ESG, Taxes, and Profitability of Insurers

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ESG, Taxes, and Profitability of Insurers

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Silvia Bressan^*

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Submitted:

24 August 2023

Posted:

28 August 2023

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Abstract

The growing concerns on sustainability urge insurance companies to incorporate Environmental, Social, and Governance (ESG) policies in order to remain competitive. As all dimensions of sustainability involve taxation, it is important to establish if this association reflects on financial performance. Our analysis of worldwide property and casualty (P&C) insurers during 2013-2022 reveals that high ESG insurers pay more taxes, while are less profitable compared to low ESG insurers. This pattern is confirmed using instrumental variable regressions and simultaneous equations systems. We argue that sustainable insurers are less tempted to avoid taxes, and don't shift their tax burdens on policyholders and investors. However, the interplay between taxes and sustainability seems to harm insurers' profitability, potentially sorting negative consequences on investment and economic growth. This is an important insight for tax authorities and insurance managers.

Keywords:

Subject: Business, Economics and Management - Finance

1. Introduction

Sustainability has three main dimensions Environmental, Social and Governance (ESG), and taxation is an essential element in each of them.For example, the environmental dimension could include tax exemptions incentivizing “green” investments. The social side, instead, could use tax revenues to improve firms’ infrastructure, education, or health care. Finally, sustainable governance would ensure transparent tax reporting, establishing trustworthy relationships between tax authorities and tax payers.

The insurance industry is quite a problematic area for tax policymakers. Its accounting systems are based on complex actuarial computations due to the lags between the assumptions of liabilities and payments, therefore calculating insurance income can be a difficult task. Among different insurance segments, taxation of property and casualty (P&C) insurers has generally been less problematic than the taxation of life insurance. The reason is that the P&C business is a purer insurance business than life insurance, as its contracts are generally of a short term (one year) and do not have an explicit savings motive.1

For this reason, in this article we focus on the P&C segment to study taxation effects in the insurance sector. More precisely, we are interested in the effect that taxes sort on insurers’ profitability, and our goal is to test whether this impact is affected by corporate sustainability. We analyze worldwide insurers during 2013-2022, and show that high ESG insurers pay more taxes while are less profitable than low ESG insurers. Our interpretation is that sustainable insurers are less tempted to manipulate earnings in order to avoid taxes, and their tax burdens are not passed on to policyholders and investors.

Our work follows the insights from previous research in the fields of accounting and of sustainability. Among accounting studies focused on financial intermediaries, we follow [1,11], who examined banking institutions. The authors find that profitability increases in effective tax rates, concluding that banks are able to shift their tax bills on depositors. We argue that investigating this behavior inside insurers is much important as well, as insurance customers include the wide base of policyholders.

Within the literature on corporate sustainability, [36] use data from Korean firms to prove that ESG mitigates incentives for tax avoidance. This argument can plausibly explain also our outcomes, as we show that effective tax rates decrease in ESG scores. The innovative and interesting contribution from our analysis is that we build a bridge between two topics of research, highlighting that promoting sustainability in the insurance business would require also a careful tax planning, to avoid that declined profitability could ultimately constrain the firm growth.

Our analysis delivers important insights for insurance managers engaged in strategic decisions, as we recommend to trade off the benefits from enhanced sustainability with pitfalls related to increased tax burdens and harmed profitability. Furthermore, regulators and policymakers should be wise to devise tools preventing that the implementation of ESG in insurance leads to high taxes that reduce investment or quality, thereby causing harm.

The article is organized as follows. Section 2 connects our article to the most recent literature. Section 3 presents the data and the variables we use in the regression analysis. Section 4 shows the results. Section 5 concludes.

2. Review of the Literature

This article examines empirically the impact of corporate taxes on insurers’ profitability, testing whether this relationship can partly be explained by ESG performances. Few streams of previous research relate to this topic. The first is the stream of research on the association between corporate sustainability and financial performance. Previous studies focused mainly on non financial firms, revealing heterogeneous evidence. [2,15] offer comprehensive reviews of the literature dealing with the effect of corporate sustainability on performance, reporting that these two aspects are positively correlated in the large majority of the articles. In a recent meta-analysis instead, [34] contend that the existing evidence is more mixed, with considerable shares of research pointing to a negative, mixed, or even not-existing relationship.

In this article we focus on the insurance sector. The unique nature of the insurance business makes the taxation of insurers a difficult task, which in many aspects is not comparable to other sectors. The main reason is that insures’ accounting systems involve actuarial computations due to the lags between the assumptions of liabilities and payments, thus calculating pre-tax income is not straightforward. Within the insurance sector though, taxation issues seem to be less complex for property and casualty (P&C) insurance (also called “general” insurance) compared to life insurance.2. P&C insurers accept premiums from policyholders for the insurance of risks related to damage to property, personal injury, or public liability for damage to third parties or to their property. Therefore, insurance contracts are generally of a short term (one year) and do not have an explicit savings motive. This implies that many tax issues of P&C insurers are different than in life insurance. For these reasons, we opt to focus on the P&C segment in order to investigate the role of taxes in channelling the impact from sustainability on insurance profitability.

In the previous literature the evidence about effects from sustainability on insurers’ financial performance is much narrow. [3] measures the stock market performance of United States insurers using monthly stock market returns during 2013-2022, and shows that high ESG insurers exhibit also highly positive abnormal returns. In this article we assess insurers’ financial performance from their return-on-assets (ROA), i.e. a quantity largely employed by academics and practitioners to assess profitability. Few studies use ROA to assess ESG effects on corporate performance. However, the evidence is mixed, as for example [10,32] discover that ESG has a positive effect on ROA, while [18] find the opposite.

Previous research proves that other aspects of insurers vary significantly with sustainability policies. These include, for example, distress risk [7], financial strength [3], and reinsurance [4]. Our article contributes to this literature by corroborating the importance of sustainability for insurers’ decision making, pointing out that ESG sorts a joint impact on taxes and financial performance.

The relationship between taxes and profits is informative on tax avoidance activities, i.e. one of the most important and studied questions in tax research. In the literature the empirical evidence on this subject is mixed. For example, [5,21,30] find that firms that are more profitable have lower effective tax rates. In contrast, [26,28] report a positive and significant association between profitability and effective tax rates, while the correlation is not statistically significant in the articles of [9,16]. For the banking industry there is evidence on this matter too, as [1,11] show that banks’ profitability increases in effective tax rates, and interpret the findings arguing that banks shift their tax bills to depositors and lenders. We build a bridge with this literature showing results for insurance companies. In fact, insurers, likewise other financial intermediaries, are much often excluded from samples of empirical analyses, due to the nature of their business and regulation, which make them hardly comparable to other sectors.

It is not well established in the literature whether taxes and ESG are effectively linked.3 Recently [36] find a negative relationship between Korean firms’ ESG scores and tax avoidance, measured in terms of book-tax income differences computed during 2011-2017. This suggests that high ESG firms pay high taxes, and the authors contend that such behavior is explained by corporate culture theory [25,29], as firms with good corporate social performance are not tempted to manipulate taxable profits. In contrast, [27] illustrate that corporate social responsibility engagement reduces the reputation risk of tax avoidance, thus hedging the value of more aggressive firms. [9] point to heterogeneous effects, as the analysis of Compustat firms during 2002-2011 reveals that corporate social responsibility is negatively related to five-year cash effective tax rates, while positively related to tax lobbying expenditures. Our article extends this previous evidence, testing whether in the insurance sector the interplay between taxes and ESG is economically significant.

3. Data and Variables

We use Standard and Poor’s Capital IQ, and select all property and casualty (P&C) insurers worldwide that report not missing annual information on ESG scores from 2013 until 2022. All firms are operating and publicly listed at the end of 2022, while firms that went bankrupt or were subject to mergers and acquisitions (M&As) before 2022 are excluded from our sample. For the same companies we also download from the category “S&Ps Global Universal Financials” balance sheet and income statement figures.4 The Appendix reports the list of the companies included in our sample, separated into geographical regions.

In the next section we conduct a regression analysis that uses the following variables, for which Table 1 reports descriptive statistics, while Table 2 reports correlation coefficients.

E S G

is the ESG score computed by S&P Capital IQ, i.e. a discrete number ranging 0-100 reflecting the performance of the company on key environmental, social, and governance issues according to an industry specific assessment methodology and aggregation schemes. Higher values of ESG scores indicate a stronger performance on sustainability practices. The S&P ESG ratings are based on the Corporate Sustainability Assessment (CSA), beside on information provided directly to S&P and certified by analysts, as well on public domain information. The separate pillars are called E, S, and G, and the scale is again 0-100.5

Using accounting figures, we measure the company’s profitability.

R O A

is the return-on-assets, i.e. the ratio of net income to total assets. For robustness, we also test the return-on-equity, and

R O E

is the ratio of net income to book value equity. Both measures are widely employed in the industry as well in the academia as proxies for profitability, and we will use the two quantities as dependent variables in our regressions.

To approximate corporate taxes, we use the effective tax rate, i.e.

E T R

is the ratio of income tax expenses to earnings before taxes (including unusual items).

E T R

is an approximation of aggressive tax reporting, defined as downward manipulation of taxable income through tax planning, that may or may not be considered fraudulent tax evasion. [14]. In general, tax aggressiveness is not measurable, therefore researchers use proxies to assess companies’ behaviors addressed to avoid taxes. Among these quantities,

E T R

is much largely used in the literature [13,19,20,22,23], and is based on the assumption that a huge weight of tax liabilities in respect to pre-tax income reflects a low incentive to avoid taxes. On average,

E T R

in our sample is close to 20% (see Table 1). For example, [9] estimate that the average

E T R

computed across all Compustat firms during 2002-2011 was 26%.6 For robustness, we test two additional measures of tax burden, i.e. the log-amounts of income taxes (

I N C T X

) and of provisions for taxes (

P R O V T X

). The quantities

E T R

I N C T X

, and

P R O V T X

are the independent variables in the regressions of the next section.

Finally, all our specifications control for corporate size, and

S I Z E

denotes the natural logarithm of total assets. Table (8) in the Appendix summarizes the definitions of our variables.

In Table 3 we separate our firms into four quantiles of

E S G

and compute averages of variables. Firms in the first (fourth) quantile are the least (most) sustainable. We observe that the most sustainable insurers have the highest

E T R

equal to 23.7%, which is approximately 28% higher than

E T R

of least sustainable insurers. The most sustainable insurers are also less profitable, as their

R O A

is 1.7% , i.e. 15% smaller than

R O A

of least sustainable insurers.

4. Results

Our goal is to test whether effects from sustainability on taxes would impact financial performances. Therefore, we first check the relationship between profitability and taxes without controlling for sustainability. In this way, we can establish if important changes in this association would occur as we consider sustainability effects. Our regressions are summarized with the following equation (1), where the subscripts j and t denote respectively the company and the year:

\begin{matrix} {P r o f i t a b i l i t y}_{p, j, t} = α_{0} + α_{1} {T a x e s}_{, s, t - 1} + α_{2} {S I Z E}_{, j, t - 1} + τ_{t} + ω_{j, t} . \end{matrix}

(1)

The subscript p indicates insurer j’s profitability, measured alternatively by

R O A

and

R O E

. The subscript s denotes the measure for taxes among

E T R

I N C T X

, and

P R O V T X

τ_{t}

are time fixed effects, while

ω_{j, t}

is the error term. Standard errors are robust.7

We find that taxes are positive and significant in all regressions, meaning that increasing taxation seems to enhance financial performances. Now, implementing a two-step instrumental variable procedure, we consider the effect of sustainability on the estimated relationship. In the first step we regress taxes on ESG scores, while in the second step we regress profitability on the series of values predicted from the first step regression.8 The procedure is summarized as follows:

\begin{matrix} {T a x e s}_{s, j, t} = α_{0} + α_{1} {E S G}_{, j, t - 1} + α_{2} {S I Z E}_{, j, t - 1} + τ_{t} + ω_{j, t} \\ {P r o f i t a b i l i t y}_{p, j, t} = β_{0} + β_{1} {\hat{T a x e s}}_{s, j, t} + ψ_{t} + ϵ_{j, t} . \end{matrix}

(2)

\hat{T a x e s}

are taxes predicted from the first step regression.

τ_{t}

and

ψ_{t}

are time fixed effects, while

ω_{j, t}

and

ϵ_{j, t}

are error terms. Standard errors are robust. With this method, we measure the effect on profitability from taxes predicted by sustainability, at the net of size effects and time fixed effects. Table 5 reports the outcomes from the second step regression of

R O A

(Panel A) and

R O E

(Panel B). We observe that, differently than in Table 4, the sign on taxes is negative. That is, profitability declines in the taxes explained by corporate sustainability. This pattern would reveal that insurers do not shift a considerable share of their tax bills to their customers.

Table 4. Results of OLS models in (1) with dependent variables

R O A

and

R O E

. See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 4. Results of OLS models in (1) with dependent variables

R O A

and

R O E

. See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	ROA	ROA	ROA	ROE	ROE	ROE
ETR	0.0094			0.0151
	(0.9162)			(0.5211)
INCTX		0.0023***			0.0170***
		(3.8295)			(5.2048)
PROVTX			0.0017***			0.0125***
			(2.7457)			(3.7443)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Observations	305	319	305	305	321	307
R-squared	0.1112	0.0654	0.0466	0.0335	0.0878	0.0552

Table 5. Results of second step regression in (2) for

R O A

(Panel A) and

R O E

(Panel B). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 5. Results of second step regression in (2) for

R O A

(Panel A) and

R O E

(Panel B). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Panel A
	(1)	(2)	(3)
Variables	ROA	ROA	ROA
$\hat{E T R}$	−0.1670 **
	(−2.044)
$\hat{I N C T X}$		−0.0028 ***
		(−3.2962)
$\hat{P R O V T X}$			−0.0030 ***
			(−3.5023)
Controls	Yes	Yes	Yes
R-squared	0.0218	0.0696	0.0671
Panel B
	(1)	(2)	(3)
Variables	ROE	ROE	ROE
$\hat{E T R}$	−0.4297 *
	(−1.5912)
$\hat{I N C T X}$		−0.0057
		(−1.2511)
$\hat{P R O V T X}$			−0.0075 *
			(−1.6483)
Controls	Yes	Yes	Yes
R-squared	0.0219	0.0672	0.0671

In order to test the robustness of the baseline outcomes, we estimate simultaneous equations systems, in which we contemporaneously determine profitability, effective tax rates, and corporate sustainability. In this way, we test how ESG scores at t relate to profitability and taxes at the same point in time.9 The equations are summarized as follows:

\begin{matrix} {T a x e s}_{s, j, t} = γ_{0} + γ_{1} {E S G}_{j, t} + γ_{2} {S I Z E}_{j, t} + ζ_{j, t} \\ {P r o f i t a b i l i t y}_{p, j, t} = λ_{0} + λ_{1} {E S G}_{j, t} + η_{j, t} . \end{matrix}

(3)

Table 6 displays results for the equations systems in (3) for

R O A

(Panel A) and

R O E

(Panel B). Overall, the simultaneous equations systems exhibit results consistent with the regression models in (2). In the large majority of the systems we estimate a positive and significant sign on

E S G

in the first equation, while a negative and significant sign on

E T R

in the second equation. That is, assuming profits and taxes are determined jointly, we observe that insurers’ profitability declines in the taxes explained by sustainable practices. The sign of

E S G

E T R

is positive and highly significant, suggesting that taxes increase with corporate sustainability, in line with previous evidence of [36].

Finally, we report estimates for the instrumental variable model in (2) testing the separate E, S, and G pillars.10 In the panels of Table 7 we show second step regressions of

R O A

on the three aspects of sustainability. The estimated signs are always negative, higher in magnitude for S and E, while G does not have explanatory power for

E T R

. This pattern is plausible, as we consider that corporate tax policies are strictly connected to the social pillar of ESG. The social element of ESG principles typically provides a perspective that focuses on the human aspects of business, such as human rights, labor standards, social justice, pay equity, product safety, community engagement, and inclusion. Companies could demonstrate their commitment and build trustfully relationships with stakeholders by means of tax policies, like for example tax credits or enhanced transparency in the tax reporting frameworks.

5. Conclusions

Taxes are a crucial part in the transition of the economy to ESG. Especially for insurers, where income calculation is particularly challenging due to the unique nature of the business, taxes should feature prominently on the ESG agenda. Analysing worldwide P&C insurers during 2013-2022, we show that high ESG insurers pay more taxes while are less profitable than low ESG insurers. Our argument is that, incorporating ESG in their business, insurers are less tempted to avoid taxes, and don’t shift tax burdens to policyholders.

These results deliver important insights for insurance managers and policymakers. Managers should consider that integrating ESG in the business would require also a careful tax planning, aimed at preventing negative consequences on profitability, like decreased competitiveness [33] or harmed reputation [8].

Policymakers would learn that strong ESG performances can potentially increase customers’ welfare, as policyholders of high ESG insurers seem to pay premiums that do not bear huge tax shifts. However, our findings raise also some warnings, as regulators and policymakers should implement tax policies aimed to avoid that incorporating ESG would lead firms to face high tax expenditures, which could ultimately reduce both investment [12] as well as economic growth [17].

Therefore, our results corroborate the recent interventions of regulators all over the world giving taxes a key role in stimulating sustainable behaviors among taxpayers. For example, the European Commission has included a number of tax measures in the European Green Deal, such as the Carbon Border Adjustment Mechanism (CBAM)11, the Energy Taxation Directive (ETD)12, and the country-by-country reporting Directive (CbCR)13 The United States Inflation Reduction Act (IRA)14 is also based on a wide range of tax credits and incentives that are meant to facilitate the United States economy’s sustainable transformation.

Overall, interventions on the taxation of insurance companies should always take into consideration the importance of sustainable insurers for the transition of the whole economy to ESG. For example, the Geneva Association highlights the key role of insurers in creating “social benefits by weaving social considerations through their core insurance activities”.15 The European Insurance and Occupational Pensions Authority (EIPOA) gives emphasis to the role of insurers in tackling climate change, filling the existing gap in worldwide insurance against catastrophes and climate related events.16

While this article contributes to fill an important gap in the knowledge surrounding ESG and taxes of insurers, further tasks were not addressed and left to future research. For example, we measured taxes primarily using the company’s effective tax rate, which nonetheless does not capture not conforming tax avoidance. For this reason, scholars have implemented also other measures for tax aggressiveness based on publicly available financial statement data. A few measures suggested by the literature include for example the cash effective tax rate [13], the book tax different [35], and the permanent book tax different [31]. [14] uses the discretionary value/residual value of the permanent book tax difference to develop the discretionary permanent different, which would be a quantity more suitable to capture conforming tax avoidance. Therefore, we leave to accounting research the task of corroborating our results using these (and in case other) measures, verifying how sustainability would induce changes in tax behaviors and tax compositions, ultimately reflecting on profits.

Moreover, to research focused on sustainability issues, we leave the goal of extending our approach to other businesses, testing whether also in other sectors the interplay between taxes and sustainability would sort a (not negligible) economic impact on profitability. It would be interesting to address this question on industrial firms in relation to the environmental aspect, as the E pillar is much affected by recent tax policies.

Finally, the changes in tax regulations mentioned above could be used as an exogenous shock for event studies aimed at testing whether firms would effectively be more transparent, without that the new regimes could make them financially weaker. These analyses require a sufficient number of observations, which in respect to the more recent regulatory interventions are not available yet, therefore will be part of future research agendas.

Appendix A. Appendix

List of companies in the sample:

Asia-Pacific: Anicom Holdings Inc., DB Insurance Co. Ltd., Dhipaya Group Holdings Public Company Limited, Dream Incubator Inc., Hyundai Marine & Fire Insurance Co. Ltd., ICICI Lombard General Insurance Company Limited, Insurance Australia Group Limited, MS&AD Insurance Group Holdings Inc., Meritz Financial Group Inc., QBE Insurance Group Limited, Samsung Fire & Marine Insurance Co. Ltd., Shinkong Insurance Co. Ltd., Sompo Holdings Inc., Suncorp Group Limited, The People’s Insurance Company (Group) of China Limited, Tokio Marine Holdings Inc.

Europe: Admiral Group Plc, Alm. Brand A/S, Beazley Plc, Chubb Limited, Direct Line Insurance Group Plc, Linea Directa Aseguradora S.A., Sabre Insurance Group Plc, Tryg A/S.

Latin America and Caribbean: Qualitas Controladora S.A.B. de C.V.

Middle East: Qatar Insurance Company Q.S.P.C.

Unite States and Canada: AMERISAFE Inc., AXIS Capital Holdings Limited, Ambac Financial Group Inc., American Financial Group Inc., Arch Capital Group Ltd., Argo Group International Holdings Ltd., Assured Guaranty Ltd., Cincinnati Financial Corporation, Employers Holdings Inc., Erie Indemnity Company, Fairfax Financial Holdings Limited, HCI Group Inc., Hallmark Financial Services Inc., Heritage Insurance Holdings Inc., Hiscox Ltd, Intact Financial Corporation, James River Group Holdings Ltd., Kemper Corporation, Kinsale Capital Group Inc., Lancashire Holdings Limited, Loews Corporation, Markel Corporation, Mercury General Corporation, Old Republic International Corporation, Palomar Holdings, Inc., ProAssurance Corporation, RLI Corporation, Safety Insurance Group Inc., Selective Insurance Group Inc., The Allstate Corporation, The Hanover Insurance Group Inc., The Progressive Corporation, The Travelers Companies Inc., Trisura Group Ltd., United Fire Group Inc., United Insurance Holdings Corp., Universal Insurance Holdings Inc., W. R. Berkley Corporation, White Mountains Insurance Group Ltd.

1	https://www.oecd-ilibrary.org/taxation/taxing-insurance-companies_9789264188396-en.
2	https://www.oecd-ilibrary.org/taxation/taxing-insurance-companies_9789264188396-en
3	Analyzing data from 2003 to 2020, [24] use a scientometric approach to investigate the nexus between corporate social responsibility and corporate tax aggressiveness research.
4	This information is provided based on the company filings.
5	More information on the methodology employed by S&P Capital IQ to compute ESG ratings can be found at https://www.spglobal.com/esg/csa/methodology/. While the majority of the studies use levels of ESG scores, we checked that using natural logarithms of ESG scores [6] won’t change the quality of our outcomes.
6	Following the definition of [14], tax reporting aggressiveness would reflect a broad range of activities, e.g. transfer pricing arrangements, location of intangible property in low-tax locations, utilization of flow-through entities in structured transactions, synthetic lease arrangements, and tax shelter transactions. We acknowledge that countries present different accounting systems. Nonetheless, we opted to include all geographical regions within our sample instead than focusing only on one country, because ESG ratings for insurance companies are available in the database starting from 2013. Thus, if we would select only one country, our sample would be too small for conducting meaningful statistical analyses. In fact, one caveat in the study of insurers’ ESG scores, is that datasets assembled combining (annual) ESG scores and balance sheet items may result to have limited size. Financial intermediaries, like insurers and banks, are hardy comparable to industrial firms, due to the unique nature of their business and regulation. Therefore, it is appropriate to analyze them separately. This, however, poses an issue in collecting a sufficient number of observations for econometric and statistical analyses. Therefore, as we build our sample, we decided to include all P&C insurers for which the database provided ESG scores, so that our firms can be regarded to be homogeneous in terms of business model and (to a larger extent) taxation issues. Nonetheless, to overcome the concern that reporting differences across countries could drive the empirical outcomes, we have estimated our regressions also clustering standard errors by country or by geographical region. The results are qualitatively similar to the results reported in the paper and are available upon request.
7	We tested also models where we clustered standard errors by firm, by geographical regions, or by country. The results are consistent with those reported in the text and are available upon request.
8	We have tested also models with contemporaneous $E S G$ , and obtained results consistent with those reported in the paper. These outcomes are available upon request.
9	We verified that results do not change in quality as we approximate taxes with $I N C T X$ and $P R O V T X$ . These results are available upon request.
10	We have tested the three pillars also on $R O E$ , and also using the simultaneous equations systems in (3). Results don’t change in quality and are available upon request.
11	https://taxation-customs.ec.europa.eu/carbon-border-adjustment-mechanism_en.
12	https://taxation-customs.ec.europa.eu/green-taxation-0/revision-energy-taxation-directive_en.
13	https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32021L2101.
14	https://www.congress.gov/117/bills/hr5376/BILLS-117hr5376enr.pdf.
15	https://www.genevaassociation.org/sites/default/files/2022-11/social_sustainability_report.pdf.
16	https://www.eiopa.europa.eu/publications/role-insurers-tackling-climate-change-challenges-and-opportunitiesen .

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Table 1. Descriptive statistics. See appendix Table 8 for the definitions of all variables.

	Mean	Median	Min	Max	St Deviation	N
$E S G$	31.54	22.50	1	85	23.19	388
E	27.56	16	0	97	29.97	388
S	25.1	15	0	86	25.66	388
G	38.71	35	1	87	20.89	388
$R O A$	0.023	0.020	0.000	0.118	1.994	354
$R O E$	0.109	0.101	0.000	0.583	11.280	357
$E T R$	0.198	0.205	0.000	0.847	17.66	332
$I N C T X$	11.735	11.86	7 4.771	15.949	1.753	348
$P R O V T X$	11.691	11.783	6.457	15.945	1.724	333
$S I Z E$	16.953	17.131	10.290	21.042	1.596	357

Table 2. Correlation coefficients. See appendix Table 8 for the definitions of all variables. *** p<0.01, ** p<0.05, * p<0.1.

	ESG	E	S	G	ROA	ROE	ETR	INCTX	PROVTX	SIZE
$E S G$	1.000
E	0.964***	1.000
S	0.979***	0.947***	1.000
G	0.951***	0.870***	0.887***	1.000
$R O A$	-0.127***	-0.149**	-0.148**	-0.078	1.000
$R O E$	-0.002	-0.033	-0.002	0.027	0.800***	1.000
$E T R$	0.165**	0.151**	0.188***	0.138*	0.055	0.035	1.000
$I N C T X$	0.172**	0.154**	0.159**	0.200***	0.179**	0.275***	0.222***	1.000
$P R O V T X$	0.168**	0.144**	0.148**	0.205***	0.126*	0.207***	0.223***	1.000***	1.000
$S I Z E$	0.259***	0.264***	0.231***	0.292***	-0.105*	0.049	0.001	0.846***	0.838***	1.000

Table 3. Averages of variables inside quantiles of

E S G

. See appendix Table 8 for the definitions of all variables.

Table 3. Averages of variables inside quantiles of

E S G

. See appendix Table 8 for the definitions of all variables.

Quantile	$E T R (%)$	$I N C T X$	$P R O V T X$	$R O A (%)$	$R O E (%)$
1	16.9833	10.6697	10.6023	2.0501	7.6192
2	18.2180	11.7594	11.7432	2.3844	11.4781
3	19.6513	12.3129	12.2860	2.8088	14.4319
4	23.7482	12.0489	11.9629	1.7479	9.7313

Table 6. Results of simultaneous equations systems in (3) for

R O A

(Panel A) and

R O E

(Panel B). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 6. Results of simultaneous equations systems in (3) for

R O A

(Panel A) and

R O E

(Panel B). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. z-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Panel A
Variables	ETR	ROA	INCTX	ROA	PROVTX	ROA
$E S G$	0.0012 ***		0.0020		0.0027 *
	(2.6991)		(1.3083)		(1.7023)
$E T R$		−0.1595 **
		(−2.3397)
$I N C T X$				−0.0024 ***
				(−3.0675)
$P R O V T X$						−0.0032 ***
						(−3.9293)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Observations	305	305	319	319	305	305
R-squared	0.0601	−2.7773	0.0719	−0.1177	0.0703	−0.1551
Panel B
	(1)	(2)	(3)	(4)	(5)	(6)
Variables	$ETR$	$ROE$	$INCTX$	$ROE$	$PROVTX$	$ROE$
$E S G$	0.0013 ***		−0.0011		−0.0012
	(2.7581)		(−0.5753)		(−0.6445)
$E T R$		−0.4411 *
		(−1.8171)
$I N C T X$				−0.0025
				(−0.6143)
$P R O V T X$						−0.0078 *
						(−1.8531)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Observations	305	305	319	319	305	305
R-squared	0.0691	−0.0797	0.0722	0.0161	0.0710	0.0601

Table 7. Results of second step regression in (2) for

R O A

explained by E (Panel A), S (Panel B), and G (Panel C). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 7. Results of second step regression in (2) for

R O A

explained by E (Panel A), S (Panel B), and G (Panel C). See appendix Table 8 for the definitions of all variables. Controls include a constant term,

S I Z E

, and time fixed effects. t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Panel A: Effects from Taxes Explained by E
	(1)	(2)	(3)
Variables	ROA	ROA	ROA
$\hat{E T R}$	−0.1723 **
	(−2.0827)
$\hat{I N C T X}$		−0.0027 ***
		(−3.2092)
$\hat{P R O V T X}$			−0.0029 ***
			(−3.4059)
Controls	Yes	Yes	Yes
Observations	254	252	263
R-squared	0.0146	0.0675	0.0675
Panel B: Effects from Taxes Explained by S
	(1)	(2)	(3)
Variables	$ROA$	$ROA$	$ROA$
$\hat{E T R}$	−0.1423 **
	(−2.4425)
$\hat{I N C T X}$		−0.0028 ***
		(−3.2807)
$\hat{P R O V T X}$			−0.0029 ***
			(−3.4611)
Controls	Yes	Yes	Yes
Observations	254	252	263
R-squared	0.0258	0.0694	0.0694
Panel C: Effects from Taxes Explained by G
	(1)	(2)	(3)
Variables	$ROA$	$ROA$	$ROA$
$\hat{E T R}$	−0.2134
	(−1.4122)
$\hat{I N C T X}$		−0.0029 ***
		(−3.3854)
$\hat{P R O V T X}$			−0.0031 ***
			(−3.6197)
Controls	Yes	Yes	Yes
Observations	254	252	263
R-squared	0.0095	0.0640	0.0641

Table 8. Definition of variables.

Variable	Definition
E	Company’s environmental score. The environmental score is a discrete number and ranges 0-100.
$E S G$	Company’s environmental, social, and governance (ESG) score. The ESG score is a discrete number and ranges 0-100.
$E T R$	Ratio of income tax expenses to earnings before taxes (including unusual items)
G	Company’s governance score. The governance score is a discrete number and ranges 0-100.
$I N C T X$	Natural logarithm of income taxes.
$P R O V T X$	Natural logarithm of provisions for taxes.
$R O A$	Ratio of net income to total book value assets.
$R O E$	Ratio of net income to total book value equity.
S	Company’s social score. The social score is a discrete number and ranges 0-100.
$S I Z E$	Natural logarithm of total assets.

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ESG, Taxes, and Profitability of Insurers

Abstract

1. Introduction

2. Review of the Literature

3. Data and Variables

4. Results

5. Conclusions

Appendix A. Appendix

References

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