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Socially Responsible Corporate Customers
Rui Dai, Hao Liang, and Lilian Ng∗
Current Version: April 15, 2018
∗Dai is from WRDS, The Wharton School, University of Pennsylvania, Philadelphia, USA; Liang from Sin-gapore Management University; Ng from the Schulich School of Business, York University, Toronto, Canada.We thank Ekkehart Boehmer, Simba Chang, Zhenhui Chen, Tao Chen, Travers Child, Hyunsoo Doh, ItamarDrechsler, Ying Duan, Caroline Flammer, Aurobindo Ghosh, Olga Hawn, Jie (Jack) He, Johan Hombert,Dashan Huang, Chuan Yang Hwang, Oguzhan Karakas, Andy Kim, Lloyd Kurtz, Mauricio Larrain, Long(Jason) Li, Weikai Li, Karl Lins, Roger Loh, Christopher Marquis, Ron Masulis, Clemens Otto, HoonsukPark, Hyeong-sop Shim, Victor Song, Choi Hyun Soo, Neal Stoughton, Alexander Verdarushko, Cong Wang,Heli Wang, Chishen Wei, Frank Yu, Xiaoyun Yu, Chendi Zhang, and seminar participants at the China Eu-rope International Business School, China International Conference in Finance (CICF), City University ofHong Kong, Deakin University, Nanyang Technological University, the Schulich School of Business, MonashUniversity, Singapore Management University, Simon Fraser University, Sungykunkwan University, TelferAnnual Conference on Accounting and Finance at the University of Ottawa, Third International Conferenceon Corporate Finance (Tokyo), and WRDS of The Wharton School, for their many helpful comments andsuggestions. Authors’ contact information: Dai: rui.dai.wrds@outlook.com; Liang: hliang@smu.edu.sg; Ng:lng@schulich.yorku.ca.
Socially Responsible Corporate Customers
Abstract
Using corporate social responsibility (CSR) ratings of 34,117 corporate customer-supplier rela-
tionships from 50 countries worldwide, we find that customers’ CSR ratings are associated with
suppliers’ subsequent CSR performance, but not vice versa and that their locations matter. Results
from quasi-natural experiments and M&A acquisitions suggest that customer activism for promot-
ing CSR in suppliers is causal. The bargaining power of firms and their network connectedness are
channels through which customers affect suppliers’ CSR practices. Finally, increasing collaborative
CSR efforts between customers and suppliers helps improve their operational efficiency and firm
valuation but increase only the customers’ future sales growth.
Keywords: Corporate Social Responsibility, Corporate Customers, Global Supply Chains, Eco-
nomic Benefits.
JEL Classification Number: G23, G30, G34, M14
More than 1,000 of the world’s largest companies ... have emissions-reduction targets
for their own operations. Now, they want the thousands of companies that supply them
with goods and services to reduce their own emissions.1
1. Introduction
Today’s changing global businesses and corporate environments have brought about a new
wave of corporate social responsibility (CSR) activities. Corporations worldwide are increasingly
integrating CSR into their business operations as consumers place growing importance on ethical,
safe and sustainable business practices, and hold corporations to high standards.2 As the above
opening quote suggests, there is mounting anecdotal evidence that many corporate customers are
concerned about not only their own CSR standards but also those of their suppliers. Some scholars
argue that the increasing popularity of CSR activities around the world is, in part, in response to the
repeated failures of laws and regulations protecting stakeholders, raising the need from stakeholders
to protect their own interests through pushing the company to engage in CSR (Benabou and Tirole,
2010; de Bettignies and Robinson, 2017). However, it is not apparent whether corporate customers,
one of the most important stakeholders, are really taking actions to push suppliers to engage in
socially responsible business practices, or whether their public mention of CSR is simply a sideshow.
Furthermore, there is limited academic research that shows that corporate customers do play an
active role in suppliers’ CSR engagements. Thus, the goal of our study is to explore whether
corporate customers are an important driver for CSR practices in global supply chains around the
world and possible channels that customers employ to influence CSR in suppliers.
Increases in economic globalization, advancements in production and information technologies,
and improvement in logistics have facilitated a dramatic growth in global supply chains in many
industries and across countries. However, as corporations exploit these expanding opportunities in
supply chains, they face new challenges to ensuring that these economically-linked firms commit
to CSR practices which are consistent with the corporations’ values. For example, Nike has long
1https://www.greenbiz.com/article/how-get-suppliers-act-climate2As of 2017, more than 9,500 corporations from 160 developed and developing countries have become participants
of the United Nations Global Compact program, a global initiative to encourage “companies to align strategies andoperations with universal principles on human rights, labour, environment and anti-corruption, and take actions thatadvance societal goals.”https://www.unglobalcompact.org/what-is-gc/participants.
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been accused of abusive labor practices since the early 1970s when outsourcing its manufacturing to
developing economies with labor practices which would be considered illegal in the U.S. In light of
these allegations, one Nike director admitted that it is difficult for corporations to pressure suppliers
to assume responsibility for the ways their operations affect societies and the environment.3 In 2012-
2013, devastating workplace disasters (i.e., the factory fires in Pakistan and the collapse of a factory
in Bangladesh)4 together claimed more than 1,300 lives.5 Activists have raised concerns and said
international retailers, such as the Gap, Walmart, among others, need to take responsibility for
the working conditions in these factories that produce their clothes. These are few examples of
numerous cases that have drawn public outcry and questioned the role of corporate customers in
CSR across global supply chains.
To begin, we test whether socially responsible corporate customers can infuse similar socially
ethical business behavior in suppliers. Our tests exploit two unique international databases: (1)
a newly available FactSet Revere database that provides information on firm-level networks of
customers and suppliers around the world, and (2) Thomson Reuter’s ASSET4 Environmental (E),
Social (S), and Corporate Governance (G) database (ASSET4) that contains ASSET4 ratings (i.e.,
a composite firm-level CSR rating) as well as more than 750 constituent ESG ratings of global
publicly listed firms. After merging these two databases, our sample consists of 34,117 unique
corporate customer-supplier pairs from 50 countries worldwide for the period from 2003 to 2015.
Using this large international sample of corporate customer-supplier relationships, we find evidence
of uni-directional effects of customers’ socially responsible behavior on their suppliers’. Specifically,
customer CSR ratings are strongly and positively correlated with the suppliers’ subsequent CSR
performance. In terms of economic significance, a one standard-deviation-change in customer CSR
rating will generate a 4% increase in CSR performance of suppliers in the customer’s direct network.
Our key results are robust to the inclusion of a multitude of firm-level control variables, the country’s
gross domestic product per capita (GDPC), as well as different combinations of supplier-customer,
country, industry, and year fixed effects. Furthermore, the one-directional CSR effect is statistically
robust to (i) social and environmental aspects of CSR, and (ii) alternative CSR databases (i.e.,
3https://en.wikipedia.org/wiki/Nike sweatshops.4http://www.nytimes.com/2012/12/07/world/asia/bangladesh-fire-exposes-safety-gap-in-supply-chain.html.5http://www.nytimes.com/2012/09/13/world/asia/hundreds-die-in-factory-fires-in-pakistan.html.
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MSCI Intangible Value Assessment and Sustainalytics).
However, we find that locations of customers and suppliers matter for the working of CSR
in supply chains. Customers play a crucial role in improving CSR standards at their suppliers
when the latter are from different countries, but not when they are from the same country. Firms
from the same country are typically subject to the same regulatory requirement, and perhaps they
voluntarily engage in similar CSR practices. Our findings also suggest that customers are unable to
drive CSR of suppliers in developing countries, unless catastrophic events elicit customers’ responses
to offer a platform of support for these suppliers, as implied by our results from quasi-natural
experiments. Nor do our results show that corporate customers in emerging markets can affect
suppliers’ CSR. In emerging markets, business practices are typically shaped by socio-economic
and political conditions, which have aggravated many environmental and social problems. Hence,
our results are not unexpected.
One may contend that our baseline result of CSR effects from customers to suppliers simply
captures the endogenous relationships in our sample of economically-linked firms. It is possible that
customers selectively choose suppliers that are likely to align their CSR engagements with theirs.
Alternatively, it is probable that suppliers may want to win customers’ businesses by conforming
to their customers’ CSR practices prior to the link. To address this potential endogenous issue and
examine whether customers can exert pressure on supplier’s CSR beyond selection, we conduct a
multitude of tests to establish causality. We employ several quasi-natural experiments of exogenous
shocks to societal demand for CSR to test the causal relationship. Specifically, we focus on a number
of unexpected global corporate scandals and disasters (i.e., Nike’s first admission to worker abuse
scandal in 2005, the deadly collapse of a garment factory in Bangladesh in 2013, the Chinese
milk scandal in 2008, and Takata/Toyota recalls in 2013). Our results show stronger corporate
customer effects of CSR on suppliers following these unexpected global shocks. In terms of economic
significance, a one-standard-deviation increase in the customer’s product responsibility rating in the
scandal year will generate a 9.1%-10.9% rise in the supplier’s mean product responsibility rating.
Similarly, a one-standard-deviation rise in the customer’s human rights rating associated with
workplace disaster and worker abuse scandal will lead to increases of 2.7% and 19.8%, respectively,
in the supplier’s mean human rights rating. The large increase in the latter perhaps suggests that
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the labor abuse scandal must have greatly shocked the world as customers put substantial efforts
to improve CSR practices along the supply chain. Finally, we implement an identification strategy
using a corporate event, particularly M&As, where target firms are a source of exogenous variation
of the CSR effect. When a supplier or a customer is targeted (i.e., such firms are targeted not
by their own choice) and successfully acquired by another firm which is not part of the supplier
chain, the stakeholder effect of CSR becomes weaker or statistically insignificant. Combined, these
findings allay potential endogeneity concerns on the impact of corporate customers on the socially
responsible behavior of suppliers.
Next, we investigate two potential channels through which customers drive CSR in suppliers.
The first possible channel is through the bargaining powers of customers and suppliers. We argue
that the bargaining power of a customer depends on its reliance of relationship-specific investment
(RSI) made by its supplier and the competition intensity of an industry. When the customer
depends heavily on its supplier’s RSI, it has less power to impose greater, typically costly, CSR
commitment on the supplier. Prior literature suggests that customers from research-intensive in-
dustries tend to involve in specialized inputs that require their suppliers to make investments
consistent with their own (e.g., Armour and Teece, 1980; Levy, 1985; Allen and Phillips, 2000;
Dhaliwal, Shenoy, Williams, 2016; Chu, Tian, and Wang, 2017). Following this strand of literature,
we employ a supplier’s level of R&D and number of patents registered as measures of its RSI.
Similarly, we expect a customer to be powerful when its industry is more concentrated, or when
its supplier’s industry is highly competitive. The results suggest that customers are less inclined to
affect their supplier’s CSR performance when the supplier is highly innovative, or when the sup-
plier’s Herfindahl-Hirschman index (HHI, a measure of industry competitiveness) is low, or when
the customer’s HHI is high.
The other mechanism is network connectedness. Existing studies suggest that common own-
ership produces positive externalities as shareholders aim to maximize the value of firms in their
portfolio as opposed to individual firm value. For example, Freeman (2017) provides evidence that
common institutional ownership strengthens customer-supplier links and have synergistic effects on
the related firms. Therefore, we expect that common ownership in both the customer and supplier
would promote or facilitate CSR propagation from the customer to the supplier. Furthermore,
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our analysis also looks at the impact of interlocking directorates, where a member(s) of a cus-
tomer’s board of directors also serves on its supplier’s board. We expect interlocking directorates
to facilitate greater alignment of CSR practices of the customer and supplier. Our evidence sup-
ports our expectation of greater CSR effects in economically-linked firms with common owners and
interlocking directorates.
Finally, we examine the economic implications of collaborative CSR efforts between customers
and suppliers that arise from the customer effect of CSR on suppliers. Previous studies show value
enhancements in corporations that implement CSR initiatives, such as issues related to human
rights, the community, the environment, and the treatment of employees (e.g., Dowell, Hart, and
Yeung, 2000; Gillan et al., 2010; Edmans, 2011; Kruger, 2015; Ferrell, Liang, and Renneboog,
2016). However, implementing these CSR initiatives is costly and has negative financial implications
(e.g., greater cost structure and agency problems) for their corporations (Balotti and Hanks, 1999;
Masulis and Reza, 2015). Unlike these studies that focus on corporations’ own CSR activities and
performances, our analyses look at the economic impact of collaborative CSR efforts of customers
and suppliers through their alignment of CSR standards. The increase of collaborative efforts helps
improve operational efficiency and firm valuation for both the customer and supplier but enhance
only the customer’s future sales growth.
Our research makes two significant contributions to the literature. First, our paper represents
the first to examine the role of a specific group of stakeholders – corporate customers – in propa-
gating CSR along global supply chains, and to show that the propagation is uni-directional from
customers to suppliers only. While this evidence is interesting on its own, our study further ad-
dresses a more important question: What are the economic forces that drive this behavior?6 Such
analyses contribute to our understanding of how CSR gets transmitted around the world. Existing
studies attribute CSR to a firm’s strategic pursuit for superior financial performance (Flammer,
2015a), or a manifestation of agency problems (Masulis and Reza, 2015; Cheng, Hong, and Shue,
2016). Recently, researchers begin to investigate how a firm’s surrounding environment, such as
national institutions (Ioannou and Serafeim, 2012; Liang and Renneboog, 2017) and interactions
6A contemporaneous study by Schiller (2018) shows that the global supply chain acts as a mechanism throughwhich CSR spills over from customers to suppliers. Instead, our analysis shows that the stakeholder bargainingpower and the network connectedness in customer-supplier relationship are two important channels through whichcustomers drive CSR in suppliers.
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with other firms (Flammer, 2015b; Cao, Liang, and Zhan, 2018), plays a role in CSR. However,
little is known about how CSR is influenced by economically-linked stakeholders. Our focus on an
important type of stakeholders, namely corporate customers, helps to reconcile some puzzles in the
emerging CSR literature, especially why firms often engage in costly CSR activity. The fact that
such activity is increasingly prevalent worldwide may be a result of forces by other market players,
such as powerful customers. This is especially the case when societal demand for CSR becomes
greater following numerous CSR-related scandals in recent years. Our findings not only enhance our
understanding on what drives CSR but also, more generally, shed light on non-economic incentives
and practices of modern corporations around the world.
Second, our research contributes to the understanding of how corporate policies and behavior
spillover along global supply chains and the value implications of such spillovers. It also expands
the supply chain literature, such as the spillover of corporate tax avoidance (Cen et al., 2017),
innovation knowledge transfers (Chu, Tian, and Wang, 2017), and information diffusion along
supply chains (Cen, Doidge, and Schiller, 2016; Cen, Hertzel, and Schiller, 2017). By focusing
on international corporate customer-supplier relationships, our study joins this strand of literature
and further demonstrates that some corporate behaviors, such as CSR, propagate uni-directionally
across countries, except those emerging ones. These institutional and firm-level nuances are often
overlooked in the extant literature.
2. Data and Summary Statistics
This study employs data from several different sources: (i) information on the global network of
customer-supplier relationships from the FactSet Revere global supply chain data obtained through
the Wharton Research Data Services (WRDS); (ii) information on firm-level CSR ratings provided
by Thomson Reuters ASSET4 ESG (i.e., Environment, Social, and Governance) database, together
with alternative ratings information from MSCI Intangible Value Assessment, and Sustainalytics;
(iii) M&A information from SDC Platinum from Thomson Reuters; (iv) R&D and sales informa-
tion for computing a firm’s industry concentration intensity from Worldscope, and patent data
from the European Patent Office’s worldwide Patent Statistical Database (PATSTAT); (v) interna-
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tional ownership information from the FactSet Global Ownership data, available from WRDS; (vi)
records of interlocking directorates from BoardEx company-level networks data from WRDS, which
covers over 550,000 interlocking individuals worldwide; and (vii) control variables from Datastream
Worldscope. The definitions of all key variables are depicted in Appendix Table A.3.
2.1. Global economic links
FactSet Revere offers a unique database of supply chain relationships that identifies companies’
interrelationships and a comprehensive geographic revenue exposures, starting from April 2003.
It covers about 23,400 global companies, whose information is culled from company regulatory
filings, websites, and daily updates based on new filings, press releases, and corporate actions
releases. FactSet Revere gathers information on corporate direct relationships disclosed by the
reporting company and on indirect relationships not disclosed by the reporting company but by
companies doing business with the reporting company. For example, their public sources of US
firms include regulatory filings (e.g., 8-K, 10-Q, and 10-K), investor presentations, websites, and
press releases. One advantage of Factset Revere data is that they contain information of both
major and minor private and publicly-listed customers, as well as their identities. To illustrate the
information contained in the FactSet Revere database, Figure 1 shows in 2013, a snapshot of BMW
with some examples of its suppliers from the U.S., the Euro markets, Canada, China, Japan, South
Korea, Mexico, and 87 other suppliers worldwide. For example, Alfa is BMW’s supplier in Mexico,
Hankook, Hyundai, and Mobiis are its suppliers in South Korea, and Baosteel in China. Under
Regulation SFAS No. 131, firms are required to disclose any major customer that represents at
least 10% of the firms’ total reported sales. Unlike FactSet Revere, the Compustat segment data,
which are commonly employed in existing studies, obtain the supply chain relationship information
only from companies’ annual 10-K filings and hence, contain a revenue distribution of firms’ major
customers. A critical limitation of Compustat segment data to a worldwide CSR research is that
it only collects information of significant customers for suppliers that are required to file 10-K (or
equivalent) to the SEC.
We merge FactSet Revere data with other sources of data, mentioned above, and our final
sample consists of 34,117 unique corporate customer-supplier pairs, with customers and suppliers
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from 50 different countries worldwide. Columns (1)-(2) of Appendix Table A.1 list the numbers of
suppliers and customers in our final sample, by year, and Appendix Table A.2 presents the same
by country. In Appendix Table A.1, the numbers of suppliers and customers are increasing over
time from 5,484 and 5,388 in 2003 to 23,556 and 20,749 in 2015, reflecting the growing coverage
of firms by FactSet Revere and the expanding network of supply chain relationships. As seen in
Appendix Table A.2, there are 78,823 suppliers and 82,113 customers in our sample. Morocco has
the smallest number of suppliers with CSR ratings (i.e., 4), whereas the U.S. has the largest (i.e.,
38,546). On the other hand, the number of customers is the smallest in Egypt and Kuwait (i.e., 2)
and is the largest in the U.S. (i.e., 43,741).
2.2. Global CSR Ratings
Thomson Reuters’s ASSET4 database provides ESG ratings of more than 6,000 publicly-listed
companies worldwide and are employed in studies such as Ferrell, Liang, and Renneboog (2016),
Dyck et al. (2017), Hsu, Liang, and Matos (2017), among others. The database focuses on global
companies whose stocks are members of the S&P 500, Russell 1000, NASDAQ 100, MSCI Europe,
FTSE 250, ASX 300, STOXX 600, the MSCI World Index, the MSCI Emerging Market index,
among other major equity indices. The ratings consist of more than 750 environmental, social, and
corporate governance data points, including all exclusion (ethical screening) criteria and all aspects
of sustainability performance. Every data point goes through a multi-step verification process,
including a series of data entry checks, automated quality rules, and historical comparisons. These
data points reflect more than 280 key performance indicators and are rated as both a normalized
score (0 to 100) and the actual computed value. Note that the ASSET4 score assigned to each firm
is evaluated based on the firm’s CSR performance relative to the CSR performance of all firms in
the same industry around the world; in other words, the ratings are industry-benchmarked. All
ratings are provided at the corporate subsidiary-level and on a yearly basis. The information is
available from 2002 onwards, and our sample period covers from 2003 to 2015.
For all companies, at least three years of historical information are available, and most companies
have coverage for at least 10 years. It is worth mentioning that firms in the ASSET4 sample
are rated based on both their ESG compliance (regulatory requirements) and ESG engagement
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(voluntary initiatives), and the effectiveness of their endeavor. Therefore, a firm’s CSR rating
or score reflects a comprehensive evaluation of how the firm engages in stakeholder issues and
complies with regulations. Columns (3)-(4) of Appendix Table A.1 show average composite values
of supplier (CSRS) and customer CSR (CSRC) scores, respectively, by year. These statistics suggest
that customers, on average, are socially more responsible than suppliers. In Table 1, we present
a more detailed distribution of CSRS and CSRS , with their means, medians, standard deviations,
minimum and maximum values, and their values at 25th and 75th percentiles. In addition, we
also report ESG component scores, particularly product responsibility and human rights ratings.
Except for product responsibility ratings, CSR and ESG component scores are generally greater for
customers than for suppliers, further confirming that suppliers tend to be socially less responsible,
compared to customers.
For robustness, our study also employs two alternative firm-level ESG databases, namely MSCI
ESG Research Intangible Value Assessment (IVA) and Sustainalytics’ ESG Research & Ratings
(Sustainalytics). Both databases provide research, ratings, and analysis of companies’ risks and
opportunities arising from ESG factors. IVA ratings are between 0 and 10 and Sustainalytics’ are
between 0 and 100. Both ratings gauge how well companies manage CSR issues that are related
to their businesses and provide an assessment of firms’ ability to mitigate risks and capitalize on
opportunities. Similar to ASSET4, these two alternative ratings are also industry-adjusted. In a
subsequent section, we show that our baseline evidence is robust to CSR ratings provided by the
two alternative databases.
2.3. Channel variables
Our study explores whether the stakeholder bargaining power and network connectedness are
two possible channels through which customers can drive CSR in suppliers. To assess the de-
gree of bargaining power of customers, we look at a supplier’s investment specific to the needs
of its customers and the intensity of industry competitiveness. Our study measures the sup-
plier’s relationship-specific investment by the supplier’s R&D and number of patents registered
to measure the level of innovation. A firm’s R&D information is obtained from Datastream World-
scope, whereas patent information is available from PATSTAT, which contains information on
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patents awarded to companies, individuals, and other institutions. We employ a firm’s Herfindahl-
Hirschman index (HHI) to measure the degree of industry competitiveness. Network connectedness
is measured by the network of institutional ownership and directorates common in both the supplier
and customer. Common ownership holdings information is obtained from the FactSet ownership
database. FactSet gathers US institutional holdings from mandatory quarterly 13F filings with the
Securities and Exchange Commission (SEC) and holdings of non-US equities from sources such as
national regulatory agencies or stock exchange announcements (e.g., the Regulatory News Service
in the U.K.), local and offshore mutual funds, mutual fund industry directories (e.g., European
Fund Industry Directory), and company proxies and annual reports. Our analysis employs the
percentage of shares held in customer and supplier firms by common owners and number of com-
mon owners in a customer-supplier pair. Our interlocking directorates (and executives) records
are obtained from BoardEx company-level networks data from WRDS, which covers over 550,000
interlocking individuals worldwide.
Summary statistics of these channel variables are shown in the second panel of Table 1. The
statistics suggest that suppliers tend to invest more in R&D than their customer counterparts. The
mean (median) ratio of R&D relative to total assets is 0.5 (0.04) for suppliers and 0.04 (0.02) for
customers. While there are significantly fewer firm-year observations for the number of registered
patents, suppliers have slightly larger R&D and number of registered patents than customers. The
average percentage of common ownership held is 1% and the average number of common owners is
135.34. The mean number of common directors is 0.06 with the mean number of board positions
held by common directors is 0.07.
2.4. Control Variables
Our analysis controls for firm-level covariates commonly employed in the existing literature,
such as leverage, return on assets (ROA), Tobin’s Q, total assets (TAssets), closely held shares
(CloseShares), and sales growth (SalesGrowth). Given the cross-country variation of our data,
we control for country-level GDP per capita (GDPC) obtained from World Bank Indicators. The
distribution of these control variables is displayed in the bottom panel of Table 1. The descriptive
statistics show that on average, suppliers have marginally lower ROA and total assets, while greater
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Tobin’s Q, the level of closely-held shares, sales growth, and GDPC, compared with those of their
customers.
3. CSR and Customer-Supplier Relationships
In this section, we examine whether corporate customers affect social and environmental engage-
ments at their economically linked firms – the suppliers. Specifically, we investigate the CSR effect
along the supply chain and determine whether any evidence of such effects is robust to a specific
component (i.e., environmental, social, or governance aspect) of CSR, and alternative databases.
Finally, we analyze whether locations of the customer and supplier play a role in the customer
effect of CSR and whether our baseline evidence reflects how customers choose and terminate their
suppliers.
3.1. Stakeholder (‘Corporate Customer’) effects of CSR
To examine for evidence of stakeholder effects of CSR in suppliers’ CSR practices, we regress
supplier CSR (CSRS) Score on the lagged customer CSR (CSRC) Score while controlling for various
firm- and country-level characteristics, as denoted by Xk, that have previously found to affect CSR.
In our key regression below, we also control for different combinations of fixed effects (FE), namely
(1) customer industry x supplier industry fixed effect, customer country x supplier country fixed
effect, and year fixed effect, or (2) customer, supplier, industry, country, and year fixed effects
separately. Given that the results from controlling different fixed effects are qualitatively similar,
in most of our regressions following Table 2, we report only results that control for fixed effects
described in (1). The baseline regression is given by
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) +K∑k=1
bkXk(t) + FE(t) + ε(t+ 1). (1)
In regression model (1), Xk include the supplier’s leverage (Leverage), return on assets (ROA),
Tobin’s Q, total assets (TAssets), closely-held shares (CloseShares), sales growth (SalesGrowth),
as well as country’s gross domestic product per capita (GDPC). All these variables are defined in
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Appendix Table A.3. We also evaluate whether customer characteristics play a role in explaining
next period’s supplier CSR Score by expanding (1) to include the customer’s firm-level character-
istics and country’s GDPC as well. While (1) allows us to investigate the stakeholder effect on
the supplier’s CSR, we cannot rule out the possibility that there may be many large, reputable
suppliers that are socially responsible and want their customers to behave responsibly. To examine
this possible supplier effect of CSR, we estimate (1) by reversing the roles of supplier and customer
CSR ratings. Results are shown in Table 2 with reported standard errors clustered at the customer-
supplier-pair level. Columns (1)-(4) show estimates of (1), while columns (5)-(8) report those with
CSRC Score as the dependent variable and CSRS Score as the key independent variable of interest.
We employ the random-effects GLS approach to estimate the different model specifications in the
table and throughout the study.
Several interesting results emerge from the table. First, there exists strong evidence of a uni-
directional CSR effect from the customer to the supplier only, an indication that suppliers do not
have a similar influence on customer CSR score. For example, the coefficient of CSRC Score, as
shown in columns (1)-(4), is positive and statistically significant, while the coefficient of CSRS
Score, as indicated in columns (5)-(8), is small and statistically insignificant. In terms of economic
significance, a one standard deviation increase in a customer’s CSRC Score will generate, on average,
a 4% (= 0.021 × 26.73 × 4.55/64.21) improvement in the performance of its suppliers’ total CSRS
Score relative to the mean supplier CSR rating in the customer’s direct network.7 We also test
whether customers in general, or a specific group of customers have the ability to influence suppliers
to align their CSR activities and practices with theirs. To address this issue, we split the sample
into two sub-groups: major versus non-major customers. As mentioned above, under SFAS 131, it
is mandatory that firms disclose the existence and sales to principal customers representing more
than 10% of total revenues. Following the SFAS definition, we define major customers as those that
are considered important customers, hence accounting for at least 10% of a supplier’s sales. Our
Online Appendix Table OA.1 suggests that only major customers are able to influence suppliers
to conform to their standard of CSR practices. Combined, the results suggest that customers,
primarily major customers, are able to exert pressure on suppliers to commit to higher standards
7For the entire sample period, a customer has, on average, about 4.6 suppliers.
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of CSR but not the supplier on the customer.
Second, including the customer’s control variables in column (4) has no material impact on the
coefficient of CSRC Score,8 nor does it qualitatively affect the coefficients of the supplier’s control
variables. Except for a marginally significant customer’s leverage effect, the other customer-specific
variables are all statistically insignificant. Our untabulated results produce consistent evidence
that when estimated without the presence of supplier-specific characteristics, the customer-specific
control variables still display no effect on a supplier’s CSR rating. In light of these results, adding
the set of mostly insignificant customer-level control variables to the model would simply increase
noise in the estimation, thereby mitigating the significance of our key variable. Hence, in subsequent
analyses, we do not report such estimations.
Finally, the effects of supplier-specific characteristics on supplier CSRS Score are broadly con-
sistent with those obtained in the existing literature (e.g., Ferrell, Liang, and Renneboog, 2016,
Liang and Renneboog, 2017). For example, the coefficients of leverage, closely-held shares, and
sales growth are negative and significantly associated with CSRS Score, whereas those of return on
assets, Tobin’s Q, and total assets are positive. GDPC is statistically and negatively significant in
column (1), but becomes insignificant after we include supplier-country and customer-country fixed
effects in the model, suggesting that GDPC captures country varying and fixed effects on CSRS
Score.
Furthermore, we also conduct robustness tests to verify that our key finding does not necessarily
imply that customers with weak CSR performance will induce suppliers to become less socially
responsible. To carry out our tests, we construct two additional measures of the CSR Gap; one
measures the difference between CSRC and CSRS ratings, and another is a binary indicator that
equals one if CSRC Score is greater than CSRS Score and zero otherwise. The results reported in
Online Appendix Table OA.2 show that the coefficients of both the CSRC Score and the interaction
variable (CSR Gap × CSRC Score) are positive and strongly significant at the 1% level. These
findings further corroborate our baseline evidence of a positive customer effect of CSR on supplier
CSR.
8We have also included the lagged supplier CSR in our model, and our untabulated results show that the correlationbetween the lagged customer CSR and future supplier CSR does not capture the autocorrelation in supplier CSR.
13
In summary, we find that, in aggregate, corporate customers, as a group of influential stake-
holders, play an important role in improving CSR performance of suppliers across the world. This
evidence also reflects customer activism as a disciplining mechanism in suppliers’ corporate social
responsible behavior.
3.2. Environmental, Social, and Governance Aspects of CSR
In this subsection, we investigate whether the customer effect of CSR is concentrated in a
specific aspect of corporate social initiatives, such as those pertaining to environmental (Env),
social (Soc), and governance (Gov) issues. We re-estimate model (1) by replacing the composite
CSR score by each component score (i.e., Env, Soc, or Gov rating). The results are presented in
Table 3, with the format similar to that of Table 2. In columns (1)-(3), the dependent variable
is supplier CSR component, and hence, the regression analysis also controls for supplier-specific
characteristics (Supplier Controls), as well as customer-supplier, industry, country, and year fixed
effects. However, in columns (4)-(6), the dependent variable is customer CSR component, and
the results also controls for customer-specific characteristics (Customer Controls), as well as the
different fixed effects. However, to conserve space, we only report the key coefficients with their
robust standard errors in parentheses in the table as well as the remaining tables of our study.
The findings underscore the robustness of uni-directional CSR effects in environmental and social
issues only from customers to suppliers, but show bi-directional effects in governance. Specifically,
columns (1)-(2) indicate that the greater the customer’s environmental and social ratings, the
higher is its supplier’s subsequent environmental and social ratings. The coefficients of EnvC and
SocC Scores are 0.032 and 0.013, respectively, and both coefficients are statistically significant at
conventional levels. In contrast, columns (4)-(5) show no statistically significant relation between
the supplier’s environmental and social ratings and its customer’s. Interestingly, the bi-directional
governance effects (columns (3) and (6)) imply that both the customer and supplier are concerned
about this aspect of CSR and perhaps consider the importance of governance relative to social and
environmental issues. The evidence of governance effects is consistent with the results reported in
Aggarwal et al. (2011) that governance “travels” around the world through institutional investors’
efforts to promote good governance.
14
3.3. Alternative CSR databases
Oftentimes, one contend that the key evidence of a study is specific to the sample of data it
employs.9 In other words, it is plausible that our baseline finding of uni-directional CSR effects of
customers to suppliers is attributed primarily to the ASSET4 data used in our study. The coverage
of ASSET4 data is fairly extensive, and the database is also employed in a number of important
studies, as cited in the Introduction section. Still, it is arguable that the assignment of individual
firm ratings may be biased toward the methodology ASSET4 adopts. To rule out this possible bias,
we employ two alternative CSR ratings databases, namely the MSCI IVA and Sustainanalytics
databases. We therefore replicate the baseline results of columns (2) and (6) in Table 2 using firm-
level ratings assigned by the two databases. Results in columns (1) and (2) of Table 4 are based
on MSCI IVA, whereas those of columns (3) and (4) are based on Sustainanalytics. However, the
results remain materially unaffected; the coefficients of CSRC in columns (1) and (3) are positive
and strongly significant, whereas those of CSRS in the other two columns yield inconsistent signs
and are insignificantly different from zero. Thus, these findings further underscore the robustness
of our baseline evidence that the uni-directional CSR effect along the supply chain is not specific
to the ASSET4 ratings employed in our earlier analyses.
3.4. Locations of customers and suppliers
CSR efforts along the supply chain may vary, depending on the country in which customers
and suppliers do their businesses. Such variations arise due to disparate costs and benefits faced
by firms when implementing a CSR initiative. For example, doing business in emerging economies
is probably challenging as many are characterized by “either bad or weak public governance and
administration, lack of public transparency, high levels of bribery and corruption, poor records of
human rights, inadequate environmental, safety and labor standards, and high levels of poverty and
inequality” (Nelson, 2004, p. 31). It is therefore apparent that a country’s socio-cultural orientation
with the business cultures customer firms embody will determine the level of difficulty it is for them
to influence their suppliers in emerging economies to be good corporate citizens. We argue that a
9Chatterji et al. (2016) suggest that for every CSR research, one has to cross-validate the results using severaldifferent ESG samples/data sources.
15
customer’s attempt to create certain CSR standards at their suppliers is determined by their local
socio-cultural contexts and hence, their location. Thus, we test this conjecture by examining CSR
propagation along the supplier chain, where suppliers and customers are from the same country,
or from different countries, such as developed (DEV) versus emerging (EMG) markets. We also
replicate the results of column (3) in Table 2 on a subsample of customer-supplier pairs from non-
US countries. The test is to ensure that our baseline evidence is not driven by US firms, which
form the largest portion of both suppliers and customers in the full sample (see Appendix Table
A.2). Results are shown in Table 5.
The table reveals a number of striking findings. First, the stakeholder effect of CSR is positive
and statistically significant when both the customer and supplier are from different countries, but
not statistically significant when they are from same country. Most firms from the same country
typically take regulatory and voluntary approaches to CSR issues, as well as commit to a similar
CSR standard. Hence, expectedly, there ought to be substantially less CSR variation between
customers and suppliers from the same country than from different countries. Second, our key
evidence remains qualitatively unaffected even after removing US firms from the sample (column
(3)). For instance, the coefficient of customer CSRC Score is 0.023 with standard error of 0.009,
suggesting a more pronounced stakeholder effect of CSR in global supply chains with no U.S.
customer or supplier.
In columns (4)-(5), we test whether the location of suppliers from emerging and developed
markets matter, and in columns (6)-(7), we look at the location of customers. Consistent with our
expectations, we find no evidence that customers are able to push for better CSR at their suppliers
from emerging markets, and that customers from emerging markets can significantly influence their
suppliers from other markets. The results therefore suggest that it is indeed difficult for customers
or suppliers to implement certain CSR initiatives when they are located in countries with poor
records of human rights, or inadequate environmental, safety and labor standards.
Thus, the overall evidence suggests that the locations of customers and suppliers matter for the
effect of CSR propagation along global supply chains.
16
3.5. Linked and delinked relationships
Our baseline evidence may also give rise to an alternative interpretation that customers prefer
suppliers that are more likely to engage in better socially responsible practices. To test this po-
tential selection channel, we construct a sample of customer-supplier pairs that have experienced
a change in relationships at any point in our entire sample period. For example, if a supplier
(customer) reports a relationship with a customer (supplier) for the first time in the sample period,
we classify the two firms as linked. Alternatively, if a supplier and a customer no longer report
their relationship which has previously existed in the sample period, we consider the chain to be
delinked. It is important to highlight that customer-supplier pairs that do not experience a change
in the relationship during our sample period (i.e., always linked or never linked firms) do not enter
into the sample.
To capture the change-status effect, we use two binary indicators, namely Post Link and Post
Delink. Post Link (Delink) is a binary indicator that equals 1 if the customer and supplier first
establish (severe) the relationship and 0 otherwise. We then run the following regression.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × Post Link/Delink(t) + a2CSRC Score(t)
+ a3Post Link/Delink(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1),(2)
In (2), the key independent variable is the interaction between CSRC Score and Post Link (or
Post Delink). Results shown in Table 6 reveal that the customer’s influence on newly established
supplier’s CSR becomes positive and significant after the two firms establish an economic link. As
seen in column (1), the overall customer CSR effect on supplier CSR is 0.013 (=0.020-0.007), sug-
gesting that there is selection evidence associated with the initial establishment of the relationship.
On the other hand, Column (2) shows that when supply chains are terminated, customers exhibit
no statistically significant influence on suppliers. The interaction term of CSRC Score and Post
Delink is statistically insignificant in the post delinked period, even though Post Delink itself is
still significant at conventional levels. The latter perhaps indicates that suppliers do not necessar-
ily eliminate their existing CSR activities immediately following the termination of economic links
17
with customers.
While the baseline evidence could be driven by customers having preferences for suppliers who
would be willing to assume greater corporate social responsibilities, our unreported results suggest
that the customer effect of CSR still holds even after we remove the observations when customers
and suppliers first establish their relationship. Nevertheless, in the subsequent section, we conduct
several causality tests to further corroborate the baseline finding that the CSR effect from customers
to suppliers is causal – customers do push suppliers for greater CSR performance.
4. Evidence from Global Scandals and Acquisitions
We have, thus far, established evidence of significant stakeholder effects in CSR and that cus-
tomer CSR has a statistically significant effect on future supplier CSR. However, such stakeholder
effects might be due to either customers’ ability to induce their suppliers to behave socially re-
sponsibly or the deliberate decisions of customer firms to selectively pick suppliers with a greater
likelihood to align their CSR practices with the customers’. In this section, we conduct several tests
to establish the direction of causality consistent with our baseline evidence. Specifically, we ex-
plore a number of exogenous shocks related to unexpected workplace disasters and product safety
scandals that have created global shocks to consumerism and the general public on our sample
of existing linked customer-supplier pairs. We also implement an identification strategy using a
corporate event, particularly M&As, to further evaluate the stakeholder effect in CSR.
4.1. Evidence from quasi-natural experiments
Our analysis focuses on some of the worst human rights and product safety issues or scandals in
history that have drawn global attention and heightened societal awareness and activism demanding
for more socially responsible practice along supply chains. Specifically, for human rights issues, we
look at the impact of Nike’s labor abuse and the collapse of a textile building in Bangladesh. For
product safety scandals, we focus on the Chinese milk scandal and Toyota car/Takata airbag recalls.
It is important to point out that one can alternatively use government-mandated initiatives or
18
country-level ESG regulations as an instrument variable for firm-level CSR.10 However, a critical
problem with such approaches is that these regulatory outcomes typically bear different implications
from those arising from voluntary CSR engagements. While these mandates are usually targeted
at specific aspects of CSR (e.g., the 2008 emissions reporting scheme mandated by the Australian
government) and even if it is a broader mandate that applies to the whole CSR spectrum, the
compliance information is often missing for most firms due to cross-country regulatory differences.
As Li and Wu (2017) point out, such before-and-after comparisons have limited causal power than
difference-in-differences analyses. Due to the lack of power, we avoid using government-mandated
CSR initiatives or mandatory ESG regulations as an instrument variable for firm-level CSR.
4.1.1. Human rights issues
We begin by evaluating the first lengthy 108-page report released by Nike in 2005, detailing
the company’s admissions to the widespread use of sweatshops in various Asian factories that
produce Nike’s products.11 Prior to this report, there have been numerous allegations of Nike’s
worker abuse. Human rights activists and aid groups have long criticized Nike for relying on
sweatshops and child labor to keep production costs low, and for not doing enough to improve
the poor working conditions in its supply chain. Nike’s 2005 report confirming the longstanding
allegations causes a global shock to all industries that outsource their production and operations in
developing economies or the Third World countries, and has raised corporations’ awareness about
their responsibility to monitor factory standards aimed at improving working conditions in supply
chains.
We also assess the 2013 garment factory collapse in Bangladesh that has brought the world’s
attention to worker safety issues and human costs of cheap fashion. The disaster, which killed 1,138
workers, has sparked serious questions about the fashion industry’s use of sweatshops and cheap
labor within the supply chain. Many workers were found to work long hours in overheated factories
without proper fire exits. Among the various textile businesses in this garment factory include
Phantom Apparels Ltd., New Wave Style Ltd., New Wave Bottoms Ltd., and New Wave Brothers
10Schiller (2018) employs mandatory ESG regulations that affect customers’ countries.11https://www.theguardian.com/business/2005/apr/14/ethicalbusiness.money.
19
Ltd., which produce clothing for major brands including H&M, Gap, Walmart, The Children’s
Place, Dress Barn, Primark, among others.
To test whether the above shocks strengthen customer CSR effects on supplier CSR, we follow
Liang and Renneboog (2017) and use the following difference-in-differences approach.
CSRS ComScore(t+ 1) = a0 + a1CSRC ComScore(t) × Event + a2CSRC ComScore(t)
+ a3Event +K∑k=1
bkXk(t) + FE(t) + ε(t+ 1),(3)
where CSRS ComScore is the supplier’s human rights component score,12 CSRC ComScore is its
customer’s human rights component score, Event is a binary indicator that equals 1 if it is the
specific event year (i.e., the event year for Nike’s worker abuse case is 2005, and for Bangladesh’s
garment factory collapse is 2013) and 0 otherwise,13 Xk(t) is the vector of supplier firm charac-
teristics employed as control variables, and FE represents the set of fixed effects. We conduct our
regression of (3) on a subsample of already linked suppliers in developing countries and customers
from both developed and developing countries. The results are contained in Table 7, with columns
(1)-(2) showing estimates of (3) associated with 2005 and 2013 events, respectively.
The variable of interest is the interaction between CSRC ComScore and Event indicator, and
its coefficient is positive and highly statistically significant in both cases. Their coefficient is 0.343
(standard error= 0.093) in column (1) and 0.031 (standard error= 0.012) in column (2). These
findings suggest that the effect of a customer’s human rights rating on the supplier’s next-period
human right rating is more pronounced in the event year, an indication that suppliers pay increasing
attention to human rights issues following the workers abuse report and workplace disaster. In
terms of economic significance, a one-standard-deviation increase in the customer’s human rights
rating (i.e., 31.43, Table 1) in the event year will lead to a 2.7% (= (0.031 + 0.019) × 31.43/58.56)
to 19.8% (= (0.343 + 0.025) × 31.43/58.56) increase in the supplier’s mean human rights rating
in columns (2) and (1), respectively. Clearly, corporate customers have responded strongly to
12The human rights component rating gauges a company’s management commitment and effectiveness towardsrespecting the fundamental human rights conventions. It reflects the company’s capacity to maintain its license tooperate by guaranteeing the freedom of association and excluding child, forced or compulsory labor.
13Note that the Event indicator does not capture post-event years because we expect the CSR gap would not belong-lasting after such large-scaled scandals and media exposure.
20
the labor abuse scandal in 2005 more than to the workplace disaster in 2013 and are able to
influence their suppliers to improve their CSR performance in terms of employee treatment and
workplace conditions. It is worthwhile to mention that when we interact CSRC ComScore with
other year dummies, our unreported results show that none of the coefficients of the interaction term
is statistically significant. Such placebo tests provide further reinforcing evidence of stakeholder
effects in CSR activities along the global supply chain.
4.1.2. Product responsibility
We select two major product safety events for our analysis. The first is the 2008 Chinese
milk scandal involving infant milk formula adulterated with melamine exploded when 16 infants in
China’s Gansu Province were diagnosed with kidney stones after consuming the melamine-tainted
milk powder. Subsequently, an estimated 300,000 babies in China were sick from consuming the
contaminated milk, and the kidney damage led to six deaths. The World Health Organization
claimed this incident was one of the largest food safety events it has had to deal with in recent
years, and the incident raised serious concerns about food safety, not only in China but also across
the world.
The other event is the recalls issued by Takata and Toyota in 2013. At least 7 million cars
worldwide had been recalled for defective airbags made by Takata, and potentially 42 million
cars in the United States were affected. The problem was the defective metal airbag inflators.
When deployed, the huge explosive force may disintegrate the canister of the inflator sending metal
shrapnels into the passenger cabin and possibly injuring or killing the occupants in the vehicles.
According to the Consumers Report,14 this problem resulted in 16 deaths and about 180 injuries
reported worldwide, and affected 19 automakers. Toyota recalled more than one million vehicles
sold in the United States over faulty airbags and windshield wipers. The National Highway Traffic
Safety Association has called this “the largest and most complex safety recall in U.S. history.”15
Similar to the analysis of the human rights issue in the preceding subsection, we also employ
model (3) to examine exogenous shocks arising from product safety scandals. In (3), ComScore
14https://www.thestar.com/business/2017/02/27/attorneys-say-five-automakers-knew-takata-airbags-were-dangerous.html.
15https://www.theverge.com/2016/5/4/11591724/takata-air-bags-largest-recall-nhtsa.
21
denotes a firm-level rating on product responsibility. The Event indicators are 2008 for the China
milk scandal and 2013 for the Takata and Toyota recalls. Column (3) of Table 7 shows estimates
of model (3) on a subsample of existing linked firms in the global food-related industry, whereas
column (4) reports those using a subsample of firms in the global auto industry with suppliers from
Japan and corporate customers from the auto industry in both developed and developing countries.
Consistent with those of columns (1)-(2), the coefficients of the interaction variable (i.e., CSRC
ComScore x Event) are positive and statistically significant. For example, the coefficient of CSRC
ComScore x Event in column (4) is 0.268 and is statistically significant at the 5% level. In terms of
economic significance, a one-standard-deviation increase in the customer’s product responsibility
rating (i.e., 28.04, Table 1) in the scandal year will lead to a 9.1% (= (0.268−0.082)×28.04/57.43)
to 10.9% (= (0.250− 0.026)× 28.04/57.43) rise in the supplier’s mean product responsibility rating
in columns (4) and (3), respectively. This time-series evidence suggests that the pressure from
corporate customers driving suppliers to improve their CSR becomes more intense when public
demands for responsible business practices are greater following major product safety scandals.
This evidence lends further credence to our causal inferences of our baseline finding (based on cross-
sectional analyses) that the stakeholder orientation of CSR is only from customers to suppliers.
It is important to draw some comparison of the contradicting results between columns (1)-(2) of
Table 7 and column (4) of Table 4. The former suggest that customers are able to coerce suppliers
from developing countries to improve their CSR standards, following major labor abuse scandals
and workplace disasters. In contrast, column (4) of Table 4 shows that customers are, on average,
unable to pressure suppliers from emerging economies to better their CSR performance. Given that
corporate customers are exploiting cheap workforce and low production costs in these economies,
it is probably unlikely that the margins given to suppliers are large enough to cover the latter’s
costs of implementing certain corporate responsibility initiatives. Furthermore, it is challenging
to implement for these suppliers operating in developing economies with inadequate infrastructure
and institutions and with poor education, widespread corruption, and little or no accountability.
On the other hand, it may not be surprising that in response to the spate of major business ethics
scandals, customers are more willing to put more resources for assisting their suppliers to meet
increased pressures and demands for corporate responsibility. The customers may evaluate the cost
22
of such resources and efforts to be significantly lower than the cost of a bad reputation or brand.
4.2. Identification issue – M&As
In this subsection, we resort to using an identification strategy to further verify the direction of
causality associated with our key result. We focus on target firms of M&As as a source of exogenous
variation of CSR propagation from customers to suppliers along the supply chain. The reason why
we examine only targets, and not acquirers, is that target firms are typically not being acquired
by their own choices. For example, when a supplier or a customer is acquired by a third party, the
economic link between the supplier and customer could probably be weakened after either of the
firm gets acquired by a firm with no economic link prior to the acquisition. Such an acquisition
would be different from an M&A, where a customer (supplier) intentionally merges with a supplier
(customer) to establish an upstream (downstream) integration. As a result, these M&A cases would
be more appropriately considered as an endogenous choice of upstream/downstream integration.
Our sample contains 839 customer or supplier firms with ASSET4 ratings that are M&A targets
and eventually acquired by another firm. To implement our test, we define a binary indicator, Post
M&A, which captures the supply chain relationship following the completed acquisition of a target
supplier or customer and run the following regression.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × Post M&A S or C(t) + a2CSRC Score(t)
+ a3Post M&A S or C(t) +K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).(4)
Results are shown in Table 8. In columns (1)-(2), we estimate the impact of all acquisitions on
the supplier’s subsequent CSR performance. Columns (3)-(4) and (5)-(6) show separate results
of suppliers as targets and customers as targets, respectively. Columns (2), (4), and (6) report
the effect of customers whose CSR ratings are above the median level of the sample customer
CSR rankings. If a customer or supplier is acquired by a third-party firm with no prior link to
either the customer or supplier, the CSR propagation effect becomes weaker after the supplier or
customer is being taken over. For instance, in column (1), the combined customer’s CSR effect on
the supplier’s next period CSR Score is -0.038 (= −0.051 + 0.013). A closer analysis suggests that
23
the negative effect is driven mainly by acquisitions of suppliers. As seen in columns (3)-(4), when
a supplier is acquired by another firm, its subsequent CSR practice becomes negatively associated
with its customer’s, especially when the latter is above the median level. It is plausible that the
supplier pays less attention to its CSR practice during and immediately after the acquisition process.
Alternatively, this may imply that the supplier’s acquirer is unwillingly to expend more resources
to maintain the sustainability of the supplier’s CSR standard, or that the supply chain is severed
following the acquisition. In contrast, any acquisition of a customer has no statistically significant
effect on the supplier’s subsequent CSR performance. The implication is that such acquisitions do
not break down the supply chain and hence, bear no effect on its existing CSR alignment.
Collectively, all the above tests bolster a causal interpretation of our baseline evidence that
customer CSR exhibits a significant impact on the subsequent supplier CSR.
5. The Channels
The preceding sections show that customers have the ability to influence or coerce suppliers
to commit to higher CSR standards as they do. Thus, a natural question that arises is: How do
corporate customers make their suppliers improve CSR practices? In this section, we explore two
possible channels through which corporate customers are able to influence suppliers to act socially
responsibly, and they are (i) the degree of stakeholder bargaining power, and (ii) the network
connectedness in customer-supplier relationships.
5.1. Degree of stakeholder bargaining power
The extent to which customers can exert considerable pressure on suppliers and demand for
improved CSR depends on the customers’ bargaining power. We argue that when customers have
low bargaining power, suppliers can afford to withhold and avoid incurring cost of concession to
meet the customers’ demand for better CSR practice. To test this bargaining power channel of
CSR effect along the supply chain, we look at suppliers’ investments specific to customers and the
intensity of industry competition for customers and suppliers.
24
5.1.1. Relationship-specific investments
Existing studies have established that customers from research-intensive industries tend to in-
volve in specialized inputs that require their suppliers to make transaction-specific investments,
consistent with their own investments (e.g., Armour and Teece, 1980; Levy, 1985; Allen and Phillips,
2000; Dhaliwal, Shenoy, Williams, 2016; Chu, Tian, and Wang, 2017). Extending this strand of
research, we argue that suppliers with more investments in innovation are more likely to engage
in customer-specific investments. We therefore hypothesize that the greater the supplier’s innova-
tion capacity, the customers would have less power to influence their suppliers to be more socially
responsible.
Following Chu, Tian, and Wang (2017), we employ the amount of R&D relative to total assets
and the log of the number of registered patents as proxies for a supplier firm’s investments in
innovation specific to the customer’s needs and conduct the following regression.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × InnovS(t) + a2CSRC Score(t)
+ a3InnovS(t) +K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).(5)
In (5), we evaluate the impact of a supplier’s innovation (InnovS) on the supplier’s CSR Score.16
We expect the coefficient of the interaction between InnovS and the customer’s CSR Score (i.e.,
CSRC Score) to be negative if the customer relies heavily on the supplier’s innovation. In other
words, when suppliers make large investments specific to customers’ needs, they would be in a
better bargaining position to decide whether or not to to align their CSR practices with those of
customers. The results reported in columns (1)-(2) of Table 9 are consistent with our expectations.
We find that the interaction effect of CSRC Score and InnovS is negative and statistically
significant for both proxies of the customer-specific investment. For instance, the coefficient of
CSRC Score x R&DS is -0.323 and is statistically different from zero. This finding suggests that
when a supplier’s customer-specific investment is low, the supplier is more inclined to meet the CSR
16We have also examined whether a customer’s innovation activity has any effect on a supplier’s CSR performance.Unreported results suggest that investment in innovation does not give customers strong bargaining power to driveCSR in suppliers.
25
standard of its customer. Alternatively, when there is less resource dependence in the customer-
supplier relationship, the customer would have more power to demand for a higher CSR standard
at its supplier. Thus, the evidence suggests that the extent of the customer-specific investment
is one mechanism that drives the supplier’s desire to align its CSR with that of its more socially
responsible customer.
We recognize that our findings are also consistent with a learning channel. A significant part
of CSR initiatives relates to product innovation and production process by using environmental-
friendly technologies and engaging in R&Ds that enhance product safety and responsibility. Such
processes involve learning from customers and feedback to suppliers; more innovative suppliers
would have less of a need to learn from their customers. Hence, in line with the evidence, it would
be harder for customers to influence highly innovative suppliers.
5.1.2. Intensity of industry competition
The intensity of competition in an industry is one determining force that drives the bargaining
power of suppliers and customers. A supplier has strong bargaining power if there are barriers to
entry, fewer threats of substitutes, the industry is highly concentrated or low intensity of industry
rivalry, and customers have weak or no power. Conversely, a customer has high bargaining power if
it has fewer competitors, substitutes are available, little product differentiation, and its purchases
comprise a large portion of the supplier’s sales. Thus, the degree to which customers are able
to pressure suppliers to achieve high CSR standards is determined by the intensity of industry
competition of the supplier and customer. We measure the intensity of competition using the
Herfindahl-Hirschman Index (HHI). A customer’s HHI is measured by the summation of squared
market share (based on sales) of each firm within the same industry, whereas a supplier’s HHI
is measured within the same global industry to approximate the level of competition suppliers
encounter globally by squaring the market share of the firm’s sales in the industry and then summing
the squares.17 We then estimate (5) by replacing the innovation variable with HHI and present the
results in columns (3)-(4) of Table 9.
Column (3) evaluates the effect of a supplier’s HHIS on the supplier’s CSR Score, whereas col-
17An industry is defined using Fama and French’s 48 industry classifications.
26
umn (4) assesses the impact of a customer’s HHIC . When the supplier is in a highly competitive
industry, it tends to align its CSR practice with that of its customer. The coefficient of the inter-
action variable, CSRC Score x HHIS is negative and statistically significant at conventional levels.
This finding probably suggests that the supplier may lose its customer if it does not improve its
CSR standard. On the other hand, column (4) shows that when a customer is in a less competitive
industry, it has strong bargaining power to influence its suppliers to be more socially responsible.
The coefficient of CSRC Score x HHIC is positive and statistically significant at the 5% level (i.e.,
0.048 with robust standard error of 0.023). Combined, these results suggest that the stakeholder
bargaining power plays a critical role in propagating CSR along the supply chain.
5.2. Network connectedness
Extant literature has shown that corporate behavior propagates through networks of decision-
making bodies, namely owners and directors. Common ownership exists between two firms when-
ever an investor owns shares of both firms, and two firms are board-linked whenever they have
shared directors on their boards. Such network structures are one potential mechanism for spread-
ing corporate policies from firm to firm. This subsection examines whether shared ownership and
interlocking corporate boards affect CSR efforts along the supply chains.
5.2.1. Common ownership
There is substantial robust evidence that institutional investors, who are large equityholders,
play a critical role in corporate policies of the firms they invest in. In recent years, several studies
find that institutional cross-ownership influences the outcomes of mergers and acquisitions (e.g.,
Matvos and Ostrovsky, 2008; Harford, Jenter, and Li, 2011), industry competitiveness (e.g., Azar,
Raina, and Schmalz, 2016; Azar, Schmalz, and Tecu, 2017; He and Huang, 2017), return correlations
(Anton and Polk, 2014), CEO pay incentives (Anton et al., 2016), and customer-supplier links
(Freeman, 2017). In particular, Freeman shows that common institutional ownership in both the
customer and supplier strengthens their supply chain relationship. Based on her finding, it is
plausible that common ownership is a channel through which a supplier’s CSR is aligned with its
customer’s.
27
Our analysis employs two different measures of common institutional ownership. Our first
measure computes the sum of the maximum percentage of ownership held by all common owners
in the supplier (customer) in a given year, and this measure is labeled, “% of Shares HeldS”. We
compute the same for the customer firm and label it “% of Shares HeldC”. The other measure is
based on the number of common institutional investors that invest in both firms in a given year
and is represented by “# of Common Owners”. Using these measures, we estimate the following
model.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × OwnershipS or C(t) + a2CSRC Score(t)
+ a3OwnershipS or C(t) +K∑k=1
bkXk(t) + FE(t) + ε(t+ 1),(6)
where Ownership represents either % of Shares Held or # of Common Owners in a supplier firm
or customer firm. If common institutional investors can influence their supplier firm to engage in
socially responsible activities as their customer firm does, we expect the interaction between CSR
score and measures of common institutional ownership to be positively correlated. Results of model
(6) are reported in columns (1)-(3) of Table 10.
The table reveals one distinct evidence – the important role of common institutional investors
in aligning the CSR of their portfolio of customer and supplier firms. We find the two measures
of common institutional ownership and their interactions with the customer CSR Score to yield
positively and statistically significant effects on the next period’s supplier CSR Score. For example,
in column (1), the interaction term, CSRC Score×% of Shares HeldS , is 0.033 with robust standard
error of 0.015. Column (3) produces a qualitatively similar finding based on the number of common
institutional investors in both the supplier and customer firms. The coefficient of the interaction
term is 0.068 and is statistically significant at the 1% level. Thus, common institutional investors
do seek to mobilize investor voice towards positive social impact along the supply chain.
5.2.2. Board interlocks
Economically-linked firms can also be connected through shared directors, where directors serve
on boards of both the customer and supplier. One advantage of having shared directors is that
28
directors can act in concert to promote similar CSR practices at both corporations. We therefore
examine whether board connections through shared directors can, in part, influence CSR policies.
To evaluate this channel, we construct two measures of common directors. The first measure is
based on the log of the number of directors who serve on both the supplier’s and customer’s boards.
To cite an example of interlocking directorates, in year 2006, Rudy Provoost was an Executive Vice
President, division CEO, and board member of Philips, a Dutch technology company headquartered
in Amsterdam, and at the same time, he was the head of board of directors (Chairman) at Philips’s
supplier, LG Display Company. An alternative measure is the log number of positions held by
common board members. For example, in 2003, Garo Armen was CEO and Chairman of Agenus,
Inc., a Lexington, Massachusetts-based biotechnology company focused on immunotherapy, and
was also Board Chair and Acting CEO of Elan Corp, Agenus’s supplier located in Dublin, Ireland.
In this case, Armen held two positions at the supplier. Results are presented in Table 11. Consistent
with the findings of common ownership in Table 9, we also find that common directors serve as
another channel through which the customer firm is able to influence better CSR practice at its
supplier firms. The coefficients of both interaction terms (CSRC Score × # of Common Directors
and CSRC Score × # of Board Positions) in columns (1) and (2) are positive and statistically
significant at the 10% level.
Overall, the results provide corroborating evidence that the stakeholder bargaining position
and collaboration among common institutional owners and common directors are instrumental in
propagating CSR practices from customers to suppliers.
6. Economic Consequences of Customer Effects of CSR
In the preceding sections, we have shown that customers have a positive impact on suppliers’
CSR practices, suggesting that suppliers do respond to their customers and behave similarly in
socially responsible ways. However, a question that remains is whether there is any economic
benefit arising from customers pushing suppliers for greater social responsibilities.
Existing studies dispute whether the benefits of CSR outweigh its costs. Some studies find that
CSR initiatives can help firms build a social reputation (e.g., Fombrun, 2005), attract more produc-
29
tive employees (Burbano, 2016), exploit new markets for environmentally friendly products (Arora
and Gangopadhyay, 1995), and can be financially profitable through branding/reputation effects
on different stakeholders (Baron, 2001). Benabou and Tirole (2010) argue that CSR engagements
are beneficial to firms in the long run and help strengthen their market positions. Other studies,
however, show that adopting CSR policies is likely to increase costs and hurt firm performance,
as firms redefine their corporate social responsibilities under the pressure of various stakeholders.
CSR costs include major investment costs involving construction, equipment, or new environmen-
tal technologies and processes, permanent contributions such as scholarships, and other operating
costs of CSR implementation. Margolis, Elfenbein, and Walsh (2010) provide a meta-analysis of
the relationship between corporate social and financial performance, and document that the overall
effect is positive but small.
In this section, we ask whether customers are financially incentivized to impose better CSR
practices on their suppliers, and whether suppliers benefit from taking greater social responsibilities.
The economic implications of these collaborative CSR efforts between customers and suppliers are
closely related to the growing literature linking CSR to firm financial performance (e.g., Gillan
et al., 2010; Edmans, 2011; Deng, Kang, and Low, 2013; Servaes and Tamayo, 2013; Flammer,
2015a; Krueger, 2015; Lins, Servaes, and Tamayo, 2017). Most of these studies consider CSR
engagements as a firm’s own strategic choice, and investigate their direct and indirect effects on the
firm’s profitability and valuation. Some recent work also studies the role of various stakeholders,
such as institutional investors (Dimson, Karakas, and Li, 2015; Dyck et al., 2017) and competitors
(Cao, Liang, and Zhan, 2018) in driving CSR engagements and their value implications. Unlike
these studies, our analysis focuses on the economic consequences of the stakeholder (customer)
effect of CSR on suppliers and not of firms’ own CSR activities.
We postulate that collaborative CSR efforts between customers and suppliers would lead to
increased operating efficiency, sales growth, and firm value, probably through enhancing branding
and reputation effects, attracting more consumers, and generating greater sales. Improved CSR
standard along a supply chain may involve the increased focus of product responsibility and safety,
which in turn lower discretionary expenses, such as selling, general, and administrative expenses,18
18Kalwani and Narayandas (1995) show that maintaining long-term relationships with their customers decreasesdiscretionary expenses, such as selling, general, and administrative expenses, and hence, improves profitability.
30
through improved operational efficiency. Customers and suppliers have desires to promote a sus-
tainable relationship, through greater CSR efforts, if such efforts produce better future sales growth
and enhanced firm valuation for both the customer and supplier. Our analysis uses the ratio of
selling, general, and administrative expenses to total assets (SG&A), 3-year annualized future sales
growth, and market-to-book ratio as measures of firm performance (Performance) associated with
increased collaborative CSR efforts in a customer-supplier relationship. To evaluate the economic
benefits associated with such efforts, we run the following regression model.
Performance S or C(t+ 1) = a0 + a1CSRC Score(t− 1) × CSRS Score(t) + a2CSRC Score(t− 1)
+ a3CSRS Score(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).(7)
The variable of interest is the interaction of CSRC Score(t − 1) and CSRS Score(t) in (7). If the
CSR effect on suppliers benefits both the supplier and customer, we should expect the coefficient
of a1 to be negative for SG&A whereas positive for future sales growth and market-to-book ratio.
Estimates of (7) are shown in Table 12. The dependent variables are SG&A, sales growth, and
market-to-book ratio of the supplier in columns (1), (3), and (5), while those of its customer
counterparts are shown in columns (2), (4), and (6).
We find that the stakeholder effect of CSR generates favorable economic outcomes. It therefore
pays for customers to influence their suppliers to act socially responsibly, as such behavior has an
overall positive impact on the customer’s future performance. Customers enjoy not only improved
operational efficiency in terms of lower SG&A, but also greater future sales growth and, albeit
small,19 firm valuation. For example, the a1 coefficient is negative in column (2) but is positive in
columns (4) and (6). Similarly, it is also worthwhile for suppliers to strive for better CSR standard
that adheres to that of their socially responsible corporate customer – suppliers also experience
decreased SG&A and enhanced firm value following their adoption of improved CSR practices.
However, unlike their customers, suppliers do not experience any statistically significant increase
in 3-year annualized future sales growth, even though the sign of the coefficient is positive.
In summary, this section shows evidence of economic benefits associated with the customer
effect of CSR along the supply chain.
19In their meta analysis of several existing empirical studies, Margolis, Elfenbein, and Walsh (2010) conclude thatthe overall correlation between CSR and corporate firm performance is positive but small.
31
7. Conclusion
Our study represents the first to examine the role of a specific group of stakeholders, namely
corporate customers, as one important driver of CSR in supply chains worldwide and then to inves-
tigate the possible channels through which customers push suppliers to take greater responsibilities
for social and environmental issues. We employ two unique international databases on firm-level
networks of customers and suppliers around the world and on Thomson Reuter’s ASSET4 Environ-
mental (E), Social (S), and Corporate Governance (G) ratings of publicly-listed global firms from
50 countries for the 2003-2015 period. Using this large international sample of corporate customer-
supplier relationships, we find strong evidence of uni-directional customer effects of CSR practices
on suppliers, but not when either the supplier or the customer is from an emerging market. This
baseline result is robust to the inclusion of a multitude of firm-level control variables, the country’s
GDP per capita, social and environmental aspects of CSR, alternative CSR databases, as well as
different combinations of supplier-customer, country, industry, and year fixed effects. Further, using
several quasi-natural experiments and M&A acquisitions provides causal evidence that customers
improve suppliers’ subsequent CSR ratings.
We show two key channels that corporate customers are able to influence or pressure suppliers
to commit to better CSR standards. The first channel is through the stakeholder bargaining power,
and the other is through common ownership and interlocking directorates. Our analysis provides
supporting evidence that the two channels facilitate the customer effect of CSR on suppliers.
Finally, we find that the collaborative CSR effect delivers economic values to both suppliers
and customers. Customers have incentives to aim for better CSR at their suppliers as higher CSR
standard results in improved operational efficiency, sales growth and firm value through socially
and environmentally friendly production and through enhancing branding and reputation effects.
Similarly, suppliers also have the desire to engage in responsible business practices that adhere to
those of their customers, as such adherence contributes to improving both operational efficiency
and firm valuation.
If we view the evidence of our study at face value, several ideas emerge for the improvement
of social welfare and firm performance. As multinational corporations around the world are in-
32
creasingly interconnected via global supply chains and making significant impacts on billions of
consumers, their value creation process and social responsibilities have become the foremost issues
in public debate. Despite their importance, we still have limited knowledge on how their CSR
practices are influenced by each other and spilled over along supply chains. Therefore, a firm’s own
socially responsible practice might have a multiplier effect through economic links and generate big-
ger positive social payoffs. However, our finding of the uni-directional CSR effect from customers
to suppliers and only in some subsamples of countries suggest that such effects are bounded by a
firm’s relative position in the global network and its socio-cultural and institutional environment.
This potentially indicates that policies aiming at promoting socially responsible practices among
public companies cannot be universally applied.
Our findings also shed light on some fundamental issues in industrial organization and strategic
management, such as why some firms are incentivized to coerce others to adopt certain practices.
The answer might simply be that it makes economic sense because as corporate customers, they
can benefit from increased sales throughout the supply chain. It is therefore unsurprising that
these companies are also more responsive to product safety and human rights scandals, as these
events are closely related to consumer perception and future purchases of their products. These are
important strategic considerations for managers, especially pertaining to indirect costs and benefits
incurred by other upstream and downstream firms, when trading off their social investment and
other capital expenditures with limited corporate resources.
33
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36
Figure 1A Snapshot of FactSet Revere Information on BMW and its Worldwide
Suppliers
87 suppliers
37
Tab
le1
Su
mm
ary
Sta
tist
ics
The
table
rep
ort
sth
enum
ber
of
cust
om
er-s
upplier
pair
obse
rvati
ons
(NO
bs)
and
sum
mary
stati
stic
sof
vari
ous
corp
ora
teso
cial
resp
onsi
bilit
y(C
SR
)sc
ore
s,firm
-lev
elch
ara
cter
isti
csand
GD
Pp
erca
pit
aem
plo
yed
as
contr
ol
vari
able
s,and
the
vari
able
sem
plo
yed
for
the
mec
hanis
ms
of
CSR
pro
pagati
on
effec
ts.
Thes
eva
riable
sare
rep
ort
edfo
rb
oth
supplier
sand
cust
om
ers.
The
CSR
vari
able
sare
rati
ngs
ass
oci
ate
dw
ith
the
com
posi
teC
SR
score
,en
vir
onm
enta
l(E
nv),
soci
al
(Soc)
,gov
ernance
(Gov
),pro
duct
resp
onsi
bilit
y,and
hum
an
rights
CSR
issu
es.
Fir
mch
ara
cter
isti
csin
clude
lever
age,
retu
rnon
ass
ets
(RO
A),
Tobin
’sQ
(Q),
log
of
tota
lass
ets
(TA
sset
s),
close
ly-h
eld
share
s(C
lose
Share
s),
sale
sgro
wth
,as
wel
las
the
countr
y’s
gro
ssdom
esti
cpro
duct
per
capit
a(G
DP
C).
The
channel
vari
able
sin
clude
R&
D,
pate
nts
,H
erfindahl-
Hir
schm
an
Index
(HH
I),
%of
share
shel
d,
num
ber
sof
com
mon
owner
sand
dir
ecto
rsin
both
cust
om
erand
supplier
firm
s,and
#of
com
mon
dir
ecto
rhold
ing
mult
iple
board
posi
tions
at
the
supplier
.A
llth
eva
riable
sare
defi
ned
inA
pp
endix
Table
A.3
.
Variablesassociate
dwith
Suppliers
Variablesassociate
dwith
Customers
Vari
able
NO
bs
Mea
nM
edia
nStd
evM
in25th
75th
Max
NO
bs
Mea
nM
edia
nStd
evM
in25th
75th
Max
CSR
Composite
andComponen
tScores
CSR
82,1
13
64.2
174.3
828.9
62.5
338.8
290.6
898.4
982,1
13
71.7
383.5
126.7
32.5
356.8
292.6
198.6
0E
nv
82,1
13
60.9
374.0
232.1
48.2
725.2
891.4
497.4
982,1
13
69.4
486.3
530.1
28.2
746.4
892.8
297.5
0Soc
82,1
13
60.1
668.5
130.3
63.5
431.5
188.2
998.8
082,1
13
69.3
081.0
127.2
83.5
452.1
890.9
698.8
8G
ov82,1
13
66.0
374.1
125.2
71.2
253.2
085.4
898.7
782,1
13
65.0
875.4
127.5
61.1
847.2
587.0
698.7
7P
roduct
Res
ponsi
bilit
y81,6
80
57.4
359.6
029.4
02.1
932.1
386.2
199.2
281,6
80
58.6
059.4
728.0
42.1
935.5
186.2
299.2
2H
um
an
Rig
hts
81,6
80
58.5
662.5
632.8
02.6
421.2
892.8
699.9
581,6
80
67.1
287.2
831.4
32.6
429.5
893.9
799.9
5
TheChannel
Variables
R&
D82,1
13
0.0
50.0
30.0
50.0
00.0
10.0
70.1
852,2
51
0.0
50.0
20.0
30.0
00.0
00.0
50.1
4P
ate
nts
2,6
33
243.3
812
567.3
11.0
02.0
054.0
02803
3,8
02
222.0
710.0
0533.3
91.0
02.0
081.0
02803
HH
I82,1
05
0.1
80.0
80.2
20.0
10.0
40.2
31.0
077,3
44
0.2
20.1
10.2
50.0
10.0
50.3
01.0
0%
of
Share
sH
eld
82,1
13
0.3
70.3
00.2
90.0
00.1
20.6
02.1
382,1
13
0.0
10.0
00.0
40.0
00.0
00.0
00.9
6#
of
Com
mon
Ow
ner
s82,1
13
212.9
7150
218.1
0.0
071
280
2280
82,1
13
212.9
7150
218.1
0.0
071
280
2280
#of
Com
mon
Dir
ecto
rs82,1
13
0.0
60.0
00.4
90.0
00.0
00.0
026
82,1
13
0.0
60.0
00.4
90.0
00.0
00.0
026
#of
Board
Posi
tions
82,1
13
0.0
70.0
00.5
60.0
00.0
00.0
030
ControlVariables
Lev
erage
82,1
13
0.2
50.2
30.1
70.0
00.1
30.3
60.6
078,8
24
0.2
50.2
30.1
50.0
00.1
40.3
60.5
8R
OA
82,1
13
0.0
80.0
70.0
7-0
.10
0.0
40.1
20.2
178,8
24
0.0
80.0
70.0
7-0
.05
0.0
40.1
20.2
2Q
82,1
13
1.7
91.5
10.8
70.8
61.1
52.1
34.2
778,8
24
1.6
21.3
60.7
20.8
91.0
81.9
03.5
8M
B82,0
64
2.9
42.3
02.1
70.5
41.3
73.7
48.7
778,7
47
2.6
92.0
22.0
50.5
11.2
33.4
18.2
8T
Ass
ets
82,1
13
22.9
722.8
11.5
914.8
721.8
924.0
228.8
678,8
24
24.0
924.1
21.6
115.6
322.9
825.2
928.9
6C
lose
Share
s82,1
13
0.1
60.0
60.2
00.0
00.0
10.2
31.0
078,8
24
0.1
70.0
70.2
20.0
00.0
00.2
81.0
0Sale
sGro
wth
82,1
13
0.0
80.0
60.1
4-0
.16
-0.0
10.1
40.5
178,8
24
0.0
60.0
50.1
2-0
.15
-0.0
20.1
20.4
0G
DP
C82,1
13
10.7
510.7
99.4
96.9
010.6
410.8
711.6
978,8
24
10.7
110.7
89.5
96.9
010.6
210.8
711.6
9
38
Tab
le2
Eff
ects
of
Corp
ora
teS
ocia
lR
esp
on
sib
ilit
y(C
SR
)P
rop
agati
on
alo
ng
Glo
bal
Su
pp
lyC
hain
s
Th
ista
ble
rep
orts
resu
lts
from
the
regr
essi
onof
sup
pli
erC
SR
(CS
RS
)S
core
on
cust
om
erC
SR
(CS
RC
)S
core
,as
foll
ows.
CS
RS(C
)S
core
(t+
1)
=a0
+a1C
SR
C(S
)S
core
(t)
+
K ∑ k=1
b kX
k(t
)+
FE
(t)
+ε(t
+1).
Th
ed
epen
den
tva
riab
leis
sup
pli
er(c
ust
omer
)C
SR
Sco
rean
dth
eke
yin
dep
end
ent
vari
ab
leis
cust
om
er(s
up
pli
er)
CS
RS
core
inco
lum
ns
(1)-
(4)
(col
um
ns
(5)-
(8))
.C
ontr
olva
riab
lesX
kin
clu
de
firm
-ch
ara
cter
isti
csof
the
sup
pli
erin
the
firs
tth
ree
colu
mn
san
dth
ose
of
cust
om
erin
the
last
thre
eco
lum
ns.
Inco
lum
ns
(4)
and
(8),
the
contr
ol
vari
ab
les
incl
ud
eb
oth
of
the
sup
pli
eran
dcu
stom
er.
Th
eyare
lever
age,
retu
rnon
ass
ets
(RO
A),
Tob
in’s
Q,
log
ofto
tal
asse
ts(T
Ass
ets)
,cl
ose
ly-h
eld
share
s(C
lose
Sh
are
s),
sale
sgro
wth
,as
wel
las
GD
PC
.A
llth
eva
riab
les
are
defi
ned
inA
pp
end
ixT
able
A.3
.N
Ob
sis
the
nu
mb
erof
firm
-yea
rcu
stom
er-s
up
pli
erp
air
ob
serv
ati
on
s.T
he
regre
ssio
ns
als
oin
clu
de
inte
rcep
tsand
com
bin
atio
ns
ofd
iffer
ent
cust
omer
-su
pp
lier
,in
du
stry
,co
untr
y,an
dye
ar
fixed
effec
ts(F
E),
an
dall
stan
dard
erro
rsre
port
edin
pare
nth
eses
are
clu
ster
edat
the
cust
omer
-su
pp
lier
-pai
rle
vel
.*,
**,
***
are
sign
ifica
nce
leve
lsd
enote
dat
the
10%
,5%
an
d1%
,le
vels
,re
spec
tive
ly.
Su
pp
lier
CS
RS
Sco
reC
ust
om
erC
SR
CS
core
Var
iab
le(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
CS
RC
0.04
4***
0.02
1***
0.0
12**
0.0
11*
(0.0
05)
(0.0
05)
(0.0
06)
(0.0
07)
CS
RS
0.0
04
-0.0
04
-0.0
08
0.0
09
(0.0
05)
(0.0
05)
(0.0
05)
(0.0
06)
Lev
erag
eS-1
8.38
4***
-11.
340*
**
-11.7
68***
-11.4
11***
1.1
33
(1.0
42)
(1.0
23)
(1.0
08)
(1.0
40)
(0.9
38)
RO
AS
38.6
64**
*36
.732
***
38.1
40***
38.5
47***
-1.4
80
(1.4
43)
(1.4
23)
(1.4
45)
(1.5
04)
(1.2
90)
QS
0.71
9***
1.24
5***
1.2
96***
1.3
72***
0.0
61
(0.1
51)
(0.1
58)
(0.1
57)
(0.1
62)
(0.1
50)
Tot
alA
sset
sS10
.097
***
10.9
89***
11.5
39***
11.5
34***
-0.7
77***
(0.1
05)
(0.1
05)
(0.1
07)
(0.1
09)
(0.1
25)
Clo
seS
har
esS
-11.
319*
**-1
3.49
4***
-13.3
16***
-13.3
71***
0.7
56
(0.6
91)
(0.6
89)
(0.7
18)
(0.7
33)
(0.6
50)
Sal
esG
row
thS
-7.2
47**
*-6
.879
***
-7.5
34***
-7.6
12***
0.1
62
(0.5
09)
(0.5
11)
(0.5
30)
(0.5
59)
(0.4
60)
GD
PC
S-1
.973
***
0.35
50.7
85
0.5
35
1.4
77
(0.3
40)
(0.4
24)
(1.0
60)
(1.1
61)
(1.1
52)
39
Lev
erag
eC1.7
91*
-7.3
36***
-7.7
28***
-8.0
60***
-8.4
96***
(0.9
65)
(1.0
41)
(1.1
08)
(1.0
72)
(1.1
20)
RO
AC
-1.1
33
47.1
20***
44.0
96***
44.8
34***
45.7
44***
(1.8
22)
(1.7
50)
(1.7
68)
(1.7
49)
(1.8
42)
QC
-0.3
28
-0.0
85
-0.3
03
-0.2
96
-0.2
55
(0.2
35)
(0.2
04)
(0.2
16)
(0.2
12)
(0.2
24)
Tot
alA
sset
sC0.0
48
7.6
79***
8.8
05***
8.9
23***
9.0
02***
(0.1
29)
(0.1
34)
(0.1
55)
(0.1
49)
(0.1
46)
Clo
seS
har
esC
0.5
81
-10.6
29***
-10.9
07***
-10.7
74***
-11.2
84***
(0.6
66)
(0.6
63)
(0.6
70)
(0.6
78)
(0.7
01)
Sal
esG
row
thC
-0.4
02
-8.1
38***
-7.6
98***
-7.8
30***
-8.3
81***
(0.6
00)
(0.5
47)
(0.5
51)
(0.5
56)
(0.5
95)
GD
PC
C-1
.627
0.5
89*
1.0
43***
0.8
60
0.4
34
(1.2
46)
(0.3
04)
(0.3
52)
(0.9
53)
(1.0
21)
NO
bs
55,6
9455,6
85
52,5
55
48,1
63
51,1
16
51,1
15
51,1
05
45,8
43
Yea
rF
EY
esY
esY
esY
esY
esY
esY
esY
esS
up
pli
erIn
du
stry
xN
oY
esN
oY
esC
ust
omer
Ind
ust
ryF
ES
up
pli
erC
ountr
yx
No
Yes
No
Yes
Cu
stom
erC
ountr
yF
ES
up
pli
erIn
du
stry
FE
Yes
Yes
Yes
Yes
Cu
stom
erIn
du
stry
FE
Yes
Yes
Yes
Yes
Su
pp
lier
Cou
ntr
yF
EY
esY
esY
esY
esC
ust
omer
Cou
ntr
yF
EY
esY
esY
esY
es
40
Table 3
Environmental, Social, and Governance Issues in Global Supply Chains
This table reports results from the regression of supplier (customer) CSR Component score on customer(supplier) component CSR score, as follows.
CSRS(C) ComScore(t+ 1) = a0 + a1CSRC(S) ComScore(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is supplier (customer) CSR ComScore and the key independent variable is customer(supplier) CSR ComScore in columns (1)-(4) (columns (5)-(8)). The CSR components are environmental(Env), social (Soc), and governance (Gov) issues. Unreported control variables Xk include firm-characteristicsof the customer (C) or supplier (S), and they are leverage, return on assets (ROA), Tobin’s Q, log of total assets(TAssets), closely-held shares (CloseShares), sales growth, as well as GDPC. All the variables are defined inAppendix Table A.3. NObs is the number of customer-supplier pair observations. The regressions also includeintercepts and combinations of different customer-supplier, industry, country, and year fixed effects (FE), androbust standard errors reported in parentheses are clustered at the customer-supplier-pair level. *, **, *** aresignificance levels denoted at the 10%, 5% and 1%, levels, respectively.
EnvS SocS GovS EnvC SocC GovC
Variable (1) (2) (3) (4) (5) (6)
EnvC Score 0.032***(0.006)
SocC Score 0.013**(0.005)
GovC Score 0.024***(0.005)
EnvS Score -0.005(0.005)
SocS Score -0.003(0.005)
GovS Score 0.023***(0.005)
NObs 55,685 55,685 55,685 51,115 51,115 51,115Supplier Controls Yes Yes Yes No No NoCustomer Controls No No No Yes Yes YesYear FE Yes Yes Yes Yes Yes YesSupplier Industry x Yes Yes Yes Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes Yes Yes Yes
Customer Country FE
41
Table 4
Alternative CSR Rating Databases
This table reports results that replicate those of Table 2 using two alternative databases, namely MSCIIntangible Value Assessment (IVA) and Sustainalytics, and the regression model is as follows.
CSRS(C) Score(t+ 1) = a0 + a1CSRC(S) Score(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is either the supplier or customer CSR score and the key independent variableis its corresponding economically-linked firm’s CSR score. Unreported control variables Xk include firm-characteristics of the customer (C) or supplier (S), and they are leverage, return on assets (ROA), Tobin’sQ, log of total assets (TAssets), closely-held shares (CloseShares), sales growth, as well as GDPC. Allthe variables are defined in Appendix Table A.3. NObs is the number of firm-year customer-supplier pairobservations. The regressions also include intercepts and combinations of different customer-supplier, indus-try, country, and year fixed effects (FE), and standard errors reported in parentheses are clustered at thecustomer-supplier-pair level in columns (1) and (3) and at the customer-year level in (2) and (4). *, **, ***are significance levels denoted at the 10%, 5% and 1%, levels, respectively.
CSRS CSRC CSRS CSRC
Variable (1) (2) (3) (4)
MSCI IVA CSRC Score 0.014**(0.005)
MSCI IVA CSRS Score 0.010(0.006)
Sustainalytics CSRC Score 0.020***(0.008)
Sustainalytics CSRS Score -0.002(0.008)
NObs 28,056 23,600 22,173 20,805Supplier Controls Yes No Yes NoCustomer Controls No Yes No YesYear FE Yes Yes Yes YesSupplier Industry x Yes Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes Yes
Customer Country FE
42
Tab
le5
Locati
on
sof
Cu
stom
ers
an
dS
up
pliers
an
dS
up
plier
CS
R
Th
ista
ble
rep
orts
resu
lts
from
the
regr
essi
on
of
sup
pli
erC
SR
score
(CS
RS
Sco
re)
on
cust
om
erC
SR
score
(CS
RC
Sco
re),
wh
ere
cust
om
ers
and
sup
pli
ers
may
be
from
the
sam
eor
diff
eren
tco
untr
ies
CS
RS
(t+
1)S
core
=a0
+a1C
SR
C(t
)S
core
+
K ∑ k=1
b kX
k(t
)+
FE
(t)
+ε(t
+1).
Th
ere
gres
sion
isap
pli
edto
sup
pli
eran
dcu
stom
erfi
rms
from
the
sam
eco
untr
y,d
iffer
ent
cou
ntr
ies,
or
non
-US
cou
ntr
ies,
or
sup
pli
ers
and
cust
omer
sfr
omem
ergi
ng
(EM
G)
ord
evel
op
edco
untr
ies
(DE
V).
Un
rep
ort
edco
ntr
ol
vari
ab
lesX
kin
clu
de
firm
-ch
ara
cter
isti
csof
the
cust
omer
(C)
orsu
pp
lier
(S),
and
they
are
leve
rage,
retu
rnon
ass
ets
(RO
A),
Tob
in’s
Q,
log
of
tota
lass
ets
(TA
sset
s),
close
ly-h
eld
share
s(C
lose
Sh
ares
),sa
les
grow
th,as
wel
las
the
cou
ntr
y’s
gro
ssd
om
esti
cp
rod
uct
per
cap
ita
(GD
PC
).A
llth
eva
riab
les
are
defi
ned
inA
pp
end
ixT
able
A.3
.N
Ob
sis
the
nu
mb
erof
cust
om
er-s
up
pli
erp
air
obse
rvati
on
s.T
he
regre
ssio
ns
als
oin
clu
de
inte
rcep
tsan
dco
mb
inati
on
sof
diff
eren
tcu
stom
er-s
up
pli
er,in
du
stry
,co
untr
y,an
dye
ar
fixed
effec
ts(F
E),
an
dro
bu
stst
an
dard
erro
rsre
port
edin
pare
nth
eses
are
clu
ster
edat
the
cust
omer
-su
pp
lier
-pai
rle
vel.
*,**
,***
are
sign
ifica
nce
level
sd
enote
dat
the
10%
,5%
an
d1%
,le
vel
s,re
spec
tive
ly.
Sam
eD
iffer
ent
Non
-US
Su
pp
lier
sfr
om
Cu
stom
ers
from
Cou
ntr
yC
ou
ntr
ies
Cou
ntr
ies
EM
GD
EV
EM
GD
EV
Var
iab
le(1
)(2
)(3
)(4
)(5
)(6
)(7
)
CS
RC
Sco
re0.
008
0.0
14**
0.0
23**
-0.0
23
0.0
12**
0.0
23
0.0
16***
(0.0
09)
(0.0
07)
(0.0
09)
(0.0
20)
(0.0
06)
(0.0
15)
(0.0
06)
NO
bs
22,6
7229,8
83
14,3
14
3,3
66
49,2
19
52,0
63
49,8
69
Con
trol
sY
esY
esY
esY
esY
esY
esY
esY
ear
FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Su
pp
lier
Ind
ust
ryx
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Cu
stom
erIn
du
stry
FE
Su
pp
lier
Cou
ntr
yx
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Cu
stom
erC
ountr
yF
E
43
Table 6
Linked and Delinked Customer-Supplier Relationships
This table reports results from the regression of supplier CSR score on customer CSR score,linked/delinked indicator, and their interaction as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × Post Link/Delink(t) + a2CSRC Score(t)
+a3Post Link/Delink(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is supplier CSR score (CSRS Score) and the key independent vari-ables are the customer CSR score (CSRC Score), Post Link/Delink variable, and their in-teraction. Post Link (Delink) is a binary variable that equals 1 if the customer and supplierfirst establishes (severes) the relationship and 0 otherwise. Unreported control variables Xk
include firm-characteristics of the customer (C) or supplier (S), and they are leverage, returnon assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares (CloseShares),sales growth, as well as the country’s gross domestic product per capita (GDPC). All thevariables are defined in Appendix Table A.3. NObs is the number of customer-supplier pairobservations. The regressions also include intercepts and combinations of different customer-supplier, industry, country, and year fixed effects (FE), and robust standard errors reportedin parentheses are clustered at the customer-supplier-pair level. *, **, *** are significancelevels denoted at the 10%, 5% and 1%, levels, respectively.
Variable (1) (2)
CSRC Score x Post Link 0.020***(0.004)
Post Link 0.209(0.275)
CSRC Score x Post Delink 0.001(0.004)
Post Delink 0.808***(0.296)
CSRC Score -0.007* 0.001(0.003) (0.003)
NObs 233,881 233,881Controls Yes YesYear FE Yes YesSupplier Industry x Yes Yes
Customer Industry FESupplier Country x Yes Yes
Customer Country FE
44
Table 7
Human Rights and Product Responsibility
This table reports results from the regression of supplier CSR component Score (ComScore) (i.e., humanrights or product responsibility measures of CSR) on its customer CSR counterpart, the year humanrights or product responsibility event occurred (Event), and the interaction between Event and customerCSR component as follows.
CSRS ComScore(t+ 1) = a0 + a1CSRC ComScore(t) × Event + a2CSRC ComScore(t)
+a3Event +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is the supplier CSR component score (CSRS ComScore) and the key independentvariables are the customer CSR subcomponent score (CSRC ComScore) and Event variable. We evaluatetwo human rights scandals (the 2005 Nike’s workers abuse report and the 2013 building collapse inBangladesh) and two product responsibility scandals (the 2008 food safety scandal in China and the2013 Takata airbag and Toyota car recalls). Unreported control variables Xk include supplier’s leverage,return on assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares (CloseShares),sales growth, and GDPC. All the variables are defined in Appendix Table A.3. NObs is the numberof customer-supplier pair observations. The regressions also include intercepts and combinations ofdifferent customer-supplier, industry, country, and year fixed effects (FE), and robust standard errorsreported in parentheses are clustered at the customer-supplier-pair level. *, **, *** are significancelevels denoted at the 10%, 5% and 1%, levels, respectively.
Worker Workplace Food Safety Airbag & CarAbuse Safety Scandal Recalls
Variable (1) (2) (3) (4)
Human RightsC x 2005 0.343***(0.093)
Human RightsC x 2013 0.031**(0.012)
Human RightsC 0.025 0.019(0.019) (0.020)
Product ResponsibilityCx 2008 0.250*(0.145)
Product ResponsibilityCx 2013 0.268**(0.122)
Product ResponsibilityC -0.026 -0.082(0.057) (0.076)
NObs 3,940 3,940 753 423Controls Yes Yes Yes YesYear FE Yes Yes Yes YesSupplier Industry x Yes Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes Yes
Customer Country FE
45
Table 8
Stakeholder Effects of CSR in Target Supplier and Customer Firms
This table reports results from the regression of supplier CSR score on customer CSR score, binary indicator for post-M&A event of either a customer or a supplier being a target firm, and the interaction between post-M&A indicator (PostM&A) and customer CSR score as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × Post M&A S or C(t) + a2CSRC Score(t) + a3Post M&A S or C(t)
+
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is supplier CSR score (CSRS Score) and the key independent variables are customer CSR score(CSRC Score) and Post M&A indicator when a customer or a supplier is a target firm. The table also shows results whenevaluating a customer whose CSR is above the median rating (CSRC
High) in the sample of customer firms. Unreportedcontrol variables Xk include firm-characteristics of the customer (C) or supplier (S), and they are leverage, return onassets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares (CloseShares), sales growth, as well as thecountry’s gross domestic product per capita (GDPC). All the variables are defined in Appendix Table A.3. NObs isthe number of customer-supplier pair observations. The regressions also include intercepts and combinations of differentcustomer-supplier, industry, country, and year fixed effects (FE), and robust standard errors reported in parentheses areclustered at the customer-supplier-pair level. *, **, *** are significance levels denoted at the 10%, 5% and 1%, levels,respectively.
Target=Supplier/Customer Target=Supplier Target=Customer
Variable (1) (2) (3) (4) (5) (6)
CSRC Score x Post M&A -0.051** -0.075** -0.028(0.023) (0.032) (0.032)
CSRC Score 0.013** 0.013** 0.012**(0.006) (0.006) (0.006)
CSRCHigh x Post M&A -1.595* -2.307** -0.038
(0.896) (1.153) (1.419)
CSRCHigh 0.382* 0.372* 0.336
(0.219) (0.218) (0.218)
Post M&A 4.492** 1.514* 7.894*** 3.381*** 1.603 -0.504(1.860) (0.830) (2.482) (1.044) (2.639) (1.308)
NObs 52,555 52,555 52,555 52,555 52,555 52,555Controls Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesSupplier Industry x Yes Yes Yes Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes Yes Yes Yes
Customer Country FE
46
Table 9
The Effect of Bargaining Power in the Supply Chain and Supplier CSR
This table reports results from the regression of supplier CSR score (CSRS Score) on customer CSR score(CSRC Score), a measure of the bargaining power (Power) in the customer-supplier relationship, and theinteraction between Power and CSRC Score as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × Power(t) + a2CSRC Score(t) + a3Power(t)
+
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
Our analysis examines the degree of bargaining power in a supply chain through the supplier’s level ofinnovation, measured by its R&D to total assets and the log of the number of patents, in columns (1)-(2),and the industry competitiveness of suppliers and customers, as measured by the Herfindahl-HirschmanIndex (HHI), in columns (3)-(4). The key independent variables are CSRC Score, the proxy for Power,and their interaction. Unreported control variables Xk include firm-characteristics of the customer (C)or supplier (S), and they are leverage, return on assets (ROA), Tobin’s Q, log of total assets (TAssets),closely-held shares (CloseShares), sales growth, as well as the country’s gross domestic product per capita(GDPC). All the variables are defined in Appendix Table A.3. NObs is the number of customer-supplierpair observations. The regressions also include intercepts and combinations of different customer-supplier,industry, country, and year fixed effects (FE), and robust standard errors reported in parentheses areclustered at the customer-supplier-pair level. *, **, *** are significance levels denoted at the 10%, 5%and 1%, levels, respectively.
Variable (1) (2) (3) (4)
CSRC Score x R&DS -0.323***(0.114)
R&DS 65.482***(9.280)
CSRC Score x PatentsS -0.013*(0.008)
PatentsS 0.795(0.737)
CSRC Score x HHIS -0.567***(0.191)
HHIS 32.811**(13.939)
CSRC Score x HHIC 0.048**(0.023)
HHIC -4.235**(2.004)
CSRC Score 0.037*** 0.042 0.034*** 0.010(0.009) (0.042) (0.007) (0.007)
NObs 35,493 1,364 55,682 52,552Controls Yes Yes Yes YesYear FE Yes Yes Yes YesSupplier Industry x Yes Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes Yes
Customer Country FE
47
Table 10
Common Institutional Ownership and Supplier CSR
This table reports results from the regression of supplier CSR score on customer CSR score, Ownership, andthe interaction between Ownership and customer CSR score as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × OwnershipS or C(t) + a2CSRC Score(t)
+a3OwnershipS or C(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is supplier CSR score (CSRS Score) and the key independent variables are thecustomer CSR score (CSRC Score) and Ownership variable. Ownership is measured using the percentageof shares held by common institutional investors in a supplier firm or customer firm, or the log number ofinstitutional investors holding both the supplier and customer firms (# of Common Owners). Unreportedcontrol variables Xk include firm-characteristics of the customer (C) or supplier (S), and they are leverage,return on assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares (CloseShares), salesgrowth, as well as the country’s gross domestic product per capita (GDPC). All the variables are definedin Appendix Table A.3. NObs is the number of customer-supplier pair observations. The regressions alsoinclude intercepts and combinations of different customer-supplier, industry, country, and year fixed effects(FE), and robust standard errors reported in parentheses are clustered at the customer-supplier-pair level.*, **, *** are significance levels denoted at the 10%, 5% and 1%, levels, respectively.
Variable (1) (2) (3)
CSRC Score x % of Shares HeldS 0.033**(0.015)
% of Shares HeldS 3.421***(1.244)
CSRC Score x % of Shares HeldC 0.035**(0.014)
% of Shares HeldC 4.160***(1.078)
CSRC Score x # of Common Owners 0.068***(0.024)
# of Common Owners 0.040**(0.018)
CSRC Score -0.010 -0.004 -0.026(0.008) (0.008) (0.018)
NObs 52,555 52,555 55,685Controls Yes Yes YesYear FE Yes Yes YesSupplier Industry x Yes Yes Yes
Customer Industry FESupplier Country x Yes Yes Yes
Customer Country FE
48
Table 11
Common Directors and Supplier CSR
This table reports results from the regression of supplier CSR score on customer CSR score, commondirectors (CDirectors), and the interaction between Directors and customer CSR score as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × CDirectors S or C(t) + a2CSRC Score(t)
+a3CDirectors S or C(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is supplier CSR score (CSRS Score) and the key independent variables arethe customer CSR score (CSRC Score), CDirectors variable, and their interaction. CDirectors ismeasured using the log number of directors who serve on the boards of both the supplier and thecustomer, or the log number of common directors holding multiple positions in the supplier firm.Unreported control variables Xk include firm-characteristics of the customer (C) or supplier (S), andthey are leverage, return on assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares(CloseShares), sales growth, as well as the country’s gross domestic product per capita (GDPC).All the variables are defined in Appendix Table A.3. NObs is the number of customer-supplier pairobservations. The regressions also include intercepts and combinations of different customer-supplier,industry, country, and year fixed effects (FE), and robust standard errors reported in parenthesesare clustered at the customer-supplier-pair level. *, **, *** are significance levels denoted at the10%, 5% and 1%, levels, respectively.
Variable (1) (2)
CSRC Score x # of Common Directors 0.053*(0.027)
# of Common Directors -4.858**(2.229)
CSRC Score x # of Board Positions 0.048*(0.025)
# of Board Positions -4.348**(2.022)
CSRC Score 0.019*** 0.019***(0.007) (0.007)
NObs 34,024 34,024Controls Yes YesYear FE Yes YesSupplier Industry x Yes Yes
Customer Industry FESupplier Country x Yes Yes
Customer Country FE
49
Tab
le12
Fir
mP
erf
orm
an
ce
an
dC
ust
om
er
Eff
ects
of
CS
R
Th
ista
ble
rep
orts
resu
lts
from
regr
essi
ng
firm
per
form
an
ce,
mea
sure
dby
SG
&A
,3-y
ear
an
nu
ali
zed
sale
sgro
wth
,an
dfi
rmva
luati
on
on
sup
pli
erC
SR
Sco
re,
cust
omer
CS
RS
core
,an
dth
ein
tera
ctio
nb
etw
een
cust
om
erC
SR
Sco
rean
dsu
pp
lier
CS
RS
core
as
foll
ows.
Per
form
ance
Sor
C(t
+1)
=a0
+a1C
SR
CS
core
(t−
1)×
CS
RS
Sco
re(t
)+a2C
SR
CS
core
(t−
1)
+a3C
SR
SS
core
(t)
+
K ∑ k=1
b kX
k(t
)+
FE
(t)
+ε(t
+1).
Th
ed
epen
den
tva
riab
leis
the
sup
pli
er’s
per
form
an
ce(P
erfo
rman
ce)
inco
lum
ns
(1),
(3),
an
d(5
),an
dof
cust
om
er’s
in(2
),(4
),an
d(6
),an
dke
yex
pla
nat
ory
vari
able
sin
clu
de
sup
pli
erC
SR
score
(CS
RS
Sco
re),
cust
om
erC
SR
score
(CS
RC
Sco
re)
an
dth
eir
inte
ract
ion
.P
erfo
rman
ceis
mea
sure
dby
SG
&A
,3-
year
annu
aliz
edsa
les
grow
th,
and
mark
et-t
o-b
ook
equ
ity
valu
e.U
nre
port
edco
ntr
ol
vari
ab
lesX
kin
clu
de
firm
-ch
ara
cter
isti
csof
the
cust
omer
(C)
orsu
pp
lier
(S),
and
they
are
leve
rage,
retu
rnon
ass
ets
(RO
A),
Tob
in’s
Q,
tota
lass
ets
(TA
sset
s),
close
ly-h
eld
share
s,sa
les
gro
wth
,as
wel
las
the
cou
ntr
y’s
gros
sd
omes
tic
pro
du
ctp
erca
pit
a(G
DP
C).
All
the
vari
ab
les
are
defi
ned
inA
pp
end
ixT
ab
leA
.3.
NO
bs
isth
enu
mb
erof
cust
omer
-su
pp
lier
pai
rob
serv
atio
ns.
Th
ere
gre
ssio
ns
als
oin
clu
de
inte
rcep
tsan
dco
mb
inati
on
sof
diff
eren
tcu
stom
er-s
up
pli
er,
ind
ust
ry,
cou
ntr
y,an
dye
arfi
xed
effec
ts(F
E),
and
rob
ust
stan
dard
erro
rsre
port
edin
pare
nth
eses
are
clu
ster
edat
the
cust
om
er-s
up
pli
er-p
air
leve
l.*,
**,
***
are
sign
ifica
nce
level
sd
enot
edat
the
10%
,5%
and
1%
,le
vel
s,re
spec
tive
ly.
SG
&A
3-Y
ear
An
nu
ali
zed
Sale
sG
row
thM
ark
et-
to-B
ook
Valu
e
Su
pp
lier
Cu
stom
er
Su
pp
lier
Cu
stom
er
Su
pp
lier
Cu
stom
er
Var
iab
le(1
)(2
)(3
)(4
)(5
)(6
)
CS
RC
*CS
RS
-0.0
003*
**-0
.0003***
0.0
21
0.0
29*
0.0
04**
0.0
003*
(0.0
000)
(0.0
000)
(0.1
49)
(0.0
17)
(0.0
01)
(0.0
001)
CS
RS
0.05
48**
*0.0
257***
0.0
06
0.0
25
0.2
70**
-0.0
65***
(0.0
0747
)(0
.00868)
(0.1
22)
(0.0
15)
(0.1
21)
(0.0
13)
CS
RC
0.02
35**
*0.0
341***
0.3
40***
-0.0
21*
-0.2
11*
-0.0
14
(0.0
0771
)(0
.00720)
(0.1
08)
(0.0
12)
(0.1
09)
(0.0
14)
NO
bs
31,2
3228,2
38
32,2
42
32,2
44
33,2
02
32,2
42
Su
pp
lier
Con
trol
sY
esN
oY
esN
oY
esN
oC
ust
omer
Con
trol
sN
oY
esN
oY
esN
oY
esY
ear
FE
Yes
Yes
Yes
Yes
Yes
Yes
Su
pp
lier
Ind
ust
ryx
Yes
Yes
Yes
Yes
Yes
Yes
Cu
stom
erIn
du
stry
FE
Su
pp
lier
Cou
ntr
yx
Yes
Yes
Yes
Yes
Yes
Yes
Cu
stom
erC
ountr
yF
E
50
Appendix Table A.1
Distribution of the numbers of customers and suppliers, together with their meanCSR scores by year.
This table shows numbers of suppliers and customers, as well as their average CSR (CSRS and CSRC ,respectively) scores, by year.
Number of AverageSuppliers Customers CSRS Score CSRC Score
Year (1) (2) (3) (4)
2003 5,484 5,388 58.21 64.11
2004 6,159 6,161 64.73 72.29
2005 6,181 6,588 67.30 75.55
2006 6,494 7,111 66.37 75.12
2007 6,638 7,355 67.51 74.49
2008 7,150 7,835 66.60 73.46
2009 9,207 9,173 65.30 72.67
2010 12,371 12,544 68.18 73.09
2011 15,273 16,131 68.05 74.03
2012 18,792 19,902 66.02 72.43
2013 23,097 23,493 65.92 71.94
2014 20,866 20,758 67.01 71.89
2015 23,556 20,749 64.35 73.00
51
Appendix Table A.2
Distribution of numbers of customers and suppliers, together with their mean CSRscores by country.
This table shows the numbers of suppliers and customers, as well as their average CSR (CSRS and CSRC ,respectively) scores, by year.
Number of AverageSuppliers Customers CSRS Score CSRC Score
Country (1) (2) (3) (4)
Australia 1,580 2,195 81.30 63.05Austria 115 186 69.63 62.97Belgium 178 193 76.61 72.43Bermuda 107 303 21.95 22.40Brazil 1,338 768 70.17 59.98Canada 2,450 3,195 75.20 57.79Chile 458 293 38.64 34.32China 874 469 50.11 41.04Colombia 61 18 80.45 74.22Czech Republic 19 4 45.02 49.64Denmark 246 238 75.24 72.71Egypt 6 2 4.86 5.46Finland 437 760 90.39 90.99France 3,857 4,500 88.55 83.90Germany 3,622 3,061 82.36 73.41Greece 104 45 77.83 45.13Hong Kong 871 639 60.75 62.45Hungary 35 12 50.62 56.90India 713 398 76.58 74.54Indonesia 348 244 58.05 58.63Ireland 215 261 62.43 64.56Israel 274 185 38.98 56.75Italy 811 503 82.80 88.58Japan 5,786 3,997 70.73 67.26Kuwait 10 2 75.13 24.99Luxembourg 346 219 80.14 66.48Malaysia 174 225 62.56 46.07Mexico 298 288 40.02 43.98Morocco 3 5 26.38 26.38Netherlands 1,665 1,224 88.33 79.34New Zealand 135 113 59.06 26.50Norway 435 287 89.01 82.95Peru 43 19 22.07 35.90Philippines 101 61 49.10 47.32Poland 172 267 46.75 26.23Portugal 162 39 86.15 87.05Qatar 13 5 7.12 9.45Russia 554 434 51.06 51.25Saudi Arabia 62 52 62.64 73.71Singapore 329 678 65.80 58.92South Africa 591 651 82.01 67.96South Korea 2,235 1,903 68.35 64.44Spain 799 630 90.64 87.32Sweden 693 1,052 86.36 80.74Switzerland 956 1,300 86.53 64.85Thailand 197 170 81.07 83.93Turkey 163 84 56.83 49.62United Arab Emirates 4 3 20.00 15.38United Kingdom 5,632 6,192 87.58 78.67United States of America 38,546 43,741 71.13 59.12
52
Ap
pen
dix
Tab
leA
.3
Vari
ab
leD
efi
nit
ion
an
dD
ata
Sou
rce
Vari
ab
leD
efi
nit
ion
an
dD
ata
Sou
rce
CS
RC
om
pon
ent
Sco
res
CS
RS
core
An
equ
alw
eigh
ted
CS
Rra
tin
gsc
ore
(AS
SE
T4)
Env
Asc
ore
asso
ciat
edw
ith
the
envir
on
men
tal
pilla
rof
CS
RR
ati
ng
(AS
SE
T4)
Soc
Asc
ore
asso
ciat
edw
ith
the
soci
al
resp
on
sib
lep
illa
rof
CS
RR
ati
ng
(AS
SE
T4)
Gov
Asc
ore
asso
ciat
edw
ith
the
corp
ora
tegov
ern
an
cep
illa
rof
CS
RR
ati
ng
(AS
SE
T4)
Hu
man
Rig
hts
Asc
ore
asso
ciat
edw
ith
the
hu
man
rights
pilla
rof
CS
RR
ati
ng
(AS
SE
T4)
Pro
du
ctR
esp
onsi
bil
ity
Asc
ore
asso
ciat
edw
ith
the
pro
du
ctre
spon
sib
ilit
yp
illa
rof
CS
RR
ati
ng
(AS
SE
T4)
Mec
han
ism
Vari
abl
esR
&D
Res
earc
han
dD
evel
op
men
tE
xp
ense
(Worl
dsc
op
eit
em01201)/
Tota
lass
ets
(Worl
dsc
op
eit
em02999)
Pat
ents
Nat
ura
llo
gari
thm
valu
eof
nu
mb
erof
succ
essf
ul
pate
nt
ap
pli
cati
on
sfi
led
plu
son
ein
aye
ar
(PA
TS
TA
T)
HH
IH
erfi
nd
ahl-
Hir
sch
man
Ind
exm
easu
red
by
the
sum
mati
on
of
squ
are
dm
ark
etsh
are
(base
on
sale
s)of
each
firm
wit
hin
the
sam
ein
du
stry
)(D
ata
stre
am
Worl
dsc
op
e)%
ofS
har
esH
eld
Su
mof
the
max
imu
m%
of
own
ersh
iph
eld
by
all
com
mon
inst
ituti
on
al
own
ers
inth
esu
pp
lier
(or
cust
om
er)
ina
giv
enyea
r(F
actS
etG
lob
alO
wn
ersh
ipD
ata
)#
ofC
omm
onO
wn
ers
#of
com
mon
own
ers
wh
oow
nsh
are
sof
both
the
sup
pli
eran
dcu
stom
erin
agiv
enye
ar
(Fact
Set
Glo
bal
Ow
ner
ship
Data
)#
ofC
omm
onD
irec
tors
#of
com
mon
dir
ecto
rsw
ho
serv
eon
the
board
sof
both
the
sup
pli
eran
dcu
stom
erin
agiv
enye
ar
(Board
Ex)
#of
Boa
rdP
osit
ion
s#
ofb
oard
pos
itio
ns
hel
dby
com
mon
dir
ecto
rsat
the
sup
pli
erin
agiv
enye
ar
(Board
Ex)
Con
trol
Vari
abl
esL
ever
age
Book
Val
ue
ofD
ebt
(Worl
dS
cop
eit
em03255)
/T
ota
lass
ets
(Worl
dsc
op
eit
em02999)
RO
AE
arn
ings
bef
ore
Inte
rest
an
dT
axes
(Worl
dsc
op
eit
em18191)/
Tota
lass
ets
(Worl
dsc
op
eit
em02999)
QM
arket
valu
eof
com
mon
equ
ity
(Worl
dsc
op
eIt
em08001)
+T
ota
lass
ets
(Worl
dsc
op
eIt
em02999)
-B
ook
valu
eof
com
mon
equ
ity
(Wor
ldsc
ope
item
03501)
an
dth
end
ivid
edby
the
net
valu
eby
Tota
lass
ets
(Worl
dsc
op
eit
em02999)
TA
sset
sN
atura
llo
gari
thm
valu
eof
tota
lass
ets
(Worl
dsc
op
eit
em02999)
plu
son
eC
lose
Sh
ares
Nu
mb
erof
Clo
sely
Hel
dS
hare
s/C
om
mon
Sh
are
sO
uts
tan
din
g(W
orl
dsc
op
eit
em08021)
Sal
esG
row
thO
ne
year
net
sale
sgro
wth
(Worl
dsc
op
eit
em08631)
GD
PC
Gro
ssd
omes
tic
pro
du
ctin
US
Dd
ivid
edby
dom
esti
cp
op
ula
tion
(Worl
dB
an
kIn
dic
ato
rs)
Per
form
an
ceV
ari
abl
esS
G&
AS
elli
ng,
gen
eral
and
ad
min
istr
ati
veex
pen
ses
(Worl
dsc
op
eit
em08631)
div
ided
by
tota
lass
ets
(Worl
dsc
op
eit
em02999)
3-Y
ear
An
nu
aliz
edS
ales
Gro
wth
3-ye
arn
etsa
les
grow
th(W
orl
dsc
op
eit
em08633)
Mar
ket-
toB
ook
Rat
ioM
arke
tva
lue
ofco
mm
on
equit
y(W
orl
dsc
op
eIt
em08001)
div
ided
by
Book
valu
eof
com
mon
equ
ity
(Worl
dsc
op
eit
em03501)
53
ONLINE APPENDIX
To accompany
Socially Responsible Corporate Customers
54
Online Appendix Table OA.1
Major versus Non-Major Customers and Supplier Corporate Social Responsibility
The table shows the regression of a supplier’s CSRS score on its customer firm’s CSRC
score, as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The dependent variable is the supplier CSR score and the key independent variable is itsmajor or non-major customer CSR score. Unreported control variables Xk include thesupplier’s leverage, return on assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-held shares (CloseShares), sales growth, and GDPC. NObs is the number of customer-supplier pair observations. The regressions also include intercepts and combinations ofdifferent customer-supplier, industry, country, and year fixed effects (FE), and robuststandard errors reported in parentheses are clustered at the customer-supplier-pair level.*, **, *** are significance levels denoted at the 10%, 5% and 1%, levels, respectively.
Variable (1) (2)
Major CSRC Score 0.020***(0.005)
Non-Major CSRC Score 0.017(0.020)
NObs 51,866 3,819Controls Yes YesYear FE Yes YesSupplier Industry x Yes Yes
Customer Industry FESupplier Country x Yes Yes
Customer Country FE
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Online Appendix Table OA.2
CSR Gap and Supplier CSR
This table reports results from the regression of supplier CSR score (CSRS Score) on customer CSR score(CSRC Score), CSR Gap and the interaction between CSR Gap and CSRC Score as follows.
CSRS Score(t+ 1) = a0 + a1CSRC Score(t) × CSR Gap(t) + a2CSRC Score(t)
+a3CSR Gap(t) +
K∑k=1
bkXk(t) + FE(t) + ε(t+ 1).
The key independent variables are CSRC Score and the interaction between CSRC Score and CSR Gap. Incolumns (1)-(2), CSR Gap is measured by the difference between CSRC and CSRS Scores, and in columns(3)-(4), the CSR gap is a binary indicator which equals 1 if CSRC >CSRS (I(CSRC >CSRS)), and 0otherwise. Unreported control variables Xk include firm-characteristics of the customer (C) or supplier(S), and they are leverage, return on assets (ROA), Tobin’s Q, log of total assets (TAssets), closely-heldshares (CloseShares), sales growth, as well as the country’s gross domestic product per capita (GDPC).All the variables are defined in Appendix Table A.3. NObs is the number of customer-supplier pairobservations. The regressions also include intercepts and combinations of different customer-supplier,industry, country, and year fixed effects (FE), and robust standard errors reported in parentheses areclustered at the customer-supplier-pair level. *, **, *** are significance levels denoted at the 10%, 5%and 1%, levels, respectively.
Variable (1) (2)
CSRC Score x (CSRC−CSRS) 0.016***(0.001)
CSRC−CSRS -0.807***(0.458)
CSRC Score x I(CSRC >CSRS) 0.121***(0.916)
CSRC Score 0.804*** 0.056***(0.0038) (0.006)
NObs 48,283 48,283Controls Yes YesYear FE Yes YesSupplier Industry x Yes Yes
Customer Industry FESupplier Country x Yes Yes
Customer Country FE
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