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Implications in Research – Types, Examples and Writing Guide

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Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

• Restate the main findings: Begin by restating the main findings of the study in a brief summary .
• Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
• Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
• Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
• Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
• Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

• To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
• To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
• To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
• To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
• To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

• Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
• Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
• Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
• Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
• Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

• Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
• Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
• Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
• Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
• Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
• Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
• Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

• Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
• Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
• Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
• Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
• Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
• Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

• Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
• Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
• Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

• Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
• Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
• Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
• Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
• Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

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• A Research Guide
• Research Paper Guide

How to Write Implications in Research

• Implications definition
• Recommendations vs implications
• Types of implications in research
• Step-by-step implications writing guide

Research implications examples

What the implications of the research definition?

• Theoretical implications stand for all the new additions to theories that have already been presented in the past. At the same time, one can use a totally new theory that provides a background and a framework for a study.
• Practical implications are about potential consequences that show the practical side of things.

Recommendations VS Implications

• Implied content versus proposed writing. It means that an implication should provide an outcome from your study. The recommendation is always based on the outcome, along with your words as a personal opinion.
• Potential impact a study may have versus a specific act. When you are composing your research paper, your implications have the purpose of discussing how the findings of the study matter. They should tell how your research has an impact on the subject that you address. Now, unlike the implications section of the research paper, recommendations refer to peculiar actions or steps you must take. They should be based on your opinion precisely and talk about what must be done since your research findings confirm that.

What are the types of implications in research?

• Political implications. These are mostly common for Law and Political Sciences students basing implications on a certain study, a speech, or legislative standards. It is a case when implications and recommendations can also be used to achieve an efficient result.
• Technological implications. When dealing with a technological implication, it serves as special implications for future research manuals where you discuss the study with several examples. Do not use a methodology in this section, as it can only be mentioned briefly.
• Findings related to policies. When you have implemented a special policy or you are dealing with a medical or legal finding, you should add it to your policy. Adding an implications section is necessary when it must be highlighted in your research.
• Topical (subject) implications. These are based on your subject and serve as a way to clarify things or as a method to narrow things down by supporting the finding before it is linked to a thesis statement or your main scientific argument.

Step-by-step implications in research writing guide

Step 1: talk about what has been discovered in your research., step 2: name the differences compared to what previous studies have found., step 3: discuss the implications of your findings., step 4: add specific information to showcase your contributions., step 5: match it with your discussion and thesis statement..

Green energy can benefit from the use of vertical turbines versus horizontal turbines due to construction methods and saving costs. 

The use of AI-based apps that contain repetition and grammar-checking will help ESL students and learners with special needs. 

Most studies provide more research on the social emphasis that influences the problem of bullying in the village area. It points out that most people have different cultural behavior where the problem of bullying is approached differently.

If you encounter challenges in terms of precise replication, you can use a CR genetic code to follow the policies used in 1994. Considering the theoretical limitations, it is necessary to provide exact theories and practical steps. It will help to resolve the challenge and compare what has been available back then. It will help to trace the temporal backline. 

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Making a Case for Policy Implications in Research: A Reflexive Contemplation and Call to Action

Very few people will argue against the importance of policy in daily life, yet there are still many scholars, both novice and experienced, who do not include policy implications in crafting the research and discussion sections of their research articles. It is commonly accepted that implications are provided in research studies with the intention of interpreting the meaning and results of your research. When taking a systems lens, there are very few research topics that would not have a direct or indirect relationship to policy. Policy implications add a holistic lens to the meaning and interpretation of your research beyond the traditional discussion of how your results can be enhanced by other research and how your results can be applied to practice. Implications for policy are most often meso- and macro-level considerations and can include programmatic, community-oriented, state-level, or federal-level reflections of how your research can influence existing systems, how existing systems or societal contexts influence your research or the application of your research, and potential options for how to develop research-informed programs and policies.

If you feel that policy is an area in which you do not have much knowledge or experience, you have some options for strengthening your competence.

First, and in no particular order of importance, combining forces with a co-author or mentor who has some experience with or knowledge of policy can help you to develop a specific understanding of where to begin, especially as it relates to your area of study. As a new professional who has been teaching family and social policy courses for many years and a researcher with an interest in policy, I have found that my students and mentees often have more policy knowledge than they realize. Gaining policy knowledge is more a manner of learning about the formal systems and governing structures that are in place, identifying pre-existing policies and programs related to their area of research, exploring how macro-level policies influence the individuals and families they research, and using the appropriate language targeted to the various audiences who would be interested in the results.

Second, policy implications should be included as an important aspect of conceptualizing your research plan and developing your scholarly article. Implications are not an afterthought but an intentional part of your purpose and research design. Consider the audience for your research and the impact you want your research to have. Are you writing articles that can be easily picked up and digested by readers who make important policy or program-level decisions?

Third, explore policy research related to your area of interest. You will often need to digest journals you may never have read or search for materials such as policy briefs, program or policy reports written by government or contracted organizations, or even go straight to government websites. Take note of the language and think critically about how the information presented to you fits the reality or context of your research. Some of you may identify new research studies, find articles that need to be critiqued or disputed, or find ways to make your research more impactful. And finally, you may identify new research partners or stakeholders for promoting or enhancing your research.

As family scholars who are committed to systems thinking and viewing research through a systemic lens, leaving out policy implications would be like eliminating a subsystem from a family or surveying only one parent about a family-oriented issue. Most of us pursue research as much more than a personal accolade, viewing it as a way to add to the collective and research-informed discourse, as well as a means of creating positive change in our world. Policy may be the only practical way of making systemic or large-level change. As the context of our research changes—from the micro (e.g., participants and families) to the macro (i.e., political and ideological)—it is time to take responsibility and action for creating digestible and impactful research.

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• Writing Tips

How to Write an “Implications of Research” Section

4-minute read

• 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

Are you currently working on a thesis or dissertation? Once you’ve finished your paper (implications included), our proofreading team can help ensure that your spelling, punctuation, and grammar are perfect. Consider submitting a 500-word document for free.

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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I am taking a Research Design and Statistical Methods class. I am wondering if I can get tutors to help me with my homework to understand more about research and statistics. I want to pass this class. I searched on YouTube and watched some videos but I still need more clarification.

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Implications in research: A quick guide

Last updated

11 January 2024

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Short on time? Get an AI generated summary of this article instead

Implications are a bridge between data and action, giving insight into the effects of the research and what it means. It's a chance for researchers to explain the  why  behind the research. 

When writing a research paper , reviewers will want to see you clearly state the implications of your research. If it's missing, they’ll likely reject your article. 

Let's explore what research implications are, why they matter, and how to include them in your next article or research paper. 

• What are implications in research?

Research implications are the consequences of research findings. They go beyond results and explore your research’s ramifications. 

Researchers can connect their research to the real-world impact by identifying the implications. These can inform further research, shape policy, or spark new solutions to old problems. 

Always clearly state your implications so they’re obvious to the reader. Never leave the reader to guess why your research matters. While it might seem obvious to you, it may not be evident to someone who isn't a subject matter expert. 

For example, you may do important sociological research with political implications. If a policymaker can't understand or connect those implications logically with your research, it reduces your impact.

• What are the key features of implications?

When writing your implications, ensure they have these key features: 

Implications should be clear, concise, and easily understood by a broad audience. You'll want to avoid overly technical language or jargon. Clearly stating your implications increases their impact and accessibility. 

Implications should link to specific results within your research to ensure they’re grounded in reality. You want them to demonstrate an impact on a particular field or research topic . 

Evidence-based

Give your implications a solid foundation of evidence. They need to be rational and based on data from your research, not conjecture. An evidence-based approach to implications will lend credibility and validity to your work.

Implications should take a balanced approach, considering the research's potential positive and negative consequences. A balanced perspective acknowledges the challenges and limitations of research and their impact on stakeholders. 

Future-oriented

Don't confine your implications to their immediate outcomes. You can explore the long-term effects of the research, including the impact on future research, policy decisions, and societal changes. Looking beyond the immediate adds more relevance to your research. 

When your implications capture these key characteristics, your research becomes more meaningful, impactful, and engaging. 

• Types of implications in research

The implications of your research will largely depend on what you are researching. 

However, we can broadly categorize the implications of research into two types: 

Practical: These implications focus on real-world applications and could improve policies and practices.

Theoretical: These implications are broader and might suggest changes to existing theories of models of the world. 

You'll first consider your research's implications in these two broad categories. Will your key findings have a real-world impact? Or are they challenging existing theories? 

Once you've established whether the implications are theoretical or practical, you can break your implication into more specific types. This might include: 

Political implications: How findings influence governance, policies, or political decisions

Social implications: Effects on societal norms, behaviors, or cultural practices

Technological implications: Impact on technological advancements or innovation

Clinical implications: Effects on healthcare, treatments, or medical practices

Commercial or business-relevant implications: Possible strategic paths or actions

Implications for future research: Guidance for future research, such as new avenues of study or refining the study methods

When thinking about the implications of your research, keep them clear and relevant. Consider the limitations and context of your research. 

For example, if your study focuses on a specific population in South America, you may not be able to claim the research has the same impact on the global population. The implication may be that we need further research on other population groups. 

• Understanding recommendations vs. implications

While "recommendations" and "implications" may be interchangeable, they have distinct roles within research.

Recommendations suggest action. They are specific, actionable suggestions you could take based on the research. Recommendations may be a part of the larger implication. 

Implications explain consequences. They are broader statements about how the research impacts specific fields, industries, institutions, or societies. 

Within a paper, you should always identify your implications before making recommendations. 

While every good research paper will include implications of research, it's not always necessary to include recommendations. Some research could have an extraordinary impact without real-world recommendations. 

• How to write implications in research

Including implications of research in your article or journal submission is essential. You need to clearly state your implications to tell the reviewer or reader why your research matters. 

Because implications are so important, writing them can feel overwhelming.

Here’s our step-by-step guide to make the process more manageable:

1. Summarize your key findings

Start by summarizing your research and highlighting the key discoveries or emerging patterns. This summary will become the foundation of your implications. 

2. Identify the implications

Think critically about the potential impact of your key findings. Consider how your research could influence practices, policies, theories, or societal norms. 

Address the positive and negative implications, and acknowledge the limitations and challenges of your research. 

If you still need to figure out the implications of your research, reread your introduction. Your introduction should include why you’re researching the subject and who might be interested in the results. This can help you consider the implications of your final research. 

3. Consider the larger impact

Go beyond the immediate impact and explore the implications on stakeholders outside your research group. You might include policymakers, practitioners, or other researchers.

4. Support with evidence

Cite specific findings from your research that support the implications. Connect them to your original thesis statement. 

You may have included why this research matters in your introduction, but now you'll want to support that implication with evidence from your research. 

Your evidence may result in implications that differ from the expected impact you cited in the introduction of your paper or your thesis statement. 

5. Review for clarity

Review your implications to ensure they are clear, concise, and jargon-free. Double-check that your implications link directly to your research findings and original thesis statement. 

Following these steps communicates your research implications effectively, boosting its long-term impact. 

Where do implications go in your research paper?

Implications often appear in the discussion section of a research paper between the presentation of findings and the conclusion. 

Putting them here allows you to naturally transition from the key findings to why the research matters. You'll be able to convey the larger impact of your research and transition to a conclusion.

• Examples of research implications

Thinking about and writing research implications can be tricky. 

To spark your critical thinking skills and articulate implications for your research, here are a few hypothetical examples of research implications: 

Teaching strategies

A study investigating the effectiveness of a new teaching method might have practical implications for educators. 

The research might suggest modifying current teaching strategies or changing the curriculum’s design. 

There may be an implication for further research into effective teaching methods and their impact on student testing scores. 

Social media impact

A research paper examines the impact of social media on teen mental health. 

Researchers find that spending over an hour on social media daily has significantly worse mental health effects than 15 minutes. 

There could be theoretical implications around the relationship between technology and human behavior. There could also be practical implications in writing responsible social media usage guidelines. 

Disease prevalence

A study analyzes the prevalence of a particular disease in a specific population. 

The researchers find this disease occurs in higher numbers in mountain communities. This could have practical implications on policy for healthcare allocation and resource distribution. 

There may be an implication for further research into why the disease appears in higher numbers at higher altitudes.

These examples demonstrate the considerable range of implications that research can generate.

Clearly articulating the implications of research allows you to enhance the impact and visibility of your work as a researcher. It also enables you to contribute to societal advancements by sharing your knowledge.

The implications of your work could make positive changes in the world around us.

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Why Scholars Should Consider Policy Recommendations

Understanding the policymakers’ dilemma, how to begin developing policy recommendations, questions to ponder, yes, you can, writing policy recommendations for academic journals: a guide for the perplexed.

Professor in the Edmund A. Walsh School of Foreign Service at Georgetown University and a Senior Fellow in the Transnational Threats Project at the Center for Strategic & International Studies.

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Daniel Byman; Writing Policy Recommendations for Academic Journals: A Guide for the Perplexed. International Security 2024; 48 (4): 137–166. doi: https://doi.org/10.1162/isec_a_00485

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Academic research can inform decision-makers on what actions to take or to avoid to make the world safer, more peaceful, and more equitable. There are many good works on bridging the gap between policymakers and academics but few on how scholars writing in academic journals can influence the policy process. In contrast to most policy-focused research, academic journals have long shelf lives and provide space for scholars to present heavily researched empirical evidence, theories, and analyses. Long, well-researched articles can, over time, shape the broader narrative for how to think about complex issues. Scholars also tend to be more objective and less partisan than policymakers. Despite the potential importance of academic work to the policy debate, many scholars receive little training on why and how to make policy recommendations. To remedy this problem, steps are offered to guide scholars as they begin developing policy recommendations for their articles. These include recognizing the dilemmas that policymakers themselves face, considering the audience before starting to write, identifying and using policy option menus, among others. When crafting recommendations, scholars should consider the long-term implications of their research on current policy as well as recommendations that might lead to more effective approaches. At the same time, scholars should consider the costs and limits of their recommendations.

Climate change. The rise of China and the U.S. response. The dangers posed by ISIS and other terrorist groups. The ability of the United Nations, the IAEA, the NPT, and other institutions to manage nuclear proliferation. Civil unrest and the potential for peaceful change. Globalization's benefits and perils. These are among the most important security issues facing the world today—and they are issues that scholars can, and do, speak to regularly. Thomas Homer-Dixon's “Environmental Scarcities and Violent Conflict,” John Mearsheimer's “The False Promise of International Institutions,” Andrew Kydd and Barbara Walter's “The Strategies of Terrorism,” Maria Stephan and Erica Chenoweth's “Why Civil Resistance Works,” and Henry Farrell and Abraham Newman's “Weaponized Interdependence” are among the most-cited articles ever published in International Security , and their scholarly impact is considerable. 1 Beyond their theoretical contributions, these articles illustrate the potential that International Security and other academic journals have for speaking to policy issues. All of them contain important ideas proposing what decision-makers should consider, do, and not do to make the world a better place.

Despite the potential importance of academic work to the policy debate, new scholars receive little training on why and how to make policy recommendations. Some academic journals only pay lip service to policymaking or ignore it altogether. There are many insightful works on the gap between policymakers and academics and why it should be bridged, and a host of training workshops, government funding programs, and other efforts push in this direction, often with valuable results. 2 Other articles stress how to craft relevant recommendations in general. 3 Yet almost all these efforts focus on activities outside publishing in top academic journals, ignoring the important role that scholarly journals should play in shaping thinking on policy. This article seeks to fill this gap, advising contributors on how to write policy recommendations for articles in both International Security and, I hope, other high-quality academic publications that seek to inform the policy debate.

Providing helpful commentary on policy is challenging—as difficult as the academic research that leads to publication in a top journal—and it is doubly difficult when doing so for an academic journal. Publication time is measured in months or even years, in contrast to blogs and current affairs journals that offer more immediate turnaround. Policymakers rarely have time to read long articles, which are the staples of journals like International Security , and indeed “rarely have time to read what's not urgent in their inbox,” as one senior policymaker noted. 4 Perhaps most important, it is often difficult for academics to understand a policy, let alone the pressures that policymakers face and the conflicting objectives that they try to juggle.

Yet it is vital for scholars to learn the dilemmas that policymakers face and to be relevant to public and private policy debates so that their research can help make the world a better place. Engaging policy also makes for better research. By focusing on questions important to the policy world, scholars avoid the trap of scholasticism—that is, when they concentrate on internal debates rather than on the original problems that first inspired academic research. But writing serious policy recommendations requires considerable modesty: crafting effective policy is hard, and academics should recognize the limits of their findings and the difficulties of moving the policy needle.

When crafting recommendations, scholars should take advantage of their objectivity and ability to challenge the prevailing wisdom. They are also well-placed to use history to learn lessons and to draw insights from large datasets. Academic journals, for their part, endure: they have long shelf lives and allow deep dives, providing space for scholars to present heavily researched empirical evidence, theories, and analyses. Long, well-researched articles can, over time, shape the broader narrative for how to think about a complex issue such as the potentially peaceful nature of democracy or how to make deterrence more robust. 5 They may also help provide context when unexpected events occur and the policy community has little to draw on but theory and analogy.

As scholars begin crafting their articles, they should try to determine their policy audience—including leaders outside government in industry and civil society—to identify who might read their work and who might act on it. They should consider their variables, identifying ways to maximize better outcomes or minimize worse ones. To help academics think of ways to use their findings to influence policy, one strategy is to create or draw on existing menus of policy instruments (“What can diplomats do?” “How might financial tools assist with coercion or another goal?” and so on). At the same time, scholars should consider the costs and limits of their arguments, conveying enabling conditions and the level of certainty of their findings as well as their overall recommendations. Finally, they should use their academic journal work as a springboard for writing shorter pieces in policy journals, blogs, and opinion pages.

The remainder of this article has five sections. The first section explains why academic authors should speak to policy issues and why offering policy recommendations contributes to better scholarship. It also highlights the unique role of academic publications like International Security . The second section describes the dilemmas that policymakers themselves face—difficulties that scholars must recognize even if their ultimate advice criticizes or transcends these dilemmas. In section three, the heart of the article, I explain how an academic author might begin thinking about policy relevance. For some scholars, this process may involve identifying steps to take from the start of their research project. For others, it may involve considering how work undertaken with an academic audience in mind might also speak to policymakers. Section four poses a set of questions that scholars should consider as they craft their recommendations. It also illustrates how to apply the article's framework. The article concludes with a brief exhortation for scholars to engage the policy world in their academic research.

Policy-relevant scholarship is intended to produce findings that feature in the deliberations of government officials and others involved in policy decisions. This section makes three arguments. First, journals like International Security value policy relevance, as do most of the scholars who work on international security, and policy-relevant research can improve scholarship as well as inform policy. Second, academics have much to contribute to the broader policy debate. Third, academic journals have their own niche in the broader policy analysis ecosystem, complementing blogs, the opinion pages of major newspapers, and policy journals like Foreign Affairs .

the so what of the so what

Not every International Security article should be policy relevant, but most should. Scholarship seeks to expand human knowledge, but for international security, much of that knowledge is interwoven with policy challenges. 6 The vast majority of articles that appear in International Security speak to some aspect of policy, ranging from avoiding international and civil wars to improving alliances to the nature of the international system. 7

The editors of International Security consider the “so what” hurdle when they evaluate a submission—Why should a reader bother with your article when there are so many other ones to read instead? 8 This hurdle is much easier to clear if the author makes the policy connection unambiguous. If an article's central question matters to those responsible for waging war, preserving the peace, maintaining fiscal stability, improving governance, and otherwise trying to promote security, then that article—and its recommendations for avoiding dangerous outcomes and increasing the chances of positive ones—is worth considering for inclusion in a journal.

Aside from this important question regarding publication, many scholars seek to do relevant research. 9 It is likely that a policy question or world event piqued a scholar's interest in international relations. Many scholars believe (rightly!) that they can contribute to both internal government debates and broader, more public discussions of complex policy issues.

what scholars bring to the policy table

Scholars have much to offer the policy debate. Although scholars may be removed from the policymaking fray, that distance gives them a chance to present new ways of thinking about a problem and to take the long view. Unlike some policymakers, scholars are not driven by their inbox. This flexibility allows scholars to set long-term agendas. As the policy community celebrated the fall of the Soviet Union, for example, Graham Allison, Owen Cote, Richard Falkenrath, and Steven Miller presciently identified the threat of loose nuclear weapons and material from the former Soviet Union. Their evidence spurred policy attention and eventually action. 10

In the near term, scholars can challenge prevailing wisdoms. In 2002, John Mueller questioned the post-9/11 consensus that Al Qaeda would continue to conduct numerous mass-casualty attacks like 9/11 or even more destructive ones. As time went on, Mueller built on his research and proved his initial argument that 9/11 was an outlier for U.S. casualties. 11

Scholars have time to dig deep: research for an article can take many years, a luxury the policy community lacks. Scholars can also create large datasets and survey significant amounts of open-source material. Some of this analysis occurs within the intelligence community, but scholars often create better-designed datasets and have more time to read and digest publicly available material.

In addition, scholars are bureaucratically (though not politically) neutral. 12 They do not have a vested interest in whether the State Department or the Defense Department oversees a peacekeeping operation, for example. Policymakers often reason by analogy, whereas scholars are trained to thoroughly research particular cases in an unbiased way and draw conclusions across cases, both of which add more insight than using a simple analogy. 13 Perhaps most important, International Security and other leading journals publish rigorous, peer-reviewed articles that use precise and careful research methods and analyses to answer questions, challenge the conventional wisdom, validate empirical findings, and advance understanding about complex topics.

This reasoning may seem Pollyannaish, but imagine if scholars rejected policy contributions in their writing. There would be less work that is deeply informed, methodologically rigorous, and carefully reviewed. Daniel Drezner points out that many nonacademic public intellectuals are more partisan and less open to criticism than their academic counterparts. Such partisanship decreases the quality of public intellectuals’ work though not their influence. 14

More focus on policy can also lead to better scholarship. Making policy is difficult, and making good policy is even harder. By speaking to these challenges, scholars are forced to ask themselves knotty questions and to better understand what they study and the data on which they rely for their analyses. For example, an academic who engages policy seriously may recognize that the paper trail of memoranda and strategy documents is often more spin than substance. 15 With policy concerns in mind, scholars are less likely to emphasize elegant scholarship that elides real-world difficulties. As Rebecca Adler-Nissen argues, “Part of the reason why ‘bad ideas’ are allowed to develop in the first place is that we have established a hierarchy of prestige that values ‘clean’ and ‘elegant’ scholarly ideas over the ‘messy’ ones of practitioners.” 16 She adds that policy engagement forces scholars to “begin to develop ideas that acknowledge the complexities, paradoxes, and hidden politics of ‘policy.’ We become curious about what practitioners find appropriate, shameful, or important.” 17

Policy recommendations can be dangerous things. Policymakers might take them out of context to “prove” that their desired outcome is the best option. Work on the democratic peace, for example, was used to justify the 2003 invasion of Iraq. But this justification overlooked important context: many findings that extol the benefits of the peace do not focus on forced regime change. 18 Paul Musgrave warns of “lab leaks” in political science, whereby magazines like the Atlantic and Foreign Policy present concepts with little context and fewer caveats, making them more likely to be misused. 19 Scholars may also fear being labeled as activists if they promote particular policies. They are also taught to avoid normative language—a luxury that policymakers do not share.

The cures for these potential ills, however, are not to avoid policy engagement but to take it more seriously. Scholars cannot control the conclusions that people draw from reading their work. But clear writing and speaking directly to policy concerns make it more difficult for others to misuse a scholar's ideas for their misguided policies. As Erica De Bruin argues, “If irresponsible public scholarship is the issue, then developing a more rigorous ethic of public engagement is vital.” 20 Similarly, activism is a good thing when it reduces the risk of war, improves human rights, and otherwise makes the world a better place. In addition, as Charli Carpenter points out, engaging with advocacy organizations often helps researchers overcome academic biases because it requires them to consider different issues and learn about new problems. 21 The key for scholars is to ensure that their work remains rigorous and objective, which at times means recognizing that their preferred approach has flaws and limits.

the unique role of academic journals in the policy debate

Academic journals like International Security are part of a vast ecosystem of outlets that all claim to speak to policy issues. These include foreign-policy-oriented long-form journals like Foreign Affairs and Survival , general media outlets such as the New York Times , Yomiuri Shimbun , and the Economist , podcasts like those featured at War on the Rocks , specialized outlets like Arms Control Wonk and Lawfare , and numerous others. As articles on policy relevance argue, scholars should publish in these outlets—and many do! These platforms publish articles more quickly than academic ones, enabling scholars to speak directly to the issues of the day.

Academic journals have their niche as well. In some cases, particularly when the danger is possible but not imminent (e.g., if China were to invade Taiwan), an academic journal might be an ideal outlet for in-depth work. Michael O'Hanlon wrote such a piece in 2000, and its findings remained relevant for years even as the international environment and China's military capabilities changed. 22 Scholars can try to provide general guidance on a more specific problem. A 2020 piece explaining the sources of Russian bellicosity, for example, would still be helpful to policymakers in 2022, though it would need to be updated with insights on the invasion of Ukraine.

The longer length of an academic article also has trade-offs. A typical International Security article has 10,000–15,000 words, and some are even longer. For Foreign Affairs , the recommended length is 2,000–5,000 words, and for the New York Times it is 800–1,200 words. 23 Shorter pieces are more likely to be read, especially by more senior policymakers with crammed schedules. Conversely, it is harder to go into depth in a short piece.

The flip side of a long review process and longer length is a long shelf life. 24 Articles for International Security deliberately speak to broader issues and long-term problems. Consequently, they may remain relevant for many years. Twenty years after publication, Andrew Kydd and Barbara Walter's work on the strategies of terrorism still speaks to terrorist groups’ goals and methods. 25 Similarly, Caitlin Talmadge's focused study on Iran's possible blocking of the Strait of Hormuz remains highly relevant almost fifteen years later, identifying the many challenges to Iran, the possible responses for the United States, and so on. 26 Additional scholarly work, technological advances, and geopolitical change may affect the issues discussed in both articles, but the authors provide a set of concepts that create a valuable foundation on which to build policy.

In addition, the long shelf life changes the readership and value of the piece, allowing scholars to shape worldviews that inform a host of decisions. An International Security article may be on a syllabus for years or even decades after publication, framing how an issue is thought of for years to come, as RAND research on nuclear strategy did in the 1950s and 1960s. 27 It is plausible that a twenty-year-old student who reads an academic piece may retain its concepts and arguments as a mid-level policymaker two decades later. In the words of John Maynard Keynes, “practical men, who believe themselves quite exempt from any intellectual influence, are usually the slaves of some defunct economist.” 28 Be that defunct economist.

Journals like International Security are also more open to historical deep dives and reinterpretations. Because analogical reasoning is so prevalent, changing an understanding of an important historical event, such as the outbreak of World War I or the Cuban missile crisis, can inform how to think about what to do in the present. Keir Lieber points out that historians and policymakers like Henry Kissinger misunderstood World War I as an inadvertent conflict. This misreading has profound implications for how to gauge the likelihood of small great power disagreements accidentally spiraling into major war. 29

In some cases, rare ones but with high impact, a discontinuous event takes an article off the shelf. Should China invade Taiwan in 2025, an in-depth piece written in 2020 in a journal like International Security might be among the most comprehensive external guides to a policy challenge that would consume world attention, even if it does not discuss this specific crisis. Naazneen Barma and James Goldgeier note that Swedish Nobel laureate Gunnar Myrdal's The American Dilemma , a masterful 1944 study of race in the United States, was largely ignored when published, and its sponsors disowned it for years. In 1956, however, its findings helped shape the Brown v. Board of Education decision, one of the most consequential Supreme Court rulings in U.S. history. 30

Because academic articles are often explicitly theoretical, they also offer insights into new or related but distinct situations. For example, the 2011 Arab Spring upended long-standing policies toward area regimes. Even though pre-2011 articles on democratic transitions, the impact of military coup-proofing, civil war resolution, and similar topics that are common in security-related academic journals do not necessarily focus on the conditions in Egypt, Tunisia, or other affected countries, they nevertheless offered many potential insights during the turbulence. 31 Policymakers might have learned ways to promote successful elections, avoid (or predict) coups, prepare for civil violence, and so on. The key concepts endure, even if the dates and places are different.

Bureaucracies often amass considerable knowledge on complex issues, and policymakers are rarely stupid. Yet many policies seem foolish and ill-informed, especially in hindsight. To understand why the policy outcome often differs from the ideal, it is vital to understand the many pressures and restrictions that policymakers face. Academics should not treat these difficulties as immutable or even excusable, but recommendations should reflect an understanding of the policy itself and how to improve outcomes. Identifying the actual policy is a difficult aspect of research that should accompany the broader academic research process. Perhaps most important, academics should approach policy influence with considerable humility: in providing advice to smart, knowledgeable people, often the academic is not aware of all, or even most, of the challenges confronting the policy community.

policy constraints

Policymakers make their own policies, but they do not make them just as they please. Some factors, such as geography or the polarity of the international system, are invariable. Although many elements that constrain decision-making and agency are malleable, these constraints are often tight, making it hard for policymakers to break out of a narrow set of options.

Policymakers are beholden to their publics and to elites. These limits apply in both democratic and authoritarian systems, albeit in different ways and to different degrees. 32 It is tempting to urge “leadership” as a recommendation, and at times policymakers do go against the preferences of their constituents and supporters. But leaders are understandably wary of jeopardizing their political status.

Competing priorities and limited resources also constrain policymakers. The many issues that scholars examine—civil wars in sub-Saharan Africa, refugee flows in Southeast Asia, the destabilizing effects of new weapons systems, and problems with security assistance, to name only a few—compete with one another and with numerous other concerns. For the most senior policymakers, they also compete with domestic priorities, which are usually more salient. Recommendations that call for more aid to a region, more training to an ally's military, and so on all come at a cost, with other priorities receiving less money and attention as a result.

Policymakers also must act with only limited information. Colin Powell recalled that if he waited for enough facts to be 100 percent right, it meant it was too late to act. 33 Although U.S. intelligence proved remarkably prescient about the likelihood of the 2022 Russian invasion of Ukraine, the Joe Biden administration did not know if Ukrainian forces would hold out, how key allies in Europe would respond, or how a then-unpopular leader like Ukrainian President Volodymyr Zelensky would respond when greatness was thrust upon him. 34 Policymakers also had to worry about less probable but potentially catastrophic concerns like nuclear escalation. Recommendations must recognize the many uncertainties and scenarios that policymakers are likely to face after a terrorist attack, as a civil war is breaking out, or when a peaceful movement seeks to overthrow a dictatorial regime.

In short, policymakers are in a proverbial box, constrained by politics, resources, competing priorities, and limited information. For academics to think outside this box, they must understand why policymakers are in it, which parameters are possible to shift, and which are likely to hold firm.

the difficulties of understanding policy

Understanding policy takes time, and it is easy to get wrong or caricature. First, policies often embody multiple strategic, bureaucratic, and political interests. Second, an administration may provide conflicting or confusing signals as to what its true policy is. Third, the resulting complexity makes coding difficult. Fourth, policies are often bad because alternative policies are worse; criticism should recognize this reality.

One barrier to understanding policy is that a single issue may be interwoven with a wide array of interests. Take, for example, negotiations with Iran as embodied in the Joint Comprehensive Plan of Action. In this instance, U.S. policymakers balance a range of goals, including: stopping Iran's nuclear weapons program altogether; pausing the program for several years; shoring up international regimes like the 1968 Treaty on the Non-Proliferation of Nuclear Weapons (NPT); leading allies in Europe and elsewhere that also oppose Iran's nuclear program but are more eager for commercial ties to Tehran; reassuring regional allies that are suspicious of Iran; condemning Iran's support for militant and terrorist groups in the greater Middle East; supporting Iranian demonstrators seeking regime change; and winning over a domestic population that is highly suspicious of any relationship perceived as forgiving to Iran. 35 Judging success is difficult, as some of these goals are incompatible. Policymakers maximize some interests, satisfice others, and “fail” on still others. 36 A recommendation that improves the odds for success in one area may hinder it in others. For example, the Barack Obama administration succeeded in nuclear negotiations with Iran in part because U.S. policymakers avoided entangling the nuclear discussions with demands regarding Iran's support for terrorist groups.

Multiple signals from an administration make determining the actual policy difficult. Governments issue public strategy documents but engage in private and even covert diplomacy that can be more consequential. Diplomats may join treaty negotiations but have private instructions to ensure that negotiations fail. Officials use public statements not only to delineate a policy but also to close off rival approaches. For example, an administration publicly condemning the assassination of Saudi journalist Jamal Khashoggi on one day would find it more difficult to sell Riyadh weapons the next. At times, the public statement is virtue signaling, staking the moral high ground even as most administration policies either do not follow through on lofty goals such as promoting human rights or opposing aggression or even go in the opposite direction. Western leaders, for example, called for Syrian dictator Bashar al-Assad's ouster but did not provide the Syrian opposition with sufficient military support to make it happen. 37 Was regime change truly their goal?

The many interests involved in policy decisions, the multiplicity of signals coming out of governments, and the possibility of virtue signaling all pose coding problems. It is challenging for scholars to determine success or failure or to assign values to other binary measures of policy that are often used in large datasets. Similarly, the signaling confusion makes it hard to for researchers to know which coding they should use.

For academics, the many interests and confusing signaling also pose a data problem. Official strategy documents can be great sources, but they can also be misleading. President Donald Trump's 2017 National Security Strategy seemed to bear little relationship to his administration's overall foreign policy. Indeed, in some ways actual policy contradicted the guidance, with the strategy embracing a strong role for U.S. leadership in the face of great power competition when President Trump was highly critical of traditional U.S. allies and was cozying up to Moscow. 38 Often deliberations occur in private before a formal meeting. The meeting record is thus a ratification, not a reflection of discourse. Understandably, scholars often have a bias toward the written word, whereas it is the briefing or private conversation that matters the most to many policymakers. Some written policy documents may accurately reflect the authors’ views. But policymakers are less likely to document their political and bureaucratic interests, which leads scholars to have a bias toward strategic explanations.

Indeed, almost every policy addressing a complex problem is insufficient and often the only alternatives are bad ones—so which bad one is the best? As David Baldwin notes in his assessment of economic sanctions, “If the menu of choice includes only the options of sinking or swimming, the observation that swimming is a ‘notoriously poor’ way to get from one place to another is not very helpful. And if the principal alternative to economic sanctions is appearing to condone communism, racism, terrorism, or genocide, the observation that they are a ‘notoriously poor tool of statecraft’ may miss the point. In the context of the logic of choice, the evaluation of one policy alternative in isolation from others makes little sense.” 39 Policymakers may have an ambitious declared goal (“stopping human rights abuses”), but in practice they may settle for a range of lesser achievements, such as slightly reducing human rights abuses by making it more difficult for a regime to access resources, signaling disapproval to gain allied support, avoiding pressure to use military force that may backfire, and so on. Recommendations that do not at least acknowledge the poor range of options available to policymakers will not be convincing.

Taylor Fravel and Charles Glaser's work on the South China Sea and U.S. policy is an excellent example of how scholars can avoid these traps. 40 Fravel and Glaser are careful not to caricature current policy as they describe alternatives such as greater retrenchment and more intense military resistance. They detail the conditions under which different alternatives might be appropriate and suggest specific policies to accompany the alternatives, such as clarifying ambiguous treaty arrangements, imposing substantial economic sanctions, implementing shaming measures when China violates norms, and deploying surface and air forces, among many others. Overall, the reader is left with a better understanding of the balance that current U.S. policy is trying to strike, which Chinese actions would suggest the policy is failing, and the many potential downsides of different approaches, particularly how more aggressive efforts risk unwanted escalation.

the need to transcend the dilemma

Scholars must understand the policymakers’ dilemma and factor it into their recommendations, but they should not be bound by it. Pointing out the weakness of a policy in addressing a problem is an important service, and strong arguments can help policymakers advocate for more resources, shift priorities, or even take political risks in the face of domestic and elite opinion. Even better, however, is offering a plausible alternative. A particularly important role for scholars is to help policymakers reconsider factors that the policy community sees as insurmountable rather than malleable, such as identifying ways to overcome long-standing animosities, to reshape public support, to reprioritize regional objectives, and so on.

Scholars are taught to dissect complex problems, but often they focus on a lacuna in the literature rather than the policy implications of their research findings. 41 If they focus more on the advantages of a particular method or on why a variant of one major paradigm is better than another, such elements by themselves are of little interest to the policymaker. Below I both offer advice for authors as they begin to craft a piece designed to increase policy influence and list factors to consider as their research progresses. Some of these steps may prove useful even if the scholar sees the research as primarily academic.

step one: help a policymaker solve a problem

For those interested in speaking directly to policymakers as well as academics, start with the “so what” that motivated the research in the first place to determine how it fits into the policy world. What factors shape current policies, how malleable are they, and what alternatives are on the table? What will policymakers learn from reading the article that may improve their understanding about the problems they face and that may offer potential solutions? The article's focus should help solve a problem that is in a policymaker's inbox or, just as important, should be in their inbox. As the then deputy secretary of state James Steinberg noted, policymakers are “desperate” for ideas and solutions. 42

step two: consider, realistically, the audience

Before scholars begin their research, they should examine the issues that different kinds of policymakers or policy influencers see as important. Make sure to include those topics in the essay in a way that their audience can recognize, understand, and appreciate. Some policymakers write strategic documents, others evaluate weapons systems, still others negotiate treaties, and so on. Sometimes scholars decide that their work will have the greatest impact if they inform the media. The research findings should help reporters enlighten the public and ask nuanced and informed questions of government officials. Likewise, if the research will have the greatest impact by shaping the thinking of undergraduates and masters’ candidates—the next generation of policymakers—consider how to structure the whole essay, and possibly the research more broadly, to be most useful and effective for that audience.

With the issue in mind, identify the target policy audience. For those writing on a common topic like alliances and war, North Atlantic Treaty Organization (NATO) countries’ ministries of defense and foreign affairs, the leaders of Asian democracies, and intelligence chiefs are some (ambitious) possibilities. But authors should think broadly, beyond just governments, even if their piece is focused on traditional interstate security issues. For example, social media companies have emerged as important players in the information realm. With three billion monthly active users, Facebook's decisions on who to allow on its platform and what can be discussed are often more consequential than various foreign ministries’ statements. Companies’ content moderation policies and crisis response protocols, or lack thereof, can hinder or enable genocide. 43 Schools can teach new subjects (or old subjects differently), affecting reconciliation between once-embittered communities and public attitudes toward age-old problems like the impact of discrimination. Civil society and advocacy organizations such as the International Campaign to Ban Landmines can shift discourse, rally domestic coalitions (remember the box and how domestic politics shapes it!), and advance international law. Also consider where on the policy food chain your audience is. The U.S. president can change things more quickly than the assistant secretary of state for the Bureau of International Security and Nonproliferation, and the assistant secretary has more power than a desk officer. But guess which one has more time to read your article?

Authors who want to speak directly to policymakers should think of ways to infuse their entire project—puzzle, theoretical approach, hypothesis testing, and presentation of findings—with answers to both scholarly and policy questions. In addition to presenting rigorous methods and building on existing literatures, an article's theoretical and hypothesis sections must also make sense to a curious policymaker. Why is comparing many states’ approaches to military training better than delving deep into one example (or vice versa)? Why present some explanations but not others? A policymaker working on this issue should nod her head as she goes along rather than wonder why vital, obvious details that are necessary to make progress on a problem are missing from an article's analysis.

step three: emphasize useful variables and proper linkages

Scholars can also emphasize certain variables in their analyses to identify ways that policymakers can achieve the best results. If civil wars are correlated with ethnic disputes, economic inequality, or poor governance, then policies that foster ethnic harmony, greater equality, and reduced corruption should be encouraged. Policymakers would eagerly listen to scholars who provide specifics on what has worked. Articles that focus on methods can also be useful to policymakers, though drawing policy insights from such works often requires a bit more effort. Nicolas Sambanis, for example, argues that changes to both the threshold of violence used to define a civil war and the coding of intrastate, interstate, and extrastate wars can dramatically alter findings regarding peace duration and the causal power of economic problems, among many others. 44 If articles that draw on such findings are not robust, as Sambanis's research suggests, then policy recommendations built on them should be reconsidered.

Some scholars may prefer to skip the above steps and instead focus on the article's contributions to the academic literature. If they do so, however, they can still write useful policy recommendations. Having read a scholar's research, the intended current or future policymaker may be more informed about the causes of war, why alliances fracture, barriers to ethnic reconciliation, and other grave problems and perhaps better equipped to reduce these dangers. The author should now ask, “Given the findings from my research, what makes desirable policy outcomes more likely?” This might involve pointing out tensions between different policy approaches. Lindsey O'Rourke, for example, finds that policymakers prefer covert regime change over overt measures because they can deny responsibility for failures and reduce criticism for meddling in general. Efforts to ensure deniability, however, make the operations less likely to succeed. Highlighting this trade-off between two competing benefits (deniability versus improved chances of success) is important and increases the article's utility to policymakers. 45 This step tends to be easier if scholars have designed their work with policy concerns in mind from the start. Even so, articles that are more academic facing may still make desirable policy outcomes more likely.

Scholars’ recommendations should flow from their analyses. Such a point seems straightforward, even obvious. But often policy recommendations stray from the analytic foundations on which they claim to rest. For example, the 9/11 Commission issued a powerful report condemning intelligence failures and calling for major structural reforms, particularly to centralize intelligence under a director of national intelligence. 46 As Richard Posner pointed out, however, among intelligence agencies it was only the Federal Bureau of Investigation (FBI) that demonstrated major structural failures in combating terrorism. 47 The 9/11 Commission nevertheless recommended major institutional changes elsewhere in the intelligence community but not in the FBI. Although the report indicated that post-9/11 information sharing worked well and that Central Intelligence Agency (CIA) paramilitary operations were effective and agile, it recommended centralizing information sharing and moving the CIA's operations under the Defense Department. To be clear, such recommendations may have been good ones, but they did not match the substance of the commission's findings on information sharing or paramilitary operations.

Finally, avoid offering hackneyed recommendations. A recommendation that urges policymakers to focus on economic growth may be vacuous. A call for more intelligence may be hard to enact. Instead, provide thoughts on why intelligence is currently lacking. 48

step four: create a menu of policy options

Depending on the research topic, there are many practicable ways to influence policy. It is useful to consider all options, even if most do not end up being relevant to your findings. The U.S. military teaches its students that the instruments of power are summarized by the acronym DIME (diplomatic, informational, military, and economic)—and that is one place to start. 49 What might a diplomat do to sail the ship of state in the right direction? Is more intelligence needed? If so, could government education or propaganda campaigns help? What about the many variations of military force? Do economic tools such as sanctions help? Sometimes the answer to such questions is a clear and quick “no.” But combining these tools can often move policy forward. There are numerous variations of DIME, such as MIDFIELD (military, informational, diplomatic, financial, intelligence, economic, law, and development), which brings in financial and economic tools, international and domestic law, and so on. 50 In other cases, changes to education policy might be appropriate. Regardless of the preferred abbreviation, if any, thinking through a list of tools is a useful way to start.

Combinations are particularly important. Policymakers rarely rely on one instrument, and saying that such an instrument succeeds or fails, by itself, is less persuasive than discussing combinations.

Another approach is to think of a checklist for policymakers. Alexander George's writing on coercive diplomacy, for example, offers both contingent generalizations on when it works and factors for policymakers to think through. In essence, George shows how structured, focused comparisons of past cases help policymakers assess what they need to know and do for current developments. 51

step five: consider costs and limits of your policy recommendations

Some policy recommendations may be highly effective but also involve high costs in lives or money. Others help solve one problem but introduce others. Considering the findings, what current policies are making things worse or simply wasting money? Similarly, what costs and trade-offs are likely if a policymaker implements the article's recommendations? In general, it is always useful to ask, “Why is this not being done already?”

The findings that promoting regime change in Iran would lead to many casualties and would foster anti-Americanism would be useful to share with policymakers. Yet a recommendation to avoid foreign intervention might mean accepting a hostile, nuclear-armed Iran. It is easier for policymakers to dismiss research that fails to consider the latter possibility. Other policies are just expensive. Asking Asian countries to respond to China's rise by vastly increasing their anti-access/area denial capabilities may be sensible, but doing so is costly. Political leaders have other uses for the money.

By contrast, examining the cost of existing policies can generate new recommendations. For example, Kenneth Pollack finds that U.S. efforts to train Arab militaries using a U.S. military model is a recipe for failure given different political, cultural, and institutional settings. This seems like a finding that would lead to a recommendation to stop training altogether. Even though ineffective training is often useless in a military sense, stopping it would anger allied elites and harm bilateral relationships. Pollack thus recommends that the United States shift how it trains foreign militaries to better recognize these differences. 52

With the above in mind, make recommendations that are distinctive and clear. In her work on military training, Renanah Miles Joyce contends, “Liberal providers should emphasize building institutions that help to regulate military behavior rather than prioritizing individual or unit-level training with a normative component tacked on.” 53 This sentence packs a lot of substance: it identifies the actors (liberal states that provide military assistance), the policy that needs to change (prioritizing individual and unit-level training), and the proposed alternative (building institutions).

Another approach is to think about the policy box and where you stand in relation to it. It is tempting for scholars to ignore politics and simply point out the best answers. Yet recommendations that incorporate political realities are potentially more influential. Often, a mix is best. A scholar might note that today's political reality makes the ideal policy infeasible. Instead, the author might recommend a suboptimal but still useful set of steps: “As long as U.S. domestic politics makes a return to the Trans-Pacific Partnership [TPP] difficult, a less effective but valuable step would be to engage in a series of bilateral trade agreements that, cumulatively, offer lesser but still important security benefits similar to TPP.” This recommendation acknowledges the preferred solution (TPP) and offers a more politically plausible middle ground (bilateral agreements) that a policymaker could consider. Similarly, it is important to acknowledge resource constraints but not be bound by them: “Ideally, Taiwan would purchase a suite of anti-access/area denial capabilities rather than rely on more traditional systems like tanks, and it should begin with Harpoon anti-ship missiles.” 54

step six: use research as a springboard for other outputs

After conducting exhaustive research and developing informed recommendations, a scholar can repackage their research for podcasts and as shorter pieces for magazines like Foreign Affairs , outlets like Lawfare , and newspapers. These shorter pieces come to the attention of policymakers at multiple levels and make it more likely that at least a few of them may engage the longer work. After Keir Lieber and Daryl Press published their deeply researched findings on challenges to nuclear deterrence in International Security , they reached broader audiences by sharing their results in Foreign Affairs and the Atlantic . 55

Believe it or not, many editors welcome pitches from informed, serious scholars, even if the scholars have not previously written for popular publications. Most outlets have information on where and how to submit on their websites. Emailing editors directly is also an option. 56 In many cases, busy editors will not respond to inquiries or will otherwise not give a pitch the time it deserves. So be it. Curse to yourself, move on, and submit elsewhere, repeating as necessary. After initial contact is made, subsequent submissions are often easier, especially if an author proves to be authoritative, responsive, and otherwise easy to work with. I edit the “Foreign Policy Essay” at Lawfare and regularly feature content that draws on long academic articles. A simple email to me usually leads to a response—the author and I discuss if a piece might be suitable and, if so, how it might draw on the original research but reach a different, policy-focused audience.

After following some form of steps one through six, there are several important questions that scholars should pose to themselves as they draft policy recommendations for their articles. These questions have no right answers—but considering them will help properly situate the research in ways that policymakers find useful.

It is fine to think big, and it is also fine to think small—each category has different audiences and different impacts. Mid- or senior-level officials are more likely to act on smaller, more fine-grained recommendations. They can use their bureaucratic power to advocate purchasing a particular weapons system, strengthening an international organization, or using financial tools instead of military force to coerce an adversary. They cannot, however, easily establish a new norm on a controversial topic, jettison the 1947 National Security Act, or dump a long-standing ally in favor of a new one. Yet such broad recommendations, even if infeasible in the short or medium term, are part of what academics contribute to a debate. By changing public and elite perceptions over time, scholars can give policymakers more agency to overhaul their approach, thereby loosening the constraints of the policy box. Another factor to consider is the timing of a recommendation. For example, proposing that the European Union change its aid recipients as the deadline for doing so approaches might be more influential than making that same recommendation months or years after the deadline has passed.

do the recommendations solve the problem or move the needle?

A related question is whether a recommendation focuses on either solving or mitigating a problem. The former, obviously, is better, but in most cases it is unrealistic. If scholars have solutions for how to finally end civil wars, reconcile embittered ethnic groups, or ensure that nuclear war is an impossibility, then they should propose them! Yet small improvements in dangerous situations are tremendously valuable. Recommendations that make a civil war a little less likely, reduce the odds of a counterproductive intervention, or minimize wasted time or resources have measurable consequences. Offering a recommendation that reduces the number of refugees from one million to 950,000 is less consequential than preventing the disaster that created one million refugees. But it is still monumental to have fifty thousand fewer refugees, even if the reduction seems insufficient to the scale of the suffering. Most scholarship at best slightly shifts policy, and academics should be comfortable, indeed proud, that it does so. Academic authors should recognize that sometimes only limited progress is possible given resources, the limits of policy instruments, and political realities.

conveying uncertainty

Policymakers and government analysts are often wrong. And so are academics. A powerful advantage of academics, however, is (or should be) that mistakes are learning opportunities. Scholars can revisit foreign policy decisions and, by understanding why people were wrong, identify neglected variables or scope conditions. This power, however, comes with responsibility. Just as methods and sources have inevitable limits and gaps, so do policy recommendations. Small variations in findings—“sanctions always fail” versus “sanctions usually fail”—have profound policy implications, and those variations should be made clear. In addition, scholars should reevaluate their work and highlight their mistakes as ways to ensure their integrity. 57 Scholars can and will be wrong, and refusing to acknowledge this by making excuses or otherwise avoiding responsibility misses a learning opportunity and reduces the scholar's credibility. In conveying uncertainty, scholars should strike a balance between showing humility by acknowledging research limitations and offering policymakers clear advice despite unknown or conflicting variables.

are you writing implications or recommendations?

Although this article focuses on policy recommendations, an important (and at times easier) variant is to consider policy implications: How do research findings inform existing policies? This differs from a recommendation, which asks: “What should policymakers do differently in light of the research findings?” Consider this statement: “There is no need to stop Iran from acquiring nuclear weapons because it is unlikely to use them.” If policymakers are convinced by the argument, they might abandon efforts to coerce Iran or otherwise dramatically change their approach. Another variant is to warn of possible problems with a current approach: “Efforts to stop Iran from acquiring nuclear weapons will face challenges from Iran's insecurity about its own defense capabilities, from the different interests of Iran's trading partners, and from a distrust of U.S. credibility following the U.S. withdrawal from the Joint Comprehensive Plan of Action under President Trump.” This latter example offers no new policy but warns current policymakers about potential obstacles to success. Presumably, policymakers could try to mitigate these constraints, such as by providing side payments to trading partners, but the author is not making a specific recommendation.

By contrast, consider an approach that focuses on recommendations: “To stop Iran from acquiring nuclear weapons, the United States and its allies should focus less on military pressure and more on tightening economic sanctions.” Ideally, a scholar would provide examples for how to do such tightening. When in doubt, it is better to be direct than to let others draw their own conclusions.

Pointing out both policy recommendations and policy implications is valuable for the reasons discussed above. Policymaking is difficult, and solutions are not always obvious. Relatedly, sometimes a particular recommendation is uncomfortable (e.g., don't do humanitarian intervention or otherwise help a vulnerable population). This discomfort does not mean that scholars should avoid highlighting unpopular policy implications. Ideally, scholars would embrace this role as they are less likely than someone in government to suffer career harm from an unpopular position.

Table 1 presents five International Security articles that were published in the last five years. These examples highlight a wide range of topics that might matter to policymakers. 58 Some focus on a clear policy issue like civil-military relations or on an instrument like military training. Others seem more abstract, examining the nature of the international system or hostile uses of water. The authors represent a mix of both senior scholars and people at earlier career stages. Without claiming expertise on any of these issues myself, I identify potential audiences, policy recommendations, and other policy platforms for each article. (Note: the scholars themselves may disagree with my read on the implications of their work.)

Examples of Policy Audiences, Policy Recommendations, and Policy Implications of Select International Security Articles

SOURCES: See note 58 for the citations for these five International Security articles. The spin-offs listed in column four include: John J. Mearsheimer, “The Inevitable Rivalry: America, China, and the Tragedy of Great Power Politics,” Foreign Affairs , Vol. 100, No. 6 (November/December 2021), https://www.foreignaffairs.com/articles/china/2021-10-19/inevitable-rivalry-cold-war ; Isaac Chotiner, “Why John Mearsheimer Blames the U.S. for the Crisis in Ukraine,” New Yorker , March 1, 2022, https://www.newyorker.com/news/q-and-a/why-john-mearsheimer-blames-the-us-for-the-crisis-in-ukraine ; The U.S.-China Technology Relationship in Flux,” panel discussion (transcript), Brookings Institution, Washington, DC, October 4, 2019, https://www.brookings.edu/wp-content/uploads/2019/10/fp_20191004_china_tech_transcript.pdf ; Andrea Kendall-Taylor et al., “Henry Farrell and Abraham Newman Discuss ‘Weaponized Interdependence,’” Brussels Sprouts , podcast, Center for a New American Security, March 6, 2020, https://www.cnas.org/publications/podcast/henry-farrell-and-abraham-newman-discuss-weaponized-interdependence ; Henry J. Farrell and Abraham L. Newman, “This is What the Future of Globalization Will Look Like,” Foreign Policy , July 4, 2020, https://foreignpolicy.com/2020/07/04/this-is-what-the-future-of-globalization-will-look-like/ ; Henry J. Farrell and Abraham L. Newman, “The U.S. Is the Only Sanctions Superpower. It Must Use That Power Wisely,” New York Times , March 16, 2022, https://www.nytimes.com/2022/03/16/opinion/us-russia-sanctions-power-economy.html ; Henry J. Farrell and Abraham L. Newman, “America Weaponized the Global Financial System. Now Other Countries Are Fighting Back,” Monkey Cage (blog), Washington Post , December 19, 2019, https://www.washingtonpost.com/politics/2019/12/19/america-weaponized-global-financial-system-now-other-states-are-fighting-back/ ; Risa Brooks, “The Erosion of Civil-Military Relations,” Power Problems , podcast, Cato Institute, November 16, 2021, https://www.cato.org/multimedia/power-problems/erosion-civil-military-relations ; Risa Brooks, Jim Goldby, and Heidi Urben, “Crisis of Command: America's Broken Civil-Military Relationship Imperils National Security,” Foreign Affairs , Vol. 100, No. 3 (May/June 2021), https://www.foreignaffairs.com/articles/united-states/2021-04-09/national-security-crisis-command ; Risa Brooks, “What Can Military and Civilian Leaders Do to Prevent the Military's Politicization,” War on the Rocks , April 27, 2020, https://warontherocks.com/2020/04/what-can-military-and-civilian-leaders-do-to-prevent-the-militarys-politicization/ ; Sam Ratner, “The Stuff of Life and Death: Part II,” The World , May 4, 2021, https://theworld.org/stories/2021/05/04/stuff-life-and-death-part-ii ; Renanah Miles Joyce, “Rethinking How the United States Trains Foreign Militaries,” Lawfare , August 14, 2022, https://www.lawfaremedia.org/article/rethinking-how-united-states-trains-foreign-militaries .

To illustrate the framework and some of the points above more fully, consider a hypothetical article that examines alliances, a staple topic in international relations. The scholar asks, “What causes major power alliances to fail?” To make the work more policy relevant, the author should investigate the policies of the United States or other relevant countries, seeking to understand why they do what they do. Why did alliances with a particular focus, strength, and scope emerge, and what limits did they have? This investigation might involve reviewing government records, interviewing diplomats, and otherwise treating this baseline question as its own research topic. When doing interviews, it is useful to ask counterfactuals to determine why different results did not occur: Why were certain desirable countries excluded or problematic countries included in the alliance? Why was a particularly difficult coordination mechanism included or an alternative excluded? Overall, the scholar should try to get a sense of why the status quo emerged the way that it did.

With this background in mind, it is time to look forward. The question—what causes major power alliances to fail—is of obvious interest to a U.S. or an Asian diplomat, a NATO leader, or another official who might be involved in strengthening alliances. Nonetheless, it is hard to consider a specific audience for this topic. If the piece is highly relevant to the United States, the audience might be the regional bureaus at the State Department, which manage diplomatic relations for their parts of the world. Another option is the Office of the Secretary of Defense for Policy, which has departments that focus on the Indo-Pacific region, Strategy and Plans, and International Security Affairs, among others. Other entities, perhaps less central but also important, might include the International Finance office at the Department of Treasury and various shops within the intelligence community that monitor relations with countries around the world. The more scholars learn about these audiences’ agendas, remits, and resources, the better scholars’ recommendations will be.

Although the variables in play will of course depend on the research, it is worth considering two hypothetical alternative variables: shared interests versus institutional design. The former, of course, is out of the hands of almost all policymakers. But at least some (very senior) policymakers have input into institutional design.

This hypothetical example also illustrates how recommendations and implications may differ. The implications of different interests may lead to problems that are difficult to solve but must be anticipated and managed, perhaps to the point of not relying on allies under certain conditions or expecting only fitful cooperation. In contrast, a scholar may recommend a specific change to institutional design, such as a new entity, or greater powers for or new members of an existing entity. Here, as in other instances, it is important to consider the scope of the recommendation. Academics might rightly propose an entirely new alliance structure, such as an Asian version of NATO. Or they might focus more narrowly (but with more chance of influencing the debate) on how to tweak an existing structure to make it more effective.

Similarly, it is useful to consider how different elements of national power might help, and drafting a basic policy menu is a useful first step. The scholar should ask how diplomats, intelligence officers, the military, and economic actors like the Treasury Department might contribute. Imagine holding a meeting (or, ideally, interviewing people from different agencies) and think about how each might play a role.

It is also valuable for scholars to think ahead about likely problems with their recommendations. If, say, the recommendation is more resources to help gain the goodwill of a particular country, the trade-off is one that senior policymakers always face: fewer resources for other countries. But there may be less obvious costs and trade-offs. Might strengthening the alliance alarm a neighbor, perhaps leading to a dangerous spiral? Might the ally become more aggressive, creating a moral hazard, or, conversely, fear being chain-ganged into a conflict? Such possibilities need not be covered exhaustively, but it is important to acknowledge the limits of a recommendation. Again, interviewing and engaging with relevant policymakers can highlight these limits.

When the research is completed and published, it is time to consider additional publishing options. Many of these should be tied to current events: For a scholar writing in early 2024, what does research on alliance weakness tell us about how the Australia-United Kingdom-United States alliance might hold up or how Sweden and Finland's accession to NATO might be best managed? Leading newspapers might find these topics of interest, as would more specialized outlets like Foreign Affairs and Foreign Policy . When possible, scholars should give briefings on their work or otherwise promote it.

Writing policy recommendations can seem daunting, and in many ways it is. It can be done poorly and at times even counterproductively. When done well, however, recommendations can help guide decision-makers and the public on the world's more difficult issues.

In many ways, the process is the same for crafting both a better policy recommendation and a better article. Use clear, jargon-free prose and structured arguments to make recommendations more convincing. 59 Authors should seek out criticism, ideally from those with policy experience as well as from fellow scholars. The editors at International Security are an invaluable resource: they can help scholars think through and fully consider both ideas and implications. By making policy recommendations, scholars join a broader community that seeks to make the world a better place. It is not an easy task, but it is a necessary and rewarding one.

The author would like to thank Michael Desch, James Goldgeier, Matthew Kirchman, Ines Oulamene, Kenneth Pollack, Jeremy Shapiro, and the anonymous reviewers for their comments and excellent feedback on previous versions of this article.

As of April 2024, these articles are among the thirty most-cited contributions to International Security , according to data obtained by MIT Press. Thomas F. Homer-Dixon, “Environmental Scarcities and Violent Conflict: Evidence from Cases,” International Security , Vol. 19, No. 1 (Summer 1994), pp. 5–40, https://doi.org/10.2307/2539147 ; John J. Mearsheimer, “The False Promise of International Institutions,” International Security , Vol. 19, No. 3 (Winter 1994/95), pp. 5–49, https://doi.org/10.2307/2539078 ; Andrew H. Kydd and Barbara F. Walter, “The Strategies of Terrorism,” International Security , Vol. 31, No. 1 (Summer 2006), pp. 49–80, https://doi.org/10.1162/isec.2006.31.1.49 ; Maria J. Stephan and Erica Chenoweth, “Why Civil Resistance Works: The Strategic Logic of Nonviolent Conflict,” International Security , Vol. 33, No. 1 (Summer 2008), pp. 7–44, https://doi.org/10.1162/isec.2008.33.1.7 Henry Farrell and Abraham L. Newman, “Weaponized Interdependence: How Global Economic Networks Shape State Coercion,” International Security , Vol. 44, No. 1 (Summer 2019), pp. 42–79, https://doi.org/10.1162/isec_a_00351 .

See, among others, Naazneen H. Barma and James Goldgeier, “How Not to Bridge the Gap in International Relations,” International Affairs , Vol. 98, No. 5 (September 2022), pp. 1763–1781, https://doi.org/10.1093/ia/iiac102 ; Michael C. Desch, Cult of the Irrelevant: The Waning Influence of Social Science on National Security (Princeton, NJ: Princeton University Press, 2019); Stephen M. Walt, “The Relationship between Theory and Policy in International Relations,” Annual Review of Political Science , Vol. 8 (2005), pp. 29–32, https://doi.org/10.1146/annurev.polisci.7.012003.104904 ; Alexander L. George, Bridging the Gap: Theory and Practice in Foreign Policy (Washington, DC: United States Institute of Peace, 1993); Bruce W. Jentleson, “The Need for Praxis: Bringing Policy Relevance Back In,” International Security , Vol. 26, No. 4 (Spring 2002), pp. 169–183, https://doi.org/10.1162/016228802753696816 ; Henry Farrell, “Why Do Policy Makers Hate International Relations Scholarship?,” Monkey Cage (blog), Washington Post , September 18, 2013, https://themonkeycage.org/2013/09/why-do-policy-makers-hate-international-relations-scholarship ; Nicholas Kristof, “Professors, We Need You!,” New York Times , February 16, 2014, https://www.nytimes.com/2014/02/16/opinion/Sunday/kristof-professors-we-need-you.html . For workshops and other initiatives, see, for example, the Bridging the Gap project ( https://www.bridgingthegapproject.org ) as well as the Scholars Strategy Network ( https://scholars.org ). In the United Kingdom, the Research Excellence Framework ( https://www.ref.ac.uk ) links public engagement and policy relevance to funding, as have efforts like the Minerva Research Initiative ( https://minerva.defense.gov ).

Bruce W. Jentleson and Ely Ratner, “Bridging the Beltway–Ivory Tower Gap,” International Studies Review , Vol. 13, No. 1 (March 2011), pp. 6–11, http://dx.doi.org/10.1111/j.1468-2486.2010.00992.x ; Paul C. Avey and Michael C. Desch, “What Do Policymakers Want from Us?,” International Studies Quarterly , Vol. 58, No. 2 (June 2014), pp. 227–246, https://doi.org/10.1111/isqu.12111 ; Daniel Byman and Matthew Kroenig, “Reaching beyond the Ivory Tower: A How To Manual,” Security Studies , Vol. 25, No. 2 (2016), pp. 289–319, https://doi.org/10.1080/09636412.2016.1171969 .

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See Michael W. Doyle, “Kant, Liberal Legacies, and Foreign Affairs,” in Arthur Ripstein, ed., Immanuel Kant (London: Routledge, 2017), pp. 503–533. For a critique, see Sebastian Rosato, “The Flawed Logic of Democratic Peace Theory,” American Political Science Review , No. 97, No. 4 (November 2003), pp. 585–602, https://doi.org/10.1017/S0003055403000893 . A foundational deterrence book is Thomas C. Schelling, The Strategy of Conflict (Cambridge, MA: Harvard University Press, 1981).

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See the question “Does your research tend to be basic or applied?” in the 2017 TRIP Faculty Survey. Daniel Maliniak et al., 2017 TRIP Faculty Survey, Teaching, Research, and International Policy Project, Global Research Institute, Williamsburg, VA, https://trip.wm.edu/research/faculty-surveys .

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Byman and Kroenig, “Reaching beyond the Ivory Tower,” p. 295.

Yuen Foong Khong, Analogies at War: Korea, Munich, Dien Bien Phu, and the Vietnam Decisions of 1965 (Princenton, NJ: Princeton University Press, 1992), pp. 3–18.

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Rebecca Adler-Nissen, “Leaving the Lab,” Duck of Minerva (blog), September 2, 2021, https://www.duckofminerva.com/2021/09/leaving-the-lab.html .

John M. Owen IV, “Review: Iraq and the Democratic Peace: Who Says Democracies Don't Fight?,” Foreign Affairs , Vol. 84, No. 6 (November/December 2005), pp. 122–127, https://doi.org/10.2307/20031781 .

Paul Musgrave, “Political Science Has Its Own Lab Leaks,” Foreign Policy , July 3, 2021, https://foreignpolicy.com/2021/07/03/political-science-dangerous-lab-leaks/ .

Erica De Bruin, “How Can We Vaccinate against Viral Political Science?,” Duck of Minerva (blog), August 31, 2021, https://www.duckofminerva.com/2021/08/how-can-we-vaccinate-against-viral-political-science.html . De Bruin points to the program Rigor, Relevance, and Responsibility at the University of Denver's Sié Center as one such effort.

Charli Carpenter, “‘You Talk of Terrible Things So Matter-of-Factly in This Language of Science’: Constructing Human Rights in the Academy,” Perspectives on Politics , Vol. 10, No. 2 (June 2012), pp. 363–383, https://doi.org/10.1017/S1537592712000710 .

Michael O'Hanlon, “Why China Cannot Conquer Taiwan,” International Security , Vol. 25, No. 2 (Fall 2000), pp. 51–86, https://doi.org/10.1162/016228800560453 .

“Submissions,” Foreign Affairs , accessed February 21, 2023, https://www.foreignaffairs.com/submissions ; “New York Times Opinion Guest Essays,” New York Times , accessed February 21, 2023, https://help.nytimes.com/hc/en-us/articles/115014809107-New-York-Times-Opinion-Guest-Essays .

For International Security , see “Submission Guidelines,” International Security , Belfer Center for Science and International Affairs, Harvard Kennedy School, https://www.belfercenter.org/journal-international-security/overview#!submission-guidelines .

Kydd and Walter, “The Strategies of Terrorism.”

Caitlin Talmadge, “Closing Time: Assessing the Iranian Threat to the Strait of Hormuz,” International Security , Vol. 33, No. 1 (Summer 2008), pp. 82–117, https://doi.org/10.1162/isec.2008.33.1.82 .

Barma and Goldgeier, “How Not to Bridge the Gap,” p. 1768.

John Maynard Keynes, The General Theory of Employment, Interest and Money (1936; repr., London: Macmillan, 2007), pp. 383–384.

Keir A. Lieber, “The New History of World War I and What It Means for International Relations Theory,” International Security , Vol. 32, No. 2 (Fall 2007), pp. 155–191, https://doi.org/10.1162/isec.2007.32.2.155 .

Barma and Goldgeier, “How Not to Bridge the Gap,” p. 1781.

James T. Quinlivan, “Coup-proofing: Its Practice and Consequences in the Middle East,” International Security , Vol. 24, No. 2 (Fall 1999), pp. 131–165, https://doi.org/10.1162/016228899560202 ; Lise Morjé Howard and Alexandra Stark, “How Civil Wars End: The International System, Norms, and the Role of External Actors,” International Security , Vol. 42, No. 3 (Winter 2017/18), pp. 127–171, https://doi.org/10.1162/ISEC_a_00305 ; Edward D. Mansfield and Jack Snyder, “Democratic Transitions, Institutional Strength, and War,” International Organization , Vol. 56, No. 2 (Spring 2002), pp. 297–337, https://doi.org/10.1162/002081802320005496 .

Elizabeth N. Saunders, “Elites in the Making and Breaking of Foreign Policy,” Annual Review of Political Science , Vol. 25, No. 1 (2022), pp. 219–240, https://doi.org/10.1146/annurev-polisci-041719-103330 ; Mary E. Gallagher and Jonathan K. Hanson, “Power Tool or Dull Blade? Selectorate Theory for Autocracies,” Annual Review of Political Science , Vol. 18, No. 1 (2015), pp. 367–385, https://doi.org/10.1146/annurev-polisci-071213-041224 .

Colin L. Powell and Joseph E. Persico, My American Journey (New York: Ballantine, 1995), p. 393.

Shane Harris et. al., “Road to War: U.S. Struggled to Convince Allies, and Zelensky, of Risk of Invasion,” Washington Post , August 16, 2022, https://www.washingtonpost.com/national-security/interactive/2022/ukraine-road-to-war/ ; Afiq Fitri, “How President Zelensky's Approval Ratings Have Surged,” New Statesman , March 1, 2022, https://www.newstatesman.com/chart-of-the-day/2022/03/how-president-zelenskys-approval-ratings-have-surged .

Suzanne Maloney and Fred Dews, “Iran's Nuclear Aspirations,” Brookings Cafeteria , podcast, February 18, 2022, https://www.brookings.edu/podcast-episode/irans-nuclear-aspirations/ ; Mark Fitzpatrick, “Assessing the JCPOA,” Adelphi Series , Vol. 57, No. 466–467 (2017), pp. 19–60, https://doi.org/10.1080/19445571.2017.1555914 .

Steinberg, “Universities and Public Policy.”

Nikolaos van Dam, “What the West Got Wrong in Syria,” Foreign Policy , August 22, 2017, https://foreignpolicy.com/2017/08/22/what-the-west-got-wrong-in-syria/ . On variations on signaling in general, see Kai Quek, “Four Costly Signaling Mechanisms,” American Political Science Review , Vol. 115, No. 2 (2021), pp. 537–549.

Donald J. Trump, National Security Strategy of the United States of America (Washington, DC: White House, 2017), https://trumpwhitehouse.archives.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf .

David A. Baldwin, “The Sanctions Debate and the Logic of Choice,” International Security , Vol. 24, No. 3 (Winter 1999/2000), p. 84, https://doi.org/10.1162/016228899560248 .

M. Taylor Fravel and Charles L. Glaser, “How Much Risk Should the United States Run in the South China Sea?,” International Security , Vol. 47, No. 2 (Fall 2022), pp. 88–134, https://doi.org/10.1162/isec_a_00443 .

Lawrence M. Mead, “Scholasticism in Political Science,” Perspectives on Politics , Vol. 8, No. 2 (June 2010), p. 454, https://doi.org/10.1017/S1537592710001192 .

Daniel Byman and Aditi Joshi, “Social Media Companies Need Better Emergency Protocols,” Lawfare , January 14, 2021, https://www.lawfaremedia.org/article/social-media-companies-need-better-emergency-protocols .

Nicholas Sambanis, “What Is Civil War? Conceptual and Empirical Complexities of an Operational Definition,” Journal of Conflict Resolution , Vol. 48, No. 6 (2004), pp. 814–858, https://doi.org/10.1177/0022002704269355 .

Lindsey A. O'Rourke, “The Strategic Logic of Covert Regime Change: U.S.-Backed Regime Change Campaigns during the Cold War,” Security Studies , Vol. 29, No. 1 (2020), pp. 92–127, https://doi.org/10.1080/09636412.2020.1693620 .

National Commission on Terrorist Attacks, The 9/11 Commission Report: Final Report of the National Commission on Terrorist Attacks upon the United States , Vol. 3 (Washington, DC: Government Printing Office, 2004), pp. 399–428.

Richard A. Posner, “The 9/11 Report: A Dissent,” New York Times , August 29, 2004, https://www.nytimes.com/2004/08/29/books/the-9-11-report-a-dissent.html .

Daniel Byman and Jeremy Shapiro, “‘What U.S. Foreign Policy Really Needs Is …’: The 11 Worst Washington Insider Policy Clichés,” Foreign Policy , June 5, 2015, https://foreignpolicy.com/2015/06/05/the-11-worst-useless-foreign-policy-pundit-cliches/ .

Joint Chiefs of Staff, Strategy , Joint Doctrine Note 1–18 (Washington, DC: Joint Force Development, 2018), pp. II-5–II-11, https://www.jcs.mil/Portals/36/Documents/Doctrine/jdn_jg/jdn1_18.pdf .

Joint Chiefs of Staff, Strategy , p. II-8.

Alexander L. George and William E. Simons, eds., The Limits of Coercive Diplomacy (Boulder, CO: Westview Press, 1994), pp. 53–55, 267–294.

Kenneth M. Pollack, Armies of Sand: The Past, Present, and Future of Arab Military Effectiveness (Oxford: Oxford University Press, 2018), pp. 439–451.

Renanah Miles Joyce, “Soldiers’ Dilemma: Foreign Military Training and Liberal Norm Conflict,” International Security , Vol. 46, No. 4 (Spring 2022), p. 89, https://doi.org/10.1162/isec_a_00432 .

Edward Wong and Amy Qin, “U.S. Presses Taiwan to Buy Weapons More Suited to Win against China,” New York Times , May 7, 2022, https://www.nytimes.com/2022/05/07/us/politics/china-taiwan-weapons.html .

Keir A. Lieber and Daryl G. Press, “The End of MAD? The Nuclear Dimension of U.S. Primacy,” International Security , Vol. 30, No. 4 (Spring 2006), pp. 7–44, https://doi.org/10.1162/isec.2006.30.4.7 ; Keir A. Lieber and Daryl G. Press, “The Rise of U.S. Nuclear Primacy,” Foreign Affairs , Vol. 85, No. 2 (March/April 2006), pp. 42–54, https://doi.org/10.2307/20031910 ; Keir A. Lieber and Daryl G. Press, “Superiority Complex: Why America's Growing Nuclear Supremacy May Make War with China More Likely,” Atlantic , July/Aug. 2007, pp. 86–92, https://www.theatlantic.com/magazine/archive/2007/07/superiority-complex/305989/ .

See, for example, submission information for Foreign Policy at https://foreignpolicy.submittable.com/submit and for Foreign Affairs at https://www.foreignaffairs.com/submissions-0 .

Barma and Goldgeier, “How Not to Bridge the Gap,” p. 1773.

John J. Mearsheimer, “Bound to Fail: The Rise and Fall of the Liberal International Order,” International Security , Vol. 43, No. 4 (Spring 2019), pp. 7–50, https://doi.org/10.1162/isec_a_00342 ; Farrell and Newman, “Weaponized Interdependence”; Risa Brooks, “Paradoxes of Professionalism: Rethinking Civil-Military Relations in the United States,” International Security , Vol. 44, No. 4 (Spring 2020), pp. 7–44, https://doi.org/10.1162/isec_a_00374 ; Charlotte Grech-Madin, “Water and Warfare: The Evolution and Operation of the Water Taboo,” International Security , Vol. 45, No. 4 (Spring 2021), pp. 84–125, https://doi.org/10.1162/isec_a_00404 ; Joyce, “Soldiers’ Dilemma.”

Johnson, “Writing for International Security.”

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What are Implications in Research?

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Table of Contents

Manuscripts that do not mention the implications of the study are often desk-rejected by journals. What constitutes the ‘implications’ of research, and why is it important to include research implications in your manuscript?

Research implications: An overview

Once you have laid out the key findings in your paper, you have to discuss how they will likely impact the world. What is the significance of your study to policymakers, the lay person, or other researchers? This speculation, made in good faith, constitutes your study’ implications.

A research paper that does not explain the study’s importance in light of its findings exists in a vacuum. The paper may be relevant to you, the author, and some of your co-workers. But it is unclear how others will benefit from reading it.

How can the findings of your study help create a better world? What can we infer from your conclusion about the current state of research in your field or the quality of methods you employed? These are all important implications of your study.

You cannot predict how your study will influence the world or research in the future. You can only make reasonable speculations. In order to ensure that the implications are reasonable, you have to be mindful of the limitations of your study.

In the research context, only speculations supported by data count as valid implications. If the implications you draw do not logically follow the key findings of your study, they may sound overblown or outright preposterous.

Suppose your study evaluated the effects of a new drug in the adult population. In that case, you could not honestly speculate on how the drug will impact paediatric care. Thus, the implications you draw from your study cannot exceed its scope.

Practical implications

Imagine that your study found a popular type of cognitive therapy to be ineffective in treating insomnia. Your findings imply that psychologists using this type of therapy were not seeing actual results but an expectancy effect. Studies that can potentially impact real-world problems by prompting policy change or change in treatments have practical implications.

It can be helpful to understand the difference between an implication of your study and a recommendation. Suppose your study compares two or more types of therapy, ranks them in the order of effectiveness, and explicitly asks clinicians to follow the most effective type. The suggestion made in the end constitutes a ‘recommendation’ and not an ‘implication’.

Theoretical implications

Are your findings in line with previous research? Did your results validate the methods used in previous research or invalidate them? Has your study discovered a new and helpful way to do experiments? Speculations on how your findings can potentially impact research in your field of study are theoretical implications.

The main difference between practical and theoretical implications is that theoretical implications may not be readily helpful to policymakers or the public.

How to Write Implications in Research

Implications usually form an essential part of the conclusion section of a research paper. As we have mentioned in a previous article, this section starts by summarising your work, but this time emphasises your work’s significance .

While writing the implications, it is helpful to ask, “who will benefit the most from reading my paper?”—policymakers, physicians, the public, or other researchers. Once you know your target population, explain how your findings can help them.

Think about how the findings in your study are similar or dissimilar to the findings of previous studies. Your study may reaffirm or disprove the results of other studies. This is an important implication.

Suggest future directions for research in the subject area in light of your findings or further research to confirm your findings. These are also crucial implications.

Do not try to exaggerate your results, and make sure your tone reflects the strength of your findings. If the implications mentioned in your paper are convincing, it can improve visibility for your work and spur similar studies in your field.

For more information on the importance of implications in research, and guidance on how to include them in your manuscript, visit Elsevier Author Services now!

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• Published: 12 December 2017

Rethinking policy ‘impact’: four models of research-policy relations

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• Katherine Smith 1  

Palgrave Communications volume  3 , Article number:  44 ( 2017 ) Cite this article

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A Correction to this article was published on 20 February 2018

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Political scientists are increasingly exhorted to ensure their research has policy ‘impact’, most notably via Research Excellence Framework (REF) impact case studies, and ‘pathways to impact’ statements in UK Research Council funding applications. Yet the assumptions underpinning these frameworks often fail to reflect available evidence and theories. Notions of ‘impact’, ‘engagement’ and ‘knowledge exchange’ are typically premised on simplistic, linear models of the policy process, according to which policy-makers are keen to ‘utilise’ expertise to produce more ‘effective’ policies. Such accounts overlook the rich body of literature in political science, policy studies, and sociology of knowledge, which offer more complex and nuanced accounts. Drawing on this wider literature, this paper sets out four different approaches to theorising the relationship: (1) knowledge shapes policy; (2) politics shapes knowledge; (3) co-production; and (4) autonomous spheres. We consider what each of these four approaches suggests about approaches to incentivising and measuring research impact.

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Introduction.

The new research ‘impact’ agenda is likely to have a profound effect on the social science research community in wide-ranging ways, shaping the sorts of research questions and methods scholars are selecting, their networks and collaborations, as well as changing institutional structures of support within higher education institutions. Yet concepts and models for defining and measuring impact have been subject to surprisingly little social scientific scrutiny. While there is an extensive literature on research-policy relations across fields of social science (notably in sociology, science and technology studies, social policy, political science and public management), only a very narrow range of these contributions have been marshalled to develop guidance and practice on ‘impact’. Indeed, prevalent guidelines and models are frequently based on surprisingly simple and linear ideas about how research can be ‘utilised’ to produce more effective policies (Smith and Stewart, 2016 ).

In this article, we seek to advance the debate on impact by setting out four different approaches to theorising research-policy relations, drawn from wider social science literature. Each set of theories is categorised according to its core assumptions about the inter-relations between the two spheres. The first approach focuses on a ‘supply’ model of research-policy relations, examining how knowledge and ideas shape policy. The second challenges the idea that research is independent of politics and policy, instead focusing on how political power shapes knowledge. The third approach takes this line further, suggesting that research knowledge and governance are co-produced through an ongoing process of mutual constitution. And the fourth approach offers a radically contrasting account, suggesting that there is no overarching causality between science and politics, but that politics only selectively appropriates and gives meaning to scientific findings. Figure  1 offers a simple representation of these four ways of modelling the relations.

Research-policy relations

This figure represents in visual form the direction of influence between research, expert knowledge and science; and policy and politics. The first panel represents theories assuming that research shapes policy. The second panel depicts the idea that policy and politics shape the production of research. In the third panel, the circular arrows convey the idea of research and policy being mutually constitutive. While the fourth panel suggests that there is no direct causal relationship between research and policy, but that instead, the two ‘systems’ only selectively pick up on signals from the other system.

This four-way schema offers a useful resource in two main ways. First, it offers a classificatory tool for mapping, comparing and analysing a range of often disparate theoretical approaches in the emerging field of knowledge-policy relations–theories that emanate from a wide set of social science disciplines, and are informed by quite divergent assumptions about knowledge and governance. The second, more applied, use of the schema is to identify the plurality of ways of conceptualising knowledge-policy relations. In doing so, we demonstrate that prevalent models of impact are based on one particular set of assumptions about the role of research in policy, and not necessarily the most theoretically sophisticated at that. By briefly setting out each of the four sets of theories, we show how each is based on quite distinct assumptions about knowledge and policy, and that each has different implications for how we might go about defining and measuring impact.

The ‘impact’ agenda in UK research funding

The emphasis on ‘research impact’ has been increasing steadily across a number of OECD countries over the past decade, notably Australia (Donovan, 2008 ; Chubb and Watermeyer, 2016 ), Canada (Canadian Academy of Health Sciences CAHS, 2009 ), the Netherlands (Mostert et al., 2010 ) and the USA (Grant et al., 2010 ) but the influence of this agenda is particularly pronounced in the UK, which can be seen as something of a pioneer in implementing these approaches (see Bornmann, 2013 and Grant et al., 2010 for useful comparative overviews). There are currently two major incentives for social scientists in the UK to demonstrate that their research influences policy. First, the national appraisal mechanism for assessing university research (which informs decisions about the distribution of core research funding), known as the Research Excellence Framework (REF), has begun awarding 20% of overall scores to institutions on the basis of case studies of research impact (UK higher education funding bodies 2011 ). Second, accounts of the work that will be undertaken to achieve research impact (‘pathways to impact’) now form a significant section of grant application processes for the UK funding councils (Research Councils UK, Undated ). The upshot is that obtaining core research funding and project-specific grants from publicly funded sources in the UK are now strongly dependent on researchers’ abilities to respond adequately to questions about the non-academic value of their work (Smith and Stewart, 2016 ).

The current focus on ‘research impact’ reflects a longer-standing concern with the societal return on public funding of science (Brewer, 2011 ; Clarke, 2010 ). This agenda was given particular impetus by New Labour government commitments to taking a more ‘evidence-based’ approach to policymaking (Labour Party, 1997 ), with official statements evoking a simple, linear conceptualisation of the relationship between research and policy (e.g., Cabinet Office, 1999 , 2000 ; Blunkett, 2000 ). It is this kind of thinking that appears to have shaped tools and guidance on impact (Smith, 2013a ). Indeed, while different public bodies have adopted a variety of models, RCUK and REF advisory documents tend to share a number of common features (AHRC, 2014 , 2015 ; ESRC, 2014a , 2014b , 2014c ; MRC, 2014 ; Research Councils UK, Undated): (i) a consensus that researchers have a responsibility to articulate the impact of their research to non-academic audiences; (ii) an assumption (most explicit in the REF impact case studies) that this impact can be documented and measured; (iii) a belief that the distribution of research funding should (at least to some extent) reflect researchers’ ability to achieve ‘impact’; and, following from this, (iv) an expectation that researchers’ own efforts to achieve research impact will play a significant role in explaining why some research has impact beyond academia and some does not.

This approach is exemplified in HEFCE’s template for REF2014 impact ‘case studies’ (REF, 2014, 2011 ). The template calls for an account of the ‘underpinning research’ that exerted impact, implying that impact is achieved through policy-makers adjusting their beliefs in response to clearly delineated research findings. The implication is that research findings are created independently of policy or politics: research is treated as an exogenous variable that feeds into policy-making. Secondly, such findings are expected to have been published as ‘outputs’ that are rated 2*, or ‘nationally leading in terms of their originality, significance and rigour’ (REF2014, 2014 ). Thus a clear link is posited between the quality of research and the desirability of rewarding impact: impactful research should meet a certain quality threshold. Thirdly, researchers are required to chart how their findings came to exert impact, and to provide evidence to corroborate their claims. Evocative of the ‘pathways to impact’ section of RCUK grant proposals (Research Councils UK, Undated ), this requirement implies that researchers can trace the effects of their work through describing a series of concrete activities and information flows – events, meetings, media coverage, and so on.

There is currently no agreed way of tracking research impacts and, in this context, some academics have identified more specific frameworks and approaches, including the ‘payback framework’ (Donovan and Hanney, 2011 ) and the ‘research contribution framework’ (Morton, 2015 ). However, others have criticised the simplistic and linear conceptualisations of research-policy relations that appear to underpin the UK’s overarching approach to research impact, particularly those with in-depth knowledge of the policy process and/or the relationship between research and policy (Greenhalgh et al., 2016 ; Smith and Stewart, 2016 ). Theories of public policy have shown that policy-making rarely occurs in such neat sequential stages (Cairney, 2016 ), and that evidence often plays a rather limited role in decision-making (Boswell, 2009a ). In the context of such criticisms and concerns, we consider the rich body of literature from political science, policy studies, sociology of knowledge, and science and technology studies, which has informed understandings of the complex relationship between knowledge and policy. Drawing on this wider literature, we now set out four different approaches to theorising the relationship, and consider their implications for the impact agenda.

Four approaches to conceptualising research-policy relations

Knowledge shapes policy.

A range of theories and models of the relationship between academic knowledge and policy were developed by US and UK scholars in the 1970s and 1980s (Blume, 1977 ; Caplan, 1979 ; Rein, 1980 ; Weiss, 1977 , 1979 ). Notably, a number of contributions produced ‘instrumental’ models of knowledge utilisation (see Weiss, 1979 for an overview), according to which knowledge either ‘drives’ policy, or policy problems stimulate research to provide direct solutions (again, see Weiss, 1979 ). Much of the work undertaken in the 1970s and 1980s demonstrated that while there are occasional examples of research feeding into policy in this manner, such simple models failed to capture the intricacies of the interactions between research and policy (Rein, 1980 ; Weiss, 1979 ). Yet, it was precisely these simple, instrumental notions of the role of research in policy that seem to have become increasingly embedded within UK policy, including higher education policy, leading Parsons to reflect that the Labour government’s commitments to ‘evidence-based policymaking’ marked:

not so much a step forward as a step backwards: a return to the quest for a positivist yellow brick road leading to a promised policy dry ground-somewhere, over Charles Lindblom - where we can know ‘what works’ and from which government can exercise strategic guidance. (Parsons, 2002 , p 45)

Understandably, official commitments to employing evidence in a direct, linear sense triggered a raft of assessments of the extent to which particular policies do reflect the available evidence. Perhaps unsurprisingly, most of these found the government’s use of evidence has been highly selective (e.g., Boswell 2009a , 2009b ; Katikireddi et al., 2011 ; Naughton, 2005 ; Stevens, 2007 ) and this, in turn, has triggered renewed interest in two, more complex models of the ways in which research knowledge shapes policy, each of which has very different implications for the research impact agenda.

The first of these approaches seeks to address what is perceived as a ‘gap’ between the research and policy communities. On this account, research has the potential to be highly relevant to policy, but its impact is often reduced by problems of communication. Research may not be disseminated in a form that is relevant or accessible to policy-makers; or officials have insufficient resources to process and apply research findings. For example, Lomas ( 2000 ) and Lavis ( 2006 ) both underline the importance of achieving shared understandings between researchers and policymakers, arguing that increased interaction between the two groups will improve the use of research in policy. These authors tend to assume that research would be more frequently employed by policymakers if only they could better access and understand the findings and if the findings were of relevance. Thus the focus is on improving the mechanisms of communication, and the levels of trust, between researchers and policymakers. A stronger version of this ‘gap’ account posits that this reflects a deeper cultural gap between researchers and policy actors. Thus Caplan ( 1979 ) suggests that these actors should be seen as distinct ‘communities’ guided by different values and beliefs–a notion we discuss further in the fourth set of theories, considered later in the paper.

The weaker version of this ‘gap’ approach, however, suggests that there are various practical steps that can be taken to improve the flow of knowledge from research to policy. Indeed, several reviews of knowledge transfer provide practical recommendations for researchers seeking to influence policy (Contandriopoulos et al., 2010 ; Innvaer et al., 2002 ; Mitton et al., 2007 ; Nutley et al., 2003 ; Oliver et al., 2013 ; Walter et al., 2005 ), suggesting researchers should ensure research is accessible, by providing clear, concise, timely summaries of the research, tailored to appropriate audiences; and develop ongoing, collaborative relationships with potential users to increase levels of trust and shared definitions of policy problems and responses. In structural terms, the findings of these reviews call for improved communication channels, via ‘knowledge broker’ roles and/or knowledge transfer training and sufficiently high incentives for researchers and research users to engage in knowledge exchange. Of the various conceptualisations of the relationship between research knowledge and policy, it is this way of thinking which appears to have had most influence on current approaches to incentivising research impact in the UK. As we shall see, however, the approach is widely criticised by the alternative theories of research-policy relations we explore later in the article.

A second popular theory of how research shapes policy emerges from Weiss’ ( 1977 , 1979 ) notion of the ‘enlightenment’ function of knowledge in policymaking. This account proposes that knowledge shapes policy through diffuse processes, resulting from the activities of various, overlapping networks, which contribute to broader, incremental and often largely conceptual changes (Hird, 2005 ; Walt, 1994 ). Radaelli’s ( 1995 ) notion of ‘knowledge creep’ is one of several more recent conceptualisations to build on this idea, and we can find similar assumptions in ideational theories of policy change (Béland, 2009 ; Hall, 1993 ; Schmidt, 2008 ). The implication of these accounts is that research influences policy over long periods through gradual changes in actors’ perceptions and ways of thinking (an idea that is also evident in theories of co-production, as discussed later) rather than through immediate, direct impacts. Whilst this body of work does not discount the possibility that research might contribute to what eventually become significant shifts in policy approaches, it suggests that assessments aiming to trace the impact of research on particular policy outcomes are likely to miss a potentially broader, more diffuse kind of conceptual influence.

The implications of this way of conceptualising the relationship between academic knowledge and policy for ideas about research impact are more challenging (indeed, the ‘enlightenment’ model has been criticised by some scholars seeking to improve the use of evidence in policy for its lack of practical utility (Nutley et al., 2007 )). Taking the more conceptual influence of research seriously suggests that incentives for achieving impact ought to shift away from individual researchers and projects to consider how to support the collective diffusion of much more diverse (potentially interdisciplinary) bodies of work. Given that multiple authors are likely to be involved, and that various factors unrelated to the underpinning research (or its communication) are likely to inform when and how knowledge shapes policy, it seems to make little sense to reward individual researchers (or even teams of researchers) for ‘achieving’ research impact. Instead, research impact might be supported by encouraging groups of researchers to work together on developing policy messages from diverse studies on particular policy topics (or, to support knowledge brokers to do this kind of work).

This is a very different model from both the RCUK pathways to impact approach, which encourages individual researchers or research teams to try to achieve research impacts on the back of single studies, and the REF impact case study approach, which encourages single institutions to narrate stories of impact based solely on the work of researchers they employ. Indeed, recent assessments of the REF impact case study approach have specifically highlighted the tendency not to adequately support these kinds of synthesised approaches to achieving impact (Manville et al., 2015 ; Smith and Stewart, 2016 ). For the moment, while some of the guidance documents relating to the UK impact agenda do acknowledge conceptual forms of influence, the mechanisms for monitoring and rewarding impact seem preoccupied with ‘instrumental’ research impact achieved on the back of research undertaken by individual researchers or small groups within single institutions.

Politics shapes knowledge

Perhaps the most obvious critique of the ‘knowledge shapes policy’ model reverses this relationship to highlight the various ways in which policies and politics shape knowledge and the use of knowledge. There is a rich body of literature theorising how state-building and modern techniques of governance have shaped the production of social knowledge (Foucault, 1991 , Heclo, 1974 ; Rueschemeyer and Skocpol, 1996 ), as well as how power relations are implicated in the construction of expert authority (Gramsci, 2009 ). What these diverse contributions share is the notion that an underlying political project is driving research production and utilisation, whether that project is the production of self-regulating subjects (as some Foucauldian interpretations suggest) or the continuing dominance of ruling elites and ideologies (as Gramscian analyses tend to posit). From this perspective, research utilisation in policymaking is understood as profoundly constrained; whilst those involved in the construction of policy are not necessarily consciously aware of the forces shaping their decisions, any attempt to engage with research must be understood as part of a wider political project. At the very least, such analyses suggest that only research that can be used to support these dominant ideas and interests will be employed in policymaking, while research that challenges dominant ideas will be discounted (see Wright et al., 2007 ). A stronger interpretation would hold that the research process is itself shaped by the ‘powerful interests’ directing policy agendas (e.g., Navarro, 2004 ).

The more applied literature concerning the relationship between research and policy also provides examples of this way of thinking about the relationship. In her overview of various ‘models’ of the relationship between research and policy, Weiss, for example, describes what she calls the ‘political model’, where research is deployed to support pre-given policy preferences; as well as a ‘tactical model’, where research is used as a method of delaying the decision-making process, providing policymakers with some ‘breathing space’ (Weiss, 1979 ). In the first case, the research process itself is not necessarily informed by politics but the decision to employ research (or not) is entirely political. In other words, political ideology and/or more strategic party politics inform the ways in which political actors respond to research evidence (e.g., Bambra, 2013 ). In the second, the commissioning of research might itself be understood as a political act (or, at least, an act that creates political benefits–see Bailey and Scott‐Jones 1984 ). In either case, efforts to reward researchers for ‘achieving’ research impact would seem misplaced.

The extent to which politics can shape research is perhaps most overt in research that is directly commissioned by sources with particular political/policy interests; reviews have repeatedly demonstrated that research funded by commercial sources, such as the pharmaceutical (e.g., Lundh et al., 2012 ) and tobacco industries (e.g., Bero, 2005 ), is more likely to present findings that are useful to those interests (see also Bailey and Scott‐Jones, 1984 ). In other contexts, it has been suggested that researchers may struggle to maintain their independence where research is commissioned directly, or indirectly, by government sources (e.g., Barnes, 1996 ; Smith, 2010 ). This kind of political influence may be felt both overtly and subtly, with researchers responding to signals from research funders as to what is likely to be funded (and what is not), what they are hoping (or expecting) to be found and what they are not (Knorr-Cetina, 1981 ; Smith, 2010 ), as we discuss further in the following section.

A second group of theories which call attention to the way in which politics can shape knowledge focus on the impact of institutions and organisational structures on policymaking and research. Similar to the previous group of theories, such accounts assume that the wider structures in which actors are located are key to explaining policy outcomes. Whilst the more political accounts discussed above highlight the ways in which power relations and elite interests can shape research and its use, these theories focus on organisational and decision-making structures. The most well-known of such theories are the various forms of institutionalism, of which ‘historical institutionalism’ is one of the most widely employed forms (see Immergut, 1998 for an overview). From this perspective, rather than constituting the collective result of individual preferences, policy processes (including efforts to engage with research) are considered to be significantly shaped by the historically constructed institutions and policy procedures within which they are embedded (Immergut, 1998 ).

Those who have contributed to the development of this genre of work have emphasised that such theories do not suggest that particular policy outcomes are inevitable –and indeed, as we discussed in the previous sections, under certain conditions existing paradigms can be superseded by new ideas, leading to substantial policy change (Hall, 1993 ). However, such theories do suggest that it becomes increasingly difficult to change the overall direction of a policy trajectory as previous decisions become ever more deeply embedded in institutional structures and ways of thinking (e.g., Kay, 2005 ). Employing these kinds of theories, Smith ( 2013b ) has demonstrated how the institutionalisation of particular ideas about health and economic policy function as filters to research-based ideas about health inequalities, encouraging those ideas that support existing institutionalised ideas (or ‘policy paradigms’) to move into policy, while blocking or significantly transforming more challenging ideas.

This way of thinking about the relationship between knowledge and policy suggests that research is constantly being influenced by policy and politics and that efforts to bring researchers and policymakers closer together are like to exacerbate this in ways that may not be desirable. At best, from this perspective, the research impact agenda seems likely to reward some academics (and not others) for achieving impacts that had far more to do with political interests and agendas than the research or impact activities of those academics. At worst, the impact agenda will lead to the increasing politicisation of research (and an associated reduction in academic freedom). Indeed, some of the most critical responses to the impact agenda are informed by these kinds of concerns. Cohen ( 2000 ) and Hammersley ( 2005 ), for example, have warned that the restrictions being placed on publicly-funded research to be ‘useful’ to policy audiences is limiting the potential for academics to promote ideas that are out-of-line with government policies. Likewise, Davey Smith et al., ( 2001 ), argue that efforts to achieve evidence-based policy may, in fact, do more to stimulate research that is shaped by policy needs than to encourage better use of research in policy-making.

Co-production

A third way of theorising research-policy relations has emerged from science and technology studies (STS), and posits a much more complex inter-relationship between knowledge production and governance. This approach is encapsulated in the idea of ‘co-production’: the claim that knowledge and governance are mutually constitutive (Jasanoff, 2004 ).

Similar to the approaches discussed in the last section, such accounts see knowledge as profoundly shaped by politics. But the notion of co-production focuses not just on the social and political constitution of science. It is also attentive to the other direction of influence: the ways in which governance is itself constituted by scientific knowledge. So rather than limiting its attention to how politics shapes knowledge, the notion of co-production posits that scientific and expert knowledge contribute to the construction of political reality (an idea that is, in some ways, simply a stronger version of Weiss’ ( 1979 ) account of the enlightenment function of research, discussed earlier). Knowledge provides the concepts, data and tools that underpin our knowledge of social and policy problems and appropriate modes of steering (Voß and Freeman, 2016 ). Sheila Jasanoff ( 2004 ) is arguably the most influential exponent of this approach. In her book States of Knowledge , she explores how knowledge-making is an inherent part of the practices of state-making and governance. States ‘are made of knowledge, just as knowledge is constituted by states’ (Jasanoff, 2004 , p 3). Moreover, STS scholars have shown how science does not just produce knowledge and theories that help define social problems and appropriate responses. It also produces skills, machines, instruments and technologies that are deployed in governance (Pickering, 1995 ).

An important concept informing this approach is that of performativity. This is the idea that social enquiry and its methods are ‘productive’: rather than simply describing social reality, they help to make or enact the social world (Law and Urry, 2004 ). Indeed, social science needs to be understood as fundamentally embedded in, produced by, but also productive of the social world (Giddens, 1990 ). Social science thus has effects–it creates concepts and labels, classifications and distinctions, comparisons and techniques that transform the social world. Such concepts and techniques can also help bring into existence the social objects they describe. Osborne and Rose ( 1999 ) illustrate this idea with the case of public opinion, a social phenomenon that was effectively created in the 1930s through the emergence of new methods of polling and survey analysis, and is now thoroughly normalised as an object of social scientific enquiry. Similarly, Donald MacKenzie ( 2006 ) has explored the performativity of economic models, showing how the theory of options shaped practices in trading and hedging in the financial sector from the 1970s onwards. Similar ideas have been explored by Colin Hay ( 2007 ) in his discussion of political disaffection. He argues that public choice theory has contributed to the ‘marketisation’ of party politics, implying that such theories have been ‘performative’ (although he does not use this term).

Theories of co-production also show how science can produce social problems. Through its various scientific and technical innovations, science does not simply solve governance problems, but it also creates new ones (Jasanoff, 2004 ). The frantic pace of development and progress in science and technology produce a continuous stream of new problems and solutions, which governments often struggle to keep pace with. So new research does not just offer ways of ordering the social world, but can also destabilise existing structures and modes of governance. In areas of policy that are highly dependent on technology and science–such as energy, health, agriculture or defence - policy develops almost in pursuit of science, in an attempt to catch up with, harness and regulate the new technologies and practices it has produced. Thus science creates the very problems that need to be addressed through political intervention (Beck, 1992 ). The demand for ever more problem-solving knowledge is effectively built into the structure of policy-research relations.

What implications do these approaches have for defining and measuring impact? First, they suggest that we cannot neatly disentangle processes of knowledge production from those of governance. This is not merely an epistemological question–a challenge of finding the right methods or observational techniques to allow us to separate out how social scientific findings have influenced politics or policy (although this is of course difficult to do). It represents a more fundamental ontological problem, in that social scientific knowledge is co-constitutive of politics. Imagine, for example, trying to chart the ‘impact’ of public choice theories on politics. We would not only face the methodological challenge of charting the subtle and incremental processes through which a wide variety of social actors (including politicians, campaigners, lobbyists and the media) appropriated public choice theories about political agency. We would also need to understand the ongoing feedback effects through which such ideas brought about shifts in the behaviour of these actors, in turn gradually transforming political behaviour. If we accept the possibility of such effects, then we need to also consider how such shifts may in turn validate the theories that originally produced them, enhancing their authority and influence. The relationship between social science and politics in this example is one of continuous mutual influence and reinforcement.

Second, the notion of co-production suggests that social science may itself produce social problems that require political responses. Studies of public opinion offer a good example of this. A survey of public attitudes may ‘discover’ unarticulated claims and preferences, which produce new demands for political action. In 2014, Jeffery et al., ( 2014 ) found a strong desire on the part of the English respondents they surveyed for institutions that better represented and articulated ‘English’ views. This could be charted as ‘impact’ insofar as the findings of the survey were picked up by politicians and influenced claims-making about UK constitutional reform (and indeed it was submitted as a case study to REF2014). But the research can also be understood as producing a new set of political problems. It encouraged a number of survey respondents to articulate a set of preferences which may previously have been nascent or unspecified. These preferences were then presented as a collective and coherent political claim, which in turn implied the need for enhanced political representation and constitutional reform. Research thus contributed to the construction of a new social problem requiring a political response. As with the case of public choice theory, we can also posit a feedback effect, whereby the social and political adjustments generated by the research might in turn further validate the findings. As politicians sought to represent and mobilise these preferences, this created further political expectations and demands, thereby substantiating the initial research claim that the English desire their own institutions.

One implication of this account is that REF or HEFCE models do not do justice to the more pervasive (but often subtle) influence of social science on policy. Another is that they overlook the feedback effects described above, whereby the political adjustments enacted through social science in turn validate (or possible discredit) the authority of research findings or methods. And a third is that they may actively encourage forms of interference that create more problems than they solve. Policy impact may not always be benign, as we noted earlier.

Assuming we accept such impacts as desirable, how might these processes of co-production be best captured and accredited? They would require quite resource-intensive methodologies, as well as forms of expertise that are not necessarily available across disciplines. Each case study would effectively be a social scientific project in its own right, explored though a range of qualitative and quantitative methods, such as ethnography (as Baim-Lance and Vindrola-Padros, 2015 , argue in more detail) process tracing, discourse analysis, interviews and surveys. It is hard to imagine sufficient resource being available for such indepth enquiry, or, indeed, for buy-in to such models and methodologies from across (non-social science) disciplines.

Autonomous spheres

Our final approach to theorising research-policy relations understands science and politics as distinct spheres, each operating according to a separate logic and system of meaning. As we saw earlier, one version of this account is Caplan’s ( 1979 ) ‘two communities’ thesis, which identifies a ‘cultural gap’ between researchers and policymakers. This conceptualisation has been subject to a range of critiques, not least, as Lindquist ( 1990 ) points out, the fact that this way of thinking about the relationship excludes a range of potentially important actors, such as journalists, consultants and lobbyists. Despite this, whilst not always referring to Caplan’s ( 1979 ) work directly, many contemporary assessments of the limited use of research in policy and practice frequently mirror Caplan’s observations by highlighting perceived ‘gaps’ between researchers, policymakers and/or practitioners as a fundamental barrier to the use of research.

In this section, we focus on a more radical account of this ‘gap’, associated with the systems theory of German sociologist Niklas Luhmann (e.g., Luhmann, 1996 ). On a Luhmannian systems theory account, science and politics are both understood as self-referential or ‘autopoietic’ systems. Although mutually dependent in important ways (they could not survive in a recognisable form without one another), each operates according to its own logic or ‘communicative code’, which determines which communications are relevant to the system. There is no causality or direct influence across systems: rather, operations in one system are selectively perceived and given meaning according to the codes and logics of another system. Thus it does not make sense to conceive of flows, diffusion or causality across systems, and STS concepts such as ‘performativity’ or ‘co-production’ need to be carefully re-specified in terms of how one system ‘models’ and responds to the operations of another.

Luhmann understands the primary building blocks of modern society not as individuals or groups, but as functionally differentiated social systems. Modern societies are increasingly sub-divided into specialised, self-referential systems such as education, health, economy, religion, welfare, science or politics. Each of these systems operates according to its own distinct codes, programmes, logic and mode of inclusion. Unlike on Caplan’s account, these systems are not distinguished in terms of members or institutions. Systems do not consist of discrete groups of people, indeed one person or one organisation can participate in several different systems. However, systems are distinguished in terms of sets of differentiated roles and activities. Each system retains its distinctiveness through developing its own criteria of selection, which help it reduce complexity by only selecting those communications which are relevant to the system.

On this account, science and politics are separate function-systems. Science (including social science) operates according to a binary code of true/false. In other words, it defines relevant communication based on whether it is concerned with establishing truth claims. The system of politics, meanwhile, selects relevant communication on the basis of the binary code of government/opposition. The political system selects and gives meaning to communication based on its relevance to the pursuit of political power and the capacity to adopt collectively binding decisions. At first sight, this seems to be a very narrow way of conceiving social systems. For example, scientists are not just preoccupied with validating truth claims; they are clearly also concerned with winning grants, enhancing their academic reputation, or influencing government policies. But these preoccupations are characterised as participating in different systems. For example, a public funding decision has a distinct meaning and relevance in the systems of science, politics and the economy.

From this perspective, there can be no overarching causality operating between two systems, although it is easy to see how appealing such causal attributions might be to observers. To be sure, one event can have effects across different systems. A government research grant has meaning for both the system of politics and that of science. Yet As Luhmann puts it, the ‘preconditions and consequences of events differ completely according to system reference’, and observers should not ‘cross-identify events over boundaries’ (Luhmann, 1991 , p 1438). Instead, Luhmann conceives of the relationship as highly selective connections between systems and their environments. Systems that are reliant on other systems in their environment develop models, or assumed regularities, to help them keep tabs on the other system. For example, science will develop a certain way of observing and anticipating political decision-making relevant to science: a set of beliefs about how and when decisions are produced, what drives them, and what effects they may have on science funding or regulation. These models can be understood as internally constructed filters to help select what is relevant from what is noise or redundancy. They help the system to sort through what is expected and what is unpredicted, what is a relevant signal and what is an irritation (Luhmann, 1991 , p 1432).

If we accept that science and politics are guided by distinct logics or communicative codes, the challenge becomes one of reconstructing how each system might selectively pick up signals from the other. We need to understand what sort of perceptual filters are developed and stabilised for the purpose of screening out relevant signals from noise; and how information from the other system might be constructed and connected to the receiving system’s identities and functions. The implication is that we need to turn our attention to how the system of politics ‘models’ the system of science, and how it selectively appropriates and gives meaning to the signals produced by that system.

This segues nicely into the earlier discussion of our first set of theories, and the need for a more sophisticated theory of politics than those provided by prevalent models of research-policy relations. Such a theory would require an account of how the political system makes sense of its environment, and selectively draws on different types of resources to secure legitimacy or support (Boswell, 2009a ). A number of theories from public policy can contribute towards such an endeavour. Notably, theories of information-processing offer potential to examine how organisations in the public administration selectively pick up signals from their environment about social problems (e.g., Baumgartner and Jones, 1993 ). Cohen and colleagues’ ( 1972 ) ‘garbage can model’ of policymaking, as taken up by Kingdon ( 1995 [1984]), offers a neat way of theorising how different ideas or ‘solutions’ are picked up depending on the political and problem streams–again, an idea broadly compatible with the systems theory approach, in that it views ‘ideas’ and ‘politics’ as operating according to different temporalities and logics (Boswell and Rodrigues, 2016 ).

What are the implications of systems theory for impact? A systems theoretic approach would be wary of the attempt to demonstrate ‘impact’, as it assumes a specious causality between science and politics. Instead, we need to try to adopt the perspective of politics, and make sense of how and why the political system picks up data, methods or techniques from social science. And we can attempt to observe how, from the perspective of social science, political decisions or goals might affect the selection and framing of research questions, and the communication of research findings. But we cannot integrate these observations into a single set of causal mechanisms. Viewed from ‘inside’ of each system, the other remains a ‘black box’: an infinitely complex set of communications and operations which can only be very crudely modelled and selectively responded to.

What this implies is that an impact case study could at best chart how politics appropriated and gave meaning to particular data, methods or techniques. But the ‘underpinning research’ that produced these data or techniques, or academic efforts to promote this research, would derive rather limited credit for such take-up. Far more important would be dynamics internal to the political system, such as the political salience of the issue, or how well the research in question was attuned to dominant political framings of policy problems (Kingdon, 1995 [1984]; Cairney, 2016 ), or how far research was seen as an authoritative mode of knowledge for guiding decisions (Boswell, 2009b ). Moreover, it would remain open how far political take-up reflected a preoccupation with signalling legitimacy, rather than informing policy interventions. After all, if research is valued by politics as a means of substantiating claims or bolstering credibility, then presumably this implies a symbolic rather than instrumental rationale for using research (Boswell, 2009a ).

In short, the systems theoretic account guides us towards an interrogation of the political context of knowledge utilisation; but the more we probe the logic of knowledge appropriation in politics, the less we can accredit research. What makes for politically useful knowledge is fundamentally distinct from what makes for good science. Thus any link between high quality science and impact is exposed as contingent. It may well be that politics needs to ‘quality control’ the science it invokes to insure against its invalidation by critics–but this is only as an insurance against critique. And it may want to ensure the robustness of science as a safeguard against making mistakes that would cost political support. But again, this concern with rigour is incidental to the core concerns of politics. Politics is not fundamentally preoccupied with what is true, but with what is relevant to securing power and producing collectively binding decisions.

Current approaches to research impact appear to have been informed by simplistic supply-side models within our first category of ‘knowledge shapes policy’. As we have suggested in this article, such accounts have been widely debunked by theorists of research-policy relations, as well as by many empirical studies of research ‘impact’. And yet the REF and HEFCE models, and much of the literature on knowledge utilisation, continue to remain faithful to this problematic account. Part of the reason for the sustained commitment to these models is that they offer a reassuring narrative to both policy-makers and researchers. Politicians and public servants can demonstrate the rigour and authority of their claims by invoking research, and they can secure legitimacy by signalling that their decisions are well-grounded (Boswell, 2009a ), or they can invoke the need for research as a rationale for delaying action (Fuller, 2005 ). At the same time, researchers can secure additional resources and credit for developing compelling narratives about the impact of their research (Dunlop, 2017 ). Yet these accounts bely the complexity of research-policy relations and, indeed, of policy processes and policy change (Cohen et al., 1972 ; Smith and Katikireddi, 2013 ). If we are to avoid continually reinventing broken wheels, we suggest a new, more theoretically informed approach to thinking about research impact is required.

The existing literature on research impact has already subjected current approaches to assessing, incentivizing and rewarding impact in the UK to extensive critique, and it was not the purpose of this paper to expand on these critiques. Rather, our aim has been to set out four alternative, sophisticated accounts of the relationship between research and policy and to consider what a research impact agenda might look like if it were informed by these other approaches. Such an exercise is necessarily hypothetical and almost impossible to test in an empirical sense, since the UK’s approach to research impact has already been informed by a relatively simple and linear conceptualisation of research-policy relations (Smith and Stewart, 2016 ). This means there are strong incentives for institutions to ‘play the game’ according to the rules that have been set by providing relatively simple and linear ‘stories’ of research impact, as Meagher and Martin’s ( 2017 ) analysis of REF2014 impact case studies for mathematics attests (see also Murphy, 2017 on ‘gaming’ in REF and Watermeyer and Hendgecoe 2016 on ‘impact mercantilsm’). However, as other countries evolve different approaches to research impact, it may become possible to empirically assess both the claims we set out here and the practical implications of such alternative approaches.

The first of the four models we outline offered a subtler ‘enlightenment’ conception of how research can influence policy. It implied that research can lead to ideational adjustments through diffuse and incremental processes, typically influenced by a wide body of research rather than individual findings. This account challenges the notion that researchers or institutions should be rewarded for claims about the impact of individual studies, though potentially supports efforts to encourage knowledge exchange. The second set of theories implied that policy and politics shape knowledge production and use, and were altogether more sceptical of the impact agenda. They suggested that it was naïve to assume that researchers can speak truth to power, implying that researchers should not be rewarded for their supposed impact since policy actors employ research for political, rather than empirical/intellectual, reasons. The third set of theories on co-production implied the need for a far more sophisticated methodology for examining how research and governance are mutually constitutive. They also argued that social science should not necessarily be understood as the ‘solution’ to social problems, since it can itself create such problems. And the fourth approach, which posits that science and politics are autonomous systems, suggested that we can best understand impact through a theory of how politics selectively observes and gives meaning to communications emanating from the system of science. Viewed from this perspective, the impact agenda has been designed to suit the needs of a political, rather than scientific, system and should be treated cautiously by researchers given its potential to divert science from its core task of developing truth claims.

Both the second and fourth accounts suggest that the very idea of trying to incentivize the use of research in policy is flawed. On these accounts, we should be cautious about adopting systems that reward researchers for influencing policy. Such impacts are spurious, in that their apparent influence is down to pre-given interests or independent political dynamics; or they are the result of researchers aligning research questions and approaches to pre-fit political agendas. By rewarding researchers for achieving impact we are adopting an arbitrary incentive system that is at best decoupled from research quality, and at worst, threatens the integrity and independence of social science.

For those more sympathetic to the idea of ‘research impact’, the first and third approaches might offer more hope. Nonetheless, neither approach suggests that the current approach is likely to achieve its intended goals. Indeed, both caution against rewarding individual researchers for ‘achieving’ research impact based on narrow indicators (e.g., citations in policy documents). The enlightenment model suggests that research impact involves subtle, incremental and diffuse ideational adjustments over a long period of time, which are generated by a wide range of research insights rather than specific individual findings. This suggests that a system for rewarding impact should not focus on individual research projects or groups and their linear effects on particular policies. Rather, impact frameworks should reward collaborative endeavours that build incrementally on a wider body of work; that develop longer-term relationships with a range of non-academic audiences (not only policymakers and other ‘elites’); and that may bring about subtle conceptual shifts, rather than clearly identifiable policy changes. This in turn implies the need for more complex research designs and methodologies for charting such influence over a far longer time-frame, and avoid incentives to over-claim credit for particular groups or projects. This perspective coheres with those arguing for a shift away from trying to measure and incentivize research impacts to focus instead on incentivizing and rewarding knowledge exchange processes (e.g., Upton et al., 2014 ). From this view, Spaapen and van Drooge’s ( 2011 ) approach of focusing on ‘productive interactions’ between science and society (which emerged out of an FP7 project called Social Impact Assessment Methods for research and funding instruments-SIAMPI), seems like a more defensible means of assessing research impact. The notion of co-production similarly suggests the need for more in-depth, ethnographic or process-tracing methods for reconstructing the complex relationships between research and policy (as outlined by Baim-Lance and Vindrola-Padros, 2015 ). Systems for rewarding impact should also be aware of the two-way relationship between research and governance, including the ways in which social science can itself affect the social and political world, imagining and enacting new social problems.

Arguably, the highest impact research is that which serves to re-shape the social world it seeks to describe. This implies that models to promote engagement with knowledge users need to be attentive not just to the complex pathways to research impact, but also to the very real ethical implications of research influence (implications that do not currently appear to be considered in either REF impact case studies or RCUK pathways to impact statements–Smith and Stewart, 2016 ). Not only can the impact agenda affect the practices of social science, as is widely recognised in social science literature; social science can also instigate new policy problems. Proponents of policy impact should have a care what they wish for.

Data availability

The article does not generate or make use of any datasets.

Change history

20 february 2018.

On page 7 of the PDF, in the second paragraph under the subheading “Conclusion” the first sentence “The existing literature on research impact has already subjected current approaches to assessing, incentivizing and rewarding impact in the UK to extensive critique (e.g., ADD REFS) and it was not the purpose of this paper to expand on these critiques” has been corrected to “The existing literature on research impact has already subjected current approaches to assessing, incentivizing and rewarding impact in the UK to extensive critique, and it was not the purpose of this paper to expand on these critiques”.

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“This Research has Important Policy Implications…”

The COVID 19 pandemic has generated much interest in the relationship between research and policy. It has drawn new attention to the limitations of a linear model, where policy is based on first observing prior scientific research and then designed in response to this. Conflict researchers often motivate the importance of their work by claiming that their “research has important policy implications”, but the proposals offered are often at best incomplete. I identify a number of common limitations in claims about policy implications, including a lack of discussion of objectives and priorities, stating objectives themselves as if they were policies, claims about targeting factors without discussing the effectiveness of possible interventions, and a failure to consider uncertainty and potential tensions with other objectives or unintended effects. Research can potentially inform policy discussions and improve decisions, but the incentives in academic research are very different from policy decisions, and the latter often calls for very different evidence than what is offered by the former. Rather than attempting to offer policy prescriptions as an afterthought to academic articles, research can be more helpful to policy by trying to inform debates, focusing on what we know from the cumulative body of research than individual manuscripts, and providing new data and empirical material that allow for better problem description and analysis.

1 The Push for Demonstrating Research “Policy Relevance” and “Policy Implications”

The COVID 19 pandemic has generated much interest in the relationship between research and policy, and it is clear that there is often no simple linear path from prior medical research to policy responses to COVID 19. The call for “policy to follow the science” sound compelling, but often “the science” itself is disputed, and there is a lack of clarity or agreement on policy objectives and priorities. In many cases, people start with strong prior assumptions or preferences for specific policies and then look selectively for evidence that appear to support positions already taken. Observing the experiences during the pandemic provides an important opportunity to reflect on the relationship between research and policy in conflict research. It is common among conflict researchers to claim that research “has important policy implications”. Such statements are often added tacked onto research articles, possibly as a way to either underscore the importance of research projects or to try respond to calls by funders and home institutions for research to be “policy relevant”. In this article I examine common problems in claims about policy implications following from research. Many research articles often make it seem as if stated policy implications arise directly from the research presented. Yet, claims about policy relevance are often at best incomplete and entail a number of common problems, including confusing outcomes and policies, or stating implications claims that do not follow in any direct way from the research itself. My argument is not that research cannot speak to policy or that researchers should not be interested in policy. However, if researchers wish to speak to policy questions and dilemmas, then their comparative advantage is precisely in research and description rather than prescription, and one would often need to ask very different questions or do different analyses to speak more directly to policy discussions and decisions. The demands that researchers face when seeking to get manuscripts accepted for publication or achieving academic success often do not incentivize the type of research and analysis that could be most helpful to evaluate policy proposals or policy decisions. But the potential role of research for policy is arguably too important to be treated as an afterthought in academic research.

In Gleditsch (2022) I examine the relationship between policy and prediction in international studies, and I propose a simple four-item typology for the key elements that should underlie policy decisions. First, we would need to clarify policy objectives , or what we wish to achieve. Second, we would need to identify policy alternatives , or what we think we can do to achieve these objectives. Third, we would need to examine likely policy consequences , or what we think would happen under different alternative policies. Fourth, we need to do cost-benefit analysis of proposals. In many cases the objectives targeted by one alternative course of action may be in conflict with other objectives, or the likely consequences of one alternative could entail unintended consequences that are detrimental to other key objectives. Gleditsch (2022) primarily seeks to highlight item 3, and underscore how any statements about future consequences are in effect predictions. As such, it is hard to see how we can avoid predictions in policy proposals, and we should look at what we know about making predictions and how to predict better to do as well as we can in informing policy discussion and debate. However, the other points are also important in their own right, and claims about policy implications in conflict research often fail to engage with points 1–2 and 4.

2 The Dark Side of Stock Phrases in Research

“This research has important policy implications” is a very common stock phrase often tacked onto articles, even if not as common as the cliché that “more research is needed”. [1] A generous interpretation might see these type of stock phrases in research as largely innocuous conventions. Claims about how “more research is needed”, for example, can arguably provide an opportunity to provide an informal discussion of possible new directions in research and things that other researchers might consider. Graduate students are sometimes encouraged to look in the concluding sections of articles for ideas on potential novel contributions when planning their dissertations and research projects. By the same token, one might perhaps argue that laying out an informal discussion of policy in a research article could help draw attention to why someone should care about the research topic in the first place and the potential relevance for policy debates.

However, claims about needs and implications are also directive, and many have pointed to how the statement that “more research is needed” (MRIN) also has a darker side, with potentially negative consequences for research. Greenlaugh argues that indiscriminate MRIN statements often become a way to save a null hypothesis from actual empirical scrutiny in medical research. [2] It can become a defense of pursuing an existing research program even when the results are largely negative, on the premise that stronger results or confirmation are just around the next corner, with more time or money. A commitment to science should also entail a commitment to abandon theories and propositions if we fail to find support. Standard tenets of philosophy of science tell us that if the results of an experiment do not come out as expected after trying more than once, then we should be prepared to reject the theory, or at least identify what premises or potential auxiliary assumptions may not hold (e.g. Hempel 1966 ). Resources are invariably limited, and throwing more good money after bad is not just wasteful, but could deprive funding from other more useful projects. Of course, not all research can be experimental, and observational studies present additional layers of complexity (e.g. Morgan and Winship 2014 ; Rosenbaum 2002 ). But even so, if we have only week empirical evidence for propositions, if we wish to justify further research then we would at least need to be more precise on what may have been wrong in efforts to evaluate the implications of a proposition, or be prepared to look for incorrect assumptions in theory itself. In short, following Greenlaugh, what we need is not more open-ended research, but “more thinking” and directed research.

3 Pathologies of Claims About Policy Implications

There is an instructive parallel here to the common and often lose claims about policy implications following from research. These often seem to be added essentially as a marketing ploy or afterthought to an article, possibly as a device to motivate the importance of the research agenda or entice more interest by suggesting potential utility of the findings for policy. However, there is rarely much systematic discussion of policy objectives, policy alternatives, cost-benefit analysis, and how research can speak to these in order to inform policy debate and decisions.

The ultimate goal of research should be science, and there is nothing inherently wrong about academic research not having much to say about policy. However, researchers face many incentives to try to claim “policy relevance” for their research, either because reviewers ask for this or because institutions or research funders increasingly emphasize non-academic impacts. [3] Incentives tend to influence behavior, and researchers are often tempted to make claims about research having policy relevance that are at best incomplete. There are a number of common problems plaguing claims about “policy relevance”. There is a tendency to present empirical findings or variables reflecting particular outcomes as if they were by themselves policies (i.e. we should “reduce conflict”). In some cases, researchers suggest that we should have policies targeting some factor X based on its relationship with some outcome Y (i.e. “reduce conflict by promoting democracy”). But evidence about a relationship between two factors X and Y do not by themselves provide clear evidence of our ability to change outcomes Y through changing X. Discussions often bypass or downplay important debates about objectives and preferences that are essential for a meaningful discussion about policy decisions. Researchers sometimes treat their own preferences and objectives as if they are inherently reasonable and knowledge based, even when they are clearly not universally shared and possibly highly contentious. Boulding (1977 : 77) argued that peace research ought to be a “normative science” (which he defined as “the serious study of what we mean by saying that the state of the world goes from bad to better or from bad to worse”, see also Regan 2013) , but at the same time noted that this was a “dangerous occupation … [since] [t]here is always a danger that our norms act as a filter which leads to a perversion of our image of reality”. [4] Moreover, discussions of policy often assume that decision makers or politicians only care about stated outcomes and invariably seek to identify efficient policies for reaching these objectives. In reality, however, politicians can often have perverse incentives. There is often uncertainty often about basic facts and the attributable effects of policies. Krugman (1994) argues that the most influential “policy entrepreneurs” in economic policy peddle politically popular ideas that often lack support in academic research, and that their success is in least part due to the unwillingness or lack of effectiveness of academic economists to engage with their proposals. There is also often a clear bias towards policies that are more visible or help “signal determination” rather than the effectiveness of policies per se. Although many emphasize how “bad policies” may be “good politics” for dictators (e.g. Bueno de Mesquita et al. 2003 ), leaders in democracies also often face perverse incentives or may act rationally from their point of view yet make decisions that are counterproductive from the point of view of social welfare or stated objectives (see, e.g. Caplan 2022 ). Finally, interventions or efforts to address one concern can have important conflicts with other or unintended effects.

4 Research and Policy on Migration and Terrorism

As an illustration of the more general common problems in claims about policy implications I look in more detail at an example from an article focusing on the relationship between migration and terrorism by my occasional coauthors Bove and Böhmelt (2016) . This is a very solid piece of empirical research, and my issue is not with the analysis itself reported in the article – it is a helpful example precisely because both the empirical findings are clearly presented and the alleged implications for policy are stated explicitly. [5] The article shows that migration appears to be linked to an increase in the risk of terrorism only in cases where migrants come from locations with active ongoing conflict and political violence, and there is no general impact of immigration on terrorism. The authors argue that this has “critical implications” for “immigration policies” (p. 572) – again, perhaps because this is what we are expected to do, or because a reviewer asked for this. They endorse statements made by then EU commission head Juncker, aspiring to “a well-designed legal migration package”, which should consider both economic benefits and potential risks (p. 586). They also recommend “serious efforts to fight terrorism abroad and reduce the incidence of political violence in immigrants’ countries of origin” (p. 586).

This seems like aspirations that everyone could agree with, so what is the problem here? I see at least two. First, the idea of well-balanced migration policy sounds like a useful objective, but attaining something like this would also require much more explicit detail in identifying costs and benefits and how to trade off one against the other. The economic benefits from migration and the potential security risks from migration have no intrinsic common metric. Leaving aside uncertainty over likely costs and benefits, we would need to price one relative to the other. What would be considered “well-balanced” could differ dramatically if people assign different rates of one to the other. If one assigns a very high price for security relative to economic benefits one might conclude that “well-balanced” would imply be next to no migration, while others would argue that the cost of any security risk pale in comparison to the expected benefits from increased migration, or that these findings at most would support reducing migrants from countries with conflict but allowing more migrants from countries without conflict. I will return to this issue and provide more examples of divergent assessments later.

Second, a common and arguably even more fundamental problem in claims about policy implications is that they in essence amount to statements about objectives we wish to achieve. Less political violence in other countries may also be a laudable objective in its own right (irrespective of any impact on migration or risk of terrorism), but it is precisely an objective or outcome that we seek to achieve, not a policy to achieve the objective. Presenting objectives as policy is also common beyond academia. For example, while Prime Minister of the UK, Elizabeth Truss insisted that she wanted “higher economic growth”. [6] However, growth is an outcome, and simply stating a wish for higher growth is not by itself a policy to achieve the outcome. The policy proposals offered by her government to boost growth in terms of tax cuts without a clear plan for financing did not produce the intended outcome in the short-term – if anything, they created negative growth expectations – and Truss resigned after 44 days in office, following increased government borrowing costs and currency depreciation.

This underscores how “policy” cannot simply be about stating objectives alone, even when these are largely uncontroversial. Rather, we need to think about choosing specific policies or actions among possible alternatives that we think may be helpful to achieve target objectives. Detailing policy objectives is not trivial, and it is not always obvious or easy to reach agreement on what they ought to be (more on this later). But even reaching agreement on objectives is not enough to proceed to “policy” – we will still need to consider policy alternatives and their consequences.

In many cases, research will uncover or find evidence of associations between specific independent variables and key outcomes, leading researchers to proceed to say that we should have “policies” focused on a key independent variable. For example, if democracy is plausibly associated with less political violence (e.g. Davenport 1999 ; Rummel 1997 ), then one might argue that we should try to reduce political violence by “promoting democracy”. The problem here is that establishing that a variable X is associated with differences in Y by itself does not tell us much about our ability to change Y through changing X. Do we have clear ways to promote democracy, for example, and what do we know about the effectiveness of alternative strategies in inducing democracy? For efforts to study such initiatives, see e.g. Bollen, Paxton, and Morishima (2016) , Carnegie and Marinov (2017) , and Finkel, Pérez-Liñán, and Seligson (2007) . Effectiveness aside, could such strategies have unintended consequences that may exacerbate the risk of political violence? [7] To evaluate such questions we need evidence on interventions and changes.

Bove and Böhmelt (2016) do not discuss the effectiveness of different immigration policies or strategies for reducing political violence in other countries, and this is not the purpose of the article in the first place. To be clear, the statements are not inherently wrong or raising concerns that are not laudable or important, rather the problem is that the research presented does not allow us to say much about how such policies could be designed or pursued, and if specific proposed actions could have the intended consequences.

Research can in principle have a lot to say about the consequences of specific policy alternatives. But we need to look at very different bodies of research, moving into the domain of “effects of causes” or interventions rather than the attributable “causes of effects” producing observed outcomes, which tends to be the focus of academic research (e.g. Dawid and Musio 2022 ). In short, for evaluating policy alternatives for a specific problem we would typically need an entirely different research program.

Differences in objectives are also usually not a trivial issue, and we are unlikely to have any agreement on cost-benefit analyses without some agreement on the objectives in the first place. Even a cursory review of existing work on migration and policy reveals that there are major disagreements in work on costs and benefits on immigration, in part reflecting differences in initial assumptions or priority assigned to specific concerns or objectives. Some such as Caplan and Weinersmith (2019) and Norberg (2020) argue that the economic case for the benefits of immigration is overwhelming, pointing to plausible studies indicating benefits for economic growth from increasing labor mobility. It is likely correct that more migration will tend to increase individual welfare of migrants and probably also raise global income. However, many skeptics of immigration simply point to other objectives or issues, arguing that immigration undermines social cohesion or can have other negative consequences, perhaps pointing to potential subsequent negative economic effects of reduced social cohesion, weaker social institutions, or negative implications for specific individual actors or coups in receiving countries. More immigration could imply lower GDP per capita even if total GDP grows. For example, Jones (2022) that argues that work on the “deep roots” of economic development should make us concerned about the consequences of immigration. Others such as Collier (2015) stress plausible negative impacts of migration on social cohesion. Others again stress security above all, possibly to the point where no economic benefits could ever compensate for the potential risks (e.g. Bawer 2006 ; Huntington 2004 ). Research alone cannot tell you how you to balance these concerns.

More generally, if people have different preferences or objectives in the first place, then we have no reason to expect that people will converge or agree on policies through more research or better information. Indeed, research on cognitive dissonance indicates that contradictory information and challenges to beliefs tend to lead to hardened beliefs among more committed individuals ( Festinger 1957 ; see also Acharya, Blackwell, and Sen 2018 ; Harmon-Jones, Harmon-Jones, and Levy 2015 ; Mullainathan and Washington 2009 ). One of the first noted examples of cognitive dissonance was a study of the coping mechanisms arising among members of a UFO group after the predicted end of the world failed to materialize ( Festinger, Riecken, and Seekers 1956 ). To use an extreme example, people have different views on abortion because they hold fundamentally different values at the outset (e.g. DiMaggio, Evans, and Bryson 1996 ), not because they disagree on medical research or scientific uncertainty about issues such as when a fetus is viable. It is ultimately a political a political question how societies chose to balance divergent objectives and preferences. This does not mean that there can be no role for research – research could possibly tell you much about existing empirical findings, the bases for the claims in individual studies, divergent conclusion, or even the distribution of popular views and preferences, and if nothing else such information could at least make cost-benefit analysis more explicit and provide for more informed debate. But it is clearly not the case that doing more research will always yield convergence or allow us to conclude on policy without at least first discussing what objectives should be and how to balance potentially competing concerns.

I am by no means an expert on migration, but know a bit more about terrorism and how international factors may influence political violence. In the case of terrorism we have some efforts to conduct more formal cost-benefit analyses of counterterrorism policies. For example, Mueller and Stewart (2011) argue that it is very unlikely that current counterterrorist spending in the US and other countries could be cost effective. This is in part because the direct costs of terrorism are estimated to be low. One might of course argue that the observed risk seems lower because counterterrorism policy might have prevented or deterred costly attacks that otherwise would have occurred. However, the only way to evaluate plausible gains in security is to try to engage explicitly with the possible reduction in risk. Mueller and Stewart calculate how many attacks with an estimated cost of $100 billion would have had to be deterred or averted for US homeland security spending after 9/11 to be cost-effective. They find that there would have to be at least two credible attacks per year averted as a result. If we lower the cost threshold to a more realistic $100 million – the plausible magnitude of the 2010 Times Square attack if it had succeeded – we would need to avert an astonishing 1667 attacks per year. They argue that what we know about terrorist planning and competence makes this difficult to justify. One might contend that Mueller and Stewart have had limited influence on counterterrorism policy, but it is hard to see how policy can benefit from avoiding cost-benefit analysis. Indeed, we could have had better debates if critics engaged with their analyses and tried to point out specially what they disagree with.

Mueller and Stewart (2011) do not provide a direct answer to questions about efforts to reduce terrorism abroad, since they consider only effects of policies at home. [8] However, much research on terrorism has substantiated potential problems of transference, for example that one might inadvertently raise the risk of other types of terrorism by making it more difficult to carry a specific type of terrorist attacks. For example, efforts to better protect US targets abroad from attacks by Islamic groups after the US embassy bombing in Kenya and the attack on USS Cole of the cost of Yemen plausibly increased the risk of domestic attacks in the US such as 9/11, by lowering the costs of these attacks relative to the costs of targets abroad ( Enders and Sandler 2012 ; Gaibulloev and Sandler 2019 ). If so, we cannot simply assume all else is equal if we seek to “fight terrorism abroad”.

When it comes to efforts to reduce violence abroad, we have evidence suggesting that international peacekeeping works in the sense that it can prevent civil conflict recurrence (e.g. Walter, Howard, and Fortna 2021 ). However, good news about peacekeeping and civil war does not necessarily translate to less terrorism by implication. Di Salvatore, Polo, and Ruggeri (2022) , for example, argue that UN peacekeeping in civil wars can lead to a shift towards more irregular attacks such as terrorism even if it reduces conventional attacks. In short, it is a valuable idea that we should consider investing in efforts to reduce political violence abroad to offset potential risk of terrorism from migration, but there is clearly much that we do not yet know or research that needs to be done to evaluate properly how actual strategies for this could or are likely to work as well as possible undesired side effects.

5 Prediction can be Helpful for Policy, but Predictive Ability Does Not Imply Policy Relevance

Researchers can in principle have a lot to contribute to debates about policy, but our best bet for doing so is to use our comparative advantage in research and engage systematically in modelling and prediction (e.g. Gleditsch 2022 ). If policy is about future consequences, then it is hard to see how you can claim to be policy relevant without engaging in prediction and explicit modelling. It is easy to claim that something is predictable after the fact, but unless we actually make predictions in advance what we have is post-diction, with the benefit of hindsight. Prediction is useful in part because we need to be precise about outcomes, timing, and quantifying likelihood, and how we would score if the prediction ultimately was correct or not.

Advances in work on forecasting has taught us a great deal about the constraints on prediction in the social sciences as well as how we can predict better (see Gleditsch 2022 ; Tetlock 2006 ; Tetlock and Gardner 2016 ). First, prediction tends to work well when grounded in clear theory and more explicit propositions. The weather is very complex, for example, and Popper actually cited clouds as less predictable systems than mechanical clocks. Yet, weather forecasting is a clear success story, and advances in computing power has made it possible to apply Lewis Fry Richardson equations for atmospheric flow to data to forecast weather ahead. Second, comparing models is usually more informative than focusing on a single prediction. In the social sciences, we now know much more about what approaches work relatively better for predicting elections and conflict, in part because we have comparisons and debate. In conflict prediction, projects such as the Political Instability Task Force have emphasized comparing alternative predictions on common data sources in ways that have helped us understand what we can do relatively better as well as what we are less likely to do well. Finally, we have a better understanding of the traits and types of reason that allow some “superforecasters” to predict better than others. Tetlock and collaborators argue that forecasting is improved when we break up problems into smaller parts and reason separately about these, think about future events in terms of scenarios instead of single outcomes, and use Bayesian updating to adjust initial predictions as we learn more information (e.g. Tetlock and Gardner 2016 ). In sum, predicting political events remains difficult, but clearly some approaches are better than others, and more likely to be helpful.

Although systematic efforts at prediction can be helpful for policy, better prediction by itself does not lead to inherently better policy proposals or resolve the ambiguities in other empirical studies. My own prior work looking at evaluation through prediction helps highlight both the promise and limitations of predictive modelling to someone interested in policy. Cederman, Gleditsch, and Wucherpfennig (2017) try to evaluate whether the decline in ethnic civil could plausibly be related to changes in grievances, and how much of observed decline of ethnic conflict could be attributed to changes in factors that might induce grievances such as greater ethnic inclusion and accommodation. [9] This is not a prediction about the future per se, but we can think of it is a predictive problem where we try to avoid overfitting statistical models to the data and look at ability of models estimated on training data to predict to new data, out-of-sample. More specifically, we trained models on for ethnic groups up to 2003, and then applied the estimated results to evaluate the predicted impact over the next 10 years. We compare the predicted impact in cases where we see changes to the implied predictions in the absence of changes (a counterfactual which avoids some of the problems in drawing inferences based on comparing levels of particular covariates across observations). The results suggest notable predicted reductions in the risk of onset and higher termination rates where we observed changes toward accommodation. Moreover, we show at the aggregate level that a model incorporating grievances and accommodation predicted global trends out-of-sample better than a purely autoregressive model based on observed trends. If our variables related to accommodation only added to overfitting the model we would expect to see worse predictions out-of-sample, yet the model with accommodation performs better and captures better the observed trend.

This helps underscore the difference between studying levels and changes, and how prediction can help us assess the consequences of changes in better ways than analyses focusing exclusively on levels or observed data. For informing policy, it is often more useful to have information on the consequences of changes than simply uncovering associations. On the positive side, our analyses show that where inequalities have been reduced we tend to see less conflict. This is useful and good to know. But we are still looking at ability to predict outcomes given observed changes rather than our ability to influence such changes. Our analysis did not consider actual interventions experiments to introduce inclusion as a policy – is this feasible and does it have different effects than where inclusion emerges organically among local actors? A brief look at other cases suggests that efforts to introduce interventions to reduce exclusion or inequality often fail, even if well-intended. The US invested a large amount of resources to broker power-sharing arrangements in Afghanistan, which ultimately failed (e.g. Coyne 2022 ). Likewise, the military in Sudan agreed to a transition framework following protest in 2019, but were very reluctant to implement this after the acute crisis abated, and have subsequently tried to reassert control through a new coup in 2021. [10] Finally, the EU has spent a great deal of resources on democracy aid in neighboring countries, and the European Instrument for Democracy and Human Rights had a €1.3 billion budget over the period 2014–2020. [11] Yet, despite all this investment, the impact in terms of observed change in neighboring countries seems rather modest and clearly falls short of expectations. In sum, I think it probably is that case that we could affect the risk of conflict through various efforts to reduce grievances, but at the same time a stretch to say that we have fully worked out proposals and impact analysis on policies to achieve this. We could probably learn a great deal more if we invest more time in directed research. This would be valuable, if not necessarily a path to academic success. However, in the absence of this we should be cautious in overstating implications of our current knowledge.

6 How Research can be Helpful for Policy Debate

I stated at the outset that policy implications rarely “follow” directly from research. Claims that research “was helpful” or “relevant” for a chosen policy are often chronologically questionable, as people start out with specific views and look selectively at research to find studies that appear to provide support for actions already chosen. Many funders are often particularly interested in evidence-based research when the evidence happens to fit their existing policy initiatives or approaches. Most conflict researchers are primarily scientists, and we should focus on our comparative advantage in research rather than claim to be experts in policy. And ultimately it is unlikely that offering specific policy prescriptions is what decision makers or policy audiences seek from academics.

Beyond being careful in confusing outcomes with policy overstating policy implications from regression tables without thinking of policy alternatives, are there more productive approaches to engage research and policy? One possible answer is to try to do science as well as possible and focus on our collective contributions rather than to emphasize narrow individual contributions. Pielke (2012) suggests four ideal types of science advice, based on different models of science and models of democracy. One is the standard linear model, where scientist do their research, others then look to their results, and conclude on what this could tell policy. Alternatively, scientists can acts as arbiters – i.e. evaluate policy proposals and comment on science, but as bystanders, without making active advice. Obviously, scientists often have many views of their own on policy, and this is not inherently a bad thing per se. The issue advocate model resembles Becker’s (1983) theory of interest groups – different stakeholders conduct their own science and try to win out in public debate. This is arguably a useful pluralist perspective, recognizing how many claims over policy are not disinterested scientific advice, but very much part of efforts to influence political processes and decisions. Pielke’s final model is scientist serving as honest brokers, who comment on what policy could seek to target, try to lay out different alternatives and map likely consequences, thereby hopefully contributing to better interaction between science and policy.

How might conflict researchers be better honest brokers? First, researchers can play a useful role in laying out or reviewing what we already know about a topic as a starting point. The research frontier is a very noisy place, and new findings are likely to be erratic and more often misleading or wrong ( Ioannidis 2005 ). For an outsider it is usually more helpful to have someone detail a field more broadly and convey key results and findings, rather than to have someone focusing narrowly on their individual contribution, recent articles, and cutting-edge manuscripts most likely to get published in an academic journal. A broader collective focus on what we know first also provides a better basis for conveying what our new or individual research might add to this. Although researchers often tend to disparage more descriptive research, one of the most useful contributions is often better data or more accurate descriptive data and material on problems of interest. Indeed, better description of a problem is often far more useful than unsolicited policy advice, and it is hard to think of cases where policy decisions cannot benefit from better data.

Instead of suggesting that implications “follow” from our research, researchers could try to talk more systematically about possible concerns and clarify objectives that might guide policies and how findings could speak to this, rather than to try to suggest specific policies or initiatives. After highlighting sets of plausible assumptions and likely key objectives, researchers could then discuss what are the alternative factors or features that could be targeted, and what we know about likely consequences of efforts to do this. For example, how direct is the evidence that we have? How much is uncertainty is there about relationships or likely effect sizes? Might there be possible tension between objectives, or could actions to achieve specific objections undermine others? Again, when we do this, we predict. The more explicit we are about stating premises and how we get from A to B, the better the basis for evaluating policy proposals and claims about consequences. In addition, researchers could benefit from greater attention to communication and how to engage with non-academic audiences (see Meyer, De Franco, and Otto 2019 ). Non-academic audiences are often unfamiliar with academic jargon and prior research, and presenting results and conclusion in a clear and transparent manner intelligible to non-experts is more likely to make your work helpful or useful to others. My own limited experience suggests that non-academic audiences are quite willing to consider relatively complex or technical analyses, but they would like you to be able to convey how you get from A to B in a clear manner.

7 Policy is Too Important to be Treated as an Afterthought

In this article I have tried to show some common problems in claims about policy implications “following from research” and to offer some suggestions on how research may be presented in ways that can be more useful to inform policy, even if you do not have clear suggestions to offer or can claim direct “implications”. There are many reasons for researchers to be interested in policy and contributing to policy debate, and policy is if anything too important to be left to cliches, afterthoughts, and loose claims about implications. It is easy to stay within our comfort zone, follow standard conventions in research articles, and keep making the usual claims that our research “has important policy implications” in a casual manner. But both policy and research can be improved by thinking more systematically about this.

There is growing awareness of the problems with many conventions in research and how cliches like “more research is needed” can have potential problematic consequences. Some journals have apparently banned use of the phrase “more/further research is needed” (see Maldonado and Poole 1999 ; Phillips 2001 ). I am in favor of free speech, and blanket bans does not seem a useful approach to guide better scientific practices. Yet, if you find yourself at the point of writing that your research “has important policy implications”, then I hope you may recognize good reasons to pause and at least try to ensure that you are precise, say what the policy objectives might be, and how your research can speak to possible alternatives and their likely consequences. And although this alone should not be grounds for rejecting a submission, its entirely fair for editors to give a yellow card when a manuscript attempts to claim policy relevance and presents outcomes as if they were policies. Many researchers appear to be afraid of not having policy suggestions to offer, or perhaps concerned about discussing limitations in the evidence for claims out of fear that this will attract more scrutiny or criticism. However, it is much better to explicit and upfront than vague, overconfident, or understating limitations. If we do yet have much evidence on the effectiveness of possible strategies, then that is simply the current state of our knowledge. It is often more useful to know what we do not know than to pretend that we know more than we do. Communicating uncertainty and what we do not yet know can help set a new research agenda, and your current research may even be helpful for this. If prediction is hard, then policy must also be hard. But more thinking and predictive analysis are most likely to yield more productive input and be helpful for policy.

A previous version of this manuscript was presented as a keynote at the Annual Meeting of the Households in Conflict Network, University of Warwick, 23–24 November 2022 and the workshop on “New Pathways of Conflict Research”, Ludwig–Maximilian University Munich, 10–11 October 2022. I am grateful for helpful discussions and comments from Baris Ari, Tilman Brück, Han Dorussen, Roos van der Haer, Håvard Hegre, Faten Ghosn, Nils Petter Gleditsch, Arzu Kibris, Dominic Rohner, Andrea Ruggeri, Uwe Sunde, Håvard Strand, Paul W. Thurner, as well as the editor and reviewers.

Funding source: Economic and Social Research Council

Award Identifier / Grant number: ES/L011859/1

Research funding: This work was financially supported by the Economic and Social Research Council (ES/L011859/1).

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8 Summary, Policy Implications, and Future Research

• Published: May 2022
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This chapter concludes, summarizing the argument, the main findings, and considering the implications. The advice given should be treated as a set of ‘first bets’, or ‘compass bearings’ for policymakers, especially development partners, who are seeking to advance the cause of inclusive development. The findings might also be of interest for other inclusive development champions, whether in civil society or governments of the Global South. It also discusses several potential areas for future research, including multi-level analysis and the relationship of political settlements to a range of upstream and downstream variables. PSA has begun to deliver on its promise of understanding conflict and development, the authors argue, and there is much it can still achieve.

The argument revisited

We began this book with the argument that despite the high degree of interest in and funding for PSA in both conflict and development studies and in policy circles, not all was well in the field. Conflict and development analysts understood the term ‘political settlement’ in different ways, there was a lack of conceptual clarity, and no clear grounds for measurement. Lack of an agreed basis for measurement, in particular, posed an obstacle to PSA’s admission into the mainstream social scientific community. This book has attempted to address these problems, and to put the future of PSA on a firmer conceptual and scientific footing. It has also generated a new set of hypotheses and tests around a new typology of political settlements which can help explain the variation we find in development outcomes in real-world polities. We have also introduced a lower-level concept, the ‘policy domain’, which can explain variation at a more granular level. Combining the two concepts can provide useful pointers for policymakers. In this Conclusion we retrace the steps in our argument and our main findings, before discussing policy implications and future directions for political settlements research.

Conceptual clarification

In Chapters 1 and 2 we traced the roots of PSA to diverse strands in conflict and peacebuilding, political science, historical sociology, and development studies. We situated the growing popularity of the term within a ‘post-institutional’ turn in political studies, arguing that PSA had much in common with these developments yet promised something additional. After further scrutinizing the ordinary language roots of the term in Chapter 2 , we argued that a political settlement should be thought of as an ongoing agreement or common understanding among a society’s most powerful groups over a set of political and economic institutions expected to generate for them a minimally acceptable level of benefits, and which thereby ends or prevents generalized civil war and/or political and economic disorder.

Using the analogy of a marriage, we have argued that there is no contradiction in seeing a settlement as both a one-off agreement, such as a peace agreement, and an ongoing and evolving relationship more akin to a political order. At the same time, we have stressed that formal agreements are not necessary, that agreements or understandings may be tacit or imposed, and we have made a distinction between a political settlement and cognate political science terms such as political system, order, or social contract. We have, however, rejected the idea that a political settlement is compatible with a mere balance of power in the midst of very high levels of competitive violence, as that seems to us to contradict everyday understandings of the term, even if those levels of violence are ostensibly sustainable.

We have stressed that our definition is an expansive one, and is compatible with the study of peace agreements, political institutions, the sociological basis of the groups that have and lack power, and their configuration. We have admitted that behind political settlements or at the very least entangled with them, are ideas (although we have not included ideas as a discrete item in our definition). Most excitingly, however, we have argued that political settlements analysis (PSA) opens up the possibility of analysing the relationship among these different elements and outcomes in the areas of politics, conflict, and development.

A new typological theory

In the line of enquiry developed in this book, we have chosen to develop the theory around how the demographic and sociological composition of powerful groups, and their political configuration, can help explain elite commitment to and state capacity for development. In this respect we have drawn heavily on the previous work of Mushtaq Khan and Brian Levy, while integrating insights from comparative politics, sociology, and collective-action theory. In Chapter 3 , we argued for a typological classification of political settlements with two new dimensions. The ‘social foundation’, which refers to those powerful groups which represent the settlement’s ‘insiders’ by virtue of being co-opted by the top political leadership. And the ‘configuration of power’, which tracks the relative strength of different powerful groups and their arrangement in respect of one another.

If the social foundation is about the sociology and demography of power, the political configuration is about its geometry. We hypothesize that the social foundation is strongly related to the political elite’s commitment to development, and, in particular, how inclusive it is. Other things being equal, where the social foundation is broad and deep, we hypothesize that there will be a stronger elite commitment to providing broad-based development benefits. Conversely, where the social foundation is narrow or shallow, there will be less of an incentive to distribute development benefits broadly.

When it comes to the geometry of power, we hypothesize that the political configuration is strongly related to the political leadership’s ability to implement policies, and to create state capacity for development more generally. Other things being equal, we hypothesize that where power is concentrated— that is, where collective-action problems among political elites are effectively addressed, resulting in a coherent allocation of decision-making procedures and authority among insiders —the potential for effective implementation is greater, and where it is dispersed, it is weaker. We need to emphasize, however, that it is not impossible to implement policy effectively in dispersed power contexts, though it is generally more difficult. Moreover, we must stress that the ability to implement policy effectively does not guarantee that policy or its outcomes will be good. Power concentration can be both a blessing and a curse. And to add a little nuance to the picture, there are reasons to think that for some developmental tasks, such as delivering quality education, dispersed power may be an advantage.

Taking these two dimensions together yields a four-quadrant typology, in which political settlements can be broad-dispersed, narrow-dispersed, narrow-concentrated, and finally broad-concentrated. Each quadrant comes with its own collective-action and political development challenges, and a set of hypothesized relationships to elite commitment, state capability, and likely development outcomes.

Put simply, broad-dispersed settlements will display relatively strong elite commitment to delivering broad-based benefits, but rather weak capacity to do so. There is likely to be more of an emphasis on short-term or populist policy measures, and a greater reliance on clientelistic benefit distribution. There may also be a stronger emphasis on social as opposed to economic development, as the former is likely to require fewer short-term sacrifices of the sort that a dispersed power configuration would find difficult to impose.

Narrow-dispersed settlements are likely to face similar problems when it comes to implementing policy for broad-based development. However, unlike in the case of broad-dispersed settlements, political elites will have less incentive even to try. With either an organizationally weak or successfully repressed political opposition, the leadership will feel little pressure to distribute benefits beyond a relatively narrow circle. At the same time, the internal fractiousness of the elite is likely to make the polity inherently unstable, and weaken its ability for any type of long-term development.

Governing elites in narrow-concentrated settlements face similarly weak incentives to distribute benefits broadly, but greater capacity to implement policies that serve long-term elite interests. In particular, such settlements are particularly well placed to be able to implement long-term economic growth policies, engaging in primitive accumulation and/or imposing consumption sacrifices on the majority of the population as they invest in infrastructure and capital stock. They are also more likely to be able to design an industrial policy that is insulated from political competition for unproductive rents. In time, such growth policies may deliver more widespread benefits through trickle-down effects and trigger new cycles of expanded political inclusion, though this is not guaranteed. Moreover, mindful of the contribution of human capital to economic growth, elites in narrow-concentrated settlements may invest in education and health and have relatively strong capacity to implement such policies effectively. Again, however, one would expect educational and health gains to be tilted disproportionately towards the elite.

Broad-concentrated political settlements are on the face of things most likely to deliver inclusive development. With a broad swathe of the population endowed with disruptive power, elites will feel impelled to deliver broad-based benefits for their own political, and in some cases, physical, survival. In addition, power concentration in and around the top leadership makes it easier to plan for the long term and to implement policy through an effective state administration, and/or to build state capacity for effective implementation. Like broad-dispersed settlements, broad-concentrated ones may place a higher emphasis on social than economic development. However, they typically have greater capacity to nurture forms of economic growth that will provide a financial basis for these policies.

Complicating matters slightly, however, we hypothesize that on the right side of Chapter 3 ’s typology, that is, the ‘concentrated’ power configurations, each quadrant contains two distinct pathways that condition how serious elites are about building state capability and pursuing long-term development, inclusive or otherwise. These are related to the availability of point resources and elite threat perceptions, and help to explain some of the ‘within-quadrant’ variation we find in the real world.

Measurement and testing

To be able to test our new theory we needed to be able to code and classify political settlements. To do this, we employed an expert survey of forty-two countries in the Global South, from independence or 1960 to 2018. We began by dividing each country’s political history into distinct periods, each of which, according to our definition, marked a change or evolution in the political settlement. We then asked twenty-eight questions of each country for each political period. The bulk of the questions revolved around the composition, size, and relative strength of three conceptual blocs: the leader’s bloc (LB), the contingently loyal bloc (CLB) (which jointly make up the ruling coalition) and the opposition bloc (OB). Inspired by Mushtaq Khan, this three-bloc structure is a novel way of thinking about the power configuration of political settlements and is not found in other political science databases. As such, it represents one of PSA’s most distinctive contributions to political science and development studies. Other questions traced the modes by which these blocs were incorporated into or under the settlement, others intervening variables such as decision-making and implementation power, and others additional variables of interest such as systemic threats, the political power of indigenous capitalists, and elite commitment to social and economic development policy.

Out of the answers to these questions we constructed our two main typological variables, the social foundation and the configuration of power, and we then mapped the journey of our forty-two countries across political settlement types over time.

In Chapter 5 we described this journey in some detail for South Africa, a country that, since 1960, has experienced all four types of settlement. The emphasis was on showing how empirical developments in that country were reflected in the country codings, but also how the concepts provide a helpful language for explaining what we observe. On the one hand, we have a broad story of South Africa transitioning between settlement types, and on the other, by lifting the hood and examining the construction of these variables, we arrive at a more precise and fine-grained analytical narrative than more conventional accounts.

In Chapter 6 we turned to small- n comparative analysis. Here we picked four countries: Ghana, Guinea, Cambodia, and Rwanda, which, over the past two decades, have represented broad-dispersed, narrow-dispersed, narrow-concentrated, and broad-concentrated settlements respectively. To a large degree, our hypotheses were borne out. Narrow-concentrated Cambodia experienced the fastest growth, which, as we might expect, was of a rather rapacious kind, followed by a slightly slower but less exclusionary growth experience in Rwanda. Ghana was next, demonstrating, as expected, a disappointing capacity for industrial policy, followed by Guinea, where per capita income had actually fallen. On social development, Rwanda and Ghana, as expected, topped the bill when it came to government spending, though with much more impressive results, especially on maternal health, in Rwanda. Cambodia, despite comparatively low spending, outperformed Ghana on maternal mortality, a phenomenon perhaps only partly explained by its concentrated-power configuration. Guinea performed poorly on social indicators, despite recent and somewhat unexplained increases in health spending. Overall, and as predicted, Rwanda demonstrated the strongest commitment to building state capability for development (see also Yanguas 2017 ), even though, as acknowledged earlier, this did not always lead to positive outcomes, as in the case of educational quality.

In sum, we found a strong read-through from our political settlement types to both intervening causal mechanisms—elite commitment and state capacity—and development outcomes. However, the picture was not uniform. In four of our twelve cases we found that political settlement type only partially explained our findings. Here, we invoked the idea of the policy domain, a meso-level field of interests, ideas, and power relations nested within political settlements, to explain puzzles such as better-than-expected maternal health performance in Cambodia, and worse-than-expected education performance in Rwanda. We also pointed to several examples from other ESID work of where the idea of a policy domain was an essential complement to that of the political settlement. We return to this question later.

The results of Chapters 5 and 6 can be treated as illustrative of the method and explanatory potential of PSA. They were not designed to provide a rigorous test of the theory. That is provided to a greater degree by Chapter 7 , in which we subject the relationship between social foundation, power configuration, economic and social development to a regression analysis. Using growth in per capita income as our proxy for economic development, and employing lagged variables, country fixed effects, and a variety of controls, we find a strong positive relationship between power concentration and economic development. Taking infant mortality reduction as our proxy for social development, we find a strong positive relationship with the size of the social foundation and social development. We also find, via the ESID multiplicative index, that power concentration and breadth of social foundation reinforce each other when it comes to driving economic and social development. Although not as statistically significant—for reasons we explain—we also find some support for our typological categorical variables. To wit, narrow-concentrated settlements tend to grow the fastest, followed by broad-concentrated, then broad-dispersed and narrow-dispersed political settlements. With regard to our social development outcome, broad-concentrated settlements perform best, closely followed by broad-dispersed and narrow-concentrated settlements, with narrow-dispersed settlements trailing the pack.

Advice for policymakers

As we saw in our Introduction, some political settlements theorists have argued that PSA is more suited to policy advice than mid-range hypothesizing and prediction. Our view is that if you are to advise policymakers using a portable model, the model ought ideally to be validated by reference to empirical results across space and time. We have constructed a new typological theory and model, and we have validated it across forty-two countries in the Global South. That does not mean that it will hold without exception; it simply means that on average it is likely to hold, and is therefore a good place for policymakers to start.

The advice we give should be treated as a set of ‘first bets’, or ‘compass bearings’ for policymakers, especially development partners, who are seeking to advance the cause of inclusive development. The findings might also be of interest for other inclusive development champions, whether in civil society or governments of the Global South. 1

So, what is our advice? In broad-concentrated settlements, especially those facing resource scarcity, governing elites are already likely to be committed to broad-based development and to have created, or be in the process of creating, the state capacity to deliver it. Development partners can therefore assist the government with finances, or technical advice. In a sense, much standard bilateral and multilateral support already takes this form, although it is only in this type of settlement that it is likely to work well. Even here, however, the government may have some policy blind spots, and, although the settlement is broad, specific minority groups may still be politically marginalized. Development partners can play an advisory function or help marginalized groups to lobby the government, if the context allows.

In narrow-concentrated settlements, development partners will need to be more imaginative. Governing elites are unlikely to be committed to broad-based development, so development partners will want to either try and shift their incentives, or substitute for them. In more predatory settlements, based most likely on point-source resource exploitation, development partners might support global initiatives that make such industries less exclusionary. Where the government is resource poor and perhaps committed to more dynamic forms of economic development, they might try to leverage inclusive spillovers, as with the Better Factories initiative in Cambodia.

Another option is to impress upon the elite the human capital aspect of economic development, thereby generating increased interest in the social sectors. Indeed, political elites in narrow-concentrated settlements may be content to outsource a large measure of responsibility to development partners here. Donors can work with champions in government and civil society to develop and test social policy solutions, which might be scaled if and when the government comes to grasp their political advantages. Note that development partners should not be in too much of a hurry to move from parallel programmes to ‘systems strengthening’. The latter is only likely to work when the government is genuinely motivated to deliver broad-based benefits. If permitted, development partners can also try and encourage societal voice, a prelude to social-foundation broadening. However, the degree to which this voice is confrontational or constructive will need to be tailored to context, with backlash a distinct possibility.

In broad-dispersed settlements, the political elite will probably recognize the importance of delivering broad-based development benefits, especially on the social front, yet the government’s capacity to plan and implement effective long-term development policy is likely to be weak. Developmental initiatives will probably take the form of populist gestures or patronage handouts. Top-down, system-wide reform efforts are unlikely to work well in these contexts, governing elites not having the strength or breathing space to implement them. Our data suggest that the best chance of nurturing progress is by building pockets of effectiveness in the administration and/or nurturing multi-stakeholder coalitions around particular issues or problem areas (even if, sadly, pockets of effectiveness are harder to sustain in dispersed power settings) ( Hickey 2021 ). By definition, power in broad-dispersed societies is de facto more decentralized, and development partners should build on that, leveraging the nascent developmental coalitions that can sometimes be found in civil society, the private sector, traditional leaders, or religious organizations. Work at sub-national level may be particularly fruitful. Either way, the trick is to build capability and keep up momentum around a set of policy reforms across administrations and above (or below) the melee of patronage politics. Again, development partners may have to temper their ambitions around system-wide strengthening. This is likely to take longer than in concentrated settlements, and to be built from the bottom up or the middle outwards, as islands of effectiveness are joined to form archipelagos and then continental land masses.

Narrow-dispersed settlements represent the biggest challenge for development partners and other reformers. Development partners can try a combination of the strategies we suggested for narrow-concentrated and broad-dispersed settlements. Elites in narrow-dispersed settlements often rely on point-source resource exploitation or criminal activities, and trying to reform the international system within which such goods are traded or activities take place may help shift elite attention into economic sectors with more positive spillovers. Where such sectors, such as export manufacturing, small business, and smallholder agriculture, can be identified, development partners can provide assistance. Government is also likely to be comparatively disinterested in social development and have little capability to deliver it. Again, development partners may have to invest seriously in parallel or non-state solutions, until such time as governing elites feel genuinely motivated, probably through a broadening of the settlement, or perhaps for ideological reasons, to provide such benefits themselves.

But there are no easy answers here, and many narrow-dispersed settlements teeter permanently on the brink of conflict. Such conflicts are often a legacy of the way state boundaries were drawn at the close of the colonial period, together with the incentives created by the manner of these states’ incorporation into the global states system. Doubtless, state-building and development are highly transnationalized processes in the Global South and we are aware that the positioning of countries within their broader historical and global political economy context is not the result of their PS type. A long-term solution may require a more radical re-imagining of the global system than our largely country-based exercise has considered. To be sure, weak regulation at a global level enables a whole range of global ‘bads’ around finance, taxation, arms, drugs, etc., that directly undermine governance and embed predatory elites.

Before moving on, it is essential to add a very important caveat. We have produced a typology of political settlements, we have illustrated how the typological dimensions have developmental effects by choosing four archetypal cases, and have demonstrated a general association between variables and outcomes across cases using regression analysis. However, when we map political settlements typologically we find that they are scattered, apparently randomly, across our four quadrants, and while the differences between a Rwanda and a Guinea may be easy to discern, those between a Senegal and a Dominican Republic, sitting just either side of our typological cut-offs, are likely to be much more difficult.

Another way of putting this is to say that countries do not cross the threshold between political settlement types and suddenly start behaving radically differently, in the way that H 2 O behaves radically differently when it crosses the 0 degrees Celcius threshold. Thus, for countries that lie close to the cross-hairs of our political settlements typology (and it is a significant proportion) our advice to policymakers needs to be taken with an even bigger grain of salt. The read-through from political settlement type to policy commitment is likely to be weaker here.

In this way we believe our coding exercise points to both the strengths and limitations of PSA. In archetypal cases, and comparing statistically both ends of the spectrum, we find large effects, but countries closer together will differ less radically. One of the pitfalls of using a qualitative typology as an interpretive model, is that there is a great temptation to fit a country into one box or another and then succumb to confirmation bias when predicting for policymakers the likely political settlement effects. Our comparative coding exercise invites us to consider many, non-archetypal cases on their own terms. A plausible hypothesis is that in non-archetypal settlements, the explanatory significance of policy domain politics will rise, though that remains to be rigorously tested.

Generally speaking, however, we believe the general advice of the ESID Programme to policymakers remains sound. When devising inclusive development strategies, policymakers should focus on context, that is the political settlement (though with a caveat about political settlement type); capacity, that is, whether the state can deliver; and coalitions, which can help catalyse reform even in unpropitious contexts. 2

Another important point to flag is the finding that political settlements cross-cut regime types, and exercise their influence at least partly independently of regime type. Too much of the debate in the politics of development has focused on the relative advantages of democracy and authoritarianism. Although there is an association between power concentration and authoritarianism it is not a strong one. Moreover, we have shown that power concentration alone is a better predictor of development outcomes than regime type, and, normative issues aside, we hope this will contribute to a process of transcending the democracy–authoritarianism debate in relation to development outcomes.

Future research

The creation of our dataset and the testing of our hypotheses has answered, or at least shed light on, a few important questions in the study of politics and development. However, it opens the door to many more. In the following pages, we list a few of the areas of research that the dataset could be used for.

Small- N research

To date, comparative political settlements research programmes have had to pick case studies largely on the basis of intuition, or a vague idea that one country is ‘dominant’ and another ‘competitive’. Our comparative codings make a much more informed process of case selection, both synchronic and diachronic, possible. The foundations have been laid for qualitatively rich yet rigorously selected small- N studies to flourish.

Further, although we have only surveyed forty-two countries, our survey is available to be applied to other countries, so that adventurous scholars can extend PSA into new terrain.

One area into which we would like to see PSA extend is multi-level analysis. What is the relationship between national political settlements and sub-national political units such as states, regions, or cities? What is the relationship between national political settlements and transnational factors? Or between political settlements and the political power and technical capability of domestic capitalist classes? Our dataset and survey provide some of the building blocks for such an analysis.

Large- N research

Thus far, we have only tested our theory against a couple of development outcomes. We have not even tested all of our own hypotheses. For example, a more elaborate set of hypotheses than appear in Chapter 3 , especially as related to the coup/civil war trap, is provided in Ferguson (2020) . We have demonstrated that narrow-dispersed settlements are the least developmental and most fragile, but we have not begun to explore whether there are any ‘best-bet’ pathways from this type to other, more developmental and/or democratic ones. Indeed, it is possible to imagine our dataset being combined with others, for example, on peace agreements, to explore relationships between peace agreements, the political settlements they found, political stability, and development. A whole field of exploration is imaginable here.

Another debate in the field concerns the difference between inclusive development outcomes and inclusive development processes, which might be described as the difference between substantive and procedural accountability. By generating some initial data on the relationship between political settlement type and democracy, and the relationship of both to development outcomes of different sorts, our study has begun to shed light on that, and deeper exploration might permit more informed judgements about potential trade-offs.

It is also possible to envisage using the dataset to test the relationship between political settlements and a whole host of other downstream development indicators: economic complexity, universal health coverage, aid effectiveness, domestic revenue mobilization, Covid-19 response, ‘gross national happiness’, to name but a few. A very important area that we have not even begun to explore is gender outcomes. Yet our survey explicitly codes powerful and powerless groups by gender, so there is much useful work that could be done here.

Upstream variables could also be explored. For example, what difference does colonial heritage, ethnic diversity, resource abundance, capitalist development, or internal and external threat perception make to the emergence and sustainability of broad-concentrated political settlements? In what circumstances do broad-concentrated settlements emerge under conditions of political democracy? In what circumstances do dispersed settlements overperform?

We have also not gone very far in exploring interaction effects. How does resource abundance affect the way narrow-concentrated settlements behave? How do ideas, for example, socialist legacies, affect how effectively different political settlement types promote social policy? Exploration of these different factors might lead ultimately to the creation of more sub-types, and thus more fine-tuned advice for policymakers.

We have stressed throughout that one of the advantages of PSA is to transcend the ‘democracy–autocracy’ debate. Another recent book that attempts to do this is Acemoglu and Robinson’s The Narrow Corridor ( Acemoglu and Robinson 2019 ). The authors speak about the historical rarity of what they call, ‘shackled leviathans’, that is, powerful centralized states with the power to protect their people and deliver social goods, yet which are accountable to a society to whom they are in bondage. Shackled leviathans exist and develop in a ‘narrow corridor’ between despotism and either anarchy or the ‘cage’ of traditional norms. Shackled leviathans are not synonymous with democracies, since there are many democracies that have very weak states, and there are some pre-modern shackled leviathans that were not democracies. For us, there is an elective affinity between shackled leviathans and at least some variants of broad-concentrated settlements—especially those where the breadth of the social foundation is based on the reality of societal power rather than just a perception. By tracing the processes by which broad-concentrated settlements emerge, PSA and the dataset we have produced can thus shed more light on the pathways by which societies get into, or turn out of, ‘the corridor’. 3

Typological refinement

There may also be considerable scope for refining our indices and categories. For example, currently our cut-off points for deciding when a country crosses from one political settlement type to another are given by the survey means. Scrutiny of the dataset might, however, reveal more meaningful cut-off points, connoting more ‘freezing-point’ type transitions. Further, when it comes to variable construction, we did not play around to any great extent with the weighting of our variables’ different component parts. We chose a weighting that seemed epistemically plausible and stuck to it. However, by experimenting with different weightings, researchers might discover a mapping that makes the joints of reality more visible and delivers even stronger results, injecting more statistical significance into our categorical variable analysis.

Also, and as Chapters 5 and 6 illustrated, our main variables are composites. As such, they secrete quite a lot of potentially interesting variation. For example, Guinea was discussed as a ‘narrow-dispersed’ settlement, but it is interesting that the OB in Guinea is quite large and powerful, with the ‘narrowness’ of that country’s coding driven by the level of government repression. There may be other ‘narrow-dispersed’ settlements, however, where the opposition is small and weak, with the ‘narrowness’ of the coding driven not by government repression, but by the majority of the population’s lack of disruptive potential. It would be interesting to know whether these different variants of ‘narrowness’ have different developmental effects. The construction of our dataset makes that possible.

Readers will be conscious that our typology, by collapsing the Khanian variables onto a single axis, squashes some of the variation we find there. Some countries, for example, Ghana and Uganda, that one would expect to appear in different quadrants in his typology and were coded as such in the original ESID work, are in the same quadrant in ours. Because our data is publicly available and our methods transparent, researchers can discover precisely why that is the case. 4 However, it may be interesting to explore exactly how much explanatory power has been gained and how much has been lost by our method.

Improving and extending the data

Political settlements are not easy things to pin down; coders have limited knowledge and do not always agree on their codings. As discussed in Chapter 4 , we have taken a variety of measures to weight the data to ensure intra-country coder reliability, yet we fully admit that the results are not perfect. In the future, there may be scope to improve the data by drawing on a wider pool of experts and developing more sophisticated methods for inter-coder reliability. There may also be ways of providing better inter-country benchmarks, further improving comparability. We have no doubt that country experts and comparativists will be able to quibble with the precise positioning of some of our settlements. Eyeballing the charts, we ourselves feel there are still some countries that are positioned not quite correctly. For example, although our Kenyan coders were all quite unified, and we thus saw little reason to challenge them, we wonder whether the political settlement there is really as broad as they imply. Fortunately, our initial reading of anomalies such as these suggests to us that a recalibration, rather than weakening our results, would strengthen them still further. And if country experts and political settlement analysts continue to disagree with our codings, we invite them to specify in detail why, an exercise that we hope will elevate the quality of political settlements research.

Finally, the coding exercise could be extended to many more countries. We cannot say whether this would strengthen or weaken our conclusions, but the politics and development community deserves to find out.

In conclusion: thanks to the questions we have answered and the ones that remain to be addressed, and despite the imperfections and limitations of our data, we believe this book has succeeded in putting PSA on a much sounder conceptual and scientific footing than hitherto, demonstrating—as our opening chapter title suggests—its considerable promise for the understanding and practice of development.

Some of the advice might conceivably translate to Northern country contexts, though given that the theory was developed for the Global South and we have not surveyed Northern countries, we make no claims here.

www.effective-states.org/the-three-cs-of-inclusive-development-context-capacity-and-coalitions/ .

See also https://www.effective-states.org/how-to-work-politically-for-inclusive-development-four-principles-for-action/#principle-one .

Note that Acemoglu and Robinson also stress the importance of coalitions in bringing about far-reaching changes in governance.

The PolSett dataset can be accessed through the Political Settlements page on the ESID website: https://www.effective-states.org/political-settlements/

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• Open access
• Published: 06 March 2021

How to bring research evidence into policy? Synthesizing strategies of five research projects in low-and middle-income countries

• Séverine Erismann 1 , 2 ,
• Maria Amalia Pesantes 3 ,
• David Beran 4 ,
• Andrea Leuenberger 1 , 2 ,
• Andrea Farnham 1 , 2 ,
• Monica Berger Gonzalez de White 1 , 2 , 5 ,
• Niklaus Daniel Labhardt 1 , 2 , 6 ,
• Fabrizio Tediosi 1 , 2 ,
• Patricia Akweongo 7 ,
• August Kuwawenaruwa 1 , 2 , 8 ,
• Jakob Zinsstag 1 , 2 ,
• Fritz Brugger 9 ,
• Claire Somerville 10 ,
• Kaspar Wyss 1 , 2 &
• Helen Prytherch 1 , 2  

Health Research Policy and Systems volume  19 , Article number:  29 ( 2021 ) Cite this article

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Addressing the uptake of research findings into policy-making is increasingly important for researchers who ultimately seek to contribute to improved health outcomes. The aims of the Swiss Programme for Research on Global Issues for Development (r4d Programme) initiated by the Swiss National Science Foundation and the Swiss Agency for Development and Cooperation are to create and disseminate knowledge that supports policy changes in the context of the 2030 Agenda for Sustainable Development. This paper reports on five r4d research projects and shows how researchers engage with various stakeholders, including policy-makers, in order to assure uptake of the research results.

Eleven in-depth interviews were conducted with principal investigators and their research partners from five r4d projects, using a semi-structured interview guide. The interviews explored the process of how stakeholders and policy-makers were engaged in the research project.

Three key strategies were identified as fostering research uptake into policies and practices: (S1) stakeholders directly engaged with and sought evidence from researchers; (S2) stakeholders were involved in the design and throughout the implementation of the research project; and (S3) stakeholders engaged in participatory and transdisciplinary research approaches to coproduce knowledge and inform policy. In the first strategy, research evidence was directly taken up by international stakeholders as they were actively seeking new evidence on a very specific topic to up-date international guidelines. In the second strategy, examples from two r4d projects show that collaboration with stakeholders from early on in the projects increased the likelihood of translating research into policy, but that the latter was more effective in a supportive and stable policy environment. The third strategy adopted by two other r4d projects demonstrates the benefits of promoting colearning as a way to address potential power dynamics and working effectively across the local policy landscape through robust research partnerships.

Conclusions

This paper provides insights into the different strategies that facilitate collaboration and communication between stakeholders, including policy-makers, and researchers. However, it remains necessary to increase our understanding of the interests and motivations of the different actors involved in the process of influencing policy, identify clear policy-influencing objectives and provide more institutional support to engage in this complex and time-intensive process.

Peer Review reports

Increasingly, research funders are asking their grantees to address the uptake of research findings into decision-making processes and policy-making [ 1 , 2 ]. This growing trend is a response to a need for real-world and context-sensitive evidence to respond to and address complex health systems and health service delivery bottlenecks faced by policy-makers, health practitioners, communities and other actors that require more than single interventions to induce large-scale change [ 3 ]. Moreover, there is growing pressure for applied and implementation research to be relevant, demonstrate value for money and result in high-impact publications. The relevance of ensuring the translation of research into practice is also reflected in growing support for research projects with concrete requirements regarding the evaluation of their impact of science on society [ 4 ].

One example of the above is the Swiss Programme for Research on Global Issues for Development (r4d Programme) initiated by the Swiss National Science Foundation (SNSF) and the Swiss Agency for Development and Cooperation (SDC) covering the period 2012–2022. The r4d Programme is aimed at researchers in Switzerland and low-and middle-income countries (LMICs) conducting projects that specifically focus on poverty reduction and the protection of public goods in developing countries. Its specific objectives are to create and disseminate knowledge that supports policy-making in the area of global development and foster research on global issues in the context of the 2030 Agenda for Sustainable Development [ 5 , 6 ].

While the linkage of research to policy is strongly encouraged by research funding agencies, the uptake of research evidence by policy-makers to establish new laws and regulations or to improve policies to solve a problem or enhance implementation effectiveness, especially in LMICs, remains weak [ 2 , 7 ]. This is often referred to as the gap between research and policy [ 8 ]. One of the factors that was identified with the dearth of research uptake in previous studies is a lack of evidence that is context sensitive, timely and relevant for policy-makers; other factors include difficulties in accessing existing evidence, challenges with correctly interpreting and using existing evidence [ 7 , 9 ] and also a lack of interest from policy-makers in the use and uptake of evidence [ 10 ]. Using the SNSF r4d funding scheme, our aim is to show how researchers have engaged with stakeholders, including policy-makers, from the onset of a research project, in order to identify strategies for evidence uptake and use.

As part of the r4d Programme, several synthesis initiatives have been launched to disseminate the research evidence from the r4d projects and increase its impact ( http://www.r4d.ch/r4d programme/synthesis ). The aim of one of these synthesis initiatives is to support knowledge translation and exchange, as well as knowledge diffusion and dissemination among 15 r4d projects focusing on public health. More specifically, the aim is to facilitate the uptake of findings for the benefit of societies in LMICs, especially with regards to social inclusion and gender equity in the drive towards universal health coverage (UHC) and the 2030 Agenda for Sustainable Development [ 6 ]. The present study and resulting article are part of this synthesis initiative.

In this article, we present—through five case studies—strategies to translate and bridge evidence emerging from research into policy-making and decision-making. We rely on the experiences of five public health projects within the r4d research initiative. This paper describes these experiences, reports on the lessons learnt and outlines important features and challenges of engaging in this process using the researchers’ perspectives. This paper contributes to the body of literature on research translation by highlighting concrete examples and successful strategies for the uptake of research evidence in policy formulation.

Invitations were sent out to researchers working on projects within the r4d Programme to share their experiences with the project. Based on the interest shown by researchers, five projects were selected by the authors to demonstrate the different approaches and strategies used in the r4d projects with the aim to influence policy. Researchers were asked to share descriptions of the different approaches used in seeking to influence the uptake of research results by policy-makers. Each project represents a case study with emphasis on the main features of their translational approaches and the challenges, enablers and successes encountered.

The different research–policy engagement strategies were identified through data analysis of the interviews conducted within the framework of the five r4d case studies and were inspired by the work conducted by Uzochukwu and colleagues in Nigeria [ 2 ], who described four detailed strategies to support evidence-informed policy-making: (1) policy-makers and stakeholders seeking evidence from researchers; (2) involving stakeholders in designing objectives of a research project and throughout the research period; (3) facilitating policy-maker–researcher engagement in optimizing ways of using research findings to influence policy and practice; (4) active dissemination of own research findings to relevant stakeholders and policy-makers (see Table 1 ).

In using the term stakeholder, we apply the following definition by Brinkerhoff and Crosby [ 11 ]: “A stakeholder is an individual or group that makes a difference or that can affect or be affected by the achievement of the organization’s objectives”. Hence, individual stakeholders can include politicians (heads of state and legislators), government bureaucrats and technocrats from various sectors (e.g. health), but also representatives of civil society organizations and support groups [ 12 ].

Data collection

Eleven in-depth interviews with principal investigators and their research partners from five r4d projects were conducted by the first author, using a semi-structured interview guide. The interview guide covered the following themes: (1) How were stakeholders involved in the research project? (2) Was there uptake of research evidence in national/international policies? (3) How were research results disseminated? (4) What were the challenges or obstacles encountered in disseminating and translating evidence from research to policy? The interview duration was between 30 and 45 min. Seven interviews were conducted with researchers based in Switzerland and four with researchers in LMICs. At least two interviews were conducted for each r4d case study.

Data management and analysis

Of the 11 interviews, nine were audio recorded and notes taken. Audio files were transcribed verbatim by the same researcher. Two interviews were not recorded, but detailed notes were taken during the interview.

A qualitative content analysis method was used in order to organize and structure both the manifest and latent content [ 13 ]. Aligned to overall study questions, essential content was identified by the first author, which involved a process of generating a provisional list of themes of interest that were based on the study objectives, including stakeholder involvement in the generation of research questions, research process, generation of results and dissemination of research findings, as well as challenges to research dissemination and policy uptake. In a next step, the transcripts were sorted and grouped by the first author according to the coding scheme for analysis. This involved using the content summary analysis method, which consists of reducing the textual content and preserving only the essential content in order to produce a short text [ 14 ]. As several co-authors were interviewed, they validated that their perspective was not misinterpreted or misrepresented.

Three key strategies were identified for research uptake into policy and practice throughout the data collection of this synthesis initiative: (S1) stakeholders directly engaged with and sought evidence from researchers; (S2) stakeholders were involved in the design and throughout the implementation of the research project; and (S3) stakeholders engaged in participatory and transdisciplinary research approaches to co-produce knowledge and inform policy. The first two strategies (S1, S2) are in line with Uzochukwu and colleagues’ work [ 2 ], and the third strategy (S3) is an additional category based on the experiences of researchers in r4d projects [ 2 ]. Each r4d project is described in more detail as a case study in one of these three strategies (Table 2 ).

S1: stakeholders directly engaged with and sought evidence from researchers

In this strategy, international stakeholders requested evidence from the research team. This is a unique (and rare) strategy, as stated by Uzochukwu et al. [ 2 ], and often involves a policy window of opportunity in which stakeholders, including policy-makers, are looking to solve a particular problem, which coincides with the publishing of a scientific report or paper and the interests of these same groups [ 15 , 16 ].

Improving the HIV care cascade in Lesotho: towards 90-90-90—a research collaboration with the Ministry of Health of Lesotho

In this r4d project, the research team was approached by the International Aids Society (IAS) and the World Health Organization (WHO) in Geneva, based on the publication of their study protocol [ 17 ], introducing their innovative research approach of same-day antiretroviral therapy (ART) initiation in rural communities in Lesotho:

“They [international stakeholders] were all keen of getting the results out and requested evidence of the randomized controlled trials. We shared the results confidentially with WHO as soon as we had the data and thereafter published the results in a journal with a wide reach. WHO as well as other international guidelines and policy committees took up the recommendation of same-day ART initiation and informed global guidelines” (Researcher 1).

As a result, many HIV programmes in sub-Saharan Africa as well as in the global north have adopted the practice of offering rapid-start ART to persons who test HIV positive even outside a health facility. In this example, the policy window and direct stakeholder engagement was crucial for the effective translation and uptake of research evidence.

Furthermore, by closely collaborating with national policy-makers, the research team advocated for the setting up of a research database and of knowledge management units within the Ministry of Health (MoH) of Lesotho, which have been successfully established. The members of the research project consortia have also initiated a national research symposium on a bi-annual basis, which is chaired by the MoH with the aim of facilitating the dissemination and uptake of research findings.

S2: Stakeholders were involved in the design and throughout the implementation of the research project

In this strategy, policy uptake is facilitated through stakeholder engagement from the beginning as well as during the conduct of research activities, through participating at workshops or functioning in the governance of the projects. Two r4d projects illustrate this strategy.

Health system governance for an inclusive and sustainable social health protection in Ghana and Tanzania

This project established a Country Advisory Group (CAG) at the start that included representatives of the main stakeholders of the social health protection systems. The CAGs were involved in all phases of the project, from the definition of the research plans to the dissemination of the results. The specific research questions addressed by the project emerged from the interactions with these main stakeholders, i.e. national policy-makers, healthcare providers and members of the social health protection schemes (the NHIS and the Livelihood Empowerment Against Poverty schemes in Ghana; and the National Health Insurance Fund, the Community Health Funds and the Tanzania Social Action Fund in Tanzania). Specifically in Ghana, the following stakeholders played a major role in shaping the research plan: the Ministry of Gender Children and Social Protection (MGCSP), the Ghana Health Service (Policy Planning and Monitoring and Evaluation Division; Research and Development Division), the National Health Insurance Authority (NHIA) and the Associations of Private Health Care Providers. In Tanzania, a major role was played by the Ministry of Health, Community, Development, Gender, Elderly and Children, the President’s Office—Regional Administration and Local Government, by representatives of civil society organizations, such as Sikika, by the SDC (Swiss Agency for Development and Cooperation) Health Promotion and System Strengthening project and by the SDC-supported development programme.

These stakeholders were subsequently involved in steering the research, as captured by a researcher:

“In Ghana, it was a balanced relationship. They were involved since the very beginning of the project in articulating what the information gap at policy level is, formulating the research questions and understanding the methods/what is feasible. In Tanzania, where the policy landscape is more fragmented, it was very important to listen to the voices of several different stakeholders” (Researcher 2).

The stakeholder consultations in Ghana and Tanzania initially involved discussions on the relevance of the research plans to address the existing gaps in strengthening the social health protection scheme, the synergies with other research initiatives and the feasibility of implementing the proposed research. Later on in the project, the consultation process involved reviewing and discussing the focus of the research and the appropriateness of the research aims in light of decisions and reforms that were under discussion by the government but not in the public domain. This led to revision of the research questions as they would have become redundant when such reforms were made public, especially in Ghana. These consultation processes were more formal in Ghana and more informal in Tanzania, but they were very informative and had a tangible impact on the research plans, which were revised according to the feedback received. However, the research teams were always independent in deciding on the research methodology and in interpreting the results. The in-country dissemination of the results at the end of the first phase of the project informed the decisions to be made on the research plan for the second phase and provided the opportunity to discuss policy implications based on the results of the first phase. Because of this close collaboration and engagement with stakeholders, the results of the studies were widely disseminated in Ghana. Two of the main findings of the project were particularly considered by these stakeholders. According to the researcher:

“First, the study results showed that even though people registered with the NHIS they continued to pay out of pocket for health services. The reasons for this were delays in reimbursement by NHIS, escalating prices of drugs and medical products, low tariffs, lack of trust between providers and NHIA and inefficiencies. Secondly, the results showed that the current system of targeting the poor is not working properly, with more than half of people registered in the NHIS as indigents being in the non-poor socio-economic groups. These results contributed to inform decisions regarding the revision of the NHIA reimbursement tariffs, and to improve the identification of the poor to be exempted from paying the NHIS premium, in collaboration with the MGCSP” (Researcher 3).

In Tanzania, research was conducted to assess the effects of the public private partnership, referred as the Jazia Prime Vendor System (Jazia PVS), on improving access to medicines in the Dodoma and Morogoro regions in Tanzania. This is one of the reforms in the area of supply chain management taking place in the country. Results showed that a number of accountability mechanisms (inventory and financial auditing, close monitoring of standard operating procedures) implemented in conjunction with Jazia PVS contributed positively to the performance of Jazia PVS. Participants’ acceptability of Jazia PVS was influenced by the increased availability of essential medicines at the facilities, higher-order fulfilment rates and timely delivery of the consignment [ 18 , 19 , 20 ].

The findings from this study were disseminated during the national meeting attended by various stakeholders, including CAG members, government officials and policy-makers. In addition, the findings were used to inform the national scale-up of the Jazia PVS intervention as the government of Tanzania decided to scale up the Jazia PVS to all the 23 regions in 2018. Moreover, the results/manuscripts were published or submitted to peer-reviewed journals [ 18 , 19 , 20 ], enabling other countries intending to adopt such innovate public–private partnerships for improvement of the in-country pharmaceutical supply chain to learn from Jazia PVS in Tanzania.

Health impact assessment for engaging natural resource extraction projects in sustainable development in producer regions (HIA4SD)

In this r4d project, stakeholders were involved from the outset through their participation in the project launch meeting and in regular consortium meetings. The project is a collaboration between the Swiss Tropical and Public Health Institute (Swiss TPH), the Center for Development and Cooperation (NADEL) at the Swiss Federal Institute of Technology in Zurich/Switzerland and national research institutes, namely the Institut de Recherches en Sciences de la Santé in Burkina Faso, the University of Health and Allied Sciences in Ghana, the Centro de Investigação em Saúde de Manhiça in Mozambique and the Ifakara Health Institute in Tanzania [ 21 ]. The involvement of key stakeholders from the government, civil society, private sector and research community in an engaged dialogue from the beginning iss of central importance in this project, as in most cases mining is a highly politicized topic. To promote the immediate integration of research findings into policy, the project is organized into two streams, namely an “impact research stream” and a “governance stream”, that work in parallel. While the impact research stream is focused on evidence generation to support the uptake of health impact assessment (HIA) in Africa, the governance stream is focused on understanding the policy terrain and consequently the pathways that need to be utilized to support translation of the evidence into policy and practice. The second phase of the study is devoted to the dissemination of research findings into policy at the national and local levels, including capacity-building activities for national stakeholders. As the HIA4SD project examines operational questions of relevance for guiding both policy-making and decision-making, team members sought to regularly engage with and inform the national stakeholders. According to the researcher:

“Strategies employed to influence policy vary according to the country, but included regular stakeholder workshops, participation in a new national platform launched to discuss issues around mining in Mozambique, development of policy briefs, strengthened collaborations with national ministries of health, discussion of results and advocacy with policy makers, and conference presentation of findings” (Researcher 4).

In these two case examples, continuous stakeholder engagement was considered essential to translate and disseminate research evidence. Thus, beyond the stage of setting the objectives, contact with stakeholders was active and maintained on a regular basis through regular exchanges with stakeholder groups during workshops or meetings, which facilitated the dissemination and uptake of the research results. While the time and level of meaningful interaction varied across the countries and workshops, all meetings were well attended by participants from varied levels of government, MoHs, nongovernmental organizations and private industry, prompting spirited discussion and insight from these groups. All stakeholders were willing to attend these workshops as part of the scope of their professional duties.

S3: stakeholders engaged in participatory and transdisciplinary research approaches to co-produce knowledge and inform policy

In the two examples presented in this section, the research questions and approaches arose through community and stakeholder participation in the research and intervention design itself. The methodology adopted allowed them to engage, design research, act, share and sustain partnerships between the communities, the involved stakeholders and researchers [ 22 ]. These participatory research approaches facilitated grassroot-level policy and practice changes which were not researcher nor policy maker led, and that show promising approaches for developing culturally aligned solutions [ 23 ]. Policy makers at both the regional and national levels were invited to be part of the participatory research approach: they were interviewed during the initial stage, then the research results were presented and discussed with them; thereafter, we had several meetings to co-create potential interventions to address the identified problems, with the aim to directly engage in the research and intervention design itself in partnerships with the community stakeholders, including local leaders, and the researchers.

Surveillance and response to zoonotic diseases in Maya communities of Guatemala: a case for OneHealth

The research was embedded in a collaboration between the Universidad del Valle in Guatemala, the MoH, the Ministry of Animal Production and Health, the Maya Qéqchi’ Council of Elders, TIGO Telecommunications Foundation and the community development councils. The objective of this r4d programme was to set up integrated animal–human disease surveillance (OneHealth) in Maya communities in Guatemala. The research approach arose from a context of medical pluralism, where communities have access to and use two different medical systems: (1) the modern Western medical system and (2) traditional Maya medicine [ 24 ].

Researchers and community members collaborated at all stages of the research process, including the planning stage. Even before the grant proposal was finalized, researchers met with the communities that, should the funding come through, would be invited to participate in the research. According to the researchers:

“The project was set up through a transdisciplinary process, with academic and non-academic actors—including national, local and traditional authorities—involved in the problem through a collaborative design, analysis, dissemination and research translation. It was a co-producing transformative process—transferring knowledge between academic and non-academic stakeholders in plenary sessions and through group work. These meetings were held every year to continuously follow up the progress of the process” (Researcher 7).

The active engagement and collaboration by the community and stakeholders facilitated the acceptability of the study results and hence its dissemination, captured by the researchers as follows:

“The main result was that they allowed a frank discussion between Maya medical exponents in human–animal health and Western medicine, which allowed the patients and the animal holders to avoid the cognitive dissonance and so that the patients or the animal holders can choose freely what they want. Cognitive dissonance exists if one system dominates the other—or refutes the other” (Researcher 7).

“After all stakeholders discussed the research evidence produced jointly, an unprecedented process of collaboration between Government authorities and communities followed to develop three joint responses: a) education campaigns led by local teachers in tandem with the Ministry of Education, b) communication strategies at regional levels led by the Human and Animal Health authorities along with traditional Maya Ajilonel (medicine specialists), and c) a policy framework for producing a OneHealth approach led by Central Government authorities” (Researcher 8).

The process of mutual learning throughout the project produced a new level of awareness, facilitating culturally pertinent and socially robust responses that overcame a historical tendency of unilateral policy making based solely on Western values and preferences. As the project implemented a new approach to monitoring animal and human populations, the involvement of regional teams from the different ministries (Health, Livestock and Agriculture) throughout all the phases of methodological design, data collection, posterior data analysis and design of specific interventions for the local population (transformation of scientific results into actions for public health improvement) was essential to ensuring that the approach used secured the regional authorities’ commitment to defining new policies for immediate application in their territory. Accordingly, this also contributed towards the development of a OneHealth national strategy for Guatemala in which ministries start to cooperate to take up priority issues.

Addressing the double burden of disease: improving health systems for non-communicable and neglected tropical diseases (Community Health System Innovation [COHESION])

Together with three Swiss academic partners, this r4d project examined the challenges of a double burden of non-communicable and neglected tropical diseases at the primary healthcare level in vulnerable populations in Mozambique, Nepal and Peru. Community participation and co-creation were key elements of the project’s approach. The work conducted in Peru illustrates this approach:

“At the beginning, the people who were involved were respondents, but then they became active participants. So it was this active engagement and the changing of roles, giving feedback not just from the research responses but also from being involved in the process, which helped to design and create interventions together with the research team” (Researcher 5).

This participatory approach to co-creation actively sought a diverse range of stakeholders, including community members, primary healthcare workers, and regional and national health authorities. The co-creation approach to participatory research enables context-specific variation in methodological design, a critical element when studying three very different countries and health systems. Central to all aspects was a feedback loop whereby early findings were shared with research participants for further elaboration and iteration.

As active co-creators of the research process, local communities developed high levels of trust in the methodology and data, with the result that researchers achieved deeper “buy-in” which in turn is known to enhance the uptake of findings by decision-makers [ 25 ] as communities in which research is being undertaken play a central role in the decision-making process [ 26 ].

Challenges to research uptake in health policy identified by r4d researchers

During the interviews, r4d researchers identified several challenges to research utilization and uptake into policy. These challenges are summarized and highlighted in Table 3 .

Three key strategies identified for research uptake in policy and practice are described in this paper, namely: (S1) stakeholders directly engaged with and sought evidence from researchers; (S2) stakeholders were involved in the design and throughout the implementation of the research project; and (S3) stakeholders engaged in participatory and transdisciplinary research approaches to co-produce knowledge and inform policy. These strategies are in line with the overall objectives of the r4d projects, which are to generate scientific knowledge and research-based solutions to reduce poverty and global risks in LMICs, and also to offer national and international stakeholders integrated approaches to solving problems [ 5 ]. In the course of our synthesis work, we found that several lessons could be learned from the three strategies identified for research uptake in policy and practice.

S1: raising awareness of planned research to attract stakeholder involvement

The actual uptake of research findings in policy was most direct in the case of the first strategy (S1), in which IAS and WHO stakeholders were wanting new knowledge on HIV and same-day ART initiation, and were actively seeking new evidence on these specific topics. The findings published in peer-reviewed journals were then taken up by these stakeholders to update international policies and guidelines on rapid ART initiation [ 27 ]. This was also found in other studies, highlighting the importance of the timeliness and relevance of findings and the production of credible and trustworthy reports, among others, as key factors in promoting the use of research evidence in policy [ 2 , 28 ].

S2: sustainable collaborations in a supportive policy environment with stakeholder engagement from early on and throughout the research process

With regards to the second strategy (S2), we found that constant collaboration with an advisory and steering group composed of diverse stakeholders, including policy-makers, from early on promotes the uptake and use of research evidence. In line with findings from other studies [ 2 ], the experiences encountered in the r4d public health projects show that early involvement of stakeholders in the processes to identify the research problem and set the priorities facilitated the continuous exchange of information that might ultimately influence policy. The r4d project on social governance mechanisms in Ghana highlight that the evidence produced influenced policy documents (identification of the poor and tariff adjustments), but that frequent changes government officials made it difficult to maintain a close relationship between the researchers and the governmental agencies/policy stakeholders. From this, we draw the conclusion that research approaches need to be more adaptive and flexible to be successful in an unsupportive or unstable policy environment to ensure continuity in promoting the dissemination and uptake of research evidence in policy-making. One possible manner to secure this transformation is for researchers to apply for additional funding after the grant is finished. Other studies have also come to this conclusion, thereby demonstrating the key role of a supportive and effective policy environment that includes some degree of independence in governance and financing, strong links to stakeholders that facilitate trust and influence and also the capacity within the government workforce to process and apply policy advice developed by the research findings [ 29 ]. By involving stakeholders in the process of identifying research objectives and designing the project, as seen particularly in the r4d case studies on social health protection in Ghana and Tanzania and the HI4SD, but also in the HIV care cascade in Lesotho, the research approach responded to the need of locally led and demand-driven research in these countries, strengthening local research capacities and institutions, but also investing in research that is aligned with the national research priorities. As highlighted by other authors, advantages of this “demand-driven” approach is that it tailors research questions to local needs, helps to strengthen local individual and organizational capacities and provides a realized stringent framework on which a research project should deliver outcomes [ 30 , 31 ].

S3: co-creation and equal partnerships

The third strategy with a strong participatory approach, such as that adopted by two r4d projects, OneHealth in Guatemala and COHESION, demonstrates benefits to promoting co-learning as a way to minimize the impact of unequal power dynamics and to work effectively across the local policy landscape through equal partnerships. It also facilitates identifying solutions that are culturally pertinent, socially more robust and implementable.

The approaches of co-creation, equal participation and stakeholder involvement used in the research projects raise questions of ‘governance’, that is the way rules, norms and actions are structured, sustained and regulated by public and para-public actors to condition the engagement and impact of public involvement activities [ 32 , 33 ]. Through stakeholder involvement in setting the agenda and designing the research projects, as shown in the case studies on social protection in Ghana and Tanzania and the HI4SD project, but particularly in the two projects using a co-creation approach, the engagement of a range of stakeholders serves to make the health research systems a participaant in the endeavor that then has the capacity to promote changes in the healthcare system it aims to serve. By establishing a shared vision with a public involvement agenda and through the collaborative efforts of various stakeholders, as we found particularly in the co-creation approach, supportive health research systems are established. This leads to greater public advancement through collaborative actions, thereby tackling the stated problems of the health systems [ 34 ].

There were four key challenges mentioned by the respondents during the interviews to research uptake in policy making. The first was the necessary time investment by researchers to translate the result and develop policy advocacy products for the different audiences. This challenge is all the more difficult because research evidence and tangible products only become available towards the end of a research project, leaving only a short window of opportunity for exchange and engagement. There seems to be a need for wider discussion on the role of researchers in influencing policy. The concerns raised included whether influencing policy is actually a role for researchers and whether researchers have the right skills to be effective in persuasion or network formation [ 35 ]. Conversely, researchers may be in a good position to engage in the policy process if they enjoy finding solutions to complex problems while working with diverse and collaborative groups in partnerships [ 36 , 37 ]. The rationale for engaging in such a process needs to be clarified in advance: is the aim to frame an existing problem, or is it to simply measure the issues at stake and provide sound evidence according to an existing frame? Regarding the the former, how far should researchers go to be useful and influential in the policy process or to present challenges faced by vulnerable populations [ 37 ]? While fully engaging in the policy process may be the best approach for researchers to achieve credibility and impact, there may also be significant consequences, such as the risk of political interests undermining the methodological rigour of academic research (being considered as academic ‘lightweight’ among one’s peer group) [ 38 , 39 , 40 , 41 ]. For researchers there is also considerable opportunity costs because engaging in the policy-influencing process is a time-consuming activity [ 35 ], with no clear guarantee of the impact of success [ 37 ]. It is therefore crucial to consider the investment and overall time researchers may have to spend to engage [ 35 ], and how this time and investment can be distributed between actual research and the production of outreach products, such as policy briefs, presentation of research findings as policy narratives [ 35 ] and the setting-up of alliances, building of networks and exploitation of windows of opportunity for policy change [ 37 ].

The second challenge included the issue of scale and objectivity, as most of the projects are not scaled or national-level studies and thus are highly context specific. The difficulty to measure the contributions of a single research project or study in terms of policy outcomes was also highlighted, particularly in view of the different understandings among researchers and funders on the possible policy impacts of the research, which can range from guiding policy-makers to understand a situation or problem (awareness raising) to influencing a particular course of action by establishing new or revising existing policies. This has also been emphasized in the Evidence Peter Principle [ 42 ], showing that single studies are often inappropriately used to make global policy statements for which they are not suitable. To make global policy statements, an assessment of the global evidence in systematic reviews is needed [ 42 , 43 ].

The third challenge mentioned was the frequent changes in staff at the governmental level, which demanded continuous interactions between r4d researchers and stakeholders, highlighting the need for more adaptive and flexible research approaches. These should include a thorough analytical process prior to implementation in historical, sociopolitical and economic aspects, power differentials and context; backward planning exercises to check assumptions; and conflict transformation and negotiation skills in order to be able to constantly adapt to changing contexts. In line with our research findings, when researchers make the time investment needed to engage in the policy-influencing process, an opportunity is provided to getting know the involved stakeholders better and improve their understanding of the policy world in practice, but also to build diverse and longer-term networks [ 37 , 44 ] and to identify policy problems and the appropriate stakeholders to work with [ 45 , 46 ]. Engaging a diverse range of stakeholders through co-designing the research is widely held to be practically the best way to guarantee the uptake and use of evidence in policy through a more dynamic research approach [ 47 ]. However, the development of networks and contacts for collaboration, as well as the skills to do so, takes time and effort and is an ongoing process [ 48 ], factors which need to be acknowledged more widely.

Lastly, the fourth challenge related to research uptake was the diverging interests between researchers, research funding bodies and stakeholders. Time was identified as a limiting factor from the perspective of the design of the research project. Most research projects, including the r4d projects, are funded for 3–4 years [ 5 ]. It takes a considerable amount of time to generate new research results, and often these are more likely to be produced for further use at the end of a project. If researchers should engage more fully in the policy process to secure meaningful impact, it is critical to discuss the extent to which they have the skills, resources and institutional support to do so [ 37 ], as well as how projects could be set up differently. This could be done either by the funders in providing the necessary support that allows researchers to have the means to impact policy, or by the researchers in the design of their project to take on board the different strategies to influence evidence use and uptake. In moving forward, defining shared goals from the outset between funders and the researchers might translate to more achievable milestones in terms of which policy issue, theme or process a research project aims to change in order to effectively influence policy [ 49 ]. This would help to identify the resources and budget needed by the funders in order for the researchers to engage with more resources over a longer time span in this process.

Limitations

Interviews were limited to researchers of the r4d projects and did not include local stakeholders. Therefore, the synthesis work, including the analysis and results, reflects solely the perspective of researchers. We are aware that had we included a range of stakeholders, including policy-makers, in the sample, we would have potentially been able to identify additional factors relating to social, cultural and political barriers to the use and uptake of research findings in politics and practice. However, constraints such as access to local stakeholders, language barriers and time zones drove our decision to focus on researchers. A future synthesis effort would need to include the other voices.

There is ever growing awareness of how critical it is to close the gap between policy-makers, practitioners and researchers. Using the researchers’ perspectives, in this article we give insight into three different strategies that can facilitate this process, with the first strategy requiring proactive searching for the latest findings on the part of well-informed policy-makers, the second requiring researchers to take steps to ensure an active exchange of ideas and information with diverse stakeholders when designing the research project and ensuring the latter’s involvement throughout; and the third using a transdisciplinary and/or co-creation approach to establish equal partnerships and trust among all involved stakeholders.

The five case studies reported here also show some of the difficulties that prevail for research to be taken up into policy and practice, despite everyone’s best intentions and efforts. Researchers may not always be best placed for communication, dissemination and advocacy work, all activities which are very time intensive or become important only towards the end of a research project when clear and high-quality evidence is produced. Moreover, it takes a strong body of evidence, advocacy and coalition building with appropriate stakeholders to influence policy, and then a further major effort of resources to see policy followed through into practice. It is through experiences such as this synthesis initiative that precious insights and learning can be gained for the common good of all involved moving forward, and it is crucial that funders continue to support and/or adapt their funding schemes to ensure some of these strategies are implemented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Antiretroviral therapy

Country Advisory Group

Community Health System Innovation

Health impact assessment

Health impact assessment for engaging natural resource extraction projects in sustainable development in producer regions

Human immunodeficiency virus

International Aids Society

Jazia Prime Vendor System

Low- and middle-income countries

Ministry of Health

Ministry of Gender Children and Social Protection

Center for Development and Cooperation at the Swiss Federal Institute of Technology

National Health Insurance Authority

National Health Insurance Scheme

Swiss Programme for Research on Global Issues for Development

Swiss Agency for Development and Cooperation

Swiss National Science Foundation

Swiss Tropical and Public Health Institute

Universal health coverage

World Health Organization

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Acknowledgements

The authors would like to acknowledge the contribution of Dr Claudia Rutte from the r4d programme/SNSF for her inputs to the history and background of the r4d programme.

The r4d synthesis initiative is implemented by the Swiss Tropical and Public Health Institute, which funded the costs of publishing this paper.

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Erismann, S., Pesantes, M.A., Beran, D. et al. How to bring research evidence into policy? Synthesizing strategies of five research projects in low-and middle-income countries. Health Res Policy Sys 19 , 29 (2021). https://doi.org/10.1186/s12961-020-00646-1

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This chapter considers the specific implications for research, policy and practice derived from this study. It begins by exploring the policy implications of the conceptualisation of ‘illicit labour’ and then reflects on implications for practice. The chapter then discusses theory—exploring gangs in extant research and proposing new ways to develop the discourse further. The chapter ends by considering the implications for global understandings of street children’s involvement in criminal groups and will pose the question of how useful the conceptualisation of ‘illicit labour’ is outside of Bangladesh, and wider afield.

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Atkinson-Sheppard, S. (2019). Implications for Research, Policy and Practice. In: The Gangs of Bangladesh. Palgrave Advances in Criminology and Criminal Justice in Asia. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-18426-1_8

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Policy recommendations in the discussion section of a research article

Short abstract.

Should a research article in Injury Prevention make policy recommendations in the discussion section?

Imagine that the Acme Auto Company has designed Device X to prevent death in a vehicle crash. You and I have completed the first randomized controlled trial of Device X; we studied 400 drivers who were randomly assigned to X or a placebo device at the time of their crash and ascertained which drivers died.

The risk of death among the drivers with Device X was 0.075 compared with 0.15 for drivers without X (table): risk ratio 0.5, 95% confidence interval 0.28 to 0.90. Our draft manuscript concludes: “We found that drivers who crashed in vehicles with Device X had a risk of death which was half that of similar drivers in similar vehicles without X. If our findings represent the casual effects of X, this device can prevent about half the driver deaths that would otherwise occur in a crash.” After reading our draft, a colleague suggests we make a policy recommendation in the discussion section of our paper. Should we?

Interpreting results or stating preventive implications does not require a statement about policy; the two sentences in quotations above provide an adequate interpretation. Calls for more data or research are often unnecessary, but they are not my topic. 1 A policy recommendation is advice that some action should be taken by someone to promote health : a behavior should be adopted, advice should be given, a public education campaign should begin, a product should be purchased, a law should be enacted, and so on.

I will offer three reasons for not giving a policy recommendation in the discussion section of our research study of Device X. Similar ideas have been expressed by others. 2 , 3 , 4 , 5

Reason 1: The results of the study may not be correct

Any study, even a randomized trial, may produce biased estimates of casual associations. Even if our hypothetical study had no apparent limitations (I have never written or read a study without limitations), our estimate of Device X's effect is still uncertain. Given the observed data, the maximum likelihood estimate for the risk ratio is 0.5, but risk ratios from 0.26 to 0.94 all have likelihoods at least 1/8th the maximum (fig). 6 , 7 A second study might find that Device X reduced mortality by only 10%, not 50%. There is a small possibility that X has no causal influence on the risk of death or might even be harmful. If we studied Device X using a design other than a randomized trial, we would want to be even more cautious about recommendations, as our estimate of the effect of X might be biased by differences between the drivers with X and those without X. 8 , 9

The relative likelihood of a range of risk ratio values, from 0.1 to 2.5, for the risk ratio of death among drivers who crashed in vehicles with Device X compared with otherwise similar drivers and vehicles without Device X.

Reason 2: All the important risks and benefits have not been studied

We studied the effect of X on death only. But perhaps X greatly increases the risk of quadriplegia, traumatic brain injury, and limb amputation. Perhaps X is ineffective or harmful for subgroups of drivers or vehicles. Even if X prevents bad health outcomes in a crash, we have no information about whether it might affect the risk of a crash—if it halved the risk of death, but doubled the risk of crashing, it might offer no net benefit for driver health. Could X have detrimental effects for other occupants of the same vehicle or occupants in other vehicles? If Device X adds 281 kg of weight to each vehicle, what does that imply for fuel consumption, dependency on imported oil, and increased emissions that might contribute to global warming (which may have health effects). If X adds $9481 to the cost of each vehicle, do the benefits justify that cost?

We can never have all the evidence we might want, so lack of information alone does not prohibit policy making. But a serious discussion of policy regarding Device X should consider what evidence is needed and whether some policy is justified by what is known. It is rare that the discussion section of a research paper will have the space for this review or that the findings of a single research study will fill the gap in knowledge that tips the balance in favor of a particular policy. 2

Reason 3: manuscript space rarely allows a full review of alternative policy choices

Even if we are convinced that Device X is beneficial, has no adverse consequences, and the costs are justified, what policy choice would be best? Choices include advising drivers to purchase X, offering X on some new vehicles, putting X into all new vehicles, retrofitting all vehicles with X, or requiring X in vehicles by law. Before recommending a public campaign to encourage the purchase of X, we might want evidence that a substantial part of the public would respond favorably to such a campaign. Before suggesting that health practitioners should advise patients to use X, we might want evidence that patients would follow this advice and we should consider whether the time needed to deliver this advice is justified, compared with other demands upon the time of health professionals.

Is there any harm in policy recommendations in research articles?

It would be tragic if a recommended policy was so misguided that its implementation produced harm. Ineffective advice would waste time and money. Even a useful policy may be wasteful if a superior policy is feasible. We cannot expect that policy advice will always be correct, but if research can contribute to effective policies, I think the best policy is more likely to emerge if it is based upon thorough review of the evidence and thoughtful consideration of alternatives.

I doubt that the policy suggestions given in the discussion section of some injury research papers are likely to result in serious harm; I suspect most readers recognize the offhand nature of these ideas and do not take them seriously. The harm from facile policy recommendations in research studies is probably subtle: (1) a paragraph of policy advice makes the article longer, wasting journal space and reader time; (2) policy discussion diverts attention from the strengths and limitations of the research; (3) casual policy recommendations can give the impression that injury research does not demand critical thinking. In the most egregious instances, recommendations are so loosely related to the research that the discussion becomes an editorial expressing the unsupported opinions of the authors.

Can researchers contribute to policy formation?

Research papers provide evidence that can contribute to policy. For example, early case‐control studies of injuries examined the association between alcohol use and traffic crashes. 10 , 11 , 12 These studies all reported evidence of harm associated with alcohol use, although none made policy recommendations. Today we have laws, educational campaigns, and other policies regarding drinking and driving. The early case‐control studies helped provide evidence for those policies.

Researchers can contribute to policy beyond their research. Some testify before legislative panels, some provide expert advice to public information campaigns, and some write thoughtful articles about policy alternatives. A recent commentary 13 and article 14 in Injury Prevention are examples of thinking about policy, and the journal encourages articles about policies. 15 I am not recommending that injury researchers avoid policy recommendations—I am suggesting that they do so in a serious manner and that policy discussions at the end of research papers are usually too short to be useful.

Should injury prevention have a policy about policy?

I am not advocating a ban on policy recommendations in research studies. I have no objection to a policy recommendation that is clearly supported by the research; I just think that situation is rare. 2 But I worry that Injury Prevention actually encourages authors to make policy statements. The journal's advice to authors states “Whenever possible, the Discussion should conclude with a separate section entitled Implications for Prevention.” I think some authors interpret these words as an invitation to recommend policies. I suggest these words could be cut from the instructions with no important loss. Or replaced with language that explains what “implications” are and why they are not usually policy recommendations.

Competing interests: None.