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The Ultimate Guide to Getting Your Thesis Published in a Journal

• 7-minute read
• 25th February 2023

Writing your thesis and getting it published are huge accomplishments. However, publishing your thesis in an academic journal is another journey for scholars. Beyond how much hard work, time, and research you invest, having your findings published in a scholarly journal is vital for your reputation as a scholar and also advances research findings within your field.

This guide will walk you through how to make sure your thesis is ready for publication in a journal. We’ll go over how to prepare for pre-publication, how to submit your research, and what to do after acceptance.

Pre-Publication Preparations

Understanding the publishing process.

Ideally, you have already considered what type of publication outlet you want your thesis research to appear in. If not, it’s best to do this so you can tailor your writing and overall presentation to fit that publication outlet’s expectations. When selecting an outlet for your research, consider the following:

●  How well will my research fit the journal?

●  Are the reputation and quality of this journal high?

●  Who is this journal’s readership/audience?

●  How long does it take the journal to respond to a submission?

●  What’s the journal’s rejection rate?

Once you finish writing, revising, editing, and proofreading your work (which can take months or years), expect the publication process to be an additional three months or so.

Revising Your Thesis

Your thesis will need to be thoroughly revised, reworked, reorganized, and edited before a journal will accept it. Journals have specific requirements for all submissions, so read everything on a journal’s submission requirements page before you submit. Make a checklist of all the requirements to be sure you don’t overlook anything. Failing to meet the submission requirements could result in your paper being rejected.

Areas for Improvement

No doubt, the biggest challenge academics face in this journey is reducing the word count of their thesis to meet journal publication requirements. Remember that the average thesis is between 60,000 and 80,000 words, not including footnotes, appendices, and references. On the other hand, the average academic journal article is 4,000 to 7,000 words. Reducing the number of words this much may seem impossible when you are staring at the year or more of research your thesis required, but remember, many have done this before, and many will do it again. You can do it too. Be patient with the process.

Additional areas of improvement include>

·   having to reorganize your thesis to meet the section requirements of the journal you submit to ( abstract, intro , methods, results, and discussion).

·   Possibly changing your reference system to match the journal requirements or reducing the number of references.

·   Reformatting tables and figures.

·   Going through an extensive editing process to make sure everything is in place and ready.

Identifying Potential Publishers

Many options exist for publishing your academic research in a journal. However, along with the many credible and legitimate publishers available online, just as many predatory publishers are out there looking to take advantage of academics. Be sure to always check unfamiliar publishers’ credentials before commencing the process. If in doubt, ask your mentor or peer whether they think the publisher is legitimate, or you can use Think. Check. Submit .

If you need help identifying which journals your research is best suited to, there are many tools to help. Here’s a short list:

○  Elsevier JournalFinder

○  EndNote Matcher

○  Journal/Author Name Estimator (JANE)

○  Publish & Flourish Open Access

·   The topics the journal publishes and whether your research will be a good fit.

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·   The journal’s audience (whom you want to read your research).

·   The types of articles the journal publishes (e.g., reviews, case studies).

·   Your personal requirements (e.g., whether you’re willing to wait a long time to see your research published).

Submitting Your Thesis

Now that you have thoroughly prepared, it’s time to submit your thesis for publication. This can also be a long process, depending on peer review feedback.

Preparing Your Submission

Many publishers require you to write and submit a cover letter along with your research. The cover letter is your sales pitch to the journal’s editor. In the letter, you should not only introduce your work but also emphasize why it’s new, important, and worth the journal’s time to publish. Be sure to check the journal’s website to see whether submission requires you to include specific information in your cover letter, such as a list of reviewers.

Whenever you submit your thesis for publication in a journal article, it should be in its “final form” – that is, completely ready for publication. Do not submit your thesis if it has not been thoroughly edited, formatted, and proofread. Specifically, check that you’ve met all the journal-specific requirements to avoid rejection.

Navigating the Peer Review Process

Once you submit your thesis to the journal, it will undergo the peer review process. This process may vary among journals, but in general, peer reviews all address the same points. Once submitted, your paper will go through the relevant editors and offices at the journal, then one or more scholars will peer-review it. They will submit their reviews to the journal, which will use the information in its final decision (to accept or reject your submission).

While many academics wait for an acceptance letter that says “no revisions necessary,” this verdict does not appear very often. Instead, the publisher will likely give you a list of necessary revisions based on peer review feedback (these revisions could be major, minor, or a combination of the two). The purpose of the feedback is to verify and strengthen your research. When you respond to the feedback , keep these tips in mind:

●  Always be respectful and polite in your responses, even if you disagree.

●  If you do disagree, be prepared to provide supporting evidence.

●  Respond to all the comments, questions, and feedback in a clear and organized manner.

●  Make sure you have sufficient time to make any changes (e.g., whether you will need to conduct additional experiments).

After Publication

Once the journal accepts your article officially, with no further revisions needed, take a moment to enjoy the fruits of your hard work. After all, having your work appear in a distinguished journal is not an easy feat. Once you’ve finished celebrating, it’s time to promote your work. Here’s how you can do that:

●  Connect with other experts online (like their posts, follow them, and comment on their work).

●  Email your academic mentors.

●  Share your article on social media so others in your field may see your work.

●  Add the article to your LinkedIn publications.

●  Respond to any comments with a “Thank you.”

Getting your thesis research published in a journal is a long process that goes from reworking your thesis to promoting your article online. Be sure you take your time in the pre-publication process so you don’t have to make lots of revisions. You can do this by thoroughly revising, editing, formatting, and proofreading your article.

During this process, make sure you and your co-authors (if any) are going over one another’s work and having outsiders read it to make sure no comma is out of place.

What are the benefits of getting your thesis published?

Having your thesis published builds your reputation as a scholar in your field. It also means you are contributing to the body of work in your field by promoting research and communication with other scholars.

How long does it typically take to get a thesis published?

Once you have finished writing, revising, editing, formatting, and proofreading your thesis – processes that can add up to months or years of work – publication can take around three months. The exact length of time will depend on the journal you submit your work to and the peer review feedback timeline.

How can I ensure the quality of my thesis when attempting to get it published?

If you want to make sure your thesis is of the highest quality, consider having professionals proofread it before submission (some journals even require submissions to be professionally proofread). Proofed has helped thousands of researchers proofread their theses. Check out our free trial today.

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• Publication Process

How to Write a Journal Article from a Thesis

• 3 minute read
• 205.4K views

Table of Contents

You are almost done with your PhD thesis and want to convert it into a journal article. Or, you’re initiating a career as a journal writer and intend to use your thesis as a starting point for an article. Whatever your situation, turning a thesis into a journal article is a logical step and a process that eventually every researcher completes. But…how to start?

The first thing to know about converting a thesis into a journal article is how different they are:

Thesis Characteristics:

• Meets academic requirements
• Reviewed by select committee members
• Contains chapters
• Lengthy, no word limits
• Table of contents
• Lengthy research of literature
• IRB approval described in detail
• Description and copies of tools used
• All findings presented
• Verb tenses may vary

Journal Article Characteristics:

• Meets journalistic standards
• Reviewed by a panel of “blind” reviewers
• Word limits
• Manuscript format
• Succinct research of literature
• IRB described in 1 to 3 sentences
• Essential and succinct tool information
• Selected findings presented
• Verb tenses are fairly consistent

Converting your thesis to a journal article may be complex, but it’s not impossible.

A thesis is a document of academic nature, so it’s more detailed in content. A journal article, however, is shorter, highlighting key points in a more succinct format. Adapting a thesis for conversion into a journal article is a time-consuming and intricate process that can take you away from other important work. In that case, Elsevier’s Language Editing services may help you focus on important matters and provide a high-quality text for submission in no time at all.

If you are going to convert a thesis into a journal article, with or without professional help, here is a list of some of the steps you will likely have to go through:

1. Identify the best journal for your work

• Ensure that your article is within the journal’s aim and scope. How to find the right journal? Find out more .
• Check the journal’s recommended structure and reference style

2. Shorten the length of your thesis

• Treat your thesis as a separate work
• Paraphrase but do not distort meaning
• Select and repurpose parts of your thesis

3. Reformat the introduction as an abstract

• Shorten the introduction to 100-150 words, but maintain key topics to hold the reader’s attention.
• Use the introduction and discussion as basis for the abstract

4. Modify the introduction

• If your thesis has more than one research question or hypothesis, which are not all relevant for your paper, consider combining your research questions or focusing on just one for the article
• Use previously published papers (at least three) from the target journal as examples

5. Tighten the methods section

• Keep the discussion about your research approach short

6. Report main findings in the results

• Expose your main findings in the results section in concise statements

7. Discussion must be clear and concise

• Begin by providing an interpretation of your results: “What is it that we have learned from your research?”
• Situate the findings to the literature
• Discuss how your findings expand known or previous perspectives
• Briefly present ways in which future studies can build upon your work and address limitations in your study

8. Limit the number of references

• To choose the most relevant and recent
• To format them correctly
• Consider using a reference manager system (e.g. Mendeley ) to make your life easier

If you are not a proficient English speaker, the task of converting a thesis into a journal article might make it even more difficult. At Elsevier’s Language Editing services we ensure that your manuscript is written in correct scientific English before submission. Our professional proofers and editors check your manuscript in detail, taking your text as our own and with the guarantee of maximum text quality.

Language editing services by Elsevier Author Services:

• Research Process

How to Choose a Journal to Submit an Article

• Publication Recognition

How to Submit a Paper for Publication in a Journal

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• Public Resources
• Publishing Your Dissertation in a…

Publishing Your Dissertation in a Scholarly Journal

So you’ve decided you want to earn a PhD. You have read about the road blocks , you’ve selected your committee, and you’ve started writing your dissertation. But why start thinking about a publication? What does that have to do with earning the ultimate degree?

Writing a dissertation without a publication is like going to the trouble of making a cake but not baking it. No one can taste your cake, no one can benefit from your hard work—of course, no one can criticize your work either. But without a publication, or two or three, the dissertation is not technically a total success. You’d be amazed at how many people don’t get a publication out of their dissertation. Without advanced planning, a publication likely won’t happen, because, once again, there are often unanticipated roadblocks.

1. Publication will be completely driven by you and no one else. A publication will not be on the priority list of your dissertation committee. And you will likely be the only one to understand where to publish.

2. Planning is the most important step and the only way to be successful. If you wait until after you go through the dissertation process, you will be too exhausted to publish, and it won’t happen for a year or two. You must plan.

3. Configure your dissertation for three separate publications. This may be three separate chapters, or it may be three different data sets or arms of your data. Link this to the selection of your committee. In my previous articles, I recommended choosing a committee wisely, with different members being associated with different jobs. Committee members should not have similar areas of expertise. Their “jobs” should not overlap. There should be a content expert or literature review expert, a methods person, a results person and a “whip.” Ideally, the “whip” (think politics, as in majority whip or minority whip in Congress) is your dissertation chair. The jobs of each member should be distinct to avoid having members fighting or making conflicting suggestions. Each article should be aligned with a separate expert. So there can be a Review article, a Methods article, and a Results article.

4. You get to decide who is listed as an author on your articles. You will be first author, and your mentor should be last author (a position of honor). If a committee member does not contribute, they ethically should not be included in the list of authors. Many journals now ask you to list the authors along with their meaningful contributions.

5. Decide ahead of time the journals in which you wish to publish. Full disclosure — I am on the board of the Journal of the American Academy of PAs (JAAPA), and I’m a reviewer for the Journal of Physician Assistant Education (JPAE). I’m going to recommend these PA journals for one of your publications. Here is my pitch (although biased): We are THE scholarly journals for the profession. If your data is completely focused on PA education, then select JPAE. Think about JAAPA for any research on PAs themselves or PA analogues. Select a PA journal for a personal touch and some really important feedback. Your article will be on a stage with your peers. This gives you the home crowd advantage.

6. Lose ownership. Remember how I said your dissertation is like your baby? Well, your baby now has gone through elementary school and the tumultuous teenage years and currently is entering college. Your publication needs you to back off and give it some space. It will leave home for a while (often for the long review process). When it comes back, it might need to do some laundry, but it should essentially not look at all like the baby you once knew. A publication should look like a publication, not a dissertation. It should be neat and mature and all grown up.

7. Try for the highest level journal you can realistically get published in. Here is the main problem: time. You cannot ethically submit the same article to multiple journals. You will need to make sure you have three completely separate articles in order to submit to multiple journals. There are many people who believe you should publish in the most prestigious journal you can make it into. I do not disagree with this philosophy; however, the alternative is that your work might not get read in a higher scholarly journal because PAs and PA educators don’t tend to read these other journals. This is for you to decide. There are websites that can help you determine where to publish. But be careful and check out Beall’s List, a list of potentially predatory “scholarly” open-access publishers.

8. Best of luck — and don’t fail to publish. Don’t bake your cake and not put it in the oven. Remember to reach out to your peers within the profession to aid in your success. Ultimately, your committee will be pleased to add a publication to their CVs. They will remember this as their reward for all the hard work that they (and you) put into your PhD.

A publication is the lasting legacy for all of your tough efforts and sacrifice. It is the “so what” of all you have put into this academic Mt. Everest. Consider the impact that your work will have on the profession. Don’t run the marathon (or climb the mountain) and not cross the finish line!

Jennifer Coombs, PhD, MPAS, PA-C

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Revising Your Dissertation for Publication

While a dissertation’s in-depth research and analysis can provide a strong foundation for a book, the dissertation itself is not a book and will not be published by an academic press without substantial revisions. Some acquisitions editors are interested first books, especially if they bring new perspectives and fresh ideas to a field, while others do not often publish first books. If you are considering submitting your dissertation for publication, we recommend that you contact editors at university presses that publish in your subject area for guidance on revising your work. Many editors prefer to be involved in the early stages of this process so they can advise you on how to structure the book and your arguments to create a publishable book. Editors generally require changes in the length, content, tone, and style of a dissertation in order to produce a book that will appeal to buyers in the academic market. Read more about submitting a proposal in our Scholarly Publishing Guide .

Below are selected resources to help you revise your dissertation for publication as a book or journal article(s).

Advice from publishers

• Harvard University Press
• Palgrave Macmillan
• Rowman & Littlefield
• Taylor & Francis
• University of North Carolina Press
• Yale University Press
• Publisher Policies on using content in both a thesis or dissertation and an article (from MIT Libraries)
• From Dissertation to Book by William Germano (Lauinger Library, 4th Floor, PN162 .G37 2013)
• Revising Your Dissertation, Updated Edition : Advice from Leading Editors (updated edition, 2008) edited by Beth Luey (online; GU NetID and password required)
• From Dissertation to Book , Duke University (February 27, 2018)
• From Dissertation to Book ( full transcript ), Harvard University (December 17, 2010)
• How To Turn Your Dissertation into a Book , Yale University (April 6, 2016)
• From Dissertation to Book by Leonard Cassuto (Chronicle of Higher Education, May 30, 2011)
• From Dissertation to Published Book (lanugageandphilosophy.com report on an American Comparative Literature Association workshop)
• Give It a Rest by Laura Portwood-Stacer (Inside Higher Ed, August 6, 2019)
• The Stages of Revising a Dissertation into a Book by Amy Benson Brown (Journal of Scholarly Publishing, vol. 52 no. 2, 2021, p. 127-140) (GU NetID and password required)
• Turning Your Dissertation into a Book (University of Washington)
• Publishing your Dissertation (American Psychological Association)

How to Turn Your Thesis Into a Journal Article

In many cases, publishing thesis is often one of the requirements for graduate students to finish their academic program. Publishing research findings is one of the more important ways to share research data with the scientific community. However, the structure of it is different from that of a research article. In this article, we shall discuss how to turn your thesis to journal article.

Characteristics of a Thesis and a Journal Article

Differences between a thesis and a journal article.

While both contain similar sections, you cannot simply publish your thesis research as a journal article. Converting it into a journal article has many steps. It is important to recognize that an article is much shorter than the thesis. However, turning your thesis into a journal article will not be a simple matter of copy and paste. You will need to use the data in your thesis as the starting point for writing your article.

Related: Planning to publish your Ph.D. research in a good journal? Check these journal selection guidelines now!

The  many differences  between a thesis research and a journal article are as follows:

• A thesis meets academic requirements while a journal article meets journalistic standards.
• The abstract of an article is usually shorter than that of a thesis.
• The introduction in a thesis is used to show that you are familiar with the literature in your chosen field. In a research paper , the introduction is much shorter as it is assumed that your target audience is familiar with the necessary background to understand your work. The introduction to your paper will, therefore, focus more on setting the stage for the data/research output that you are about to present.
• The results section in a thesis will include all your findings. In a paper, this would be too much detail. The data in this section should be only what you need to support your research problem or hypothesis. Often, the results in former may represent two to three different papers.
• The discussion in your paper will be much more focused than in your thesis. It will be guided by the results presented in the paper. Finally, only citations of articles actually mentioned in your paper will be listed in the references section.

Turning Thesis Research to Journal Article

As a researcher, you need to publish your work to advance your career and make contributions to the research field. Now that the differences have been outlined, how do you actually write one?

1. Identify a Suitable Journal

You could start by  journal selection . Look at your reference list. Chances are at least some of the papers you read were published in journals whose scope would match your work. Selecting a journal also allows you to tailor the paper to the specific requirements of that journal. Ensure that your research article coincides with the aim and scope of the journal. Understand the journal’s guidelines, recommended manuscript structure, and reference style

2. Reduce Redundant Length of Your Thesis

An important aspect of turning your thesis research to journal article is focusing on the word count without deleting crucial information. In order reduce word count , extract the data that answers just one research question. This should result in a more focused information than your thesis research presented. Discuss results in context with your problem statement-that is the focus of your paper. Good language and structure are crucial – your paper may get rejected even though it contains valuable data if it is difficult to understand. Use your data to tell a coherent story and avoid sweeping conclusions your data cannot support. Ensure that your title matches the contents of your paper. Paraphrase the content without changing the meaning.

3. Modify Introduction as Abstract

Repurpose the introduction as an abstract by shortening your thesis introduction to 100-150 words. Remember to maintain key points of the introduction to hold the reader’s attention. Formulate the introduction and discussion of thesis as basis for the journal article’s abstract. Furthermore, consider combining multiple research questions or focus on just one for the journal article.

4. Focus on Relevant and Selective Information

Since the discussion, methods and methodology, and results section of your thesis is an in-detail explanation of your research, these sections must be kept short while writing in a journal article.  Familiarize yourself with the target journal’s standards by referring previously published papers and understanding their format. Most importantly, provide interpretation of main findings in the results section in concise statements or tabular formats. Avoid repeating your results in the discussion section. However, discuss how your findings expand and support previous perspectives of the research. Finally, mention how future studies can build upon your work and address limitations in your study.

5. Limit the Number of References

As your thesis is a work of several years put together, it involves numerous literature reviewing. However, while turning your thesis to journal article, you must include only limited references that are relevant to the research question addressed in the journal article. Focus on using most recent references. Consider using reference management tools such as Zotero, Mendeley, Quiqqa, etc. to make the referencing process easier and efficient.

It is an academic requirement that you publish your data for the benefit of the scientific community. Considering that the structure of journal article is different from the structure of a thesis, turning a thesis to journal article must be done following logical steps as mentioned above.

Did you ever have to convert your thesis to journal article? How did you plan it? What strategies did you use while reducing the word count of your thesis? Let us know in the comments section below! You can also visit our  Q&A forum  for frequently asked questions related to different aspects of research writing and publishing answered by our team that comprises subject-matter experts, eminent researchers, and publication experts.

I was Searching For This From So Many days. Thank you for Sharing

Thanks! Glad you liked it.

Are we also going to talk Code of Conduct in Research, as authorship is part of the conduct (ethics)?

Regards, Elvia

I guess you are referring to our upcoming webinar on Assigning Authorship & Contributorship | Tips for Researchers. We will be discussing the ethical dilemmas in authorship during the session.

We would request you to register and attend the webinar for additional insights on this topic.

Meanwhile, we would recommend you to share your queries with us on our FREE Q&A forum . In addition, you may download our FREE mobile app to stay updated on the latest resources in research writing and academic publishing.

What about the Topic? we need to rephrase the topic or keep it same with Thesis topic?

Hi Shahid, Thank you for your question. Your thesis topic would be a cumulative title for all the chapters described in your thesis. When you publish your thesis as a journal article, every chapter would be published as an individual article in most cases. You may or may not use the same title that you have used for that particular chapter in the thesis. Your thesis would have chapter names that are more suited to the overall objective of your thesis. On the other hand, your manuscript should ideally have a catchy and search-optimized title highlighting a general perspective. It may not be the same as your thesis title. However, if your thesis chapter title meets the requirement of the manuscript you intend to publish, you can go ahead with the same.

You could also go through our articles on writing good research paper titles: https://www.enago.com/academy/top-10-tips-on-choosing-an-attractive-research-title/ https://www.enago.com/academy/writing-a-good-research-title-things-to-avoid/ https://www.enago.com/academy/write-irresistible-research-paper-title/

Did you get a chance to install our FREE mobile app . Make sure you subscribe to our weekly newsletter: https://www.enago.com/academy/subscribe-now/ .

Hi Dr. Durga, Amazing article and I am sure it will surely help the writers to write more carefully and also plagiarised free.

Greeting from Enago Academy! Thank you for your positive comment. We are glad to know that you found our resources useful. Your feedback is very valuable to us. Happy reading!

i just read the article and also the comments section it’s so helpful. thank you so much for sharing it.. good work!

Thanks a lot for this informational blog which surely going to help the students pursuing the Phd. Nowdays due to assignment burden students started taking the help of professional academic experts. There are many writing services.

Thank you for the very useful article. I will definitely look into it.

Writing a book: needed advice and help at one point. I found enago academy in my search of Answers. You were a Great Help! I hope to use your services again, if I am stuck on correct writing principles! Thank You for being here. K.R. Plante

This helped me a lot; thank you for this informative article.

Thanks for writing such an informative blog which will surely be a great help for the students as well as the institutions

Great article! One question…. should I cite the thesis in the paper? If so, how do I do that efficiently since it would be all over the place?

good, insightful piece of text.

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Publishing a Master’s Thesis: A Guide for Novice Authors

Robert g. resta.

1 Swedish Cancer Institute, Swedish Medical Center, Seattle, WA USA

Patricia McCarthy Veach

2 Department of Educational Psychology, University of Minnesota, Minneapolis, MN USA

Sarah Charles

3 Jefferson Kimmel Cancer Center, Thomas Jefferson University Hospital, Philadelphia, PA USA

Kristen Vogel

4 Center for Medical Genetics, NorthShore University HealthSystem, Evanston, IL USA

Terri Blase

5 Department of Maternal Fetal Medicine, Advocate Christ Medical Center, Oak Lawn, IL USA

Christina G. S. Palmer

6 Department of Psychiatry & Biobehavioral Sciences, UCLA, Los Angeles, CA USA

7 Department of Human Genetics, UCLA, Los Angeles, CA USA

8 UCLA Semel Institute, 760 Westwood Plaza, Room 47-422, Los Angeles, CA 90095 USA

Publication of original research, clinical experiences, and critical reviews of literature are vital to the growth of the genetic counseling field, delivery of genetic counseling services, and professional development of genetic counselors. Busy clinical schedules, lack of time and funding, and training that emphasizes clinical skills over research skills may make it difficult for new genetic counselors to turn their thesis projects into publications. This paper summarizes and elaborates upon a presentation aimed at de-mystifying the publishing process given at the 2008 National Society of Genetic Counselors Annual Education Conference. Specific topics include familiarizing prospective authors, particularly genetic counseling students, with the basics of the publication process and related ethical considerations. Former students’ experiences with publishing master’s theses also are described in hopes of encouraging new genetic counselors to submit for publication papers based on their thesis projects.

Introduction

Scholarship is important for growth of a profession and for clinical care. For these reasons, the American Board of Genetic Counseling (ABGC) endorses scholarly activities through Practice Based Competency IV.5 (American Board of Genetic Counseling 2009 ). Boyer ( 1990 ) describes four types of scholarship (Scholarship of Discovery, Scholarship of Integration, Scholarship of Application, and Scholarship of Teaching), all of which are endorsed by ABGC and required of accredited genetic counseling training programs. The first three types of scholarship, which involve generating new knowledge or applying existing knowledge to an important problem, are the basis of the ABGC’s requirement that students in accredited programs engage in scholarship and complete a scholarly product. The ABGC defines a scholarly product to include: a master’s thesis, an independent research project, a literature review/case report, a formal needs assessment, design and implementation of an innovative patient, professional, or community educational program, and/or preparation of a grant proposal.

The purpose of this article is to encourage students to disseminate their scholarly work (except grant proposals) through a journal publication. This article was developed from an Educational Breakout Session (EBS) at the 2008 National Society of Genetic Counselors (NSGC) Annual Education Conference and draws upon the experiences of a past editor and current assistant editor of the Journal of Genetic Counseling ( JOGC ), a student mentor, and recent genetic counseling graduates who successfully turned their student thesis projects into peer-reviewed publications.

Engaging in scholarship is important for increasing genetic counselors’ self-knowledge, but dissemination of scholarship is essential for the growth of the genetic counseling field. McGaghie and Webster ( 2009 ) identify a wide range of types of scholarly products that promote broad dissemination of information, including peer-reviewed journal articles (e.g., original research, case reports, review articles), book chapters, books or monographs, edited books, essays, editorials, book reviews, letters, conference reports, educational materials, reports of teaching practices, curriculum description, videos, simulations, simulators, and web-based tutorials. As evidence of the importance of disseminating scholarship to the field of genetic counseling, dissemination of scholarly products is actively promoted by the NSGC, the major professional organization for the genetic counseling profession. A prominent example of NSGC’s commitment to dissemination is the JOGC , a professional journal devoted to disseminating peer-reviewed information relevant to the practice of genetic counseling. The success of this journal over nearly two decades is a strong indicator of the value genetic counselors place on publishing journal articles as an essential product of scholarship.

Individuals who have completed a master’s thesis or equivalent should consider publication. This “call to publish” student work is based on evidence that a large proportion of students engage in a scholarly activity with publication potential. A recent survey of 531 genetic counselors suggests that 75% of respondents fulfilled their scholarly activity requirement via a master’s thesis (Clark et al. 2006 ). Among this group, 21% classified their thesis as “hypothesis driven” and 20% classified it as a “descriptive study.” Although the research may be relatively small scale given the time and resource constraints of short training programs (≤2 years), it nonetheless offers a rich and varied source of information about the practice of genetic counseling that could be shared with the broader community through publication. Yet Clark et al. ( 2006 ) found that only 21.6% of respondents who completed a master’s thesis had submitted a manuscript for publication in a peer-reviewed journal. It appears that many students do not submit their research for professional publication, perhaps due to a combination of time constraints, lack of mentoring and support, unfamiliarity with the publication process, lack of professional confidence, and fear of rejection (Clark et al. 2006 ; Cohen et al. 2008 ; Driscoll and Driscoll 2002 ; Keen 2006 ). Because this is one aspect of scholarship that has received limited attention, guidance regarding the details and vicissitudes of the publication process, and acknowledgement that master’s theses can be successfully published, are needed.

Of course, one might question why students should or would publish the results of their graduate work. The answer is complex, without a “one size fits all,” because scholarship can be intrinsically and/or extrinsically motivated. McGaghie and Webster ( 2009 ) describe intrinsic motives as including sharing knowledge, career advancement, status improvement, collegial approval, personal pleasure, and response to challenge; extrinsic motives include academic pressure, commitment to patient care, practice improvement, and promoting the use of new technologies. Although the reasons genetic counselors publish articles have not been empirically evaluated, Clark et al. ( 2006 ) (i) concluded that a substantial number of genetic counselors consider active involvement in research (a form of scholarship and precursor to publication) to be a core role, and (ii) found that respondents endorsed a range of intrinsic and extrinsic motives for their involvement in research. These reasons included interest in the subject, contributing to the field, personal development/satisfaction, diversifying job responsibilities, job requirements, lack of existing research on a particular topic, and career advancement. It is reasonable to infer that these reasons would extend to publication as well.

The work that culminates in a master’s thesis provides the basis for a professional journal article. However, writing a professional journal article differs from writing a master’s thesis. This article, therefore, provides practical ideas and considerations about the process for developing a master’s thesis into a peer-reviewed journal article and describes successful case examples. Research and publication occur in stages and include many important topics. Previous genetic counseling professional development articles have partially or comprehensively addressed the topics of developing and conducting a research project (Beeson 1997 ), writing a manuscript (Bowen 2003 ), and the peer-review process (Weil 2004 ). This paper expands on previous articles by describing the publication process and discussing publication ethics, with emphasis on aspects pertinent to publishing a master’s thesis. It is hoped that this article will encourage genetic counselors to publish their research.

The primary audience for this article is genetic counselors who are conducting a master’s thesis or equivalent or who completed a thesis in the last few years which remains unpublished. The secondary audience is other novice authors and affiliated faculty of genetic counseling training programs. Although the focus of this paper is on journal publications which are subject to a peer-review process (e.g., original research, clinical reports, and reviews), some of the basic information applies to a variety of publishing forms.

The Publication Process

Publish before it perishes.

Like produce and dairy products, data have a limited shelf life. Research results may be rendered marginal by new research, social changes, and shifts in research trends. For example, a study of patient reluctance to undergo genetic testing due to concerns about health insurance discrimination conducted in December 2007 would have been obsolete when the Genetic Information Nondiscrimination Act (Pub.L. 110–233, 122 Stat. 881, enacted May 21, 2008) was enacted 5 months later. Or studies of whether patients think they might undergo testing if a gene for a particular condition were identified become less relevant once the gene is actually mapped and sequenced.

The hardest part about writing is actually writing. Making the time to sit down and compose a report of research findings is a very difficult first step. As noted in the three case examples, this is particularly true for a recent graduate whose time is occupied with searching for a new job, moving to a new city, and learning the details of a new job. However, the longer you wait, the more difficult it becomes, and the greater the risk that your data will grow stale. If you do not write it, the paper will likely not get written. The three case examples identify strong mentorship, ongoing communication with co-authors, constructive criticism, and commitment to publication by every author as key elements for successfully preparing a manuscript. The following sections describe basic processes for preparing a paper. See also Table  1 for helpful references about technical aspects of manuscript preparation.

Table 1

Selected Resources For Manuscript Preparation

Choosing a Journal

Research delivered to an inappropriate audience is ignored. Many journals publish genetic counseling research—as demonstrated by the three case examples—and therefore, choosing the right journal is critical (Thompson 2007 ). The first step is to decide who the audience should be. Is it important to reach genetic counselors? Medical geneticists? Or is the audience outside of the genetic counseling community? Some genetic counseling research is of interest to researchers in patient education, decision-making, or the social sciences. Clinicians such as surgeons, radiology technicians, psychologists, and family practice physicians might benefit from a greater understanding of genetic counseling and how it interfaces with their specialties.

The next step is to decide whether the journal is interested in the type of research conducted. For example, does the journal publish articles mostly on medical and clinical issues? Does it publish qualitative research? A description of the scope, aims, and types of research that are published is located in the “Instructions to Contributors” section on the web page of most journals. A look at the journal’s editorial board might also provide a good idea of a journal’s theoretical approaches, philosophical orientation, and research interests. Another strategy is to contact the journal’s editor or a member of the editorial board prior to submitting a manuscript to discuss the appropriateness of the manuscript for the journal. Many editors welcome such pre-submission contact since it reduces their workload of reading inappropriate manuscripts.

A journal’s “impact factor” may be important to some authors when considering where to publish a manuscript. The impact factor is a—perhaps imperfect—statistical measure of a journal’s importance. The impact factor was developed in the early 1960s by Eugene Garfield and Irving Sher and is technically defined as A/B, where A = the number of times articles published in that journal were cited and B = the number of citable articles published by the journal (letters and editorials are not usually citable articles) (Garfield 1994 ). An impact factor of one indicates that on average, articles published in the journal were cited once by other authors.

A journal’s impact factor can vary greatly from year to year, and its practical utility is widely debated (Andersen et al. 2006 ; Chew et al. 2006 ; Greenwood 2007 ; Ha et al. 2006 ; The PLoS Medicine Editors 2006 ). Genetic counselors often publish small studies and case reports. The journals that might publish such papers usually have impact factors of ten or less. Thus the impact factor may be a less important consideration for many genetic counselors when deciding where to publish.

A publisher’s copyright policy may also influence the choice of where to publish. The majority of publishers own the copyright (United States Copyright Office 2008 ) and authors do not have the right to copy, re-use, or distribute their own publications without buying reprints, which can be a significant source of income for publishers. Some journals, like the Public Library of Science (PLoS), are completely Open Access and make all articles fully available online. Other journals have Delayed Open Access, which makes articles publicly available after a specified period of time, often a year or two. Many journals, such as the JOGC , promote Hybrid Open Access in which authors, for a fee, can make their articles publicly available. Some journals will make select articles publicly available, usually those that attract media attention. For grant-funded research, consider the requirements of the funding source; some granting agencies require that the research results be made publicly available at some point.

Peer Review

Peer review is the process in which two or three experts evaluate a manuscript to determine whether it is worthy of publication. Peer review is the backbone of scholarly publishing; no research manuscript gets published until a team of reviewers and journal editors vets it. Ideally, reviewers are objective, constructively critical, open-minded, fair, and insightful. Some journals blind the reviewer to the author’s identity, in hopes that the authors’ reputations or professional relationships will not influence the review. Some journals will let authors suggest reviewers or request that certain people not review a manuscript. A journal’s peer review policies may be another important consideration in choosing where to submit a manuscript.

In practice, peer review is not always ideal (Benose et al. 2007 ; Curfman et al. 2008 ; Hames 2007 ; Wager et al. 2006 ). Nonetheless, no better or viable alternative has been proposed. Reviews may sometimes appear to be arbitrary, unfair, and poorly performed. Reading such reviews can be very difficult and frustrating, even for experienced authors. However, it is a reviewer’s job to be critical, and there may be elements of truth in even the most negative reviews. Some editors may be willing to send a manuscript to another reviewer if an original reviewer produces a harshly critical or poorly thought out critique. Some journals have a formal appeals process if a manuscript is rejected or an author feels a review is inaccurate, inappropriate, or biased. However, sometimes it is simply easier to submit the manuscript to a different journal. Case # 2 describes a successful example where submitting a manuscript to a different journal led to publication.

The manuscript rejection rate varies widely across journals, but about half of all manuscripts are rejected or require significant revisions (Armstrong et al. 2008 ; Hall and Wilcox 2007 ; Liesegang et al. 2007 ). About half of rejected manuscripts are published in other journals (Armstrong et al. 2008 ; Hall and Wilcox 2007 ; Liesegang et al. 2007 ). Even among articles that are accepted for publication, the vast majority will require significant revisions. All three case examples describe manuscripts that underwent significant revision. Thus, prospective authors should not be disheartened if a manuscript is rejected or needs extensive re-writing; this is the rule rather than the exception . Many editors are willing to work with authors who have questions about specific comments or how best to incorporate the reviewers’ suggestions. Busy journal editors would rather answer questions up front than have to laboriously edit a revised manuscript and send it back for further revisions.

Peer review, and the subsequent manuscript revisions, along with the number of manuscripts submitted to the journal, are probably the most critical bottlenecks in determining how long it takes before a manuscript appears in print. Typically, a year or more may pass from the time of submission to the publication date. The three case examples include their timeframes to highlight the need for perseverance and patience with the publication process.

The clearest way for authors to respond to editors’ and reviewers’ comments is to prepare a table that lists each comment and how the authors addressed them, item by item. Some reviewers’ comments may be inaccurate or simply unrealistic (e.g. “The authors should re-do the entire research study...”); these can be discussed in the table or in the cover letter that accompanies the table. Additional information about the peer-review process can be found in Weil ( 2004 ).

Acceptance!

Once a manuscript is accepted for publication, the publisher or the journal editor will send a copyright transfer statement that spells out ownership of the article. This statement must be signed and returned in short order before the manuscript will be published. The corresponding author will receive page proofs, usually electronically, which must be read by the author for accuracy and returned fairly quickly (usually 2–3 days). Many publishers are reluctant to make significant changes in the page proofs, and they may charge for substantial revisions. Thus, the version of the manuscript that is submitted to the journal before the page proofs are generated should be very close to what the author wishes to see in print. Usually at this time publishers will offer the author the option to purchase reprints to allow the author to share the publication with other researchers, co-authors, and colleagues. Some journals will provide a limited number of free reprints or a complimentary copy of the issue of the journal in which the paper appears. The steps in the publication process are summarized in Table  2 .

Table 2

Steps in the Publication Process

a ∼50% of manuscripts are rejected or require significant revision before being accepted for publication

Ethics of Publishing

“Scholarship (like life) is not always fair or precise.” (Thompson 1994 )

Manuscript preparation and submission for publication can be complicated by ethical issues. Many authors may not be aware of these ethical conundrums, let alone have a plan for addressing them. Ethics is not a stagnant concept. As research methodologies and research questions evolve, new ethical issues in publishing arise. This section contains a description of several issues broadly relevant to the publishing practice of genetic counselors, particularly as students or recent graduates. However, it is important for genetic counselors-as-authors to keep abreast of ethical issues relevant to their own work.

“Ethics” are principles that govern the behavior of individuals or groups (Merriam-Webster 1974 ). Ethical codes of conduct exist in order to preserve the integrity of a profession, ensure the public’s welfare, and protect scholars. Ethical issues particularly relevant to writing for publication, include: (1) authorship determination, (2) disclosure and conflicts of interest, (3) plagiarism, (4) subject confidentiality, (5) accuracy of information, and (6) publishing in multiple sources.

Authorship Determination

Consider the following situation: A student conducted an excellent study for her master’s thesis project. At the beginning of the project, her supervisor promised her that she would have first authorship on any manuscripts based on the project. However, when the time came to write the paper, the student procrastinated. Finally, after the supervisor repeatedly “nagged” her, she submitted a draft to her, but it was very poorly written. The supervisor decided the only way to salvage the paper was to totally rewrite it herself. Now the supervisor thinks that she deserves to be the first author. Is this ethical? Does it matter if the project was the student’s master’s thesis rather than a project in which she was voluntarily involved? Are there guidelines that might be implemented in advance to handle this kind of situation?

This complex situation may be all too familiar for many supervisors and students. It raises issues about valuing contributions to the publication process, the power differential between supervisors and students, determining when renegotiation of authorship is warranted, and setting expectations and priorities up front. Whenever manuscripts are authored by more than one individual, order of authorship should be negotiated as early in the process as possible. Only individuals who have actually contributed to the work should be listed as authors. Their order should indicate “...the relative scientific or professional contributions of the individuals involved, regardless of their status” (Shadish 1994 ) (p. 1096). In the sciences, the first and last authors typically are the individuals that made the greatest contributions to the project (Laflin et al. 2005 ). Many journals require a listing of each author’s contribution to the manuscript in order to make sure each person meets the journal’s requirements to be listed as an author.

Student authors pose a special situation. Doctoral students usually are the first authors of papers based on their dissertation research (Nguyen and Nguyen 2006 ). Authorship order is less clear for masters’ projects because masters’ students may lack sufficient knowledge and skills to conduct a project and prepare a manuscript of publishable quality without considerable input from their supervisor (Shadish 1994 ). Thompson ( 1994 ) recommends that when there is any question as to who made the primary contribution, the student should receive higher authorship. His recommendation helps to protect the person who has less power in the situation. Often students are involved in studies that are not based on their own master’s or doctoral research, but rather are connected to an existing research program, such as case examples 1 and 2. In those situations, some authors contend that their involvement should be creative and intellectual in order to warrant authorship; otherwise, student input can be credited in an acknowledgement section (Fine and Kurdek 1993 ; Holaday and Yost 1995 ; Thompson 1994 ).

Negotiating authorship is an important step that should begin in the initial stages of a project. This step usually involves assessing and agreeing upon each person’s tasks, contributions, and efforts. The amount of supervision required for an individual’s contributions is usually considered as well (Fine and Kurdek 1993 ). Sometimes renegotiation of authorship order is necessary due to unexpected changes and/or substantial revision of the manuscript. The key is to remember that authorship is negotiated. Questions to consider throughout this negotiation process include: Who had the original idea for the basis of the publication? Who designed and conducted the study that generated the data? Who will write most of the first draft of the paper? Is the study part of someone’s research lab? Students should maintain early and on-going communication with their co-authors about their investment of time and efforts and the outcomes of those efforts (Sandler and Russell 2005 ). However, scholarly contribution is more important than actual time and effort expended when determining authorship. For more information regarding authorship determination, it may be useful to review guidelines for discussing and clarifying authorship order (Gibelman and Gelman 1999 ) or developing individualized contracts for research collaboration (Stith et al. 1992 ). These guidelines also may be useful for initiating discussion of authorship as part of the curriculum in genetic counseling training programs.

Take another look at the authorship scenario. At the time of the original negotiation of authorship, it is likely that the supervisor (and other parties) believed the student warranted first authorship due to her creative contributions and time allotted to the study. In most authors’ minds, first authorship is equated with substantial contribution to writing the manuscript, usually the first draft, so it is important the student understand this is part of the responsibilities of being first author. Typically students have no experience writing a journal article, and so some procrastination is likely. In this scenario, the authorship dilemma may have been averted by having in place a plan to mentor the student, providing support, and delineating a specific process for writing the first draft of the manuscript.

Manuscripts invariably undergo substantial revision as co-authors and reviewers weigh in, so it is not unusual that the supervisor would revise the student’s first draft. This activity does not prima facie warrant a change in authorship order. However, by developing a specific plan to support the student’s writing, it may minimize the extent of the supervisor’s revisions. It is possible, though, that the student’s procrastination and poor writing should initiate a renegotiation of authorship order because the level and nature of her contributions to the work may be changing. The supervisor and student should discuss the reasons for changing authorship order; the supervisor should not unilaterally make this change without discussion. Keep in mind that the bar for changing authorship should be much higher if the paper is based on the student’s master’s thesis than if it is based on a project in which she was voluntarily involved. It is also important to inform students early in the process that most research is a collaborative effort, requiring time, energy, and sometimes funding, and therefore their collaborators have expectations that their contributions will be rewarded through publication. Developing an a priori policy for renegotiation may often reduce misunderstandings and minimize conflict.

Disclosure and Conflicts of Interest

Consider the following situation: A student conducted a study to evaluate a new program that her clinic is offering to its patients. She interviewed ten patients who participated in the program about their experience. Nine of these patients were in general agreement about the value of the program, while the 10th patient was quite negative about her experience. The student’s impression of this patient is that she is a generally negative person. The student believes that the patient came into the program expecting not to like it. Furthermore, the student is concerned her clinic will lose funding for this program if she reports this patient’s responses. The student decides to exclude her data from the paper. Is this decision ethical? Why or why not?

One ethical issue raised in this scenario involves determining when it is appropriate to exclude data points. Data collected from research can be messy, and it is not unusual for some data points to be excluded from analyses. However, there must be an explicit methodology for excluding data points or subjects, and this information usually is reported in the manuscript. Examples for exclusions include: missing data (e.g., a participant did not complete a majority of the items on a questionnaire); measurement error (e.g., the recorded measurement of a biological process or part of the anatomy is simply impossible); small sample sizes (e.g., an insufficient number of individuals from a minority group participated in the research resulting in numbers too small for meaningful analysis). In the scenario described above, the rationale provided for excluding the 10th patient’s experience is not sufficient to warrant exclusion. Instead, it appears that exclusion of this individual is based on a desire to promote the new program in the student’s clinic. In order to eliminate this form of conflict of interest, one could consider involving a clinic outsider in the analysis and interpretation of the data. By including a clinic outsider in the project, editor and reviewer concerns about the integrity of the data, analyses, and conclusions will be allayed.

Most journals provide another “safeguard,” by requiring a statement about possible conflicts of interest. A conflict of interest statement requires the author to acknowledge in writing the nature of any circumstances that might bias the process and/or outcome of their work. For example, any project and published report that might result in direct financial gains for an author(s) should be disclosed to a journal’s editor and to the readership. Examples of possible conflicts of interest include conducting a study of the effectiveness of a genetic test funded by the company that developed and is marketing the test, or a program evaluation study whose outcome would determine the continuation of the investigators/authors’ jobs.

Plagiarism is a familiar concept to most people. Everyone generally understands the importance of “giving credit where credit is due.” Yet, the National Science Foundation estimates that the prevalence of plagiarism may be as high as 50% (Roig 2001 ). Probably many of these incidents are unintentional and/or occur because the authors were unaware of some of the nuances regarding plagiarism. Although there is some variability within and across disciplines about the specific behaviors that constitute plagiarism, there is general agreement about two broad types (Roig 2001 ): cryptamnesia -an individual thinks their idea is original when it actually was presented by someone else previously; and inappropriate paraphrasing —an individual uses another person’s published text without properly citing that use, and/or using their statements with little or no modification. Specific examples of inappropriate paraphrasing include: (1) publishing another person’s work as one’s own; (2) copying part of another author’s paper and claiming it as one’s own; (3) copying text from another source without using quotations marks and without citing that source in the text; (4) paraphrasing text from another source without providing an in-text citation; (5) summarizing material from another source without clearly connecting the summary to that source; and (6) using copyrighted materials without author/publisher permission (East 2006 ; Lester and Lester Jr. 1992 ).

Additional types of plagiarism include ambiguous use of citations. For instance, an individual includes a citation in a paragraph but does not clearly indicate which content in the paragraph is from the cited work. Another type of plagiarism is self-plagiarism . Self-plagiarism occurs when an individual includes published work of their own for which they do not own the copyright (e.g., reprinting a table from one of their previously published papers); repeating verbatim text from a previously published article. Permission to reprint material from the publisher must be obtained.

Plagiarism is a serious ethical breach which can result in a legal penalty. Strategies for avoiding plagiarism include limiting the use of direct quotes; avoiding the use of secondary sources—it is always better to read and cite an original source when available; and restating ideas in one’s own words while providing in-text citation of the work that contains the original ideas (East 2006 ; Lambie et al. 2008 ; Lester and Lester Jr. 1992 ). When in doubt regarding the originality of one’s words, it is best to cite the source(s) on which they are based. In this regard, it may help to bear in mind that readers will assume all words in the paper are the author’s unless the source(s) are cited.

Subject Confidentiality

Published papers must be written in a way that no subjects can be recognized by others without their written consent (Gavey and Braun 1997 ). Given the unique nature of genetics, family members may also need to provide written consent (McCarthy Veach et al. 2001 ). When possible, identifying information should be removed or disguised (e.g., use of pseudonyms) and data based on multiple subjects should be reported in aggregate (group) form. Institutional review boards (IRBs) play a critical role in assuring protection of subject confidentiality. Many journals require authors to indicate either in the paper or a cover letter that they have obtained institutional review board approval to conduct their animal or human subjects study. In some cases, an ethics board may have been consulted regarding ethical dilemmas reported in a clinical paper and this should be acknowledged in the paper.

Accuracy of Information

Authors are responsible for rigorously checking the accuracy of their facts, data, and conclusions. However, despite one’s best efforts, substantial errors sometimes are not discovered until after a paper is published. In that case, the corresponding author should contact the journal immediately and ask that an erratum be published. On a related note, authors have a professional responsibility to make data sets reported in published papers available to other professionals. This practice allows for verification of the findings and conclusions, and it also makes possible research replications and extensions of the original study. The length of time for retaining research records depends on institutional policy and sponsor policy, so it is important to be aware of how these policies apply to the research generated by a master’s thesis. Often institutional review boards require researchers to state how long they will maintain a data set, and the researchers must adhere to that time frame.

Another accuracy issue concerns modifying and reporting the use of published material (e.g., an interview protocol, psychological instrument, curriculum) without clearly describing the precise nature of the modifications. Interpretation of findings and their comparison to other studies using the “same” instrumentation may be severely compromised when an author fails to report modifications. Further, professional courtesy suggests that permission be sought from the author before changing her or his material. Also, use of published material requires crediting the author(s) of that material by including relevant citations.

Publishing in Multiple Sources

In the sciences, a manuscript should not be under review by more than one journal at a time. It is, however, acceptable to submit material for presentation at a conference prior to its actual publication in a journal, as the authors in case examples 1 and 3 did. Some conferences publish proceedings , and some journals will not publish work that is already published in a Proceedings unless the two papers differ substantially. When in doubt, it is good practice to contact a journal’s editor to determine the journal’s policy. Journals typically only publish original work, but on occasion there may be interest in reprinting an article. Reprinting a previously published paper requires written permission from the owner of the publication copyright. As a matter of courtesy, one should also seek the corresponding author’s permission, even if the author does not own the copyright.

Examples of Success

The benefits of sharing knowledge within the medical community and with the public via publication have been delineated. The publication of original work contributes to the advancement of the genetic counseling field overall, and at the individual level, authorship establishes a level of professional credibility, enhancing opportunities for future employability, funding and job satisfaction. The opportunity to develop a genetic counseling master’s thesis into a manuscript should therefore not be overlooked. Below are the personal accounts of three recent graduates who successfully transformed their individual master’s theses into published manuscripts. These examples were not systematically ascertained, and as such, do not necessarily represent all experiences with trying to publish a master’s thesis. These stories provide “first-hand accounts” of the authors’ experiences and, while acknowledging the challenges, demonstrate commitment to publishing their own projects throughout their careers. Table  3 contains a list of helpful hints gleaned from these cases.

Table 3

Helpful Hints for First Time Authors

Case 1: Consider Writing Your Thesis and Journal Article Concurrently

As a result of personal determination, and above all, strong mentorship, I was able to turn my master’s thesis work into a manuscript published in Patient Education and Counseling , titled “Satisfaction with genetic counseling for BRCA1 and BRCA2 mutations among African American women” (Charles et al. 2006 ). My work was a small component of an existing research project being conducted within a university academically affiliated with my genetic counseling training program. The project was an evaluation of the overall effects of “Culturally Tailored vs. Standard Genetic Counseling Protocol” among African American women.

I started by reviewing previous publications this group of researchers had produced and using these as a guide for my first draft, followed by multiple revisions. Approximately 17 months elapsed between first submission and publication. We submitted the manuscript in its original form in May 2005. We received the reviewers’ comments later that summer, and submitted revisions five months later. The article was accepted in that same month, published online five months later and in print seven months after the online version appeared. Shortly after graduating from my program I submitted an abstract of the work to NSGC for presentation at the 2005 Annual Education Conference, and subsequently learned that it was selected for the NSGC Beth Fine Student Abstract award.

My experience may be unusual because I worked on the manuscript and thesis project concurrently. Composing separate but related documents while still juggling second year genetic counseling student responsibilities was certainly a challenge. Preparing a comprehensive thesis project is a very different task than manuscript composition, the latter of which is more focused and narrow in scope. Challenges posed by this concurrent approach included ensuring that text requirements and deadlines specific to each document were met, as well as incorporating and addressing the reviews of both the training program and peer-reviewers. The main benefits of this approach were that I was still in school and therefore geographically close to my mentors, which facilitated ongoing communication throughout the process, and that the manuscript was under review by a journal before I started my new job.

Factors contributing to the successful publication of this project include mentorship, accountability, and commitment to publication by every author. Supportive, constructively critical, and well published, my mentors had high standards and knew the process. Frankly, I did not want to disappoint them. I found setting deadlines and meeting them, along with the accountability of in-person meetings (as opposed to email), to be effective approaches. Finally, publishing the project was a stated goal of the authors at the initiation of the project. I will not claim that the process was easy, but the goal is certainly attainable and worthwhile.

Case 2: You Need Not Publish Every Thesis Finding—Pick The Most Interesting and Relevant

As is the case for many graduate students, the first time I attempted to publish was after I completed my thesis. My thesis concerned the development of a minority research recruitment database and was the result of my graduate research on underserved populations.

Following graduation, I started my first job as a genetic counselor in a new city. During the overwhelming process of adjusting to “my new life,” my thesis advisor asked me to submit a manuscript to the American Journal of Public Health in response to a call for abstracts on genetics topics. Unfortunately, the deadline was only one week away. I scrambled to cut down my lengthy thesis to a reasonable length and submitted it, knowing that it was not my best work given the time constraint. Needless to say, it was rejected.

I decided that before resubmitting the manuscript to a different journal, I would need to take a different approach to the paper, more or less starting over. While my research results were interesting, they were limited in their application. I decided to publish instead on the success of our research initiative, as other researchers could learn from our process. Since I was changing the focus of the manuscript, I had to do an additional literature search and produce much of the writing from scratch. Most of this work had to be completed in my free time. While it was difficult to stay motivated, working on my manuscript when first starting a job was manageable as my caseload was lightest in the beginning. After several weeks of hard work, I submitted the manuscript to Health Promotion Practice .

About one month later, the editor contacted me and asked me to resubmit my manuscript with revisions. Three different reviewers provided feedback. Initially, it was overwhelming to read through their comments and frustrating, particularly when the reviewers contradicted each other. Despite my frustration, with my co-authors’ guidance I forged ahead and resubmitted, only to have the editor and reviewers ask for additional revisions. There were comments from the same three reviewers, however, far fewer in number. Still, I was beginning to think they would never accept the manuscript. I once again called upon my co-authors for guidance and was able to address the reviewers’ comments and resubmit the manuscript once again.

This time when I heard from the editor, the manuscript was finally accepted. What started out as a 120 page thesis ended up being published as an eight page paper (Vogel et al. 2007 ). It took approximately 8 months of writing and revising before the manuscript was finally accepted and an additional year before it came out in print. While the entire process was a true test of patience and determination, it was ultimately worth it. The experience gave me the foundation to carry on my research career and continue to publish successfully.

Case 3: Expectations and Mentorship are Crucial

I defended my thesis, received my Master’s degree, and was about to move back to the Midwest to start my new job as a genetic counselor, but my long “To-Do” list had one remaining item: Publish master’s thesis. I started the initial master’s thesis process with the expectation from one of my thesis advisors, and now a co-author, that research is not “put down and set aside” until published. I never questioned the process; if I was going to work with this advisor, I would be publishing. I was excited to undertake this challenge and impressed by my thesis advisor’s dedication, mentorship, and desire to see our hard work recognized. Nearly two years later, I could proudly say that this expectation, held by all of my thesis advisors and me, was accomplished. The manuscript, published in the JOGC , describes qualitative research regarding communication of genetic test results within a family (Blase et al. 2007 ).

In the beginning, I was unfamiliar with the publication process, but because of the support and guidance of my advisors, I began to learn the process, and so the frustrations and uncertainties were minimal. I also had a great working relationship with my co-authors that included communicating regularly and setting and meeting deadlines. After deciding the JOGC was the most appropriate venue for my research, I spent a good deal of time reducing and reformatting the 80 page thesis to a 20–25 page manuscript to meet the journal’s guidelines. Given the page constraints, this process necessitated determining which data to focus on and re-framing some information to appropriately fit the readers of my selected journal. Conversations with my advisors were instrumental in this phase.

There was nothing quick about publishing my master’s thesis. I graduated in June 2005, received an email shortly thereafter from one of my advisors about how to begin constructing a first draft of a manuscript, and began working on the manuscript in July 2005. I submitted the manuscript to JOGC in May 2006 and subsequently was informed by the editor that based on the reviews, revisions were required before the manuscript could be considered for publication. In September 2006, after two rounds of revisions, my manuscript was accepted, and by June 2007 it was published in the journal.

Although ultimately I was successful in publishing my master’s thesis, the process had its moments of frustration. I remember getting my first round of comments from the reviewers; I thought I was never going to get to the point of publication. My co-authors supported and encouraged me by explaining that revisions are truly part of the process. I was overwhelmed by the reviewers’ list of questions and changes after my initial submission, followed by additional reviews and revisions. Not only did I have to figure out how to keep the manuscript a priority in light of my new job, but I had to weed through and address the reviewers’ comments, and the suggestions of each co-author. The guidance of my thesis advisors, now co-authors, helped me navigate this process.

I have gained much through this experience. The process has opened doors for me including opportunities to work with other professionals with impressive publishing experiences, as well as speaking and poster presentation opportunities at national conferences. I also have greater confidence about the publishing process. What seemed like such a daunting and impossible task is now an attainable outcome. Although my master’s thesis was my most recent publication, the thought of taking on the publication process again is not nearly as intimidating as I once thought.

Publication of original research, clinical experience, and literature reviews are vital to the growth of the genetic counseling field and to the delivery of genetic counseling services. Publishing also promotes personal growth by counting toward maintenance of ABGC-certification as well as establishing the author as a credible and respected authority both within and outside the genetic counseling field. This professional recognition in turn can lead to employment opportunities, speaking engagements, research funding, and career advancement.

Submitting a manuscript for publication also can be an intellectually challenging, emotionally trying, and time-consuming task. But similar to life’s other difficult tasks, the rewards and satisfaction are commensurately great—to see your name in print, have your work cited by other authors, and know that you have contributed in a meaningful way to the practice and understanding of genetic counseling. Transforming a master’s thesis into a journal article is an obvious first step in developing and sustaining a commitment to publishing for our genetic counseling profession. Common themes in the three success experiences include the importance of mentorship and clear expectations for publishing, recognition of the length of the process and concomitant need for perseverance in the face of revisions, awareness of personal and professional benefits in terms of presentations at national meetings, awards, and motivation to continue publishing. Hopefully the information provided in this article will help to de-mystify the publishing process, promote consideration of ethical issues in publishing, and stimulate genetic counseling students and new graduates to embrace a “Publish for Success” philosophy.

Acknowledgments

This paper was developed from an Educational Breakout Session (EBS) sponsored by the Jane Engelberg Memorial Fellowship Advisory Group at the 2008 NSGC Annual Education Conference.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Thesis content and article publishing

Journal publishers usually acquire the copyright to scholarly articles through a publication agreement with the author. Their policies then determine what authors can do with their work.

Below are publisher policies regarding graduate students’ reuse of their previously published articles in their theses, and policies on accepting journal submissions that first appeared in an author’s previously released thesis.

If an article is co-authored with a member of the MIT faculty, or if you have opted-in to an OA license , the MIT open access policy  is likely to apply to the article, which allows for the extension of additional rights to graduate student authors through MIT for reuse.  Short excerpts of published works may also be available for reuse under the MIT Libraries license agreements .

See this page for information about who owns the copyright to your thesis (generally, it’s either MIT or you).

Please contact [email protected] with questions or if you need information that does not yet appear below.

American Association for the Advancement of Science (AAAS)

Reuse of author’s previously published article in author’s thesis

Check the terms of your publication agreement .

Submission of new article by author that first appeared as part of author’s thesis

Allows :  “ We do not regard dissertations/theses as prior publications.”

American Chemical Society

Allows : “Authors may reuse all or part of the Submitted, Accepted or Published Work in a thesis or dissertation that the author writes and is required to submit to satisfy the criteria of degree-granting institutions…. Appropriate citation of the Published Work must be made as follows “Reprinted with permission from [COMPLETE REFERENCE CITATION]. Copyright [YEAR] American Chemical Society.”

“If the thesis or dissertation to be published is in electronic format, a direct link to the Published Work must be included using the  ACS Articles on Request link.”

See also this FAQ for thesis info.

Each ACS journal has a specific policy on prior publication that is determined by the respective ACS Editor-in-Chief. Authors should consult these policies and/or contact the appropriate journal editorial office to ensure they understand the policy before submitting material for consideration.

American Geophysical Union

Allows : “If you wish to reuse your own article (or an amended version of it) in a new publication of which you are the author, editor or co-editor, prior permission is not required (with the usual acknowledgements). However, a formal grant of license can be downloaded free of charge from RightsLink by selecting “Author of this Wiley article” as your requestor type.”

Allows : “Previously published explicitly does not include oral or poster presentations, meeting abstracts or student theses/dissertations.”

American Institute of Aeronautics and Astronautics

Allows : “ Upon publication of an article or paper in an AIAA journal or conference proceeding, authors can use in their own theses/dissertations (with permission of AIAA if required by copyright).”

From here : “In most cases when AIAA is the copyright holder of a work, authors will be automatically granted permission by AIAA to reprint their own material in subsequent works, to include figures, tables, and verbatim portions of text, upon request. Explicit permission should be sought from AIAA through Copyright Clearance Center (CCC) ; all reprinted material must be acknowledged and the original source cited in full.” 

American Institute of Physics

Allows : “A uthors do not need permission from AIP Publishing to reuse your own AIP Publishing article in your thesis or dissertation (please format your credit line: “Reproduced from [FULL CITATION], with the permission of AIP Publishing”)” Author agreement says the version of record can be used . 

Allows : Publishing the work in a thesis is not considered prior publication according to author warranties in the publication agreement.

American Meteorological Society

AMS seems to require permission. Email [email protected] . It can take 10 days to hear back. They will then ask that you include the complete bibliographic citation of the original source, as well as the following statement with that citation for each: © American Meteorological Society. Used with permission.

American Physical Society

Allows , “ provided the bibliographic citation and the APS copyright credit line are given on the appropriate pages.”

Allows Language from Physical Review journals page: “Publication of material in a master’s or doctoral thesis does not preclude publication of that material in the Physical Review journals.”

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Prior to Jan 4, 2022 : “ Permission can be obtained via Copyright Clearance Center . Copyright or license information is noted on each article.”

Likely allows : D oesn’t explicitly call out theses but says posting on preprints isn’t prior publication. 

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Allows : “Authors can include partial or complete papers of their own (and no fee is expected) in a dissertation as long as citations and DOI pointers to the Versions of Record in the ACM Digital Library are included. Authors can use any portion of their own work in presentations and in the classroom (and no fee is expected).”

Likely allows . Prior publication rules apply to “peer reviewed” publications.  

Cambridge University Press

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Policies set by individual journals.

Allows : “ ​​ Use and share their works for scholarly purposes (with full acknowledgement of the original article): Include in a thesis or dissertation (provided this is not published commercially).”

Allows : “ Elsevier does not count publication of an academic thesis as prior publication.”

Emerald Publishing

Allows . Authors should use the submitted version or accepted manuscript version.  Use of the final published version is permitted in print, but not electronic versions of theses.

Allows : “We are happy for submissions to Emerald to include work that has previously formed part of your PhD or other academic thesis. Please submit your paper in the usual way but declare the existence of the uploaded thesis to the Editor of the journal. If the Editor wished to consider the paper further, the paper would go through our standard anonymised peer-review process.”

Allows , with some requirements

Theses not specifically addressed , but permitted subject to editorial discretion. Individual journals may have their own policies.

Institute of Physics

Allows. A uthor may use the final published version or figures/text, and should include citation and a link to the version of record. “When you transfer the copyright in your article to IOP, we grant back to you certain rights, including the right to include all or part of the Final Published Version of the article within any thesis or dissertation.”

Inter-Research Science Center

Reuse of published content is “generally free of charge,” but you must get permission. Email: [email protected]

Not addressed , except to say, “ Permission to re-use any previously published material must have been obtained by the authors from the copyright holders.”

International Speech Communication Association (ISCA) INTERSPEECH conference

Allows , with citation. “ISCA grants each author permission to use the article in that author’s dissertation….”

Mathematical Sciences Publishers

Allows , with citation. “The Author may use part or all of this Work or its image in any future works of his/her/their own.”

Unclear, but the author agreement says , “The Author warrants that the Work has not been published before, in any form except as a preprint.” We suggest asking your editor.

National Academy of Sciences

Allows , with citation. “ PNAS authors do not need to obtain permission in the following cases: …to include their articles as part of their dissertations.”

Not addressed . “ What constitutes prior publication…will be determined on a case-by-case basis.

Allows , with citation. “ Authors have the right to reuse their article’s Version of Record, in whole or in part, in their own thesis.”

Allows . “ Nature Portfolio will consider submissions containing material that has previously formed part of a PhD or other academic thesis which has been published according to the requirements of the institution awarding the qualification.”

Oxford University Press

Journals have their own policies . OUP uses the Copyright Clearance Center for permissions . Contact your editor.

Royal Society of Chemistry

Allows , but says, “ Excerpts or material from your dissertation that have not been through peer review will generally be eligible for publication. However, if the excerpt from the dissertation included in your manuscript is the same or substantially the same as any previously published work, the editor may determine that it is not suitable for publication in the journal.”

Allows — in addition, a special agreement with Springer for MIT authors allows for reuse for scholarly and educational purposes.

Policy varies by journal but according to Springer: “There are no overriding ethical issues as long as the dual publication is transparent and cross referenced. Transparency is key, though a few journals might reject such an article for the reason of non-originality.”

Taylor & Francis

Allows — authors retain the right to “Include your article Author’s Original Manuscript (AOM) or Accepted Manuscript(AM), depending on the embargo period, in your thesis or dissertation. The Version of Record cannot be used.”

Allows : “ Y ou may share any version of your article with individual colleagues and students… as submission of thesis, or doctorate.”

Allows : “ The following types of “prior publication” do not present cause for concerns about duplicate or redundant publication: Dissertations and theses in university archives.”

Page last updated: April 10, 2024

Patterns of dissertation dissemination: publication-based outcomes of doctoral theses in the social sciences

• Open access
• Published: 29 February 2024

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• Anastasiya-Mariya Asanov   ORCID: orcid.org/0000-0003-3080-4213 1 ,
• Igor Asanov   ORCID: orcid.org/0000-0002-8091-4130 1 ,
• Guido Buenstorf   ORCID: orcid.org/0000-0002-2957-5532 1 ,
• Valon Kadriu   ORCID: orcid.org/0009-0006-1101-5349 1 &
• Pia Schoch   ORCID: orcid.org/0009-0006-9471-4590 1  

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Dissemination of knowledge through the publication of findings is a cornerstone of the academic research system. Doctoral dissertations document the findings made by early-stage researchers during their doctoral studies. However, prior research suggests that dissertations may not be effective in disseminating these findings to the broader community of researchers. We study how knowledge documented in doctoral dissertations is disseminated. Specifically, we investigate which dissertation characteristics and institutional factors are related to the number of journal publications based on these dissertations and the number of citations that these publications receive. Our analysis uses a random sample of doctoral dissertations from German universities in economics, political science, and sociology. We find that “cumulative” dissertations—dissertations consisting of a number of separate articles—are turned into three times more publications which receive three times more citations than monographic dissertations. We also find explorative evidence that dissertations written in English and empirical dissertations have higher publication-based outcomes. We conclude that a policy allowing doctoral candidates to write their dissertations in a cumulative format provides them with an opportunity to share the results of their research through publications in peer-reviewed journals.

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Avoid common mistakes on your manuscript.

Introduction

Doctoral candidates contribute in many ways to the productivity of their universities—for example, as research and teaching assistants (Kifor et al., 2023 ; Larivière, 2012 ; Rodríguez-Montoya et al., 2023 ). However, their primary task and the key prerequisite for being awarded a doctoral degree is to make a research contribution to the respective discipline, which is documented in the doctoral dissertation. Moreover, dissertations turn into published papers serving dissemination of the acquired and documented knowledge. Yet, not many factors are known that determine the success of knowledge dissemination from dissertations (Mayir et al., 2017 ; Paglis et al., 2006 ; Rojko et al., 2020 ).

Depending on the country and discipline, it takes about 3 to 6 years for a doctoral candidate to fulfil all requirements for the doctoral degree, including submitting a comprehensive dissertation of 100 to 400 pages and to graduate (Günauer et al., 2013 ; Siegfried & Stock, 2001 ). Prior evidence indicates that dissertations contain high-quality research. For example, one review (McLeod & Weisz, 2004 ) shows that the methodology in experiments from dissertations was stronger, while the mean effect size reported in dissertations was smaller than in the published articles in the same field, suggesting the dissertations were less susceptible to overestimating the effect size than the published articles. Despite this, compared to journal publications, the citation rate of dissertations in the scientific literature has declined over time (Larivière et al., 2008 ). Less than 12% of the dissertations produced by UK doctoral candidates have at least one citation on platforms like Scopus, Microsoft Academic, or Google Books (Kousha & Thelwall, 2020 ).

Even though dissertations themselves are not cited much in the academic literature, one might expect that the research and data that dissertations are based on contribute to publications in peer-reviewed journals. However, research shows that only about 25–29% of dissertations in psychology, counseling, and social work (Evans et al., 2018 ; Maynard et al., 2014 ; Osborn et al., 2023 ) ended up with at least one article derived from them published in a peer-reviewed journal. Similarly, only around 40% of electronic theses and dissertations in engineering produced at a South African university received at least one citation, and only 16.8% of them were converted into research outputs such as books, journal articles, or conference proceedings (Bangani, 2018 ). Studies of medical theses dissemination show similarly low publication rates of 17% in France (Salmi et al., 2001 ), 17.6% in Peru (Arriola-Quiroz et al., 2010 ), and 23.8% in Finland (Nieminen et al., 2007 ). Finally, 53.2% of dissertations in Turkish language education are turned into journal publications (Karagöz & Şeref, 2021 ).These prior findings indicate that substantial resources are dedicated to producing high-quality research that is documented in doctoral dissertations but often not disseminated to the broader community of researchers. Yet, little is known about how universities can enhance dissertation-based research dissemination. Several studies have explored individual factors associated with the research productivity of doctoral candidates. Paglis et al. ( 2006 ) found no significant association between advisor mentoring and research productivity, which was defined as the total number of conference papers, journal publications, book chapters, and grant proposals accepted. Rojko et al. ( 2020 ) did not find a significant difference in the average publication performance of doctoral candidates before and after the implementation of the Bologna reform in Slovenia.

Other research has explored differences in the extent of doctoral dissertation dissemination. For example, making doctoral dissertations available through open-access repositories at universities resulted in higher citation counts (Ferreras-Fernández et al., 2013 ). Mayir et al. ( 2017 ) did not reveal an association between the publication rate and citation counts of dissertations in surgery and the type of study on which a dissertation was based (e.g., randomized study, case study, cross-sectional study). Closest to our study, Smaldone et al. ( 2019 ) compared the number of peer-reviewed publications based on dissertations from the Columbia University School of Nursing that were written in a monographic or an article-based (i.e., “cumulative”) format. The study found that article-based dissertations were associated with larger numbers of publications in peer-reviewed journals. Similarly, a survey of Australian students and alumni (Thomas et al., 2016 ) from instructional technology programs found that those who chose an article-based dissertation format reported receiving more citations on their dissertations.

In this study, using a random sample of German dissertations, we investigate dissertation characteristics and institutional factors that may be related to higher research output. We quantify research output as the number of papers based on the dissertation that are published in peer-reviewed journals and the number of citations from these papers.

We aim to answer the following four questions: First, do rates of publications based on dissertations and their citations differ between economics, sociology, and political science; second, do monographic and “cumulative” (article-based) dissertations differ in publication and citation rates; third, do dissertations from universities with and without an established graduate school or graduate academy differ in publication and citation rates; and fourth, do dissertations from universities that were successful in the German excellence initiative differ in publication and citation rates.

Materials and methods

Pre-analysis plan.

Prior to conducting the empirical analysis underlying the present paper, we specified in a pre-analysis plan the process of sampling, data collection, a set of outcomes and explanatory variables, a set of control variables, a set of hypotheses, and the empirical strategy. We store the pre-analysis plan and a replication package at Open Science Framework: https://doi.org/10.17605/OSF.IO/U7M2A .

We selected a sample of 1500 doctoral dissertations from 2004 to 2006 and 1500 doctoral dissertations from 2012 to 2014, drawn randomly from the German National Library’s database of all dissertations published in Germany during those years. We determined the desired sample size and sampling strategy based on statistical power calculations before data collection to have a representative sample of dissertations for the sampling frame years. We focused on dissertations classified under the fields of Economy (“Wirtschaft”), Politics (“Politik”), and Social Sciences (“Sozialwissenschaften, Soziologie, Anthropologie”). To ensure accuracy, our team manually classified the dissertations in the sample, particularly distinguishing between those in economics and those in management sciences, which are both classified as “Wirtschaft” in the database. After this classification process, we were left with a total of 1840 dissertations from 73 German universities. Given 1840 dissertations (observations) across 73 universities (clusters) with observed intra-university (-cluster) correlation (ICC) of 0.04 for the main outcomes, assuming a conventional significance level of 5% and 80% of statistical power, the minimum detectable effect (MDE) size is 0.19 of a standard deviation. Thus, we have sufficient statistical power even to detect a correlation of about 0.1 ( r ), which can be considered a small effect size given empirically observed effect sizes in observational research in economics (Ioannidis et al., 2017 ).

Outcome variables

We were interested in two primary outcomes—the number of publications based on the doctoral dissertation and the total number of citations from these publications. Relevant publications were identified as follows. Initially, our team of research assistants, under the supervision of the research team, exhaustively searched for one peer-reviewed publication of each author in various sources (the author’s personal website, the author’s university page, Web of Science, Google Scholar, and WISO) and recorded the author identifiers. To ensure the correct person was found, we verified that the author’s publication lists contained the dissertation as well. Author identifiers were then matched with the Scopus database, and the list of publications was cross-referenced with the dissertation itself to identify which publications were based on the dissertation. The identification of dissertation-based publications was conducted manually by research assistants based on a formalized algorithm created by us, which entailed comparing titles, abstracts, and, if necessary, the introductions of the dissertation and each publication. Research assistants were encouraged to leave comments regarding uncertainties, which were then resolved by a member of the research team. To additionally ensure accuracy, the research assistants’ work was systematically and independently double-checked at random by senior researchers, i.e., a random sample of 30% dissertations was extracted to check for potential mistakes. In both instances—resolving an uncertainty and double-checking a random sample of dissertations—the senior researchers followed the same formalized protocol: check whether the title, abstract, and if necessary, introduction match. To determine whether the paper and a dissertation match, the algorithm required to compare the topic and the object of the study, the study sample, the method and the location of the study. When there was a significant overlap in these categories between the paper and a dissertation/dissertation chapter, the paper was classified as a match. After independent double-checking, the rate of agreement between research assistants’ and senior researchers’ classification was 94%.

Most of the publications from Scopus matched to the dissertations were classified as journal articles. We base our analysis on these observations. However, some of the Scopus items classified as conference proceedings, reviews, book chapters, notes, etc., were (later) also transformed into journal articles. Therefore, two senior researchers independently re-classified these cases manually (agreement between researchers in this classification was more than 95%). We performed a robustness check based on the dataset including these cases (Online Appendix S12 ).

Explanatory variables

We pre-specified four explanatory variables:

Cumulative dissertation

A “cumulative” doctoral dissertation is a dissertation written in a specific format. In addition to introductory and concluding chapters, it includes three or more chapters written in the format of journal articles. Writing cumulative dissertations is a recent and still relatively uncommon practice at German universities. According to the German Federal Statistical Office, only 13% of doctoral candidates in 2021 pursued a cumulative dissertation, with the remaining doctoral candidates opting for the traditional monographic dissertation format. For doctoral candidates in law, economics, and social science, the share of cumulative dissertations in 2021 was 18% (Bildung & Kultur, 2021 ). Cumulative dissertations have been suggested to address the low rates of dissertation citations observed in the past (Francis et al., 2009 ; Larivière et al., 2008 ). We accordingly hypothesize that cumulative dissertations have a higher number of publications and citations (hypothesis 1.1).

Graduate academy

Graduate academies are specialized institutions within universities that offer comprehensive support and guidance to doctoral candidates from all academic disciplines. The first graduate academies in Germany were established in 2000 and have since become an integral part of most German universities (Bundesbericht Wissenschaftlicher Nachwuchs, 2017 ). In addition to offering general support and advice, graduate academies typically provide additional quality assurance measures and offer specialized training and mentorship programs for doctoral candidates. These programs are designed to enhance the academic and professional skills of doctoral candidates and to help them succeed in their respective fields.

We hypothesize that dissertations written at universities with established graduate academies have higher numbers of publications and citations (hypothesis 1.2.a).

Graduate school

The traditional format of doctoral education at German universities relied on on-the-job training under the supervision of an individual doctoral advisor. The adoption of the graduate school model in Germany originated from the establishment of the first Research Training Groups (“Graduiertenkollegs” in German), funded by the German Research Foundation (DFG) in the mid-1980s.

In Research Training Groups, a team of professors and post-doctoral researchers jointly provide guidance and supervision to a number of doctoral candidates, all working on dissertations within the group’s common thematic focus. In addition to on-the-job learning opportunities, research training groups offer specialized training programs to enhance doctoral candidates’ academic and professional skills. Research Training Groups emphasize the training of early-career researchers subsequently embarking on academic careers (DFG, 2010 ). In the 2000s, they provided the template for graduate schools funded by the German Excellence Initiative.

We classify both the Research Training Groups funded directly by the German Research Foundation and the graduate schools funded by the Excellence Initiative as graduate schools. We hypothesize that dissertations written at universities with a graduate school in the respective discipline have a higher number of publications and citations (hypothesis 1.2.b).

Excellence university

The Excellence Initiative is a large-scale funding program that was jointly established by Germany’s federal government and the individual federal Länder in 2006. Its objective was to promote the best German universities to top positions in international university rankings and increase collaboration between German universities and the non-university research sector. The Excellence Initiative encompassed three funding lines: graduate schools, clusters of excellence funding thematically focused research centers connecting universities and research institutes or businesses, as well as university-wide development strategies (“future concepts”). Success in the Excellence Initiative entailed substantial resource and reputation effects on the respective universities. In particular, winning universities in the funding line for development strategies were often considered “excellence universities” (Buenstorf & Koenig, 2020 ; Möller et al., 2016 ).

There is mixed evidence of the changes in universities that received Excellence Initiative funding. Some evidence shows a decrease in the number of citations per researcher at universities funded in the first round of the Excellence Initiative compared to universities that did not receive funding (Menter et al., 2018 ). Other evidence points out that universities funded for their development strategy attracted students with higher GPAs—the effect remained for three years after the funding was awarded—and that students perceived these universities as having higher quality (Fischer & Kampkötter, 2017 ). Based on these findings, we hypothesize that dissertations written at excellence universities have a higher number of publications and citations (hypothesis 1.3).

Control variables

We collected control variables available from the dissertations, the German National Library portal, and the university websites for each dissertation in the sample. We used the post-double-selection Lasso procedure (Belloni et al., 2014 ) to select relevant control variables from the set of available control variables. This machine learning procedure relies on a two-step method to identify control variables for inclusion: (1) fitting a lasso regression to predict the outcome variable and (2) fitting a lasso regression to predict the explanatory variables of interest. The union of the variables selected by the procedure is included in the regression. The post-double-selection Lasso procedure reduces the risk of omitted variable bias, while at the same time avoiding overfitting in the presence of many potential control variables (Belloni et al., 2014 ). It is popular in many social sciences (Kreif & DiazOrdaz, 2019 ) and in medical research (Dukes & Vansteelandt, 2020 ), but has not yet been widely adopted in the scientometric literature despite the abovementioned advantages. The full list of control variables is available in Table S1 in the Online Appendix.

We pre-specified that a set of available control variables used in the post-double-selection Lasso procedure will consist of variables with less than 20% missing values. Most variables like language, university and field were retrieved from the German National Library portal and have no missing values. Some explained and controlled variables that could only be obtained from the dissertation text have missing values as 23 dissertations could not be obtained. The analysis included these variables due to a very low missingness rate.

Empirical strategy

We use the following main regression to estimate the relationship between publication-based outcomes and the format of the dissertation (cumulative or monographic), as well as the presence of graduate academies, graduate schools, or excellence funding:

where \(Y_{ij}\) is the publication-based measure for dissertation i in university j ; \(CD_{ij}\) is a binary variable equal to 1 if dissertation i at university j is in a cumulative format; \(GS_{ij}\) is a binary variable equal to 1 if dissertation i comes from a university j with an established graduate school in economics/sociology/political science; \(GA_{ij}\) is a binary variable equal to 1 if dissertation i comes from a university j with an established graduate academy; \(EU_{ij}\) is a binary variable equal to 1 if dissertation i comes from an excellence university j ; \(Controls_{ij}\) is a vector of control variables selected through the post double-selection Lasso procedure (Belloni et al., 2014 ). We cluster at the university level. \(f\) stands for a general functional form in regression analysis. We mainly use negative binomial regression as the number of papers and citations (publication-based measures) is prone to have a skewed distribution and to be overdispersed. We also estimate the Poisson regression model following Azoulay et al. ( 2019 ) and a simple linear regression as robustness checks. We performed control variables selection based on the post-double Lasso procedure for each hypothesis tested to see the sensitivity of the results with respect to the second stage of the procedure. In addition, we estimated negative binomial regressions including (i) the full set of institutional variables collected, (ii) the full set of individual author-dissertation variables collected, and (iii) the union of them as additional robustness checks. All estimations were done with R software version 4.0.3. except we had to use STATA for the robustness check regressions with full sets of controls (to ensure model convergence with many controls).

Additional outcome variables

While we hypothesize that cumulative dissertations convert into more journal publications that receive more citations, monographic dissertations may get more citations themselves. We test this conjecture and supplement the main pre-specified analysis using the methodology developed by Donner to estimate the number of citations the dissertations received (Donner, 2021 ). We followed the algorithm he described and searched for citations to the dissertations in Google Books (using Webometric Analyst) and Scopus and combined the results (Table S4 in the Online Appendix).

Specifically, we used a snapshot of the Scopus data from April 2022. In the first step, we restricted the cited reference data to the publication years of our dissertation sample ± 1 year. In the second step, we looked for exact matches with our dissertation sample regarding the author’s surname and initials and the dissertation publication year being around ± 1 year. Lastly, we compared the dissertation title to the Scopus cited item title and cited source title after standardizing them to the length of the shorter title. We calculated the similarity using the Optimal String Alignment (OSA) method and divided the result by the length of the standardized dissertation title, which led to outcomes between 0 and 1 (with 0 being an exact match and 1 being no match). If the outcome was between 0.00 and 0.25, we deemed the citation valid. After manually checking some dissertations at random, we observed that some authors were occasionally stored in the reference data with their full first name instead of their initials, so we also considered those cases.

Furthermore, we considered names containing German Umlaute (ä, ö, ü) by turning those into a, o, u, and ae, oe, ue. As we focus on the dissertations, we do not include the non-dissertation cited source titles containing words like “Journal”, and “Conference”. Following Donner’s ( 2021 ) approach, we applied the same approach to find indexed Scopus source publications matching with our dissertation sample. Lastly, to make sure that we do not have false-positive matches, we sampled 100 citations from the 2376 citations found and manually checked in the actual publications if the references list contains matched dissertations. We did not find any mismatch.

After obtaining the dissertation citations from Google Books and Scopus, we searched for the overlap between both sources. We found 18 citations present in both citations retrieved from the Google Books database and the Scopus database and removed them from the Google Books citations. We then combined the citations from both sources as described in (Donner, 2021 ).

We provided the analysis on combined Scopus and Google Books citations in line with the pre-specified empirical strategy above, being interested in whether monographic dissertations receive more citations. Finally, we manually collected Google Scholar citations, which have been used before to estimate the scholarly impact of dissertations (Kousha & Thelwall, 2020 ), and applied the above empirical strategy to assess if the results also hold for Google Scholar citations.

Our data shows that 26% of the dissertations in economics, 11% in sociology, and 7% in political sciences end up with at least one publication in a peer-reviewed journal. Additionally, the average number of papers based on the dissertations is 0.52 for economics, 0.18 for sociology, and 0.1 for political science. The corresponding citation counts of papers resulting from these dissertations are 14.63, 8.74, and 1.28, respectively (Table  1 ). We also observe that variance exceeds the mean on both primary outcomes—overdispersion for the number of papers and citations from these papers—suggesting that the negative binomial is the preferred specification.

Interestingly, we observe a considerable increase in the number of publications for dissertations in economics during the years 2012–2014, as shown in Fig.  1 .

Share of cumulative dissertations and average number of publications based on the dissertations per field over time

Results based on tests of the pre-specified hypotheses

Results based on tests of the pre-specified hypotheses with and without control variables selected by the double-lasso selection algorithm (Belloni et al., 2014 ) can be found in Table  2 . We find that cumulative dissertations are associated with a significantly higher number of journal articles than monographic dissertations ( p value < 0.00001). Furthermore, the total citation count of papers based on cumulative dissertations is also significantly higher than for monographic dissertations ( p value = 0.06).

On average, cumulative dissertations turn into three times as many publications as monographic dissertations (Table  2 \({\text{Model }}2:{ }\beta_{CD} = 1.13;e^{{\beta_{CD} }} = 3.1\) ), even if we account for a large set of controls algorithmically selected by double lasso. Moreover, the average total citation count of the papers from the cumulative dissertations is more than three times as high as for monographic dissertations (Table  2 \({\text{Model }}4:{ }\beta_{CD} = 1.11;e^{{\beta_{CD} }} = 3.03\) ). In addition, we observed a notable increase in the share of cumulative dissertations in economics during the second period, followed by a higher number of publications, as shown in Fig.  1 .

Our analysis indicates that dissertations from universities with established graduate academies are initially associated with a higher number of publications in peer-reviewed journals. This association becomes insignificant with the inclusion of algorithmically selected control variables and we do not observe any significant difference in citation counts with or without control variables. We also investigated whether the presence of graduate schools in the respective discipline or recognition as an excellence university was related to the publication-based outcomes. However, we did not find statistically significant difference at any conventional level of significance in the number of publications or citation counts between dissertations from universities with or without graduate schools and universities with or without excellence status.

Out of all journal publications for which the year of publication is known, 62.2% were published in the years after the dissertation defense, and 37.8% were published in or before the year of the defense. We re-ran Model 2 from Table  1 separately for publications from the years before and after the defense (Table S2 in the Online Appendix). The significantly positive relationship between cumulative dissertations and the number of publications holds for publications both before and after the defense.

One might expect that, while cumulative dissertations are turned into more journal publications that receive more citations, monographic dissertations receive more citations themselves. We scrutinize this conjecture using the same empirical strategy as before on the following two outcome variables: (a) dissertation citations in Google Books and Scopus constructed following (Donner, 2021 ) and (b) Google Scholar citations.

We find a significantly negative relationship between cumulative dissertations and the number of dissertation citations. On average, a cumulative dissertation receives 36% fewer citations in Google Books and Scopus (Table  3 \({\text{Model }}2:{ }\beta_{CD} = - 0.45;e^{{\beta_{CD} }} = 0.64\) ). In other words, monographic dissertations receive only 1.5 times more citations in Google Books and Scopus than cumulative ones. We also find a significantly negative relationship between cumulative dissertations and Google Scholar citations. The average number of Google Scholar citations is 63% lower for cumulative dissertations than for monographic dissertations (Table  3 \({\text{Model }}4:{ }\beta_{CD} = - 0.99;e^{{\beta_{CD} }} = 0.37\) ), which implies less than a threefold increase in Google Scholar citations for monographic dissertations compared to cumulative ones. Finally, we do not see a stable association between dissertations from excellence universities and the number of dissertation citations.

We assess our results using Poisson and linear regressions as robustness checks. All results hold with these alternative specifications, both for primary outcomes (Tables S4 and S5 in the Online Appendix) and dissertation citations (Tables S6 and S7 in the Online Appendix).

In addition to estimating the regressions with covariates selected by the post-double Lasso selection procedure, we also estimate regressions with all institutional (Kifor et al., 2023 ; Rojko et al., 2020 ) and/or individual author-dissertation factors (Larivière, 2012 ; Mayir et al., 2017 ; Maynard et al., 2014 ; Paglis et al., 2006 ) as robustness checks (Tables S8 , S9 , S10 and S11 in the Online Appendix, columns 1–3). The results remain robust to the inclusion of these control variables. They are also robust to the inclusion of covariates selected at the second stage of the post-double Lasso algorithm for each pre-specified explanatory variable, except for the association between graduate academy and the number of papers (Tables S8 , S9 , S10 and S11 in the Online Appendix, columns 4–7). Thus, we consider the association between the number of papers and the presence of a graduate academy non-robust.

In summary, our findings suggest that cumulative dissertations are turned into more publications in peer-reviewed journals and receive more citations to these peer-reviewed publications. In contrast, monographic dissertations receive more citations as separate works.

Explorative results

We also exploratively examine the variables selected as control variables by the double-lasso procedure. Of the dissertations in our sample, 33.4% were written in English, with the rest being written in German (except for four dissertations in French, two in Italian, and two in Spanish). With 45.1%, the share of dissertations in English is highest in economics, while in sociology and political science, the shares were 18% and 16.6%, respectively.

Our findings indicate that dissertations written in English have significantly higher publication-based outcomes. On average, English dissertations turn into almost twice as many published papers as other dissertations (Table  2 \({\text{Model }}2:{ }\beta_{English} = 0.94;e^{{\beta_{English} }} = 2.56\) ). In addition, the average citation count for papers based on English dissertations is more than three times as high as that for dissertations in German and other languages (Table  2 \({\text{Model }}4:{ }\beta_{English} = 1.07;e^{{\beta_{English} }} = 2.92\) ). These results are consistent with other research comparing publication and citation levels of dissertations written in English versus the local language (Nieminen et al., 2007 ; Donner, 2021 ).

Empirical dissertations comprise 34.6% of our sample, with economics having the highest share at 48%, followed by sociology at 21.2%, and political science at 6.4%. We defined a dissertation as empirical if it contained hypothesis-testing statistical procedures, including moments of statistical distribution (mean, median, variance, etc.), regression coefficients, standard errors, p values, t values, or z values. Empirical dissertations had 50% more publications than other dissertations (Table  2 \({\text{Model }}2:{ }\beta_{Empirical} = 0.45;e^{{\beta_{Empirical} }} = 1.57\) ) and more than three times as many citations to these papers (Table  2 \({\text{Model }}4:{ }\beta_{Empirical} = 1.21;e^{{\beta_{Empirical} }} = 3.35\) ). Furthermore, our analysis shows an upward trend in the share of dissertations written in English and in the share of empirical dissertations (see Fig.  2 ).

Share of dissertations in English and share of empirical dissertations per field over time

The algorithm also selected control variables denoting online dissertations, the number of pages, and the field of the dissertation as control variables, but these variables are not consistently associated with significantly different publication-based outcomes in our analysis. Finally, we inspected control variables that were not selected by the algorithm but that we included in additional robustness estimations (with the full set of institutional variables collected, the full set of individual author-dissertation variables collected, and the union of them). We do not observe any associations between these additional control variables and publication-based outcomes that are robust across model specifications. Notably, we do not observe gender differences in publication-based outcomes of dissertations across model specifications.

In this study, we investigated how publication-based outcomes of social science dissertations in Germany are associated with dissertation characteristics and institutional factors. Consistent with our hypothesis specified in a pre-analysis plan, we observe that cumulative dissertations lead to a higher number of publications in peer-reviewed journals as well as a higher number of citations from these publications. We also find that the share of cumulative dissertations increased over time in economics. Our analysis does not suggest that the citation advantage enjoyed by publications based on cumulative dissertations is offset by a lower number of citations to the dissertations themselves. While we found that monographic dissertations receive more citations than cumulative ones, their implied advantage in direct citations is smaller than their disadvantage in publication-based citations. We thus conclude from our analysis that results of social science dissertation research documented in cumulative dissertations tend to be disseminated more extensively than results documented in monographic dissertations.

As dissertations are not randomly allocated into cumulative and monographic formats, the patterns we observe in our data cannot be interpreted as causal effects. Indeed, our analysis suggests that publication and citation outcomes for dissertations are affected by dissertation characteristics and institutional factors and that controlling for these variables helps explain some of the differences in outcomes. Regarding institutional factors, we do not see any robust significant difference in publication-based outcomes of dissertations from universities with or without graduate academies, graduate schools in the respective discipline and recognition as “excellence universities”.

Going beyond the hypothesized associations that were specified in our pre-analysis plan, we explored how differences in control variables selected by the double-lasso procedure (Belloni et al., 2014 ) are related to publication and citation outcomes. These exploratory analyses indicate, first, that dissertations written in English are associated with significantly more publications in peer-reviewed journals and higher citation counts compared to those written in German or other languages. Second, empirical dissertations in our sample also had higher publication-based outcomes compared to other types of dissertations. Overall, shares of dissertations in English and empirical dissertations seem to be increasing over time. In a nutshell, it appears that dissertations written in English or empirical dissertations increase in number and tend to particularly contribute to the dissemination of knowledge produced by doctoral students in German universities.

Various factors, however, like author characteristics and institutional conditions can affect the choice of dissertation language and topic. Moreover, we did not hypothesize in the pre-analysis plan if dissertations written in English or empirical dissertations are associated with higher publication-based outcomes, barely allowing us to post-hoc speculate about the cause of the higher publication-based outcomes of the dissertations with these characteristics. Thus, we interpret these findings as indicative and encourage further empirical work to probe into their robustness in other settings.

It is hard to distinguish the causal effect of the dissertation features and institutional factors on the publication-based outcomes as this study is based on observational data. More research can be done in the future to identify causal effects and extrapolate the results for other countries. Additionally, automatic, or alternative formal matching algorithms between publications and dissertations could be used to cover more research fields, languages or countries (e.g. Donner, 2022 ; Echeverria et al., 2015 ; Heinisch & Buenstorf, 2018 ). However, based on the results of the study, we can conclude that a policy that allows doctoral students to write cumulative dissertations permits them to strengthen their research output counted as papers published or cited.

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We would like to thank for excellent research assistance Denise Bornewasser, Maria Paz Catalan Palma, Besmir Daci, Lea Hartwig, Alana Hindiyeh, Irila Kola, Christina Lerke, James Macumber, Dariia Melnyk, Veronica Vela, Jana Zaremba. Special thanks to Johannes Koenig for finding efficient ways to work with Scopus data and to Maria Mavlikeeva.

Open Access funding enabled and organized by Projekt DEAL. All authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) under Grant number 16PH20011 (PI: GB) and via the German Competence Network for Bibliometrics under Grant number 16WIK2101A. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Maximilian Kotz, Anders Levermann & Leonie Wenz

Institute of Physics, Potsdam University, Potsdam, Germany

Maximilian Kotz & Anders Levermann

Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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DOI : https://doi.org/10.1038/s41586-024-07219-0

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NYU Named Nature Index “Rising Star” as Research Output Climbs

NYU ranked #8 research institution in North America—up from #25 in 2016—based on increase in articles in top science journals

New York University has been named a Nature Index 2023 Rising Star and is ranked #8 in North America for its growth in research published in the world’s leading science journals.

The Nature Index 2023 Rising Stars list highlights universities, health systems, nonprofits, and other research organizations that have posted the greatest increases—from 2015 to 2022—in authored or co-authored papers appearing in a list of leading journals chosen by an independent group of scientists. NYU was previously named a Nature Index 2016 Rising Star , ranking 25th among institutions in North America at the time.

“Our designation as a Rising Star and top-10 ranking is yet another proof-point  of the growth and momentum of NYU’s research enterprise; the high-caliber and impactful work our researchers undertake each day in their labs; and the University’s increased commitment to science, technology, and the generation of knowledge,” says Stacie Grossman Bloom, NYU’s vice provost for research and chief research officer.  

Examples of research by NYU scientists and NYU Grossman School of Medicine researchers appearing in Nature Index journals in the most recent year measured include:

• A study published in Science revealing the fountain of youth for ants: an insulin-suppressing protein called Imp-L2. The protein blocks the part of the insulin pathway that is responsible for aging in queen ants, providing clues about aging in other species.
• A Nature study demonstrating a new way to self-assemble particles—an advance that offers new promise for building complex and innovative materials at the microscopic level.
• The first-ever rigorous biophysical model of lizard tail autotomy, revealing the mechanism behind how lizards can lose—and later regenerate—their tails to escape predators. The findings are also published in Science .
• A study in Nature Neuroscience showing alternate origins of Alzheimer’s plaques, with neuronal damage taking root inside of cells well before plaques develop, which could explain why drugs to remove amyloid deposits have failed. 
• A discovery published in Nature Communications of the role that specific cells in the nervous system play in migraine pain, providing potential targets for new treatments.
• A study in the Proceedings of the National Academy of Sciences on bias in search engines that finds gender-neutral internet searches yield results that produce male-dominated output, which may promote gender bias and potentially influence hiring decisions.
• Research published in Macromolecules on gel-forming protein molecules, which can inform developing new gel materials for biomedical applications such as tissue engineering and drug delivery.

In addition to strong growth in the volume of articles published in top journals, NYU recently announced record rankings in annual R&D spending —another key measure of a university’s research performance. NYU reached #15 nationally and #7 among private universities, according to the National Science Foundation’s annual HERD rankings, with $1.29 billion in research expenditures for fiscal year 2022. In moving up eight spots in the ranking, NYU had the greatest climb among the top 50 U.S. research universities.

Founded in 1831, NYU is one of the world’s foremost research universities (with more than $1 billion per year in research expenditures, it is ranked seventh among private research universities) and is a member of the selective Association of American Universities. NYU has degree-granting university campuses in New York, Abu Dhabi, and Shanghai and has 13 other global academic sites, including London, Paris, Florence, Tel Aviv, Buenos Aires, and Accra, and US sites in Washington, DC, Los Angeles, CA, and Tulsa, OK. Through its numerous schools and colleges, NYU is a leader in conducting research and providing education in the arts and sciences, law, medicine, business, dentistry, engineering, education, nursing, the cinematic and performing arts, music and studio arts, public service, social work, public health, and professional studies, among other areas.

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