DBJ Discussion Paper Series, No. 2401

Selection and Pricing of Green and Sustainability-Linked Corporate Bonds

Maria Gonzalez Ramirez（University of Texas at San Antonio）
John K. Wald
（University of Texas at San Antonio）

April 2024

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in order toprotect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the views of
the Institute.

Selection and Pricing of Green and Sustainability-Linked Corporate
Bonds
Maria Gonzalez Ramirez
University of Texas at San Antonio
maria.gonzalezramirez@utsa.edu
John K. Wald*
University of Texas at San Antonio
john.wald@utsa.edu
April 23, 2023
Abstract
We examine when firms choose to issue green or sustainability-linked bonds (SLBs), and how
these bonds are priced relative to regular bonds. We hypothesize that firms are more likely toissue green bonds when credit spreads are high (“reaching for features”). Using recent data on
corporate bond issues in US dollars, yen, and euros, we estimate a trivariate probit model andfind evidence consistent with this hypothesis. Further, higher emission firms are more likely toissue SLB securities, while companies that do not disclose emissions are less likely to issue
environmental securities. We consider matching, OLS, and using a heteroskedasticity-based
instrument to see how green and SLB securities are priced. During the 2019 to 2022 period,
regression methods suggest sustainability-linked bond spreads were issued at 29 to 43 basis
points tighter than regular bonds. Green bonds were also priced tighter than regular bonds,
although this difference is not always statistically significant.

Keywords: Greenium; Green Bonds; Sustainability-linked Bonds; Selection ModelJEL Classification: G12; G15; Q30; M48*

We thank Binay Adhikari, Arturo José Galindo Andrade, Bruno Georges Kamdem, Andreas Kern, Iva Kalcheva,
Alexander Vasa, and seminar participants at the Green Bond Transparency Platform Conference, at the Development
Bank of Japan Inc., at the 2023 FMA Meetings, and at the University of Texas at San Antonio for comments on earlier
versions. Part of this research was conducted while John Wald was the Shimomura Fellow at the Development Bankof Japan.

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I.

Introduction
The first green bond was issued by the World Bank in 2008, while the first sustainability-

linked bond (SLB) was issued by the energy company Enel in September of 2019.1 Since then,the market for both types of securities has expanded greatly, with municipalities, agencies, andcorporations issuing both types of securities. In this study, we address two issues. First, weanalyze what market and firm characteristics lead companies to issue green or SLB securities.
Second, we expand on the unresolved question of whether these types of securities carry a pricepremium (or greenium as the additional value of a green bond is sometimes called).
We hypothesize that issuers may be more willing to add potentially restrictive
environmental features when markets for regular corporate bonds are less favorable. That is,
Becker and Ivashina (2015) describe how insurance companies which invest in corporate bonds“reach for yield” within a given rating. Similarly, we expect issuers to consider adding bondfeatures which reduce spreads if market conditions are less advantageous, and we term this process“reaching for features.” Specifically, we use a trivariate multinomial probit to model firms’
decisions on whether to issue a regular, green, or SLB security. We gather all large public
corporate bond issues in US dollars, Yen, or euros by firms from the 10 countries with the mostobservations from September 2019 through 2022. We consider country-level, industry-level,
currency-level, and firm-level determinants of the type of bond issue.
The results from our first-stage specification show that firms are more likely to choose
green bond –and to some degree SLB issues –when spreads are high. Based on our selection model
estimates, a one standard deviation increase in the quality spread (approximately 46 basis points)

1

Green bonds are issued with the understanding that their proceeds will go towards pre-determined environmentally
related projects, whereas SLBs include covenants which make the cost of debt contingent on the achievement ofsustainability performance targets.

2

implies a 1.33% increase in the probability of issuing a green bond. Given that 5.65% of oursample is made up of green bonds, a one standard deviation increase in the quality spread translates
to a 23.6% increase in the probability of green bond issuance. Thus, firms reach for these additionalfeatures when market conditions are worse. This self-selection makes correcting for market
conditions important when examining pricing. Otherwise, even within the same firm, spreads ongreen bonds are likely to be high due to the market conditions when these bonds are issued rather
than due to the green feature.
The increase in green bond and SLB issuance when firms face higher yields is consistent
with either firms switching to greener projects when financing is more expensive or with firms
misclassifying projects as green to take advantage of lower spreads. While detailed data on projecttype (and alternatives) is not available, Flammer (2021) finds that green bond issuers improve theirenvironmental impact, which is consistent with at least some firms switching to greener projects.2
Additionally, firms that do not report CO2 emissions are much less likely to issue either
green bonds or SLBs. After all, SLBs typically include contractual obligations to reduce emissions,and therefore firms that do not report are unlikely to credibly issue these securities. SLB issuers
are also more frequently firms with low growth opportunities, high tangible assets, and low credit
ratings. The average Moody’s credit rating for an SLB issuer is only Ba1, whereas the averagegreen bond issuer is rated A3, and the average regular bond issuer is rated Baa2.
To provide some sense of how demand for these securities has increased, we note that in
the U.S. the number of sustainability-focused bond funds grew from 39 in 2018 to 112 in 2021,
with 99 out of these 112 funds focusing on taxable bonds while the rest invest in municipal bonds.
At the same time, fund inflows to sustainability-focused bond funds grew from approximately $1.7

2Moreover, we find no evidence that markets value third-party opinions certifying the environmental quality for oursample. This suggests that markets believe firms are actually undertaking green projects when they issue green bonds.

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billion in 2018 to $11.1 billion in 2021 (Morningstar, 2018 and 2021). Given this growth in the
demand for ESG (environmental, social, and governance) bonds, as well the recent increase in thegreenium for German sovereign bonds documented by Pastor, Stambaugh, and Taylor (2022), we
expect to find a larger greenium than in Flammer (2021), who only considers bonds issued up to
2018.

Given the stricter nature of SLB securities, which commit the firm to reaching asustainability goal otherwise they provide additional coupon payments, we expect a larger
greenium on SLB issues.
After examining the issuance decision, we consider whether green and SLB securities are
priced differently from regular bonds. The prior research on this topic has been mixed, with
Larcker and Watts (2020) finding that yields on green and regular municipal bonds issued by thesame entity on the same day are equal, and Flammer (2021) finding no difference between the
spreads of green corporate bonds and a matched sample of regular bonds. In contrast, Deng, Tang,and Zhang (2020) find a greenium of 79 basis points for securities issued for a fully green purposein their sample of Chinese corporate bonds, and Kolbel and Lambillon (2022) find a spread of 9basis points on their sample of SLBs. We consider a more recent and larger sample than priorstudies using corporate bond issues in US dollars, euros, or yen from 10 different countries.
Corporations do not issue both green and regular bonds on the same day, thus we require adifferent method to determine the pricing of green and SLB issuances than that used by Larcker
and Watts (2020).

Following Flammer (2021), we first consider several matched sample

techniques, where we separately match green and SLB securities to regular securities based on
firm and market characteristics. However, nearest neighbor matching (as used by Flammer, 2021),
produces estimated spreads which vary depending on the choice of matching characteristics.

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We therefore consider regression methods with controls for industry and market
conditions, as well as an instrumental variable procedure where the first stage models the firms’
trivariate selection decision. Specifically, we use the heteroskedasticity in our selection model as
one possible set of instruments (see Lewbel 2012, 2018).
These regression methods provide a range of greeniums, from 14 to 19 basis points forgreen bonds and from 29 to 43 basis points for SLBs. The estimates for green bonds are only
significantly different from zero in the instrumental variable specification, whereas the estimatesfor SLB securities are significant in all specifications. The magnitudes are consistent with thenotion that the market currently provides a lower cost of capital to issuers who provide green or
SLB securities.
We contribute to the literature by estimating an empirical model for when firms are more
likely to issue green or SLB securities. We show that when companies face higher market spreads,
they are more likely to issue green securities. Rather than “reaching for yield,” issuers “reach forfeatures” to keep costs down. Second, we show that firms with greater emissions are more likely
to issue SLB securities, while firms that do not report emissions are much less likely to do so.
Third, we demonstrate how different methods affect the estimation of the premium for green and
SLB securities. We compare the estimates from using different matched sample, OLS, andinstrumental variable models to estimate the premiums for green and SLB securities.

II.

Literature Review

II.A. Prior Empirical Findings on Spreads
The growth in green bond issuance has been accompanied by a growth in the literature,with Cheong and Choi (2020) providing an early review of the literature. Many of the findings

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have focused on municipal bonds, with Larcker and Watts (2020) showing that green and nongreen bonds issued simultaneously by a municipality have similar spreads. However, Baker,
Bergstresser, Serafeim, and Wurgler (2022) show that municipal green bonds trade at a slightpremium (about 5 to 9 basis lower spreads) except when issued at the same time as a non-greenbond, and in that case, the premium emerges over time. Pastor, Stambaugh, and Taylor (2022)
also find that the greenium increased after 2020 for green German government bonds relative toregular German government bonds.
Flammer (2021) considers corporate green bonds issued in the 2013 to 2018 period andfinds a positive stock price response to green bond issuance. Similar to Tang and Zhang (2020),
Flammer also documents an improvement in corporate environmental performance after a green
bond issuance, and that there is no significant pricing difference between green bonds and amatched sample of regular bonds. That said, Flammer’s findings on pricing are based on arelatively small sample of 152 green bonds and an equally sized matched sample of regular bonds,
and her sample ends in 2018, whereas our sample begins in 2019 (when the first SLB was issued)and ends in 2022.
In contrast to Flammer (2021), Deng, Tang, and Zhang (2020) and Ehlers and Packer(2017) find a significant difference between the pricing of green and nongreen bonds. Using asample of Chinese bonds issued from 2016 to 2018, Deng et al. find that bonds whose purpose isfully green trade at spreads which are 79 bp lower than similar nongreen bonds. Deng et al. alsofind that firms which employ third-party verification for the green projects have lower bondspreads, and this difference is as high as 126 basis points for all green projects which use
international third-party verification. Ehlers and Packer find a smaller 18 bp difference in theirsample of US dollar and euro denominated green bonds relative to similar regular bonds. More

6

recently, Kolbel and Lambillon (2022) examine the pricing of SLBs and find that they had an
average 9 basis point lower spread than regular corporate bonds.
We also find tighter spreads on green bonds at issuance, although not as large as thosefound by Deng et al. Our results for SLB securities suggest a larger spread difference than in
Kolbel and Lambillon (2022).

II.B. Theory
The theoretical literature on the issuance of these securities is still at an early stage.
Barbalau and Zeni (2022) provide a theoretical model where firms choose between issuing green
and SLB securities. They show that both types of securities may be issued depending on signaling
and how much firms are able to manipulate green outcomes. Additionally, firms with higher
historic emission intensity are more likely to issue SLB securities. Our choices of variables to
explain firms’ issuance choices are partly motivated by this theory, as we also consider firm-level
or industry-level pollution and ESG reporting as determinants of issuance type.
Ramadorai and Zeni (2021) show that current and future abatement of environmental
emissions can be explained by a two-firm model with cross-firm information asymmetry andreputational externalities. Additionally, Hsu, Li, and Tsou (2022) provide a general equilibrium
model of how uncertainty about future environmental regulation impacts the pricing of equity
securities. Empirically, they show that equity returns from high pollution emissions firms generate
a 4.42% annual premium over the returns from low pollution emissions firms. Prior papers also
show that carbon risk and hazardous chemical emissions are priced in financial markets; see, forexample, Chava (2014), Bolton and Kacperczyk (2021), and Sautner, van Lent, Vilkov, and Zhang(2023).

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II.C. Prior Empirical Findings on Issuance Choice
In contrast to the relatively large literature on green bond spreads, the literature on whether
a firm chooses to issue green bonds is much smaller. Recently, Cicchiello, Cotugno, Monferra,and Perdichizzi (2022) examine the choice between issuing regular and green bonds for a sampleof European issuers. They find that firms with more long-term debt and with a higher current ratio
are more likely to issue green bonds. Garcia, Herrero, Miralles-Quiros, and Miralles-Quiros (2023)
show that firms with higher environmental scores and with sustainability committees are more
likely to issue green bonds. Using a sample of European and Chinese bond issues, Dutordoir, Li,
and Neto (2024) find that firms are more likely to issue green bonds if they get more reputational
gains from being seen as environmentally friendly. We are not aware of any prior works thatexamine the choice of issuing green, SLB, or regular bonds simultaneously.

III.

Methods
We provide two types of analysis in this study: a choice model for whether a firm decides

to issue regular, green, or SLB fixed income securities, and a pricing model which measuresdifferences in how the market prices these different types of bonds. We also combine these models
by using the choice model to instrument for self-selection in the pricing equation.

III.A. Selection Model8

We model the firm’s issuance decision using a multinomial probit, where the outcome
variable is whether the firm issues a regular, green, or SLB security.3 The advantage of using a
trivariate multinomial probit is that it does not suffer from the independence of irrelevantalternatives (IIA) problem. As McFadden (1974) shows, multinomial logit models require the IIA
property to be valid, and we do not expect this condition to hold among the bond choices weconsider.4 The disadvantage of using the multinomial probit is that maximizing the likelihood canbe slow, although with only three alternatives this issue is still manageable.
Let 𝑌𝑖 denote the choice of which type of security firm i issues. And denote the latentvariables associated with firm i’s profit from issuing regular, green, and SLB securities as𝑟𝑒𝑔𝑢𝑙𝑎𝑟∗𝑌𝑖𝑔𝑟𝑒𝑒𝑛∗, 𝑌𝑖, and 𝑌𝑖𝑆𝐿𝐵∗ . The multinomial probit can be specified as corresponding to thefollowing equations:
𝑟𝑒𝑔𝑢𝑙𝑎𝑟∗

1 𝑖𝑓 𝑌𝑖𝑔𝑟𝑒𝑒𝑛∗> 𝑌𝑖

𝑟𝑒𝑔𝑢𝑙𝑎𝑟∗𝑎𝑛𝑑 𝑌𝑖> 𝑌𝑖𝑆𝐿𝐵∗

𝑌𝑖 = { 2 𝑖𝑓 𝑌𝑖𝑔𝑟𝑒𝑒𝑛∗ > 𝑌𝑖𝑟𝑒𝑔𝑢𝑙𝑎𝑟∗ 𝑎𝑛𝑑 𝑌𝑖𝑔𝑟𝑒𝑒𝑛∗ > 𝑌𝑖𝑆𝐿𝐵∗ }𝑔𝑟𝑒𝑒𝑛∗

3 𝑖𝑓 𝑌𝑖𝑆𝐿𝐵∗ > 𝑌𝑖

𝑟𝑒𝑔𝑢𝑙𝑎𝑟∗𝑎𝑛𝑑 𝑌𝑖𝑆𝐿𝐵∗ > 𝑌𝑖

The firms’ managers do not need to be pure profit maximizers; they may instead have some otherutility function based on their own or the firms’ stakeholders’ preferences. In that case, the latentvariables would correspond to the unobserved utilities rather than profits.

3

An alternative method would use a bivariate choice model to compare environmental (green and SLB) vs. nonenvironmental issues. However, such an approach would overlook the differences between the motivations of green
and SLB issuers that are highlighted in the trivariate model.
4
The IIA property requires that the odds ratio between any two alternatives is independent of whether a third
alternative is present. McFadden (1974) provides the red bus/blue bus example, where a commuter has a choice
between taking a red bus or driving to work. If a second type of bus, the blue bus, also becomes available, the IIA
property requires that this not affect the odds ratio between the red bus and driving. If the buses are otherwise identical,
the commuter would be indifferent between taking a red or blue bus, and the IIA property would not hold. In general,
we do not expect the IIA property to hold in the bond issuance space we consider.

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We then assume a linear specification for the latent variables, and we normalize against
one of the alternatives; thus, the estimated coefficients correspond to how much a particularvariable impacts the choice for green relative to regular or SLB relative to regular. We estimate:𝑔𝑟𝑒𝑒𝑛∗

𝑌𝑖

{

𝑔𝑟𝑒𝑒𝑛

= 𝑋𝑖 𝛽 𝑔𝑟𝑒𝑒𝑛 + 𝜀𝑖

(1)

𝑌𝑖𝑆𝐿𝐵∗ = 𝑋𝑖 𝛽 𝑆𝐿𝐵 + 𝜀𝑖𝑆𝐿𝐵Note that our estimated parameters for the green choice capture the difference between the impactof a particular variable on the choice of green relative to the choice of a regular bond. Our X
variables include Tobin’s Q (market value of equity plus book value of debt divided by total
assets), the firm size (log of total assets), leverage (debt as a fraction of total assets), tangibility(property, plant, and equipment divided by total assets), and ROA. We also include the rating of
the firm, from 1 for C-rated firms to 21 for AAA rated firms, as well as a dummy variable if thefirm is unrated.

To test our hypothesis about market conditions and issuance choice, theindependent variables include the treasury rate and the quality spread — equal to the average BAA
rate minus the treasury rate for a given currency and maturity.5
Given the theoretical setup in Barbalau and Zeni (2021), we include the firm’s CO2
emissions and the firm’s ESG score from the prior year. Barbalau and Zeni’s model predicts that,in the presence of asymmetric information, firms with higher historical CO2 emission levels findit optimal to issue contingent green debt, such as SLBs. Firms with greater discrepancy between
their sustainable image—measured by the firm’s ESG score—and a credible signal ofenvironmental commitment also find it optimal to issue SLBs. Thus, these variables are expected
to capture the key factors that determine optimality in a sustainable debt contract. We also include

5

We use the quality spread from 10 business days prior to the issuance date, as this would more likely capture thefirm’s timing of what type of security to issue, although our findings are not sensitive to this lag. We use the Germantreasury rate as the benchmark for the euro market.

10the country-industry-year average of CO2 and ESG, as these variables may determine the market
demand for green or SLB issues. While firm-level CO2 and ESG variables may be endogenous
with the later bond pricing, we expect the country-industry-year average of CO2 and ESG to berelated to ease of issuance for these securities but not to the pricing of individual bonds. We also
include indicator variables for whether CO2 or ESG are missing. The choice regression also
incorporates industry (based on 2-digit GICS codes), country, and currency fixed effects. As thetime period we use is relatively short, we include a time trend; and including non-linear time trend
variables does not meaningfully change the results.
Given the short time period and our inclusion of country fixed effects, we are unable toconsider institutional differences between countries. That said, we add an indicator variable forwhether the issuer’s country added a new environmental law. For instance, Germany added a new
climate protection law on December 12, 2019, and thus we set our new_law indicator equal to zerofor German issuers prior to that date and equal to one after that date.

III.B. Pricing Models
We consider several methods to estimate the difference in issuance spreads between green
and regular, or SLB and regular, bonds. Our first method matches green bonds to similar regularbonds and SLBs to similar regular bonds. As in Flammer (2021), we use the nearest-neighbortechnique which requires an exact match for country and industry, then minimizes the Mahalanobis

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distance based on five characteristics: Tobin’s Q, issue size, ROA, leverage and ESG
environmental scores.6 The characteristics are from the year prior to the issuance date.7
We then extend this method to include an exact match on currency and rating. We add
other distance-matching variables including quality spreads, treasury rates, CO2 emissions, andtangibility ratios as these variables are significant in the selection model. We also consider aweighted matching using the MD Kernel procedure—where green bonds and SLBs are separately
matched against regular bonds using an Epanechnikov weighting function—rather than a one-toone nearest neighbor match.
Next, we provide a simple OLS regression to show the partial correlations between thevariables of interest. Our pricing equation is a linear specification with indicator variables for theissuance type. We estimate:𝑛𝑆𝑝𝑟𝑒𝑎𝑑𝑖 = 𝛼 + 𝛾1 𝐺𝑟𝑒𝑒𝑛𝑖 + 𝛾2 𝑆𝐿𝐵𝑖 + Σ𝑘=3
𝛾𝑘 𝑋𝑖𝑘 + 𝜇𝑖

(2)

The additional control variables in the pricing equation include firm characteristics(Tobin’s Q, size, leverage, tangibility, and ROA), as well as bond characteristics including
maturity and whether the security is callable. To control for other credit risk features, we include
a numeric value for the firm’s Moody’s rating category and a separate indicator for if the firm isunrated. We also include dummy variables for industry (2-digit GICS code), country, andcurrency. Deng et al. (2020) find that green bonds with third-party opinions (TPO), that is thosewhere the issuer commits to having a third-party verify the nature of the green investment, have

6

Flammer also matches by coupon and maturity in some specifications. However, we find this problematic; as mostbonds are priced at par, the coupon equals approximately the treasury rate plus the spread. Thus, matching on coupon
would also match on spread if treasury rates do not change. Matching on coupon therefore biases the results towards
finding no difference between green and regular bonds.
7
Flammer (2021) matches based on these variables from both one and two years before the issuance.

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lower spreads than green bonds without such opinions. We therefore run additional regressions
with interactions between TPO and green and TPO and SLB indicators as robustness checks.8
A potential issue with an OLS estimation of equation (2) is that the choice of green or SLB
is endogenous. We therefore consider an instrumental variable (IV) estimation procedure. Lewbel(2012) suggests that heteroskedasticity in the first stage estimation can be used as an instrument,
and Lewbel (2018) extends this method to considering binary endogenous regressions. We apply
his method, using the multinomial probit to estimate both binary endogenous regressors (green
and SLB) simultaneously.
While our instruments show varying levels of statistical significance in the selection model,
the Kleibergen-Paap underidentification tests suggest they meet the instrumental variable (IV)relevance criteria. We also believe these variables meet the exclusion criteria as, after controllingfor company characteristics, they are unlikely to impact spreads – see Lewbel (2012 and 2018) for
additional assumptions. Wooldridge (2002) discusses how using a parametric estimation in thefirst stage improves estimation efficiency, and we therefore use the fitted values from themultinomial probit as instruments. While Wooldridge discusses the application for a single
endogenous indicator variable, the extension to multiple indicator variables (green and SLB in ourcase) using multinomial probit is straightforward.
The first SLB was issued in September of 2019, but the number of SLB issues is very
sparse until the third quarter of 2020.9 Given this issuance pattern, we also test how OLS and IV
estimates fare using a sub-sample that starts in the third quarter of 2020.
In all estimation procedures, the continuous variables are winsorized at the upper andlower 1%. Standard errors in all pricing regressions are clustered by country.

89Regular bonds do not have third-party opinions, therefore TPO by itself would be collinear to the other variables.
SLBs account for less than 0.1% of corporate bond issues from the 3rd quarter of 2019 until the 3rd quarter of 2020.

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IV.

Data and Summary Statistics
Our selection analysis incorporates data for 8,306 bonds and 825 issuers covering theperiod from September of 2019—the issuance date of the first sustainability-linked bond—through December of 2022. As spreads are not available for all issues, the pricing analysis includes
only 6,408 bonds and 745 issuers. To create the sample, we begin with Securities Data Company’s(SDC) New Issues database and screen for all corporate bonds issued in U.S. Dollars, Euros, orJapanese Yen with proceeds greater than $100 million USD during the period of interest. We
further exclude issues that are equity-related, credit sensitive, payable-in-kind, collateralized debt
obligations, floating rate notes, or hybrid securities. We use Bloomberg’s database of self-labeledenvironmental bonds in the corporate market to identify the green and sustainability-linked bonds
from our original sample. Our initial screen yields a sample of 26,677 regular bonds, 373
sustainability-linked bonds and 949 green bonds. SDC’s database provides us with bond issuancedata, including issuance and maturity dates, proceeds, callability, and the bond’s spread tobenchmark. From issuance and maturity dates, we compute each issue’s time horizon in years. Our
pricing measure and dependent variable, the spread to benchmark, is computed as the difference
between the bond’s yield at issue and the comparable maturity treasury yield based on currency.
We further limit our sample to only issuers from countries with at least 25 bonds and at
least some green and SLB securities. Without this restriction, the sample would include issues
from countries with only a small number of securities, and these are likely to either be dropped in
the multinomial probit or to add noise to the estimation. That said, our results are similar if we do
not use this additional filter.

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We gather financial statement variables—such as total assets, Tobin’s Q, tangibility ratios,
return on assets, and debt to assets— from Capital IQ and environmental data—such as ESG scores
and CO2 emissions per sales— from Bloomberg. The firm-level independent variables are fromthe year preceding issuance. Our final sample of observations used in the spread analysis consistsof 5,757 regular bonds, 362 green bonds, and 289 sustainability-linked bonds. Appendix A
displays a full list of the variables included in our analysis along with their respective descriptions
and sources.
To control for macroeconomic conditions, we include quality spreads and treasury yields
from 10 days prior to the issuance date.10 These spreads and yields are matched to the maturity
and currency of similar treasury bonds. For instance, a 5-year bond issued by a Mexican company
is US dollars would be compared against the quality spread for 5-year US BAA corporate bonds
and against 5-year US government bonds.11 We compute quality spreads as the differential yield
between currency-matched Baa rated corporate bonds and similar maturity treasury rates. Yields
for Baa bonds and treasuries for each currency and each issuance day are obtained from
Bloomberg, with the exception that Baa yields for Japanese bonds are obtained from the Japan
Security Dealers Association web page. We also construct an indicator variable for whether thecountry passed a new environmental law during the sample period by searching the Carrots andSticks database for mandatory laws and regulations for the countries in our sample.
Panel A of Table 1 provides a breakdown of the sample by country. The sample includes
companies from 10 countries, although US bonds make up approximately 85% of the observations.
However, only 49% of the green bonds come from US issuers, and only 42% of the SLBs are from10

We use a 10-day lag to roughly capture when the issuer would decide on bond features, although our results holdwithout a lag as well.
11
We match euro bond issues against German government treasury bonds.

15

US issuers. Panel B of Table 1 provides a breakdown of the sample by issuing currency. Just over
80% of the sample is in US dollars.
Panel A of Table 2 shows descriptive statistics for all our variables, and Panel B of Table
2 provides descriptive statistics grouped by bond type. Both green and sustainability-linked bonds
have lower average spreads, by 73 bp and 23 bp, respectively, than regular bonds. A t-test showsthat these means are significantly different than the mean spreads for traditional bonds at the 1%
level. Moreover, green and sustainability-linked bond issuers emit significantly more CO2/sales
and show lower environmental scores than the regular bond issuers.
Table 3 provides the correlations between the primary variables of interest. Spreads arenegatively correlated with both green and SLB issuance types.

V.

Estimation Results

V.A. Issuance Choice
Table 4 presents the estimates from our multinomial probit regression. Our full-sample
selection model is displayed in columns 1 and 2, while our later-sample estimates – after SLB
issues became more widely used– are displayed in columns 3 and 4.
Consistent with our hypothesis regarding “reaching for features,” the Quality Spread issignificantly positively associated with green bond issuance for both the full sample (column 1)
and the latter period sample (column 3). A one standard deviation increase in the quality spread(approximately 46 basis points) implies a 1.33% increase in the probability of issuing a green bondgiven the estimate in column 1—or a 23.6% increase in issuance over the unconditional
probability. Higher quality spreads are also significantly related to SLB issuance in the later time
period (column 4). Treasury yields are also positively significantly associated with green bond16

issuance for the full sample, with a one standard deviation increase in treasury yields(approximately 71 basis points) implying a 0.9% increase in the probability of green bondissuance. Thus, firms which face less favorable market conditions are more likely to reach for
additional features to lower the yield. This effect helps create a bias toward similar spreads onregular and green securities in studies which match only on firm characteristics.
Consistent with Garcia et al. (2023), Column 1 of Table 4 also shows that better firm ESG
scores are associated with a higher probability of issuing green bonds for the full sample. A one
standard deviation increase in company ESG score is associated with a 0.6% increase in theprobability of issuing a green bond, which corresponds to a 10.6% change in issuance. Higher
firm-level emissions are also associated with a greater likelihood of issuing SLBs in the fullsample, and issuing either green or SLB securities in the later sample. The estimate in column 2
implies that a one standard deviation increase in company CO2/sales is associated with a 0.9%
increase in the probability of issuing an SLB. This is consistent with the findings of Barbalau andZeni (2022), who conclude that firms with greater emission intensity are more likely to issue
contingent environmental debt contracts. Moreover, for the full sample, companies which do notreport CO2 emissions are 38% less likely to issue SLBs.12 There are several potential explanations
for these results. Barbalau and Zeni (2022) argue that, in the presence of low information
asymmetry, firms which are better able to manipulate environmental impact information and lessable to achieve green outcomes are more inclined towards regular debt.13 This is because investors
are able to distinguish whether firms have an incentive to implement the green project. However,12

All SLB issuers in our sample reported CO2 emissions, thus this coefficient is estimated based on the impact ofreporting on regular and green issuance. The maximization is not able to estimate a significance level for thiscoefficient in column 4.
13
Barbalau and Zeni (2022) define manipulation as the selective disclosure of information about a company’senvironmental performance so as to create a positive corporate image.

17

disclosure is often not optional for firms; 84.5% of our sampled bonds are issued by US firms,
which are subject to the mandatory reporting rule established by the EPA in January of 2010.
Disclosure thresholds vary by source category, the most common being 25,000 tons of CO2e(Tomar, 2023). This implies that, at least in some countries, firms that do not disclose CO2
emissions are often low polluters. Low-emission firms may simply not have an opportunity toissue green or SLB securities.
Several of the firm-level control variables are also significantly related to the type ofissuance. Companies with higher Tobin’s Q are significantly less likely to issue both green bondsand SLBs, and larger firms are less likely to issue SLBs. Higher leverage is associated with anincreased probability of issuing green bonds and a lower probability of issuing SLB securities forthe full sample, and greater tangibility is positively associated with SLB issuance. ROA is also
negatively associated with green issuance, while firm rating is positively associated with green
bond issuance and negatively associated with SLB issuance. Overall, the evidence shows thatgreen and SLB securities are chosen by different types of firms and that this choice is sensitive tomarket conditions.

V.B. Spreads on Green and SLB Securities Using Matching
We next examine several estimation methods for how much green and SLB securities differ
in pricing terms from regular bonds. Our first analysis of spreads uses a matched sample analysis.
Table 5 reports the match using the same variables as Flammer (2021) in columns 1 and 2 forgreen and SLB securities. Columns 3 and 4 additionally force an exact match for currency andrating. Columns 5 and 6 add quality spreads, treasury rates, CO2 emissions, and tangibility ratios
as distance matching variables. Columns 7 and 8 use a kernel match rather than a nearest neighbor18match, allowing the treated (green or SLB issues) to be matched against a weighted average of
non-treated issues. We report the average treatment effect (ATE), and, in all cases, the averagetreatment on the treated and the average treatment on the untreated are similar to the ATE.
When using the matching method in Flammer (2021), we find a large and statisticallysignificant premium of 40 basis points for green bonds in column 1, and a large but not significant
premium of 17 basis points for SLB securities in column 2. Adding an exact match for currency
and rating, we find a smaller but still significant premium of 27 basis points for green bonds, anda larger 35 basis point premium on SLBs.
As firms are more likely to issue green bonds when market spreads are high, we anticipate
that not matching on the quality spread will lead to lower estimates for the greenium. That is, aparticular issuer is more likely to choose green bonds when overall market spreads are high, andtherefore a comparison of spreads (even for the same issuer) are likely to be biased towards higher
spreads if not adjusting for market conditions. Consistent with this self-selection, spreads for green
bonds in column 5 are significantly more negative (at 53 basis points) than without correcting formarket conditions in column 3.
Columns 7 and 8 of Table 5 use a kernel rather than a nearest neighbor match, and thismethod produces economically and statistically significant estimates of 18 and 55 basis point
premiums for green and SLB securities, respectively.

However, these different matchingprocedures produce relatively large differences in estimated premiums, and these are largely due
to choosing different subsamples of green and SLB observations for comparison. Interestingly,
the kernel matching procedure produces estimates close to those from OLS, and we next turn toour regression greenium estimates. In additional untabulated analyses, we consider matching

19

within the same issuers. However, the sample size from this procedure is small, and the results
are highly dependent on the choice of matching characteristics.

V.B. Regression Estimates of Green and SLB Spreads
In Table 6, we provide OLS and IV estimations of the spread equation. Columns 1 showsan OLS estimate of spread, controlling for market conditions using quality spread and treasury
yield; security characteristics including issue size, maturity, and callability; and firm
characteristics including size, ROA, leverage, tangibility, an indicator if the issuer is unrated, andthe numerical value for the Moody’s rating. 14 All specifications include industry, country, andcurrency fixed effects.
In column 1, green securities are issued at 19 basis point tighter spreads, while SLB
securities are issued at 43 basis point tighter spreads. The difference for SLBs is significantly
different from zero, while the difference for green bonds is not.
In column 2, we consider an instrumental variable specification using heteroscedasticity
generated instruments. The underidentification test rejects the null hypothesis, and therefore theinstruments meet the relevance criterion. Moreover, the tests for weak identification are muchlarger than the Stock-Yogo statistics, suggesting that weak identification is not an issue. Both thegreen and SLB coefficients are negative and significant (at the 5% and 1% levels, respectively) at
14 and 29 basis points. Overall, the analysis provides strong evidence that over this time period,
SLB securities were priced significantly tighter than regular bonds. The evidence for green bonds

14

One possible alternative would be to use dummy variables for each rating. However, our tests show that such a
specification suffers from considerable multicollinearity, as most of the rating dummies have VIF statistics greaterthan 100 (where a value of 10 suggests an unacceptable level of multicollinearity).

20

is more mixed in terms of statistical significance, although economically the impacts aremeaningful in all the analyses.
In additional untabulated tests we consider how third party opinions affect green and SLB
pricing. Unlike the findings in Deng et al. (2019), we find no significant impact on how tightly
green or SLB bonds trade at issuance from third party opinions in either OLS, fitted IV, orheteroskedastic IV regressions. This suggests that third party opinions are less important for thelargely developed country sample we consider. We also consider estimations with additional nonlinear time trend variables. The magnitude and significance of our estimates remain similar to our
original specification with these additional controls.
The other control variables in the spread regressions mostly have the expected signs.
Firms with better credit ratings issue lower-spread securities. Securities issued when quality
spreads are high show higher spreads. Longer maturity issues and callable bonds have higher
spreads as well.

VI.

Conclusion
We analyze which firms are more likely to issue green or SLB securities relative to regularbonds using a trivariate multinomial probit. Our estimates show that firms facing higher yields,
as reflected by a higher quality spread, are significantly more likely to issue green bonds. This
result is consistent with firms “reaching for features” when market conditions are morechallenging.

This finding suggests that controlling for market conditions is crucial when

examining pricing, otherwise green bond issues appear to have higher spreads because they areissued when market spreads are generally high.

21

We also find that firms with greater emissions are much more likely to issue SLB securities.
Moreover, firms that do not report emissions are less likely to issue either green or SLB securities.
Credit ratings also differentiate green bond issuers from SLB issuers. Better rated firms are more
likely to issue green bonds, while lower rated firms are more likely to issue SLBs. Results fromthe selection model suggest that green and SLB securities cater to different types of firms’
financing needs.
We then consider several different techniques for estimating how spreads on green and
SLB issues differ from spreads on regular bonds. These methods include matching, OLS, andusing instruments either based on industry characteristics and country policies orheteroskedasticity generated instruments.

Matching methods provide a wide dispersion of

estimated values, depending on which securities are included in the sample, while regression
methods provide more consistent estimates of spreads. The estimates for SLB securities suggestthat spreads at issuance are 29 to 43 basis point tighter than spreads for comparable regular bonds.
Economically, green bonds are also associated with tighter spreads, although the significance of
this finding is not consistent across estimation methods. Overall, the estimates suggest larger
greeniums in the recent 2019 to 2022 period, and they provide new evidence on how different
estimation methods impact greenium estimates.

22

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Green Bonds. Annual Review of Financial Economics 14, 415-437.

Barbalau, A. and F. Zeni. 2022. The Optimal Design of Green Securities. University of Alberta
Working Paper.

Becker, B. and V. Ivashina. 2015. Reaching for yield in the bond market. Journal of Finance 70,
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Chava, S. 2014. Environmental externalities and the cost of capital. Management Science 60(9),2223-2247.

Cheong, C. and J. Choi. 2020. Green Bonds: A Survey. Journal of Derivatives and Quantitative
Studies: 선물연구 28, 175-189.

Cicchiello, A., Cotugno, M., Monferra, S., and Perdichizzi, S. 2022. Which are the factors
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Deng, Z, Tang, D., and Y. Zhang, 2020. Is “Greenness” Priced in the Market? Evidence fromGreen Bond Issuance in China. The Journal of Alternative Investments 23 (1), 57-70.

Dutordoir, M., Li, S., Neto, J. 2024. Issuer motivations for corporate green bond offerings. British
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Ehlers, T, and F Packer. Green bond finance and certification. BIS Quarterly, 89-104.

Flammer, C. 2021. Corporate Green Bonds. Journal of Financial Economics 142, 499-516.

Garcia, C., Herrero, B., Miralles-Quiros, J., and Miralles-Quiros, M. 2023. Exploring thedeterminants of corporate green bond issuance and its environmental implication: The role ofcorporate board. Technological Forecasting & Social Change 189, 122379.

Hachenberg, B. and D. Schiereck. 2018. Are Green Bonds Priced Differently from Conventional
Bonds? Journal of Asset Management 19, 371-383.

Kolbel, J, and A. Lambillon, 2022. Who pays for sustainability? An analysis of sustainabilitylinked bonds. University of St. Gallen Working Paper.

Larcker, D. and E. Watts. 2020. Where’s the Greenium? Journal of Accounting and Economics
69 (2-3), 101312.

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Lewbel, A., 2012. Using heteroscedasticity to identify and estimate mismeasured and endogenous
regressor models. Journal of Business and Economics Statistics 30, 67–80.

Lewbel, A. 2018. Identification and estimation using heteroscedasticity without instruments: The
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Morningstar. 2018. Sustainable Funds U.S. Landscape Report. Morningstar Research.

Morningstar. 2021. Sustainable Funds U.S. Landscape Report. Morningstar Research.

Pastor, L., R. Stambaugh, and L. Taylor. 2022. Dissecting Green Returns. NBER Working Paper
28940.

Ramadorai, T. and F. Zeni. 2021. Climate Regulation and Emissions Abatement: Theory andEvidence from Firms’ Disclosures. Management Science, forthcoming.

Sautner, Z., van Lent, L., Vilkov, G., and Zhang, R. 2023. Firm-level Climate Change Exposure.
Journal of Finance 78(3), 1449-1498.

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Tang, D. and Y. Zhang. 2020. Do Shareholders Benefit from Green Bonds? Journal of Corporate
Finance 61, 101427.

Tomar, S. 2023. Greenhouse Gas Disclosure and Emissions Benchmarking. Journal of Accounting
Research, 61: 451-492. https://doi-org.libweb.lib.utsa.edu/10.1111/1475-679X.12473

Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press.
Cambridge, Massachusetts.

Zerbib, O. 2019. The Effect of Pro-Environmental Preferences on Bond Prices; Evidence fromGreen Bonds. Journal of Banking and Finance 98, 39-60.

26

VII.

Table 1Panel A: Number of issues and bond spreads by country and type
Total Issues
Percent
Green
Country
Bonds
9
Canada44
0.69
34France132
2.06
37
Germany
106
1.65
6
Ireland
36
0.56
18
Italy
34
0.53
60
Japan
446
6.96
5
Mexico41
0.64
8Netherlands
92
1.44
7
United Kingdom
61
0.95
178
United States
5,416
84.52
Total
6,408
100.00
362

SLBs
1
26
11
8
10
36
21
39
16
121
289

Regular
Bonds
34
72
58
22
6
350
15
45
38
5,117
5,757

Spread (%)

Regular
Bonds
4,825
586
346
5,757

Spread (%)

2.484
1.042
1.333
1.040
1.302
0.348
2.404
2.838
3.089
1.802

Panel B: Number of issues and bond spreads by currency and typeCountry
USD
EUR
JPY
Total

Total Issues

Percent

5,168
790
450
6,408

80.65
12.33
19.35
100.00

Green
Bonds
181
117
64
362

27

SLBs
162
87
40
289

1.901
1.186
0.347

Table 2
Panel A: Full-Sample Descriptive Statistics
VariableCount
Mean
Spread (%)
Green
SLB
Tobin’s Q
Firm Size
Leverage
Tangibility
ROA
Quality Spread
Treasury Rate
Rating

6,408
6,408
6,408
6,408
6,408
6,408
6,408
6,408
6,408
6,408
6,408

UnratedCO2/Sales
CO2 Missing
Environmental
Score
Environmental
Score Missing
Industry CO2
Industry ESG
New Law
Issuance
AmountMaturity
Callable
Time

MedianStandard
Deviation
1.426
0.230
0.199
0.956
1.386
0.419
0.451
6.342
0.463
0.710
4.253

Minimum

Maximum

0.235
2.745
0.486

0.116
0.000
0.000
0.306
6.421
0.001
0.001
0.100
0.343
-0.675
1.000
(C)
0.000
0.000
0.000

7.961
1.000
1.000
3.859
13.8042.973
3.833
33.9202.864
2.390
21.000(Aaa)
1.000
10.806
1.000

6,408
6,408
6,408

1.704
0.056
0.042
1.276
10.270
0.391
0.281
6.579
1.088
0.844
12.308(Baa3)
0.059
0.599
0.619

1.150
0.000
0.000
1.000
10.114
0.308
0.143
4.980
1.064
0.860
13.000(Baa2)
0.000
0.315
1.000

6,408

50.64452.27020.8110.000

96.220

6,408
6,408
6,408
6,408
6,408

0.158
0.503
50.8750.022
20.1280.000
0.488
56.1990.000
20.2070.364
0.705
12.922
0.147
0.909

0.000
0.000
0.000
0.000
12.2131.000
10.80672.9851.000
22.468

6,408
6,408
6,408

2.495
0.429
434.471

2.083
0.000
458.000

1.575
0.495
263.904

.032
0.000
1.000

9.603
1.000
1,206.00028

Panel B: Descriptive statistics by bond type
Variable
Full Sample
Observations
6,408
Spread to Benchmark
Mean
1.704
Std. deviation
1.426
Tobin’s Q
Mean
1.276
Std. deviation
0.956
Firm Size
Mean
10.270Std. deviation
1.386
Leverage
Mean
0.391
Std. deviation
0.419
Tangibility
Mean
0.281
Std. deviation
0.451
ROA
Mean
6.579
Std. deviation
6.342
Quality Spread
Mean
1.088
Std. deviation
0.463
Treasury Rate
Mean
0.844
Std. deviation
0.710
Rating12.308Mean(Baa3)
Std. deviation
4.253
UnratedMean
0.059
Std. deviation
0.235
CO2/Sales
Mean
0.599
Std. deviation
2.745
CO2 Missing
Mean
0.319
Std. deviation
0.486
Environmental Score
Mean
50.644
Std. deviation
14.141
Environmental Score Missing
Mean
0.158
Std. deviation
0.364
Industry CO2
Mean
0.503
Std. deviation
0.705
Industry ESG
Mean
50.875
Std. deviation
12.922Issuance Amount (in millions USD)
Mean
726
Std. deviation
542

Green
362

SLBs
289

Regular
5,757

1.024c0.775

1.520c
1.131

1.754
1.458

1.073c0.935

0.853c
0.727

1.309
0.961

11.171
1.872

9.997
1.451

10.2271.326

0.835c1.001

0.608c
0.669

0.353
0.309

0.472c0.942

0.612c
1.090

0.254
0.328

2.449c2.562

4.431c
3.158

6.936
6.505

1.014c
0.500

0.854
0.212

1.104
0.470

0.626c0.794
14.802c(A3)
3.016

0.693c0.729
11.247c
(Ba1)
3.719

0.864
0.700
12.202(Baa2)
4.289

0.014c0.117

0.052c
0.223

0.062
0.242

1.481c
3.174

3.332c
4.485

0.418
1.567

0.192c0.394

0.000c
0.000

0.675
0.469

32.948c27.88528.637c30.63352.76218.4990.536c0.499

0.566c
0.497

0.115
0.319

0.801c1.556

1.019c
1.293

0.461
0.552

36.759c22.95831.476c25.86352.650
9.414

494c
372

704
354

742
555

See Appendix A for variable definitions. c and b indicate statistical difference from traditional bond means at 0.01 and 0.05, respectively.

29

Table 3
Correlation between select variables.
SpreadGreen
SLB
Tobin’s Q
Firm Size
Tangibility
Leverage
ROA
Quality Spread
Treasury Rate
CO2/Sales
Environmental Score

SpreadGreen

SLB

Tobin’s Q

Firm Size

Tangibility

Leverage

ROA

1-0.1163-0.0268-0.0602-0.3595
0.0602-0.0655-0.04290.3339-0.0068
0.0383-0.16161-0.0509-0.0520.15860.10420.2592-0.1589-0.0387-0.07480.1076-0.2075

1-0.0924-0.04110.153
0.1083-0.0706-0.1052-0.04420.2849-0.22051
0.03450.0091-0.0101
0.05220.0088-0.0345-0.08190.034

1-0.13120.0544-0.224-0.0194-0.1344-0.0561
0.1421

10.1532-0.0192-0.01880.05410.21-0.1817

1-0.0938-0.1586-0.14930.0146-0.125110.00090.2291-0.07360.215230

Quality
SpreadTreasuryRate

1
0.2789-0.0679
0.06271-0.0049
0.1477

CO2/Sales1-0.1244

Environmental
Score

1

Table 4
Multinomial probit regression on whether a firm issues regular, green, or SLB bonds
(1)
(2)
(3)
VARIABLES
Green
SLB
Green
Tobin’s Q-0.386c-0.592c-0.309c(-7.190)(-5.200)(-4.478)[-0.017][-0.009][-0.013]
Firm Size-0.061-0.194c0.105b
(-1.510)(-2.690)(1.970)[-0.002][-0.003][0.005]
Leverage
0.354c-0.747a0.325c(4.330)(-1.810)(3.112)[0.018][-0.013][0.019]Tangibility
0.101
0.780c0.114(1.220)(4.860)(1.139)[0.003][0.013][0.003]
ROA-0.106c
0.034-0.072c(-5.820)(1.610)(-3.083)[-0.005][0.001][-0.003]
Quality Spread
0.626c0.042
0.949c(5.410)(0.130)(3.172)[0.029][-0.001][0.042]c
Treasury Rate
0.287
0.166
0.235
(3.170)(0.830)(1.355)[0.013][0.002][0.011]ccRating0.125-0.2190.084c(6.040)(-5.640)(3.049)[0.006][-0.004][0.015]
Unrated-0.482-1.241-0.596(-1.080)(-1.240)(-1.047)[-0.020][-0.020][-0.018]
CO2/Sales
0.037
0.175c0.032(1.590)(7.890)(1.166)[0.001][0.003][0.001]CO2 Missing-0.796c-22.581-1.336c(-6.810)(-0.000)(-8.028)[-0.017][-0.378][0.001]c
Environmental Score
0.012
0.011
-0.003(2.690)(1.550)(-0.593)[0.001][0.000][-0.000]
Environmental Score Missing
2.096c2.216c1.562c(8.540)(3.790)(4.898)[0.093][0.032][0.063]c
Industry CO20.000-0.0030.001(0.000)(-3.480)(0.927)

31

(4)
SLB
-0.603c(-4.825)[-0.013]-0.176b(-2.120)[-0.004]-1.283c(-2.774)[-0.031]0.679c(3.801)[0.016]0.037(1.446)[0.001]0.938b
(1.968)[0.019]-0.048(-0.172)[-0.002]-0.347c(-6.822)[-0.008]-3.316c(-3.037)[-0.077]
0.198c(8.150)[0.005]-22.067(-0.000)[-0.521]0.019b
(2.381)[0.000]3.387c(4.800)[0.076]-0.003c(-3.047)

Industry ESG

New Law

Time

Industry Fixed Effects
Country Fixed Effects
Currency Fixed Effects
Observations
Number of issues
Log-Likelihood[0.000]-0.005(-0.570)[0.000]-0.069(-0.280)[0.001]0.001c(6.080)[0.000][0.000]-0.044c(-3.150)[-0.001]-1.048(-1.560)[-0.018]0.005c(9.620)[0.000]Yes
Yes
Yes
24,918
8,306-1,385.054[0.004][-0.008]

0.008-0.040c(0.729)(-2.609)[0.051][-0.097]-0.062-1.033(-0.206)(-1.360)[0.000][-0.024]0.002c0.005c(3.869)(6.049)[0.000][0.000]
Yes
Yes
Yes
16,044
5,348-951.263

This table displays coefficients, z-statistics (in parentheses), and marginal effects (in brackets) for the self-selection model. The dependent variable
is an indicator variable for whether the firm chooses to issue regular, green or SLB securities. See Appendix A for variable definitions. The
analysis includes currency, nation, and industry fixed effects. c, b, and a indicate statistical significance at 0.01, 0.05 and 0.10 level, respectively.

32

Table 5
Panel A: Matched sample treatment effects
Similar to Flammer (2021)
(1)
(2)
ATE
-0.400c-0.173(-5.460)(-1.260)
Treatment Group
Additional
Variables
Country andIndustry Exact
Match
Currency and
Rating Match
MethodTreatedObservations
Untreated
Observations
Total
Observations

Currency and Rating(3)(4)-0.265c-0.353b(-3.260)(-2.340)

Additional Controls(5)(6)-0.529c-0.303b(-5.560)(-1.990)

Multivariate Distance Match(7)(8)-0.182b-0.554c(-2.480)(-5.960)

Green
No

SLB
No

Green
No

SLB
No

Green
Yes

SLB
Yes

Green
Yes

SLB
Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

NearestNeighbor
334

NearestNeighbor
186

NearestNeighbor
249

NearestNeighbor
138

NearestNeighbor
249

NearestNeighbor
138

MD Kernel

MD Kernel

264

144

334

186

249

138

249

138

3482

2082

668

372

498

276

498

276

3746

2226

Column 1 and 2 display treatment effects for SLB and Green bonds resulting from sample matching using following the matching methodology of Flammer (2021). Columns 3 and 4 display treatment
effects adding country, currency, and quality spread as matching variables. Columns 5 and 6 further add exact country matching into the methodology. Average Treatment Effect (ATE) refers to theaggregated causal effects over all individuals. Standard errors clustered by country are presented in parentheses. c, b, and a indicate statistical significance at 0.01, 0.05 and 0.10 level, respectively.

33

Table 6
OLS and instrumental variable regressions

Green
SLB
Tobin's Q
Firm Size
Leverage
Tangbility
ROA
Quality Spread
Treasury Rate
Rating
UnratedCO2/Sales
CO2 Missing
Environmental Score
Env. Score Missing
Issuance Amount
Maturity
CallableTime
Industry Fixed Effects

34

(1)
OLS

(2)
Heteroskedastic IV-0.190(-1.247)-0.433b(-2.011)0.016(0.312)0.103a(1.829)0.198b
(2.161)
0.031(0.246)-0.003(-0.338)
0.783c(6.349)-0.610c(-4.472)-0.304c(-10.258)-4.100c(-7.938)0.000(-0.210)0.048(0.371)-0.001(-0.206)
0.403a(1.792)-0.081(-1.484)
0.247b
(2.181)0.203a(0.110)-0.001c(-3.790)Yes-0.139b(-2.056)-0.286c(-3.040)-0.012(-0.348)0.049a(1.662)0.196c(3.550)-0.027(-0.452)0.001(0.100)0.792c(8.519)-0.470c(-5.542)-0.285c(-18.759)-4.113c(-16.258)0.000(0.981)
0.072(0.846)-0.002(-0.871)
0.397b
(2.566)-0.034(-1.040)0.158b
(2.478)0.184c(0.063)-0.001c(-5.363)
Yes

Country Fixed Effects
Currency Fixed Effects
Observations
Adjusted R-squared
Underidentification K.P. lm(p-value)
Weak Ident. K.P. Wald
[Stock-Yogo 10% level]
Hansen’s J p-value

Yes
Yes
6,408
0.619

Yes
Yes
6,408
0.535
199.704(0.000)436.149[10.88](0.201)See Appendix A for variable definitions. The dependent variable is the bond’s spread to benchmark at issuance. SLB and Green refer tosustainability-linked bond and green bond indicators. The estimates in column 1 are from an OLS regression. The estimates in column 2 are froman instrumental variable regression using heteroskedasticity-based instruments. All regressions include country, industry, and currency dummies.
Standard errors are clustered by country. t-statistics are presented in parentheses. c, b, and a indicate statistical significance at 0.01, 0.05 and 0.10level, respectively.

35

Appendix A
Variable definitions
Variable
Definition
SpreadA bond’s spread, relative to its benchmark. Our benchmark
measure is the treasury yield that matches the bond’s currency andmaturity
CO2
Total emissions (measured in hundreds of metric tons of CO2),
Emissions/Sales
standardized by total sales
ESG
Environmental
Sustainability component of a company’s ESG score
Industry CO2Country-industry-year average levels of CO2 emission intensity
Industry ESG
Country-industry-year average ESG ratings
CO2 Missing
Indicator set to 1 for firms that do not report CO2 emissions
Environmental
Missing
Indicator set to 1 for firms that are not ESG rated
Size
The natural logarithm of total assets. Total assets are measured inmillions of USD
Tobin’s Q
Market value of equity + book value of debt, standardized by total
assetsLeverageFirm-level debt-to-asset ratio. Total debt standardized by total
assetsROA
Return on total assets, computed as net income standardized bytotal assets
Tangibility
Property, plant, and equipment standardized by total assets
RatingA numerical value corresponding to the firm’s Moody’s ratings,
with AAA equal to 21 and C equal to 1. Unrated issuances havethis variable set to 0
Rating Missing
Indicator variable set to 1 for firms that are not rated by Moody’s
Issuance Amount Total proceeds resulting from the bond issuance, in millions of
USD
Quality spread
Currency-average Baa rate minus the similar maturity Treasury
rate (lagged by 10 business days)
Benchmark Yield
New LawTime

Treasury yield used as a benchmark to calculate the spread. Our
treasury benchmarks must the bond’s maturity and currency
Indicator variable for whether the issuer’s country added a newenvironmental law
A time trend measuring the days between September 1st, 2019—the first day in our sample—and the issuance date of the bond36

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