Public Policy Research

How can we successfully transform scientific research results into Government policy? This book chapter presents innovative processes that have been developed in University College Cork and used to bridge the interface between the research ecosystem and policy-making ecosystem. Available here https://lnkd.in/evFNv9Hu. While the insights can apply across many areas of policy, the specific example here focuses on how energy systems modelling has been used to inform energy and climate mitigation policies in Ireland. From our experience over a 15 year period, motivation is critically important in order to overcome the challenges and to take on the extra effort to move beyond the traditional research process towards any or all of: actively informing, influencing, underpinning and co-producing policy. Engagement is not about communicating research findings, but critically also about listening to the policy practitioners needs, and developing a clear understanding of the policy making process, which is significantly different from the research process. Building trust with policy practitioners can take a lot of time and effort, but is hugely important. This includes developing personal relations respecting their role, their position, and when conversations are confidential in nature (especially when this not explicitly stated). Based on this experience, coupled with the examples provided, our approach can be summarised in a seven step plan that other research teams may find useful, in particular those who wish to bridge between the research and policy eco-systems: 1. Undertake scientifically robust research, submit it for peer review, publish it in scientific journals and make methods and results openly and publicly available. 2. Frame research questions that respond to specific policy needs, and then submit the results and insights to policy practitioners to inform policy 3. Translate research results into policy insights—including through the use of ‘policy briefs’ 4. Improve communications of research findings through the development of infographics 5. Engage actively with policy practitioners and policy makers—this is critical to move beyond informing and towards influencing policy, mindful of the different roles and responsibilities of each. 6. Co-produce policy—challenging but can be very successful. 7. Build absorptive capacity in the policy system—the focus here is on equipping the policy makers to understand the strengths and limitations of the approaches used, and improved interpretation of the scenario results generated. Thanks to co-authors Paul Deane and Fionn Rogan, and to MaREI, Science Foundation Ireland, Environmental Protection Agency (EPA) Ireland, Sustainable Energy Authority of Ireland (SEAI), Minister Eamon Ryan, International Energy Agency (IEA), IEA-ETSAP | Energy Technology Systems Analysis Program

If Public Policy is meant to be hands-on, shouldn’t public policy research be that too? Too often, I see policy students buried in secondary data, producing neat reports from government websites or the World Bank database. Useful? Sure. But if that’s all we needed, the economists and sociologists already had it covered. Public policy as a discipline was born to be pragmatic; to reflect ground realities instead of only theorizing them. And yet, much of our research still feels like “watch-and-wait” instead of “test-and-learn.” Here’s the truth: our knowledge gaps about how things work on the ground are still enormous. Policy isn’t failing because we don’t have enough PDFs; it’s failing because too few people are running real-world experiments. What we desperately need is more action-oriented, experimental research. Think of examples like: >Testing whether SMS reminders improve pension enrollment in one taluka before scaling. >Trying two grievance redressal systems in different districts and comparing results. >Piloting cash vs. in-kind transfers to see which impacts nutrition better. This is how public policy builds knowledge that actually works in messy, real contexts. And here’s the encouraging part: you don’t need to be a tenured professor with a million-dollar grant to do this. Even as a student or early-career professional, you can start small: 3 steps to move from “desk-based” to “hands-on” research: ~Start with micro-experiments. Pick a small public program in your city/village. Test something measurable. It could be as simple as comparing how two different posters influence awareness about a scheme. ~Collaborate with NGOs/startups. They are often open to researchers testing small innovations. You get access to the field, they get actionable insights. Win-win. ~Document and share. Publish your findings (even if modest) as short blogs, LinkedIn posts, or working papers. Remember: visibility attracts collaboration, and collaboration creates larger projects. Here’s the mindset shift: don’t wait for the perfect dataset. Create the dataset. So, to every MPP student, researcher, or young professional: step beyond Excel sheets and borrowed numbers. Immerse yourself. Run pilots. Shadow a government department. Collect primary data. Learn by doing. Because policy isn’t guesswork but if we don’t generate evidence rooted in lived reality, guesswork is all we’ll ever have. What’s one experiment you’d love to test if you had the chance? #PublicPolicy #Research #Impact #PolicyEducation #Evidence #LearningByDoing

📌 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

When science is under attack and budgets are shrinking, “publish and pray” isn’t a strategy—it’s a risk to public health. Our new Nature Portfolio piece—“Maximizing researcher–policymaker engagement in global public health”—sets out a practical playbook so every pound/dollar of research translates into policy impact. As researchers who have held high level roles in the #UN (including #WHO) and in government-facing roles, we wrote this for researchers who need to move evidence beyond journals and into decisions—now. What the paper offers (ready to use): 👉 A 6-question framework (Why, What, With whom, When, Where, How) to plan engagement from day zero—not after publication. 👉 Mechanisms you can deploy immediately: concise policy briefs; rapid “science-on-demand” syntheses; deliberative dialogues and roundtables; embedded advisors/knowledge brokers; advocacy coalitions that combine diverse skills and networks; and digital evidence hubs. 👉 Timing & politics: how to spot policy windows, manage trade-offs, and show contribution (not just attribution). 👉 Roles for funders & universities: ring-fence time/budget for engagement; reward policy outcomes alongside citations. Do this in the next 90 days: 1.    Map your decision-makers & calendars (who decides, when). 2.    Turn your latest findings into a 2-page brief + 10-minute deck. 3.    Convene a small roundtable with policy leads and one civil-society partner. 4.    Join or form an advocacy coalition for your topic: identify 1–2 civil-society groups, a policy entrepreneur, and a comms ally; agree a shared objective (e.g., wording in a guideline), split roles (research, convening, media, legislative outreach), and set a 12-week action plan. Shaping policy is hard work, and far from a science, but if publicly funded research stops at publication, it underserves the public. Let’s fix that—together. Read the paper: Maximizing researcher–policymaker engagement in global public health https://lnkd.in/eruJ_d-R J. Jaime Miranda David Berlan Camila Corvalan Taufique Joarder Arpita Raja Raja Yoong Khean Khoo Sunoor Verma Brig Gen Prof Dr Mohd Arshil Moideen (Rtd) Anne Marie Thow Helena Legido-Quigley David Peiris Rogers Kanee PhD, MPH, CSCA Ertila Druga MD MBA PhD Adeeba Kamarulzaman

Why does so much “policy-relevant” research fail to shape policy when it matters most? This paper ⬇️ offers a grounded answer from international behavioural science units. 👉 Quality and relevance are not produced by methods alone. They emerge from #relationships, #timing, and #InstitutionalPositioning. 🔔 What stood out to me is how consistently policy influence depends on proximity to decision-making rather than to journals. Research that informs policy is co-produced early, aligned with real policy questions, and embedded in organzations that understand political constraints, trade-offs, and moments of opportunity. Rigor matters, but relevance is negotiated, not delivered. The paper quietly challenges a familiar myth: that better evidence automatically leads to better policy. In practice, evidence travels through trust, credibility, and interpretive work. Behavioural science units succeed not because they simplify politics away, but because they learn how to work within it. The implication is uncomfortable for academia. If we want research to matter, we must invest in #PoliticalLiteracy, institutional interfaces, and long-term engagement, not only in methodological excellence. 📢 Policy-relevant research is not a technical output. It is a governance achievement. #PolSci4Health

68% of Europeans believe scientists should intervene in political debates to ensure decisions are evidence-based (Eurobarometer 557). Yet, too often, the bridge between research results and policymaking remains underused. The European Research Executive Agency (REA) Agency has published a kit for EU-funded projects on how to share scientific evidence with policymakers. Its logic is simple but powerful: if research is publicly funded, it should not only advance knowledge but also inform policy choices. What this means The document outlines three principles for achieving policy impact: • Understand the policy context – track priorities, identify the right timing, and make results relevant. • Join forces with stakeholders – academics, industry, civil society, and other EU projects. • Plan for impact from the start – define audiences, key messages, and the right channels. It also lists the most effective formats to reach policymakers: policy briefs, consultations, workshops, and direct reporting. Interestingly, it stresses that researchers’ own social media accounts can also play a role in authenticity and engagement. Why this is interesting and for whom • For researchers: the kit provides 10 concrete steps and links to EU tools such as CORDIS, Horizon Dashboard, and the Horizon Results Platform, turning evidence into actionable insights. • For policymakers: it offers a structured way to receive scientific input in real time, aligned with the EU policy cycle. • For citizens: it strengthens the expectation that public policies are backed by evidence, not just political negotiation. The message is clear: EU-funded research is not complete until its results have reached the people shaping Europe’s future laws and strategies.

I recently discovered a fascinating paper by a recent PhD graduate Alix Bonargent - now at IDInsight - that provides cool new evidence about the research to policy pipeline. (Link in comment) She constructs a dataset of over 500 research projects, all conducted in the context of International Growth Center rearch initiatives, and seeks to explore what characteristics of projects predict actual policy change. She finds that projects developed in partnership with policymakers are dramatically more likely to result in observed change (15 to 20 percentage points), even conditional on publication. But, she also finds that there is a pronounced political cycle to this form of collaboration: it's more likely to be effective when projects are launched earlier in the electoral cycle, when there is more time to act on the findings. Interestingly, though, researchers from elite institutions seem to be relatively insulated from political cycles. Her hypothesis is that they can command so much funding and such large project teams that those teams can "protect" projects from political fallout. Fascinating analysis - worth reflecting on as we all attempt to craft meaningful and policy-relevant projects, particularly when working within ever-tighter constraints on time and resources.

An important new The World Bank World Bank Development Economics Policy Research Working Paper by Christopher Hoy, Yeon Soo Kim, Saad Imtiaz, Ana Maria Rojas Mendez, Moritz Meyer, Gustavo Canavire-Bacarreza, Lydia Soojin Kim, William Seitz, Imane Helmy, Ikuko Uochi, Sering Touray, Juni Singh, Bambang Suharnoko Sjahrir, Utz Pape, Alan Fuchs, Trang Nguyen, Defne Gencer, Min Lee, and Akiko Sagesaka looks at what drives opposition to energy price reforms. Key insights: 💬 Public opposition to reforms like subsidy removal depends more on design and communication than on cost. 📊 In surveys of 10,000 people across five countries, about 70% opposed an immediate 100% price increase. 🔄 Opposition fell by nearly half when reforms were phased in, targeted to high users, or paired with compensation. 🧠 Informational messages also reduced resistance, equivalent to halving the perceived price increase. 🎓 Experts misjudged public reactions, underestimating design effects and misunderstanding coping and compensation preferences. ⚖️ Behavioral biases, like present bias, loss aversion, and fairness concerns, shape opposition as much as economic costs. 📑 Read the paper: https://lnkd.in/gxp9eAj9

Do YOU analyze public policies before you propose them? I often see scientists, engineers, and health professionals take less care in their pronouncements about public policy than they would for their own research and professional activities. That's fine if you are just chatting among friends during a casual conversation. It's not good practice, however, to speak to the public and policymakers about science and technology policy issues before rather than taking the time to research and analyze the different policy options. Doing so reduces the credibility of the scientific and technical community. Instead, you should conduct a policy analysis to develop an evidence-based policy position. Policy analysis is a systematic process of analyzing potential policy options to respond to a societal problem and prioritizing those options based on their effectiveness, efficiency, equity, and ease of political acceptability. Another important consideration is thinking about your audience. A policy that works in Pittsburgh may not work in Phoenix, and vice-versa. This is not just for geographical reasons but sometimes for other historical or cultural reasons. In addition, some communities have more financial resources than others, which impacts how they prioritize issues. So, public policies should be developed in the context of community discussion and considerations. What to learn more? Below are the ten policy analysis steps from my book, From Expertise to Impact: A Practical Guide to Informing And Influencing Science and Technology Policy. Disagree with me? Do you think that scientists are always right when they speak about policy issues? Even without conducting an analysis? Even in fields where they are not experts? Let me know in the comments.

Who's Afraid of Policy Experiments? Our paper – which has now been accepted 🎉 for publication in The Economic Journal – shows that voters strongly support randomized policy experiments, and particularly so when they do not hold a strong opinion about the policy. When learning about voters' favorable opinion, politicians conform to voters' views about policy experimentation. Full paper: https://lnkd.in/eHY8Qd23 (Open Access!) Joint work with Arjan Non, Paul Prottung, and Benedetta Ricci. Abstract: In many public policy areas, randomized policy experiments can greatly contribute to our knowledge of the effects of policies and can thus help to improve public policy. However, policy experiments are not very common. This paper studies whether a lack of appreciation for policy experiments among voters may be the reason for this. Collecting survey data representative of the Dutch electorate, we find clear evidence contradicting this view. Voters strongly support policy experimentation and particularly so when they do not hold a strong opinion about the policy. In a subsequent survey experiment among a selected group of Dutch politicians, we find that politicians conform their expressed opinion about policy experiments to what we tell them the actual opinion of voters is.