ESG-SDG Alignment in Kazakhstan: Firm Disclosures Versus Employee Perceptions

2022–2023 survey data from 90 managers and employees at 63 large Kazakhstani firms, matched with 2024 ESG disclosure ratings for 25 firms. The dataset, created by Neafie et al., includes anonymized firm-level scores, response-level scores, and survey matrices. It is accompanied by Python scripts for reproducing statistical analyses from the associated paper.

Use Cases

• Compare firm-level ESG disclosure ratings with internal survey perceptions based on the matched dataset of 25 firms.
• Conduct cluster analysis to identify patterns in sustainability practices based on the encoded survey item matrices.
• Perform reliability and missing-value sensitivity analyses on the survey response data.
• Run statistical tests, such as Wilcoxon signed-rank tests, to analyze gaps between disclosed and perceived ESG-SDG alignment.

Strengths

• Includes matched data for 25 firms, allowing direct comparison of external ESG ratings and internal survey perceptions.
• Provides replication code in Python for descriptive statistics, hypothesis tests, cluster analysis, and robustness checks.
• Survey data were collected under a formal IRB protocol (Nazarbayev University IRB 531/05032022), suggesting ethical oversight.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The sample is limited to 63 large firms in Kazakhstan, which may not be generalizable to other regions or smaller companies.

Provenance

Source

Harvard Dataverse, deposited by author Jessica Neafie.

Collection Method

Combines an original survey of firm employees and managers with third-party ESG disclosure ratings (ESGQ, 2024).

Time Range

Survey data from 2022–2023; ESG ratings from a 2024 evaluation.

Freshness

Last updated 2026-07-17 20:51:23; freshness should be verified.

Geography

Kazakhstan (large firms).