Market competition and firms' social performance

Journal Pre-proof Market competition and firms' social performance Chee Kian Leong, Yung Chiang Yang PII:

S0264-9993(19)30047-1

DOI:

https://doi.org/10.1016/j.econmod.2019.12.002

Reference:

ECMODE 5095

To appear in:

Economic Modelling

Received Date: 9 January 2019 Revised Date:

4 November 2019

Accepted Date: 3 December 2019

Please cite this article as: Leong, C.K., Yang, Y.C., Market competition and firms' social performance, Economic Modelling (2020), doi: https://doi.org/10.1016/j.econmod.2019.12.002. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Market Competition and Firms’ Social Performance

ABSTRACT This paper investigates whether market competition encourages firms to be more socially responsible. We find that firms in more competitive markets exhibit better overall social performance, as measured by doing well (“strength”) and doing badly (“concern”) in areas such as community, environment, human rights, and treatment of employees. To deal with endogeneity, we instrument market competition on entry barrier and observe that market competition only significantly reduces social concerns but not increases social strengths. Thus, firms are more reactive in reducing social concerns than proactive in augmenting their social strengths. Amongst these concerns, firms appear to be more active in reducing environmental concerns. The paper underscores the limitations in relying on the “invisible hand” of the market to deal with the multi-dimensional challenges of firms’ social performance. Keywords: Social Performance, Market Competition, Instrumental Variable

JEL Classification Number: D22, D40, L21, M14

1

Introduction

Economists commonly believe that competition is good in providing the incentives for firms to be more efficient and innovative in production. Nickell (1996) cites Adam Smith in remarking that “monopoly ... is a great enemy to good management”. Yet is market competition also the driving force behind firm’s socially responsible behaviour? This is the question we attempt to address in this paper. The importance of this question cannot be understated. Markets fail in many ways, such as pollution, dodgy labor practices and adulterated product quality. Such failures are manifested in a slew of recent examples, such as the horse meat scandal in Europe, contaminated milk powder and worker suicides in Foxconn factories in China and the BP oil spill from the Deepwater Horizon drilling rig explosion in the United States. A common prescription to “internalise” such externalities is to impose a tax on pollution and other socially irresponsible behaviours equal to their net social costs or to provide a subsidy to encourage pro-social behaviours equal to their net social benefits. Such an idea dates as far back as Pigou (1920). In asking whether market competition can nudge firms to take up their social responsibility, we are at the same time exploring whether the market failure can be internalised through the “invisible hands” of the market rather than the visible hand of the government. At first glance, it is not so evident why firms should be concerned with being socially responsible. After all, Friedman (1970) famously argues that the primary goal of a firm is to maximise returns to its shareholders and as such, operating within the laws of the jurisdictions already constitutes socially responsible behaviour. Further, pursuing socially responsible behaviour incurs considerable costs on the part of firms: according to a 2015 report released by the Varkey Foundation, global Fortune 500 businesses spent a total budget of $19.9bn on such efforts. Possibly firms may be willing to incur such expenditures purely out of altruism or to project a positive social images (Benabou and Tirole, 2010). On the other hand, Porter and Kramer (2006) have argued that sustaining such a large outlay may confer on firms a competitive advantage in the market competition. Exogenous events, such as scientific or news reports of health risk associated with colouring used in the food supply chain, of the environment, or about sweat shop-like working conditions, may trigger off awareness of consumers about specific CSR issues. Such exogenous events are driving forces which prompt firms to realise that adopting a socially responsible behaviour by addressing public concern pays off and hence they will be prompted to adopt socially responsible behaviour. In this respect, Ding et al (2016) have shown that firms that distinguish themselves over their peers in terms of CSR performance are associated with increased firm value, while Kopel and Brand (2012) demonstrate that socially concerned firms have larger market share and even higher profit, so that it pays to take into account CSR. In examining the global banking sector, Shen et al (2016) observe that socially responsible banks overwhelmingly outperform not socially responsible banks in terms of return on assets and return on equity. Further, as noted by Becchetti et al (2014), the implementation of CSR involves a trade-off between incurring higher production costs and at the same time the accumulation of ethical capital. The costs of implementation also require firms to be strategic about which aspect of CSR to focus on. For instance, Nollet et al (2016) discover 1

that corporate governance is a key CSR driver affecting the financial performance of the firm and suggest that investment should be focussed on this component. Taken together, all these appear to suggest that strategic motivations may influence firms’ social performance. Thus we assume a strategic perspective in this paper with the primary focus in analysing how competitive market pressures may induce firms’ social performance. Using a large panel of US firms, we examine empirically how market competition affects firms’ social performance. To facilitate our discussion, we will sometimes refer to social performance using the commonly-used acronymn CSR (corporate social responsibility), which has become an overarching phrase which encompasses different aspects of a firm’s social performance, such as its corporate governance, product quality, labour practices (fair wage, child labour), consumer protection and environmental responsibility. At a later stage in the paper, we will examine how market competition affects firms’ social performance in each of these individual dimensions. The paper contributes to the nascent economic literature on corporate social responsibility, which is exhaustively surveyed in Kitzmueller and Shimshack (2012). Benabou and Tirole (2010) draw on recent developments in the psychology and economics of prosocial behaviour to link individual concerns to CSR, highlighting three different perspectives: the firms’ adoption of a more long-term perspective, the delegated exercise of prosocial behaviour on behalf of stakeholders and insider-initiated philanthropy. But understanding the role of market competition in CSR performance is a relatively new development in this literature. Most of these studies are empirical reduced-form models, regressing some measures of CSR on some measures of competition. Fisman et al (2008) find that CSR activities are more pronounced in markets with more intense product market competition. In Fernandez-Kranz and Santalo (2010), better social ratings for firms are associated with more competitive industries. The results from a more recent study by Dupire and M’Zali (2016) suggest that competitive pressures prompt firms to increase their positive social actions without necessarily reducing their social weaknesses, but all these are contingent on the specific dimensions of CSR and industry specifications. Our main departure from these empirical studies is our attempt to control for the endogeneity in the market competition and social performance nexus through an instrumental variable (IV) approach. The endogeneity problem in this nexus may arise because of omitted variables, measurment errors or reverse causality. Because social performance is multi-dimensional, variables which could explain social performance may have been omitted in specifications examining the relationship. The second source of endogeneity arises because there is a plethora of measures pertaining to social performance and market competition. Invariably, errors in measurements are inevitable and create bias and inconsistency in the estimation. Thirdly, while market competition may prompt firms to improve on their social performance, good social performance could also confer a competitive edge for firms and the resulting market power may alter the dynamics of the market competition. This potential reverse causality renders the task of establishing a causal relationship more difficult. In this paper, we use a measure of the entry barriers to the industry as an instrumental variable to the market competition in order to estimate the impact of market competition on firms’ social performance. We derive this instrument by taking the average of the number of new firms entering the industry in the last three years. Our contention is that the entry 2

barriers should be correlated with the intensity of market competition but are unlikely to be correlated to the variations in the social performance of firms. This allows us to establish a causal relationship between market competition and firms’ social performance rather than a correlation as is the case with most extant empirical studies in this literature. We are not alone in attempting to deal with the endogeneity problem in such studies. Flammer (2015a) examines how CSR-related shareholder proposals can impact financial performance using a quasi-experiment. Specifically, she focuses on “close call” CSR proposals that pass or fail by a small margin of votes. She discovers that the adoption of close call CSR proposals leads to positive announcement returns and superior accounting performances, implying that these proposals are value enhancing. Taking a more restricted perspective of firms competing in the export markets, Flammer (2015b) applies a natural experiment approach and finds that domestic exporters respond to tariff reductions and thereby the higher foreign competition by increasing their engagement in CSR. However, the study is restricted to manufacturing firms engaging in international trade1 . Our paper differs from Flammer (2015a, 2015b) in two ways. While we agree that quasi-experiments and natural experiments offer alternatives to deal with the endogeneity problem, the IV approach can be applied more generally. Thus, in our current paper, we are able to consider a broader spectrum of firms across industries, not just firms with records of “close call” CSR proposals or those engaging in international trade. By taking into account endogeneity using the IV approach, we obtain consistent results that firms in more competitive markets exhibit better performance in social responsibility, possibly because the escape competition effect predominates. However, we discover that competitive pressures motivate firms to modify significantly the concerns in their social performance, rather than their strengths. We also find that, by allowing for endogeneity, the findings from other empirical studies cannot be corroborated by our results. In a sense, failing to adjust for endogeneity in existing studies may have resulted in mistaken inferences. The rest of this paper is organised as follows. Section 2 develops the conjectures and empirical hypothesis of the paper. We describe the dataset and the empirical strategy in Section 3. The empirical results are discussed in Section 4. Section 5 examines the robustness of our empirical results. Finally, concluding remarks are presented in Section 6.

2

Conjectures and Empirical Methodology

There are many possible approach to model the relationship between market competition and firms’ social performance. One possible approach, as adopted by Fernandez-Kranz and Santalo (2010), is to consider CSR as a form of innovation (Porter and Kramer, 2006). For instance, if firms decide to reduce their carbon footprint or invest in clean energy, the ensuing social performance is associated with an innovation in the production process. Or if firms reorganise their production processes to improve workplace safety, a more conducive place for the workers is created through such innovative practices. Granted that CSR can be regarded as a form of innovation, Fernandez-Kranz and Santalo (2010) invoke Aghion and Griffith 1

The sample used by Flammer (2015b) includes only manufacturing firms with SIC 2000-3999 from 19722005.

3

(2005) to model the relationship between product market competition and CSR. In this case, there could be two possible conjectures for the CSR and market competition nexus, namely escape competition and rent dissipation. The escape competition conjecture posits that firm has more incentive to “innovate” in CSR in a more competitive environment. The rationale is that a better CSR performance confers a competitive advantage for firms and translates into higher profitability because consumers are attracted by firms doing good. In contrast, the rent dissipation conjecture posits that market competition reduces the profitability of a firm and as CSR implementation are costly, firms will have less incentive to “innovate” in CSR. Both conjectures appear likely but most empirical studies (Fisman et al, 2008; FernandezKranz and Santalo, 2010; Flammer, 2015a, 2015b; Dupire and M’Zali, 2016) generally report a positive relationship between social performance and market competition. This may suggest a stronger escape competition effect. On the other hand, a more dominant rent dissipation appears to dominate in a theoretical model by Bagnoli and Watts (2003) who define intense competition as a larger number of competitors and find an inverse relationship between CSR and market competition. Unfortunately, each study employs different proxies for market competition, making it difficult to compare results across studies. For instance, Fernandez-Kranz and Santalo (2010) adopt the common Hirschman-Herfindahl Index (HHI) and number of firms as competition measures, whereas competition is proxied by fitted-HHI (Hoberg and Phillips, 2010) in Dupire and M’Zali (2016) and by tariff-based measures in Fernandez-Kranz and Santalo (2010) and Flammer (2015b). Although a key conjecture in these studies is that market competition prompts better social performance, a number of issues can be raised. Firstly, most of these studies do not account for the endogeneity issue. For instance, market competition is proxied by different competition measures in each study. This not only makes it difficult to compare the results across studies, but also raises the possibility that the market competition is measured with error, in which case the regression estimates will be biased and inconsistent. This concern justifies the need to address the endogeneity problem using an instrumental variable in this paper. In the process, we also investigate how robust the key results are across different measures of market competition. Secondly, it is conceivable that firms may consider it costly to invest in efforts to address both social performance concerns and strengths and they may tend to focus on the less costly option. An example would be a firm deciding to reduce the carbon footprint of their administrative division by reducing paper printing and thus augmenting its positive environmental performance. This may be less costly to implement for the firm, which will be less willing to make their production process more environmentally friendly, thus aggravating their negative social performance. To address the possibility that firms may perform differently in terms of strengths and concerns, we analyse not only the aggregate performance of firms but also their positive and negative social performances in this paper. Specifically, we hypothesise that competitive pressures will prompt better positive social performance and reduce negative social performance. Thirdly, the many dimensions of social performance suggest that the market competition and social performance relationship is not straightforward. Social performance generally encompasses a broad range of concerns, such as the community, the environment, human 4

rights, and the treatment of employees. For example, firms operating in localities where issues in labour practices may undermine their competitive advantages may focus on addressing these issues, rather than environmental issues. Because of the costs incurred by such social performance efforts, firms would be judicious in selecting the dimensions which they would like to focus on. As such, the effects of market competition on the social performance of firms may not be uniform across the CSR dimensions. Hence, we explore how the endogeneity issue can affect the results across different CSR dimensions. Fourthly, a good part of the current literature on the market competition and CSR nexus do not attempt to explain the complexity inherent in this relationship. In our paper, we hypothesize that how promptly and effectively firms adopt new CSR behaviours depend on the degree of competition in their industry and their market power. Firms in very competitive markets will tend to react promptly, in an attempt to catch up with the industry leaders. If the majority of firms in the industry adopts a new CSR initiative, the remaining firms cannot exempt themselves lest their sales suffer as a consequence. In addition, when the new CSR standard is widely adopted in the industry, the corresponding costs may be lowered, thus encouraging even more firms to jump onto the CSR bandwagon. In less competitive (oligopolistic or monopolistic) markets, there are more options for the adoption of socially responsible behaviour. One is to adopt the socially responsible behaviour and raise the price of the CSR friendly product or absorb part of the corresponding cost. Alternatively, these may be part of their R&D effort to improve the CSR content of their product or part of their marketing and lobbying effort aimed at reducing consumer awareness (such as the agreement to hide the percentage of OGM ingredients in food labels, promoted by Monsanto, Dupont, and Syngenta among others). The preceding discussion generates the following conjectures: Hypothesis 1 An increase in the degree of market competition results in a higher CSR performance amongst firms. Hypothesis 2 Competitive pressures induce more positive social performance and reduce negative social performance. To test these empirically, we propose a fixed effects panel data model, as illustrated in Figure 1. The panel regression specification is given by CSRM easurei,t = γCompetitionM easurei,t−1 + Xi,t−1 β + di Y eart + i,t ,

(1)

where CSRM easurei,t is a measure of firm i’s social performance and CompetitionM easurei,t−1 is a market competition measure. The term di Y eart captures the year fixed effects or timespecific characteristics which could influence social performance. Across time, the proportion of manufacturing firms decreases from about 50% of the sample in the 1990s to about 30% in the 2000s. Further time variations are introduced by the different industry mixes over time. The year fixed effects should control for any potential biases in the estimation arising from these time-related variations. The covariate control matrix Xi,t−1 is composed of firmspecific control variables, as indicated in Figure 1, with the details of these control variables provided in the next section. The control variables are lagged because implementing a CSR 5

Figure 1 Flow Chart Showing Panel Regression with Fixed Effects program usually takes time and thus the current year CSR performance is dependent on the previous year decision on CSR actions to be taken. In turn, such decisions may be based on the financial performance of the firms and other factors unique to the firms. Our results are robust even if contemporaneous controls are adopted. For space reasons, we do not report these contemporaneous results, which are available from the authors upon request. We do not include any industry fixed effects in the reported results as we are concerned that there are already many control variables which are influenced by the industry and the additional dummy variables introduced by the industry fixed effects may produce multi-collinearity and thus reduce both the power and precision of our estimates. However, we also perform but do not report the regressions with the industry fixed effects separately, since the addition of the industry fixed effects does not add significantly to the insights obtained in their absence. The real dotted line in Figure 1 indicates that market competition is endogenous. We can deal with this endogeneity using an instrumental variable (entry barriers) which is related to the market competition, but not to the error term i,t . This is illustrated in Figure 2. We implement the instrumental variable estimation using two-stage least squares (2SLS). The first-stage model is given by CompetitionM easurei,t−1 = g IVi,t−1 + Xi,t−1 b + di Y eart−1 + ei,t−1 ,

(2)

where IVi,t−1 denotes the instrumental variable, which will be discussed in the following section. In the second stage, the estimated value of CompetitionM easurei,t−1 from the first-stage model is then used in the panel model specified earlier. In the next section, we explain how the social performance measure, the market competition measure and the instrumental variable are derived, along with other firm-specific covariates. 6

Figure 2:Flow Chart Showing the Two-Stage Least Squares (2SLS) using an Instrumental Variable

3 3.1

Data and Variable Definitions Social Performance Measures

The social performance measures are derived from data in the Kinder, Lyndenberg and Domini (KLD) database. The KLD data used in this paper cover the period from 1991 to 2015. The KLD database examines seven dimensions of social performance, namely community relations, corporate governance, diversity, employee relations, environment, human rights and product quality and safety. Positive social performance is termed as a “strength” while negative social performance is termed as a “concern”. For each of these strengths or concerns, “1” indicates the presence of a CSR strength or concern and “0” denotes that there is nothing good or bad about the firm’s behaviour in that particular area. Between 1991 and 2000, the social performance data in the KLD database are obtained from firms either in the S & P 500 Index or in the Domini 400 Social Index, which is an index of 400 socially screened stocks selected by KLD. From 2001 onwards, the social performance ratings of all firms belonging to the Russell 1000 Index, which is an index comprising of the large-cap segment of the United States equity market. This represents about 92 percent of the United States market. From 2003, social performance data on companies were further expanded to include the small-cap equity segment of the equity market in the United States. Our baseline sample consists of firms in the KLD database for which firm-specific information can also be derived rom the Compustat database. This results in an unbalanced panel dataset comprising of 36,026 US firm-year observations for the period between 1991 and 2015. In comparison to other earlier studies, such as Fernandez-Kranz and Santalo (2010) and Dupire and M’Zali (2016), our sample covers a longer period and also includes firms in both the mid-cap and small-cap segments of the equity market. 7

We first construct the strengths and concerns (STR and CON) which is simply the total number of strengths or concerns across all seven CSR dimensions for each firm-year. We then construct aggregate social performance score (CSR) by taking difference between the strengths and concerns across dimensions for each firm-year. As shown in equations (3) to (5), all measures are taken for firm i in year t, j is one of the seven CSR dimension, and p represents the specific CSR measure which takes a value of 0 or 1. The considerable variations in the numbers of strengths and concerns every year may pose a problem for comparing social performance across years. To address this, we follow Deng et al (2013) and adjust these three sets of scores by first dividing the strengths and concerns for each j dimension by the respective maximum possible number of strengths and concerns (NST R j and NCON ). The adjusted aggregate social performance score is then obtained by taking the difference between the adjusted strengths and concerns across domains. The mathematical expressions of the adjusted CSR measures are presented in equations (6) to (8). XX ST Rit = strengthjp,it (3) j

CONit =

p

XX

concernjp,it

CSRit = ST Rit − CONit

AdjST Rit =

X

P

p

strengthjp,it j NST R

j

AdjCONit =

X

P

p

concernjp,it j NCON

j

AdjCSRit = AdjST Rit − AdjCONit

3.2

(4)

p

j

(5)

(6)

(7) (8)

Market Competition Measures

We use different proxies for measuring market competition. The first is the HirschmanHerfindahl Index (HHI) for each industry, which is widely applied in the industrial organisation literature as an index for market concentration. This index is obtained by adding the squares of the market share of all players operating in an industry in a given year. An HHI value below 0.01 indicates low concentration and a highly competitive industry, whereas a value above 0.25 indicates a highly concentrated or less competitive industry. The market share of a firm is obtained by the proportion of its sales in the industry that it belongs to. We follow Moskowitz and Grinblatt (1999) in classifying all the firms into 20 industries with their two-digit SIC codes, derived from the Compustat database. The second market competition measure is the number of players in each industry. Bagnoli and Watts (2003) use this as the market competition measure in their theoretical study. Presumably, with more players in each industry, the more competitive the industry will be. For our estimation, we take the natural logarithm of the number of players. As a third 8

measure, we consider the proportion of total sales for the top 4 players in the industry. In a less competitive market, a large proportion of sales tends to be concentrated on the top 4 players. We also employ the fitted HHI measure (denoted by HHIFITTED) by Hoberg and Phillips (2010), which is used by Dupire and M’Zali (2016) in their study. The fitted HHI measure is derived from the US Census Bureau and employee data from the Bureau of Labor Statistics (BLS) and accounts for firms in all industries. Matching the data to the HHIFITTED classification reduces the dataset to 10,547 firm-year observations. With the exception of number of players in each industry, all these market competition measures are measures of market concentration. On the other hand, the number of players in each industry measures the inverse of market concentration.

3.3

Instrumental Variable

The endogeneity problem may arise in estimating the relationship between market competition and firms’ social performance. One potential source of endogeneity may be omitted variables which may explain the social performance but not included in our specification. A second source of endogeneity may be due to errors in measuring both social performance and market competition. Such measurement errors will result in biased and inconsistent OLS estimates. Finally, it may be possible that the direction of causation is reversed, in which market competition is driven by social performance, rather than the other way round. To deal with the endogeneity problem, we instrument the market competition measure using a measure of the entry barriers to the industry. This instrumental variable is obtained by taking the average of the number of new firms entering the industry in the last 3 years. It is likely to be correlated with the intensity of market competition but is unlikely to be correlated to the variations in the social performance of firms2 . Using this instrument, we perform two-stage least squares (2SLS) estimations. To ensure that our instrument is relevant and not weak, we perform the test proposed by Stock and Yogo (2005).

3.4

Other Firm-Specific Control Variables

A firm’s social performance may be influenced by firm-specific factors other than market competition. We therefore include these variables as control variables in our regression analysis. First, a size effect may be present in social performance in the sense that large firms operating in less competitive market may be subjected to greater public scrutiny. This will increase the probability of their social performance to be recorded in the KLD database. To 2

We also use the tariff-cut events outlined by Fresard (2010) as an instrument variable in our study. We follow Fresard and Valta (2016) in employing difference-in-differences method to analyse the effect of tariffcut on CSR. Our result suggests that a reduction in import tariff increases the CSR on average. However, this result is weak given the small sample size. The tariff data covers only manufacturing firms (SIC 2000-3000) until 2001 which has a small overlap with our main data.

9

correct for this, we control for firm size, which is obtained by taking the natural logarithm of the book value for a firm’s assets. Further, firms in more competitive markets tend to have higher expenditure on advertisement and research and development (R&D). Fisman et al (2008) argue that advertising social performance allows firms to signal quality in more competitive industries. In contrast, Servaes and Tamayo (2013) interpret a firm’s advertising intensity as a proxy for consumer awareness. They find a positive relationship between social performance and firm value for firms with high customer awareness. On the other hand, the R&D relationship is fairly well-established (Aghion and Griffith, 2005; Aghion et al, 2005). Innovation through R&D enables a firm to differentiate its products and thereby “escape competition”; alternatively, a very competitive market quickly dissipates the rent accrued from R&D investment. Investing in R&D may inadvertently divert resources from social performance efforts and thus impact social peformance negatively. On the flip side, R&D may enable a firm to improve its environmental compliance in production and produce more socially responsible goods which will have a positive bearing on social performance. Hence, we control for both advertising and R&D expenditures, both deflated by sales of the same period. However, we encounter missing values for these two variables, so for each of these two variables, we include a missing dummy, which is equal to 0 if the firm reports the respective type of expenditures and 1 if the respective value is missing. Firms’ social performance may also be influenced by its profitability and its financial status. More profitable firms are likely to have more resources to engage in investing to improve their social performance. To account for profitability, we include a variable measuring firm’s profitability, namely the profit margin (PM), defined as the net operating income deflated by sales. In addition, the financial status of firms may affect their ability to carry out projects related to social performance. To control for firms’ financial status, we employ the following measures: the book-to-market (BM) ratio, which is the ratio of equity’s book value to its market value; leverage, which is the ratio of total debts (sum of current liabilities and long term debt) to assets; cash holding (CASH), which is the ratio of total cash holdings and equivalent liquidities deflated by total asset values; and sales, defined as the ratio of sales to assets. To limit the influence of extreme values, we winsorise all the continuous control variables at the 1st and 99th percentiles. Since the data are also grouped in industry clusters, the regression model errors may be independent across clusters but correlated within clusters. To prevent the default standard errors overstating the estimation precision, we employ cluster robust standard errors (Cameron et al, 2011; Cameron and Miller, 2015) for all the regressions.

3.5

Summary Statistics

Table 1 shows the sample distribution according to the classification of 20 industries with their corresponding SIC codes. The largest industry groups represented in the sample are Financial, Retail, Chemical and Electrical Equipment. The overall social performance (CSR) scores for most industries are negative, suggesting that there are more concerns than strengths. The industries with positive overall social performance scores are Manufacturing, 10

Table 1 Sample Distributions 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

Industry Mining Food Apparel Paper Chemical Petroleum Construction Prim. Metals Fab. Metals Machinery Electrical Eq. Transport Eq. Manufacturing Railroads Other Transport. Utilities Dept. Stores Retail Financial Others

SIC Codes 10-14 20 22-23 26 28 29 32 33 34 35 36 37 38-39 40 41-47 49 53 50-52, 54-59 60-69 others

Observations 1,330 797 372 457 2,907 244 156 464 427 1,967 2,358 870 2,040 116 725 1,648 321 3,204 7,405 8,218

% 3.7% 2.2% 1.0% 1.3% 8.1% 0.7% 0.4% 1.3% 1.2% 5.5% 6.5% 2.4% 5.7% 0.3% 2.0% 4.6% 0.9% 8.9% 20.6% 22.8%

CSR -1.083 0.775 -0.349 0.230 -0.084 -2.135 0.019 -1.101 -0.445 0.097 0.198 -0.707 0.100 -1.284 -0.252 -0.135 -0.340 -0.169 0.095 -0.188

STR 1.229 2.675 1.169 2.287 1.868 3.098 1.455 1.185 1.119 1.673 1.631 2.062 1.417 1.862 1.348 2.281 2.573 1.287 1.278 1.294

CON 2.313 1.900 1.519 2.057 1.951 5.234 1.436 2.287 1.564 1.576 1.433 2.769 1.317 3.147 1.600 2.416 2.913 1.457 1.183 1.482

HHI 0.041 0.067 0.070 0.092 0.042 0.190 0.173 0.093 0.081 0.063 0.058 0.134 0.047 0.288 0.069 0.021 0.278 0.021 0.022 0.020

PLAYER 174 81 40 37 403 25 19 47 46 217 296 81 280 9 86 146 23 363 940 1,038

TOP4 0.305 0.426 0.427 0.528 0.335 0.754 0.744 0.519 0.493 0.416 0.369 0.628 0.362 0.930 0.451 0.168 0.758 0.197 0.214 0.221

Notes. This table provides the sample distribution by industry. The two-digit SIC code grouping is used to define the industry classification. “Observations” and “%” are the number of unique firm-year observations included in the study and the proportion of these observations out of total observations for each industry, respectively. CSR, STR, and CON the three main corporate social responsibility measures, while HHI, PLAYER, and TOP4 are the main industrial competition measures. CSR is aggregate CSR score equal to the number of CSR strengths minus the number of CSR concerns; STR is the number of strengths across all seven issue dimensions; CON is the number of concerns across all seven issue dimensions; HHI is Hirschman-Herfindahl Index computed as the sum of squares of market share measured by proportion of sales of all player in the industry; lnPLAYER is log of number of players in the industry; TOP4 is proportion of sales of the top 4 players in the industry. Sample period is from 1991 to 2015.

Food, Paper, Construction, Machinery, Electrical Equipment and Financial. Higher negatives in the overall scores seem evident in resource-based industries such as Petroleum, Mining, Railroads and Primary Metals. However, it is interesting to note that amongst these industries, Mining and Primary Metals have moderate market concentration as measured by the HHI, whereas Railroads and Petroleum are highly concentrated industries. Based on HHI, highly concentrated industries include Railroads, Department Stores, Petroleum, Construction and Transport Equipment. Incidentally, these are also industries with a lower number of players (with the exception of Transport Equipment) and also higher concentration of sales amongst the top 4 players. These highly concentrated industries also have the highest scores in terms of social performance concerns. On the other hand, there are also less concentrated industries, such as Mining, Chemical and Department Stores with similarly high concern scores in their social performances. In terms of overall social performance, Petroleum fared the worst alongside Railroads, but besides these two highly concentrated industries, there are other less concentrated industries such as Mining and Primary Metals who exhibit similarly bad overall social performance. Thus, the nexus between social performance and market competition cannot be easily discernible from examining the sample distribution and their corresponding statistics. The summary statistics and correlation matrix are given by Tables 2 and 3 respectively. In Table 2, both the average overall social performance scores with and without adjustment are negative. This is because the average concerns are higher than the average strengths; further, 11

Table 2 Summary Statistics Variable CSR STR CON AdjCSR AdjSTR AdjCON

Observations 36,026 36,026 36,026 33,824 33,824 36,026

Mean -0.114 1.513 1.627 -0.161 0.273 0.414

Std. Dev. 2.421 2.315 1.809 0.541 0.450 0.440

P5 -3.000 0.000 0.000 -0.917 0.000 0.000

P25 -1.000 0.000 0.000 -0.500 0.000 0.000

Median 0.000 1.000 1.000 -0.167 0.125 0.333

P75 1.000 2.000 2.000 0.100 0.333 0.583

P95 4.000 6.000 5.000 0.685 1.142 1.202

HHI lnPLAYER TOP4 HHIFITTED

36,026 36,026 36,026 10,547

0.042 5.934 0.301 0.060

0.042 1.050 0.135 0.028

0.018 3.738 0.161 0.035

0.020 5.371 0.211 0.043

0.025 5.961 0.237 0.051

0.046 6.888 0.355 0.068

0.110 7.048 0.573 0.106

SIZE RD ADVER MISSRD MISSADVER PM BM LEVERAGE CASH SALES

36,026 36,026 36,026 36,026 36,026 36,026 36,026 36,026 36,026 36,026

7.444 0.098 0.011 0.455 0.597 -0.039 0.512 0.228 0.164 0.876

1.747 0.430 0.026 0.498 0.490 0.688 0.375 0.206 0.197 0.737

4.750 0.000 0.000 0.000 0.000 -0.373 0.072 0.000 0.005 0.058

6.163 0.000 0.000 0.000 0.000 0.016 0.261 0.045 0.026 0.323

7.373 0.000 0.000 0.000 1.000 0.060 0.445 0.195 0.080 0.728

8.566 0.035 0.010 1.000 1.000 0.120 0.683 0.349 0.228 1.209

10.518 0.271 0.058 1.000 1.000 0.273 1.206 0.626 0.623 2.355

Notes. This table reports descriptive statistics of variables used in the analysis. Three main social performance measures (CSR, STR, CON) and market competition measures (HHI, PLAYER, TOP4) are defined in Table 1. AdjCSR is equal to the adjusted strength (AdjSTR) score minus adjusted concern score (AdjCON); AdjSTR is sum of the adjusted strength scores across all seven issue categories, the adjusted strength score in each category is number of strengths in the category scaled by number of items of strength in that category; similarly, AdjCON is sum of the adjusted concern scores across all seven issue categories; SIZE is equal to the log of total assets in $ million; RD is research and development expense deflated by net sales; ADVER is advertising expense deflated by net sales; MISSRD (MISSADVER) is an indicator variable taking the value of 1 if R&D (advertising) expense is missing; PM is net income deflated by net sales; BM is book value of equity over market value of equity; LEVERAGE is ratio of total debt (sum of current liabilities and long-term debt) divided by total assets; CASH is total cash and equivalent deflated by total assets; SALES net sales deflated by total assets. HHIFITTED is firm-level competition measure based on the firm product market similarities and network obtained from Hoberg and Phillips (2010). All continuous variables are winsorised at the 1st and 99th percentiles. Sample period is from 1991 to 2015.

12

13

CSR 1 0.709 -0.431 0.930 0.690 -0.437 -0.042 0.050 -0.042 -0.066 0.190 -0.016 0.097 0.049 -0.071 -0.045 0.000 -0.029

CON

1 -0.540 0.285 0.956 0.168 -0.191 0.156 0.170 0.351 -0.051 -0.018 0.031 -0.004 0.074 -0.091 0.025

STR

1 0.331 0.556 0.945 0.290 0.087 -0.096 0.078 0.081 0.473 -0.057 0.088 0.075 -0.077 0.011 -0.071 -0.010 1 0.620 -0.597 -0.059 0.049 -0.058 -0.083 0.122 -0.010 0.080 0.040 -0.043 -0.048 -0.010 -0.018

AdjCSR

1 0.260 0.086 -0.099 0.077 0.087 0.455 -0.053 0.076 0.069 -0.056 0.008 -0.076 -0.015

AdjSTR

1 0.154 -0.161 0.143 0.161 0.304 -0.043 -0.020 0.022 0.005 0.062 -0.067 0.012

AdjCON

1 -0.733 0.924 0.444 0.050 -0.004 -0.036 -0.002 -0.010 -0.026 -0.040 0.180 1 -0.774 -0.355 -0.027 0.027 0.051 0.000 0.021 -0.013 0.105 -0.239

lnPLAYER

1 0.361 -0.023 0.045 -0.037 -0.038 -0.048 -0.051 0.027 0.161

TOP4

1 0.236 -0.135 0.010 0.091 0.113 0.104 -0.255 0.302

HHIFITTED

Table 3 Correlation Matrix HHI

1 -0.250 -0.032 0.226 0.136 0.268 -0.438 -0.229

SIZE

1 -0.025 -0.859 -0.137 -0.084 0.498 -0.169

RD

1 0.002 -0.104 -0.033 0.081 0.081

ADVER

1 0.058 0.019 -0.378 0.118

PM

1 -0.046 -0.230 -0.139

BM

1 -0.363 -0.157

LEVERAGE

1 -0.048

CASH

1

SALES

Notes. This table presents the Pearson correlation matrix for variables used in the analysis. All variables are defined in Tables 1 and 2. All continuous variables are winsorised at the 1st and 99th percentiles. Sample period is from 1991 to 2015.

Variable CSR STR CON AdjCSR AdjSTR AdjCON HHI lnPLAYER TOP4 HHIFITTED SIZE RD ADVER PM BM LEVERAGE CASH SALES

Figure 3:Empirical Relationship between Social Performance (CSR) and Market Competition (HHI) the mean and median of the adjusted concerns are also higher the mean and median of the adjusted strengths. It would appear that most of the overall social performance is driven by concerns than strengths. Next, we consider the market competition measures. The average HHI in the sample is 0.042, while its median is 0.025. This indicates that most industries in the sample are neither highly concentrated nor overly competitive. Similar observations can be made for HHIFITTED. The Pearson correlation between the overall CSR score (both adjusted and unadjusted) and HHI is negative. The same negative correlation is observed between these overall social performance scores and TOP4 as well as HHIFITTED. On the other hand, the correlation between the overall social performance score with lnPLAYER is positive, which is expected as we have mentioned earlier that lnPLAYER is the inverse of market concentration. Similar correlation patterns can be observed for the adjusted social performance score.

4

Empirical Results

Figure 2 illustrates the relationship between social performance and market competition graphically. Using market level information, we average the CSR scores across firms in the same industry and plot these against the industry average HHI. Overall, it appears that a lower market concentration or a higher level of market competition is associated with higher CSR scores. 14

Table 4 contains the results of the fixed effects panel data regression models, while Table 5 presents the results of the 2SLS regressions with HHI being instrumented by the entry barriers measure discussed earlier. In models (1) to (3) for both tables, we use the unadjusted CSR score, CSR strength score and the CSR concern score. For models (4) to (6), we employ the corresponding adjusted versions of these scores. We note that in Table 4, market competition are significant for both the overall social performance scores and the concern scores. This applies to both the unadjusted and adjusted scores. The overall CSR scores (both unadjusted and adjusted) and HHI are negatively correlated, suggesting that a higher concentrated market is associated with lower overall CSR scores. While this corroborates similar results in Fernandez-Kranz and Santalo (2010), it does not factor into endogeneity. Once endogeneity is incorporated using the instrumental variable in Table 5, the market competition is no longer significant for predicting the overall social performance, whether this is measured adjusted or unadjusted. Besides the aggregate social performance scores, we also examine the scores for both social performance strengths and concerns. In Table 4, we note a negative relationship between CSR concerns and HHI and a positive relationship between CSR strengths and HHI. However, the latter relationship is not statistically significant. Our results also contrast with those of Fernandez-Kranz and Santalo (2010) who report a positive relationship bewtween CSR concerns and HHI and a negative relationship between CSR strengths and HHI. These results are obtained without considering endogeneity. Yet we obtain similar IV results in Table 5, albeit with a larger magnitude for the coefficients of HHI for both the adjusted and unadjusted concerns. Our results suggest that once endogeneity is accounted for, market competition is significant in reducing performance concerns with a magnitude larger than previously estimated. A concern is whether the instrument chosen is weak. As pointed out by Bound et al (1995), a weak instrument can lead to large inconsistencies in the IV estimates, which will be biased in the same direction as OLS estimates. We report the Craig-Donald statistics for all the models at the bottom of Table 5. All these exceed the 10% critical value proposed by Stock and Yogo (2005). This suggests that the instrument used in our 2SLS estimations is not weak. The control variables R&D expenditure and advertising expenditure are positively correlated with CSR and adjusted CSR scores, whether HHI is instrumented or not. The coefficients for advertising are particularly large. In Fisman et al (2008), the large positive coefficient for advertising is interpreted as higher advertising intensity to signal their social performance efforts. On the other hand, Servaes and Tamayo (2013) regard a larger advertising outlay as a measure of consumer awareness in CSR ; thus, the results would imply that a larger advertising outlay is required to increase consumer awareness of firms’ social performance. However, our results suggest that advertising is not significant in reducing firms’ social performance concerns. On the other hand, a larger R&D expenditure can significantly reduce social performance concerns. Concomitantly, a larger R&D outlay could enable firm to achiever better overall CSR performance3 . 3

We thank a reviewer for pointing out the high correlation between two highly correlated control variables – profit margin (PM) and research and development expense (RD). We address the collinearity issue using

15

Table 4 Panel Regressions of Market Competition on CSR Variable HHI SIZE RD ADVER MISSRD MISSADVER PM BM LEVERAGE CASH SALES Intercept Fixed Effects Observations Adj-R2

(1) CSR

(2) STR

(3) CON

(4) AdjCSR

(5) AdjSTR

(6) AdjCON

-4.105** (1.450) 0.308*** (0.050) 0.340*** (0.089) 5.836*** (1.944) -0.256** (0.090) -0.224** (0.091) 0.228*** (0.060) -0.454*** (0.103) -1.119*** (0.133) 0.367 (0.222) -0.009 (0.050) -1.634*** (0.420) Year 36,026 0.13

1.537 (1.515) 0.810*** (0.070) 0.144* (0.079) 7.045*** (2.182) -0.491*** (0.121) 0.010 (0.088) 0.032 (0.044) -0.693*** (0.092) -1.049*** (0.226) 1.139*** (0.204) 0.252* (0.143) -4.270*** (0.333) Year 36,026 0.31

5.642** (1.976) 0.503*** (0.081) -0.196*** (0.029) 1.209 (1.161) -0.234** (0.103) 0.234* (0.125) -0.196*** (0.039) -0.239** (0.092) 0.070 (0.198) 0.771*** (0.212) 0.261* (0.130) -2.636*** (0.446) Year 36,026 0.32

-1.016*** (0.286) 0.040*** (0.011) 0.089*** (0.018) 1.138*** (0.345) -0.029 (0.018) -0.039* (0.021) 0.051*** (0.013) -0.058** (0.024) -0.232*** (0.027) -0.017 (0.046) -0.003 (0.010) -0.308*** (0.090) Year 33,824 0.12

0.280 (0.282) 0.148*** (0.013) 0.032** (0.015) 1.304*** (0.404) -0.081*** (0.022) 0.012 (0.017) 0.004 (0.008) -0.116*** (0.018) -0.214*** (0.035) 0.178*** (0.035) 0.046 (0.028) -0.783*** (0.062) Year 33,824 0.28

1.267*** (0.391) 0.107*** (0.018) -0.054*** (0.006) 0.162 (0.262) -0.049** (0.020) 0.048* (0.027) -0.046*** (0.009) -0.057** (0.022) 0.015 (0.051) 0.191*** (0.049) 0.049* (0.027) -0.485*** (0.098) Year 36,026 0.29

Notes.: This table reports the results of our fixed effects panel regressions. In models (1) to (3), the dependent variable is one of the social performance scores (CSR, STR, or CON). In models (4) to (6), the dependent variable is one of the adjusted social performance score (AdjCSR, AdjSTR, or AdjCON). All variables are defined in Tables 1 and 2. The coefficient estimates for year fixed effects are not tabulated. Robust standard errors adjusting for industry clusters are reported in parentheses. Sample period is from 1991 to 2015. *, **, *** indicate that the estimated coefficient is significant at the 10%, 5%, and 1% levels, respectively.

Next, we consider the relationship between the profitability of the firms and social performance. In both Tables 4 and 5, we observe that a higher profit margin is positively and significantly correlated with better overall social performance while negatively correlated with a higher social performance concern score. Because investments in social performance are costly, a more profitable firm is more likely to be able to harness the financial resources to invest and turn out a better social performance and reduce social concerns. Mirroring the results for HHI, performance in social strengths is not significantly affected by profit margin. This would imply that firms tend to focus more on reactively reducing social performance concerns, rather than proactively augmenting their social performance strengths. three methods: (a) by dropping PM from the regression; (b) by dropping RD from the regression; and (c) orthogonalizing the two collinear regressors. The results from all these analyses suggest that there are little qualitative and quantitative differences from our existing results.

16

Table 5 Panel Regressions of Market Competition on CSR: Instrumental Variable Variable HHI SIZE RD ADVER MISSRD MISSADVER PM BM LEVERAGE CASH SALES Intercept Fixed Effects Cragg-Donald Statistics (critical value) Observations

(1) CSR

(2) STR

(3) CON

(4) AdjCSR

(5) AdjSTR

(6) AdjCON

-2.430 (2.802) 0.311*** (0.050) 0.335*** (0.086) 5.629*** (1.841) -0.238*** (0.084) -0.241*** (0.091) 0.227*** (0.061) -0.459*** (0.105) -1.122*** (0.129) 0.399* (0.205) -0.017 (0.059) -1.344** (0.560) Year 8,884 (16.38) 35,495

8.077 (5.643) 0.798*** (0.066) 0.145** (0.073) 7.258*** (2.309) -0.398*** (0.148) -0.019 (0.084) 0.045 (0.033) -0.689*** (0.098) -1.022*** (0.252) 1.172*** (0.210) 0.212* (0.121) -4.918*** (0.630) Year 8,884 (16.38) 35,495

10.507* (5.612) 0.486*** (0.074) -0.190*** (0.036) 1.629 (1.353) -0.160 (0.152) 0.222* (0.115) -0.182*** (0.038) -0.230*** (0.084) 0.100 (0.210) 0.773*** (0.235) 0.229** (0.108) -3.574*** (0.578) Year 8,884 (16.38) 35,495

-0.255 (0.623) 0.041*** (0.011) 0.088*** (0.017) 1.100*** (0.315) -0.019 (0.018) -0.045** (0.019) 0.051*** (0.013) -0.060** (0.025) -0.230*** (0.025) -0.005 (0.041) -0.007 (0.013) -0.238** (0.121) Year 8,078 (16.38) 33,298

1.800* (1.092) 0.145*** (0.012) 0.032** (0.014) 1.345*** (0.426) -0.059** (0.029) 0.004 (0.015) 0.007 (0.006) -0.115*** (0.019) -0.208*** (0.040) 0.188*** (0.036) 0.037 (0.023) -0.904*** (0.123) Year 8,078 (16.38) 33,298

1.972* (1.164) 0.103*** (0.016) -0.054*** (0.008) 0.239 (0.300) -0.039 (0.029) 0.047* (0.025) -0.044*** (0.009) -0.054*** (0.021) 0.020 (0.051) 0.189*** (0.053) 0.044* (0.023) -0.650*** (0.121) Year 8,884 (16.38) 35,495

Notes.: This table reports the results of our results of the second stage of the instrumental variable (IV) regressions. All variables are defined in Tables 1 and 2. The coefficient estimates for year fixed effects are not tabulated. To test for weak instrument, the Cragg-Donald statistics are reported along with test the critical values for the 10% maximal IV size taken from Stock and Yogo (2005). Robust standard errors adjusting for industry clusters are reported in parentheses. Sample period is from 1991 to 2015. *, **, *** indicate that the estimated coefficient is significant at the 10%, 5%, and 1% levels, respectively.

17

5 5.1

Robustness Checks and Further Discussions Alternative Measures of Market Competition

In our baseline models, we employ the HHI as a proxy measure for market competition. As a robustness check, we investigate how the results are affected by different measures of market competition. For this purpose, we re-estimate the models with year fixed effects using three alternative measures of market competition: lnPLAYER, TOP4 and HHIFITTED. The results for both the models with or without IV are displayed in Table 6. Considering overall social performance, the coefficient for lnPLAYER is positive, while those for TOP4 and HHIFITTED are negative. All these parameters are significant for predicting overall social performance in the panel regressions without the instrumental variable. For lnPLAYER, the positive coefficient would suggest that the higher the number of players in the industry, the higher would be the CSR score, confirming that more market competition (as measured by more players) is beneficial to social performance. A similar conclusion can be obtained from the signs for the TOP4 and HHIFITTED. All these three alternative measures of market competition are not significant for predicting social performance strengths, except for lnPLAYER, but the sign appears to be wrong and the significance is weak. On the other hand, these market competition measures are significant for predicting social performance concerns. In comparison, the results of the IV regressions indicate that coefficients for all these three market competition measures are insignificant for predicting overall social performance or strengths. However, lnPLAYER and TOP4 are significant for predicting social performance concerns. To a certain extent, our results corroborate our earlier results obtained using HHI as the market competition measure, namely that once endogeneity is factored in, market competition is only crucial for reducing social performance concerns. It is also instructive to compare our results of the HHIFITTED with those of Dupire and M’Zali (2016). Like this paper, they also find a negative and significant relationship between HHIFITTED and adjusted overall social performance scores, as well as a negative and significant relationship between HHIFITTED and total strengths. They do not find any relationship between HHIFITTED and total concerns. Therefore, they suggest that firms compete on the basis of improving their social performance strengths, and not the reduction of social performance concerns. Our panel regressions with HHIFITTED but without instruments obtain a contrary result. In fact, once the HHIFITTED is instrumented with the entry barriers, all the coefficients are no longer significant. Although we repeat our regressions for the HHIFITTED measure with adjusted scores, the IV regressions remain insignificant. This highlights the importance of accounting for endogeneity in understanding the nexus between market competition and social performance of firms.

5.2

Dimensions of Social Performance

So far, our analysis has demonstrated that market competition is important for reducing social performance concerns. In this section, we disaggregate the concerns further into their sub-components and analyse which of these components are influenced by market competi18

Table 6 Alternative Measures of Market Competition CSR

lnPLAYER Other Controls Fixed Effects Adj-R2 Cragg-Donald Statistics (critical value) Observations

TOP4 Other Controls Fixed Effects Adj-R2 Cragg-Donald Statistics (critical value) Observations

HHIFITTED Other Controls Fixed Effects Adj-R2 Cragg-Donald Statistics (critical value) Observations

STR

CON

(1) Panel

(2) IV

(3) Panel

(4) IV

(5) Panel

(6) IV

0.124* (0.061) Yes Year 0.13

0.050 (0.060) Yes Year

-0.148* (0.086) Yes Year 0.31

-0.167 (0.106) Yes Year

-0.272** (0.100) Yes Year 0.33

-0.218** (0.108) Yes Year

36,026

45,848 (16.38) 35,495

36,026

45,848 (16.38) 35,495

36,026

45,848 (16.38) 35,495

Panel

IV

Panel

IV

Panel

IV

-1.062** (0.488) Yes Year 0.13

-0.622 (0.722) Yes Year

0.765 (0.550) Yes Year 0.31

2.068 (1.446) Yes Year

1.827*** (0.576) Yes Year 0.32

2.690* (1.447) Yes Year

36,026

11,031 (16.38) 35,495

Panel -6.369*** (1.613) Yes Year 0.09

10,547

36,026

11,031 (16.38) 35,495

36,026

11,031 (16.38) 35,495

IV

Panel

IV

Panel

IV

-42.010 (41.383) Yes Year

-1.196 (1.303) Yes Year 0.27

25.245 (25.712) Yes Year

5.173** (1.934) Yes Year 0.34

67.255 (55.459) Yes Year

69.38 (16.38) 10,339

10,547

69.38 (16.38) 10,339

10,547

69.38 (16.38) 10,339

Notes: This table reports the results of the regressions using alternative measures of industrial competition. The dependent variable is one of the social performance scores: CSR for models (1) and (2), STR for models (3) to (4) and CON for models (5) to (6). The alternative competition measures are lnPLAYER, TOP4 and HHIFITTED. The coefficient estimates for other control variables and year fixed effects are not tabulated. Robust standard errors adjusting for industry clusters are reported in parentheses. Sample period is from 1991 to 2015. To test for weak instrument, the Cragg-Donald statistics are reported along with test the critical values for the 10% maximal IV size taken from Stock and Yogo (2005). Sample period is from 1991 to 2015. *, **, *** indicate that the estimated coefficient is significant at the 10%, 5%, and 1% levels, respectively.

19

tion. Table 7 reports the effect of market competition as measured by HHI on the concerns of seven separate dimensions of social performance concerns, namely corporate governance, community, diversity, employee relations, environment, human rights and product quality and safety. If market competition encourages firms to reduce the social performance concerns associated with the individual firm, we should observe a positive relationship between HHI and social performance concerns. In Table 7, positive correlations between HHI and CSR concerns are observed for all concerns, except for corporate governance. However, only the HHI coefficients for diversity, employment and human rights are statistically significant in the panel regressions without the instrument. When HHI is instrumented using entry barriers, the only significant HHI coefficient is that for the dimension of environmental concerns. Hence, if endogeneity is not considered, the wrong inferences may be drawn; in this case, it would appear that market competition is important in reducing diversity, employment and human rights concerns, but not environmental concerns. But once endogeneity is considered, market competition is only crucial for reducing firm’s environmental concerns. One possible explanation why market competition is critical in reducing firm’s environmental concerns is that there are more awareness of environmental issues, making the environmental dimensions of social performance particularly salient for firms. Since social performance efforts are costly, firms rationalise their resources towards the more salient issue of the environment. No firm likes to be identified as harming the environment as this will affect their market shares and hence alter the market competition in favour of their competitors. Thus, in response to the market competition, firms are likely more active in trying to reduce their environmental concerns. It is worthwhile here to consider the generalisation of the results in our study. While our study focuses primarily on the CSR performance of US-based firms in different industries, we are confident that our finding can be generalised to other country. Hence, the cross-country differences of the effect of market competition on CSR is worth investigating in the future research. Nevertheless, we defer further discussions of these limitations of our study to the concluding remarks.

6

Concluding Remarks

Corporate social responsibility is an important issue in firms and deserves investigation. Our paper contributes methodologically and practically to a better understanding in developing a more socially responsible behaviour in firms: Methodologically, a key implication of our paper is that endogeneity is very important in understanding the relationship between CSR and market competition. Our results demonstrate that in the absence of accounting for such endogeneity, the wrong inference about the CSR and market competition nexus may be drawn. By accounting for endogeneity, we reached a more nuanced conclusion that market competition may not be sufficient in motivating social performance of firms and that firms can be selective in dealing with CSR, with firms reactively dealing with CSR concerns, rather than proactively promoting their CSR 20

Table 7 Dimensions of Social Performance Concerns Corporate Governance

Community

Diversity

Employee Relations

Environment

Human Rights

HHI Panel -0.161 (0.257)

Other Controls Fixed Effects Adj-R2 Yes Year 0.30

Cragg-Donald Statistics

Observations 36,026

IV

-1.190 (1.086)

Yes

Year

8,884 (16.38)

35,495

Panel 0.558 (0.341)

Yes

Year

IV

Yes

Year

Panel 0.679*** Yes (0.212)

Year

IV

Yes

Year

Panel 1.493*** (0.306)

Yes

Year

IV

2.180 (1.416)

Yes

Year

Panel 2.364 (1.412)

Yes

Year

IV

7.968** (3.190)

Yes

Year

Panel 0.478** (0.193)

Yes

Year

IV

Yes

Year

Yes

Year

Yes

Year

1.119 (0.681)

0.384 (0.849)

0.690

(0.325) Product Quality Panel 0.233 & Safety (0.325) IV

-0.645 (0.747)

0.11

36,026

8,884 (16.38) 0.31

35,495

36,026

8,884 (16.38) 0.17

35,495

36,026

8,884 (16.38) 0.17

35,495

36,026

8,884 (16.38) 0.09

35,495

36,026

8,884

0.21

35,495

36,026

8,884 (16.38)

35,495

Notes: This table reports the results of our panel and IV regression models of market competition and seven key dimensions of corporate social responsibility concerns. The dependent variable is the concern score from one of the key dimensions: corporate governance, community, diversity, employee relations, environment, human rights, and product quality and safety. All variables are defined in Tables 1 and 2. The coefficient estimates for other control variables and year fixed effects are not tabulated. Robust standard errors adjusting for industry clusters are reported in parentheses. To test for weak instrument, the Cragg-Donald statistics is reported along with test the critical values for the 10% maximal IV size taken from Stock and Yogo (2005). Sample period is from 1991 to 2015. *, **, *** indicate that the estimated coefficient is significant at the 10%, 5%, and 1% levels, respectively.

21

strengths. From a practical perspective, our paper contributes to the debate on whether it is possible to rely purely on the “invisible hand” operating through market competition to ensure good social performance amongst firms. Conventionally, corporate social irresponsibility is regarded as a form of market failure which should be corrected by government intervention a la Pigou (1920). Our results suggest that firms are probably more willing to incur the higher costs of social performance efforts if doing so confers on them a competitive edge over their competitors. Further, competitive pressures nudge firms to reduce social performance concerns in the industry, and in the process addressing some of the market failures. The nice corollary of this result is that in general, we neither need to rely on the benevolence of the firms in the form of “morally motivated self-regulation” (Baron, 2010) nor their concerns for self image and social image (Benabou and Tirole, 2010) for good social performances. Further, there is no need to tweak the compensation systems to encompass social performance (Baron, 2008). Instead, firms may rely on strategic interests and competitive forces to realise certain aspects of social performance. On the other hand, purely relying on strategic interests have only limited effects in reducing social performance concerns. Even with market competition, firms are selective in addressing their social performance concerns. Our results suggest that they expend a large amount of efforts towards addressing environmental concerns. Consequently, relying on the “invisible hand” of the market to deal with the multi-dimensional challenges of firms’ social performance has its limitations. Viewed in this way, the role of more competition is not so much as a driving force, but arguably a dominating conformism inducing all firms to conform to an (occasionally) changing CSR standards shared by all the firms. The driving force in this context derives from a demand of such behaviour (through heightened consumer awareness, legislation or organised labour), which prompts firms to bother with CSR behaviour. On the other hand, in the absence of a high degree of competition, firms with large market power will be able to resist consumers’ demand from heightened consumer awareness (such as not colouring food), legislation (such as those promoting food safety) and workers’ demand (such as better pay and working conditions). As such, the promotion of good social performance requires the collective efforts of firms, consumers, workers and government. It is worthwhile to consider some limitations of our study. Our dataset is limited to US firms and may not be representative of the CSR performance of firms in general. While our finding can be generalised to other country, the effect of market competition on CSR may be conditioned on the education, cultural, legal, or other aspects of the specific country. However, information on CSR performance of firms worldwide are currently scarce and the KLD database is probably the most complete dataset on CSR performance on firms available, which is why it is widely used in the literature. The determinants of cross-country differences in relation between market competition and CSR is another of the avenue of future research. In our paper, we do not consider “natural” monopolies which are operated and regulated by the government (such as Vesta in Denmark) which may be deeply concerned with CSR and act in a socially responsible way. It would be interesting for future research to analyse if such CSR behaviour from “natural” monopoly is due to government regulation and whether such 22

regulation can also be applied to non-state-owned firms, like those studied in this paper. A practical implication of our results is the need to encourage firms to take a more proactive role in promoting their CSR strengths. How this can be best done (whether through appropriate regulation or moral persuasion) remains an area for future research. Finally, another area worth exploring for future research is whether the effect of market competition on CSR may be different for business-to-business and business-to-consumer firms.

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Highlights • • • • •

Firms in more competitive markets exhibit better social performance. Market competition only significantly reduces social concerns. Firms appear to be more active in reducing environmental concerns. Social performance is multi-dimensional in challenges. Market competition is limited in dealing with multi-dimensional social performance.