on longer time horizons.
Theoretical background

9. 9
• After the issue of the final version of IFRS 9 by International Accounting Standard Board
(IASB), a noticeable attention is assumed by the banking sector for models able to estimate
firms’ multiperiod default probability (Duffie et al, 2007; Xu, 2016; Gubareva, 2021);
• The implementation of ESG metrics into loan assessment and on-going evaluation became
mandatory following the introduction of loan origination monitoring guidelines issued by
European Banking Authority (EBA, 2019; EBA, 2020a; EBA, 2020b; EBA, 2020c; EBA, 2021);
• Companies in sectors most exposed to environmental problems will benefit most from positive
changes in the ESG profile. On the other side, on the market will exist firms that because of
their core business, technical impediments or exogenous issues react less to ESG
performance (Eccles et al., 2012).
Theoretical background

10. 10
HYP2: ESG performance impact on probability of default changes in
function of the sector to which a firm belongs.
Theoretical background

11. Database and Methodology
11
Number of firms Index N. Firms
50 EUROSTOXX 15%
20 BEL 20 6%
40 CAC40 12%
30 DAX30 9%
100 FTSE100 30%
35 IBEX 10%
20 SMI 6%
40 FTSE MIB 12%
335 Sum 100%
The initial dataset  335 European firms (2010-2020) + Bloomberg Industry Classification
Systems (BICS)
ESG scoring disclosure available starting from 2015;
Bloomberg PD calculated over a time horizon of 1, 2, 3, 4 and 5 years.
Code Sectors N %
MAT Materials 16 14%
DIS Consumer Discretionary
Products 16 14%
IND Industrials 18 16%
HEA Health care 12 11%
TEC Technology 9 8%
CON Consumer Staples 13 12%
COM Communication 11 10%
ENE Energy 5 5%
UTI Utilities 11 10%
Sum 111 100%

12. Database and Methodology
12
Code Variable Fonte
ZSC Altman Z-Score Bloomberg
B1Y Bloomberg PD 1 year Bloomberg
B2Y Bloomberg PD 2 years Bloomberg
B3Y Bloomberg PD 3 years Bloomberg
B4Y Bloomberg PD 4 years Bloomberg
B5Y Bloomberg PD 5 years Bloomberg
ENV Environment Disclosure Bloomberg
GOV Governance Disclosure Bloomberg
SOC Social Disclosure Bloomberg
ESG ESG Total Disclosure Bloomberg
BIC Bloomberg Industry Classification System Bloomberg
The unavailability of data referring to the Z-Score and the ESG report for two periods
after 2015 represents the trigger for missing observation elimination. Finally, the
companies subject to double listing were eliminated, and the data used refer to the
index of the country at which the firm belongs  111 firms and 10.410 observations.

13. Database and Methodology
13
ZSC B1Y B2Y B3Y B4Y B5Y ENV GOV SOC ESG
Mean 3,15 0,21% 0,67% 1,23% 1,76% 2,29% 43,48 61,53 51,66 49,60
Variance 7,88 0,01% 0,03% 0,05% 0,07% 0,08% 227,08 84,16 201,40 135,60
Standard
Deviation 2,81 1,21% 1,79% 2,26% 2,60% 2,82% 15,07 9,17 14,19 11,64
Minimum 0,05 0,00% 0,01% 0,06% 0,15% 0,33% 2,33 26,79 8,77 11,57
1st Quartile 1,55 0,00% 0,07% 0,25% 0,49% 0,83% 34,11 57,14 43,86 43,39
Median 2,55 0,02% 0,25% 0,63% 1,06% 1,55% 44,72 62,50 54,39 50,83
3rd Quartile 3,94 0,09% 0,64% 1,35% 2,02% 2,65% 55,81 67,86 61,40 57,85
Maximum 38,63 25,59% 33,71% 38,52% 41,44% 43,04% 79,07 96,24 85,96 77,27

14. Database and Methodology
14
with: (i) 𝑃𝐷𝑖𝑡 = probability of default with yearly time frame t, (t=1;2;3;4;5); (ii) 𝛼 = constant;
(iii) 𝑍𝑆𝐶𝑖𝑡 = Z-Score; (iv) 𝐸𝑁𝑉𝑖𝑡 = environmental score; (v) 𝑆𝑂𝐶𝑖𝑡 = social score; (vi) 𝐺𝑂𝑉𝑖𝑡 =
governance score; (vii) 𝐷𝑡𝑖𝑚𝑒𝑖𝑡
= dummy which takes a value of 1 for the years after 2014 and
0 in the other cases; (viii) 𝐷𝑡𝑟𝑚𝑖𝑡
= dummy which takes value 1 in the group of companies
treated and 0 in the rest; (ix) 𝐷𝑡𝑟𝑚𝑖𝑡
∗ 𝐷𝑡𝑚𝑖𝑡
= interaction variable that assumes a value of 1 in
the case of companies belonging to the treatment group for the years after 2014; (x) 𝑢𝑖𝑡 =
error term.
𝑃𝐷𝑖𝑡 = 𝛼 + ß1 ∗ 𝑍𝑆𝐶𝑖𝑡 + ß2 ∗ 𝐸𝑁𝑉𝑖𝑡 + ß3 ∗ 𝑆𝑂𝐶𝑖𝑡 + ß4 ∗ 𝐺𝑂𝑉𝑖𝑡 + 𝛾1 ∗ 𝐷𝑡𝑖𝑚𝑒𝑖𝑡
+ 𝛾2 ∗ 𝐷𝑡𝑟𝑚𝑖𝑡
+ 𝜆1 ∗ 𝐷𝑡𝑟𝑚𝑖𝑡
∗ 𝐷𝑡𝑖𝑚𝑒𝑖𝑡
+ 𝑢𝑖𝑡

15. Database and Methodology
15
Before ESG (2010-
2015)
After ESG (2015-2020) After - Before
Control Firms 𝛼 𝛼 + 𝛾1 𝛾1
Treatment Firms 𝛼 + 𝛾2 𝛼 + 𝛾1 + 𝛾2 + 𝜆1 𝛾1 + 𝜆1
Control - Treatment 𝛾2 𝛾2 + 𝜆1 𝜆1

16. Database and Methodology
16
with: (i) 𝑃𝐷𝑖𝑡 = probability of default with yearly time frame t, (t=1;2;3;4;5); (ii) 𝛼 = constant and utility
sector given the omission of a sector; (iii) 𝑍𝑆𝐶𝑖𝑡 = Z-Score; (iv) 𝐸𝑁𝑉𝑖𝑡 = environmental score; (v) 𝑆𝑂𝐶𝑖𝑡 =
social score; (vi) 𝐺𝑂𝑉𝑖𝑡 = governance score; (vii) 𝑀𝐴𝑇𝑖𝑡 = dummy for materials sector; (viii) 𝐷𝐼𝑆𝑖𝑡 =
dummy for discretionary product sector; (ix) 𝐼𝑁𝐷𝑖𝑡 = dummy for industrial sector; (x) 𝐻𝐸𝐴𝑖𝑡 = dummy for
health sector; (xi) 𝑇𝐸𝐶𝑖𝑡 = dummy for technology sector; (xii) 𝐶𝑂𝑁𝑖𝑡 = dummy for consumer staple
sector; (xiii) 𝐶𝑂𝑀𝑖𝑡 = dummy for communication sector; (xiv) 𝐸𝑁𝐸𝑖𝑡 = dummy for energetic sector; (xv)
𝐷𝑡𝑖𝑚𝑒𝑖𝑡
= dummy which takes a value of 1 for the years after 2014 and 0 in the other cases; (xvi)
𝐷𝑡𝑟𝑚𝑖𝑡
= dummy which takes value 1 in the group of companies treated and 0 in the rest; (xvii) 𝐷𝑡𝑟𝑚𝑖𝑡
∗
𝐷𝑡𝑚𝑖𝑡
= interaction variable that assumes a value of 1 in the case of companies belonging to the
treatment group for the years after 2014; (xviii) 𝑢𝑖𝑡 = error term.
𝑃𝐷𝑖𝑡
= 𝛼 + ß1 ∗ 𝑍𝑆𝐶𝑖𝑡 + ß2 ∗ 𝐸𝑁𝑉𝑖𝑡 + ß3 ∗ 𝑆𝑂𝐶𝑖𝑡 + ß4 ∗ 𝐺𝑂𝑉𝑖𝑡 + 𝛿1 ∗ 𝑀𝐴𝑇𝑖𝑡 + 𝛿2 ∗ 𝐷𝐼𝑆𝑖𝑡 + 𝛿3 ∗ 𝐼𝑁𝐷𝑖𝑡 + 𝛿4 ∗ 𝐻𝐸𝐴𝑖𝑡 + 𝛿5
∗ 𝑇𝐸𝐶𝑖𝑡 + 𝛿6 ∗ 𝐶𝑂𝑁𝑖𝑡 + 𝛿7 ∗ 𝐶𝑂𝑀𝑖𝑡 + 𝛿8 ∗ 𝐸𝑁𝐸𝑖𝑡 + 𝛾1 ∗ 𝐷𝑡𝑖𝑚𝑒𝑖𝑡
+ 𝛾2 ∗ 𝐷𝑡𝑟𝑚𝑖𝑡
+ 𝜆1 ∗ 𝐷𝑡𝑟𝑚𝑖𝑡
∗ 𝐷𝑡𝑖𝑚𝑒𝑖𝑡
+ 𝑢𝑖𝑡

17. Results
17
PD 1 year
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 9.031e-03 2.780e-03 3.249 0.001197 **
ZSC -1.095e-03 3.120e-04 -3.509 0.000469 ***
ENV 1.540e-05 3.524e-05 0.437 0.662164
SOC -1.090e-04 3.685e-05 -2.957 0.003174 **
GOV 4.209e-05 4.421e-03 0.952 0.341305
Time (𝛾1) -3.779e-03 1.283e-03 -2.945 0.003300 **
Treated (𝛾2) -9.957e-04 7.473e-04 -1.332 0.183057
Treated * Time
(𝜆1)
8.004e-04 3.431e-04 2.333 0.019851 *
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 4.431 on 7 and 1.033 DF p-value 7.255e-05
PD 2 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 2.026e-02 4.060e-03 4.989 7.13e-07 ***
ZSC -2.524e-03 4.556e-04 -5-540 3.83e-08 ***
ENV -7.626e-06 5.147e-05 -0.148 0.882229
SOC -1.463e-04 5.383e-05 -2.717 0.006697 **
GOV 6.010e-05 6.457e-05 0.931 0.352157
Time (𝛾1) -7.387e-03 1.874e-03 -3.941 8.65e-05 ***
Treated (𝛾2) -9.719e-04 1.092e-03 -0.890 0.373477
Treated * Time
(𝜆1)
1.721e-03 5.011e-04 3.434 0.000618 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 8.179 on 7 and 1.033 DF p-value 1.008e-09
PD 3 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 3.176e-02 5.083e-03 6.249 6.03e-10 ***
ZSC -3.848e-03 5.703e-04 -6.747 2.51e-11 ***
ENV -3.427e-05 6.442e-05 -0.532 0.5949
SOC -1.645e-04 6.738e-05 -2.441 0.0148 *
GOV 6.614e-05 8.083e-05 0.818 0.4134
Time (𝛾1) -1.050e-02 2.346e-03 -4.476 8.43e-06 ***
Treated (𝛾2) -7.718e-04 1.366e-03 -0.565 0.5723
Treated * Time
(𝜆1)
2.557e-03 6.273e-04 4.076 4.94e-05 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 11.29 on 7 and 1.033 DF p-value 7.579e-14
PD 4 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 4.172e-02 5.818e-03 7.171 1.41e-12 ***
ZSC -4.855e-03 6.529e-04 -7.437 2.16e-13 ***
ENV -5.748e-05 7.375e-05 -0.779 0.4359
SOC -1.724e-04 7.713e-05 -2.236 0.0256 *
GOV 6.500e-05 9.252e-05 0.702 0.4825
Time (𝛾1) -1.276e-02 2.686e-03 -4.750 2.32e-06 ***
Treated (𝛾2) -5.402e-04 1.564e-03 -0.345 0.7299
Treated * Time
(𝜆1)
3.186e-03 7.180e-04 4.437 1.01e-05 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 13.4 on 7 and 1.033 DF p-value < 2.2e-16

18. Results
18
PD 5 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 5.018e-02 6.263e-03 8.013 3.01e-15 ***
ZSC -5.546e-03 7.028e-04 -7.892 7.56e-15 ***
ENV -7.504e-05 7.938e-05 -0.945 0.3447
SOC -1.737e-04 8.302e-05 -2.092 0.0366 *
GOV 6.152e-05 9.959e-05 0.618 0.5369
Time (𝛾1) -1.427e-02 2.891e-03 -4.936 9.31e-07 ***
Treated (𝛾2) -3.381e-04 1.6884e-03 -0.201 0.8409
Treated * Time
(𝜆1)
3.617e-03 7.729e-04 4.680 3.24e-06 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 14.92 on 7 and 1.033 DF p-value < 2.2e-16

19. Results
19
PD 1 year
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 6.957e-03 3.136e-03 2.218 0.026742 *
ZSC -1.205e-03 3.232e-04 -3.728 0.000203 ***
ENV -2.851e-05 3.874e-05 -0.736 0.461904
SOC -7.182e-05 3.988e-05 -1.801 0.071984 .
GOV 7.107e-05 4.687e-05 1.516 0.129712
MAT 5.191e-04 1.579e-03 0.329 0.742390
DIS 4.285e-03 1.591e-03 2.693 0.007189 **
IND 3.796e-04 1.479e-03 0.257 0.797440
HEA -2.746e-04 1.708e-03 -0.161 0.872288
TEC -7.182e-04 1.842e-03 -0.390 0.696749
CON -9.311e-04 1.725e-03 -0.540 0.589475
COM -1.100e-03 1.698e-03 -0.648 0.517155
ENE 1.169e-03 2.060e-03 0.568 0.570346
Time -4.006e-03 1.295e-03 -3.093 0.002032 **
Treated -9.885e-04 7.623e-04 -1.297 0.195954
Treated *
Time
8.610e-04 3.475e-04 2.478 0.013373 *
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 3.356 on 15 and 1.025 DF p-value 1.477e-05
PD 2 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 1.737e-02 4.548e-03 3.820 0.000142 ***
ZSC -2.661e-03 4.688e-04 -5.676 1.80e-08 ***
ENV -9.002e-05 5.619e-05 -1.602 0.109415
SOC -8.234e-05 5.784e-05 -1.424 0.154891
GOV 1.074e-04 6.798e-05 1.581 0.114257
MAT 1.409e-03 2.290e-03 0.615 0.538571
DIS 7.295e-03 2.307e-03 3.162 0.001614 **
IND 7.014e-04 2.145e-03 0.327 0.743687
HEA -1.242e-03 2.477e-03 -0.501 0.616237
TEC -2.279e.03 2.672e-03 -0.853 0.393888
CON -2.581e-03 2.502e-03 -1.031 0.302588
COM -3.212e-03 2.463e-03 -1.304 0.192471
ENE 1.610e-03 2.988e-03 0.539 0.590189
Time -7.619e-03 1.878e-03 -4.056 5.37e-05 ***
Treated -1.128e-03 1.106e-03 -1.021 0.307684
Treated *
Time
1.796e-03 5.040e-04 3.563 0.000384 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 6.156 on 15 and 1.025 DF p-value 1.545e-12

20. Results
20
PD 3 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 2.846e-02 5.666e-03 5.023 5.98e-07 ***
ZSC -3.992e-03 5.839e-04 -6.837 1.39e-11 ***
ENV -1.493e-04 6.999e-05 -2.133 0.033201 *
SOC -7.935e-05 7.205e-05 -1.101 0.271019
GOV 1.265e-04 8.468e-05 1.494 0.135470
MAT 2.323e-03 2.853e-03 0.814 0.415683
DIS 9.733e-03 2.874e-03 3.386 0.000735 ***
IND 8.670e-04 2.672e-03 0.325 0.745623
HEA -2.234e-03 3.086e-03 -0.724 0.469386
TEC -3.761e-03 3.329e-03 -1.130 0.258846
CON -4.187e-03 3.117e-03 -1.343 0.179505
COM -5.155e-03 3.068e-03 -1.680 0.093234 .
ENE 1.808e-03 3.722e-03 0.486 0.627283
Time -1.069e-02 2.340e-03 -4.568 5.52e-06 ***
Treated -1.054e-03 1.377e-03 -0.765 0.444452
Treated *
Time
2.634e-03 6.278e-04 4.195 2.96e-05 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 8.389 on 15 and 1.025 DF p-value < 2.2e-16
PD 4 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 3.825e-02 6.467e-03 5.915 4.51e-09 ***
ZSC -4.999e-03 6.665e-04 -7.500 1.38e-13 ***
ENV -1.957e-04 7.989e-05 -2.450 0.014444 *
SOC -7.301e-05 8.224e-05 -0.888 0.374856
GOV 1.345e.04 9.665e-05 1.392 0.164315
MAT 2.950e-03 3.256e-03 0.906 0.365096
DIS 1.138e-02 3.281e-03 3.469 0.000543 ***
IND 8.352e-04 3.049e-03 0.274 0.784236
HEA -2.986e-03 3.523e-03 -0.848 0.396874
TEC -5.015e-03 3.799e-03 -1.320 0.187112
CON -5.493e-03 3.558e-03 -1.544 0.122900
COM -6.662e-03 3.502e-03 -1.902 0.057390 .
ENE 1.736e-03 4.248e-03 0.409 0.682861
Time -1.289e-02 2.671e-03 -4.828 1.59e-06 ***
Treated -9.013e-04 1.572e-03 -0.573 0.566564
Treated *
Time
3.261e-03 7.166e-04 4.550 6.00e-06 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 9.851 on 15 and 1.025 DF p-value < 2.2e-16

21. Results
21
PD 5 years
Coefficients Estimate Std. Error t – value Pr ( > |t|)
Intercept 4.669e-02 6.948e-03 6.720 3.01e-11 ***
ZSC -5.685e-03 7.160e-04 -7.940 5.30e-15 ***
ENV -2.284e-04 8.583e-05 -2.661 0.007917 **
SOC -6.558e-05 8.835e-05 -0.742 0.458109
GOV 1.365e-04 1.038e-04 1.315 0.188943
MAT 3.374e-03 3.498e-03 0.964 0.335064
DIS 1.242e-02 3.525e-03 3.523 0.000446 **
IND 7.250e-04 3.276e-03 0.221 0.824899
HEA -3.512e-03 3.784e-03 -0.928 0.353684
TEC -5.912e-03 4.082e-03 -1.448 0.147845
CON -6.416e-03 3.822e-03 -1.679 0.093511 .
COM -7.658e-03 3.762e-03 -2.036 0.042042 *
ENE 1.600e-03 4.564e-03 0.351 0.725910
Time -1.436e-02 2.869e-03 -5.004 6.60e-07 ***
Treated -7.437e-04 1.689e-03 -0.440 0.659787
Treated * Time 3.688e-03 7.699e-04 4.790 1.91e-06 ***
Signif. codes 0 ‘ *** ’ 0.001 ‘ ** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ‘
F-statistic 10.9 on 15 and 1.025 DF p-value < 2.2e-16

22. Conclusion
22
Tendency observed in literature  a firm is able to reduce its own probability of default
through the improvement of ESG performance.
Not every component of ESG scoring has the same importance:
• time-based analysis: social sphere accounts for the most in the enhancement of firms’
credit worthiness;
• time-sectorial analysis: the social dimension accounts only for the reduction of 1-year
probabilities of default  other horizons  environmental score improvements.

23. Conclusion
23
Contributions:
1. confirmation of the default risk mitigation effect increases as the default likelihood reference
time increases. Banks can adequately assess firms’ credit worthiness involving adjusted
probabilities of default for ESG performance with time spans greater than 1 year;
2. Through the validation of hypothesis 1 we have also identified the existence of a time lag
between ESG compliant strategies and the effects on firms’ probability of default. This lag is
generated by the dynamics linked to reputation; any improvement that impact on corporate
dimension require a non-negligible amount of time to be perceived by the market;
3. existence of a differential effect in firms’ risk mitigation effect in function of the sector at which a
company belongs  banks can dynamically define the amount of capital that they desire to
allocate on a specific sector and geographical area, coherently with their risk appetite framework,
and adjust their allocation according to sectorial sensibility to ESG scoring improvement.

24. Conclusion
24
The present study is characterized by model limits:
1. we assume that Z-score is able to express the entire profile of risk represented by
balance sheet items, but it doesn’t consider the risk appetite framework and the
strategic plan;
2. the sample refers only to European listed companies, but we do not consider
different geographical area neither SME;
3. probabilities of default are strictly correlated with market cycles dynamics;
4. the time period took in consideration is characterized by unconventional monetary
policy, such as, negative interest rates and quantitative easing.

25. Conclusion
25
Future studies could try to implement a sensitivity analysis to reveal the influence of exogenous
variables on probability of defaults. In addition, observing a more geographically diversified sample
verify the persistence of the time lag effect in risk mitigation effect.