Dividend Policy Reform and Shareholder Wealth in Emerging Markets: Evidence from Synthetic Control Method

Abstract

This paper investigates the causal effect of dividend policy change on shareholder wealth in the emerging market framework, by employing Synthetic Control Method (SCM). Although a positive relationship between dividend policy and firm value has been described previously in the literature, most of the results have been obtained using the traditional regression methods where the endogeneity and weak causal identification can be a problem. This study tries to fill this void by building a counterfactual baseline and using a weighted sample of emergent economies with similar characteristics that were not subjected to comparable dividend policy changes. Shareholder wealth is calculated using market capitalization in constant USD, and alternative measures of shareholder wealth such as the level of the stock market index and market capitalization as a ratio of GDP are used for robustness checks. The empirical results suggest that dividend policy reform has a significant and long-lasting effect on shareholder value, and that a distinct difference between the treated unit and its synthetic can be clearly seen after the dividend policy reform. Placebo-in-time tests, placebo-in-unit tests, RMSPE analysis and leave-one-out sensitivity tests validate the estimated effects and test for their stability across the sample. Findings indicate that dividend policy change contributes to market valuation in the sense of less information asymmetry and the improvement of governance mechanism in the emerging markets. The overall findings of the study offer new causal evidence that dividend policy is an important institutional mechanism in the creation of shareholder value in financial systems in development.

Received: 2026-04-13 | Revised: 2026-06-24 | Accepted: 2026-06-26 | Published: 2026-06-30

Declarations

Competing interests: The authors declare no competing interests.

Data availability statement: Data will be made available on reasonable request from the corresponding author.

Funding: This research received no external funding.

Clinical Trial Number: Not Applicable.

Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions: M.M.R.: Conceptualization, methodology, writing—original draft preparation, writing—review and editing. All authors have read and agreed to the published version of the manuscript.