Climate Policy Consulting

MEAL/MERL/ MEL/ M&E/ MERLA The evolution of project management frameworks, particularly in the international development and non-profit sectors, shows a steady shift from simple data collection to complex, people-centered systems. The Evolution of Monitoring, Evaluation, and Learning (MEL)** 1. M&E: Monitoring and Evaluation** Focus: Tracking Results Definition: The foundation of the framework. Monitoring is the continuous collection of data to see if a project is on track; Evaluation is the periodic assessment of the project’s overall impact and relevance. 2. MEL: Monitoring, Evaluation, and Learning Focus: Learning from Results Definition: Adds a "Learning" component to ensure that the data collected in M&E isn't just filed away. It emphasizes using data to improve current and future project decision-making. MEAL: Monitoring, Evaluation, Accountability, and Learning** Focus: Accountability to Communities Definition: This introduces "Accountability," shifting the focus to the stakeholders. It ensures there are mechanisms for beneficiaries to provide feedback and that the organization is answerable to the people it serves. 4. PMEL: Planning, Monitoring, Evaluation, and Learning Focus: Planning with Measurement in Mind Definition: Explicitly integrates "Planning" into the cycle. It highlights that effective monitoring and evaluation cannot happen unless the project is designed from day one with measurable indicators. 5. MERL: Monitoring, Evaluation, Research, and Learning Focus: Research-Informed Programming *Definition: Introduces "Research" as a formal pillar. This approach uses rigorous scientific methods or deep-dive studies to understand the "why" behind trends, rather than just tracking the "what." 6. MERLA: Monitoring, Evaluation, Research, Learning, and Adapting Focus: Adapting Based on Evidence Definition: Adds "Adapting" to create a circular feedback loop. It’s not enough to learn; the organization must have the agility to change its strategy mid-course based on what the evidence suggests. 7. MEALK: Monitoring, Evaluation, Accountability, Learning, and Knowledge Management Focus: Knowledge Management & Learning Definition: Adds "Knowledge Management" to ensure that the insights gained are documented, stored, and shared across the entire organization or sector, preventing "reinventing the ....

I’m pleased to share WIPO’s new Innovation Economics and Policy Design webpage. A great resource curated by our Innovation Economics team for policymakers, entrepreneurs and researchers. This platform is designed to provide you with the latest economic insights and innovation trends to help you understand how innovation can elevate income, boost economic growth and improve standards of living. It breaks down complex economic insights into concise, easy-to-digest articles. Key highlights include: -      The role of innovation capabilities: How strategic policy design and smart specialization can transform innovation ecosystems. -      Policy impact: How government policies influence innovation. -      Resources Hub: A collection of datasets, economic papers and guidelines to help you conduct your own analysis. Explore the webpage here: https://lnkd.in/esGmUNJV One of the highlights is our latest World Intellectual Property Report (WIPR), a flagship WIPO publication that focuses on how policymakers can make innovation policy work for development. You can explore the full report or explore specific insights: https://lnkd.in/gCAGtnFT The IP and Innovation Ecosystem sector also assists countries develop National IP Strategies that align with economic goals, boost entrepreneurship and industrial growth, and help countries specialize in high-value industries. Explore the new webpage and join us in driving the future of innovation policy. #InnovationEcosystem #WIPO #WorldIPReport #InnovationCapabilities #SmartSpecialization #NationalIPStrategies #IPforDevelopment #Policymaking #InnovationEconomics #PolicyDesign

Navigating Liquidity and Policy: Evaluating India's Bond Buyback Strategy Amid Global Economic Tensions I'm excited to share my latest article where I delve into the complexities of India's recent bond buyback initiative and its impact on the financial markets. With the global economic landscape as a backdrop, this piece explores how domestic fiscal strategies and international monetary policies intertwine to shape India's economic decisions. Key insights include an analysis of the RBI's liquidity management, the influence of US Federal Reserve policies on Indian monetary decisions, and the broader implications for India's fiscal health. This article is a must-read for professionals interested in finance, economics, and policy-making. Explore the full article for a deeper understanding of these dynamics and their implications on the Indian and global economies. #Finance #MonetaryPolicy #BondYields #FinancialMarkets #EconomicPolicy #InflationRates #GlobalEconomy #RBI

📌 Rethinking Policy Evaluation: The Growing Importance of Advanced Difference-in-Differences Methods Difference-in-Differences (DID) has become one of the most influential methods in applied economics, public policy, and social science research. While the traditional DID framework remains a valuable tool for estimating causal effects, modern policy environments often present challenges that require more sophisticated approaches. Among the various tools available for causal analysis, Difference-in-Differences (DID) has emerged as one of the most widely used methods in economics and the social sciences. By comparing changes over time between treated and untreated groups, DID provides a practical framework for estimating policy impacts when randomized experiments are not feasible. Yet, as economic systems and policy interventions become increasingly complex, the traditional DID framework faces important limitations. Many contemporary policies are implemented gradually across regions, affect populations differently, and generate impacts that evolve over time. In such settings, the standard two-group, two-period DID model may fail to capture the full picture. This challenge has led to the development of advanced DID approaches, including fixed-effects models, event-study analyses, staggered adoption estimators, synthetic DID methods, and triple-difference designs. These innovations allow researchers to examine treatment dynamics, test key assumptions, address heterogeneous effects, and improve the credibility of causal estimates. For example, in climate and health economics, advanced DID methods can help quantify how floods, cyclones, heatwaves, or salinity intrusion affect healthcare expenditures, productivity losses, household welfare, and long-term economic resilience. Rather than simply identifying whether climate shocks have an impact, researchers can explore how those impacts change over time and which populations are most vulnerable. From my perspective, the evolution of DID reflects a broader transformation in empirical research. Policymakers today require more than average treatment effects; they need detailed evidence on timing, distributional consequences, and long-term outcomes. Advanced DID methods help bridge the gap between rigorous econometric theory and practical policy questions, enabling researchers to generate findings that are both scientifically credible and socially relevant. As data availability continues to expand and policy challenges become more interconnected, mastering advanced causal inference techniques will be increasingly important for economists, public health researchers, and development practitioners seeking to contribute meaningful evidence for informed decision-making. . . #Economics #Econometrics #DifferenceInDifferences #AdvancedEconometrics #CausalInference #PolicyEvaluation #ResearchMethods #DataScience #EvidenceBasedPolicy #AcademicResearch #PublicPolicy #ImpactEvaluation #HigherEducation #UmmeySalma

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗽𝘂𝗯𝗹𝗶𝗰 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗳𝗮𝗶𝗹, 𝗲𝘃𝗲𝗻 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗴𝗼𝗼𝗱? This paper ⬇️ on policymaking under complexity argues that the answer lies in 𝘩𝘰𝘸 policies are conceived, designed, and implemented in the face of unpredictable, interconnected systems. Traditional policy models assume linear cause-and-effect relationships: identify the problem, design the solution, implement it, and evaluate the results. Reality, however, is far messier. Public policies often fail because they underestimate 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, that is, the multiple actors, shifting incentives, feedback loops, and external shocks that shape policy outputs and outcomes. In such environments, even well-designed interventions can trigger unintended consequences, be captured by vested interests, or lose momentum as political priorities change. Another reason is the 𝗶𝗹𝗹𝘂𝘀𝗶𝗼𝗻 𝗼𝗳 𝗰𝗼𝗻𝘁𝗿𝗼𝗹: policymakers often believe they can steer complex systems through top-down plans, but they evolve in ways that cannot be fully predicted or controlled. Policies also falter when they ignore 𝘁𝗵𝗲 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 of local actors, those on the ground who interpret, modify, and sometimes resist policy directives. The paper suggests that success under complexity requires a shift in mindset: 👉 Design policies as 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀, not fixed blueprints. 👉 Build 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗹𝗼𝗼𝗽𝘀 that capture feedback early and adjust the course. 👉 Invest in 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 and 𝘁𝗿𝘂𝘀𝘁 among actors to improve coordination. In short, public policy is less about engineering perfect solutions and more about navigating a dynamic, uncertain landscape. Failure is not inevitable, but avoiding it means embracing complexity, not denying it. #PublicPolicy #PolicyFailure #SystemsThinking

In a recent study, we analyse 145,000 point estimates and confidence bounds on the effects of monetary policy shocks on output and inflation collected from more than 400 primary studies. We show that interest rate hikes by central banks are less effective in reducing inflation than conventional wisdom suggests. Correcting for publication bias, the output cost of reducing inflation increases. Our results suggest that we need realistic expectations about what monetary policy can achieve in steering inflation - and a broader mix of policy instruments, including fiscal, industrial, and competition policies, to ensure price stability at a reasonable macroeconomic cost. Policy brief in English: https://lnkd.in/dSJfrzu2 Policy brief in German: https://lnkd.in/dCATquGS Full study: https://lnkd.in/dBjXWVQ8

Can you learn everything you need for effective monitoring and evaluation in a few hours? In my work, I get this question quite often. And I usually answer honestly: I will not be able to teach this in such a short time. But I can do something else — show the full picture of what M&E actually includes, where to start if you want to build a systematic approach, and what kind of nuances you need to consider. In other words, I can give a direction and a framework on which you can then build your own practice of working with M&E. M&E is difficult to “fit” into a short format, not because of the number of tools or the complexity of the terminology. It is a system of interconnected decisions in which each step affects the next. In real work, this means you need to be able to: - see the difference between symptoms and root causes of a problem - build the logic of change: how exactly activities lead to results - formulate results in a way that they can be measured - define indicators that reflect real change, not just completed activities - choose appropriate data collection methods and work with data quality And then comes the most difficult part — analysis. When it is not enough to just collect data, but you need to answer “what has changed and why”, separate project influence from other factors, and make conclusions that can actually inform decisions. This is where it becomes clear that M&E is not only about tools. It is about a way of thinking that develops through practice. Short formats still have their value. In a few hours, you can explore one tool and try it in practice. In one day, you can work through one thematic block. In a few days, you can go from a project idea to a system of indicators and evaluation questions. But depth comes later, in the actual work. What were your shortest and longest MEL trainings — and when did they actually start working in practice?

Your project started without a baseline? Welcome to 90% of real-world Monitoring and Evaluation. Most programmes launch with urgency, political pressure, or donor timelines, not perfect data systems. That doesn’t mean you can’t measure change. It just means you need to reconstruct the “before” using the tools seasoned evaluators rely on: 🔹 Start with what already exists Intake forms, early reports, planning documents, grant proposals, even if they weren’t created for MEL, they often contain reference points you can extract. 🔹 Use recall methods strategically Ask participants and staff to describe conditions before the intervention, but anchor their memory to major events: ↳ “Before the school opened…” ↳“Before the water point was installed…” This reduces bias and increases accuracy. 🔹 Pull secondary data to fill the gaps Census tables, ministry surveys, NGO assessments, anything close in geography and timeframe can provide a credible reference. 🔹 Triangulate relentlessly Never rely on one source. Cross-check community recall with government data, staff insights, and documentation. Retrospective baselines aren’t shortcuts. They’re structured, defensible methods for rebuilding the past and they’re what experienced evaluators use when perfection isn’t possible (which is most of the time). 🔥 If you want more practical MEL techniques like this with no jargon, no theory-only talk, join my mailing list for weekly insights that will sharpen your practice. #Baseline

Most frameworks count how many people with disabilitied are in the room. Ours measures what’s keeping them out. There’s a fundamental difference and it changes everything about what we do next. Organisations celebrate diversity numbers while the environment around people with disabilities stay exactly the same. Inaccessible. Exclusionary. Unchanged. A Monitoring & Evaluation Framework that doesn’t ask “are people with disabilities included?” It asks: → What barriers exist in this environment? → How deep does the exclusion run? → What has persisted and why? This is the social model applied to measurement. We stop locating the problem in the person. We locate it in the system. Because you can’t fix what you’re not accurately tracking. This framework is the foundation of how AccessInclusion conducts accessibility assessments and it’s reshaping how our clients understand what inclusion actually requires of them. Remediation without rigorous measurement is just guesswork with good intentions. We deserve better than that. I’m building out the methodology white paper. If you work in disability inclusion, policy, or organisational M&E and you’re tired of metrics that celebrate presence without interrogating barriers let’s talk #DisabilityInclusion #MonitoringAndEvaluation #InclusionFramework #AccessInclusionInstitute

Baseline → Midline → Endline Over time, I’ve stopped seeing these as three separate donor requirements. They’re really one continuous learning journey—and how you handle them can shape whether a program actually makes a difference. Baseline This is your moment to pause and really understand what’s going on. Are your assumptions in the Theory of Change actually true? Are your targets grounded in reality? When this step is rushed, nothing breaks immediately—but everything that follows is built on shaky ground. Midline This is where honesty matters most. Are things working the way you expected? What’s not going as planned? And what can you still change while it matters? The strongest teams use this moment to adjust, not just report. Endline This is more than closing a project. It’s about understanding the story: what changed, what didn’t, and what we need to do differently next time. When it all connects Something shifts. You move from reporting to learning. From sticking to plans → to adapting them. From activity → to real impact. But the reality? Too often, these moments are treated as isolated tasks—done late, filed away, and forgotten. The reports look good. But the learning gets lost. What really makes the difference It’s not better tools or nicer reports. It’s being intentional about connecting these moments—so evaluation becomes a living process that actually improves decisions and impact. #MEAL #MonitoringAndEvaluation #AdaptiveManagement #DataForImpact