Impact of Regulatory Impact Assessment on Policy Effectiveness in India: Evidence from ARDL Model
DOI:
https://doi.org/10.66348/jpa.26.v1.n1.a27Keywords:
Regulatory Impact Assessment (RIA); Policy Effectiveness; Governance Quality; ARDL Model; Institutional Economics; India; Time-Series Analysis; Evidence-Based Policymaking; State Capacity; Regulatory GovernanceAbstract
This study investigates the impact of Regulatory Impact Assessment (RIA) on policy effectiveness in India by employing a time-series econometric framework. Drawing on annual data for India and incorporating key governance indicators and macroeconomic controls, the study applies the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine both long-run and short-run relationships. The empirical results confirm the existence of a stable long-run equilibrium among RIA, governance quality, macroeconomic variables, and policy effectiveness. The findings reveal that RIA has a statistically significant and positive effect on policy effectiveness, indicating that systematic regulatory evaluation enhances the quality, coherence, and implementation of public policies. Governance indicators such as regulatory quality, government effectiveness, rule of law, and control of corruption also significantly contribute to improved policy outcomes. Additionally, GDP per capita and trade openness are found to strengthen policy effectiveness, highlighting the role of macroeconomic conditions in institutional performance. The Error Correction Model further confirms rapid adjustment toward long-run equilibrium, reinforcing the dynamic stability of the system. Overall, the study underscores the importance of integrating regulatory evaluation mechanisms with strong institutional frameworks to improve governance outcomes in India. The findings provide important implications for policymakers seeking to strengthen evidence-based policymaking and enhance regulatory efficiency.
Received: 2026-04-10 | Revised: 2026-06-02 | Accepted: 2026-06-23 | Published: 2026-06-30
Declarations
Ethics and Guidelines: Not Applicable.
Consent to participate: Not Applicable.
Consent to publish: The authors have provided consent to publish.
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: Conceptualization, S.K.S.; methodology, S.K.S.; formal analysis, S.K.S.; writing—original draft preparation, S.K.S.; writing—review and editing, S.K.S. All authors have read and agreed to the published version of the manuscript.
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