KenyaESG: Sentence-level ESG Disclosure Classification Dataset (NSE)

Sentence-level dataset for three independent binary ESG classification tasks (Environmental, Social, Governance) built from corporate reports of firms listed on the Nairobi Securities Exchange (NSE), 2010–2024, combined with reference sentences from Schimanski et al. (2024). Companion data to the fine-tuned classifiers josephagossa/KenyaESG-RoBERTa-{env,soc,gov}.

Structure

Three pillars, each with a training set and a disjoint human-annotated evaluation set, provided as Excel files (one per pillar):

Pillar	Training file	Evaluation file	train	eval
Environmental	KenyaESG_train_environmental.xlsx	KenyaESG_evaluation_environmental.xlsx	3,900	100
Social	KenyaESG_train_social.xlsx	KenyaESG_evaluation_social.xlsx	3,900	100
Governance	KenyaESG_train_governance.xlsx	KenyaESG_evaluation_governance.xlsx	3,900	100

The training and evaluation sentences are disjoint (the 100 evaluation sentences per pillar were removed from training to prevent leakage). Together they reconstruct the full 4,000-sentence pool per pillar. Because the three pillars are modelled as independent binary tasks, a sentence may be positive on more than one pillar.

You can read the files directly with pandas:

import pandas as pd
train = pd.read_excel("KenyaESG_train_environmental.xlsx")   # or social / governance
test  = pd.read_excel("KenyaESG_evaluation_environmental.xlsx")

Fields

Training files (KenyaESG_train_<pillar>.xlsx): a row index, text, <pillar> (the 0/1 label, named env, soc, or gov), and source.

Evaluation files (KenyaESG_evaluation_<pillar>.xlsx): id, text, <pillar>_A, and <pillar>_B (the two independent annotators' binary judgments, e.g. env_A, env_B). Inter-annotator agreement (Cohen's κ): 0.94 (environmental), 0.86 (social), 0.88 (governance). Both annotations are released rather than a single adjudicated label, so users can recompute agreement and adjudicate themselves; the three evaluation samples are drawn independently per pillar (the sentences differ across pillars).

Companion models (performance vs. human benchmark)

Model	Pillar	F1	DOI
KenyaESG-RoBERTa-env	Environmental	0.917	10.57967/hf/9126
KenyaESG-RoBERTa-soc	Social	0.882	10.57967/hf/9127
KenyaESG-RoBERTa-gov	Governance	0.916	10.57967/hf/9128

Sources

The source field distinguishes two origins. Kenya: sentences from annual, integrated, and sustainability reports of NSE-listed firms (2010–2024); training labels were assigned by a keyword-based filter and refined by a single-reviewer pass (not full manual annotation). Schimanski et al. (2024): reference sentences from "Bridging the gap in ESG measurement" (Finance Research Letters 61, 104979), available under Apache-2.0.

Privacy and anonymization

Company and personal names are masked in the text field ([COMPANY], [PERSON]) in line with the anonymisation principles of the Kenya Data Protection Act, 2019 (No. 24 of 2019) and Regulation (EU) 2016/679 (GDPR). Masking of personal names follows these principles; company names (from public filings) are masked as a precaution. Masking is semi-automatic and not guaranteed exhaustive; a small number of false-positive masks on common words (e.g. "equity", "equality", "Standard") were subsequently restored — see KenyaESG_correction_log.txt. The dataset must not be used to attempt re-identification of individuals or firms.

Intended use and limitations

Intended for training and evaluating sentence-level ESG text classifiers. The training labels for the Kenya portion are keyword-derived and single-reviewer-checked; the evaluation files are the appropriate benchmark for honest performance estimates. The dataset captures disclosure intensity, not disclosure quality; sample sizes are modest. The corpus is predominantly English but retains a small number of Swahili and OCR-degraded sentences, reflecting the original NSE reports.

License and citation

© 2026 Joseph Agossa. The dataset compilation, structure, and annotations are licensed under Apache-2.0; the underlying verbatim report text remains under the copyright of the respective issuing companies and is included for research purposes under a fair-dealing rationale, with sources documented.

This dataset has two persistent identifiers, which may be used interchangeably: the Hugging Face DOI 10.57967/hf/9193 and the Zenodo concept DOI 10.5281/zenodo.20608236 (resolving to the latest archived version).

@misc{agossa2026kenyaesg,
  author    = {Agossa, Joseph},
  title     = {KenyaESG: A Sentence-level ESG Disclosure Classification Dataset
               for Nairobi Securities Exchange Corporate Reports},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.20608236},
  note      = {Also available on the Hugging Face Hub, DOI 10.57967/hf/9193}
}

Working paper: Agossa, J. (2026). Pricing the Cost of Compliance: Equity Reactions to Mandatory ESG Disclosure in a Frontier Market. SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6966682

@unpublished{agossa2026compliance,
  author = {Agossa, Joseph},
  title  = {Pricing the Cost of Compliance: Equity Reactions to
            Mandatory ESG Disclosure in a Frontier Market},
  year   = {2026},
  note   = {Working paper, SSRN 6966682},
  url    = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6966682}
}

Schema adapted from Schimanski, T., Reding, A., Reding, N., Bingler, J., Kraus, M., & Leippold, M. (2024). Bridging the gap in ESG measurement. Finance Research Letters, 61, 104979.