Cited by 651Corporate Social Responsibility ReportingSustainable Finance and Green BondsGovernance, Compliance, and Sustainability

Four Things No One Will Tell You About ESG Data

Open access
Kotsantonis, Sakis, Serafeim, George·2019·Journal of Applied Corporate Finance

Abstract

As the ESG finance field and the use of ESG data in investment decision‐making continue to grow, the authors seek to shed light on several important aspects of ESG measurement and data. This article is intended to provide a useful guide for the rapidly rising number of people entering the field. The authors focus on the following: The sheer variety, and inconsistency, of the data and measures, and of how companies report them. Listing more than 20 different ways companies report their employee health and safety data, the authors show how such inconsistencies lead to significantly different results when looking at the same group of companies. ‘Benchmarking,’ or how data providers define companies' peer groups, can be crucial in determining the performance ranking of a company. The lack of transparency among data providers about peer group components and observed ranges for ESG metrics creates market‐wide inconsistencies and undermines their reliability. The differences in the imputation methods used by ESG researchers and analysts to deal with vast ‘data gaps’ that span ranges of companies and time periods for different ESG metrics can cause large ‘disagreements’ among the providers, with different gap‐filling approaches leading to big discrepancies. The disagreements among ESG data providers are not only large, but actually increase with the quantity of publicly available information. Citing a recent study showing that companies that provide more ESG disclosure tend to have more variation in their ESG ratings, the authors interpret this finding as clear evidence of the need for ‘a clearer understanding of what different ESG metrics might tell us and how they might best be institutionalized for assessing corporate performance.’ What can be done to address these problems with ESG data? Companies should ‘take control of the ESG data narrative’ by proactively shaping disclosure instead of being overwhelmed by survey requests. To that end, companies should ‘customize’ their metrics to some extent, while at the same time seeking to self‐regulate by reaching agreement with industry peers on a ‘reasonable baseline’ of standardized ESG metrics designed to achieve comparability. Investors are urged to push for more meaningful ESG disclosure by narrowing the demand for ESG data into somewhat more standardized, but still manageable metrics. Stock exchanges should consider issuing—and perhaps even mandating—guidelines for ESG disclosures designed in collaboration with companies, investors, and regulators. And data providers should come to agreement on best practices and become as transparent as possible about their methodologies and the reliability of their data.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/jacf.12346

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • For an audit to successfully measure performance, its scope should be sufficiently delineated.

    “For an audit to successfully measure performance, its scope should be sufficiently delineated.”

    From Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance (2022) · cites this paper as [42]

    The sections on benchmarking and defining a peer group say that how a comparison group and performance range are defined is crucial to assessing a company's ESG performance. That supports the general need to define the basis of an assessment, but the paper does not address audits or state a requirement about an audit's scope (searched: audit, scope, delineated). No page number was available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cited by

Sorted by citations

Authors
Kotsantonis, S., & Serafeim, G.
Affiliations
  • SAKIS KOTSANTONIS is the managing partner of KKS Advisors, a consulting firm advising leading organizations on bold and effective strategies that pave the way to a sustainable society.
  • Harvard University (US)
Venue
Journal of Applied Corporate Finance
Publisher
Wiley
Published
2019-06
Volume
31
Issue
2
Pages
50-58
ISSN
  • 1078-1196 (ISSN-L)
  • 1745-6622
OpenAlex
W2953492091
Cited by
651
References
0
Citations by year
2026: 183 · 2025: 187 · 2024: 96 · 2023: 77 · 2022: 57 · 2021: 24 · 2020: 23 · 2019: 3 · 2017: 1
FWCI
41.18
Type
journal article
Language
en
Open access
Locations
  • Journal of applied corporate finance (publishedVersion, other-oa)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Corporate Social Responsibility Reporting
  • Social Sciences › Economics, Econometrics and Finance › Finance › Sustainable Finance and Green Bonds
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Governance, Compliance, and Sustainability
Keywords
ESG data, ESG measurement, ESG disclosure, ESG ratings, benchmarking, peer groups, data gaps, imputation methods, ESG disclosure standards, corporate sustainability performance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review