Country-Level Institutions, Firm Value, and the Role of Corporate Social Responsibility Initiatives *

Sadok El Ghoul Associate Professor, Campus Saint-Jean, University of Alberta

8406, Rue Marie-Anne-Gaboury (91 Street), Edmonton, AB T6C 4G9, Canada elghoul@ualberta.ca. Phone: 780-465-8725, Fax: 780-465-8760

Omrane Guedhami

Associate Professor, Moore School of Business, University of South Carolina 1705 College Street, Columbia, SC 29208, U.S.A.

omrane.guedhami@moore.sc.edu, Phone: 803-777-2175, Fax: 803-777-3609

Yongtae Kim Professor, Leavey School of Busines, Santa Clara University

500 El Camino Real, Santa Clara, CA 95053, U.S.A. y1kim@scu.edu, Phone: 408-554-4667. Fax: 408-554-2331

Abstract

We posit that the value of corporate social responsibility (CSR) initiatives is greater in countries with weaker market institutions because firms can adopt CSR activities to fill institutional voids. Using a large sample of 11,672 firm-year observations representing 2,445 unique firms from 53 countries over the period 2003-2010 and controlling for firm-level unobservable heterogeneity, we find that CSR is indeed more positively related to firm value in countries with weaker market institutions. The results persist when we address endogeneity using the instrumental variables approach. Our findings provide new insights on the mechanism through which firms can overcome institutional voids.

Key words: Institutions; Institutional void; Corporate social responsibility; Firm value * We thank Tammy Madsen, Niki den Nieuwenboer, Carrie Pan, Andrew Spicer, and participants at the 2014 Academy of International Business Meeting (Vancouver) for constructive comments. We appreciate the generous financial support of the Social Sciences and Humanities Research Council of Canada.

Country-Level Institutions, Firm Value, and the Role of Corporate Social Responsibility Initiatives

Abstract

We posit that the value of corporate social responsibility (CSR) initiatives is greater in countries with weaker market institutions because firms can adopt CSR activities to fill institutional voids. Using a large sample of 11,672 firm-year observations representing 2,445 unique firms from 53 countries over the period 2003-2010 and controlling for firm-level unobservable heterogeneity, we find that CSR is indeed more positively related to firm value in countries with weaker market institutions. The results persist when we address endogeneity using the instrumental variables approach. Our findings provide new insights on the mechanism through which firms can overcome institutional voids.

Key words: Institutions; Institutional void; Corporate social responsibility; Firm value

1

INTRODUCTION

Institutions are often referred to as the “rules of the game” that support market activity

(Mair, Marti, and Ventreasca, 2012; North, 1990; Campbell and Lindberg, 1990; De Soto, 2000;

Greif, 2006; Sen, 1999). Institutions support the effective functioning of the market mechanism

by allowing firms and individuals to engage in transactions without incurring undue costs or

risks (Meyer, Estrin, Bhaumik, and Peng, 2009). If market-supporting institutions are absent or

weak, resulting in institutional voids, firms should find ways to overcome these voids (Khanna

and Palepu, 1997, 2011).1 As emerging economies evolve, institutional structures move from

‘relationship’ contracting to ‘arms-length transactions’ and market-supporting formal institutions

replace informal constraints (Peng 2003; Narayanan and Fahey 2005). However, firms cannot

operate in emerging markets without encountering institutional voids and hence need to develop

strategic responses to overcome these voids (Khanna and Palepu, 2011). The quality of

institutional conditions varies even in developed countries, requiring strategic adaptations (Khanna

and Palepu, 2010).

In this paper we argue that firms may use corporate social responsibility (CSR) initiatives

to overcome institutional voids. In the absence of market-supporting institutions, firms rely more

heavily on implicit contracts and non-market mechanisms in creating value. One such non-

market mechanism is investment in CSR activities, as the intangible resources created by CSR

increase firm value (Surroca, Tribo, and Waddock, 2010). Since this role of CSR is likely to be

more important when markets fail due to institutional voids, we predict that the value implication

of CSR is greater in countries with weak market-supporting institutions.

1 In this paper we use the term ‘institutional void’ to refer to less developed, inefficient, or poor-functioning institutions as well as missing institutions.

2

Institutional voids occur when capital markets, regulatory system, and contract-

enforcement mechanisms are underdeveloped. For example, if information intermediaries, such

as financial analysts, investment banks, and a financial press, are not fully developed,

transactions costs are likely to be high due to a high level of information asymmetry (Meyer,

Estrin, Bhaumik, Peng 2009). Further, without strong equity and credit markets, firms may be

unable to raise adequate financing. If the state intervenes extensively in business operations,

firms will have difficulty predicting the actions of regulatory bodies, reducing managers’ ability

to make value-maximizing decisions. If the country lacks effective mechanisms to enforce

contracts, firms will have to find other ways to ensure that their partners will hold up their end of

bargain.

By adopting CSR initiatives, firms can develop longer-term relationships with their

stakeholders and as a result expand the set of value-creating exchanges with these stakeholders

beyond what would be possible based on market transactions alone (Hillman and Keim, 2001). For

instance, socially responsible behavior can increase a firm’s access to finance by reducing agency

costs and information asymmetry (Cheng, Ioannou, and Serafeim, 2014). In addition, CSR can

help a firm leverage its reputation to increase the value of implicit contracts when explicit

contracts are not protected by a sound legal system or regulatory interventions prevent efficient

contracting. CSR activities can further help firms in countries in weak legal institutions signal

their commitment not to expropriate their stakeholders. Thus when markets fail due to

institutional voids, intangible resources generated through improved stakeholder relationships

resulting from CSR can increase firm value, and this value-added is likely to be greater in

countries with weak institutions.

3

To test our prediction we analyze 11,672 firm-year observations representing 2,445 firms

from 53 countries over the period 2003-2010. We begin by examining the valuation effects of

CSR, abstracting from the country-level institutional environment. The OLS results suggest a

positive relation between CSR and firm value. However, this relation is sensitive to accounting

for time-invariant unobservable firm heterogeneity using firm fixed effects, which highlights the

importance of controlling for firm-level heterogeneity in examining the relation between CSR

and firm value (Servaes and Tamayo, 2013).

Next, we investigate whether the value implication of CSR is greater in countries with

weak institutions. We use proxies for the strength of market-supporting institutions in four areas:

stock market efficiency, credit market efficiency, business freedom, and legal system & property

rights. In regressions that control for firm fixed effects, we find that CSR is significantly and

positively related to firm value and that this relation is stronger in countries with weaker market-

supporting institutions. These results are consistent with the view that CSR increases firm value

by mitigating the negative impact of market failures associated with institutional voids. These

findings persist when we use the instrumental variables approach to address potential

endogeneity. The results are also robust to employing alternative CSR data.

Our study contributes to the literature in several ways. First, we provide evidence

suggesting that relationship building through CSR initiatives can help firms overcome institutional

voids. Prior studies examining mechanisms that fill such voids focus primarily on business groups

and strategic alliances (Chang and Hong 2000; Fisman and Khanna 2004; Khanna and Palepu

2000; Peng et al. 2005; Siegel, 2004), which internalize functions typically carried out by external

markets. In contrast, we show that CSR initiatives increase firm value in countries with weak

institutions, by leveraging long-term relationships with primary stakeholders to decrease

4

transaction costs and increase the value of implicit contracts. Second, we extend the recent

literature that examines factors mediating the relation between CSR and firm value (Surroca,

Tribo, and Waddock 2010; Wang and Bansal 2012; Servaes and Tamayo 2013; Luo, Wang,

Raithel, and Zheng 2014). Our results suggest that CSR is a non-market mechanism that can fill

voids in market-supporting institutions. This new evidence on the role of CSR improves our

understanding of how CSR affects firm value. Third, our study contributes to the CSR literature

that examines the influence of the country-level institutional environment. Third, while prior

empirical evidence on the link between CSR and firm value comes mainly from the U.S., our study

provides evidence on this relation using the largest international sample to date (spanning 53

countries).2 In addition, our study is among the first to examine cross-country variation in the value

implication of corporate CSR.

COUNTRY-LEVEL INSTITUTIONS, INSTITUTIONAL VOID, AND CSR

The role of institutions and institutional voids

Institutions have an essential role of supporting the effective functioning of markets by

allowing firms and individuals to engage in transactions without incurring undue costs or risks

(Meyer, Estrin, Bhaumik, and Peng, 2009). A country’s institutions are considered strong if they

support the voluntary exchange underpinning an effective market mechanism, whereas they are

viewed as weak if they fail to ensure effective markets or if they undermine markets (Meyer,

Estrin, Bhaumik, and Peng, 2009). If institutions are absent or weak, institutional voids occur

2 Of 167 studies surveyed by Margolis, Elfenbein, and Walsh (2007), only 22 use non-U.S. data. Of these 22 studies, 11 examine mutual fund performance or the performance of a portfolio of firms, leaving only 11 that employ firm-level non-U.S. data. Of these 11 studies, 8 use single-country data (Canadian firms in 4 studies, UK firms in 2 studies, and Australian firms in 2 studies). The number of firms examined in the 3 studies that use more than one country data ranges from 33 to 153.

5

and an alternative social structure is needed to spur market formation and operation (Greif, 2006;

Khanna and Palepu, 1997).

Institutional voids and the associated market failures can occur in many ways. Absent

strong equity and credit markets, firms may not be able to raise adequate financing. A shortage of

financial intermediaries such as financial analysts, investment banks, and the financial press, may

lead to an increase in information asymmetry (Meyer, Estrin, Bhaumik, and Peng, 2009), and thus

further reduce access to financing by increasing transactions costs.

If the state intervenes extensively in business operations, managers may have difficulty

predicting the actions of regulatory bodies, which would impact their ability to make value-

maximizing decisions. Direct government involvement, through state control of enterprises or

banks, may further distort the ability to compete fairly (Kuppuswamy, Serafeim, and Villalonga,

2012). Furthermore, if regulators place political goals over economic efficiency, they can distort

the functioning markets. Regulatory roadblocks can also impede the ability of firms to exploit new

business opportunities that might emerge through the introduction of new products or services

(Kuppuswamy, Serafeim, and Villalonga, 2012). Under weak legal institution and limited

enforcement of liability laws, consumers have no ability to seek redress if a product does not

deliver on its promise, which can lead to product market failure (Khanna and Palepu, 1997). And if

a country lacks effective mechanisms to enforce contracts, firms have to find other ways to

ensure that their partners hold up their end of an agreement.

The role of CSR in filling institutional voids

Firms cannot operate in emerging markets without encountering institutional voids and

hence need to develop strategic responses to overcome these voids (Khanna and Palepu, 2011).

6

Prior studies mainly focus on strategies that internalize functions that are typically carried out by

external markets. For example, a large diversified conglomerates or business groups can internalize

product and capital market intermediation and thus reduce transaction costs (Chang and Hong

2000; Fisman and Khanna 2004; Khanna and Palepu 2000; Peng et al. 2005). Business groups can

also use their broad scope to smooth out income flows and ensure access to internal finance

(Khanna and Palepu 2000). Siegel (2004) suggests forming a tight strategic alliance with a foreign

multinational firm from a country with stronger legal institutions as a mean to overcome

institutional voids. Shared investment motivates the foreign multinational to monitor the quality of

the investment and help access to cheap finance. However, business groups and strategic alliances

incur significant costs. Unrelated diversification may lead to inefficient business decisions and

poorly developed market for control in emerging countries makes inefficiencies to persist.

Conflicts between controlling family shareholders and minority shareholders can also exacerbate

the problem (Khanna and Palepu 2000).

Another strategy to fill the institutional void is to rely on alternative forms of contracting

such as trust (e.g., Narayanan and Fahey 2005). Although managers may rely on trust within

networks as well as informal agreements and connections to overcome weak institutions (Puffer,

McCarthy, and Boisot 2009), prior studies pay little attention to non-market strategies such as trust

and reputation building as means to address institutional voids.

CSR activities help develop long-term relationships with primary stakeholders such as

customers, suppliers, employees, and local communities, and allow firms expand the set of

value-creating exchanges with these groups beyond that which would be possible with

interactions limited to market transactions (Hillman and Keim, 2001). Superior CSR

performance allows firms contract with stakeholders based on mutual trust and cooperation

7

(Jones 1995) and limit short-term opportunistic behavior (Benabou and Tirole 2010), which

reduces agency costs and transaction costs (Cheng, Ioannou, and Serafeim 2014). Firms with

better CSR performance are also more likely to be transparent (Dhaliwal, Li, Tsang, and Yang

2011; Gelb and Strawser 2001), which reduces information asymmetry. Reduced agency costs

and reduced information asymmetry in turn allow firms better access to finance (Cheng,

Ioannou, and Serafeim 2014). When stock and credit market inefficiencies arise from

institutional voids, the role of CSR in facilitating access to finance is likely to be more important.

CSR activities can also help firms to overcome market failures arising from the state

intervention and weak legal institution. A firm is a nexus of contracts between shareholders and

other stakeholders whereby each group of stakeholders supplies the firm with critical resources

or effort in exchange for claims outlined in explicit contracts (e.g., product warranties) or those

in implicit contracts (e.g., promises of continued service to customers). 3 Unlike explicit

contracts, implicit contracts are nebulous and firms can default on their implicit commitments

without legal recourse. As such, the value of implicit contracts depends on stakeholders’

expectations about a firm honoring its commitments (Cornell and Shapiro, 1987). A higher

degree of CSR is a signal that the firm behaves in accordance with stakeholders’ expectations

(Brammer and Pavelin, 2006). Thus, implicit contracts of socially responsible firms are likely to

be valued more highly than those of socially irresponsible firms. As such, stakeholders are likely

to have stronger incentives to contribute resources and effort to socially responsible firms (Deng,

Kang, and Low, 2013). When explicit contracts are not protected by the sound legal system and

regulatory roadblocks prevent efficient contracting, firms rely more heavily on implicit contracts

3 Stakeholder theory is closely related to the issue of CSR to the extent that stakeholder theories define appropriate and inappropriate corporate behavior in terms of how corporations act with regards to their stakeholders (Driver and Thompson, 2002).

8

in creating firm value and therefore the strategic value of CSR is likely to be greater when

market-supporting institutions are weaker.

In summary, we expect that the value implication of CSR is greater in countries with

weaker market institutions because firms can adopt and initiate CSR activities to fill institutional

voids. Thus, we predict the relation between CSR performance and firm value to be more

positive when country-level institutions are weaker.

DATA AND MEASUREMENTS

Sample construction

To test our prediction that CSR is more valuable for firms that operate in countries with

weak institutions, we construct our sample by combining two data sources. We begin by

collecting data on CSR from Thomson Reuters’ ASSET4. This database “provides objective,

relevant and systematic environmental, social and governance (ESG) information”4 for 3,400

listed companies including S&P 500, Russell 1000, MSCI Europe, FTSE 250, ASX 300, MSCI

World Index, and 250 MSCI emerging markets companies. Thomson Reuters states that “using

only publicly available information, our 100+ specially trained analysts scour through company

reports and other public sources, and transform the results into comparable and consistent units

to enable assessment of different entities.” ASSET4 data have been used in recent studies that

examine CSR issues of U.S. and non-U.S. companies (e.g., Ioannou and Serafeim, 2012; Cheng,

Ioannou, and Serafeim, 2014; Luo, Wang, Raithel, and Zheng, 2014). Next, we match ASSET4

with Compustat North America and Global files to obtain financial data for our sample firms.

4 http://im.thomsonreuters.com/solutions/content/asset4-esg/

9

Our final sample consists of 11,672 firm-years representing 2,445 firms from 53 countries over

the period 2003-2010.

Table 1 provides a breakdown of our sample by country and year. Panel A shows that there

is wide variation in the number of firm-years in each country. The U.S. is the most representative

country, accounting for 3,658 firm-years, while Panama and Qatar are the least representative

countries, with just one firm-year each. Panel B shows that the number of firm-years increases

steadily over the sample period from 644 in 2003 to 2,344 in 2010. Table 2 provides a breakdown

of our sample by industry according to Fama and French’s (1997) classification.

-------------------------------- Tables 1 & 2 about here --------------------------------

Measurements

Firm value

Following extensive prior research in economics, finance, and strategy (e.g., Morck,

Shleifer, and Vishny, 1988; Waddock and Graves, 1997; Servaes and Tamayo, 2013), we

measure firm value using Tobin’s q (TOBQ), which is the market value of the firm divided by the

replacement value of the firm’s assets. We compute Tobin’s q as (market value of equity + book

value of assets – book value of equity) / book value of assets. Our measure of Tobin’s q is

similar to that of Servaes and Tamayo (2013), except that we do not subtract deferred taxes from

the numerator, as this variable is not available for the majority of non-U.S. firms.

CSR

ASSET4 data consist of four pillars: environmental, social, economic, and governance

performance. For each firm, over 250 objective indicators are used to calculate these four

10

performance scores. Following previous studies (e.g., Ioannou and Serafeim, 2012; Luo, Wang,

Raithel, and Zheng, 2014), we use the environmental and social performance scores to capture a

firm’s CSR. In particular, we compute an overall CSR performance score (CSR_P) as the

average of the environmental performance score (CSR_EP) and the social performance score

(CSR_SP). CSR_EP measures a firm’s impact on living and non-living natural systems,

including the air, land, and water, as well as complete ecosystems. This measure captures, for

example, a firm’s resources reduction, emission reduction, and product innovation benefiting the

environment. CSR_SP measures a firm’s capacity to generate trust and loyalty with its

workforce, customers, and society through the use of best management practices. This measure

captures a firm’s investment in customer/product responsibility, community, human rights,

diversity, training and development, health and safety, and employment quality.

Control variables

We include a set of controls commonly used in Tobin’s q regressions: firm size measured

as the natural logarithm of total assets in millions of US$ (SIZE); return on assets measured as

the ratio of net income before extraordinary items to total assets (ROA); leverage measured as the

ratio of total debt to total assets (LEV); the ratio of research and development expenses to total

sales (R&D/S), where missing research and development expenses are set to zero; sales growth

measured as the change in sales from the previous year (SGR); and the natural logarithm of GDP

per capita in constant year 2000 US$ (LOG_GDP). Table 3 provides summary statistics for

Tobin’s q and the CSR measures in Panel A, and all other firm-level variables as well as the

natural logarithm of GDP in Panel B.

------------------------ Table 3 about here ------------------------

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Country-level institutions

To examine how market-supporting institutions shape the relation between CSR and firm

value, we rely on IMD World Competitiveness Yearbook (WCY) and the Fraser Institute’s

Economic Freedom of the World (EFW) data to assess each country’s institutional environment.5

We focus on proxies for the strength of market-supporting institutions in the areas of stock

market efficiency, credit market efficiency, business freedom, and legal system & property

rights. More specifically, we derive the following four indices: (1) WCY’s Stock market

efficiency, which assesses whether stock markets provide adequate financing to companies, (2)

WCY’s Credit market efficiency, which captures whether credit is easily available for business,

(3) EFW’s Business freedom, which assesses freedom from regulations as reflected by

administrative requirements, bureaucracy costs, starting a business, extra payments/bribes/

favoritism, licensing restrictions, and cost of tax compliance, and (4) EFW’s Legal system &

property rights, which evaluates the quality of the legal system and the extent of property rights

protection based on judicial independence, impartial courts, protection of property rights, military

interference in rule of law and politics, integrity of the legal system, legal enforcement of contracts,

regulatory restrictions on the sale of real property, reliability of police, and business costs of crime.

Appendix A provides variable definitions and data sources. Appendix B presents the index score

for each market-supporting institution by country, averaged over the sample period.

EMPIRICAL RESULTS

The relation between CSR and firm value: Preliminary evidence

5 These databases have the advantage of consistently providing a time series of relevant institutional variables for a large number of countries.

12

As a first-step, we examine the relation between CSR and firm value abstracting from the

influence of country-level institutions. In particular, we estimate the following regression model:

TOBQt = 0 + 1 CSRt-1 +

n

ii

1

CONTROLSt-1 + FIXED EFFECTS + (1)

where TOBQ is Tobin’s q, CSR is one of the CSR proxies discussed above (CSR_P, CSR_EP,

CSR_SP), CONTROLS includes the set of firm- and country-level control variables discussed

above (SIZE, ROA, LEV, R&D/S, SGR, LOG_GDP), FIXED EFFECTS includes firm, country,

industry, and year fixed effects, and ε is an error term. To mitigate concerns about potential

reverse causality and simultaneity, we lag the CSR proxies and control variables by one period.

We estimate equation (1) using ordinary least squares (OLS) with robust standard errors adjusted

for clustering by firm.

Table 4 reports the results for the full sample of 11,672 firm-year observations from 53

countries. Models 1-3 include year, industry, and country fixed effects. In Model 1, we find a

positive and statistically significant (at the 1% level) coefficient on the overall CSR score (CSR_P),

suggesting a positive relation between socially responsible behavior and firm value. This finding

remains when we examine CSR scores related to environmental performance (CSR_EP) in Model

2 and social performance (CSR_SP) in Model 3; the coefficients on both CSR proxies load

positively at the 1% level. These preliminary findings provide support for the strategic stakeholder

management model, which predicts positive valuation effects of CSR. The coefficients on the

control variables are generally consistent with those in earlier studies. Specifically, larger and more

established firms are associated with lower q ratios because these firms tend to have limited

investment opportunities. Better performing firms, firms with greater research and development

13

expenditures, and firms with higher annual sales growth have higher q ratios, while firms with high

leverage have lower Tobin’s q.

------------------------ Table 4 about here ------------------------

A potential concern with these findings is endogeneity stemming from omitted variables

correlated with CSR and firm value. Servaes and Tamayo (2013) argue that the mixed findings in

the literature on the link between CSR and firm value are due in part to model misspecification

arising from the omission of controls for time-invariant unobservable firm characteristics. To

address this concern, in Models 4-6 we estimate equation (1) using firm fixed effects. These

models also include year fixed effects. The results portray a different picture. In Model 4, the

coefficient estimate on CSR_P is positive but statistically indistinguishable from zero. In Model

5, the coefficient estimate on the CSR component CSR_EP is also statistically insignificant.

Model 6 shows a positive and statistically significant (at the 1% level) coefficient on the CSR

component CSR_SP, confirming a link between a firm’s social performance and value.

Collectively, these results confirm the importance of accounting for firm-level heterogeneity

when examining the link between CSR and valuation (Servaes and Tamayo, 2013).

Next, we test our prediction that country-level institutions influence the link between

CSR and firm value.

The relation between CSR and firm value: The role of market-supporting institutions

We argue that firms respond to institutional voids and related market failures by adopting

and improving their CSR practices, which ultimately affect their valuation. Based on this

argument, we expect CSR activities to contribute more to firm value in countries with weaker

14

market-supporting institutions. In our analysis, we capture the strength of a country’s market-

supporting institutions in the areas of: (1) stock market efficiency, (2) credit market efficiency, (3)

business freedom, and (4) legal system and property rights. To examine how country-level

institutions affect the relation between CSR and firm value, we estimate the following regression

model:

TOBQt = 0 + 1 CSRt-1 +2 Institutiont-1+ 3 CSRt-1×Institution t-1

+

n

ii

1

CONTROLS t-1 + FIXED EFFECTS + (2)

We interact each institutional variable (Institution, which is one of the four proxies for

country-level institutions discussed above) with the CSR proxy of interest (CSR_P, CSR_SP, and

CSR_SP) to examine the value implication of CSR across countries with varying degrees of

institutional strength. Given the evidence above highlighting the importance of controlling for

firm-level heterogeneity, all regression models are estimated with firm fixed effects. In addition,

we include year fixed effects and report t-statistics that account for firm-level clustering. Table 5

reports the results.

------------------------ Table 5 about here ------------------------

Panel A presents the results using the overall CSR rating CSR_P. Three main findings

emerge. First, we find that the coefficient on CSR_P becomes positive and statistically

significant across all models. This evidence, based on regressions that control for various

dimensions of the institutional environment as well as firm fixed effects, suggests that CSR is

value-creating. Second, the institutional variables themselves load positively and significantly (at

the 1% level in Models 2 through 5), suggesting that strong market-supporting institutions

contribute to corporate value. Third, and more importantly, consistent with our prediction, we

find that the relation between CSR and firm value is weaker (stronger) in countries with stronger

15

(weaker) market-supporting institutions. Specifically, the coefficients on the interactions

between CSR_P and the institutional proxies capturing stock market efficiency, credit market

efficiency, business freedom, and legal system and property rights are all negative and

statistically significant.6

From the results reported in Table 5, we can determine the economic significance of the

effect of market-supporting institutions on the relation between CSR and firm value.

Specifically, one can calculate the impact of a one-standard-deviation increase of CSR_P on

TOBQ for different levels of market institutions’ strength. When Stock market efficiency is set to

its first quartile value (4.93), a one-standard-deviation increase in CSR_P (28.89) leads to an

increase in TOBQ of 0.09. In contrast, when Stock market efficiency is set to its third quartile

value (6.92), a one-standard-deviation increase in CSR_P (28.89) leads to an increase in TOBQ

of 0.03. The difference is 0.06, which is not trivial given that the mean (median) TOBQ is 1.76

(1.43). The corresponding differences for the other institutions are: 0.07 for Credit market

efficiency, 0.03 for Business freedom, and 0.06 for Legal system & property rights.

Panels B and C focus on the interactions between the institutional variables and the CSR

components CSR_EP and CSR_SP, respectively. We find that the coefficients on both CSR

components are generally positive and significant and the coefficients on interaction terms are

negative and significant at the 5% level or better, suggesting that both components enhance the

6 We also estimate the regression model in equation (2) using the overall measure of market-supporting institution. We measure the overall strength of market-supporting institution by aggregating four indices for stock market efficiency, credit market efficiency, business freedom, and legal system and property rights. The untabulated results are consistent with those in Table 5. That is, the coefficient on CSR is positive and statistically significant at the 1% level, and the coefficient on the interaction of CSR and the overall strength of market-supporting institution is negative and statistically significant at the 1% level.

16

long-term value of the firm and the value-added is greater (smaller) in countries with weaker

(stronger) market-supporting institutions, reinforcing the findings in Panel A.

Endogeneity

The evidence in Table 5, based on various proxies for the institutional environment,

implies that firms use CSR to overcome institutional voids and related market failures.

However, potential endogeneity might influence our results. First, country-level institutions

may affect not only the valuation effect of CSR but also a firm’s level of CSR activity (e.g.,

Ioannou and Serafeim, 2012), which may in turn affect the valuation implication of CSR.

This issue is of less concern because in the Table 5 regressions we include not only the

interaction effect but also the main effect of country-level institutions. Second, although our

research design helps mitigate concerns about omitted firm-level heterogeneity using firm

fixed effects, it is possible that omitted country-level factors that affect both CSR and

institutional variables are driving our results in Table 5. Third, although we use the lagged

values of CSR proxies in our research design, our evidence may be driven by reverse

causality, whereby past firm value influences current CSR activities, which cannot be

addressed using firm fixed effects.

To mitigate these concerns we re-estimate regressions in Table 5 using instrumental

variables estimation. Following recent studies (e.g., El Ghoul et al., 2011; Kim et al., 2014), we

use as instrument the industry-year average CSR score. In the first-stage regression

(untabulated), we regress the CSR proxy on the industry-year average CSR score (the

instrument), other determinants of CSR (SIZE, ROA, LEV, R&D/S, SGR, LOG_GDP), along with

the institutional variable and year effects. We then replace CSR in the regression model (2) with

the fitted values of the CSR proxies form the first stage regression. Table 6 reports the results

17

using the fitted values of the CSR proxies. Panels A, B, and C show the results for CSR_P,

CSR_EP, and CSR_SP, respectively. We find that the coefficients on the CSR proxies are

positive and statistically significant, while the coefficients on the interactions of CSR and the

strength of market-supporting institutions are negative and highly significant, lending additional

support to the prediction that the strategic value of CSR is greater (smaller) in countries with

weaker (stronger) market-supporting institutions.

------------------------ Table 6 about here ------------------------

Robustness to sample composition

A large number of countries in our sample account for only a few observations (e.g.,

Qatar and Panama) while a few countries account for a large number of observations (e.g.,

United States, Japan). We investigate the possibility of heterogeneity in the number of

observations across countries influencing our results in two different ways. First, we re-run our

regressions after excluding countries with fewer than 10 observations (15 countries) and fewer

than 100 observations (35 countries), respectively. Second, we run weighted regressions where

the weight is the inverse of the number of observations per country. Untabulated results continue

to show negative and statistically significant coefficients on the interaction between CSR and

market-supporting institutions. Thus, our results are not driven by heterogeneity in the number of

observations across countries.

Excluding cross-listed firms

Firms cross-listed in major stock exchanges have access to resources in countries where

the stock exchange is located and therefore are less likely to be affected by weak market

institutions in home countries. Thus inclusion of cross-listed firms in our sample works against

18

finding the relation between country-level market-supporting institutions and the CSR-value

link. However, if cross-listed firms are more likely to be located in developed countries and such

firms invest in CSR and have higher values, inclusion of cross-listed firms in the sample may

influence our results. To rule out this possibility, we re-estimate our regressions excluding cross-

listed firms. We identify firms cross-listed in major U.S. stock exchanges by CIK information in

Compustat Global. Cross-listed firms represent roughly 30% of firm years in our sample.

Untabulated results show that all our results are robust to excluding cross-listed firms from the

sample.

Alternative CSR proxy

To examine the robustness of our results to an alternative proxy for CSR, we employ

CSR ratings from Governance Metrics International (GMI). GMI measures firm-level CSR

practices over the period 2003-2010 for firms from 50 countries covered by the MSCI World

Index and the MSCI EAFE Index. After matching GMI with Compustat data we obtain a

sample consisting of 11,025 firm-years representing 2,055 firms from 49 countries over the

period 2004-2010. We re-estimate the regression model in equation (2) by replacing ASSET4

CSR performance scores with GMI CSR ratings. Table 7 summarizes the results. Reinforcing

the evidence in Table 5, we find that the coefficient on the CSR proxy is positive and

statistically significant in Models 1 through 5 and the coefficients on the interactions

between CSR and the institutional variables are negative and statistically significant at the

1% level.

------------------------ Table 7 about here ------------------------

19

CONCLUSION

While there is a large body of empirical research on the relation between corporate social

responsibility (CSR) and firm value, little is known about the role of the institutional

environment in influencing this relation. In this paper we provide cross-country evidence on the

value implication of CSR activities by examining whether the relation between CSR and firm

value varies systematically with the country-level institutional environment. We argue that firms

rely more on CSR initiatives that build resources to overcome market failures arising from

institutional voids and hence the strategic value of CSR is more positive in countries with weaker

market-supporting institutions.

Using a large sample of 11,672 firm-year observations representing 2,445 unique firms

from 53 countries over the period 2003-2010, we show that CSR is more positively related to

firm value in countries with weaker market-supporting institutions, which is consistent with the

notion that CSR initiatives mitigate market failures associated with institutional voids. This

result persists when we use instrumental variables to address endogeneity, and is also robust to

using an alternative CSR proxy.

Prior studies suggest business groups and strategic alliances as mechanisms that firms

may use to overcome institutional voids. While these mechanisms fill institutional voids by

internalizing functions typically carried out by external markets, alternative forms of contracting

such as trust can also be used to fill institutional voids. We argue that CSR activities help

develop relationships with primary stakeholders and allow firms to build intangible resources to

overcome market failures arising from institutional voids. We contribute to the literature by

showing that CSR initiatives help overcome institutional voids. Our study also expands the scope

of the literature on the valuation effect of CSR by providing evidence of cross-country variation

20

in the relation between CSR and firm value. Our results suggest that CSR can be used as a non-

market mechanism to fill the voids in market-supporting institutions, and thus shed light on the

mechanism through which CSR affects firm value.

21

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24

Appendix A. Variable definitions and data sources

Variable Definition Source TOBQ Tobin’s q measured as the ratio of the market value of assets

to the book value of assets, where the market value of assets is total assets plus market capitalization minus book equity.

Authors’ calculations based on Compustat data

CSR_P The overall CSR performance is equal to the average of environmental performance and social performance.

ASSET4

CSR_EP The environmental performance measures a firm’s impact on living and non-living natural systems, including the air, land, and water, as well as complete ecosystems.

ASSET4

CSR_SP The social performance measures a firm’s capacity to generate trust and loyalty with its workforce, customers, and society, through its use of best management practices.

ASSET4

SIZE Firm size is measured as the natural logarithm of total assets in millions of $US.

Authors’ calculations based on Compustat data

ROA Return on assets measured as the ratio of net income before extraordinary items to total assets.

As above

LEV Leverage measured as the ratio of total debt to total assets. As above R&D/S Ratio of research and development expenses to total sales.

Missing research and development expenses are set to zero. As above

SGR Sales growth measured as the change in sales from the previous year.

As above

LOG_GDP The natural logarithm of GDP per capita in constant year 2000 $US.

Authors’ calculations based on World Development Indicators data

Stock market efficiency

Stock markets provide adequate financing to companies. Obtained from an executive survey based on an index from 0 to 10.

IMD World Competitiveness Yearbook

Credit market efficiency

Credit is easily available for business. Obtained from an executive survey based on an index from 0 to 10.

As above

Business freedom An index of business regulations. Higher values of the index imply fewer regulations. The subcomponents of the index are:

(1) Administrative requirements (2) Bureaucracy costs (3) Starting a business (4) Extra payments/bribes/favoritism (5) Licensing restrictions (6) Cost of tax compliance

Fraser Institute’s Economic Freedom of the World

Legal system & property rights

An index of the quality of the legal system and the security of property rights. Higher values of the index imply better legal systems and more secure property rights. The subcomponents of the index are:

(1) Judicial independence (2) Impartial courts (3) Protection of property rights (4) Military interference in rule of law and politics (5) Integrity of the legal system (6) Legal enforcement of contracts (7) Regulatory restrictions on the sale of real property (8) Reliability of police (9) Business costs of crime

As above

25

Appendix B. Market-supporting institutions by country

Country Stock market efficiency Credit market efficiency Business freedom Legal system & property rights

Australia 7.29 6.73 7.10 8.24Austria 5.97 6.53 6.83 8.49 Belgium 6.08 6.21 6.72 7.22 Brazil 5.57 4.23 3.72 5.06 Canada 6.89 6.75 7.41 8.20 Chile 6.52 6.79 6.94 7.22 China 4.76 4.08 5.62 6.27 Colombia 4.10 4.75 6.35 4.35 Czech Republic 3.27 4.79 5.32 6.28 Denmark 6.54 7.09 7.66 8.93 Egypt . . 5.90 5.30 Finland 6.42 7.69 7.70 8.96 France 5.83 5.49 6.78 7.46 Germany 6.33 5.39 6.87 8.44 Greece 5.07 5.51 6.05 6.07 Hong Kong 8.00 7.58 7.95 8.25 Hungary 3.38 3.16 6.13 6.33 India 6.67 6.01 5.16 5.71 Indonesia 5.46 5.17 5.93 4.56 Ireland 5.57 6.26 7.20 7.96 Israel 6.25 5.96 6.77 6.21 Italy 4.15 4.29 5.80 5.89 Japan 6.15 6.28 6.57 7.63 Kazakhstan 3.90 4.25 6.10 5.70 Korea 5.01 4.46 6.63 6.62 Kuwait . . 6.60 7.15 Luxembourg 5.94 6.76 7.19 8.30 Malaysia 7.00 6.49 6.83 6.71 Mauritius . . 7.10 6.23 Mexico 3.52 2.96 5.89 4.62 Morocco . . 6.35 6.00 Netherlands 6.76 6.71 6.76 8.37 New Zealand 5.64 6.96 7.71 8.63 Norway 6.96 7.18 7.12 8.82 Panama . . 6.00 5.10 Papua New Guinea . . 7.73 4.70 Peru 4.23 5.13 5.95 5.10 Philippines 5.12 5.29 5.78 4.45 Poland 5.19 4.78 5.75 6.25 Portugal 4.55 5.34 5.98 7.08 Qatar 5.29 6.81 8.50 7.50 Russian Federation 3.71 3.25 4.90 5.30 Saudi Arabia . . 8.20 7.70 Singapore 7.13 7.16 8.23 8.50 South Africa 6.82 5.05 6.44 5.58 Spain 5.59 5.33 6.20 6.71 Sweden 7.07 7.00 7.47 8.42 Switzerland 7.15 6.74 7.47 8.61 Thailand 6.10 6.30 6.30 5.54 Turkey 4.73 4.81 6.24 5.39 United Arab Emirates . . 8.00 7.35 United Kingdom 6.29 5.66 7.10 8.06 United States 7.10 6.84 7.01 7.39 All countries 6.54 6.35 6.93 7.67

26

Table 1. Sample distribution by country and year

Panel A. Sample distribution by country Panel A. Sample distribution by country (cont'd)

Country N % Country N %

Australia 407 3.49 Norway 116 0.99

Austria 87 0.75 Panama 1 0.01

Belgium 117 1.00 Papua New Guinea 3 0.03

Brazil 59 0.51 Peru 2 0.02

Canada 670 5.74 Philippines 4 0.03

Chile 17 0.15 Poland 11 0.09

China 36 0.31 Portugal 53 0.45

Colombia 2 0.02 Qatar 1 0.01

Czech Republic 5 0.04 Russian Federation 26 0.22

Denmark 115 0.99 Saudi Arabia 3 0.03

Egypt 2 0.02 Singapore 126 1.08

Finland 154 1.32 South Africa 22 0.19

France 461 3.95 Spain 232 1.99

Germany 384 3.29 Sweden 249 2.13

Greece 99 0.85 Switzerland 287 2.46

Hong Kong 114 0.98 Thailand 13 0.11

Hungary 3 0.03 Turkey 18 0.15

India 46 0.39 United Arab Emirates 8 0.07

Indonesia 13 0.11 United Kingdom 1,442 12.35

Ireland 86 0.74 United States 3,658 31.34

Israel 20 0.17 All countries 11,672 100

Italy 163 1.40

Japan 1,922 16.47 Panel B. Sample distribution by year

Kazakhstan 2 0.02 Year N %

Korea 73 0.63 2003 644 5.52

Kuwait 2 0.02 2004 651 5.58

Luxembourg 42 0.36 2005 1,231 10.55

Malaysia 21 0.18 2006 1,541 13.20

Mauritius 3 0.03 2007 1,555 13.32

Mexico 36 0.31 2008 1,697 14.54

Morocco 2 0.02 2009 2,009 17.21

Netherlands 183 1.57 2010 2,344 20.08

New Zealand 51 0.44 All years 11,672 100

This table presents the sample distribution by country and year. The sample comprises 11,672 observations representing 2,445 unique firms from 53 countries over the 2003-2010 period.

27

Table 2. Sample distribution by industry Industry N % Industry N %

Agriculture 30 0.26 Aircraft 125 1.07

Food Products 335 2.87 Shipbuilding & Railroad Equipment 31 0.27

Candy & Soda 48 0.41 Defense 9 0.08

Beer & Liquor 150 1.29 Precious Metals 106 0.91

Tobacco Products 61 0.52 Non-Metallic & Industrial Metal Mining 241 2.06

Recreation 66 0.57 Coal 40 0.34

Entertainment 115 0.99 Petroleum & Natural Gas 746 6.39

Printing & Publishing 213 1.82 Utilities 692 5.93

Consumer Goods 259 2.22 Communication 626 5.36

Apparel 129 1.11 Personal Services 83 0.71

Healthcare 103 0.88 Business Services 904 7.75

Medical Equipment 205 1.76 Computers 260 2.23

Pharmaceutical Products 419 3.59 Electronic Equipment 545 4.67

Chemicals 510 4.37 Measuring & Control Equipment 129 1.11

Rubber & Plastic Products 49 0.42 Business Supplies 175 1.50

Textiles 21 0.18 Shipping Containers 50 0.43

Construction Materials 296 2.54 Transportation 544 4.66

Construction 404 3.46 Wholesale 331 2.84

Steel Works 333 2.85 Retail 847 7.26

Fabricated Products 10 0.09 Restaurants, Hotels, Motels 189 1.62

Machinery 463 3.97 Almost Nothing 266 2.28

Electrical Equipment 162 1.39

Automobiles & Trucks 352 3.02 All industries 11,672 100

This table presents the sample distribution by industry according to Fama and French’s (1997) classification. The sample comprises 11,672 observations representing 2,445 unique firms from 53 countries over the 2003-2010 period.

28

Table 3. Descriptive statistics

Panel A. Descriptive statistics by country

TOBQ CSR_P CSR_EP CSR_SP

Country N Mean SD Mean SD Mean SD Mean SD

Australia 407 1.92 1.20 44.92 27.42 44.11 29.13 45.73 29.24

Austria 87 1.49 0.74 59.45 24.51 61.13 27.20 57.78 26.54

Belgium 117 1.60 0.79 49.96 28.03 52.68 30.60 47.24 28.59

Brazil 59 1.83 1.44 68.33 24.13 61.49 24.17 75.17 26.48

Canada 670 1.76 0.97 39.84 26.04 39.30 26.94 40.37 27.75

Chile 17 1.68 0.54 44.97 29.04 42.04 26.43 47.91 34.74

China 36 1.90 1.39 32.38 24.92 30.92 22.76 33.84 28.02

Colombia 2 2.52 0.42 66.85 1.48 52.70 5.37 81.00 2.40

Czech Republic 5 1.54 0.12 73.25 11.41 65.84 20.24 80.66 4.47

Denmark 115 2.09 1.36 47.77 25.23 51.19 26.22 44.35 27.49

Egypt 2 1.10 0.02 10.38 0.11 11.45 0.21 9.30 0.42

Finland 154 1.59 0.64 68.76 22.63 72.12 26.07 65.40 24.13

France 461 1.56 0.82 74.78 22.80 74.06 25.27 75.50 24.44

Germany 384 1.49 0.80 66.47 26.38 67.31 29.37 65.63 27.37

Greece 99 1.74 1.18 47.92 30.25 46.46 28.62 49.38 34.35

Hong Kong 114 1.61 0.79 45.27 28.00 41.64 30.16 48.91 28.69

Hungary 3 1.09 0.08 90.47 2.68 86.73 4.11 94.20 1.42

India 46 2.84 1.82 66.79 23.55 61.63 25.43 71.94 23.95

Indonesia 13 3.53 1.40 63.85 19.26 54.22 24.69 73.48 17.96

Ireland 86 1.88 1.10 36.88 20.03 39.56 25.60 34.21 19.60

Israel 20 2.28 0.81 26.28 19.21 26.94 19.95 25.61 21.84

Italy 163 1.46 0.56 55.07 30.88 49.63 35.60 60.51 30.07

Japan 1,922 1.31 0.57 54.69 30.10 64.17 31.82 45.21 32.53

Kazakhstan 2 1.25 0.27 19.23 0.88 17.95 0.78 20.50 0.99

Korea 73 1.48 0.92 69.30 27.52 73.75 27.87 64.85 31.17

Kuwait 2 1.70 0.48 38.63 2.86 20.50 3.25 56.75 2.47

Luxembourg 42 1.76 1.10 47.63 22.73 47.28 27.68 47.98 22.71

Malaysia 21 1.93 0.91 43.88 23.47 42.80 25.83 44.96 23.69

Mauritius 3 0.89 0.18 30.37 5.72 43.90 6.42 16.83 5.12

Mexico 36 1.72 0.89 56.25 34.78 54.08 33.40 58.42 37.27

Morocco 2 3.29 0.14 52.32 3.92 34.55 10.39 70.10 2.55

Netherlands 183 1.58 0.61 70.86 23.01 65.70 28.80 76.03 22.06

New Zealand 51 1.73 1.09 44.20 21.50 45.16 26.59 43.24 22.89

Norway 116 1.85 0.96 59.72 28.35 59.29 31.15 60.14 30.04

Panama 1 1.31 . 12.15 . 11.80 . 12.50 .

Papua New Guinea 3 2.36 0.28 39.90 25.29 29.57 23.07 50.23 27.53

Peru 2 3.88 0.51 25.77 0.39 20.00 0.99 31.55 0.21

Philippines 4 1.87 0.61 43.55 27.25 43.85 26.80 43.25 32.65

Poland 11 1.58 1.16 39.95 19.17 36.15 26.51 43.75 21.65

29

Portugal 53 1.51 0.55 70.25 22.13 68.69 24.79 71.81 25.51

Qatar 1 2.70 . 18.70 . 20.20 . 17.20 .

Russian Federation 26 2.02 1.36 40.35 24.47 34.82 24.11 45.89 27.49

Saudi Arabia 3 1.34 0.28 29.22 24.88 32.70 26.33 25.73 23.43

Singapore 126 1.71 0.75 35.20 21.02 33.23 22.96 37.16 24.45

South Africa 22 2.41 1.30 63.08 23.58 58.81 26.06 67.35 22.59

Spain 232 1.94 1.32 72.69 25.35 70.82 25.79 74.55 27.68

Sweden 249 1.71 0.85 65.39 24.35 66.53 28.97 64.25 25.26

Switzerland 287 2.14 1.13 60.07 28.93 61.50 30.76 58.64 30.77

Thailand 13 1.97 0.62 51.58 27.63 48.18 28.92 54.98 30.09

Turkey 18 2.06 1.44 47.11 27.15 45.04 29.97 49.17 26.93

United Arab Emirates 8 1.52 0.29 19.16 8.05 21.68 10.22 16.65 11.99

United Kingdom 1,442 1.78 0.92 61.14 23.91 58.60 26.88 63.68 25.04

United States 3,658 1.99 1.09 43.93 27.88 41.86 30.63 46.00 28.93

All countries 11,672 1.76 0.99 52.72 28.89 53.09 31.60 52.35 30.45

Panel B. Descriptive statistics for the full sample

Variable Mean Min Q1 Median Q3 Max SD

TOBQ 1.76 0.73 1.13 1.43 2.00 6.29 0.99

CSR_P 52.72 6.55 24.82 52.45 81.55 97.85 28.89

CSR_EP 53.09 9.40 19.20 53.20 86.20 97.20 31.60

CSR_SP 52.35 3.40 23.15 52.60 82.20 98.90 30.45

SIZE 8.63 1.69 7.72 8.53 9.52 13.59 1.36

ROA 0.14 -0.07 0.09 0.12 0.18 0.44 0.08

LEV 0.19 0.00 0.07 0.17 0.28 0.65 0.15

R&D/S 0.03 0.00 0.00 0.00 0.02 0.31 0.06

SGR 0.09 -0.48 -0.02 0.06 0.16 1.38 0.25

LOG_GDP 10.28 6.51 10.16 10.48 10.55 10.94 0.49

This table presents descriptive statistics by country (Panel A) and for the full sample (Panel B). Appendix A provides definitions and data sources for all variables.

30

Table 4. Corporate social responsibility and firm value: Preliminary evidence (1) (2) (3) (4) (5) (6) CSR_P 0.002*** 0.000 (4.438) (1.237) CSR_EP 0.001*** -0.000 (3.137) (-1.222) CSR_SP 0.002*** 0.001*** (5.028) (3.485) SIZE -0.206*** -0.196*** -0.206*** -0.278*** -0.270*** -0.284*** (-14.501) (-14.077) (-14.960) (-19.203) (-18.736) (-19.961) ROA 5.098*** 5.141*** 5.090*** 2.337*** 2.355*** 2.315*** (18.993) (19.182) (19.035) (12.910) (13.005) (12.857) LEV -0.310*** -0.313*** -0.313*** -0.239*** -0.243*** -0.241*** (-2.985) (-3.012) (-3.012) (-2.607) (-2.646) (-2.639) R&D/S 3.767*** 3.808*** 3.756*** 2.455*** 2.479*** 2.426*** (7.731) (7.791) (7.727) (5.408) (5.434) (5.383) SGR 0.179*** 0.171*** 0.179*** 0.022 0.018 0.026 (3.130) (2.982) (3.146) (0.642) (0.522) (0.749) LOG_GDP 0.342 0.283 0.365 -0.183*** -0.183*** -0.180*** (0.655) (0.538) (0.703) (-6.412) (-6.365) (-6.305) Industry effects Yes Yes Yes No No No Country effects Yes Yes Yes No No No Year effects Yes Yes Yes Yes Yes Yes Firm effects No No No Yes Yes Yes N 11,672 11,672 11,672 11,672 11,672 11,672 Adj. R2/ R2 0.490 0.488 0.490 0.250 0.250 0.251 This table reports results from regressing Tobin’s q on CSR proxies (CSR_P, CSR_EP, CSR_SP). The sample comprises 11,672 observations representing 2,445 unique firms from 53 countries over the 2003-2010 period. Models (1)-(3) include year, industry, and country fixed effects. Models (4)-(6) include firm and year fixed effects. Appendix A provides definitions and data sources for all variables. Beneath each coefficient estimate we report the t-statistic based on robust standard errors adjusted for clustering by firm in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

31

Table 5. Corporate social responsibility and firm value: The role of institutions Panel A. CSR_P Panel B. CSR_EP Panel C. CSR_SP Stock

market efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights

Stock market

efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights

Stock market

efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) CSR_P 0.008*** 0.006*** 0.009*** 0.011** (4.904) (4.486) (3.733) (2.571) CSR_EP 0.008*** 0.005*** 0.007*** 0.009** (5.412) (4.456) (2.957) (2.304) CSR_SP 0.005*** 0.004*** 0.009*** 0.009** (3.754) (4.088) (3.912) (2.422) Institution 0.109*** 0.075*** 0.072* 0.149*** 0.114*** 0.075*** 0.061 0.137*** 0.088*** 0.062*** 0.062* 0.128*** (5.690) (5.678) (1.945) (3.160) (5.919) (5.942) (1.620) (2.953) (4.934) (4.980) (1.827) (3.063) CSR_P×Institution -0.001*** -0.001*** -0.001*** -0.001** (-4.844) (-4.603) (-3.597) (-2.517) CSR_EP×Institution -0.001*** -0.001*** -0.001*** -0.001** (-5.897) (-5.209) (-3.087) (-2.393) CSR_SP×Institution -0.001*** -0.001*** -0.001*** -0.001** (-3.118) (-3.463) (-3.548) (-2.231) SIZE -0.378*** -0.375*** -0.376*** -0.375*** -0.376*** -0.373*** -0.374*** -0.373*** -0.380*** -0.376*** -0.377*** -0.378*** (-11.869) (-11.884) (-11.712) (-11.741) (-11.834) (-11.854) (-11.642) (-11.668) (-11.898) (-11.894) (-11.785) (-11.824) ROA 0.813*** 0.807*** 0.799*** 0.796*** 0.822*** 0.817*** 0.803*** 0.805*** 0.804*** 0.797*** 0.795*** 0.787*** (4.176) (4.151) (4.134) (4.129) (4.224) (4.202) (4.153) (4.171) (4.148) (4.117) (4.127) (4.102) LEV -0.193* -0.181 -0.187 -0.196* -0.188 -0.177 -0.185 -0.193 -0.196* -0.186 -0.192 -0.197* (-1.654) (-1.549) (-1.588) (-1.664) (-1.614) (-1.518) (-1.564) (-1.641) (-1.671) (-1.584) (-1.635) (-1.678) R&D/S -0.021 0.006 0.084 0.031 -0.016 0.008 0.102 0.056 -0.019 0.003 0.057 0.010 (-0.027) (0.008) (0.109) (0.041) (-0.021) (0.010) (0.131) (0.072) (-0.025) (0.004) (0.074) (0.013) SGR 0.050 0.052 0.058 0.054 0.049 0.051 0.056 0.053 0.053 0.055 0.059 0.056 (1.359) (1.410) (1.551) (1.461) (1.343) (1.386) (1.508) (1.435) (1.430) (1.487) (1.589) (1.511) LOG_GDP 0.731 0.731 1.111** 1.155** 0.662 0.675 1.066** 1.134** 0.811* 0.819* 1.155** 1.172** (1.531) (1.531) (2.297) (2.399) (1.382) (1.411) (2.204) (2.349) (1.702) (1.718) (2.398) (2.443) Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 11,648 11,648 11,672 11,672 11,648 11,648 11,672 11,672 11,648 11,648 11,672 11,672 R-squared 0.274 0.274 0.270 0.271 0.276 0.275 0.270 0.271 0.273 0.273 0.271 0.271 This table reports results from regressing Tobin’s q on CSR proxies (CSR_P in Panel A, CSR_EP in Panel B, and CSR_SP in Panel C) and the interaction between each CSR proxy and the four proxies for country-level institutions, namely, stock market efficiency in Model (1), credit market efficiency in Model (2), business freedom in Model (3), and legal system and property rights in Model (4). All models include firm and year fixed effects. Appendix A provides definitions and data sources for all variables. Beneath each coefficient estimate we report the t-statistic based on robust standard errors adjusted for clustering by firm in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

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Table 6. Corporate social responsibility and firm value: IV regressions Panel A. CSR_P Panel B. CSR_EP Panel C. CSR_SP Stock

market efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights

Stock market

efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights

Stock market

efficiency

Credit market

efficiency

Business freedom

Legal system & property

rights (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Pred. CSR_P 0.019*** 0.010*** 0.012** 0.021** (5.672) (3.683) (2.175) (2.309) Pred. CSR_EP 0.018*** 0.009*** 0.010** 0.023*** (5.883) (3.790) (2.043) (2.678) Pred. CSR_SP 0.020*** 0.012*** 0.015** 0.019** (5.902) (4.277) (2.562) (1.999) Institution 0.226*** 0.136*** 0.117** 0.243*** 0.212*** 0.131*** 0.114** 0.271*** 0.220*** 0.132*** 0.100* 0.182** (7.235) (6.556) (2.203) (3.434) (6.969) (6.536) (2.267) (4.047) (7.186) (6.465) (1.863) (2.557) Pred. CSR_P×Institution -0.003*** -0.002*** -0.002*** -0.003*** (-8.169) (-7.375) (-2.825) (-2.696) Pred. CSR_EP×Institution -0.003*** -0.002*** -0.002*** -0.003*** (-8.982) (-8.154) (-3.004) (-3.278) Pred. CSR_SP×Institution -0.003*** -0.002*** -0.002*** -0.002** (-6.805) (-6.060) (-2.641) (-2.004) SIZE -0.348*** -0.341*** -0.350*** -0.347*** -0.339*** -0.335*** -0.337*** -0.334*** -0.379*** -0.370*** -0.379*** -0.379*** (-9.840) (-9.897) (-9.663) (-9.451) (-10.114) (-10.205) (-9.781) (-9.679) (-10.654) (-10.623) (-10.392) (-10.220) ROA 0.918*** 0.917*** 0.877*** 0.892*** 0.926*** 0.914*** 0.880*** 0.904*** 0.794*** 0.807*** 0.779*** 0.777*** (4.595) (4.635) (4.438) (4.447) (4.773) (4.750) (4.565) (4.678) (3.879) (3.972) (3.837) (3.744) LEV -0.204* -0.205* -0.205* -0.215* -0.219* -0.222* -0.236* -0.242** -0.191 -0.186 -0.184 -0.195* (-1.733) (-1.737) (-1.705) (-1.812) (-1.847) (-1.869) (-1.953) (-2.023) (-1.628) (-1.588) (-1.550) (-1.662) R&D/S 0.112 0.144 0.160 0.124 0.094 0.112 0.170 0.140 0.031 0.075 0.068 0.022 (0.145) (0.183) (0.206) (0.162) (0.121) (0.143) (0.219) (0.183) (0.041) (0.095) (0.087) (0.029) SGR 0.018 0.016 0.031 0.026 0.012 0.009 0.016 0.011 0.045 0.046 0.062 0.058 (0.449) (0.405) (0.759) (0.630) (0.325) (0.242) (0.394) (0.280) (1.151) (1.155) (1.514) (1.406) LOG_GDP 0.733 0.896* 1.098** 1.084** 0.643 0.847* 1.049** 1.055** 0.829* 0.940** 1.151** 1.128** (1.539) (1.908) (2.273) (2.237) (1.357) (1.807) (2.178) (2.174) (1.729) (1.999) (2.387) (2.337) Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 11,648 11,648 11,672 11,672 11,648 11,648 11,672 11,672 11,648 11,648 11,672 11,672 R-squared 0.282 0.279 0.270 0.271 0.284 0.282 0.271 0.272 0.278 0.276 0.270 0.270 Using instrumental variable (IV) regressions, this table examines the relation between Tobin’s q and CSR proxies (CSR_P in Panel A, CSR_EP in Panel B, and CSR_SP in Panel C) and the interaction between CSR proxy and the four proxies for country-level institutions, namely, stock market efficiency in Model (1), credit market efficiency in Model (2), business freedom in Model (3), and legal system and property rights in Model (4). The first-stage regressions (not reported to save space) involve regressing the CSR proxy on all independent variables (SIZE, ROA, LEV, R&D/S, SGR, LOG_GDP), the institutional variable, year effects, and the instrument (industry-year average CSR_P). The second-stage regression results that use the predicted values of the CSR proxy from the first-stage regressions are reported. All models include firm and year fixed effects. Appendix A provides definitions and data sources for all variables. Beneath each coefficient estimate we report the t-statistic based on robust standard errors adjusted for clustering by firm in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

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Table 7. Corporate social responsibility, country-level institutions, and firm value: Alternative CSR data

Stock market efficiency Credit market efficiency Business freedom Legal system & property rights (2) (3) (4) (5) CSR_GMI 0.008*** 0.004*** 0.017*** 0.023*** (4.377) (2.634) (4.956) (2.896) Institution 0.060*** 0.047*** 0.126*** 0.215*** (3.446) (3.777) (3.020) (3.389) GMI×Institution -0.001*** -0.001*** -0.002*** -0.003*** (-4.696) (-2.984) (-4.822) (-2.876) SIZE -0.364*** -0.365*** -0.365*** -0.363*** (-10.548) (-10.550) (-10.518) (-10.478) ROA 0.603*** 0.602*** 0.588*** 0.588*** (3.185) (3.181) (3.114) (3.114) LEV -0.199** -0.197* -0.192* -0.200** (-1.979) (-1.960) (-1.914) (-2.002) R&D/S -1.081* -1.086* -1.007* -1.045* (-1.891) (-1.896) (-1.771) (-1.825) SGR 0.072* 0.073* 0.075* 0.071* (1.812) (1.841) (1.885) (1.782) LOG_GDP 1.169*** 1.019*** 1.303*** 1.256*** (3.604) (3.148) (3.979) (3.888) Year effects Yes Yes Yes Yes Firm effects Yes Yes Yes Yes N 11,006 11,006 11,025 11,025 Adj. R2 0.269 0.268 0.268 0.267 This table reports results from regressing Tobin’s q on CSR ratings from Governance Metrics International (CSR_GMI) and the interaction between CSR proxy and the four proxies for country-level institutions, namely, stock market efficiency in Model (1), credit market efficiency in Model (2), business freedom in Model (3), and legal system and property rights in Model (4). All models include firm and year fixed effects. The sample consists of 11,025 firm-years representing 2,055 firms from 49 countries over the period 2004-2010. Appendix A provides definitions and data sources for all variables. Beneath each coefficient estimate we report the t-statistic based on robust standard errors adjusted for clustering by firm in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.