# Imperfect information and the reputational proximity spillovers issue in the product-related CSR-CFP relationship: A study of 125 major listed European companies from 2010 to 2018

Pascal Ricordel, Melinda Majláth

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Pascal Ricordel, Melinda Majláth. Imperfect information and the reputational proximity spillovers issue in the product-related CSR-CFP relationship: A study of 125 major listed European companies from 2010 to 2018. Journal of Cleaner Production, 2025, 525, 10.1016/j.jclepro.2025.146578. hal-05354594

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# Imperfect information and the reputational proximity spillovers issue in the product-related CSR-CFP relationship: A study of 125 major listed European companies from 2010 to 2018

Pascal Ricordel<sup>a,*</sup> <sup>ID</sup>, Melinda Majláth<sup>b, ID</sup>

## ARTICLE INFO

**Keywords:**

Corporate social responsibility

Corporate financial performance

Spillover effect

Reputational proximities

Contamination effect

Imperfect information

## ABSTRACT

In the corporate social responsibility (CSR)–corporate financial performance (CFP) relationship, the stakeholder dimension coupled with the intangible nature of CSR generates an imperfect information environment with manipulation and misperception risks. In this context, reputation enters the relationship as a signal to fill the informational gap and reduce uncertainty. However, there are multiple reputation sources, including firm, product, industry, and country-of-origin (C-O-O) sources, with strong interdependence and therefore spillover effects. This study introduces the hypothesis of the reputational proximity spillover effect across industries and countries of origin in the empirical CSR-CFP relationship. Using a social distance weight matrix for assessing proximity, reputational spillovers are estimated with a dynamic system GMM on a panel of 125 major listed European corporations operating in four sectors, namely construction, consumer goods, energy, and engineering, is observed from 2010 to 2018. Focusing on product-related CSR, spillover contamination effects are very robustly demonstrated regardless of whether return on assets (ROA) or return on equity (ROE) is used for estimating financial performance.

## 1. Introduction

The stakeholder approach introduced by Freeman (1984) in the corporate social responsibility (CSR)–corporate financial performance (CFP) relationship presents CSR initiatives in which companies integrate social and environmental concerns as resources to increase support from consumers, investors, employees, creditors, and policy makers. This stakeholder dimension, coupled with the intangible nature of CSR, whereby the benefits cannot be assessed immediately and appropriately (Benabou and Tirole, 2010), is noted in the literature to generate a context in which information is very imperfect (Orlitzky, 2008, 2013; Wang and Choi, 2010). This context paves the way for manager manipulation and distortion (García-Sánchez et al., 2021; Cho et al., 2012), with strong decoupling risks when symbolic communication pertaining to CSR is done in place of substantive change (Delmas and Burbano, 2011; Bromley and Powell, 2012; Bothello et al., 2023). Uncertainty with regard to the quality of information can therefore generate scepticism among stakeholders, whose perceptions are pivotal

in mediating the relationship between CSR disclosure and organizational outcomes (Petrenko et al., 2016; Gu, 2023).

In this uncertainty context, CSR initiatives and disclosures are considered from a signal channel perspective to bridge the informational gap within the market by conveying qualitative information from firms to stakeholders (Orlitzky, 2008, 2013). In the signalling channel, reputation, which is defined as collective perceptions by the stakeholders of past activities and the beliefs of a firm regarding its future activities (Newburry et al., 2012), is viewed as an intangible and profitable key resource for firms generated by their CSR initiatives (Fombrun and van Riel, 1997; McWilliams and Siegel, 2001; Brammer and Pavelin, 2004, 2016; Zhao et al., 2021; Ruiz and Garcia, 2021; Bothello et al., 2023). However, in the signal transmission, the sense-making process of stakeholders is pivotal (Xu et al., 2023) and if, from the sender side, CSR is a resource for building reputation, from the stakeholder side, reputation is a resource for assessing perceived CSR signals. For this purpose, reputational environment beyond firm boundaries is central to the CSR signal perception (Park and Patel, 2015; Sekerci et al., 2022).

<footnote><sup>a</sup> Université Le Havre Normandie, EDEHN (EA7263), France</footnote>

<footnote><sup>b</sup> Budapest University of Economics and Business, Hungary</footnote>

<footnote>* Corresponding author.
E-mail addresses: pascal.ricordel@univ-lehavre.fr (P. Ricordel), majlath.melinda@uni-bge.hu (M. Majláth).</footnote>
From stakeholder side, there are multiple source of reputation sig-
nals perceived, and beyond those attached to the firm and product, two
signals perceived from the firm environment are frequently shown to
interfere with halos effects: the reputation signals associated with the
industry and the country-of-origin (C-O-O). industry reputation signal
generates a process (Conte et al., 2023) by which firms are categorized
as a controversial industry (e.g., tobacco, oil) or noncontroversial in-
dustry (Barnett and King, 2006) whereas C-O-O is a visible and consis-
tent signal that influences the effectiveness of the reputational signal of a
firm (Jin et al., 2008; Brodie and Benson-Rea, 2016; Magnusson et al.,
2011; Cowan and Guzman, 2020). Reputation interdependencies and
halo effects with CSR spillovers have recently been shown for industry
(McWilliam and Siegel, 2000; Barnett and Hoffman, 2008; Moliner-Tena
et al., 2019; Melo and Garrido Morgado, 2012; Rouvière and Soubeyran,
2011) and for C-O-O in the product-related CSR dimension (Sapic et al.,
2018; Cowan and Guzman, 2020). These halo effects distort corporate
reputational assessments in the form of a negative or positive spillover
effect on a company's reputation on the basis of the categorization of its
industry or its C-O-O. This effect constitutes a transfer of the reputation
of a firm's industry or C-O-O to the firm itself.

However, while these studies have empirically evidenced reputa-
tional spillovers and CSR, there is no attempt yet to integrate and
measure them in the CSR-CFP relationship. The aim of this study is to fill
this gap by testing the hypothesis that having a certain C-O-O or being
part of a certain industry impact the corporation's CSR-CFP relationship
in a contamination sense for product-related CSR. The contribution of
this study to the literature is twofold: This study is the first to integrate
reputational spillovers in a CSR-CFP empirical relationship to measure
the contamination effect of C-O-O and industry CSR scores on CFP.
Additionally, this study contributes to deepening the understanding of
proximity spillovers in the CSR-CFP relationship by considering the
proximity spillovers that were generated due to the high degree of
imperfect information. Only recently have proximity spillovers been
introduced in empirical measures of the CSR-CFP relationship. How-
ever, approaches that integrate proximity spillovers are very context-
specific in terms of geography (Chintrakarn et al., 2017; Gu, 2023),
scandals (Jourdan et al., 2019) and financial instability (Jahmane and
Gaies, 2020). Our study differs in that it explicitly relies on imperfect
information and signal theory (Orlitzki 2008, 2013; Cui et al., 2018;
Clarkson et al., 2008) in the stakeholder approach.

To conduct this study, reputational proximity spillovers are exoge-
nously introduced in a dynamic endogenous CSR-CFP model with
system-GMM estimators using a social lag matrix combining industry
and C-O-O. The combination of the C-O-O and industry allows industry
to be kept as a dummy variable in the equation, and the dynamic
specification associated with system-GMM allows us to eliminate
endogeneity concerns regarding this relationship that are common. The
selected panel is made up of 125 major European companies listed on 12
stock indices (FTSEE 100, DAX 30, CAC 40, etc.) operating in the con-
struction, consumer goods, energy and engineering sectors. These sec-
tors are selected due to their high degree of uncertainty with regard to
the quality of information in CSR disclosure from a stakeholder point of
view: food safety scandals are frequent in the consumer goods sector,
energy and construction are controversial sectors, and engineering is a
complex sector to track. The European context is conducive to the study
of strong national and sectoral proximity across companies. The study
period, 2010-2018, is the period after the financial crisis but before the
COVID-19 pandemic, and it was marked by a sharp increase in stake-
holders' interest in CSR performance and by stricter European regula-
tions regarding the disclosure of CSR information (European Directive,

2003/51/EC and 2014/95/UE).

The CSRHub database is used measure the CSR score. The rating methodology involves the aggregation and normalization of numerous and diverse independent sources. The product-related CSR score is used in this study, as C-O-O reputational spillovers are described by the literature as associated with products and brands. The product-related CSR score in the CSRHub database covers the company's responsibility for the development, design and management of its products and services and their impacts on customers and society as a whole.

To measure the CFP dependent variable, we use well-established
quantitative measures of accounting performance, namely, return on
assets (ROA) and return on equity (ROE), as well as market performance
(Qtobin) using the Orbis database. The model is a dynamic endogenous
fixed-effects model with CFP as the dependent variable, and the lagged
dependent variables are on estimated the right side of the model (Ullah
et al., 2018). The CSR and CFP scores of proximate corporations are
variables of interest, and control variables are those identified in the
literature (size, leverage, investment, R&D, slack resources, industry
profitability). The Arellano-Bover/Blundell-Bond system GMM esti-
mation with robust VCE (El Ghoul et al., 2018) based on the instru-
mental variable technique is used, which has the advantages of
eliminating endogeneity and unobservable variable problems.

The results of this study with the product-related CSR score show a
very significant contamination effect from proximate corporations
associated with C-O-O and industry, demonstrating the importance of
proximity reputational spillovers associated with the product-related
CSR component irrespective of ROA or ROE. The results also show a
negative impact of a firm's own CSR score on its financial performance
(ROA & ROE).

The remainder of this paper is organized as follows: the framework and hypotheses are developed in section 2, and the research methodology is presented in section 3. The results are presented in section 4, and the implications, limitations and research opportunities are presented in section 5.

## 2. Framework and hypothesis development

The proximity spillover effects in the CSR-CFP relationship refer to
the influence that the CSR of proximate firm j can have on the CFP of
firm i independently of the CSR of firm i. These effects are rooted in the
imperfect information environment associated with the stakeholder
ecosystem in the CSR-CFP relationship and in the intangible nature of
CSR, which is “a concept whereby companies integrate social & envi-
ronmental concerns in their business organization and in their interac-
tion with their stakeholders on a voluntary basis” (EU commission,
2011). Firms can create a competitive advantage by voluntarily
engaging in CSR activities (Freeman, 1984); customers, employees, in-
vestors, creditors, and regulators positively value firms that behave
responsibly and achieve environmental and societal goals. However, the
asymmetry of information between managers and stakeholders hinders
the manager-stakeholder relationship and allows managers to engage in
CSR manipulation to mislead stakeholders regarding the real practices of
the firm (Delmas and Burbano, 2011; Garcia-Castro and Arino, 2010).
Therefore, agency theory and signal theory, which address corporate
reputation and cognitive distortion, are appropriate theoretical frame-
works for understanding the proximity reputational spillover
phenomenon.

As stressed by Orlitzky (2013), cognitive distortion and market noise
are inherent to the CSR-CFP relationship. Noise refers to information
unrelated to a company's economic prospects (Black, 1986) and can
prompt stakeholders to make mistakes when it is viewed as information with valid explanatory or predictive power about business fundamentals (Schleifer, 2000). Two noise sources characterize the CSR–CFP relationship: the asymmetry of information about CSR and the ambivalent impact of CSR on CFP (Orlitzky, 2013).

In this context, signalling theory posits that CSR initiatives and disclosures convey signals to stakeholders to fill the informational gap (Forcadell and Aracil, 2023). For example, Lys et al. (2015) argue that firms' engagement in CSR expenditures correlates with their future financial outlooks, implying that a component of CSR expenditures may signal private information about a firm's future financial prospects. Reputation, in the signal channel has a key role and voluntary societal and environmental disclosures in CSR reporting are key elements which aim at building a reputation (Basdeo et al., 2006; Perez, 2015; Hetze, 2016; McWilliams and Siegel, 2000; Bothello et al., 2023; Xu et al., 2023) and establishing a corporate identity (Campbell, 2007; Connelly et al., 2011). Fombrun and van Riel (1997) argue that reputation increases stakeholders' confidence in firms' products and services. However, reputation is a collective and stable perception of the past activities and beliefs of a firm regarding its future activities (Newburry et al., 2019; Walker and Wan, 2012) and while reputation is demonstrated positively correlated with CSR (Brammer and Pavelin, 2004, 2016; Khan and Sukhotu, 2020; McWilliams and Siegel, 2001; Ruiz and Garcia, 2021), the sense-making process and stakeholders' perception are pivotal in mediating the relationship between CSR disclosure and organization performance (Dilling, 2011; Petrenko et al., 2016; Gu, 2023; Xu et al., 2023).

The existence of reputational proximity spillovers have been demonstrated for scandals associated with a firm or product reputation. Stakeholders then perceive that companies can send false signals and creatively manage their reputation (Branco and Rodriguez, 2006; McWilliams and Siegel, 2001; 2010); the literature describes this phenomenon as the decoupling risk that characterizes a divergence between CSR communication and CSR practices (Bromley and Powel, 2012). When a scandal happened, a strong contamination spillover effect occurs on proximate companies because stakeholders tend to assume that neighbouring firms may be involved in similar misconduct (Paruchuri and Misangyi, 2015; Yenkey, 2018; Piazza and Jourdan, 2018; Zeng et al., 2018; Chen and Tao, 2020). These reputational proximity spillovers associated with stakeholders' perception have received considerable attention in the corporate reputational literature which highlights the interdependence of firm reputational signals with those issued beyond firm boundaries (Barnett and Hoffman, 2008; Yu and Lester, 2008; Gao et al., 2015; Veil et al., 2016; Veil and Dillingham, 2020), particularly those originated in industry (Melo and Garrido-Morgado, 2012; Cowan and Guzman, 2020; Calvo-Iriarte et al., 2024) and in C-O-O (Tezer and Tofighi, 2021; Kang and Yank, 2010; Kim, 2016; Kelley et al., 2018). For Industry and C-O-O reputation signals, the literature has demonstrated it exists some halo effects. A halo effect is the generalization of an individual attribute to the whole or, in the other direction, the individualization of a collective attribute.

Categorization theory explains the halo effect by positing that people efficiently process information by categorizing new stimuli in a way that emphasizes similarities among objects (Mervis and Rosh, 1981; Meyers-Levy and Tybout, 1989) and that new objects are classified through comparisons with attributes of a representative example (Loken et al., 2008). This theory is accurate in the context of product recalls, whereby Gong (2024) noted that peer firms' product recall events generate

negative reputation spillovers and attract strong scrutiny from stakeholders in the focal firms. Since firms in the same industry share similar perspectives and values and experience a common environment (Barnett and King, 2006; Zavyalova et al., 2012) and since stakeholders classify objects, including firms, into categories (Porac and Thomas, 1990), stakeholders may logically assume that if some firms in the industry experience product recalls, other firms may suffer from similar product quality problems (Shi et al., 2020).

First, the halo effects indicate that the reputational signalling environment beyond firm boundaries is therefore central to the signalling process and affects the credibility of CSR signals perceived associated with the firm and product (Park and Patel, 2015; Sekerci et al., 2022). Second, the halo effects indicate that from stakeholder side, there are multiple source of reputation signals perceived associated with CSR, the individual signals perceived about the company and the product and the collective signals about the industry and the country to which the company is a part with interdependence across signals.

$$ \text{Reputation signals perceived } S = f(S_{\text{firm}}, S_{\text{product}}, S_{\text{industry}}, S_{\text{C-O-O}}) $$

The concept of the industrial “halo” and its impact on the evaluation of corporate reputation through stakeholder perceptions was recently coined by Calvo-Iriarte et al. (2024) to describe the transfer of reputation from an industry to a company via a contamination effect. The stakeholders’ perception of CSR signals relies on the company’s reputation ecosystem that includes industry reputation according to Winn et al. (2008) because its reputation is difficult to separate from that of its industry peers, what King et al. (2002) call the reputation commons problem. Corporate reputation is shown by Melo and Garrido-Morgado (2012) as a combination of social responsibility and industry. Industry is a strong signal by itself according to Conte et al. (2023), when the industry is categorized as controversial (for example, tobacco, oil) or noncontroversial. The collective informational judgement of the company and its entire sector determines the impact of the CSR–CFP (Barnett et al., 2006), a point that echoes what the leader of Amoco claimed, “we are still an oil company, and we have to live with the sins of our brothers”.

C-O-O reputation signals are also part of company’s reputation ecosystem with spillovers between the reputation of a country and the reputation of a company that originated in the country (Newburry, 2012; White, 2012). Focusing on consumer perception, product attitude and purchase intention, the reputation literature has clearly showed that C-O-O attribute is a visible and consistent signal that could undermine or enhance the effectiveness of the reputational signal of a firm. C-O-O is associated with cultural factors (Jin et al., 2008; Magnusson et al., 2011), institutional and development-based factors (Kang and Yank, 2010) or sustainability reputation (Cowan and Guzman, 2020) that interfere with CSR signals, so that country of origin branding contribute to corporate brand performance according to Brodie and Benson-Rea (2016).

The halo model of origin image associated with the product or brand image is very pregnant in the marketing literature (Wang et al., 2012; Josiassen et al., 2013) and in the customer loyalty creation process (Sapic et al., 2018). Extended to CSR reputation, Kelley et al. (2018) show that country and corporate reputation create shared value in emerging markets, explaining why multinational companies emphasize their C-O-O in CSR disclosure to add value to their product. The positive halo response in CSR signals includes perceptions of product quality according to Madden et al. (2012), CSR attributes being perceived
through product quality.

This overview of the literature clearly underscores the existence of reputational industry and C-O-O halo effects, which is, however, limited on product aspects for C-O-O.

Interdependence across reputational signals is strongly supported by the theoretical framework described in the literature on firm reputation, and must impact the CSR-CFP relationship. Proximity reputational spillovers in the CSR–CFP relationship can be conjectured in the sense of a contamination spillovers effect. Therefore, we hypothesize that industry and C-O-O reputation are both used as proximate signals by stakeholders and that CSR variation from proximate corporations that belong to the same industry and the same country create contamination effects on the CFP of corporations.

## 3. Methodology

To conduct the study and test the hypothesis, reputational proximity spillovers are exogenously introduced into a dynamic endogenous CSR–CFP model with system-GMM estimators using a social lag matrix combining industry and C-O-O.

### 3.1. Sample

To investigate this hypothesis, we studied a panel data sample of 125 large- or mid-cap European listed companies from 12 countries observed from 2010 to 2018 (1125 firm-year observations). All companies are benchmarked against their national financial market indices and operate in four sectors: energy, construction, engineering, and consumer manufacturing (see Table 7 in the Appendix). The sectors are selected due to their high degree of uncertainty with regard to the quality of information in CSR disclosure: food safety scandals are frequent in the consumer goods sector, energy and construction are controversial sectors, and engineering is a complex sector to track. These sectors are associated with the potential for spillovers. The European context is conducive to the study of strong national and sectoral proximity across companies. The 2010–2018 period is after the financial crisis but before the COVID-19 pandemic and was marked by growing stakeholder interest in CSR, as stricter rules had been issued by the European Commission regarding information disclosure (European Directive, 2003/51/EC and 2014/95/UE).

### 3.2. Variables and data

CFP. The dependent variable, CFP, is represented as either an accounting or market-based measure. CFP is measured using two accounting-based variables— ROA<fnref n="1" /> and ROE—and a market-based variable, Tobin's q (Qtobin). The Orbis database is used. European empirical studies (Meier et al., 2014) show that the signs are different when different dependent variables are used to represent financial performance. Therefore, results regarding the spillover effect should differ when ROA is used compared with when Tobin's q is used. Following the methodology of Ullah et al. (2018), the model is specified as an endogenous model, and two-year lag dependent variables or CFP are introduced.

CSR. The CSRHub score database is used in this study to evaluate the CSR variable. The CSRHub methodology relies on consistent consensus ratings based on a big data algorithm from 754 data sources. The CSRHub method involves mapping, aggregating and normalizing information to provide a data format that uses a single value ranging from 0 to 100 (Lin et al., 2019). CSRHub classifies all CSR dimensions into

four categories: community (COM), employee (EMP), environment (ENV), and governance (GOV). Each of the four categories is divided into three subcategories, and the product-related CSR that is used in the study is one of the three subcategories of the community component (see Table 7 in the Appendix). As highlighted in the theoretical section, proximity spillovers associated with C-O-O are more likely to affect product-related CSR. We therefore focus on this dimension in this study. The “product” subcategory covers a company’s responsibility for the development, design and management of its products and services and their impacts on customers and on society as a whole.

Control Variables. Following the studies of Vanacker et al. (2017) and Meier et al. (2014) for the European context and previous studies by McWilliams and Siegel (2000) and Surroca et al. (2010), several control variables likely to influence CFP are introduced: size, leverage, investment, R&D, and slack resources. Size is an important factor mentioned in any empirical study and is measured in this study with the logarithm of total assets as a proxy (Hillman and Keim, 2001). Leverage is cited in several studies (Waddock and Graves, 1997; Vanacker et al., 2017) and is measured as the level of debt in relation to market capitalization. Investment is proposed as a control variable by Meier et al. (2014) and Chintrakarn et al. (2017) and is measured as the ratio of capital expenditure to total assets. R&D has received considerable attention (Waddock and Graves, 1997; McWilliams and Siegel, 2000; Garcia-Castro and Arino, 2010; Schnippering, 2020). However, because European companies have not systematically reported their R&D, we use the ratio of intangible assets calculated as total intangible assets to total assets as a proxy. Vanacker et al. (2017) identify age, sector profitability and level of competition as affecting firm performance. However, their study was conducted among small German companies. Because our study focuses on large- and mid-cap companies, the sample is homogeneous in terms of age and level of competition: each company has a long history, and given their size, they all operate in an oligopolistic sector. We use industry profitability as a control variable at the industry level. Industry fixed effects are also included to control for unobserved heterogeneity at the industry level. Therefore, the list of variables used in our study is shown in Table 1.

### 3.3. The proximity dependence matrix (W) and the proximity variable (WCSR)

Exogenous spatial interactions (proximity spillovers) are captured using a proximity matrix (W) associated with the CSR value for each company $j \neq i$. Correctly shaping proximity interactions entails identifying the relationship between nearby individuals with an accurate dependency structure, a square matrix of proximity weight ($W_{n,n}$), which is introduced exogenously. Spatial econometrics is the field that addresses proximity regardless of the kind of distance, namely, spatial, social, or institutional. To measure interactions, the first step consists of defining a priori a diagram of neighbourhood relationships between firms. Then, it is necessary to create a hypothesis on the basis of the structure of these correlations. A square neighbourhood matrix, denoted W, whose diagonal components are zero (no element can be its own neighbour), must be defined (LeSage and Pace, 2009).

We reference the hypothesis on the joint influence of industry and C-O-O reputation and define the proximity dependence structure (W) according to the following coding scheme: 1) companies operating in the same industry and that have the same C-O-O (for example, Daimler and BMW) are given a value of 8 in the matrix, 2) companies operating in the same sector and originated from a geographically contiguous C-O-O<fnref n="2" /> (for example, Renault and Fiat) are given a value of 4 in the matrix, 3)

<footnote n="1">¹ CFP is also captured by the following two accounting-based measures: SPM (the ratio of earnings before tax to total sales) and ROE (net income to equity). However, those measures provide unstable results. The results are available from the authors on request.</footnote>

<footnote n="2">² The UK is considered contiguous with countries in the northern half of Europe (i.e., the Netherlands, Belgium, France, Germany, and Norway). Hungary is considered contiguous with Germany for the one Hungarian company represented.</footnote>
<table><caption>**Table 1**
List of variables and construction.</caption><tr><td>ROA (t): Operating income (t)/total assets (t)</td></tr><tr><td>ROE (t): Net income (t)/shareholder equity (t-1)</td></tr><tr><td>Qtobin (t): Long-term debts (t) + value of outstanding preferred stock (t) + market value (t)/total assets (t)</td></tr><tr><td>CSR (t): CSRHub score for product-related CSR</td></tr><tr><td>SIZE (t): Logarithm of total assets (t)</td></tr><tr><td>LEVERAGE (t): (Total assets (t)/total equity (t))</td></tr><tr><td>SLACK RESOURCE (t): (Current assets (t)/current liabilities (t)) × 100</td></tr><tr><td>INVESTMENT (t): Capital expenditures (t)/total assets (t)</td></tr><tr><td>R&D (t): Total intangibles (t)/total assets (t)</td></tr><tr><td>ROAIND (t), ROEIND (t) and QtobinIND (t): Sectoral industrial profitability</td></tr></table>

companies operating in the same industry but that are not originated from a contiguous C-O-O are given a value of 1 in the matrix, 4) companies operating in different sectors but that have the same C-O-O are also coded as 1 in the matrix and 5) 0 otherwise.

Thus, the proximity matrix $W^3$ is defined as follows:

$CFP_{it-1}$ is the financial performance of corporation i with a one-year lag

$CFP_{it-2}$ is the financial performance of corporation i with a two-year lag

$CSR_{it}$ is the social responsibility performance of corporation i

$WCSR_{j \neq it}$ is the social responsibility performance of corporation j

$$W \left\{ \begin{array}{ll} w_{ij} = 0 & \text{if } i = j \\ w_{ij} = 8 & \text{if } i \text{ belongs to the same industry and the same COO than } j \\ w_{ij} = 4 & \text{if } i \text{ belongs to the same industry than } j \text{ and is originated from a contiguous COO to } j \\ w_{ij} = 1 & \text{if } i \text{ belongs to the same industry than } j \text{ but is originated not from a contiguous COO to } j \\ w_{ij} = 1 & \text{if } i \text{ operates in another industry but belongs to the same COO than } j \\ 0 & \text{otherwise} \end{array} \right.$$

WCSR. Proximity interactions are introduced into the model by the exogenous spatially lagged variable that is processed by multiplying $W_{(n,n)}$ by CSR$_{(n,t)}$. We apply row normalization so that the spatial lag variable attached to neighbouring companies (WCSR) reflects the average CSR rating of nearby companies for each company considered. This step alleviates the problem of “spatial” endogeneity and simultaneity bias (Le Gallo and Fingleton, 2019) because the observations are assumed to be independent. The impact of proximity is exogenous in the specification of our model. We expect the coefficient attached to the variable WCSR to be significantly positive. This indicates a proximity contamination effect—all things being equal, there is an increase in the CSR of nearby companies that improves a corporation’s financial performance.

### 3.4. Empirical model and estimation methods

As largely echoed by the empirical literature on the CSR–CFP relationship, this relationship is prone to having endogeneity concerns such as reverse causality, unobserved heterogeneity or simultaneity bias. To study proximity spillovers in the CSR–CFP relationship, we select a dynamic panel specification as recommended by Surroca et al. (2010), Ben Lahouel et al. (2019) or Jahmane and Gaies (2020) with exogenous spatial interaction using the system GMM two-step estimator of Blundell and Bond (1998).<fnref n="3" />

The model for estimation is as follows:

$$CFP_{it} = \alpha_0 + \beta_1 CFP_{it-1} + \beta_2 CFP_{it-2} + \beta_3 CSR_{it} + \beta_4 WCSR_{j \neq it} + \beta_k X_{it} + \mu_i + \gamma_{id} + \lambda_t + \varepsilon_{it}$$

where $CFP_{it}$ is the financial performance of corporation i.

multiplied by the weighting matrix W (which captures the proximity relationships)

$X_{it}$ is the vector of control variables

$\mu_i$ is the individual fixed effect

$\gamma_{id}$ is the industry fixed effect

$\lambda_t$ is the time effect

$\varepsilon_{it}$ is the independent error term

$\alpha_0$ is the constant

$\beta_1, \beta_2, \beta_3, \beta_4, \beta_k$ are the GMM estimators

The dynamic specification addresses the time dependence of a company’s financial performance, and the combined instrumental approach does not reduce the number of observations. Using the two-step estimator of the Blundell & Bond system GMM (under the conditions of no heteroscedasticity and no serial correlation) appears to offer more robust estimates than the Arellano & Bond estimator does. However, an Arellano–Bond test for the absence of serial correlation is required for the validity of the strong exogeneity assumption, and a Hansen test for the correct specification of instruments is required to determine whether the econometric model is valid.<fnref n="4" />

The Blundell & Bond system GMM model internally transforms the data in a system of two regressions that combine difference and level instruments and include lagged values of the dependent variable as follows (Ullah et al., 2018):

$$\left\{ \begin{array}{l} CFP_{it} = \alpha_0 + \gamma CFP_{it-1} + \alpha_1 CSR_{it} + \alpha_2 WCSR_{j \neq it} + \beta' X_{it} + u_i + \gamma_{id} + \lambda_t + \varepsilon_{it} \\ \Delta CFP_{it} = \gamma \Delta CFP_{it-1} + \alpha_1 \Delta CSR_{it} + \alpha_2 W \Delta CSR_{j \neq it} + \beta' \Delta X_{it} + \Delta \lambda_t + \Delta \varepsilon_{it} \end{array} \right.$$

This estimation method presents several great advantages for our study of the CSR–CFP relationship. It controls for three potential sources of endogeneity bias that are common in the CSR–CFP relationship: unobserved heterogeneity (McWilliam and Siegel, 2000; Garcia-Castro and

<footnote n="3">³ Since two channels industry and C-O-O are combined in W, we also study the influence of two weighting matrices which only consider the spillover effect of industry and C-O-O independently. Results are presented in Section 4.5.</footnote>

<footnote n="4">⁴ The test is built on the Hansen statistic J, which uses an optimal weighting matrix. The Sargan test is appropriated only after a difference-GMM estimator under the assumption of homoskedasticity and no serial correlation (in levels) in the idiosyncratic error term.</footnote>
<table><caption>**Table 2**
Correlation coefficients (CSR = product-related CSR).</caption><thead><tr><th></th><th>ROA</th><th>ROE</th><th>Qtobin</th><th>LROA</th><th>LROE</th><th>LQtobin</th><th>SIZE</th><th>LEVER.</th><th>INVEST.</th><th>R&D</th><th>SLACK</th><th>ROAIND</th><th>ROEIND</th><th>QtobinIND</th><th>CSR</th><th>WCSR</th></tr></thead><tbody><tr><td>ROA<br>(p value)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ROE<br>(p value)</td><td>0.776<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Qtobin<br>(p value)</td><td>0.075<br>(0.011)</td><td>0.006<br>(0.826)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LROA<br>(p value)</td><td>0.559<br>(0.000)</td><td>0.336<br>(0.000)</td><td>0.107<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LROE<br>(p value)</td><td>0.261<br>(0.000)</td><td>0.145<br>(0.000)</td><td>0.000<br>(0.999)</td><td>0.573<br>(0.000)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LQtobin<br>(p value)</td><td>0.067<br>(0.032)</td><td>0.012<br>(0.695)</td><td>0.879<br>(0.000)</td><td>0.108<br>(0.000)</td><td>0.003<br>(0.905)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SIZE<br>(p value)</td><td>-0.027<br>(0.352)</td><td>0.071<br>(0.016)</td><td>-0.271<br>(0.000)</td><td>-0.093<br>(0.003)</td><td>0.041<br>(0.188)</td><td>-0.270<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LEVER.<br>(p value)</td><td>-0.192<br>(0.000)</td><td>0.109<br>(0.000)</td><td>0.098<br>(0.000)</td><td>-0.184<br>(0.000)</td><td>-0.121<br>(0.000)</td><td>0.093<br>(0.003)</td><td>0.162<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>INVEST.<br>(p value)</td><td>0.042<br>(0.151)</td><td>0.022<br>(0.454)</td><td>-0.022<br>(0.456)</td><td>0.097<br>(0.001)</td><td>0.059<br>(0.061)</td><td>-0.021<br>(0.490)</td><td>-0.001<br>(0.972)</td><td>-0.113<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R&D<br>(p value)</td><td>0.062<br>(0.037)</td><td>0.067<br>(0.023)</td><td>-0.025<br>(0.384)</td><td>0.106<br>(0.000)</td><td>0.100<br>(0.001)</td><td>-0.022<br>(0.474)</td><td>0.153<br>(0.000)</td><td>-0.003<br>(0.914)</td><td>-0.059<br>(0.046)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SLACK<br>(p value)</td><td>0.085<br>(0.004)</td><td>0.017<br>(0.569)</td><td>-0.031<br>(0.292)</td><td>0.044<br>(0.155)</td><td>-0.037<br>(0.240)</td><td>-0.032<br>(0.305)</td><td>-0.291<br>(0.000)</td><td>-0.223<br>(0.000)</td><td>-0.051<br>(0.086)</td><td>-0.126<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ROAIND<br>(p value)</td><td>0.288<br>(0.00)</td><td>0.183<br>(0.000)</td><td>-0.103<br>(0.000)</td><td>-0.299<br>(0.000)</td><td>0.136<br>(0.000)</td><td>0.096<br>(0.001)</td><td>0-0.165<br>(0.000)</td><td>-0.135<br>(0.000)</td><td>-0.114<br>(0.000)</td><td>0.254<br>(0.000)</td><td>0.045<br>(0.124)</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>ROEIND<br>(p value)</td><td>0.241<br>(0.000)</td><td>0.218<br>(0.000)</td><td>0.076<br>(0.010)</td><td>0.229<br>(0.000)</td><td>0.099<br>(0.001)</td><td>0.064<br>(0.042)</td><td>-0.112<br>(0.000)</td><td>-0.059<br>(0.046)</td><td>-0.072<br>(0.015)</td><td>0.159<br>(0.000)</td><td>0.020<br>(0.494)</td><td>0.836<br>(0.000)</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>QtobinIND<br>(p value)</td><td>0.156<br>(0.000)</td><td>0.087<br>(0.003)</td><td>0.190<br>(0.000)</td><td>0.209<br>(0.000)</td><td>0.104<br>(0.001)</td><td>0.165<br>(0.000)</td><td>-0.020<br>(0.493)</td><td>-0.127<br>(0.000)</td><td>-0.053<br>(0.075)</td><td>0.293<br>(0.000)</td><td>0.018<br>(0.541)</td><td>0.543<br>(0.000)</td><td>0.401<br>(0.000)</td><td>1.000</td><td></td><td></td></tr><tr><td>CSR<br>(p value)</td><td>0.019<br>(0.517)</td><td>0.008<br>(0.773)</td><td>-0.050<br>(0.090)</td><td>0.027<br>(0.377)</td><td>0.033<br>(0.296)</td><td>-0.055<br>(0.079)</td><td>0.298<br>(0.000)</td><td>0.041<br>(0.166)</td><td>-0.254<br>(0.000)</td><td>0.106<br>(0.000)</td><td>-0.118<br>(0.000)</td><td>0.150<br>(0.000)</td><td>0.088<br>(0.003)</td><td>0.192<br>(0.000)</td><td>1.000</td><td></td></tr><tr><td>WCSR<br>(p value)</td><td>0.132<br>(0.000)</td><td>0.095<br>(0.001)</td><td>0.150<br>(0.000)</td><td>0.170<br>(0.000)</td><td>0.110<br>(0.000)</td><td>0.142<br>(0.000)</td><td>-0.012<br>(0.674)</td><td>-0.018<br>(0.542)</td><td>-0.105<br>(0.000)</td><td>0.286<br>(0.000)</td><td>-0.051<br>(0.082)</td><td>0.389<br>(0.000)</td><td>0.245<br>(0.000)</td><td>0.364<br>(0.000)</td><td>0.242<br>(0.000)</td><td>1.000</td></tr></tbody></table>
<table><caption>**Table 3**
VIF test results.</caption><thead><tr><th>Variable</th><th>VIF</th></tr></thead><tbody><tr><td>SIZE</td><td>37,56</td></tr><tr><td>LEVERAGE</td><td>1,78</td></tr><tr><td>INVESTMENT</td><td>4,06</td></tr><tr><td>R&D</td><td>11,70</td></tr><tr><td>SLACK RESOURCE</td><td>4,62</td></tr><tr><td>ROEIND</td><td>2,42</td></tr></tbody></table>

<table><caption>**Table 4**
product-related CSR–CFP system GMM regressions.</caption><thead><tr><th></th><th>ROA</th><th>ROE</th><th>Qtobin</th></tr></thead><tbody><tr><td>L1ROA</td><td>0.361**<br>(0.165)</td><td></td><td></td></tr><tr><td>L1ROE</td><td></td><td>-0.138**<br>(0.057)</td><td></td></tr><tr><td>L2ROE</td><td></td><td>-0.41***<br>(0.052)</td><td></td></tr><tr><td>L1Qtobin</td><td></td><td></td><td>0.838***<br>(0.048)</td></tr><tr><td>L2Qtobin</td><td></td><td></td><td>-0.36***<br>(0.011)</td></tr><tr><td>CSR</td><td>-0.140<br>(0.144)</td><td>-0.723*<br>(0.427)</td><td>-0.250*<br>(0.151)</td></tr><tr><td>WCSR</td><td>0.108*<br>(0.059)</td><td>0.679**<br>(0.243)</td><td>0.301<br>(0.201)</td></tr><tr><td>SIZE</td><td>1.020<br>(2.992)</td><td>4.212<br>(9.692)</td><td>-17.2***<br>(5.691)</td></tr><tr><td>LEVERAGE</td><td>-0.388<br>(0.349)</td><td>4.728<br>(5.184)</td><td>-0.174<br>(0.669)</td></tr><tr><td>INVESTMENT</td><td>3.124<br>(19.18)</td><td>0.889<br>(0.679)</td><td>-0.610<br>(0.401)</td></tr><tr><td>R&D</td><td>-12.72*<br>(7.292)</td><td>-16.02<br>(28.93)</td><td>-28.80<br>(40.157)</td></tr><tr><td>SLACK RES</td><td>5.823<br>(3.670)</td><td>12.41<br>(7.828)</td><td>2.666<br>(4.353)</td></tr><tr><td>ROAIND</td><td>0.498**<br>3.670</td><td></td><td></td></tr><tr><td>ROEIND</td><td></td><td>0.664**<br>(0.307)</td><td></td></tr><tr><td>QtobinIND</td><td></td><td></td><td>1.200<br>(0.745)</td></tr><tr><td>constant</td><td>-10.35<br>(30.09)</td><td>-5.058<br>(9.241)</td><td>16.209***<br>(4.977)</td></tr><tr><td>Observations</td><td>1000</td><td>875</td><td>875</td></tr><tr><td>Firms</td><td>125</td><td>125</td><td>125</td></tr><tr><td>Instruments</td><td>79</td><td>74</td><td>74</td></tr><tr><td>AR1 (p-val)</td><td>0.0092</td><td>0.0352</td><td>0.3045</td></tr><tr><td>AR2 (p-val)</td><td>0.4606</td><td>0.5246</td><td>0.3108</td></tr><tr><td>Hansen (p-v)</td><td>(0.226)</td><td>(0.538)</td><td>(0.618)</td></tr></tbody></table>

Estimations: Two-step GMM with Windmeijer (2005) small sample robust corrections.

Time, industry and individual fixed effects are included in all regressions.

The standard errors are presented below the corresponding coefficients.

The symbols *, ** and *** indicate significance at the 10 %, 5 % and 1 % levels, respectively.

Arino, 2010; Hamilton and Nickerson, 2003; Orlitzky et al., 2003; Schnippering, 2020), simultaneity (Jo and Harjoto, 2012; Margolis et al., 2009; Soytas et al., 2019; Zhao et al., 2021) and dynamic endogeneity (Ben Lahouel et al., 2019; Li et al., 2021). To ensure the absence of heteroscedastic and serially uncorrelated error terms, we select a robust small-sample correction (robust VCE) from Windmeijer (2005). The Durbin–Wu–Hausman test is applied to detect and report endogenous variables on the right-hand side of the equation. We also perform a Wintoki et al. (2012) test to determine the number of lags in the dependent variable to capture the most information from the past, as required by Li et al. (2021).

# 4. Results

## 4.1. Correlations, multicollinearity diagnosis and unit root test

The Pearson correlation coefficients for the variables used in the model are provided in Table 2 (descriptive statistics are provided in Table 8 in the Appendix). Concerning the interest variable, the correlation matrix reveals that product-related CSR is negatively correlated with ROA, ROE and Qtobin, which is consistent with the findings of Ben Lahouel et al. (2019). Concerning the social performance associated with proximity from WCSR, the variable of interest is significantly positively correlated with financial performance, confirming our intuition that spillovers are important and indicating a contamination effect. Furthermore, the lagged dependent variables LROA, LROE and LQtobin are positively and significantly correlated with ROA, ROE and Qtobin. This corroborates the choice to use the dynamic panel specification suggested in the most recent studies (Ben Lahouel et al., 2019; Li et al., 2021).

Concerning the control variables, the Pearson correlation coefficient indicates that SIZE is not significantly correlated with ROA but is positively correlated with ROE and negatively correlated with Qtobin. LEVERAGE is significantly negatively correlated with ROA but positively correlated with ROE and Qtobin. INVESTMENT is not significantly correlated with any financial performance; however, that is not sufficient to exclude it from the model. R&D is positively and significantly correlated with ROA and ROE but is not significantly correlated with Qtobin. SLACK RESOURCE has a significant positive correlation with ROA but is not correlated with ROE or Qtobin.

Concerning the diagnosis of multicollinearity associated with the control variables, for each pair of variables, the Pearson correlation coefficients are far from 1, indicating that no potential collinearity exists between the control variables. However, when processing the VIF test, the results (Table 3) show a risk of multicollinearity associated with SIZE and R&D, whose values are greater than 5, indicating a severe correlation between each of these variables and the others.

The risk of multicollinearity must be addressed by skipping the correlated variable. However, given that the endogeneity test results (see 4.2.2) indicate that SIZE and R&D are endogenous and must be instrumentalized, SIZE and R&D are present in their instrumentalized form in the model to be regressed. Although our sample time series is less than the minimum value (15) referenced in Dickey and Fuller (1981) to require a unit root test, a Fisher-type unit root based on the augmented Dickey & Fuller test with the H₀ hypothesis “All panels contain unit root” was carried out and rejected at 1 % for each variable, indicating that the series is stationary.

## 4.2. Conducting the GMM regression

### 4.2.1. Lag selection

Using a dynamic model, we must first determine the number of lags in the dependent variable to capture the most information from the past. We perform the Wintoki et al. (2012) test as described by Li et al. (2021); i.e., we run a pooled OLS regression and detect how many lags associated with the dependent variable are statistically significant. For the dependent variable ROA, only one lag is significant, whereas for ROE and Qtobin as dependent variables, two lags are significant. Therefore, the estimated model is differentiated accordingly. A single lag is estimated using ROA, and two lags are introduced when the model is estimated using ROE and Qtobin as dependent variables.

$$ROA_{it} = \alpha_0 + \beta_1 ROA_{it-1} + \beta_3 CSR_{it} + \beta_4 WCSR_{j \neq it} + \beta_k X_{it} + \mu_i + \gamma_{id} + \lambda_t + \varepsilon_{it}$$

$$ROE_{it} = \alpha_0 + \beta_1 ROE_{it-1} + \beta_2 ROE_{it-2} + \beta_3 CSR_{it} + \beta_4 WCSR_{j \neq it} + \beta_k X_{it} + \mu_i + \gamma_{id} + \lambda_t + \varepsilon_{it}$$
<table><caption>**Table 5**
Differential regression specifications for product-related CSR.</caption><thead><tr><th rowspan="2">CSR = Product</th><th colspan="4">Dependent = ROA</th><th colspan="4">Dependent = ROE</th></tr><tr><th>(1) OLS Model</th><th>(2) Fixed Effects Model 2SLS</th><th>(3) Sys-GMM one lag Model</th><th>(4) Sys-GMM two lags Model</th><th>(5) Sys-GMM Model 2 L without ROAIND</th><th>(1) OLS Model</th><th>(2) Fixed Effects Model 2SLS</th><th>(3) Sys-GMM one lag Model</th><th>(4) Sys-GMM two lages Model</th><th>(5) Sys-GMM Model 2 L without ROEIND</th></tr></thead><tbody><tr><td>LDependent</td><td></td><td></td><td>0.361**<br>(0.165)</td><td>0.403**<br>(0.182)</td><td>0.444**<sup>f</sup><br>(0.191)</td><td></td><td></td><td>-0.030<br>(0.047)</td><td>-0.138**<br>(0.057)</td><td>-0.143**<br>(0.059)</td></tr><tr><td>L2Dependent</td><td></td><td></td><td></td><td>-0.110**<br>(0.055)</td><td>-0.094<br>(0.065)</td><td></td><td></td><td></td><td>-0.419***<br>(0.052)</td><td>-0.429***<br>(0.051)</td></tr><tr><td>CSR</td><td>-0.145**<br>(0.061)</td><td>-0.461**<br>(0.226)</td><td>-0.140<br>(0.144)</td><td>-0.195<br>(0.177)</td><td>-0.194<br>(0.184)</td><td>-0.253*<br>(0.154)</td><td>-1.139*<br>(0.608)</td><td>-0.676**<br>(0.317)</td><td>-0.723*<br>(0.427)</td><td>-0.930**<br>(0.481)</td></tr><tr><td>WCSR</td><td>0.080*<br>(0.050)</td><td>1.207**<br>(0.528)</td><td>0.108*<br>(0.059)</td><td>0.095<br>(0.068)</td><td>0.166**<br>(0.077)</td><td>0.181*<br>(0.098)</td><td>2.462*<br>(1.335)</td><td>0.674***<br>(0.253)</td><td>0.679***<br>(0.243)</td><td>0.670**<br>(0.264)</td></tr><tr><td>SIZE</td><td>1.721***<br>(0.520)</td><td>1.930<br>(3.780)</td><td>1.020<br>(2.992)</td><td>1.701<br>(3.363)</td><td>0.999<br>(3.063)</td><td>3.574***<br>(1.038)</td><td>3.127<br>(10.86)</td><td>3.924<br>(8.317)</td><td>-4.212<br>(9.692)</td><td>2.318<br>(8.801)</td></tr><tr><td>LEVERAGE</td><td>-0.715***<br>(0.154)</td><td>-0.674<br>(0.713)</td><td>-0.388<br>(0.349)</td><td>-0.418<br>(0.403)</td><td>-0.563<br>(0.445)</td><td>2.243***<br>(0.436)</td><td>3.119<br>(5.522)</td><td>6.353<br>(5.647)</td><td>4.728<br>(5.184)</td><td>4.704<br>(5.373)</td></tr><tr><td>INVESTMENT</td><td>2.698**<br>(1.262)</td><td>6.876<br>(4.201)</td><td>3.124<br>(19.188)</td><td>9.205<br>(23.381)</td><td>-3.712<br>(18.53)</td><td>4.839<br>(3.136)</td><td>13.06<br>(10.11)</td><td>1.059<br>(2.934)</td><td>8.896<br>(6.798)</td><td>10.586<br>(7.647)</td></tr><tr><td>R&D</td><td>-10.15**<br>(4.554)</td><td>-35.28***<br>(13.42)</td><td>-12.72*<br>(7.292)</td><td>-10.332<br>(7.502)</td><td>-9.403<br>(6.566)</td><td>-1.271<br>(9.826)</td><td>-60.329*<br>(30.10)</td><td>-36.32**<br>(18.203)</td><td>-16.021<br>(28.936)</td><td>-6.134<br>(28.571)</td></tr><tr><td>SLACK RES</td><td>2.284***<br>(0.624)</td><td>4.215*<br>(0.540)</td><td>5.823<br>(3.670)</td><td>6.371<br>(3.920)</td><td>6.185<br>(4.155)</td><td>4.657***<br>(1.523)</td><td>11.68*<br>(7.061)</td><td>13.55**<br>(6.369)</td><td>12.411<br>(7.828)</td><td>12.760<br>(8.351)</td></tr><tr><td>ROAIND</td><td>0.995***<br>(0.145)</td><td>0.974***<br>(0.272)</td><td>0.498**<br>(0.232)</td><td>0.598**<br>(0.286)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ROEIND</td><td></td><td></td><td></td><td></td><td></td><td>1.031***<br>(0.156)</td><td>1.000***<br>(0.340)</td><td>0.872**<br>(0.400)</td><td>0.664**<br>(0.307)</td><td></td></tr><tr><td>constant</td><td>-12.92**<br>(6.010)</td><td>-49.73<br>(41.90)</td><td>-10.356<br>(30.090)</td><td>-15.550<br>(34.553)</td><td>-7.266<br>(34.693)</td><td>-43.78***<br>(12.94)</td><td>-102.52<br>(106.87)</td><td>26.076<br>(51.520)</td><td>-50.584<br>(92.418)</td><td>-16.357<br>(85.565)</td></tr><tr><td>Observations</td><td>1125</td><td>1125</td><td>1000</td><td>875</td><td>875</td><td>1125</td><td>1125</td><td>1000</td><td>875</td><td>875</td></tr><tr><td>R2</td><td>0.102</td><td>0.131</td><td></td><td></td><td></td><td>0.079</td><td>0.112</td><td></td><td></td><td></td></tr><tr><td>Fisher statistic</td><td>141.19</td><td>81.76</td><td>107.13</td><td>81.46</td><td>86.94</td><td>102.85</td><td>43.17</td><td>54.65</td><td>106.2</td><td>733.65</td></tr><tr><td>Wald test (p-v)</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Wooldridge test (p-v)</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>AR1 (p-val)</td><td></td><td></td><td>(0.009)</td><td>(0.010)</td><td>(0.009)</td><td></td><td></td><td>(0.027)</td><td>(0.035)</td><td>(0.037)</td></tr><tr><td>AR2 (p-val)</td><td></td><td></td><td>(0.460)</td><td>(0.210)</td><td>(0.338)</td><td></td><td></td><td>(0.348)</td><td>(0.524)</td><td>(0.564)</td></tr><tr><td>Hansen (p-v)</td><td></td><td></td><td>(0.226)</td><td>(0.145)</td><td>(0.199)</td><td></td><td></td><td>(0.427)</td><td>(0.538)</td><td>(0.409)</td></tr></tbody></table>

Time, industry and individual fixed effects are included in all regressions.

The standard errors are presented below the corresponding coefficients.

The symbols *, ** and *** indicate significance at the 10 %, 5 % and 1 % levels, respectively.

$$ \text{Qtobin}_{it} = \alpha_0 + \beta_1 \text{Qtobin}_{it-1} + \beta_2 \text{Qtobin}_{it-2} + \beta_3 \text{CSR}_{it} + \beta_4 \text{WCSR}_{j \neq it} + \beta_k X_{it} + \mu_i + \gamma_{id} + \lambda_t + \varepsilon_{it} $$

### 4.2.2. Endogeneity and instrument selection

A Durbin-Wu-Hausman test is performed to check whether the variables in the dataset are endogenous or exogenous. A correlation of an explanatory variable using the error term leads to estimates of simultaneity bias. We follow the approach of Ullah et al. (2018), which prescribes the Davidson & McKinnon method for the creation of residual variables associated with each variable in an OLS regression. We then regress the dependent variable with all explanatory variables and their residuals and apply Student's t-test to determine whether the residual coefficient $\beta$ is significantly different from zero ($H_0: \beta = 0$) and should be declared endogenous.

SIZE, R&D and WCSR are endogenous and must be instrumentalized in the regression. SIZE is justified to the extent that companies with higher market valuations or profits can grow more easily. R&D is also justified to the extent that companies with higher market valuations or profits may choose to invest more in R&D in subsequent years (Gupta et al., 2017). The result concerning WCSR as endogenous is another indication of the presence of a spillover effect consistent with imperfect information theory: higher market evaluation or corporate financial

performance generated by a misperceived collective signal issued from the CSR of neighbours has a boomerang effect on the perceived CSR of every firm in the neighbourhood.

The system GMM controls for endogeneity by internally transforming the data and including the lagged value of the dependent variable. As a rule of thumb when selecting instruments, the number of instruments should never exceed the number of groups and should stay far below that number. Therefore, we limit the lagged values used as instruments to 3 so that the number of instruments never exceeds 79 in the regression (this number is far below the number of panels, which is 125). The use of the system GMM relies on an additional exogeneity assumption according to which any correlation between the endogenous variables and the unobserved fixed effects remains constant over time. As a postestimation test, a Hansen test for overidentification restrictions with $H_0$ "all overidentifying restrictions are valid" is thus performed to check the exogeneity of the instrument subsets. The null hypothesis is not expected to be rejected. The first- and second-order serial correlation tests of Arellano-Bond are performed to examine the serial correlation with the idiosyncratic error term and determine whether the model is correctly specified.
<table><caption>**Table 6**
Product-related CSR regressions when W is replaced by W<sub>IND</sub> and W<sub>C-O-O</sub>.</caption><thead><tr><th rowspan="2">Weighting Matrix = CSR = Product</th><th colspan="4">W<sub>IND</sub></th><th colspan="4">W<sub>C-O-O</sub></th></tr><tr><th colspan="2">ROA</th><th colspan="2">ROE</th><th colspan="2">ROA</th><th colspan="2">ROE</th></tr></thead><tbody><tr><td>Model</td><td>(1) OLS</td><td>(2) Sys-GMM</td><td>(1) OLS</td><td>(2) Sys-GMM</td><td>(1) OLS</td><td>(2) Sys-GMM</td><td>(1) OLS</td><td>(2) Sys-GMM</td></tr><tr><td>LDependent</td><td></td><td>0.381**<br>(0.195)</td><td></td><td>-0.131**<br>(0.059)</td><td></td><td>0.350**<br>(0.150)</td><td></td><td>-0.141**<br>(0.055)</td></tr><tr><td>L2Dependent</td><td></td><td></td><td></td><td>-0.408***<br>(0.047)</td><td></td><td></td><td></td><td>-0.426***<br>(0.052)</td></tr><tr><td>CSR</td><td>-0.152**<br>(0.062)</td><td>-0.161<br>(0.155)</td><td>-0.265*<br>(0.155)</td><td>-1.077**<br>(0.486)</td><td>-0.150**<br>(0.061)</td><td>-0.164<br>(0.179)</td><td>-0.244<br>(0.154)</td><td>-0.841*<br>(0.459)</td></tr><tr><td>WCSR</td><td>0.358*<br>(0.218)</td><td>0.239<br>(0.274)</td><td>0.946*<br>(0.546)</td><td>2.115*<br>(1.166)</td><td>0.210**<br>(0.095)</td><td>0.328<br>(0.230)</td><td>0.276<br>(0.199)</td><td>1.575***<br>(0.597)</td></tr><tr><td>SIZE</td><td>1.911***<br>(0.521)</td><td>0.637<br>(3.410)</td><td>4.042***<br>(1.000)</td><td>6.940<br>(8.070)</td><td>1.515***<br>(0.493)</td><td>0.054<br>(2.733)</td><td>3.205***<br>(1.029)</td><td>3.011<br>(8.078)</td></tr><tr><td>LEVERAGE</td><td>-0.712***<br>(0.154)</td><td>-0.399<br>(0.385)</td><td>2.246***<br>(0.436)</td><td>4.450<br>(4.919)</td><td>-0.731***<br>(0.154)</td><td>-0.293<br>(0.345)</td><td>2.175***<br>(0.436)</td><td>4.621<br>(5.285)</td></tr><tr><td>INVESTMENT</td><td>2.827**<br>(1.268)</td><td>-6.535<br>(18.557)</td><td>5.396*<br>(3.165)</td><td>9.175<br>(7.546)</td><td>2.667**<br>(1.254)</td><td>-4.506<br>(16.57)</td><td>4.694<br>(3.118)</td><td>13.929<br>(8.830)</td></tr><tr><td>R&D</td><td>-9.81**<br>(4.512)</td><td>-13.99*<br>(7.252)</td><td>-0.638<br>(9.772)</td><td>-9.119<br>(22.02)</td><td>-8.187*<br>(4.411)</td><td>-12.56<br>(8.927)</td><td>3.819<br>(9.422)</td><td>3.739<br>(23.617)</td></tr><tr><td>SLACK RESOURCE</td><td>2.190***<br>(0.625)</td><td>5.298<br>(3.562)</td><td>4.358***<br>(1.525)</td><td>9.821<br>(7.866)</td><td>2.171***<br>(0.621)</td><td>5.541*<br>(3.328)</td><td>4.335***<br>(1.518)</td><td>14.822*<br>(8.042)</td></tr><tr><td>ROAIND</td><td>1.028***<br>(0.142)</td><td>0.643***<br>(0.217)</td><td></td><td></td><td>1.018***<br>(0.142)</td><td>0.620**<br>(0.258)</td><td></td><td></td></tr><tr><td>ROEIND</td><td></td><td></td><td>1.071***<br>(0.154)</td><td>0.955***<br>(0.328)</td><td></td><td></td><td>1.058***<br>(0.154)</td><td>0.750**<br>(0.361)</td></tr><tr><td>constant</td><td>-30.23**<br>(13.49)</td><td>-14.75<br>(30.50)</td><td>-90.50***<br>(33.13)</td><td>-149.1<br>(108.7)</td><td>-18.55***<br>(6.899)</td><td>-14.53<br>(23.80)</td><td>-47.51***<br>(14.47)</td><td>-92.94<br>(62.37)</td></tr><tr><td>Observations</td><td>1125</td><td>1000</td><td>1125</td><td>875</td><td>1125</td><td>1000</td><td>1125</td><td>875</td></tr><tr><td>R2</td><td>0.123</td><td></td><td>0.093</td><td></td><td>0.120</td><td></td><td>0.093</td><td></td></tr><tr><td>Fisher statistic</td><td>141.30</td><td>96.68</td><td>102.34</td><td>841.46</td><td>143.42</td><td>70.21</td><td>100.91</td><td>1013.4</td></tr><tr><td>Wald test (p-v)</td><td></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Wooldridge test (p-v)</td><td></td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>AR1 (p-val)</td><td></td><td>(0.015)</td><td></td><td>(0.040)</td><td></td><td>(0.006)</td><td></td><td>(0.030)</td></tr><tr><td>AR2 (p-val)</td><td></td><td>(0.565)</td><td></td><td>(0.861)</td><td></td><td>(0.484)</td><td></td><td>(0.547)</td></tr><tr><td>Hansen (p-v)</td><td></td><td>(0.357)</td><td></td><td>(0.145)</td><td></td><td>(0.228)</td><td></td><td>(0.371)</td></tr></tbody></table>

Time, industry and individual fixed effects are included in all regressions.

The standard errors are presented below the corresponding coefficients.

The symbols *, ** and *** indicate significance at the 10 %, 5 % and 1 % levels, respectively.

### 4.3. Regression results and postestimation tests

Table 4 provides the results of the regressions and the postestimation test of Arellano-Bond and Hansen to determine whether the model specification and the instrument selection are valid. For every component except for community with ROA as the dependent variable (6 %), the P value of the Hansen test is greater than 10 %, indicating the absence of a strong correlation between the instrumental variables and the error term. Therefore, the instrumental variables used are valid, and the two-step sys-GMM estimator is consistent. For ROA and ROE, the Arellano-Bond test shows that differences in the error terms have negative first-order serial correlation (p-val <0.05) but no second-order serial correlation (p-val >0.05), meaning that we do not need to reconsider the model specification or the instrument selection. This is not the case for Qtobin, for which AR1 is largely greater than 10 %. This result, coupled with a Hansen value close to 1 for every component and subcomponent, invalidates the model specification and instrument selection. Consequently, we hereafter have to pursue a regression analysis on the robust results obtained using only ROA and ROE.

For ROA as the dependent variable, the regression shows no explanatory power of CSR on CFP. This result is in line with those of Ben Lahouel et al. (2019, p. 359), who established no effect on the relationship when system GMM was used. However, when WCSR, the CSR variable associated with the proximity matrix W, is considered, the variable is positively significant at the 10 % level for product-related CSR (β = 0,108; se = 0,059), inducing a reputational contamination spillover effect on ROA financial performance.

For ROE as the dependent variable, the regression shows a much more significant correlation of WCSR with CFP. The contamination effect is clearly demonstrated (β = 0,679; se = 0,243, statistically significant at 5 %). This result clearly indicates the reality of reputation spillovers with, as expected, a contamination effect: the product-related CSR score of neighbouring companies linked to the industry and C-O-O impacts a company's ROE financial performance. A very interesting result is the significant negative coefficient associated with the CSR variable (β = -0,723; se = 0,427, statistically significant at 10 %), which means that individual product-related CSR investment does not compensate for the advantage gained from stakeholder interest, a result that is in line with Friedman's theory.

The results for the product-related CSR measure are in line with the literature concerning the “halo” described in section 2. One interesting robustness test is to compare the results with other specifications and regressions suggested in the empirical literature on the CSR-CPF relationship to consider whether there is some convergence in the results.

### 4.4. Robustness of the product-related CSR measure using different regressions

The results associated with product-related CSR for ROA and ROE show that the selection of the CSR measure is crucial in the study of reputational spillovers in the empirical relationship between CSR and financial performance. Our results are obtained using Windmeijer's two-step system GMM with small-sample robust correction. However, other regression techniques are used in empirical studies, and it is interesting
to compare the results with those of different regressions to consider
whether there is convergence in the sign and significance of the co-
efficients. It determines the stability and robustness of the relationship.
Most empirical works on the CSR–CFP relationship rely on OLS and fixed
effects estimations using two-stage least squares regressions according
to Ben Lahouel et al. (2019). We use these two methods in the regression
in Table 5 (models 1 and 2). We also consider the system GMM esti-
mation, differentiating one and two lags (models 3 and 4). Finally, we
consider the system GMM estimation without the ROAIND and ROEIND
variables (Model 5). We take these steps because we suspect that these
variables are likely to capture most of the explanatory power, leaving a
minimal portion to other variables. Table 5 presents the results of the
five regressions.

The results show strong convergence in significance and signs across
the models for the WCSR and CSR estimators. As expected, the OLS
regression demonstrates significance for all control variables, except for
INVESTMENT and R&D, when associated with ROE as the dependent
variable. CSR and WCSR are very significant for fixed effect regressions
using two-stage least squares for CSR, with the expected negative sign
for CSR (β = -0.461; se = 0.226, statistically significant at 5% for ROA
and β = -1139; se = 0.608, statistically significant at 10% for ROE) and
a positive sign for WCSR (β = 1207; se = 0.528, statistically significant at
5% for ROA and β = 2462; se = 1.335, statistically significant at 10% for
ROE), confirming the contamination effect. For the specification
variation using the system GMM estimator, the introduction of two lags
for ROA results in WCSR being nonsignificant, whereas the contami-
nation effect reappears (β = 0.166; se = 0.077, statistically significant at
5%) when we rerun the regression and remove the ROAIND variable
(which is suspected of capturing too much explanatory power). For ROE,
regardless of the specification selected, the results demonstrate the
significance of CSR with a negative impact and WCSR with a positive
contamination effect. For the system GMM with one lag estimator, the
coefficient for CSR is β = -0.676 (se = 0.317) and statistically significant
at 5%, and the coefficient for WCSR is β = 0.674 (se = 0.253) and
statistically significant at 1%. For the system GMM with two lags and
ROEIND removed, the coefficient for CSR is β = -0.930 (se = 0.481) and
statistically significant at the 5% level, and that for WCSR is β = 0.670
(se = 0.264) and statistically significant at the 5% level. Thus, we can
conclude that our results for the “product” subcomponent are very
robust, demonstrating that in the European context, there exists a
negative effect in the CSR–CFP relationship and a contamination effect
associated with proximity spillover.

4.5. Robustness of the product-related CSR measure disaggregating the weighting matrix W

Since two channels, industry and C-O-O were combined in the weight
matrix W, it is not necessary for both to have significant effects when
considered independently. As a robustness check, W is disaggregated
into two contiguous matrices W<sub>IND</sub> and W<sub>C-O-O</sub>, which only consider the
spillover effects of industry and C-O-O separately. For W<sub>IND</sub>, w<sub>i,j</sub> = 1 if i is
in the same industry than j, 0 otherwise. For W<sub>C-O-O</sub>, w<sub>i,j</sub> = 1 if i is in the
same C-O-O than j, 0 otherwise. OLS and sys-GMM regressions are then
processed and the results are shown in the following Table 6.

Regarding ROE, the measures associated with product-related CSR
remain highly significant for both CSR and WCSR regardless W<sub>IND</sub> or W<sub>C-O-O</sub>
and the regression method chosen. However, for ROA, if the signs
associated with the variables are correctly oriented, WCSR which was
significant at 10 % with W, is no longer significant regardless of the
proximity matrix W<sub>IND</sub> or W<sub>C-O-O</sub> selected, suggesting that industry and
C-O-O operate jointly in proximity influence. These results demonstrate
the stability and robustness of the combined proximity matrix especially

for the ROE measure.

5. Conclusion

5.1. Implications for theory

This study clearly establishes the reality of reputational interferences
from industry and C-O-O in the product-related CSR–CFP relationship,
which validates the concepts of the industrial halo effect (Barnett et al.,
2006; Calvo-Iriarte et al., 2024) and the C-O-O halo effect (Newbarry,
2012; White, 2012; Kim, 2016), two concepts that have been highlighted
by the literature on spillover reputation. Our results confirm that the
spatial neutrality hypothesis mentioned by Orlitzky and Swanson (2008)
and Rost and Ehrmann (2015) should be reconsidered in the context of
the CSR–CFP relationship.

The contamination spillover effect measured in our study validates
the theoretical framework of the noise phenomenon and the role of the
collective reputation signal associated with industry and C-O-O in the
decisions of stakeholders, as stressed by Su et al. (2014). We robustly
show that an increase in the product-related CSR score of a firm’s in-
dustry or C-O-O is then profitable for any company in the same industry
or with the same C-O-O. Inversely, if the CSR performance of a firm in a
particular industry or with a particular C-O-O is perceived as declining,
all companies in these industries or with this C-O-O are negatively
financially impacted.

A second interesting theoretical implication lies in the importance of disaggregating CSR components. By focusing on the “product” sub-component, very robust results are obtained for ROA and ROE as financial measures. Moreover, this study demonstrated a significant negative relationship between a company’s individual product-related CSR investment and its financial performance. When considering previous studies using system GMM (El Ghoul et al., 2018; Ben Lahouel et al., 2019), only the “overall,” “environment” and “community” components are used, and a neutral individual relationship is demonstrated at this component level. When disaggregated to a product-related measure, a significant negative correlation is demonstrated, with its robustness established by the use of different estimates. At the “product” level, Friedman’s vision of CSR as a cost (1970) is established. There is some interest in the “product”, which is the core of a company’s activity and is key for its communication and reputation (Wang et al., 2012; Josiassen et al., 2013; Kelley et al., 2018). Our results support the findings of Sapic et al. (2018) who show that the positive halo effect originating from C-O-O is closely associated with brand and product quality.

Finally, the demonstration of the halo effect validates the postulate
in Orlitzky (2013) that most stakeholders are unable to distinguish be-
tween true and false CSR market signals, thereby establishing informa-
tion asymmetry as an accurate stakeholder-related theoretical
framework for any study of the relationship.

5.2. Managerial implications

This study has several interesting managerial implications, as the
proximity reputational spillovers of industry and C-O-O demonstrate the
positive impact of the CSR score of nearby companies on the financial
performance of a company independent of its own CSR performance.
This elucidates the incentive for an individual company to underinvest
in CSR to avoid the associated cost while benefiting from the effects of
other companies investing in CSR. This is especially true for product-
related CSR performance, which is negative in the CSR–CFP relation-
ship, meaning that an increase in CSR investment reduces financial
performance. Therefore, a free-riding strategy encourages a company
that can benefit from higher collective CSR performance from other companies in the same industry or with the same C-O-O to restrict its investment in CSR. This common problem has already been noted by King et al. (2002) and Winn et al. (2008) and involves a noncooperative Nash equilibrium, which is ultimately destructive to other companies in the same industry or with the same C-O-O as a whole.

This study also has some managerial implications related to stakeholders' informational strategies. This finding demonstrates that stakeholders used to refer to a global signal (industry, C-O-O) to filter individual signals in situations of uncertainty with regard to the quality of information. However, this behaviour is likely to blur the link between individual CSR and CFP.

### 5.3. Limitations and directions for future research

As robust as our results are for the product-related CSR measure, our study has several limitations that suggest a direction for future research. First, the results depend on a dependency structure that is exogenously introduced. In spatial econometrics, the estimation is very sensitive to proximity (Lesage and Pace, 2009). Even if the proximity matrix W is carefully drawn in our study, with a solid theoretical framework embedded in the spillover reputation literature, further research is needed to test other forms of proximity in the relationship.

Second, we introduce into this study the hypothesis of industrial

proximity by using a heterogeneous panel of four sectors. However, it is possible that what was valid for the whole sample may no longer be valid for a specific industry. There is an opportunity for future research to study other forms of industrial proximity.

Third, it is possible that the results of this study are valid only for the European panel considered and only for the time period studied. Different results are likely to be found for other geographic regions and in the long term, whereby CSR can be used to develop competitive advantages with stakeholders. Readers should be aware of the limitations of our study, which also indicate new research opportunities.

### CRediT authorship contribution statement

**Pascal Ricordel:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Melinda Majláth:** Writing – review & editing, Validation, Resources, Data curation, Conceptualization.

### Declaration of competing interest

We, authors, have nothing to declare.

Pascal RICORDEL Melinda Majlath.

## Appendix. Sample description, CSRHub Mapping, descriptive statistics

<table><caption>**Table 7**
– Sample description</caption><thead><tr><th>Country</th><th>Number of firms</th><th>Market Index</th><th>Energy</th><th>Building</th><th>Engineering</th><th>Consumer</th></tr></thead><tbody><tr><td>Hungary</td><td>1</td><td>BUX13</td><td>1</td><td></td><td></td><td></td></tr><tr><td>UK</td><td>32</td><td>FTSE100</td><td>5</td><td>8</td><td>8</td><td>11</td></tr><tr><td>France</td><td>21</td><td>CAC40</td><td>3</td><td>3</td><td>9</td><td>6</td></tr><tr><td>Germany</td><td>8</td><td>DAX30</td><td>2</td><td></td><td>3</td><td>3</td></tr><tr><td>Belgium</td><td>4</td><td>BEL20</td><td>1</td><td>1</td><td></td><td>2</td></tr><tr><td>Netherlands</td><td>4</td><td>AEX25</td><td>1</td><td></td><td>1</td><td>2</td></tr><tr><td>Spain</td><td>14</td><td>IBEX25</td><td>5</td><td>5</td><td>2</td><td>2</td></tr><tr><td>Norway</td><td>11</td><td>OBX20</td><td>7</td><td></td><td>3</td><td>1</td></tr><tr><td>Switzerland</td><td>6</td><td>SMI20</td><td></td><td>1</td><td>2</td><td>3</td></tr><tr><td>Finland</td><td>11</td><td>OMXH25</td><td>1</td><td>4</td><td>6</td><td></td></tr><tr><td>Portugal</td><td>3</td><td>PSI20</td><td>3</td><td></td><td></td><td></td></tr><tr><td>Italy</td><td>10</td><td>FTSE MIB40</td><td>5</td><td>3</td><td>1</td><td>1</td></tr><tr><td>TOTAL</td><td>125</td><td></td><td>34</td><td>25</td><td>35</td><td>31</td></tr></tbody></table>

Source: authors

<table><caption>**Table 8**
– CSRHub Mapping for CSR Ratings</caption><thead><tr><th colspan="4">Overall CSR</th></tr><tr><th>Community</th><th>Employees</th><th>Environment</th><th>Governance</th></tr></thead><tbody><tr><td>Community Development and Philanthropy</td><td>Compensation and Benefits</td><td>Energy and Climate Change</td><td>Board</td></tr><tr><td>Human Rights and Supply Chain</td><td>Diversity and Labour Rights</td><td>Environmental Policy and Reporting</td><td>Leadership Ethics</td></tr><tr><td>Product</td><td>Training and Health and Safety</td><td>Resource Management</td><td>Reporting Transparency</td></tr></tbody></table>

Source: CSRHub
<table><caption>**Table 9**

Descriptive statistics of the variables</caption><thead><tr><th>Variable</th><th></th><th>Mean</th><th>Standard Deviation</th><th>Minimum</th><th>Maximum</th><th>Observation</th></tr></thead><tbody><tr><td rowspan="3">ROA</td><td>Overall</td><td rowspan="3">11.311</td><td>14.262</td><td>-303.79</td><td>62.667</td><td>N = 1125</td></tr><tr><td>Between</td><td>8.528</td><td>-34.25</td><td>39.434</td><td>n = 125</td></tr><tr><td>Within</td><td>11.454</td><td>-264.41</td><td>63.119</td><td>T = 9</td></tr><tr><td rowspan="3">ROE</td><td>Overall</td><td rowspan="3">15.306</td><td>38.400</td><td>-738.74</td><td>442.63</td><td>N = 1125</td></tr><tr><td>Between</td><td>16.080</td><td>-79.14</td><td>66.366</td><td>n = 125</td></tr><tr><td>Within</td><td>34.897</td><td>-644.29</td><td>438.57</td><td>T = 9</td></tr><tr><td rowspan="3">QTobin</td><td>Overall</td><td rowspan="3">5.300</td><td>31.463</td><td>0.13</td><td>631.22</td><td>N = 1125</td></tr><tr><td>Between</td><td>26.209</td><td>0.36</td><td>294.73</td><td>n = 125</td></tr><tr><td>Within</td><td>17.548</td><td>-189.46</td><td>341.75</td><td>T = 9</td></tr><tr><td rowspan="3">SIZE</td><td>Overall</td><td rowspan="3">9.294</td><td>1.486</td><td>3.829</td><td>12.874</td><td>N = 1125</td></tr><tr><td>Between</td><td>1.471</td><td>4.117</td><td>12.605</td><td>n = 125</td></tr><tr><td>Within</td><td>0.245</td><td>7.886</td><td>11.063</td><td>T = 9</td></tr><tr><td rowspan="3">LEVERAGE</td><td>Overall</td><td rowspan="3">3.072</td><td>2.793</td><td>-41.44</td><td>49.49</td><td>N = 1125</td></tr><tr><td>Between</td><td>1.835</td><td>-1.935</td><td>13.36</td><td>n = 125</td></tr><tr><td>Within</td><td>2.111</td><td>-36.43</td><td>42.64</td><td>T = 9</td></tr><tr><td rowspan="3">INVESTMENT</td><td>Overall</td><td rowspan="3">0.044</td><td>0.043</td><td>0.00003</td><td>0.3352</td><td>N = 1125</td></tr><tr><td>Between</td><td>0.037</td><td>0.00002</td><td>0.2539</td><td>n = 125</td></tr><tr><td>Within</td><td>0.021</td><td>-0.1349</td><td>0.2478</td><td>T = 9</td></tr><tr><td rowspan="3">R&D</td><td>Overall</td><td rowspan="3">0.124</td><td>0.148</td><td>0.0001</td><td>0.917</td><td>N = 1125</td></tr><tr><td>Between</td><td>0.142</td><td>0.0002</td><td>0.637</td><td>n = 125</td></tr><tr><td>Within</td><td>0.044</td><td>-0.1349</td><td>0.537</td><td>T = 9</td></tr><tr><td rowspan="3">SLACK RESOURCE</td><td>Overall</td><td rowspan="3">1.475</td><td>0.913</td><td>0.152</td><td>11.23</td><td>N = 1125</td></tr><tr><td>Between</td><td>0.804</td><td>0.185</td><td>6.686</td><td>n = 125</td></tr><tr><td>Within</td><td>0.437</td><td>-1.014</td><td>8.717</td><td>T = 9</td></tr><tr><td rowspan="3">ROAIND</td><td>Overall</td><td rowspan="3">11.311</td><td>4.118</td><td>-0.936</td><td>16.473</td><td>N = 1125</td></tr><tr><td>Between</td><td>3.127</td><td>7.268</td><td>15.501</td><td>n = 125</td></tr><tr><td>Within</td><td>2.691</td><td>3.106</td><td>17.200</td><td>T = 9</td></tr><tr><td rowspan="3">ROEIND</td><td>Overall</td><td rowspan="3">15.306</td><td>8.401</td><td>-8.716</td><td>36.499</td><td>N = 1125</td></tr><tr><td>Between</td><td>4.292</td><td>9.444</td><td>21.027</td><td>n = 125</td></tr><tr><td>Within</td><td>7.231</td><td>-2.853</td><td>30.778</td><td>T = 9</td></tr><tr><td rowspan="3">QtobinIND</td><td>Overall</td><td rowspan="3">5.300</td><td>5.990</td><td>1.496</td><td>25.108</td><td>N = 1125</td></tr><tr><td>Between</td><td>4.932</td><td>1.738</td><td>13.754</td><td>n = 125</td></tr><tr><td>Within</td><td>3.424</td><td>-2.219</td><td>16.654</td><td>T = 9</td></tr><tr><td rowspan="3">CSR (product)</td><td>Overall</td><td rowspan="3">57.538</td><td>9.632</td><td>20.46</td><td>83.25</td><td>N = 1125</td></tr><tr><td>Between</td><td>8.134</td><td>25.09</td><td>71.77</td><td>n = 125</td></tr><tr><td>Within</td><td>5.204</td><td>37.84</td><td>73.44</td><td>T = 9</td></tr></tbody></table>

## Data availability

Data will be made available on request.

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