Linganisha mbinu

	Regulatory Impact Assessment	Realist Evaluation
Nyanja	Public Administration	Public Policy
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2008	1997
Mwanzilishi	OECD (Regulatory Policy programme)	Ray Pawson & Nick Tilley
Aina	Ex ante policy appraisal framework	Theory-driven, generative evaluation approach
Chanzo asilia	OECD (2008). Building an Institutional Framework for Regulatory Impact Analysis (RIA): Guidance for Policy Makers. Paris: OECD Publishing.	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
Majina mbadala	Regulatory Impact Analysis, RIA, Impact Assessment of Regulation, Better Regulation Impact Assessment	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
Zinazohusiana	4	4
Muhtasari	Regulatory impact assessment (RIA) is a systematic, ex ante framework for appraising the likely consequences of a proposed regulation before it is adopted, so that policymakers choose the option that delivers the greatest net benefit to society. Promoted internationally by the OECD as a cornerstone of regulatory quality and 'better regulation,' RIA requires governments to define the problem clearly, identify a full range of options including non-regulatory alternatives, weigh their costs and benefits, consult affected parties, recommend the preferred option, and plan for monitoring. The aim is to replace reflexive rule-making with evidence-based, transparent and proportionate regulation.	Realist evaluation is a theory-driven approach to evaluating programs and policies that asks not simply 'does it work?' but 'what works, for whom, in what circumstances, and why?'. Developed by Ray Pawson and Nick Tilley in their 1997 book Realistic Evaluation, it treats interventions as theories incarnate: programs offer resources or opportunities that trigger underlying mechanisms of reasoning and response in participants, and those mechanisms only fire in particular contexts. The unit of analysis is the Context-Mechanism-Outcome (CMO) configuration, and the goal is to build and refine middle-range theory that explains differential outcomes across settings.