The Consequences of
Mandatory Corporate
Sustainability Reporting:
Evidence from Four Countries
Ioannis Ioannou
George Serafeim
Working Paper
11-100
August 20, 2014
Copyright © 0211, 2012, 2014 by Ioannis Ioannou and George Serafeim
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the copyright holder. Copies of working
papers are available from the author.
The Consequences of Mandatory Corporate Sustainability Reporting:
Evidence from Four Countries
Ioannis Ioannou and George Serafeim
Abstract
We examine the effect of mandatory sustainability reporting on corporate disclosure practices. Specifically,
we examine regulations mandating the disclosure of environmental, social, and governance information in
China, Denmark, Malaysia, and South Africa using differences-in-differences estimation with propensity
score matched samples. We find significant heterogeneity in corporate disclosure responses across those
four countries. Relative to propensity score matched control firms, treated firms in China and South Africa
increased disclosure significantly. We also find increased propensity to receive assurance to increase
disclosure credibility in the case of South Africa, and increased propensity to adopt reporting guidelines to
increase disclosure comparability in both China and South Africa. In contrast, treated firms in Denmark
and Malaysia did not increase disclosure. Danish firms responded by embedding environmental and social
factors in their supply chain management, and by signing on the United Nations Global Compact while
Malaysian firms adopted reporting guidelines. We do not find any evidence that the disclosure regulations
adversely affected shareholders. Instrumental variables regressions suggest that increases in disclosure
driven by the regulation are associated with increases in firm value. Our results highlight the role of local
context and institutional differences in how firms in different countries respond to reporting regulations.
Keywords: sustainability reporting, mandatory reporting, corporate sustainability, corporate social
responsibility

Ioannou is an Assistant Professor of Strategic and International Management at London Business School. Email:
iioannou@london.edu. Serafeim is an Associate Professor of Business Administration at Harvard Business School. Email:
gserafeim@hbs.edu. We are grateful to Bob Eccles, Paul Healy, Mike Krzus, Costas Markides, and seminar participants in the
Business and Environment Initiative at the Harvard Business School, Boston University, Academy of Management, and at London
Business School for helpful comments. All remaining errors are our own.
1
I.
Introduction
During the last two decades, reporting of sustainability information has become widespread. While less
than 100 firms reported such information twenty years ago, by 2013 more than 6,000 companies around the
world were issuing sustainability reports. Governments and stock exchanges have promoted sustainability
reporting by adopting regulations or listing requirements that often mandate this form of disclosure. In this
paper, we focus on understanding the effect of such mandatory disclosure regulations on firms’ nonfinancial
disclosure practices and organizational processes.1
An increasing number of regulations are emerging across countries, mandating the disclosure of
environmental, social, and governance data. We collect data for four countries, China, Denmark, Malaysia,
and South Africa that mandated such disclosures before 2011; as a result, we are able to collect data both
before as well as after the enforcement of the regulation. In contrast to previous studies that have examined
the effect of disclosure regulations on stock market variables, which are typically assumed to be reflective
of changes in the underlying disclosure (Daske et al. 2008), we model actual disclosure changes. Therefore,
our setting enables us to test the first order and direct effect of the disclosure regulation.
As with all regulatory shocks that intend to affect certain organizational processes for firms that are
covered by the regulation, the main challenge is to separate the effect of the regulation from other
confounding effects. To do so, we implement propensity score matching procedures to construct control
groups for the treated firms. Specifically, in the year immediately prior to the enforcement of the regulation,
we match on firm size, profitability, Tobin’s Q, pre-existing environmental, social, and governance (ESG)
disclosure, financial leverage, and industry membership, to construct a more comparable group of control
firms. This is especially important for our setting because ESG disclosure has significantly and voluntarily
increased over the past ten years. Therefore, to observe increased disclosure by treated firms over time does
not constitute evidence that the disclosure regulation had any impact. Moreover, because firms in different
The terms “sustainability”, “environmental, social and governance” (ESG), “non-financial” or “corporate social responsibility”
(CSR) reporting are all been used interchangeably, to describe reports with different degrees of focus on environmental, social or
corporate governance issues.
1
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countries might have been affected either by voluntary disclosure initiatives, or major environmental or
social crises that put pressure on firms to disclose more, or even disclosure regulations specifically around
climate change, we present results using control firms from a set of multiple countries. Specifically, we
utilize firms from the US, Japan, India, Australia, and the UK as controls and we separately report our
empirical results for each group.
We find that firms in China and South Africa increase ESG disclosure significantly after the regulation
relative to control firms. Moreover, they significantly increase the probability of getting assurance on their
ESG disclosures (result is significant only for South Africa) and they are significantly more likely to adopt
the set of reporting guidelines of the Global Reporting Initiative (GRI), which represents the most widely
adopted set of reporting guidelines for nonfinancial information. Therefore, we conclude that firms in these
countries not only increase disclosure following the regulatory change but also, they seek to improve the
reliability and comparability of these disclosures.
In contrast, we do not find any increase in disclosure by firms in Denmark or Malaysia relative to the
control groups. Instead, we present evidence that firms in these countries responded differently. Danish
firms signed on the United Nations Global Compact (UNGC) given that complying with the
Communication of Progress in the UNGC was explicitly stated in the regulation and was regarded as a
sufficient action that would satisfy its disclosure requirements. Moreover, we find that Danish firms
emphasize the incorporation of social and environmental criteria in their supply chain management.
Because in Malaysia corporate social responsibility (CSR) is still understood as a set of philanthropic
activities (e.g. community giving), we examine whether Malaysian firms increased disclosure around
community spending. We find directionally consistent but insignificant results. Malaysian firms do
however exhibit an increased propensity to adopt GRI guidelines after the regulation.
Both Danish and South African firms exhibited relatively high ESG disclosure even before the
regulation. We speculate that the subsequent differential response could be attributed to South Africa
mandating integrated rather than sustainability reporting and that for firms with already high ESG
disclosure, integration with financial data might be necessary for increasing disclosure even further. On the
3
other hand, both Chinese and Malaysian firms have almost nonexistent ESG disclosure prior to the
regulation. However, while disclosure of ESG information was understood and framed in economic terms
in China, it was understood in the context of philanthropy in Malaysia, therefore generating very different
responses by firms across these two countries.
We also estimate instrumental-variables regressions for firms in China and South Africa to understand
the financial implications from the increase in disclosure. In the first stage, we estimate the effect of the
disclosure regulation on ESG disclosure and, in the second stage, we examine the effect of the predicted
ESG disclosure on Tobin’s Q. We find that Tobin’s Q exhibits a positive and significant relation with the
predicted component of the ESG disclosure. The results are highly significant for China and moderately
significant for South Africa. These results suggest that the effect of disclosure regulation has been valueenhancing rather than value-destroying for the firms in our sample.
The results of this paper contribute to the academic literature on the effects of disclosure regulation
(Armstrong et al. 2008; Daske et al. 2008; Joos and Leung 2013). However, in contrast to previous studies
that examine the effect of financial disclosure regulation, here we study a fundamentally different reporting
framework and a different type of information disclosure. In the sustainability setting, the target audience
for the mandated disclosures is not strictly confined to a firm’s investor base, as is typically the case with
financial reporting. Rather, by issuing a sustainability report, firms target a wide and diverse range of nonshareholding stakeholders in addition to shareholders; as a result, we suggest that a different set of
motivations is driving the decision to proceed with such disclosures compared to the set of motivations
driving traditional financial reporting. In addition, this paper contributes to an emerging literature that seeks
to understand the characteristics and consequences of nonfinancial disclosure (Simnett, Vanstraelen, and
Chua 2009; Dhaliwal et al. 2011; Cheng, Ioannou and Serafeim 2014). Our results also contribute to a
literature that examines firm noncompliance with required disclosures relating to executive compensation,
internal control weaknesses, legal liabilities and environmental liabilities (Rice and Weber 2012; Barth et
al. 1997; Robinson et al. 2011). Importantly, our findings have implications for regulators and policy
makers who have already mandated or are considering mandating sustainability or integrated reporting. Our
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findings suggest that the way and extent to which firms might respond to disclosure regulation critically
depends on the local context as well as the in-depth understanding of the motivations for ESG disclosure.
The remainder of the paper is organized as follows. Section II briefly reviews the history of
sustainability reporting to date. Section III presents the data sources and sample. Section IV discusses the
research design. Section V presents our findings and section VI concludes.
II.
Historical Background and Disclosure Regulations
Both in the US and in Europe during the 1960s and 1970s, sustainability reporting was driven by a renewed
awareness of responsibilities that remained unfulfilled by governmental institutions, and some that were
directly attributable to business organizations. Early attempts with social reporting, primarily in the
Netherlands and France, paved the way for the introduction of environmental reports in countries such as
Germany, Austria, and Switzerland. During the 1980s, ethical investment funds in the UK and the US
implemented an investment approach – broadly known as “negative screening” – excluding firms from their
investment universe based on the firms’ social and ethical performance. Originally based on religious
principles, investment decisions by such funds excluded firms operating in “sin” industries like alcohol and
tobacco. Towards the end of the 1980s and following the 1989 Exxon Valdez disaster, the US-based
Coalition for Environmentally Responsible Economies (CERES) developed The “CERES/Valdez
Principles” on behalf of the Social Investment Forum (SIF), introducing a set of environmental reporting
guidelines. In 1997, CERES and the United Nations Environment Program (UNEP) launched the Global
Reporting Initiative (GRI) to develop reporting guidelines for the “triple bottom line”: accounting for
economic, as well as environmental and social performance. The goal was to establish sustainability
reporting at par with financial reporting in terms of rigor. Not surprisingly, the 1990s witnessed an explosive
growth in voluntary corporate sustainability reports.
In recent years, growing social (e.g., poverty, deteriorating social equality, and corruption) and
environmental (e.g., climate change, water usage, and waste) global challenges have generated pressures
on companies to adopt a more systematic treatment of sustainability reporting by disclosing how they are
5
utilizing, developing (or depleting) and, more generally, affecting human capital and natural resources.
Moreover, as a result of several high-profile corporate scandals and the recent global financial crisis, there
has been a general feeling of distrust regarding companies’ ability to self-regulate (Edelman Trust
Barometer, 2009) and a belief that current company disclosures provide information mostly about past
performance rather than the future prospects of the company (e.g. Kaplan and Norton, 1992; Simnett,
Vanstraelen and Chua, 2009). Meanwhile, investors and information intermediaries in capital markets, such
as sell-side analysts, increasingly integrate ESG data in their valuation models, creating demand for
sustainability reporting (Ioannou and Serafeim, 2014). As a result, an increasing number of countries around
the world are mandating the disclosure of ESG information. In the next section, we present the cases of four
countries that adopted such regulations between 2007 and 2010. We proceed with our discussion by
grouping together countries with high versus low levels of ESG disclosure before the regulation.
The Case of Denmark and South Africa
Both Denmark and South Africa are countries in which ESG reporting has been widespread before the
regulation, at least among the large firms in the economy. For example, in Denmark the Center for Quality
in Business Regulation estimated that approximately 1,030 out of the 1,100 companies affected by the
regulation were already working on social responsibility issues. Both countries are characterized by
relatively high quality corporate reporting (Leuz, Nanda and Wycosci 2003). Corporate governance and
protection of outside investor rights are considered world-class in South Africa, largely due to the influence
of British common law that was transferred through colonization. Corporate governance is also regarded as
reasonably strong in Denmark, with an exceptionally high quality legal enforcement regime. However,
while Denmark was not facing any significant social or environmental problems, South Africa had to tackle
high levels of corruption, poverty, social inequality, and widespread HIV infection among its population.
In Denmark, the Minister for Economic and Business Affairs introduced an Act that amended the
Danish Financial Statements Act, in October of 2008. Large companies, meaning businesses that satisfy
two out of the three criteria of a) total assets more than DKK 143 million, b) net revenues of DKK 286
6
million, and c) an average number of full-time employees of 250, were required to supplement their
management’s review with a report on social responsibility. Corporate social responsibility was defined in
the report as “voluntarily include considerations for human rights, societal, environmental and climate
conditions as well as combatting corruption in their business strategy and corporate activities.” It was not
therefore mandated that companies adopt or implement such policies; however, if companies did not have
such policies they were required to disclose this fact in their management’s review. Because many of these
policies were predominantly relevant to Danish companies conducting business abroad – due to the absence
of significant environmental and social challenges domestically – emphasis was given on supply chain
relations. Moreover, the amendment put emphasis on the United Nations Global Compact (for nonfinancial
companies) and the United Nations Principles for Responsible Investment (for financial companies). It
stated that exemptions from disclosure requirements would be granted to companies that have acceded to
the principles of these two organizations and published a report on their progress as part of their accession.
The amendment entered into force and applied for the financial years commencing on 1 st of January 2009
or later.
In South Africa, the Johannesburg Stock Exchange (JSE) mandated the disclosure of sustainability
information starting in the 2010 financial year and subsequently mandated the disclosure of integrated
reporting starting in 2011, through the issuance of the King III Report on Corporate Governance in 2009.
King III was preceded by the issuance of the King I Report on Corporate Governance in 1994, and by the
King II Report on Corporate Governance in 2002. While the two earlier King reports did not specify
mandatory requirements for companies, their guidelines were selectively or holistically adopted by the JSE
as listing requirements (Eccles, Serafeim, and Armbrester 2012). Importantly, although both King I and
King II discussed sustainability issues as part of corporate management, King III emphasized to a great
extent the ideas of leadership, sustainability, and corporate citizenship.
In contrast to Denmark’s Act, – that required disclosure of ESG issues in a supplementary and nonintegrated way – King III stated that reporting on sustainability issues was to be interwoven with financial
reporting (Eccles, Serafeim, and Armbrester 2012). Therefore, the integrated report would describe the
7
value creation process inside the organization, critically putting its economic performance into context. In
doing so, companies would have to discuss the environment in which they operated as well as their impact
on stakeholders, and the strategies for mitigating any potentially negative impacts on society. In addition,
King III stated that the sustainability elements in an integrated report should be independently assured,
although it recognized that such information is more complex and more difficult to audit. Finally, King III
emphasized that integrated reporting was not only an end-of-year disclosure practice but it was more about
encouraging or even pressuring companies to integrate ESG factors into their operations throughout the
year. Consequently, in South Africa, integrated reporting required a larger investment in terms of funds,
human capital, and organizational processes commitment. The JSE made integrated reporting mandatory
for all listed companies on an “apply or explain” basis allowing companies that did not issue an integrated
report to explain why this was the case. Similarly to a company in Denmark therefore, a company in South
Africa could simply explain why they would not make any ESG disclosures. While in both countries
companies were mandated to disclose the policies that they have in relation to a series of ESG issues as
well as to report on the actions that they take to achieve the objectives of their policies, no specific
guidelines were provided or standards were set to require disclosure along a specific set of metrics.
The Case of China and Malaysia
Companies in China as well as companies in Malaysia had very low levels of ESG reporting before the
regulation. While both countries represent emerging economies and both have experienced impressive
economic growth in the past few decades, they also have significant differences. Malaysia is a more open
economy with less government intervention compared to China. Both the quality of the corporate
governance structures and corporate reporting are considered higher compared to China. On the other hand,
both social as well as environmental challenges are more severe in China due to increasing levels of social
inequality, concerns about human rights violations, and acute environmental pollution.
In China, the Shanghai Stock Exchange (SHSE) and the Shenzhen Stock Exchange (SZSE)
mandated certain listed firms to disclose ESG information starting for financial year end in December 2008.
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SHSE mandated reporting of ESG information for firms included in the SHSE Corporate Governance
Index, firms with overseas listed shares, and financial firms. SZSE mandated ESG reporting for firms
included in the Shenzhen 100 Index. In fact, in 2006 the Chinese government revised Article 5 of the
Company Law requiring companies to “undertake social responsibility” in the course of business. In
January of 2008, the State-Owned Assets Supervision and Administration Commission of the State Council
released the Guide Opinion on the Social Responsibility Implementation for the State-Owned Enterprises
controlled by the government. Both the reporting regulations and the previous government actions
emphasized the economic benefits of Corporate Social Responsibility (CSR), how CSR could be a driver
for harmonious growth, and how it could help organizational creativity, reputation, and employee
engagement. Rather than emphasizing the moral obligations of companies, government officials focused
on economic incentives and the positive effects of CSR initiatives.
In Malaysia, the stock exchange Bursa Malaysia made ESG disclosure a listing requirement for all
listed firms starting in 31st of December 2007. This followed the Malaysian Prime Minister’s speech
announcing the requirement for listed companies to report on their CSR initiatives. Specifically, there is an
obligation for firms to disclose a description of their CSR activities or, if they have none, to issue a statement
publicly acknowledging the absence of such activities. It is important to note that CSR activities are even
currently perceived and implemented by the majority of Malaysian companies as philanthropic initiatives
and are rarely, if ever, a core component of a company’s business model or understood and integrated in a
strategic manner. Similar to the regulation adopted for Danish and South African companies, no specific
guidelines were provided to require disclosure on specific metrics in China or Malaysia. A very important
difference however, is that the Chinese and Malaysian disclosure regulations did not discuss the role of
assurance and the responsibilities of the auditors in the sustainability context.
III.
Data and Sample
9
We collect data on reporting practices and other organizational processes around ESG issues from
Bloomberg. Bloomberg is by a large margin the most widely used data provider for stock market, financial,
and other corporate data. ESG data originate from company-sourced fillings including sustainability or CSR
reports, annual reports, company websites, and a proprietary Bloomberg survey that requests corporate data
directly from the companies. In contrast to other data providers, Bloomberg does not estimate or derive
from mathematical models any of the ESG data. Rather, all data points are transparent and can be traced
back to their source in a company document. Importantly for our study, Bloomberg has the widest coverage
of all ESG datasets, assessing the ESG disclosure of more than 10,000 companies around the world. By
comparison, MSCI covers less than 6,000 companies and Thomson Reuters fewer than 4,000. Due to this
rather limited overall coverage, these other datasets have a much smaller number of companies covered in
the countries that we are interested in for our purposes.
We begin our data collection process by downloading the members of the Bloomberg product
index, which includes 10,472 companies from around the world. This is a set of companies for which
Bloomberg analysts have collected ESG data since 2005; therefore our data span the period 2005-2012. For
all these companies we also collect accounting and stock market data from Bloomberg. All data are
measured in US dollars.
Bloomberg calculates an ESG Disclosure score and its three sub-scores (E, S and G) to quantify a
company’s transparency in reporting ESG information. Environmental data relate to emissions, water,
waste, energy and operational policies around environmental impact. Social data relate primarily to
employees, products and impact on communities. Governance data relate to board structure and function,
firm’s political involvement, and executive compensation. This score is based on 100 out of 219 raw data
points that Bloomberg collects, and is weighted to emphasize the most commonly disclosed fields. The
weighted score is normalized to range from zero, for companies that do not disclose any ESG data, to 100
for those who disclose every data point collected. Bloomberg accounts for industry-specific disclosures by
normalizing the final score based only on a selected set of fields applicable to the industry type; for example,
10
“Total Power Generated” is counted into the disclosure score of utility companies only. We use these
disclosure scores as our main dependent variables of interest.
We identify treated firms in each country through the criteria that each regulation stated and
required for a company to be covered. Within the group of firms with available ESG data in Bloomberg,
we identify 144 Chinese, 29 Danish, 43 Malaysian, and 101 South African firms. By construction, these
firms are the largest firms in each economy and collectively cover most of the market capitalization in the
stock exchange that are listed. Indicatively, as of the end of 2012, the aggregate market capitalization of the
treated Chinese firms was $1,880 billion (50% of total market capitalization of all Chinese stock markets
and 90% of total market capitalization of all treated Chinese firms). The respective numbers for treated
Danish, Malaysian and South African firms was $220 (90% of total market capitalization of Danish stock
exchange), $250 (60% of total market capitalization of Bursa Malaysia), and $460 billion (70% of total
market capitalization of JSE).
IV.
Research Design
To identify the effect of the disclosure regulation on firms’ reporting practices, we use a differences-indifferences approach whereby we track a reporting practice before and after the regulation both for the
treated and the control firms. Moreover, we use propensity score matching to ensure that the treated and
the control group are as comparable as possible on a number of observable characteristics. Specifically, we
match in the year before the regulation on firm size (natural logarithm of total sales), profitability (ROA),
leverage (total liabilities over total assets), market expectations about growth opportunities (Tobin’s Q), the
level of the ESG disclosure, and industry membership (financial vs. non-financial sectors). We select for
each firm the closest neighbor based on the following model:
Treatmenti = α + β1 Sizei + β2 ROAi + β3 Leveragei+ β4 Tobin’s Qi+ β5 ESGi+ µj
(1)
Treatment takes the value of 1 if firm i is covered by the regulation or otherwise it takes the value of zero.
µj is an indicator variable that takes the value of one for industry j. Each logit regression is estimated
separately for pairs of treated and control countries. Because firms in different countries might have been
11
affected by a) various voluntary disclosure initiatives, b) major environmental or social crises that put
pressure on firms to disclosure more, or c) disclosure regulations specifically about climate change issues,
we present results using control firms from a set of multiple countries. In particular, we utilize firms from
the US, Japan, India, Australia, and the UK as controls and we separately report the empirical findings for
each control group. There are 2,663, 1,863, 683, 428, and 394 potential control firms from the US, Japan,
India, Australia, and the UK, respectively. Within this set of countries probably the ‘cleanest’ control group
is US firms, which for the years included in our sample have not been subject to mandated disclosure of
any ESG metrics. In contrast, disclosures of firms in the UK might have been affected by the 2006
Companies Act broad requirements to identify and disclose environmental and social factors that could
affect the business in the director’s report; firms in Japan have increased disclosure of carbon emissions
and other environmental information due to the 2005 Mandatory GHG accounting system; the largest firms
in India were mandated in 2011 to start producing sustainability reports the following year. Overall, we
choose firms from these five countries because they have the largest number of firms covered by the
Bloomberg ESG database enabling us to match all of the treated firms in the countries of interest.
Moreover, we present results using “local” firms as a control group. For China we have a large pool
of Chinese control firms. For Denmark, we define as a local control group firms from the other
Scandinavian countries (i.e. Norway, Finland and Sweden). For Malaysia, we use companies from
Singapore, Thailand and Indonesia as controls. Finally, for South Africa we use UK companies as local
controls because of the tight business connections between the two countries and the similarities in
corporate governance regimes. However, we note that the local control group has some drawbacks
especially in the case of China where control firms are domiciled in the same country as the treated firms.
Past research has shown that corporate social responsibility practices, such as reporting on those practices,
are implemented because of institutional pressures and peer effects, where competitors mimic what other
companies are doing (Misani 2010). Therefore, even if certain Chinese companies are not covered by the
regulation, they might still increase disclosure if the Chinese firms that are covered by the regulation (i.e.
their competitors or companies in the same peer group) increase their disclosure. This is because customers,
12
employees, investors, and local communities now form expectations from companies to be transparent
about its environmental and social impact.
Table 1 presents summary statistics pertaining to the matching algorithm. Specifically, we present
the average level of each covariate of interest for the treated firms in the year of matching (i.e. the year
prior to the first year of the regulation) and moreover, we tabulate the average difference in means between
treated and a) unmatched (Δ No Match) and b) matched control (Δ Match) group as well as the corresponding
t-statistic. We match treated with control firms in the year prior to regulation as follows: 2009 for South
Africa, 2008 for Denmark, 2007 for China and 2006 for Malaysia. Because all Malaysian firms have zero
ESG disclosure prior to the regulation, we do not use this variable in the logit model for this country.
Instead, we restrict all candidate control firms to be drawn from a pool of firms that also have zero ESG
disclosure in 2006. Chinese firms have low ESG disclosure in the year of matching while both South
African and Danish firms have considerably higher levels of disclosure. Indicatively, we note that among
all firms in the Bloomberg sample, the disclosure levels of the treated South African and Danish firms rank
at the top quartile in their respective year of matching, given that the 75 percentile of all companies has an
ESG score of approximately 21.
The statistics of Table 1 suggest that the matching procedure works reasonably well with mean
differences for many covariates being significant across the treated and unmatched control group but
insignificant between the treated and the matched control group. In cases where matched and treated group
are still significantly different, the differences are smaller suggesting that the matching process increases
the similarity on the observable characteristics between the two groups actually used in the empirical
analysis. To identify the effect of the disclosure regulation on ESG reporting we estimate the following
model through ordinary least squares:
Reportingit = αi + µt + γjt + β1 Treatmenti x Mandatet + β2 Sizeit + β3 Leveragei
(2)
Reportingit is the disclosure or other reporting practice for firm i in year t. In particular, we explore the
impact of regulation on a) a composite index of ESG disclosure as well as individual indices for b)
environmental, c) social and d) governance disclosure. Mandatet is an indicator variable capturing whether
13
in year t the regulation mandates disclosure of ESG information and zero otherwise. From equation (1),
Treatmenti takes the value of 1 if firm i is covered by the regulation and zero otherwise. To mitigate the
concern of correlated omitted variables, we include firm fixed effects (αi), year fixed effects (µt) and yearindustry paired fixed effects (γjt, where we categorize all industries in our sample into two groups: financial
and non-financial industries). The average treatment effect is the estimated β 1 on the interaction term
Treatmenti*Mandatet, which captures the change in disclosure for treated firms after the regulation relative
to the change for control firms. A positive coefficient on β1 is therefore consistent with an increase in the
disclosure variable following the regulation. We control for key time-varying firm characteristics that are
likely to be correlated with disclosure levels: firm size and leverage.2 Finally, we use robust standard errors
clustered at the firm and year level throughout our analysis to mitigate serial correlation within a firm across
years and across firms within a year.
V.
Results
Disclosure Changes
Table 2 presents the estimated coefficient β1 and its statistical significance based on the regression analysis.
Specifically, each column of table 2 estimates equation (2) using a different set of control firms each time
(i.e. local firms and firms from the US, Japan, India, Australia, and the UK). For each of the four countries
of interest, we report results for four distinct measures of disclosure as the dependent variables: the
composite ESG disclosure score and its three components E, S, and G.
The findings show that the regulation has a highly significant positive effect on treated firms in
South Africa suggesting that these firms increased their level of ESG disclosure following the regulation
compared to the control group. In China, we also find a highly significant increase in disclosure by treated
firms compared to control firms from any of the other countries except China itself. We speculate that this
2
We also included other time-varying firm characteristics such as profitability, ownership concentration, and stock
return volatility but none of the variables was significant. All results reported here remain unchanged.
14
lack of significance when the control group includes other Chinese firms may potentially be attributed to
spillover effects of regulation. Past literature has shown that sustainability practices and reporting diffuse
because of institutional pressures and mimetic behavior (Misani 2010). In this setting, it appears as if when
a competitor issues a sustainability report, other companies follow, irrespective of whether they are directly
affected by the regulation. The empirical findings are consistent with such spillover effects. In fact, the
Chinese control firms in our sample increased ESG disclosure significantly after the regulation: we
propensity score match these Chinese control firms with US, Japanese, Indian, Australian, and UK firms
and we find that the Chinese control firms increase disclosure significantly relative to the control groups
after the regulation.
We also note that while the effect is highly significant for all disclosure measures in South Africa,
for China the most consistently significant effect is on social disclosures (although environmental and
governance disclosure obtain significance in several of the specifications as well). The results of table 2
also indicate that regulation in Denmark and Malaysia did not significantly affect disclosures by treated
firms relative to any control group: across almost all specifications the estimated coefficient β1 does not
obtain significance. However, these findings do not necessarily suggest that the regulation had no impact
at all. In fact, treated firms might have responded in different ways; an issue that we empirically explore in
subsequent analysis.
Interestingly, we note that firms in both South Africa as well as Denmark exhibit relatively high
ESG disclosure even before the regulation. Accordingly, we speculate that their subsequent differential
response to regulation (i.e. significant increase in South Africa and no effect in Denmark) could be
attributed to the fact that the South African regulation actually mandated integrated rather than
sustainability reporting. For countries with already high ESG disclosure, integration with financial data
(rather than separate disclosure) might be necessary for subsequently increasing disclosure even further.
We explore this explanation in two ways. First, while sustainability reporting was a listing requirement on
an “apply or explain” basis in 2010, integrated reporting was for financial year ending on or after March of
2011. Therefore, the disclosure regulation in 2010 for South Africa is similar with the disclosure regulation
15
in Denmark. We find that disclosure increased significantly in 2011 and 2012 but not in 2010 consistent
with integrated reporting being a more effective mechanism to increase disclosure in an environment with
already high levels of ESG disclosure. Second, we identify 29 firms that had voluntarily issued integrated
reports before 2011. These firms should be affected less by the mandate to issue integrated reports since
they are already practicing integrated reporting. Indeed, we find that these firms increased disclosure after
the regulation significantly less than the rest of the South African firms, consistent with integrated reporting
as opposed to sustainability reporting being the catalyst for the increased disclosure in South Africa.
Table 3 is similar to table 2 in that it presents the estimated coefficient β1 based on estimations of
equation (2) while the key difference is that we distinguish between treated firms with high versus low
disclosure scores (i.e. above and below the median level) in the year of matching. Accordingly, the models
in the first panel for each country are estimated based on a subsample of high disclosure treated firms and
their corresponding control group, whereas the models in the second panel are estimated on a subsample of
low disclosure firms and their control firms. Since all Malaysian firms have zero ESG disclosure prior to
the regulation, we cannot perform this analysis for Malaysia.
Our findings reveal significant differences between high and low disclosure firms with regards to
responding to the regulation for firms in South Africa and China. In South Africa, firms that had higher
levels of environmental or social disclosure increased their environmental or social disclosure by more
compared to firms with low levels of disclosure. In contrast, firms that had higher levels of governance
disclosure increased their governance disclosure by less. This asymmetry is consistent with the disclosure
regulation generating differential responses across different types of information, even within the
nonfinancial information category. Firms that were laggards in terms of governance disclosure increased
their disclosure significantly more and, after the regulation, reached levels of governance disclosure similar
to the leaders. In contrast, the leaders in environmental and social disclosure further widened the gap after
the disclosure regulation. This is consistent with governance disclosures being less costly to obtain and
make available compared to environmental or social information. For example, while board or
compensation level information is more readily available, information around environmental impact, or
16
employee metrics are more difficult to obtain, aggregate and release. A similar asymmetry is observed in
the case of China where firms with low governance disclosure increased governance disclosure relatively
more, while firms with high environmental or social disclosure increased environmental or social disclosure
more.
Reliability and Comparability
In table 4, we explore whether in addition to having an impact on disclosure practices (or perhaps as a
substitute for), the regulation also has an impact on firms’ propensity to seek to improve the reliability and
comparability of their ESG disclosures. Specifically, in the models of table 4 we replace the dependent
variables on disclosure with two new variables capturing a) whether firms receive assurance on their ESG
data and b) whether they adopt the reporting guidelines of the GRI, which represents the most widely
adopted set of reporting guidelines for nonfinancial information. Accounting information that is assured is
considered to be more reliable because of lower noise and/or lower bias. Assurance can decrease both the
probability of unintentional errors in calculations (by double-checking the numbers and underlying
assumptions) and the likelihood of intentional misreporting because of managerial incentives.
We estimate linear probability models because of the large number of fixed effects in our models.
We find that treated firms in South Africa are significantly more likely to receive assurance or adopt the
GRI guidelines compared to control groups of firms from any other country. Moreover, we find that treated
Chinese firms are significantly more likely to adopt the reporting guidelines of the GRI following the
regulation. These results therefore, seem to suggest that in South Africa and China, firms not only increased
disclosure but also sought to increase its reliability and comparability. On the other hand, and perhaps
consistent with the finding of no changes in disclosure, we find no evidence that firms in Denmark were
more likely to seek assurance or adopt the GRI following the regulation. We do find however, some
evidence that Malaysian firms are more probable to adopt GRI guidelines after the regulation. However,
adoption of GRI guidelines was already relatively higher in Denmark with 15% adopting them in the year
before the regulation and nonexistent in Malaysia before the regulation.
17
What happened in Denmark and Malaysia?
As we already mentioned the fact that Danish and Malaysian firms did not increase significantly their levels
of disclosure or supplemented disclosure with assurance or by following the GRI guidelines (in the case of
Denmark), does not necessarily imply that regulation was entirely ineffective. Instead, it is conceivable that
due to idiosyncrasies of the regulation, or the local context itself, firms in these countries responded
differently. For example, in Denmark, the regulation explicitly stated that compliance with the
Communication of Progress in the UNGC was considered as sufficient action for satisfying the regulation’s
disclosure requirements. Moreover, and as discussed earlier, many of the sustainability policies explicitly
referred to in the regulation were predominantly relevant to Danish companies conducting business abroad
– due to the absence of significant environmental and social challenges domestically. Accordingly, Danish
firms may have put more emphasis on supply chain relations and/or becoming signatories of the UN Global
Compact, rather than increasing disclosure levels or assurance.
We directly explore whether these alternative responses indeed materialized in Denmark.
Specifically, in table 5, we report the estimated coefficient β1 based on estimations of equation (2) with
three alternative dependent variables that capture a) whether a firm is a signatory of the UN Global
Compact, b) whether a firm is using environmental criteria in supply chain management and c) whether a
firm is using social criteria in supply chain management. All three data items are sourced from Bloomberg.
We find a highly significant and positive effect of the regulation on the propensity of Danish firms to
become signatories of the UN Global Compact, as expected. Also, we find that Danish firms were
significantly more likely to adopt social criteria in supply chain management compared to most of the
control groups and moderately more likely to adopt environmental criteria.
Similarly, we suggest that regulation may have generated different responses by firms in Malaysia;
a country in which CSR activities are perceived and implemented by the majority of Malaysian companies
as philanthropic initiatives and rarely constitute a core component of a company’s business model or are
18
understood and integrated in a strategic manner (UNICEF 2009).3 In such a context, it is likely that affected
Malaysian firms responded to the regulation by increasing their philanthropic donations. In table 6, we
present estimation results for models in which the dependent variable is an indicator variable that takes the
value of one if the firm discloses the amount of funds spent in community projects. However, we are unable
to confirm our prediction: we find no significant results across all of the specifications. This might be
because Malaysian firms might disclose nonmonetary measures of their community involvement (housing
services, education, hospitals etc.), which we are unable to measure systematically.
Financial Effects
Given all of the above findings, and in order to gain a more in-depth understanding of how mandatory
corporate sustainability reporting is affecting companies, we also estimate instrumental variable (IV)
specifications to trace the financial implications from increased disclosure. The results of table 1 for ESG
disclosure in South Africa and China essentially constitute the first stage of our IV approach and confirm
that Treatmenti*Mandatet is a valid instrument (i.e. highly significant). Accordingly, in the second stage,
we instrument for ESG disclosure using this interaction term and report results for the impact of increased
ESG disclosure on Tobin’s Q, the dependent variable of interest. Similarly for Denmark, based on the
results of table 5, we construct a composite index as the sum of UN Global Compact Signatory,
Environmental criteria in supply chain management and Social criteria in supply chain management. This
index ranges from zero to three. We instrument for this index using the interaction term
Treatmenti*Mandatet and in the second stage, we estimate the impact of this instrumented index on Tobin’s
Q. Because we are unable to find any effect of the regulation on Malaysian firms, we do not conduct the
analysis since the instrument is not relevant and as a result not valid.
We suggest that if the disclosure regulation forced firms to disclose proprietary information then
we would expect to find a negative effect on firm valuation. Moreover, a negative effect on valuation is
3
For more information about CSR in Malaysia see http://www.csr-weltweit.de/en/laenderprofile/profil/malaysia/.
19
expected if these disclosures were costly for the firm in terms of revealing bad news or direct costs in the
form of information systems, personnel, and management time. In general, if the disclosure regulation
forced firms to adopt organizational processes that are costly and with little benefit for the company then
the disclosure regulation would negatively affect the valuation of a firm (Eccles, Ioannou and Serafeim
2014). In contrast, we expect a positive effect on valuation if increased disclosure brought benefits either
in terms of improving a firm’s access to finance (Cheng, Ioannou and Serafeim 2014), or by forcing the
firm to a) discover new risks and opportunities thereby improving process efficiency that leads to cost
savings, b) manage better reputation risk thereby improving brand value, or c) identify market opportunities
and as a result growing sales (Eccles, Ioannou and Serafeim 2014). Consistent with this argument, we note
that as companies change their disclosure level they might also be changing other internal management
practices. For example, the disclosure regulation and/or other ESG regulations that become effective around
the same time might be forcing them to implement initiatives to reduce their carbon emissions, increase
employee engagement and reduce turnover, change suppliers, invest in product quality and safety
procedures etc. Therefore, our tests are capturing financial effects both from the increased level of
disclosure and any internal changes that the management is making.4
Table 7 presents the results of this analysis across the different control groups. We find significant
results for China and South Africa indicating an increase in Tobin’s Q for firms that increased disclosure
relative to the control group following the regulation. The only non-significant results are generated when
the control group includes Indian firms. We note that Indian firms are potentially the most problematic
control group, given that in 2011 it was announced that starting from 2012 the largest Indian firms would
have to issue sustainability reports. In other words, our findings suggest that by increasing disclosure for
treated firms and potentially affecting ESG management practices, the disclosure regulation generated longrun benefits for companies across these countries. We find no such effect for firms in Denmark using the
4
We also note that if some companies implement internal changes in response to the regulation but do not increase
disclosure then our results might be biased. However, it seems unlikely that a disclosure regulation would force firms
to change management practices and at the same time leave disclosure practices unaffected.
20
composite index of environmental and social supply chain management and signing on the UN Global
Compact.
Table 7 panel B shows results from IV regressions where the first stage is each sub-component of
the overall ESG disclosure index. We estimate these models to understand which of these subcomponents
may be driving the overall effect on Tobin’s Q. We run separate instrumental variable models for each
subcomponent. For South Africa we find that increased environmental, social and governance disclosures
due to the regulation are associated with increases in Tobin’s Q (except when the control group includes
Indian firms). The results are similar for China for all the specifications where the instrument in the first
stage is relevant.
VI.
Conclusion
In this paper we investigate the effect of disclosure regulations that mandate sustainability reporting on
firms’ disclosure practices and other organizational processes. We collect data for firms in China, Denmark,
Malaysia, and South Africa. We find that the disclosure regulations had different effects across the countries
in our sample. Firms in China and South Africa – countries characterized by severe social and
environmental challenges – increased disclosure significantly compared to control firms. Moreover, they
supplemented the increased disclosure with efforts to increase the comparability and credibility of the
disclosed information. In contrast, firms in Denmark and Malaysia – countries characterized by relatively
less severe social and environmental challenges – exhibited no increase in disclosure relative to control
firms. Danish firms signed on the United Nations Global Compact and concentrated on supply chain
management while Malaysian firms adopted reporting guidelines.
The economic effect of the disclosure regulations appear to be positive. Instrumenting the increases
in disclosure because of the regulation reveals a positive correlation between instrumented disclosure and
Tobin’s Q. The results are robust across several specifications both in China and South Africa. While the
regulation might have imposed costs on some firms, our estimates suggest that on average, the effect of the
regulation on companies has been value-enhancing rather than value-destroying.
21
Several opportunities exist for future research. First, an increasing number of countries are adopting
disclosure regulations similar to the ones considered here. Because firms respond differently across
countries, it is important to evaluate how companies respond differently across distinct contexts. Second,
more research is needed in order to understand how companies change resource allocations and investment
decisions as a response to changes in disclosure regulations. Finally, a fruitful avenue for future research is
investigating changes in the demand for non-financial information. While the disclosure regulations in some
cases increase the supply of such information, we still know little about how they affect the demand across
different stakeholders.
22
References
Barth, M. E., M.F. McNichols and G. P. Wilson. 1997. Factors influencing firms' disclosures about
environmental liabilities. Review of Accounting Studies 2: 35-64.
Cheng, Beiting, Ioannis Ioannou, and George Serafeim. "Corporate Social Responsibility and Access to
Finance." Strategic Management Journal 35, no. 1 (2014): 1–23.
Eccles, Robert G., Ioannis Ioannou, and George Serafeim. "The Impact of Corporate Sustainability on
Organizational Processes and Performance." Management Science (forthcoming).
Eccles, Robert G., George Serafeim, and Pippa Armbrester. "Integrated Reporting in South Africa."
Harvard Business School Case 413-038, September 2012.
Ioannou, Ioannis, and George Serafeim. "The Impact of Corporate Social Responsibility on Investment
Recommendations: Analysts' Perceptions and Shifting Institutional Logics." Strategic Management
Journal (forthcoming).
Philip P. M. Joos and Edith Leung. 2013. Investor Perceptions of Potential IFRS Adoption in the United
States. The Accounting Review: March 2013, Vol. 88, No. 2, pp. 577-609.
Misani, Nicola. 2010. The Convergence of Corporate Social Responsibility Practices (February 6, 2009).
Management Research Review, Vol. 33, No. 7, pp. 734-748.
Rice, S. C. and Weber, D. P. 2012. How Effective Is Internal Control Reporting under SOX 404?
Determinants of the (Non-)Disclosure of Existing Material Weaknesses. Journal of Accounting Research,
50: 811–843.
Simnett, R., A. Vanstraelen, and W.F. Chua. 2009. Assurance on Sustainability Reports: An International
Comparison, Accounting Review, 84(3): 937-967.
UNICEF. 2009. Corporate Social Responsibility Practices in Malaysia.
23
Table 1: Mean of covariates used in the propensity-score matching and differences between treated and a) unmatched and b) matched control
group
Treat
China
Denmark
Malaysia
ESG Disclosure
4.191
Sales
6.918
Leverage
0.571
ROA
8.727
Tobins Q
1.342
ESG
22.248
Sales
7.535
Leverage
0.610
ROA
4.777
Tobins Q
0.696
ESG
0.000
Sales
6.622
Leverage
0.566
Local
Δ No
Match
t-stat
-1.285
-2.289
-1.678
-7.696
-0.048
-2.196
-0.069
-0.052
-0.242
-4.590
-0.361
-0.110
-0.400
-1.051
-0.005
-0.117
-2.556
-1.008
-0.101
-1.550
0.506
1.108
0.179
0.781
0.024
0.632
US
Δ
Match
t-stat
-0.038
-0.050
-0.604
-3.188
-0.014
-0.610
-0.777
-0.950
-0.067
-0.067
-1.976
-0.487
-0.275
-0.533
-0.044
-0.725
-2.449
-0.668
-0.012
-0.119
0.000
-0.080
-0.291
0.000
-0.003
Δ No
Match
t-stat
1.725
2.102
-0.641
-3.322
-0.024
-1.106
-4.590
-3.914
-0.418
-10.316
-12.750
-6.047
-0.948
-2.381
-0.036
-0.739
-4.532
-1.570
-0.034
-0.460
1.845
1.682
-0.269
-0.786
-0.017
-0.444
Δ
Match
t-stat
0.341
0.421
0.370
1.824
0.014
0.496
-0.054
-0.045
-0.009
-0.145
1.263
0.300
0.592
1.230
-0.018
-0.298
1.604
0.505
0.098
1.001
0.000
0.598
1.801
0.007
0.150
24
Japan
Δ No
Δ
Match
Match
t-stat
t-stat
2.073
1.757
1.980
1.709
-0.540
0.455
-3.406
2.184
-0.030
0.074
-1.579
2.702
-4.080
-3.158
-5.544
-3.441
-0.649
-0.322
-17.491 -5.849
-7.598
-4.671
-2.642
-1.205
-0.620
-0.048
-2.215
-0.108
-0.068
0.043
-1.621
0.666
-1.795
-0.916
-1.806
-0.398
-0.240
-0.020
-4.630
-0.163
1.806
0.000
1.571
0.010
0.287
0.042
1.016
-0.018
-0.091
-0.522
-1.746
India
Δ No
Δ
Match
Match
t-stat
t-stat
-0.840
-0.442
-1.422
-0.489
-1.955
-0.258
-10.470 -1.285
0.004
0.057
0.213
2.217
-0.452
-0.399
-0.424
-0.315
-0.431
-0.194
-9.199
-3.300
-10.082 -2.341
-6.299
-0.626
-2.447
0.162
-6.610
0.393
-0.002
0.081
-0.050
1.387
2.767
1.208
1.626
0.518
0.037
-0.004
0.460
-0.038
1.223
0.000
1.844
-2.017
0.221
-6.939
0.698
0.051
-0.024
1.607
-0.476
Australia
Δ No
Δ
Match
Match
t-stat
t-stat
1.686
4.780
2.151
5.204
-1.995
-1.359
-8.760
-5.422
-0.089
-0.062
-4.001
-2.319
-3.392
-3.456
-2.294
-2.122
-0.292
-0.142
-5.895
-2.312
-7.826
2.817
-3.678
0.755
-2.548
-0.052
-4.878
-0.114
-0.167
-0.025
-3.289
-0.430
-4.694
1.566
-1.346
0.654
0.056
-0.005
0.589
-0.049
3.677
0.000
2.758
-2.187
-0.567
-5.068
-1.992
-0.112
-0.010
-2.675
-0.214
UK
Δ No
Match
t-stat
6.480
5.407
-0.920
-4.283
-0.015
-0.695
-0.794
-0.667
-0.330
-7.084
2.995
1.122
-0.519
-1.333
0.004
0.103
-0.277
-0.121
-0.014
-0.195
12.068
5.110
0.256
0.804
0.035
0.951
Δ
Match
t-stat
10.432
8.913
0.034
0.151
0.041
1.588
-0.597
-0.478
-0.294
-5.304
0.683
0.178
0.032
0.056
0.001
0.016
-1.137
-0.419
-0.139
-1.380
0.000
0.215
0.606
-0.001
-0.026
ROA
6.816
Tobins Q
0.719
ESG
22.553
Sales
South
Africa
Leverage
ROA
Tobins Q
7.124
0.588
7.830
0.702
0.843
0.683
0.089
1.410
2.513
0.880
0.553
0.074
0.902
0.499
-3.215
-1.493
0.201
3.124
-12.023
-1.910
-1.166
-0.030
-0.437
2.595
-3.153
-3.841
-0.066
-1.295
-5.455
-0.026
-0.017
0.068
0.788
-2.689
0.307
0.211
0.096
1.334
-8.397
3.309
1.622
0.138
1.400
-4.739
-5.566
-1.962
0.151
1.984
-6.197
2.218
1.015
0.026
0.300
-1.478
1.438
0.224
-9.212
0.908
-3.651
-1.154
-6.898
-2.155
-4.010
-0.652
-0.134
-0.387
-0.598
-0.099
-0.246
-0.284
-1.910
-0.158
-1.821
-0.280
-0.666
-1.624
-2.863
-0.374
-1.634
-1.242
-9.185
-0.768
-6.788
-1.147
0.014
-0.003
-0.028
0.025
-0.044
0.005
0.020
0.017
-0.134
-0.004
0.566
-0.082
-1.068
0.798
-1.817
0.148
0.879
0.537
-4.875
-0.134
-3.972
-0.379
-7.502
-1.277
-7.153
-1.439
-2.355
0.261
-8.767
1.044
-3.410
-0.271
-5.429
-0.905
-10.186
-1.244
-2.488
0.223
-4.615
0.667
0.013
0.032
0.056
0.027
-0.299
-0.007
-0.134
-0.002
-0.030
0.018
0.347
0.632
1.437
0.551
-10.790
-0.141
-3.461
-0.033
-0.629
0.347
0.049
0.028
0.194
2.972
This table tabulates the average level of each variable for the treated group that is included in a logit model as described by equation (1) in the text. The logit is estimated separately
for each treated-control country pair in the year before the first year of the regulation. Δ No Match is the difference in the means between the treated group and the potential control
group while Δ Match is the difference in the means between the treated group and the propensity-score matched control group based on propensities estimated from the logit model.
ESG is the ESG disclosure score from Bloomberg. Sales is the natural logarithm of firm sales. Leverage is total liabilities over total assets. ROA is net income plus net interest
expense over total assets. Tobin’s Q is the market value of a firm’s assets over total book value of assets. *,**,*** indicate significance at 10%, 5%, and 1% levels (two-tailed),
respectively.
25
-0.028
-0.018
0.005
0.071
Table 2: The impact of mandatory corporate sustainability reporting on ESG, Environmental, Social and Governance disclosure
South Africa (Local: UK)
ESG Disclosure
Environmental Disclosure
Social Disclosure
Governance Disclosure
China (Local: China)
ESG Disclosure
Environmental Disclosure
Social Disclosure
Governance Disclosure
Denmark (Local: Sweden/Norway/Finland)
ESG Disclosure
Environmental Disclosure
Social Disclosure
Governance Disclosure
Local
US
Japan
India
Australia
UK
11.55***
(1.187)
8.221***
(1.314)
13.07***
(1.794)
17.04***
(1.986)
10.50***
(1.338)
8.361***
(1.602)
14.42***
(1.941)
11.02***
(2.079)
8.831***
(1.276)
5.292***
(1.566)
15.27***
(1.798)
9.823***
(1.502)
9.644***
(1.435)
6.810***
(1.528)
13.76***
(2.043)
11.62***
(1.919)
11.40***
(1.264)
8.577***
(1.448)
11.20***
(1.925)
17.68***
(1.631)
11.55***
(1.187)
8.221***
(1.314)
13.07***
(1.794)
17.04***
(1.986)
1.160
(0.799)
0.953
(0.678)
1.444
(1.231)
1.422
(1.256)
6.273***
(0.918)
4.770***
(0.891)
13.58***
(1.211)
2.369
(1.662)
3.072**
(1.186)
-2.567*
(1.416)
11.04***
(1.392)
7.837***
(1.397)
3.987***
(1.037)
1.656
(1.108)
12.89***
(1.422)
0.104
(1.381)
5.214***
(0.776)
3.555***
(0.830)
9.241***
(1.202)
4.913***
(1.350)
3.903***
(1.123)
-0.317
(1.038)
6.430***
(1.478)
10.90***
(1.848)
-1.222
(2.626)
-1.019
(3.145)
0.244
(3.623)
-3.210
(3.107)
3.323
(2.278)
3.106
(2.757)
8.286***
(3.056)
-0.852
(3.276)
-2.103
(2.654)
-2.273
(3.234)
5.620
(3.399)
-9.514***
(2.724)
3.456
(2.874)
4.462
(2.979)
9.750***
(3.482)
-5.112
(3.748)
3.116
(2.526)
3.499
(2.945)
5.334
(3.352)
-0.0685
(2.879)
2.446
(2.542)
1.376
(2.637)
5.451*
(3.257)
1.444
(4.036)
26
Malaysia (Local: Singapore/Indonesia/Thailand)
ESG Disclosure
Environmental Disclosure
Social Disclosure
Governance Disclosure
1.473
(2.191)
-0.502
(1.787)
2.098
(2.762)
4.800
(3.637)
3.491
(2.333)
3.722**
(1.856)
8.657***
(3.065)
-2.696
(3.902)
-0.867
(2.720)
-4.945*
(2.742)
3.176
(3.344)
3.968
(3.403)
-1.116
(2.476)
-2.990
(2.216)
1.296
(3.188)
0.447
(3.401)
-0.370
(2.004)
1.509
(1.654)
2.908
(2.772)
-8.434***
(3.054)
-9.669***
(2.355)
-7.963***
(2.149)
-12.74***
(3.043)
-10.80***
(3.498)
This table tabulates the estimated coefficient on the interaction term between an indicator variable that takes the value of one for treated firms and an indicator variable that takes the
value of one for years after the regulation. The dependent variable is the ESG disclosure score or its subcomponents. Equation (2) in the text describes the full model which includes
firm fixed effects, year fixed effects, time-varying sector indicators, a firm size control, and a firm leverage control. *,**,*** indicate significance at 10%, 5%, and 1% levels (twotailed), respectively.
27
Table 3: The impact of mandatory corporate sustainability reporting on ESG, Environmental, Social and Governance disclosure, conditional on
levels of disclosure in the year prior to the regulation
South Africa
ESG Disclosure High
Environmental Disclosure High
Social Disclosure High
Governance Disclosure High
ESG Disclosure Low
Environmental Disclosure Low
Social Disclosure Low
Governance Disclosure Low
China
ESG Disclosure High
Environmental Disclosure High
Social Disclosure High
Governance Disclosure High
US
Japan
India
Australia
UK
11.62***
(2.010)
8.872***
(2.363)
17.10***
(2.387)
5.948*
(3.019)
9.302***
(1.688)
7.668***
(1.767)
11.19***
(2.861)
15.84***
(2.730)
12.66***
(1.617)
11.30***
(1.808)
21.22***
(2.133)
5.428***
(1.916)
8.431***
(2.067)
8.100***
(2.201)
13.75***
(2.746)
1.299
(2.210)
10.51***
(1.876)
5.230**
(2.016)
13.82***
(2.738)
20.97***
(2.701)
12.00***
(1.773)
10.98***
(1.956)
12.73***
(2.473)
11.33***
(1.927)
11.40***
(1.828)
6.885***
(1.981)
9.477***
(2.976)
23.09***
(2.406)
13.75***
(1.596)
11.26***
(1.759)
17.87***
(2.037)
11.42***
(2.560)
8.852***
(1.664)
4.506***
(1.669)
6.941**
(2.810)
22.16***
(2.936)
3.551**
(1.366)
8.232***
(2.273)
14.21***
(3.537)
-4.471*
(2.256)
-1.861
(2.058)
1.653
(6.012)
11.44***
(4.058)
-0.0703
(2.021)
2.827**
(1.297)
7.246**
(2.614)
15.25***
(3.573)
-4.917**
(2.096)
2.178*
(1.302)
7.091**
(3.160)
10.41***
(3.454)
0.185
(2.180)
-1.386
(1.973)
-3.367
(4.938)
6.902
(4.836)
0.441
(2.863)
(1.987)
4.983**
(1.902)
-1.109
(2.469)
8.625***
(2.772)
14.01***
28
ESG Disclosure Low
Environmental Disclosure Low
Social Disclosure Low
Governance Disclosure Low
Denmark
ESG Disclosure High
Environmental Disclosure High
Social Disclosure High
Governance Disclosure High
ESG Disclosure Low
Environmental Disclosure Low
Social Disclosure Low
Governance Disclosure Low
7.475***
(1.098)
4.591***
(0.926)
13.34***
(1.294)
5.457***
(2.088)
5.615***
(1.397)
-2.809*
(1.480)
10.81***
(1.500)
12.19***
(1.556)
4.331***
(1.411)
1.234
(1.193)
12.46***
(1.556)
2.385
(1.630)
6.331***
(0.880)
3.439***
(0.852)
8.949***
(1.295)
6.646***
(1.523)
6.981***
(1.186)
0.415
(0.921)
6.314***
(1.517)
16.40***
(2.236)
5.887*
(3.357)
3.075
(4.216)
7.684*
(3.804)
3.295
(4.838)
0.485
(1.861)
3.302
(2.315)
8.489
(5.592)
-4.723
-2.601
(3.620)
-2.992
(4.145)
6.536
(4.598)
-11.19**
(4.193)
-3.584
(2.602)
-1.698
(3.744)
4.328
(5.695)
-7.974**
6.900*
(3.777)
6.995
(4.200)
11.06***
(4.027)
-8.204
(5.759)
-0.174
(2.770)
1.962
(2.753)
7.760
(5.786)
-2.854
6.510*
(3.798)
9.287**
(3.430)
5.891
(3.991)
-2.254
(3.391)
-0.342
(2.027)
-0.837
(3.653)
5.294
(5.864)
4.242
8.887**
(3.488)
9.787***
(3.206)
5.180
(3.783)
2.434
(5.413)
-5.575*
(2.891)
-7.392**
(3.412)
6.188
(6.039)
2.652
(3.956)
(3.567)
(4.397)
(4.723)
(5.209)
This table tabulates the estimated coefficient on the interaction term between an indicator variable that takes the value of one for treated firms and an indicator variable that takes the
value of one for years after the regulation. The dependent variable is the ESG disclosure score or its subcomponents. Equation (2) in the text describes the full model which includes
firm fixed effects, year fixed effects, time-varying sector indicators, a firm size control, and a firm leverage control. A firm is identified as High Disclosure if it discloses more than
the median firm in the year before the first year of the regulation. A firm is identified as Low Disclosure if it discloses equal to or less the median firm in the year before the first
year of the regulation.*,**,*** indicate significance at 10%, 5%, and 1% levels (two-tailed), respectively.
29
Table 4: The impact of mandatory corporate sustainability reporting on assurance and propensity to adopt the GRI reporting guidelines
South Africa
Assurance
GRI
China
Assurance
GRI
Denmark
Assurance
GRI
Malaysia
Assurance
GRI
US
Japan
India
Australia
UK
0.114***
(0.0364)
0.273***
(0.0483)
0.145***
(0.0321)
0.301***
(0.0455)
0.0756**
(0.0380)
0.285***
(0.0465)
0.0790**
(0.0394)
0.313***
(0.0429)
0.108***
(0.0344)
0.367***
(0.0410)
0.0271**
(0.0120)
0.125***
(0.0337)
0.0137
(0.0214)
0.0521
(0.0406)
-0.0321
(0.0235)
0.0863**
(0.0384)
0.00842
(0.0175)
0.106***
(0.0345)
0.0121
(0.0188)
0.121***
(0.0368)
0.0897
(0.0700)
0.102
(0.0877)
0.122**
(0.0605)
0.0378
(0.0898)
0.0893
(0.0734)
0.175**
(0.0873)
0.0297
(0.0821)
0.0972
(0.0966)
0.0861
(0.0732)
0.176**
(0.0853)
0.0164
(0.0134)
0.150**
(0.0596)
0.0252**
(0.0117)
0.0343
(0.0750)
-0.0643*
(0.0363)
0.0415
(0.0707)
0.0118
(0.0204)
0.122**
(0.0599)
-0.0181
(0.0294)
0.134**
(0.0582)
This table tabulates the estimated coefficient on the interaction term between an indicator variable that takes the value of one for treated firms and an indicator variable that takes the
value of one for years after the regulation. The dependent variable is Assurance, an indicator variables that takes the value of one if the firm has received assurance on its ESG
disclosures, or GRI, which is an indicator variable that takes the value of one if the firm follows the Global Reporting Initiative guidelines. Equation (2) in the text describes the full
model which includes firm fixed effects, year fixed effects, time-varying sector indicators, a firm size control, and a firm leverage control. *,**,*** indicate significance at 10%,
5%, and 1% levels (two-tailed), respectively.
30
Table 5: The impact of mandatory corporate sustainability reporting on propensity to become a UNGC signatory and to adopt environmental and
social criteria in supply chain management for Danish firms
US
Japan
India
Australia
UK
UN Global Compact Signatory
0.431***
(0.0977)
0.369***
(0.0998)
0.380***
(0.106)
0.468***
(0.0921)
0.462***
(0.0887)
Environmental criteria in supply chain management
0.184*
(0.0938)
0.161*
(0.0937)
0.194**
(0.0856)
0.258**
(0.104)
0.193*
(0.100)
Social criteria in supply chain management
0.218*
(0.122)
0.264**
(0.118)
0.346***
(0.107)
0.337***
(0.104)
0.241**
(0.113)
Denmark
This table tabulates the estimated coefficient on the interaction term between an indicator variable that takes the value of one for treated firms and an indicator variable that takes the
value of one for years after the regulation. The dependent variable is a) UN Global Compact Signatory, an indicator variable that takes the value of one if the firm is a signatory of
the UN Global Compact, b) Environmental criteria in supply chain management, an indicator variable that takes the value of one if the firm has applied environmental criteria when
choosing its supply chain partners and c) Social criteria in supply chain management, an indicator variable that takes the value of one if the firm has applied social criteria when
choosing its supply chain partners. Equation (2) in the text describes the full model which includes firm fixed effects, year fixed effects, time-varying sector indicators, a firm size
control, and a firm leverage control. *,**,*** indicate significance at 10%, 5%, and 1% levels (two-tailed), respectively.
31
Table 6: The impact of mandatory corporate sustainability reporting on community spending by Malaysian firms
US
Japan
India
Australia
UK
0.0793
(0.0560)
-0.0482
(0.0722)
-0.0442
(0.0663)
-0.0169
(0.0625)
-0.664***
(0.0704)
Malaysia
Philanthropy (Community Spending)
This table tabulates the estimated coefficient on the interaction term between an indicator variable that takes the value of one for treated firms and an indicator variable that takes the
value of one for years after the regulation. The dependent variable is Philanthropy (Community Spending), an indicator variable that takes the value of one if the firm discloses the
level of community spending. Equation (2) in the text describes the full model which includes firm fixed effects, year fixed effects, time-varying sector indicators, a firm size control,
and a firm leverage control. *,**,*** indicate significance at 10%, 5%, and 1% levels (two-tailed), respectively.
32
Table 7: Instrumental variables analysis – the impact of disclosure on Tobin’s Q
Panel A: Instrumented ESG Disclosure
US
Japan
India
Australia
UK
South Africa
0.00591*
(0.00328)
0.0204***
(0.00422)
-0.00188
(0.00269)
0.00823***
(0.00257)
0.00440
(0.00268)
China
0.0314***
(0.00688)
0.0605***
(0.0233)
0.0123
(0.00809)
0.0425***
(0.00842)
0.0356***
(0.0134)
-0.320
0.0166
-0.182
-0.0536
-0.0939
(0.199)
(0.0742)
(0.123)
(0.0652)
(0.0758)
Denmark
33
Panel B: Instrumented sub-components of ESG Disclosure
South Africa
E
S
G
China
E
S
G
US
Japan
India
Australia
UK
0.00748*
(0.00433)
0.00431*
(0.00242)
0.00557*
(0.00310)
0.0347***
(0.0109)
0.0119***
(0.00239)
0.0177***
(0.00375)
-0.00271
(0.00388)
-0.00132
(0.00189)
-0.00153
(0.00220)
0.0110***
(0.00371)
0.00840***
(0.00286)
0.00527***
(0.00158)
0.00623
(0.00395)
0.00391
(0.00243)
0.00296*
(0.00175)
0.0413***
(0.0102)
0.0145***
(0.00274)
N/A
N/A
N/A
N/A
0.0165***
(0.00352)
0.0239***
(0.00608)
0.00383
(0.00235)
N/A
0.0623***
(0.0159)
0.0240***
(0.00456)
0.0452***
(0.0133)
0.0218***
(0.00731)
0.0127***
(0.00401)
This table tabulates the estimated coefficient on the instrumented ESG disclosure score (Panel A) or its subcomponents (Panel B) in the second stage of an instrumental variables
regression. The dependent variable is Tobin’s Q calculated as the sum of the market value of equity and the book value of the liabilities over the total book value of assets. Equation
(1) in the text describes the first stage. N/A means that the instrument in the first stage is not relevant. *,**,*** indicate significance at 10%, 5%, and 1% levels (two-tailed),
respectively.
34