The first four myths pertain to the belief that one needs funding or a CSN paper to be competitive for a tenure-track faculty position. We examined three categories of funding: the K99/R00 award, which, in our experience, is the award that many postdocs feel is the key to obtaining a faculty position, other types of NIH or non-NIH funding, and the F32.
Without a doubt, a K99/R00 award has many benefits both from the K99 phase of funding and the R00 phase of funding. Discussions of these benefits are beyond the scope of this paper and will be addressed elsewhere. Our data clearly demonstrate, however, that a very small percentage of individuals hired into tenure-track positions had a K99/R00. For individuals conducting research in the NINDS mission who were hired into a tenure-track academic position at an institution that has an expectation of R01 submission, our data indicate that 11% or fewer had a K99 award; and for those hired by the many institutions that do not have an expectation of R01 funding but who were running a research program as evidenced by seeking NIH funding for their research, none had a K99 award.
Equally clear from our data is that major funding of any sort is not needed to obtain a tenure-track faculty position. When one combines all sources of competitive research and non-“fellowship” career development funding, 60% of individuals hired into tenure-track faculty positions had no funding before obtaining their tenure-track position. Moreover, the willingness to hire an individual who had not received prior competitive funding was widespread. Of the 133 institutions in this study who hired an individual into a tenure-track position, 82% hired an individual who had not received prior competitive funding.
The data also clearly demonstrate that the F32 is not a critical factor in one’s ability to obtain a faculty position. A total of 464 postdoctoral neuroscientists received F32 awards from NINDS between 2007 and 2016 (individuals funded during these years would have had time to transition to faculty positions in the time relevant for this study), yet only 58 individuals in our cohort had obtained an F32. One would certainly imagine that some individuals who are hired into faculty positions would have had an F32 as a postdoc. Consequently, our data that only 11% of our faculty cohort had an F32 and no other funding, and only 17% of the cohort overall had an F32, suggest that having an F32 has a relatively unimportant role (and perhaps even no impact) in enhancing one’s ability to obtain a faculty position.
Our data also demonstrate that CSN papers are not necessary to obtain an academic faculty position; approximately half of the population in the cohort did not have a CSN paper on their CV. This leaves the possibility that one must have either funding or a CSN paper to obtain a faculty position. Within the entire cohort of the 344 individuals who obtained tenure-track or equivalent positions, 35% had neither postfellowship funding nor a first-author CSN paper. Certainly, one can look at this from two perspectives. On the one hand, 65% of those hired had obtained either a competitive grant or published a first-author CSN paper before being hired. Conversely, over one-third of the cohort had neither funding nor a CSN paper before obtaining a tenure-track faculty position. Although undoubtedly beneficial, these data argue that neither one of these two accomplishments are necessary for one to transition to a tenure-track position. Moreover, as we demonstrate below in the qualitative section of this paper, many research-intensive institutions consider other factors to be much more important than these metrics of accomplishment.
Any reasoning that is applied to these results is a bit circular. It is students and postdocs who do the experiments that generate most or all CSN papers, and there is a substantial amount of NIH and non-NIH funding that is targeted specifically to those in pretenure-track (training) career stages. Consequently, one would expect that individuals who have the training and research excellence to obtain competitive funding or publish in a CSN journal to be the same as those who have the training and research excellence to be competitive for an academic faculty position; and in a circular fashion, it would be expected that many of the individuals who have the research and training excellence to obtain a faculty position would be the same as those who have the research and training excellence to publish in a CSN journal or obtain competitive funding. Our conclusion is not that these funding or publication accomplishments are unrelated to obtaining an academic faculty position nor that these accomplishments are not looked upon favorably in the hiring process. What the data above clearly demonstrate, however, is that neither funding nor a CSN paper are necessary to obtain a tenure-track faculty position. In part II below, we provide insight gained from interviews with six individuals at a diverse set of institutions as to what is the most important for obtaining a tenure-track faculty position.
The fifth myth pertains to the belief that one needs to obtain funding or a CSN paper to transition out of an intermediate position into a tenure-track faculty position. Of the 182 individuals in this group, 55% obtained funding and/or published a first-author CSN paper during their intermediate position (i.e., after completing their postdoctoral fellowship period). Conversely, 45% did not. Thus, inasmuch as 72% of individuals who transitioned from an intermediate position had funding or a CSN paper before obtaining a faculty position, almost half got their position without obtaining one of these major accomplishments during the intermediate position and more than a quarter of this population transitioned to a faculty position without having obtained competitive funding or a first-author CSN paper at any time. These data demonstrate that one need not obtain one of these accomplishments during the intermediate period, or indeed, at all, to obtain a tenure-track position.
Although not in direct response to addressing the myths, additional interesting information emerged from the data. We found it remarkable how many institutions represented by our cohort hired individuals into tenure-track positions that had previously been trained or employed at that institution. Across the entire cohort of 133 hiring institutions, 55% hired one or more individuals that were known to it by previous training or employment. Among the individuals hired, however, this behavior was markedly more apparent for those who transitioned to an intermediate position between their postdoctoral fellowship and tenure-track position. Whereas 58% of those hired out of intermediate positions were hired by an institution where they had previously trained or worked, only 23% of those who transitioned to a faculty position directly from their postdoctoral position were hired by an institution where they had previously trained or worked.
Another interesting finding was that, of the 344 members of the entire cohort, only 27 individuals were hired into tenure-track positions after transitioning to a non–tenure-track position outside of their postdoctoral institution. With the usual caveat that we do not have a control group to evaluate the success of individuals who pursue this pathway, these data indicate that taking an intermediate position outside of the postdoctoral institution is not a common route to a tenure-track position.
The data in part I demonstrated that, although undoubtedly a beneficial addition to a CV, one does not need a K99/R00 award, funding of any sort, or a paper published in the CSN journal family to obtain a first tenure-track faculty position. Given that none of these specific prefaculty accomplishments are necessary for obtaining a faculty position, we sought to determine factors that were. To address this, we interviewed six individuals who have a long history of being involved in, and overseeing, the hiring process. We selected these individuals on the basis of their stature at their institutions, their extensive experience in hiring faculty, and their being at six different types of institutions. We posed two general questions to these individuals: (i) “What characteristics are you looking for in order to invite somebody for an interview?” and (ii) “what factors lead you to hire somebody, and what issues lead you to not hire somebody after you’ve interviewed them?” After posing these questions, we did not ask for discrete answers to these specific questions but rather asked those interviewed to talk to us about the factors that are most important in the hiring process. Below, we have used either direct quotations or paraphrased answers to provide a brief, descriptive answer. Before submission of this paper for publication, we sent it to each of these individuals to confirm the accuracy of the statements or thoughts that we attributed to them (bolding was added by us for emphasis, and confirmed by the individual interviewed as appropriately applied).
Diane Lipscombe, Ph.D.
Thomas J. Watson Sr. Professor of Science
Reliance Dhirubhai Ambani Director,
Robert J. and Nancy D. Carney Institute for Brain Science
Department of Neuroscience
Brown University
Providence, RI
We are looking for the potential to succeed in research. We don’t use a K99 as a way to triage anyone. A K99, or any other funding, is of secondary importance. We also don’t look at numbers of papers, but at their quality. We do look at the journal name, but we also look at the paper itself. A short paper in a high-profile journal is often less interesting, and less of a draw to us when hiring, than a solid scientific contribution. We value this much more than many papers that are parts of studies— we are looking for solid research pieces that demonstrate independence and creativity by the candidate.
We like to see consistency in someone’s CV. We’re looking for high quality, consistent, rigorous research. We look closely at letters of recommendation. And we look at the research statement quite deeply, which is a very important factor in choosing who to interview.
We require a statement from applicants on diversity and inclusion. In recent hires, we’ve read that first. This doesn’t mean that the person has to be an underrepresented minority. We’re looking for a sincere, demonstrated interest .
We’re looking for independence and passion, although that can be hard to define. Someone who isn’t passionate about education and mentorship won’t be happy here. This doesn’t mean we require formal teaching experience or a teaching certificate. This teaching experience can show up in a multitude of ways, even as an interest outside of your research—for example, community outreach.
Things that are turn-offs? Lack of a particular interest in Brown. We want the applicant to have thought about how they’ll interact in our community.
To get on the shortlist , the cover letter will tell you a lot about the effort they’ve put in. Do they know the faculty at Brown and how they would fit in? Video pre-screens also give us information about their interest in Brown, their understanding of their own work, how they view the impact of their work, the challenges they see in their research.
Networking does have an influence. A letter from a faculty member we know, whose opinions we value and who we know is completely honest in their assessments, counts.
At the interview, their knowledge of their work can come through in a presentation, but the chalk talk is where we really learn about their understanding of their work and how they see their work intersecting with the faculty here .
Ted Abel, Ph.D.
Director, Iowa Neuroscience Institute
Chair and DEO, Department of Neuroscience and Pharmacology
Roy J. Carver Chair in Neuroscience
Carver College of Medicine
University of Iowa
Iowa City, IA
We are looking for a colleague who is intellectually curious . This comes from an ability to ask insightful questions and to use techniques that are appropriate to answer those significant questions. Having a hot new technique is not sufficient without knowing important and interesting questions that can be addressed with these new approaches.
Funding and journals count but are not the key issue. We’ve known applicants with a K99 and a CSN paper who couldn’t articulate the importance of their research, so we don’t base decisions solely on funding and the journals in which research is published. One aspect that is important is consistent productivity at a high level throughout a candidate’s graduate and postdoctoral work.
We want to know that the candidate is driving their research project, and we seek individuals who understand the importance of their work, the strengths and weaknesses of their technical approaches, and have a sense of where the field is headed.
We’re interested in people who have thought about what big questions they’d like to address and how they might study them in their own lab. What would they pursue that might make it into the textbooks? How would their research make a difference either in our fundamental knowledge of neuroscience or how we might better understand brain disorders.
The research statement is very important, and it should not read like it was copied from an NIH Biosketch or a Specific Aims page. The research statement should clearly and concisely describe the advances that the candidate has made in their research and outline where they are headed. A “graphical abstract” as a part of this statement can make things much clearer. The cover letter and CV can break an application but can’t really make it. The research statement can make it.
Community matters. The best neuroscience is carried out by collaborative communities of faculty, fellows, and students. As faculty candidates look at potential institutions in which to launch their careers, it is important to look closely at the neuroscience community at the institutions you are considering. Are faculty appropriately mentored? Are students and fellows part of a collaborative community that supports their training? Find out about faculty whose research connects with yours and determine if there are appropriate resources to help you grow your research program. Our search committee looks for candidates who have sought answers to these questions.
We all focus too much on metrics. Just because we can measure things does not mean that they are important or significant. We seek to focus on the unique strengths of individuals to identify their potential to make discoveries in their lab in the future that will make a difference in how we understand how the brain works.
Joseph LoTurco, Ph.D.
Department Head and Professor
Physiology and Neurobiology
University of Connecticut
Out of 150-200 applications, we usually whittle down to 20-25 for a remote interview by grants and publications. We are mainly interested in 1) someone who wants to be in our department, wants to work with our people, in our kind of environment, and 2) someone who will be successful in getting tenure here. But we don’t typically get to these issues until we get down to the 20-25.
We don’t care too much about what graduate school or postdoctoral institutions people come from.
For the first cut, we look at where they’ve published as an indication of quality of work. We do not require CSN publications. We are looking for top-field journals. We are also looking for a CV that is not filled with short papers. Once the first cut is made, we will go back and read some of the papers. We have hired plenty of people who don’t have CSN publications. In fact, we may actually be a bit suspicious about a CSN publication vs. a 2-3 author paper in a really good field journal. We look for balance. Almost all of the applicants we look at have 8-10 papers minimum. Probably 3 of those will be first-author or communicating author papers. We are looking for at least 2 to be in really good field journals and at least one to be during their postdoc (recent).
The vision for their research is really critical. They need to prove that they have a real idea of what they want to do and that it is going to excite a group of 4-5 people, including people who aren’t experts in their area.
There is typically a noticeable difference between a candidate who has at least written a research grant and those who have not. This becomes particularly evident in the chalk talk portion of the interview. You can also tell which applicants have written grants because their research statements are much more polished.
At the Skype interview, we get a general sense of whether the applicant knows what they are going to do. That probably whittles the pool down to about 10 people. They have to demonstrate that they own their research and have thought about it. People still answer questions factually wrong at this level—that will sink them. The other critical thing we ask is, “why do you want to come to our institution and our department?” Some people can’t answer this; eliminates 2-3 people every round. A lot rides on this initial Skype interview.
Once we narrow applicants down to an interview list of 10 people, grants and publications become less important ; we actually don’t find that having a K99 is a huge predictor of success when they get here.
We are looking at how well they communicated in their talk—it’s a diverse audience—undergrads, grad students, lots of people that aren’t in their field. They have to be able to communicate well. The buzz in the hallway after a job talk takes on a life of its own. Then, we do a chalk talk. We also want to make sure that the applicant is conscientious about teaching.
Leslie C. Griffith, M.D., Ph.D.
Nancy Lurie Marks Professor of Neuroscience and
Director of the Volen National Center for Complex Systems
Department of Biology
Brandeis University
Waltham, MA
The people who have been successful here are people we chose because they fit us. This will be very different than a very large department, which looks for a different kind of fit. We are high quality but small. We look for someone highly collaborative, who extends boundaries but isn’t separate from the core group. People that are scientifically diverse end up having really good interactions, because they’re imaginative . Candidates need to do their homework, figure out what people work on, be interested, and collaborative. We want to see that they will be able to get along with the department .
We look really carefully at publication record as well. We value someone who shows judgment in their publications as a postdoc . An 8-year postdoc with 1 Cell paper with 10 authors—that is a red flag to us … it shows terrible judgment. There are 4-5 papers worth of data in that Cell paper. We look for someone who published in a distributed manner with some high-profile papers (i.e., in “good” journals) but with also some solid work in what some people may consider “lesser” journals—but still good science.
My process is to look at the CV, papers, where they are publishing, what the topics are. Then, I look at the research statement. They have to convince me in a 4-page research statement that what they did was important, sound, interesting. The research statement makes a really big difference and the letters of reference do, too . If I’m really interested, I’ll go back to the papers. I have confidence that if a paper is published in a reputable journal, it was adequately reviewed. It’s the person’s plans, ideas, and way of expressing themselves that make a difference .
They should have a strong letter from postdoc mentor, graduate mentor, and maybe a collaborator letter . You can tell when someone writes a letter and they don’t really know the person. It’s bad when somebody has a letter from someone who doesn’t really know them. That rings false.
Communication matters. The 5-minute pitch, the ability to respond to questions without panicking is important . They have to be able to give a good talk that will not only engage neuroscientists but biologists, psychologists, biochemists, physicists, etc. I would say, though, that the chalk talk is the major separator. Some people give beautiful presentations but go down in flames during the chalk talk.
Marc Freeman, Ph.D.
Vollum Institute, OHSU
Portland, OR
Creativity is an invaluable commodity that can serve a person in science their whole life. I personally gravitate toward applicants where I read their package and learn something new and interesting, and I get convinced that there’s room for a lot of exciting and important questions to be explored. With the assumption that the science will be high quality, novelty is a big deal . Even before looking at the papers, we want to know whether an applicant is looking at a really interesting biological question. The good science always wins out. Usually that means the funding follows.
One doesn’t need a paper in a so-called “high-profile” journal to be competitive, but having papers only in lower tier journals probably won’t cut it. Very interesting, well-done science that appears in highly respected journals will do it. It is important to see that the candidate has been successful at each career stage—history will repeat itself when they are PIs.
Grants and papers are nice, but certainly don’t guarantee anything. One gets the interview based on what they’ve done and how they’ve presented it to us in their application. Did we get excited enough to offer them one of a limited number of slots to visit? Having funding is unimportant. If somebody is doing novel, interesting, important research, we can then help them get funding. It’s our job to mentor them to help them get funding. I don’t see a lack of current grant funding as a problem at all. In fact, many people that get funding like K awards do so because their PI basically writes it with them. It’s not necessarily a reflection of the candidate’s ability to get funding.
We want to be convinced that the person is excited to join us. Would they look forward to being here and why? Does their reasoning make sense? Not all people are a great fit for us, nor our environment a great fit for them. The match is key.
One ultimately gets hired by convincing us that they’ll do something interesting and that the ideas are the applicant’s (not just fed to them by their boss) . The chalk talk is the most important part of the visit. Anybody can give a polished presentation given enough practice. The chalk talk is where we see their understanding of their work, creativity, and ability to make a compelling argument.
We’re looking for the kind of person who has the disposition to run a lab; some don’t, so we’re also looking at management potential. You want someone who’s going to be comfortable working with a whole lot of people and personality types and can inspire them to work hard. If someone comes into an interview and has bad interactions with faculty or doesn’t interact well with trainees, that’s a red flag.
Networking is important. An applicant will be helped if someone on our faculty knew them, heard them give a talk, or met them somewhere. It can really help. It will help get them through the door. It’s important to be known in your field even before you are a PI. You can get a lot of credit in your application if people who are outside your immediate orbit and who have no vested interest in your success are vouching for you in recommendation letters. I encourage my postdocs to get to know PIs at other places and build relationships. These types of references indicate that you have started to gain the respect of your field.
Matthew N. Rasband, Ph.D.
Professor and Vivian L. Smith Endowed Chair in Neuroscience
Baylor College of Medicine
Houston, TX
We do not use funding as a litmus test for any applicant that we’re interested in, it’s simply not one of the major criteria. If a person has a K99, great—we view it as a bonus—but it is not considered as a requirement.
I am interested in applicants who can demonstrate continued and sustained high productivity, regular publishing of papers. In the neuroscience field—if I saw one applicant with 1 CSN paper and another applicant who had 3 papers in a top tier journal, I would go for the one with three papers in a heartbeat. I am far more interested in people who show and demonstrate that they know how to “walk the walk” and “talk the talk” again and again. That is the most important criterion—continuous, sustained productivity. I want to see that they’ve climbed the mountain, gotten to the top, and started to climb another mountain, over and over. Some mountains will be higher, and some lower. But I want to see that hungry to climb mountains.
In fact, it is a bit of red flag if I see only CSN papers—because I wonder whether their perception is that early on in their faculty position they have to publish in big name journals. That may be their personality or possibly their experience in their prior labs. My impression is that, as faculty, they often waste time spinning their wheels going through reviews only to be rejected by the vanity journal and then they go to their perceived lower journal. They could have spent that time starting another project (“climbing up another mountain”).
During the hiring process, we ask the committee to come up with their top 6-10 applicants and then we look at their research statement . What is their vision for what they want to do? How would they fit in the department? We are interested in looking for the very best scientists and people who have the best vision and ideas, who can clearly articulate what they want to do, and why they want to do it. It is a subjective evaluation, but if somebody can write a really compelling vision in their research statement, that puts them way ahead.
There should be at least a couple of labs that a candidate can work synergistically with and collaborate with . I want someone who I could talk with to bounce ideas off of each other.
Frequently, many of the top candidates we get are from colleagues who we know through previous interactions. The best cases are where there are outstanding people, who are reaching out, and their mentors are reaching out—mentors reaching out is very important, maybe more important— it does matter who the letters are from. If the letter is from someone who we know and trust, the letter carries more weight .
Applicants can cold-call, but a more effective strategy is if you have a mentor that has relationships with chairs and deans that can reach out. The mentor can have much more of an impact that the applicant cold calling themselves.
The most important component of the interview for me is the chalk talk —it is the thing that always sells it. Candidates have spent years thinking about their particular projects, so if they can’t knock their presentation out of the park, that is an obvious problem. But can they stand up at a chalkboard, respond to faculty questions and defend their ideas? We want to know what it is that they cannot wait to get into the lab to do: we want to know their vision. The chalk talk is the deciding factor.
The six individuals selected for these interviews represented a variety of types of research institutions. The institutions ranged in size from large to small; some were affiliated with medical schools and some were not; some were public institutions and some private. A common feature among all, however, was excellence in research and a high expectation on faculty to obtain major grants to support their research as faculty. Our interviews suggested that, whereas funding and papers in prestigious journals can play a role in hiring, individuals doing the hiring are fundamentally looking for thoughtful, highly creative, and well-trained individuals who are in pursuit of novel discoveries, fit well into their departments, and are well-suited to personal interactions with people that have different perspective and experiences. Critically, all of those interviewed placed a high value on an individual being the driver of their research, a person with a vision for where their work will go in the future, and a sense that the work will be important. All stated that one of the most important components of an interview was the chalk talk in which the applicant needs to be able to discuss their research ideas and answer potentially unexpected questions from faculty that may be experts or may know nothing about their field. A clear negative is where the applicant is perceived to have been working on the mentor’s research, with a lack of clear vision of how they themselves will contribute something new.
In the current paper, we set out to address some of the frequent myths that we hear at NINDS about perceived metrics of success to obtain a tenure-track faculty position. We used a data-driven approach that examined the funding history and publication record of NINDS ESI R01 applicants who obtained their first tenure-track faculty position during the K99 era. We found that, whereas a history of funding and publication in high-profile journals may be beneficial to an applicant, these factors are not necessary to be successful in the academic job market. Comments by some of those we interviewed suggest that caution should be applied to the pursuit of a CSN paper. Certainly, there may be individual departments that require applicants to have funding, or potentially a CSN publication, to be considered for hiring. J. LoTurco at the University of Connecticut stated that this was an important factor in an initial screen of applicants. Overall, however, 82% of the institutions that hired an individual in our cohort hired somebody who did not have funding before being hired, and 60% of the individuals hired did not have prior funding.
Trainees with transition funding receive more job offers ( 1 ) and virtually all NINDS K99/R00 awardees obtain independent research positions. However, the number of K99/R00 awards is very small relative to the number of research positions available. The key point, however, is that inasmuch as most K99/R00 awardees obtain independent research positions, few who obtain academic positions had a K99/R00 award.
Similarly, whereas just over half of the individuals in our cohort had a first-author CSN paper before obtaining a faculty position, nearly half did not. Consistent with our data, a survey study by Fernandes et al. ( 1 ) suggested that neither funding nor publication metrics were able to distinguish between those who were hired into faculty positions and those who were not. A study by van Dijk et al. ( 2 ) suggested that publications in high impact factor journals could be used to predict success in becoming an academic PI. These findings are not inconsistent with ours based on a similar distinction as that made between K99/R00 awardees getting positions and needing a K99/R00 award to get a position. It is not unusual that individuals with one or more outstanding publications in a high impact factor or high visibility journal are highly competitive for academic positions. Our data support this conclusion, in that approximately half of our cohort had first-author CSN publications. Our data demonstrate, however, that such publications are not necessary to obtain a faculty position in that approximately half of those hired did not have one. Similarly, a survey study by Martinez et al. ( 3 ) suggested that publication in a high impact journal was relatively unimportant relative to other factors in the success of underrepresented minorities obtaining faculty positions.
Our data suggest that there are other factors beyond the scientific accomplishments of the individual that can also play a role in obtaining a faculty position. For example, 41% of our cohort was hired into an institution at which they had previously conducted research (i.e., where they were known). This was apparently more important for individuals who took an intermediate position between the postdoctoral fellowship and the tenure-track faculty position than for those who transitioned directly from postdoctoral position to the faculty position The basis for this latter distinction is beyond our ability to explain with our data, as most of the individuals who took intermediate positions did so at their postdoctoral institution. Perhaps related to this, previous studies have shown that the doctoral institution at which individuals train is a factor that influences the competitiveness of an applicant for an academic position ( 4 , 5 ).
Previous studies also found a relationship between the career support a postdoctoral advisor provides and the likelihood that an individual would obtain a tenure-track position ( 3 , 5 ). One might surmise that this support can go hand-in-hand with providing strong recommendations and even proactively promoting candidates for faculty positions, which some hirers we interviewed stated as strong indicators for selecting candidates for interviews.
The results of our study are consistent with an opinion piece by Martin ( 6 ) on tips for obtaining a faculty position. To summarize, a broad set of factors is involved in obtaining a faculty position. There is no question that publication of high-quality science is important. Moreover, one can surmise that publications in high-profile journals and obtaining funding can be beneficial (but see caveats suggested in part II above). However, our data clearly demonstrate that neither publishing in high-profile journals nor obtaining funding during training periods are required. Moreover, the faculty interviewed in our study indicated that they valued a few significant papers (significance relating to the science, not to the prestige of the journal) over many shorter, less important papers. Our data, together with the information gained from interviews, indicates that doing high quality science, being able to articulate a vision for your science, owning a project that serves as a starting point to realize your vision, communication skills, and personal fit within an environment are all key factors in obtaining a faculty position. On the basis of both our data and interviews with hirers, accomplishments such as competitive funding or publication in a CSN journal, although likely beneficial, are not necessary.
Last, there is a growing understanding of the importance of diverse viewpoints and perspectives to scientific progress, such as the benefits of bringing diverse perspectives to innovation and problem solving ( 7 – 9 ). In line with these findings, several of the hirers we interviewed described an increased emphasis in the hiring process of seeking individuals who valued and/or provided a larger diversity of perspectives.
Ideally, to do the analyses in part I of this paper, we would have had a list of all neuroscientists hired into tenure-track positions in a given year. To our knowledge, such a list does not exist. We created a cohort from a complete, defined group of individuals who recently obtained tenure-track positions and asked what accomplishments they had before being hired. Although our cohort creation did not depend upon any assumptions, our approach benefited from the knowledge that virtually all individuals who were supported by a K99/R00 award and transitioned into tenure-track assistant professor positions applied for an R01 within a few years of starting their faculty position. Of course, our cohort included only a subset of those hired during the specified time period, but this limitation likely led to an overestimate of the role of funding and publishing in high-profile journals in obtaining a faculty position. For example, many are hired into tenure-track faculty positions that do not have an expectation of applying for an NIH R01 or equivalent. These might include individuals who took faculty positions at smaller institutions, such as liberal arts institutions, who would not frequently be applying for R01s. Similarly, we did not include individuals who have applied for smaller NIH awards (e.g., R21, R03) or individuals whose work is well suited to nonbiomedical research funding. We consider it is unlikely that these individuals have a higher rate of pre-hire funding or CSN journal publications than our cohort. These assumptions are supported by the fact that 96% of NINDS R00 awardees apply for an R01 by the end of their 3-year R00 grant period; we would not have missed a lot of individuals who had K99/R00 funding by not including these other groups.
Our cohort was limited to a 3-year window of application to NINDS and did not include individuals who applied to other NIH institutes for their funding. However, there is no basis for believing that the results would be fundamentally different had we chosen a different set of grant application deadlines or included neuroscience applicants to other NIH institutes. Moreover, because of the ease of collecting the information, we expanded our K99/R00 analysis to both a 5-year window of applicants for an NINDS R01 and to include NIs and ESIs. Even with this expansion to almost 1000 individuals who applied for their first NINDS R01 over a 5-year period, the upper limit of the percentage of tenure track faculty hired during the K99 era remained at 11%.
We chose the six faculty members to interview on the basis of their experience in hiring individuals into neuroscience faculty positions at different types of institutions. One can reasonably ask whether these six institutions represent a larger number of hiring institutions in the country. Two observations suggest to us that the answer is yes. First, the six hirers we interviewed independently provided remarkably consistent descriptions of critical issues for hiring an individual into a tenure-track position. Second, the expressed lack of importance placed on funding by five of six of these individuals is supported by our data that 82% of institutions in our study hired an individual who did not have prior competitive funding.
Our approach was intended to directly address the myths that we framed. We did not seek to determine what accomplishments provide a competitive advantage for obtaining a faculty position nor did we address whether one accomplishment was more important than another. Our goal was to address the very pervasive myths that we hear almost daily that relate to whether certain accomplishments are needed to obtain an academic faculty position. Postdoctoral fellows often feel quite stressed about the perceived need for a K99 to obtain a faculty position. We at NINDS have known since the initiation of the K99 award, simply by knowing who is applying for NIH grants, that this myth was not true. The pervasiveness of these myths is potentially damaging in several ways: (i) It might cause trainees to focus on these metrics of accomplishment rather than their training and the pursuit of important scientific questions that might not quickly turn into publications in prestige journals or funding opportunities, (ii) it may discourage trainees from pursuing academic faculty positions because they feel they have not fulfilled these perceived requirements, (iii) it can mislead trainees regarding their understanding of what is important for their future, and (iv) it can put undue, and unnecessary, stress on trainees who believe they must achieve these specific metrics of accomplishment. Equally damaging, in our view, is the potential for prolongation of time in graduate school or postdoctoral training that occurs for some based on the belief that their work must be published in a very prestigious journal to be competitive for a faculty position. This focus on journal prestige or, similarly, on the importance of obtaining competitive funding during training, can lead trainees to miss opportunities to obtain critical skills and broad education that their time in a training position allows them the time to explore and that will benefit them greatly in the long run. This was reinforced through our interviews with hirers, all of which placed great emphasis on applicants having a clear vision and passion for their science, a detailed understanding of both the technical aspects and significance of their project, and an understanding of how they might fit into a new scientific environment. Not only were funding and publications in prestigious journals not critical factors for obtaining first faculty positions at five of six of the institutions we interviewed, but it was also pointed out quite clearly that excessive pursuit of a very high-profile paper at the expense of steady publication of important work may be viewed by some as a negative.
Data sources.
Data were obtained from the Information for Management, Planning, Analysis, and Coordination (IMPAC II) database, which is used by NIH staff to track and manage grant applications and awards. Most of the data were manually extracted from the NIH biosketches included with grant application submissions. When biosketches did not contain key information or lacked adequate detail, data were sourced from other repositories of publicly available information including departmental and laboratory websites, PubMed, LinkedIn, and Twitter.
From these collective sources, we were able to ascertain the following for all individuals in the study cohort:
1) The positions held and the time spent in each position from matriculation into graduate school to the start of the tenure-track or equivalent position. The positions considered were graduate student, “postdoctoral positions” (positions that start immediately after obtaining a doctorate, often called “postdoctoral fellows”), and what we termed intermediate positions before beginning the assistant professor (or equivalent) position (see below for definition of intermediate positions).
2) Funding history (including NIH F32, K-series and R-series grants, and non-NIH grants).
3) Authored publications before the start of the faculty position. For each publication, we identified the journal, whether the individual was listed as first author (including co–first author) and whether the paper was published from work performed during graduate school, postdoctoral training, or the period identified as within an intermediate position (papers that included the predoctoral advisor as an author were considered to have been associated with graduate school and papers that included the postdoctoral advisor were considered to have been associated with the postdoctoral period).
Data were organized in Excel for the basic descriptive statistics performed in the study. No inferential statistics were performed as the intent of our study was to determine whether certain accomplishments are required to obtain an academic faculty position, not to ascertain statistical differences in the prevalence of accomplishments by different groups of individuals nor to determine what accomplishments might make one more competitive for a position.
We defined intermediate positions as those that occur subsequent to a postdoctoral fellow position and before starting the tenure-track or equivalent position. Titles in our cohort included: research assistant professor, instructor, research associate, adjunct assistant professor, assistant professional researcher, assistant project scientist, assistant scientist, lecturer, research investigator, research scientist, and staff scientist.
We thank E. Marder who read an early draft of the manuscript and provided very helpful suggestions. Funding: The authors acknowledge that they received no funding in support of this research. Author contributions: N.S.H., K.P.R., M.S.T., and S.J.K. were involved in study conception, design, and manuscript editing. N.S.H. and K.P.R. carried out all data collection, analysis and data visualization, and wrote the initial draft of the manuscript. S.J.K., N.S.H., and K.P.R. conducted the interviews and interpreted the data. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper.
View/request a protocol for this paper from Bio-protocol .
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Introduction, types of positions, tenure review, the job: teaching, the job: research, the job: service, getting to know your department, being a professional.
Finally, the opportunity to teach your own syllabus and not have to worry about how to diplomatically deal with your student’s complaints that the lectures and readings are boring, out of date, and lack the intellectual frisson that you can bring to your chosen field of study. What does it mean to make the move from GSI and research assistant to assistant professor? No single summary can provide an adequate description of the variance to be found among different departments and the many different types of colleges and universities.
The paragraphs below are designed to give you a broad sense of what is expected of you as a tenure-track, junior faculty member. For more about the specifics of your field, talk to the faculty in your department and ask for the names of some recent PhDs from your department’s placement advisor. If there is a bias in what follows, it is towards smaller colleges and universities because in the smaller departments found in such institutions you are less likely to find peers who can advise you, and expectations based on your experience at Berkeley are likely to be less helpful as a guide.
With rare exceptions, there are two types of junior faculty positions: visiting/adjunct faculty and tenure-track. The difference between them is merely night and day. The other key distinction is between institutions that emphasize research and those which stress teaching. Here, the differences can be more subtle, and are often very difficult to gauge.
Visiting/Adjunct Professor – These positions are either part-time and/or limited, fixed term appointments. Visiting positions range from one semester to three years, and at times are renewable. Typically, visiting professors are hired to replace faculty on leave or to provide coverage in an area where the administration doesn’t want to commit a tenure slot. Visiting/adjunct faculty generally carry higher teaching loads at a significantly lower salary than their tenure-track brethren. Often they must share an office, sometimes located in the basement or some other out of the way place, and lack access to computers and other resources. You are also less likely to have control over which courses you teach and how you teach them. Given the heavy teaching demands, and especially if you have a lengthy commute, you may find it difficult to get much scholarly work done. Visiting positions can provide you with teaching experience and help keep body and soul together, but they are rarely an avenue into a tenure-track position. When such positions come open, they almost always entail a national search. Your status as the incumbent may help, but is not likely to be a decisive factor.
Lecturer – Lectureships are typically longer term than visiting/adjunct positions, but are non-tenured positions. Contracts can range up to five years, often renewable, but as above with a higher teaching load and less infrastructural support than a tenure-track position. These positions are generally found in areas such as foreign language instruction or the arts which may or may not require a PhD.
Tenure-Track (aka The Promised Land) – These are positions for which there is every expectation, and administrative budgetary commitment, that the person will receive a tenure review within seven years that if passed successfully provides for lifetime employment with the college or university. Most newly-minted PhDs are hired as assistant professors, promoted to associate upon achieving tenure, and go through an additional review, five to seven years later, for promotion to full professor. The rank of associate professor does not necessarily imply tenured status. An experienced, assistant professor who moves to another university or a PhD with significant, relevant, non-academic experience may be hired as a non-tenured associate professor generally with tenure review to follow within a year or two. An instructor is generally an ABD (All But Dissertation) hired for an assistant professor slot and is usually listed as such as soon as the degree is awarded.
Once you are hired, the tenure clock begins to tick, and usually you will come up for tenure in your sixth year. Typically, you will receive an initial two to four year contract, and go through a review in your second or third year. At some schools, these reviews are perfunctory, but at others they are a major production requiring you to assemble a substantial file including outside letters of support for your scholarship at least some of which are from people who were not on your committee or in your placement file. If you are successful in this first review, you receive an additional contract that will take you through the probationary period. If not, you usually have a remaining academic year on your contract to find a new position. At most research-oriented colleges and universities, you will receive a semester or year-long paid sabbatical after successfully navigating this review.
The tenure review generally occurs in your sixth year, though at most institutions you can choose to come up for tenure earlier. If you are hired as an ABD, are injured or disabled for a significant period of time, get grants to take an unpaid leave, or have a child during your probationary period you may be able to negotiate having your clock stopped for a semester or a year. If you are offered a tenure-track job as an ABD, the time to raise the possibility of an extension is when you are hired (and they are still dazzled by you) and not two years later.
The review process is one of the most demanding and nerve-wracking experiences you will ever have to go through – with good reason. You are asking your department and institution to allocate a significant share of their resources to you for the next thirty to forty years. On the other hand, if you receive it you gain a measure of security and freedom in your chosen profession that is extremely rare in contemporary society. You need to start thinking about what you want to have in your tenure file from the minute you accept their offer.
The tenure file begins with
The file is usually completed by October and made available to all the tenured members of the department. Letters are then written by every tenured member of the department (if it is small) or tenured members of your sub-field and interested others (if it is large) which then become part of the file. There is a formal vote by the department, and the resulting recommendation is communicated in the form of a final letter from the chair, representing the overall view of the department. The file is now complete.
At most but not all schools, the recommendation of the department is then forwarded along with your dossier to a committee of tenured faculty drawn from a range of departments which may or may not endorse the recommendation of the department. Depending on the size of the institution, your file may pass up through more than one such committee. Finally, it is up to the president, provost, or chancellor to make the final decision. Presidents et. al. usually have absolute discretion in this regard, and may choose to reject unanimous recommendations from below.
Throughout the following paragraphs, there are references to choices that should be made by junior faculty (the non-tenured) with at least some consideration of how it will impact your ability to present as impressive a tenure file as possible. This is not meant to convey cynicism, but it’s important to realize that absent tenure you will be unable to accomplish most if not all of the goals you set for yourself when you decided to enter academia. You will need to ask yourself whether a given project, course, or commitment should be started now, or deferred until after you’ve satisfied the powers that be that you deserve the commitment that tenure entails.
As an assistant professor your job consists of three components: teaching, research, and service to the institution (serving on academic and administrative committees). The relative importance of these three varies widely depending on the institution and its requirements for tenure.
At a major research university or top-ranked small college, the teaching load is typically 2-2 (two courses per semester, and at a university you may teach graduate and undergraduate versions of the same course each semester) in the social sciences and humanities – less in the sciences and engineering. At the other end of the spectrum, there are many colleges and some universities where faculty carry a 4-4 teaching load. Even in the latter case, it is unlikely that you would be asked to teach eight different courses, and a distinction is commonly made between the number of courses you teach and the number of preparations (i.e., teaching the same syllabus more than once in a semester or year). The number of preparations you are required to teach may be almost as important as the number of courses, and this is often negotiable for first year faculty if you remember to ask.
Creating new courses can require an enormous investment of time and energy especially if you teach in a field where textbooks are rarely utilized. No one (rather no “sane” one) teaches five new courses their first year. Borrow from your friends, and remember imitation is the sincerest form of flattery.
Some institutions and departments have set curriculum and teaching methods, especially for intro and core courses, but for the most part you will enjoy wide latitude in designing and teaching your courses as you see fit. It is important, however, to think about how your style of pedagogy fits with the prevailing culture of your new home. Students at small colleges (especially the better ones) will resist having to listen to lectures on an ongoing basis without the opportunity to participate. At the same time, a purely Socratic approach is likely to bog down in an intro class of 500. You want to find a style and approach that fits your personality and your pedagogical philosophy, but it also makes sense to recognize that you are not teaching in a vacuum. If you encounter difficulty, as most do, talk to your new colleagues. Everyone has gone through the same adjustment, and most are happy to help.
In addition to coursework, the teaching function typically involves advising incoming first year students, majors, and supervising independent studies and senior theses. First year faculty are usually exempt from these duties. They can be among the most satisfying parts of the job, but they can also be very time consuming. In a similar vein, you may be asked to teach as a part of a multi-disciplinary team (e.g., The Renaissance, or The Emergence of the Pacific Rim). This can be a fun and stimulating experience and a good way to get to know faculty from other departments. But team-taught courses tend to be more work, and you are very unlikely to get much credit for being a good corporate citizen when the tenure committee meets.
Higher education is getting increasingly competitive, and there are very few colleges and universities that are not keenly interested in their relative status and prestige as reflected in guidebooks and, especially, US News and World Reports. One of the keys to increasing an institution’s visibility and ability to attract good students, in the minds of most senior officials, is the reputation of its faculty as reflected by publications and other markers of recognition and achievement (e.g., getting grants). You may find yourself at a place where many tenured and senior faculty haven’t published for years, if ever, but times have changed. Many schools which used to look only at teaching, service, and general amiability, now expect publications in a tenure file.
Across the spectrum of institutions, expectations have ratcheted upwards. Where a few articles would have sufficed a few years ago, you now need a book. Instead of a book, you need a book (at a university or prestigious commercial press) and clear evidence of progress on a post-dissertation project.
You are unlikely to ever get a clear answer to the question how much am I expected to publish for tenure. The best you can do is try to assess what recently successful candidates have done in similar fields. You need to be aware that different disciplines, even if closely related, may have different standards. Political scientists for example write books, economists write monographs and articles. If your primary medium of scholarly expression is relatively new (computer software, multimedia, internet-based journal) or unusual (e.g., plays directed, dances choreographed, exhibitions curated) you need to educate those who will evaluate your scholarly production sooner rather than later. Don’t assume that they must have done it before, especially at a smaller institution.
Don’t delay sending out draft articles and manuscripts until you have it just right. You will likely have to revise it on the basis of reviewers’ comments anyway. Let it go. Time is of the essence, and passes shockingly fast, even if you don’t have small children. There are few places in life where the perfect is more of an enemy of the good/publishable.
After your first year, you will probably be asked to serve on one or more faculty committees. These committees are responsible for governing and supervising a wide range of activities at the institution. Here again you need to practice moderation. Many committees tackle important issues that will have a substantial impact on an aspect of the institution that interests you deeply, but will also be very time consuming.
Other forms of service include organizing a conference or lecture series, serving as advisor to a student organization, taking on a part-time administrative position (e.g., assistant director of Asian Studies).
Beware, it is important to interact with colleagues from other departments , (some of whom will sit on the committee that will review your tenure file) on a professional basis and many service activities are both interesting and important. On the other hand, it is a rare institution where great service can overcome mediocre research and teaching. You need to find a balance; you need to be careful.
Your department is where you live, your family. Like many today, it may be an extremely dysfunctional one, but it’s yours. The first hurdle you must overcome on the road to tenure is to obtain the strong endorsement of your department. You may not like some of them, but you need to gain their respect.
Your first challenge is to learn the lay of the land. The first few departmental meetings will be very disorienting as names and phrases fly across the table as a series of allusions, metaphors, and shorthand evoking laughter or derision while you sit there dumbfounded. It will take some time to learn the informal patterns and organizational culture that characterize your new home, but it is important to make the effort. Many of the opinions and positions held by individuals and factions and the bases of their unwillingness to “try that again” (no matter how compelling your logic), will remain inexplicable absent an understanding of the departmental and institutional history.
It is unlikely that you have ever been exposed to politics as pervasive and at times as vicious as you will find in many institutions of higher learning. People live together for many years, and insults real and imagined can fester for a long time. Your job is not to be consumed by it, but to learn enough not to be caught in the middle.
Most of what you need to know will not be expressed at formal meetings. If your department has informal get togethers, attend them. Ask innocuous-sounding questions about names you’ve heard or issues you don’t understand, and allow them to tell stories. If the members of your department aren’t collectively very social, suggest some ways of getting together as a group or individually. Even if you’re not terribly athletic, going to the gym or playing racquetball is an excellent way of relieving stress and getting to know your colleagues in a less guarded setting.
As a professor you need to engender the respect of your fellow faculty members and create an appropriate social distance between yourself and your students. A senior colleague once described his first semester at the college where he had dressed very informally and treated the students as peers only to have one of them express dismay and disappointment at the low grade he had received from his buddy the professor.
Women and those whose hair has not yet begun to gray may have a more difficult time engendering the respect they deserve. It may seem odd at first, but let students call you “Dr.” or “professor” (even if you’re still ABD), even if you’d rather go by your first name. If your colleagues neglect to use your title (especially in front of students, parents, colleagues, or administrators) and refer to you as “Mr.” or “Ms.,” gently but firmly correct them in private. They probably don’t mean anything by it, but you have enough to worry about without the added confusion about your professional status.
You need not carry the burden of appearing omnipotent and all knowing. It is perfectly acceptable to respond to a question with “I don’t know. It’s an interesting question. I’ll look it up before next class.” One of the greatest benefits of a Berkeley PhD is that for the rest of your life, you can say “I don’t know,” and not feel stupid because you have a piece of paper from one of the world’s leading universities attesting to that fact.
At the same time, don’t overestimate your relative ignorance in areas outside of your specific research specialty. An undergrad is not going to challenge your interpretation of the origins of the Dead Sea Scrolls by citing the new article in American Scholar that you haven’t yet gotten around to reading. Relax, if you say it, they’ll believe it.
At least for a while, in your heart of hearts you’ll be confident only that the university will soon enough discover its error in awarding you the PhD, and at some point will brand you (in public no doubt) the fraud you know you are. This too will pass as you come to realize that students are extremely gullible and many of your colleagues are even greater frauds than you. That is to say, you know more than you think you do, and students and the people you work with will appreciate the range and depth of your knowledge and abilities if you let them.
If you feel students and colleagues are not according you the respect you deserve, talk to more senior colleagues or other junior faculty who have likely shared the same experience. It’s much easier to ease up once you have established yourself as a professional, than the reverse.
Berkeley Career Engagement UC Berkeley, CA 94720
Estimation of probabilities to get tenure track in academia: baseline and publications during the phd., key takeaways:.
This article aims to estimate the probabilities of any PhD student to get a permanent position (tenure track) in academia, in order to inform career decisions. The findings have been:
The most important factor determining whether you actually get such positions is the number of first-authored articles, although the precise numbers by field are not known in Pure Science or Technological fields. They are nevertheless available in the Biomedical and Sociology fields.
Contributing with a career to one important cause is perhaps one of the most effective ways of having a positive impact in the world. However, since EA careers are not so well established, the few opportunities that are available tend to grab all the attention, and less conventional choices, although celebrated, are often difficult to assess and find.
For instance, I aim to contribute to the AI Safety problem. However, it is not clear what is the best way. In my case I see two main options for next steps:
Some cons of working in academia due to the long time it takes, the low chances of getting tenure, and the perverse incentives to publish a lot no matter how relevant the topic is. On the other hand, working at academia gives a lot of freedom, social status and influence and the possibility of making fields of interest to Effective Altruism (including e.g. AI safety and global priorities research) more respectable. Some other considerations might be found in CS PhD 80000 hours career profile .
However, one factor not analysed in depth in that profile is the probability of getting a tenure position. In this post we aim to get a first estimate that could inform people.
Just to inform the reader, the typical career path in academia usually involves a PhD (~3 years in Europe, 5 in the US which also includes the master), one or two postdocs of 2-3 years each (this step may be longer, up to 7-8 years), and then obtaining access to junior research fellows, which may be promoted to professorship with time. The most difficult part seems to be changing from the temporary postdoctoral positions to the permanent research fellow one.
Approximate academic career path. The lengths are only estimated based on the comments above, actual timelines vary a lot. Junior researcher is the name I give to the first permanent position.
In order to establish a baseline, the following might be useful:
The data is somewhat confusing and contradictory, probably because I am mixing non-comparable sources. In any case, other minor comments is that in Europe there seem to be higher chances (based on point 4) and that Biology seems harder than Physics and Chemistry
Percentage of PhD students who get a permanent position in academia according to each source above.
With respect to the amount of people who want to stay in academia I have found
Overall, my personal estimate is that I'm 90% sure that 10-30% of students get tenure, and 65% that the intervale is between 10% and 20%. Furthermore, I estimate based on the previous sources that around 70% of the PhD alumni would want to work in academia. Hence, conditional on wanting to work in academia your baseline chances should be in the 15-30% range to start with.
Update : Since I published this article, I have found this report , that decomposes probabilities by field of study in Concordia University, Canada:
The discipline with the highest percentage of tenure-track and tenured professors is business (69%) followed by social sciences (27%), humanities (22%), engineering (21%),fine arts (14%) and sciences (11%).
Update 2 : I have found a second article on 7000 US STEM graduates detailing what percentage of people progress to the next stage, indicating that overall 21% of PhD holders get tenure, and that 24% do in Computer Science.
Pipers are those who get tenure, never are those who don't stay in academia after their PhD, droppers those who drop mid way.
Some articles that are important in this respect:
Some conclusions from this section is that publishing and specially first-author publishing is the largest predictor of academic success. What is much harder to find is concrete numbers for particular fields.
I believe that having a more accurate estimate of what are the actual chances of landing a job at academia can help to gauge the pros and cons of this career path.
From the previous sections it is likely (65% chance in my opinion) that the probability baseline probability of landing a permanent job at academia conditional on trying is in the 15-30% range.
However, there is a lack of data on how exactly to use the inside view to gauge personal probabilities. In particular, most studies analysing this have been done for particular fields which may not replicate in others. However, the best estimate seems to be that if you are going this path, the main metric you should be looking at is the number of articles where you are the first author. More research is needed to calibrate estimates based on inside-view factors.
I would like to thank the incredible help of Jaime Sevilla, who provided useful feedback on a draft of this article.
Academic here:
Hey Pablo - thanks for working this up. It's nice to have some baseline estimates!
As you say, Tregellas et al. shows that the probability of tenure varies a lot with the number of first author publications. It would be interesting to know if tenure can be predicted better with other factors like one's institution or h-index - I could imagine such a model performing much better than the baseline.
Two other queries:
You're right Ryan, I'll modify the second complicated sentence. I am actually not sure what is the difference between tenure and tenure track, to tell the truth. However, in one of the documents above I saw that institution is not such a strong predictor (point 4), but h index seemed useful (in point 2 the h-index is discussed).
Interesting. The point 2 article by van Dijk seems decent. Figure 1B says that the impact factor of journals, volume of publications, and cites/h-index are all fairly predictive. University rank gets some independent weighting (among 38 features, as shown in their supplementary Table S1), but not much.
Looks like although the web version has gone offline, the source code of their model is still online!
I strongly agree with Ryan that success is to a relatively large degree predictable, as can be done in the PCA decomposition of point 2 above, figure 1C. I think it would be very valuable to have such a model, but the current code is only for biology (the impact factor will fail for instance for anything different). If one wanted to fit a model to predict it, it could probably use google scholar and arxiv, but the trickiest part would be to recover the position of those people (the target), which may partially be done using google scholar.
I just posted another article I found on average publication rates in Norway for different positions, ages, fields and gender.
This is helpful, thanks.
The information is probably here somewhere, but is that the probability of getting tenure given you finish your Ph.D.? I.e. Does this account for dropping out?
Somewhat tangential, but I think accounting for the chance of working on AI safety (or something comparably effective) outside of academia will help. I think this is more common in Economics (e.g. World Bank). But I guess OpenAI or similar institutions hire CS PhDs and working there possibly has a similar impact to working in academia.
I would say you basically cannot get tenure if you don't get a PhD, so dropouts are not taken into account in any of the previous statistics as far as I understood them. All this metrics are of the kind of: x% of PhD alumni got tenure, or similar.
I actually agree that taking into account the private sector could help, but I am much less certain about the freedom they give you to research those topics, beyond the usual suspects. That was why I was focussing on academia.
In the US, about half of people who start PhD programs get the degree. Also, a big factor that I thought I commented about here (I guess they removed comments) is that most tenure track positions at least in the US are teaching intensive, so there is not much time for research.
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Our students are engaged in a wide range of academic pursuits that include degree programs in 160 undergraduate and graduate fields delivered by 6 different colleges.
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Marking its first hundred years, Arkansas State University continues to expand in exciting ways.
Dr. brendan kelly begins tenure as new president of arkansas state university system.
LITTLE ROCK – Dr. Brendan Kelly today officially began his appointment as only the third president of the Arkansas State University System.
The ASU System Board of Trustees voted to hire Kelly, who had served as president of the University of West Georgia since 2020, on June 5. He succeeds interim president Dr. Robin Myers and the second system president, Dr. Chuck Welch, who left the system in January after nearly 13 years to become president and CEO of the American Association of State Colleges & Universities (AASCU) in Washington, D.C.
“The ASU System is open for business and will expand the state’s economy by improving lives and opportunities – starting with our students and their families,” Kelly said. “I believe strongly that if we do what's right for a student, and then we make it work for the institution, we’ll never go wrong. The energy that exists in most communities emanates from institutions like the ones that we represent. I’m excited that the ASU System comprises not only seven outstanding campuses, but also some terrific communities in Arkansas.
“We have the opportunity to galvanize communities, look to the future to ensure the ASU System is ready to meet it and work to expand the impact of higher education within Arkansas and beyond,” he added. “From cutting-edge research initiatives to skilled workforce training, I’m confident the ASU System has the ability to meet the demands of our rapidly evolving economy.”
Kelly said the ASU System role appealed to him “because it represents one of a handful of roles in the United States in which one has the ability to influence and shape higher education throughout an entire state. I am grateful to each member of the ASU Board of Trustees for their faith in me to offer leadership to a university system that has the capacity to provide access to high quality, higher education to the vast majority of Arkansans and many more beyond the state borders. This is an opportunity to support the economic evolution of the state at large.”
Prior to joining UWG, Kelly was chancellor at the University of South Carolina Upstate in Spartanburg and Greenville from 2017-2020 and was appointed as interim president of the University of South Carolina in 2019. He was vice president of university advancement and president of the UWF Foundation, Inc., at the University of West Florida in Pensacola from 2013-2017, where he led a successful $50 million capital campaign. His teaching career spanned 13 years in Florida and Michigan in communication arts.
His focus at UWG has been innovative programming both inside and outside the classroom with an emphasis on creating valuable life and career outcomes for students, with a commitment to launch and advance student careers upon graduation. He served on the board of directors of multiple regional and state economic development entities, as well as the Atlanta Regional Higher Education Consortium.
While leading and implementing the “Becoming UWG” Institutional Strategic Plan, Kelly has led UWG to record-breaking fund-raising efforts, including the largest gift in institutional history, and oversaw an enrollment resurgence. The UWG campuses in Carrollton, Newnan and Douglasville have a combined 12,769 students and are expected to exceed 14,000 this fall. Its spring enrollment was 11.4% higher than last year, while its graduate enrollment surged 41%. UWG recently completed a move in athletics from the NCAA Division II Gulf South Conference to the Division I Atlantic Sun Conference.
“We’re thrilled to have Dr. Kelly officially beginning his leadership of the ASU System,” Board Chair Christy Clark said. “I know he is eager to get busy, and our board is eager to support him and his vision for the System and our campuses. We look forward to a smooth transition as he leads our experienced System team and chancellors.”
Trustee Price Gardner, who chaired the search on behalf of the board, said: “We are excited to welcome Brendan Kelly as our new System president. We believe Dr. Kelly’s experience and outstanding record of achievement throughout his career will continue the growth and development of our System and lead us in addressing the ever-changing challenges facing higher education and our focus on student success. He has been described as a visionary, talented communicator and a person with the ability to elevate the reputation of the institution and further its mission. Our board looks forward to the opportunity to work with him.”
Kelly is a member of the AASCU board of directors and was named “One of the Most Influential Georgians” by Georgia Trend magazine and James Magazine. He was active with the National Forensic Association (NFA) and speech/debate competitions for many years and was honored as a NFA Hall of Fame inductee and with the NFA Eddie Myers Distinguished Service Award.
He received a Bachelor of Science degree in public relations and a Master of Arts degree in communication at Eastern Michigan University and his Doctor of Philosophy in rhetoric and political communication at Wayne State University.
Kelly and his wife, Dr. Tressa Kelly, have three children – Bree Luckey (married to Drew Luckey), Liam and Kieran.
The Arkansas State University System , based in Little Rock, serves more than 35,000 students annually on campuses in Arkansas and Queretaro, Mexico, and globally online. The System includes Arkansas State University , a four-year Carnegie R2 research institution in Jonesboro with degree centers at ASU-Beebe, ASU-Mountain Home and ASU Mid-South in West Memphis. Arkansas State University Campus Queretaro opened in September 2017. The system's two-year college institutions include ASU-Beebe , with additional campuses in Heber Springs and Searcy and an instructional site at Little Rock Air Force Base; ASU-Newport , with additional campuses in Jonesboro and Marked Tree; ASU-Mountain Home ; ASU Mid-South in West Memphis; and ASU Three Rivers in Malvern. Henderson State University in Arkadelphia became the system's second four-year institution member in February 2021.
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As stated in the title, I'm wondering if someone could shed some light on whether it is possible to land a tenure-track position in philosophy with a J.D. By way of background, I double majored in philosophy and psychology, and I am heading into my 3rd year of law school. A life as an academic sounds more appealing than it did a few years ago when I chose law school over a Phd., and I'm curious if a J.D. could suffice. Any helpful insight on this topic would be greatly appreciated!
Realistically, no.
There's more philosophy PhDs looking for jobs than jobs available in philosophy, and your undergraduate experience while helpful probably won't make you stand out as an expert in philosophy. Or to put it another way, while you've been earning your J.D. which prepares you for law, philosophy PhD earners have been studying the very subject material they will teach.
But you might be eligible for positions where they are looking for someone in philosophy of law. Specifically, if they want someone with practical experience (but then they wouldn't want you straight out of your J.D.). Probably a good way to ask this question would be to e-mail Brian Leiter (or someone else) who works in law and philosophy.
It is possible but you'll be competing against people with doctorates in Philosophy and dissertations and publications in philosophical journals.
Usually the requirement for faculty at colleges and university is the "terminal degree in the field." For law professors, this is the JD. For studio artists, the MFA. And most other faculty, the PhD.
I don't think the Provost would raise any issues with your hiring in terms of credentials, but the more difficult thing will be to convince the hiring committee (consisting of mostly philosophy profs with some other humanists) that you're the right person for the job.
Be prepared to articulate why you'll be capable of not only teaching PHIL101, but PHIL2xx, 3xx, and 4xx. If you're at a university, would you be capable of mentoring PhD students? The assumption will be that you don't have that experience, so the burden of proof will be on you.
Many JDs figure it's just as easy to get the PhD with a few more years of school and emerge with a JD-PhD.
[Editorial Aside: That all being said, I think you're a bit nutso. Have you seen the starting salaries for law professors? They are earning $150,000+ in the few few years and often have tenure by their 4th year. If I were you, I'd go into the teaching of law and teach very philosophical law classes.]
At a theoretical level, it's certainly possible. Saul Kripke never went to graduate school at all, but that didn't stop Princeton from giving him tenure in philosophy. If you're the next Kripke, then nobody will care what sort of degree you have.
At a practical level, you can't get hired in philosophy with just a J.D., assuming you aren't talking specifically about philosophy of law (which might draw on your legal background on an equal footing with philosophy). If you are, then that's worth a more detailed and specific question regarding the necessary background and experience. For a start, see these comments by Brian Leiter. If you want to do philosophy of law with a primarily legal background, it sounds like the chances are higher if you look for a law faculty position rather than a job in a philosophy department.
On the other hand, if you have in mind a philosophical career that does not make heavy use of your legal background, then the J.D. will be essentially useless. It's a terminal degree, but not one that certifies any level of background or experience in philosophy, so it will be irrelevant. The only way to get a job in a philosophy department at a four-year college or university is to convince them that you have the equivalent of a Ph.D. in philosophy (including not just basic knowledge, but also advanced seminars, carrying out research, and writing a dissertation - even if you won't be doing further research or teaching graduate courses).
This level of experience would be rare among law students, and even if you genuinely have the equivalent of a philosophy Ph.D. you should expect to have a difficult time making a convincing case for this.
I haven't seen your particular case (applying for philosophy jobs with a J.D.) in practice, but I've seen similar sorts of job searches in other fields (arguably with closer degrees, since Ph.D. degrees in related fields are more similar to each other than either is to a J.D.). In order to pull this off, you must have credible and compelling recommendations from mainstream faculty in the field you're applying to. So one key question is what the philosophy faculty at your current university think of you. Are they willing to write letters making a case that you are as qualified as their own Ph.D. students? If so, then you may have a shot at this, and you should talk with them for advice based on your personal situation. If you don't know any philosophers who are willing to write that sort of letter for you, then that will be a major barrier to getting a job in a philosophy department.
I think in part you're mixing up (or at least not clearly distinguishing) two separate questions:
To go to an analogy that might be more familiar, your question is a bit like asking "Can you go to Harvard if you get a GED?" The answer is yes in a certain formal sense; I'm sure there are people whose highest qualification is a GED who have gone to Harvard. Probably, somebody, somewhere, with a JD has gone on to being a philosophy professor without getting an additional degree, but that doesn't mean it's something a reasonable person should expect to do.
The important point here is that having a degree (even a very specific kind of degree) is not the primary qualification for becoming a professor. It's publishing in your field, convincing important people in the field that you are smart and good at what you do, and being able to teach undergraduate and graduate students in your field. A PhD helps you become a professor because it gives you a chance to do those things in a conducive environment, not because you get a sheepskin at the end. If you are able to do those things, maybe you can be successful in philosophy. It doesn't sound from your question like you've had much of a chance to do them yet.
The fact that you are a freshly graduated JD, and not a PhD cries out loud that you don't have any substantial research in the chosen field (neither other academic field). I don't think it is a good sign...
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Faculty hiring and retention determine the composition of the US academic workforce and directly shape educational outcomes 1 , careers 2 , the development and spread of ideas 3 and research priorities 4 , 5 . However, hiring and retention are dynamic, reflecting societal and academic priorities, generational turnover and efforts to diversify the professoriate along gender 6 , 7 , 8 , racial 9 and socioeconomic 10 lines. A comprehensive study of the structure and dynamics of the US professoriate would elucidate the effects of these efforts and the processes that shape scholarship more broadly. Here we analyse the academic employment and doctoral education of tenure-track faculty at all PhD-granting US universities over the decade 2011–2020, quantifying stark inequalities in faculty production, prestige, retention and gender. Our analyses show universal inequalities in which a small minority of universities supply a large majority of faculty across fields, exacerbated by patterns of attrition and reflecting steep hierarchies of prestige. We identify markedly higher attrition rates among faculty trained outside the United States or employed by their doctoral university. Our results indicate that gains in women’s representation over this decade result from demographic turnover and earlier changes made to hiring, and are unlikely to lead to long-term gender parity in most fields. These analyses quantify the dynamics of US faculty hiring and retention, and will support efforts to improve the organization, composition and scholarship of the US academic workforce.
Prestige plays a central role in structuring the US professoriate. Analyses of faculty hiring networks, which map who hires whose graduates as faculty, show unambiguously in multiple fields that prestigious departments supply an outsized proportion of faculty, regardless of whether prestige is measured by an extrinsic ranking or reputation scheme 11 , 12 , 13 or derived from the structure of the faculty hiring network itself 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 . Prestigious departments also exhibit ‘social closure’ 15 by excluding those who lack prestige, facilitated by relatively stable hierarchies over time, both empirically 17 and in mathematical models of self-reinforcing network dynamics 30 , 31 .
These observations are important because of the broad impacts of prestige itself. Prestigious affiliations improve paper acceptance rates in single- versus double-anonymous review 32 ; faculty at prestigious universities have more resources and write more papers 33 , 34 , receive more citations and attention 35 , 36 , 37 and win more awards 38 , 39 ; and graduates of more prestigious universities experience greater growth in wages in the years immediately after graduating 40 . Furthermore, the vast majority of faculty are employed by departments less prestigious than those at which they were trained 27 , making prestigious departments central in the spread of ideas 3 and academic norms and culture more broadly.
Less well studied are the processes of attrition that, together with hiring, shape the data underpinning the analyses reviewed above. Evidence suggests that women in science and engineering (but not mathematics) and foreign-born faculty leave the academy in mid-career at higher rates than do men 41 and US-born 42 faculty, respectively, making clear the fact that the US professoriate is structured by more than just prestige. These processes are particularly important in light of clear evidence that the topics studied by faculty depend not only on their field of study, but also on their (intersecting) identities 43 .
However, the difficulty of assembling comprehensive data on US faculty across fields, across universities and over time has limited analyses and comparisons, leaving it unclear how much of the observed patterns and differences are universal, vary by field or are driven by current or past hiring or attrition. Less visible but just as important are the inherent limitations of focusing only on the placement of faculty within the US system, to the exclusion of US faculty trained abroad. A broad cross-disciplinary understanding of academic hierarchies and their relationship to persistent social and epistemic inequalities would inform empirically anchored policies aimed at accelerating scientific discovery or diversifying the professoriate.
Our analysis examines tenured or tenure-track faculty employed in the years 2011–2020 at 368 PhD-granting universities in the United States, each of whom is annotated by their doctoral university, year of doctorate, faculty rank and gender. To be included in our analysis, a professor must be a member of the tenured or tenure-track faculty at a department that appears in the majority of sampled years, which yields n = 295,089 faculty in 10,612 departments.
This dataset resulted from cleaning and preprocessing a larger US faculty census obtained under a data use agreement with the Academic Analytics Research Center (AARC). To facilitate comparisons of faculty across areas of study, we organized departments into 107 fields (for example, Physics, Ecology) and eight domains (for example, Natural Sciences) (Extended Data Table 1 ). Field labels, provided in the AARC data, and subsequently hand-checked, are not mutually exclusive, such that 23% of faculty were assigned to multiple fields (for example, members of a Department of Physics and Astronomy were assigned to both Physics and Astronomy). For faculty associated with multiple departments, we restricted our analyses to their primary appointments only. All doctoral universities were manually annotated by country. Self-reported faculty genders were used when available, and otherwise algorithmically annotated (man or woman) on the basis of historical name–gender associations, recognizing that there are expansive identities beyond this limiting binary. These procedures resulted in gender annotations for 85% of records; faculty without name–gender annotations were not included in analyses of gender but were included in all other analyses. Comparing data collected in adjacent years, we also annotated all instances of new hiring, retention and attrition. Data preparation and annotation details can be found in Methods .
To analyse patterns of faculty hiring and exchange among US universities, we created a faculty hiring network for each of the 107 fields, eight domains and for academia as a whole ( Methods ). In such a network, each node u represents a university, and a directed edge u → v represents an individual with a doctorate from u who becomes a professor at v . Faculty employed at their doctoral universities, so-called self-hires, are represented as self-loops u → u . When aggregating field-level hiring into networks for the eight domains or for academia in toto, we take the union of the constituent fields’ edges, which avoids double-counting of faculty rostered in multiple fields. Anonymized data supporting our analyses are freely available (Data availability).
In general, although our data show that US academia largely requires doctoral training, the ecosystem of broad domains and specialized fields exhibits diversity in its credential requirements. Fully 92.7% of all faculty hold doctoral degrees yet only 1% lack a doctorate in Social Sciences compared with 19% in the Humanities (Fig. 1a ). Even within the Humanities there is wide variation, with only 7% of remaining faculty lacking a doctorate if one separates out the fields of Theatre (67% non-doctorates), Art History (44%), Music (30%) and English (11%) (Extended Data Fig. 1 ).
a , Degrees of n = 295,089 US faculty by domain, and for academia overall, separated by non-doctoral degrees (solid bars), US doctorates (open bars) and non-US doctorates (hatched bars). b , Continent of doctorate for n = 31,845 faculty with non-US doctorates by domain. Within the Europe and North America bars, darkened regions correspond to faculty from the United Kingdom and Canada, respectively. c , d , Ratios of average annual attrition risks among faculty with doctorates from Canada and the United Kingdom ( c ) ( n = 11,156), and from all countries other than Canada, the United Kingdom and the United States ( d ) ( n = 20,689), versus all US-trained faculty, for each field (colours), domain (grey) and academia (blue), on logarithmic axes. Circles, significantly different from 1.0, χ 2 test, Benjamini–Hochberg-corrected P < 0.05; crosses, not significant.
This variation in credentials is paralleled by US faculty trained internationally. Overall, our analysis finds that 11% of US faculty have non-US doctorates yet only 2% of Education faculty received their doctorates internationally compared with 19% of Natural Sciences faculty (Fig. 1a ). However, internationally trained faculty primarily receive their training from a limited range of geographical areas, with 35.5% trained in the United Kingdom or Canada compared with just 5.4% from all countries in Africa and the Americas, excluding Canada (Fig. 1b ).
Our data suggest that differences in country of doctoral training are not without consequence for the dynamics of the professoriate. Using the 10 years of observations in our data, we identified instances of attrition and estimated the annual per-capita risk of attrition for faculty trained in three groups of countries: Canada and the United Kingdom, the United States, and all others. Those with doctorates from Canada and the United Kingdom ( n = 11,156) left their faculty positions at statistically indistinguishable rates compared with US-trained faculty ( n = 238,676) in all 107 fields and eight domains, and at slightly lower rates overall (significance level α = 0.05, Benjamini–Hochberg-corrected χ 2 test; Fig. 1c ). In stark contrast, those with doctorates from all other countries ( n = 20,689) left the US tenure track at markedly higher rates overall, in all eight domains and in 39 individual fields (36%), and in no field did such faculty leave at significantly lower rates ( α = 0.05, Benjamini–Hochberg-corrected χ 2 test; Fig. 1d ). We note that our data allow us to consider hypotheses related only to country of doctoral training, not to country of citizenship or birth, leaving open questions about foreign-born yet US-trained faculty 42 .
For faculty with US doctorates, we find that academia is characterized by universally extreme inequality in faculty production. Overall, 80% of all domestically trained faculty in our data were trained at just 20.4% of universities. Moreover, the five most common doctoral training universities—UC Berkeley, Harvard, University of Michigan, University of Wisconsin-Madison and Stanford—account for just over one in eight domestically trained faculty (13.8%; Fig. 2a and Extended Data Table 3 ). Even when disaggregated into domains of study, 80% of faculty were trained at only 19–28% of universities (Fig. 2b ).
a , Proportions of US faculty produced by US universities, sorted by production rank, with the university producing the most faculty having a rank of 1 ( n = 238,676 faculty; n = 387 universities). Quintiles of production are highlighted with alternating colours and annotated with the number of universities falling within each. By production, the first quintile contains only eight universities and the bottom contains 308. b , Lorenz curves for faculty production at the field level (coloured lines) and at the domain level (grey lines). A point is placed at the site along the domain-level Lorenz curve where 80% of faculty have been produced.
Our analysis shows that universities that employ more faculty generally also place more of their graduates as faculty elsewhere (Pearson ρ = 0.76, two-sided z -test P < 10 −5 ). Nevertheless, at the level of domains and fields, faculty size alone cannot explain faculty production and placement: in academia as a whole, in all eight domains and in 91 of 107 fields (85%), faculty size and production are from significantly different distributions (Kolmogorov–Smirnov (K-S) test, Benjamini–Hochberg-corrected P < 10 −5 for academia and domains, P < 0.01 for fields), reproducing the findings of a previous analysis of faculty hiring networks in Business, Computer Science and History 27 . For the remaining 16 fields (15%), the hiring of one’s own graduates plays a key role: when self-hires are excluded, the distributions of hiring and production of only 12 fields (11%) remain statistically indistinguishable. In other words, inequalities in university or department size do not explain inequalities in faculty production.
The Gini coefficient is a standard way to quantify inequality in a distribution, with G = 0 representing perfect equality and G = 1 maximal inequality. We find that inequality in faculty production across academia as a whole is both marked ( G = 0.75) and greater than the inequalities in seven of eight domains. Of those domains, inequality is lowest in Education ( G = 0.67) and Medicine and Health ( G = 0.67) and highest in the Humanities ( G = 0.77). Similarly, inequality in faculty production at the domain level is nearly always greater than production inequality among a domain’s constituent fields. For instance, whereas G = 0.73 for Engineering as a whole, Gini coefficients for the ten fields within Engineering range from 0.58 to 0.68 and, overall, G domain > G field for 104 of 107 fields (97%; Fig. 3a ). Generally, field-level faculty production distributions are heavy tailed and the universities comprising those tails are similar across fields within a given domain and, more broadly, across domains. That is, measurements of inequality in domestic faculty production increase as aggregation or scale expands, because of university-level correlation in faculty production across related fields and domains.
a , Line segments contrast the faculty production Gini coefficients calculated for newly hired faculty (filled circles; n = 54,100) and for existing faculty (open circles; n = 184,576) for each of the 107 fields (colours), eight domains (grey) and academia as a whole (blue). Line segments are grouped and coloured by domain. b , Annual Gini coefficients for academia and for each domain showing strong interyear consistency. c , Attrition risk as a function of university production rank by domain and for academia overall, via logistic regression, showing that university production rank is a significant predictor of annual attrition risk (two-sided t -test, Benjamini–Hochberg-corrected P < 0.05) such that faculty trained at high-producing universities leave academia at substantially lower rates than those trained at less productive universities. The empirical average annual attrition rates vary around the fitted curves.
Faculty production inequalities are rooted in hiring but are exacerbated by attrition. Computing the domestic production Gini coefficients separately for newly hired faculty and their sitting colleagues across our longitudinal data frame, we find uniformly larger inequalities for existing faculty in every field, every domain and in academia overall (Fig. 3a ). However, cross-sectional Gini coefficients, computed separately for each year of observation, are stable over time, a pattern that rules out a simple cohort effect that would over time draw the Gini coefficients downward towards those of the newly hired faculty (Fig. 3b ). Combined, these observations suggest that distributions of faculty production change after hiring in a manner that increases observed inequalities. We tested this hypothesis directly by modelling annual attrition risk as a function of faculty production rank. For academia as a whole, all eight domains and 49 of 107 fields (46%), we find substantially higher rates of attrition among faculty trained at those universities that already produce fewer faculty in the first place (logistic regressions, two-sided t -test, Benjamini–Hochberg-corrected P < 0.05). Put differently, most US-trained faculty come from a small number of universities and those who do not are nearly twice as likely to leave the professoriate on an annual basis (Fig. 3c ).
In addition to inequalities in production, our analysis expands on well-documented gender inequalities 8 . Whereas the majority of tenure-track US faculty in our data are men (64%), we find substantial heterogeneity by area of study with moderate change over time. For instance, between 2011 and 2020, women’s representation rose from 12.5 to 17.1% among faculty in Engineering and from 55.4 to 58.5% among faculty in Education (Fig. 4a ). In fact, women’s representation significantly increased in academia overall, in all eight domains and in 80 (75%) of 107 fields (one-sided z -test, Benjamini–Hochberg-corrected P < 0.05; Fig. 4a ). Nursing, a majority-women field, is the single instance in which the representation of women significantly decreased. The representation of women among faculty is thus generally increasing, even as women remain broadly under-represented.
a , Representation of women over time, coloured by domain and academia ( n = 162,408 men, n = 89,429 women). b , Line segments contrast percentages of women among newly hired faculty (filled circles; n = 59,007) and women among all-cause attritions (open circles; n = 90,978) for each of the 107 fields (colours), eight domains (grey) and academia as a whole (blue). Line segments are grouped and coloured by domain. c , Representation of women by career age, quantified by years since doctorate, coloured by domain and for academia as a whole. Lines indicate empirical proportions, bands indicate 95% confidence intervals.
Changes in the overall representation of women over time could be driven by many factors, including demographic changes in new hires between 2011 and 2020 or simply demographic turnover—differences between those entering and those retiring or leaving the professoriate before retirement. Investigating these potential explanations we first found that, between 2011 and 2020, the proportion of women among newly hired faculty did not change significantly in 100 of 107 fields (93%) and significantly decreased in the remaining seven fields (7%). However, by comparing the inflows of new hires with the outflows of departing faculty over our decade of observation we found that, in academia, all eight domains, and 103 of 107 fields (96%), newly hired faculty were substantially more likely to be women than their departing counterparts (Fig. 4b ). This pattern in all-cause attrition is driven by dramatic demographic turnover, with retirement-age faculty skewing heavily towards men (Fig. 4c ), implying that the overall increases in women’s representation over this period of time (Fig. 4a ) are primarily due to changes in faculty hiring that predate our decade of observation. Importantly, the fact that women’s representation among new hires has remained flat over the past decade, combined with the observation that newly hired faculty are still more likely to be men (in academia, six of eight domains (75%) and 75 of 107 fields (70%); Fig. 4b ), suggests strongly that future gender parity in academia—and especially in Science, Technology, Engineering and Mathematics (STEM) fields—is unlikely without further changes in women’s representation among new faculty.
Professors who are employed by their doctoral university, called self-hires, account for roughly one in 11 (9.1%) of all US professors in our data (11% of US-trained professors). Whereas these rates remain generally low compared with other countries (for example, 36% in Russia 44 , 67% in South Africa 29 and 73% in Portugal 45 ), they are nevertheless consistently greater than would be expected under a network-based null model that randomizes hiring patterns while keeping faculty production (outflow) and faculty hiring (inflow) fixed 46 . Self-hiring rates were similarly higher than expected across individual fields, ranging from 1.1-fold higher in Theatre to 29.3-fold in Nursing. Self-hiring rates also vary considerably by domain, being lowest in the Humanities (4.5%) and Social Sciences (6.0%) and highest in Medicine and Health (16.7%; Fig. 5a ).
a , Self-hiring rates overall (open bars; n = 295,089), for women (solid; n = 89,429) and for men (hatched; n = 162,408), by domain and across academia. Dots overlaid on open bars indicate the expected rate of self-hiring under the network-based null model. b , Ratios of average annual attrition risks among self-hires ( n = 26,720) versus all other faculty ( n = 268,369) for each field (colours), domain (grey) and academia (blue), on logarithmic axes. Circles, significantly different from 1.0, χ 2 test, Benjamini–Hochberg-corrected P < 0.05; crosses, not significant.
Previous work found that women were self-hired at higher rates than men in Computer Science 47 . We find overall that 11.2% of women are self-hires compared with 8.2% of men (two-sided z -test for proportions, Benjamini–Hochberg-corrected P < 10 −5 ; Fig. 5a ). However, this effect is driven by a minority of fields: only 26 (24%) showed differences in self-hiring rates by gender (two-sided z -test for proportions, Benjamini–Hochberg-corrected P < 0.01), 25 of which featured more frequent self-hiring among women than men. These differences are particularly common in Medicine and Health, where in 12 of 18 fields women are self-hired at significantly higher rates than men.
We also find that self-hires are at greater risk of attrition than non-self-hires. In academia, self-hires in our data leave at 1.2-fold the rate of other faculty and rates are similarly elevated in all eight domains, as well as in 36 of 107 fields (34%; two-sided z -test for proportions, Benjamini–Hochberg-corrected P < 10 −5 for academia, P < 0.05 for fields and domains; Fig. 5b ). Relative rates of self-hire attrition are highest in Criminal Justice and Criminology and Industrial Engineering, at 1.9- and 1.8-fold the rate of other faculty, respectively. Only in Nursing was the relative rate of self-hire attrition significantly below 1.0 (0.9-fold). It is unclear what drives these differences but, given the ubiquity of self-hired faculty and differential rates of attrition, determining and addressing the causes of this phenomenon would have a wide impact.
If a faculty hiring market were to follow a strict social hierarchy, no university would hire a graduate from a university less prestigious than its own—100% of faculty would hold positions of equal or lower prestige than their doctoral training. The extent to which empirical faculty hiring networks follow perfect hierarchies has direct implications for academic careers, the mobility of the professoriate and the flow of scientific ideas 3 , 37 . Treating the flows of faculty between US universities as a network leads to a natural, recursive definition of prestige: a department is prestigious if its graduates are hired by other prestigious departments. We apply the SpringRank algorithm 48 to each faculty hiring network to find, in approximation, an ordering of the nodes (universities) in that network that best aligns with a perfect hierarchy; this ordering represents the inferred hierarchy of prestige.
Faculty hiring networks in the United States exhibit a steep hierarchy in academia and across all domains and fields, with only 5–23% of faculty employed at universities more prestigious than their doctoral university (Fig. 6a,b and Extended Data Table 4 ). Measured by the extent to which they restrict such upward mobility, these prestige hierarchies are most steep in the Humanities (12% upward mobility) and Mathematics and Computing (13%) and least steep in Medicine and Health (21%; Fig. 6b ). We tested whether these steep hierarchies could be a natural consequence of inequalities in faculty production and department size across universities, using a null model in which we randomly rewired the observed hiring networks while preserving out-degree (placements) and in-degree (hires) and ignoring self-loops (self-hires) 46 . For each rewired network we re-ranked nodes using SpringRank and measured induced upward mobility as a test statistic (fraction of up-hierarchy edges; Methods ). For academia as a whole, all domains and 94 of 107 fields (88%), empirical networks showed significantly steeper prestige hierarchies than their randomized counterparts (one-sided Benjamini–Hochberg-corrected P < 0.05; Fig. 6c and Extended Data Table 5 ). No field was significantly less steep, although networks in the fields of Pharmacy ( P = 0.88), Immunology ( P = 0.77) and Pathology ( P = 0.73) were less steep than null model randomizations most frequently. In short, the prestige hierarchies that broadly define faculty hiring are universally steep, and often substantially steeper than can be explained by the ubiquitous and large production inequalities.
a , Prestige change from doctorate to faculty job in the US faculty hiring network ( n = 238,281; Methods ), with ranks normalized to the unit interval and 1.0 being the most prestigious. The proportions of faculty at universities less prestigious than their doctorate are annotated as 'move down' (open bars), at universities more prestigious than their doctorates as 'move up' (hatched) and at the same university as self-hires (solid). b , Rank change among faculty in the US faculty hiring network, by domain, using the same shading scheme as in Fig. 1a . c , Comparison between empirical hierarchies and those from 1,000 draws from a null model of randomly rewired hiring networks ( Methods ), quantified through upward mobility. Fields above the diagonal reference line exhibit steeper hierarchies than can be explained by department size and faculty production inequalities alone. Circles, Benjamini–Hochberg-corrected P < 0.05, network null model ( Methods ); crosses, not significant; no field was significantly less steep than expected. d , Heatmap of pairwise Pearson correlations between prestige hierarchies of fields.
Inferred prestige ranks of universities are also highly correlated across fields, suggesting that many factors that drive field-level prestige operate at the university level. Among pairwise correlations of university prestige rankings across fields, the overwhelming majority are positive (all but 116 of 12,024) and nearly half (48%) have a correlation >0.7 (Pearson’s ρ ). Fields in Engineering, Mathematics and Computing, and Humanities are particularly mutually correlated whereas the field of Pathology is, on average, the least correlated with others (mean correlation 0.2).
Patterns across field-level 'top-10' most prestigious departments illustrate other aspects of the stark inequalities that define US faculty hiring networks. Among the 1,070 departments that are ranked top-10 in any field, 248 (23.2%) top-10 slots are occupied by departments at just five universities—UC Berkeley, Harvard, Stanford, University of Wisconsin-Madison and Columbia; fully 252 universities (64%) have zero top-10 departments. These findings show that, both within individual fields and across entire domains, faculty placement power is highly concentrated among a small set of universities, complementing the already enormous concentration of faculty production among the same set of universities (Fig. 2 ). Together, these patterns create network structures characterized by a closely connected core of high-prestige universities that exchange faculty with each other and export faculty to—but rarely import them from—universities in the network periphery (Extended Data Fig. 2 ).
As a result of both systematic inequality in production and steep social hierarchies, the typical professor is employed at a university that is 18% further down the prestige hierarchy than their doctoral training (Fig. 6a , Extended Data Table 6 ). Combined with sharply unequal faculty production (Fig. 2 ), this movement downward in prestige implies that the typical US-trained professor can expect to supervise 2.4-fold fewer future faculty than did their doctoral advisor. At the field level, the typical professor who moves downward descends by between 28% (Electrical Engineering) and 46% (Classics) of the prestige hierarchy whereas the typical professor who moves upward, of whom there are very few, ascends by between 6% (Economics) and 26% (Agronomy) of the hierarchy. There was no significant difference in mobility between men and women in 82 of 107 fields, but of the 25 fields in which mobility did differ by gender (two-sided z -test for proportions, Benjamini–Hochberg-corrected P < 0.05), women were less likely to move down the prestige hierarchy and more likely to be self-hires (Extended Data Table 6 ); 11 of those 25 fields were within the domain of Medicine and Health. However, we found no significant differences in the magnitudes of upward or downward movements between men and women for all fields (K-S test, Benjamini–Hochberg-corrected α = 0.05).
Prestige helps explain more than just the flows of faculty between US universities. For instance, across all domains, our analysis shows that sitting faculty are markedly more likely to be self-hires as prestige increases, yet this relationship is progressively weaker among younger faculty cohorts (Extended Data Fig. 3 ) and is either attenuated or not significant for new hires (two-sided t -test, Benjamini–Hochberg-corrected α = 0.05; Extended Data Fig. 4a ). By contrast, new hires in all domains are substantially more likely to be trained outside the United States as prestige increases, yet this relationship is either attenuated, not significant or even reversed for sitting faculty (two-sided t -test, Benjamini–Hochberg-corrected α = 0.05; Extended Data Fig. 4b ). Although we observe no common relationship across domains between prestige and gender, both new and existing faculty are more likely to be men as prestige increases for academia as a whole (two-sided t -test, Benjamini–Hochberg-corrected P < 0.05; Extended Data Fig. 4c ). Together, these observations suggest complicated interactions between prestige and the processes of hiring or retaining women, one’s own graduates and graduates from abroad, patterns that complement previously observed effects of prestige on peer review outcomes 49 , 50 and productivity 34 .
As a whole, by domain and by field, US tenure-track faculty hiring is dominated by a small minority of US universities that train a large majority of all faculty and sit atop steep hierarchies of prestige. Just five US universities train more US faculty than all non-US universities combined. As we expand our view from fields to entire domains, inequalities in faculty production further increase, reflecting elite universities’ positions at or near the top of multiple correlated prestige hierarchies across fields. In principle, universities are on equal footing as both producers and consumers in the faculty hiring market. However, the observed patterns of faculty hiring indicate that the system is better described as having a universal core–periphery structure, with modest faculty exchange among core universities, substantial faculty export from core to periphery and little importation in the reverse direction or from outside the United States.
Although significant efforts have been made over many years to make faculty hiring practices more inclusive, our analysis suggests that many inequalities at the faculty hiring stage are later magnified by differential rates of attrition. For instance, our analysis showed higher rates of attrition among US faculty who were (1) trained outside the United States, Canada or the United Kingdom, (2) trained at universities that have produced relatively fewer faculty overall and (3) employed at their doctoral alma mater. Combined with our observations of unchanging proportions of these groups over time, these differential attrition rates suggest a dynamic equilibrium of countervailing patterns of hiring and attrition. Identifying the causes of these elevated attrition rates is likely to provide insights and opportunities to improve retention strategies for faculty of all kinds.
Our analyses of the hiring and retention of women faculty point to stalled progress towards equal representation. Whereas women’s overall representation has increased steadily across all eight broad domains of study, women nevertheless remain under-represented among new hires in many fields, particularly in STEM, and women’s representation among newly hired faculty over the past decade has generally been flat. As a result, the continued increase in women’s overall representation can instead be attributed to the disproportionate number of men among retiring faculty, across all domains. Continued increases in women’s representation among faculty are therefore unlikely if the past decade’s pattern remains stable.
Around one in 11 US professors are employed by their doctoral university. Such high rates of self-hiring across fields and universities are surprising, because academic norms treat self-hiring negatively—for example, it is sometimes called 'academic inbreeding' 51 . Elevated self-hiring rates may indicate an unhealthy academic system 52 because self-hiring restricts the spread of ideas and expertise 3 , and many decades of study suggest that it can correlate with lower quantity and quality of scholarship 53 , 54 . In this light, the sharply elevated rates of self-hiring at elite universities present a puzzle 51 , with uncertain epistemological consequences, yet these trends seem to be driven less by recent new hires and more by attrition or hiring patterns preceding our decade of observation. Overall, high rates of self-hiring persist in spite of (not because of) differential rates of attrition, with self-hires leaving US academia at higher rates in most fields, all domains and academia overall.
Our analyses describe system-wide patterns and trends, and hence say little about individual faculty experiences or the causal factors that predict the outcomes of individual faculty placements in the US academic system 55 . At best, our results provide statistical estimates for the direction and distance of faculty placements up or down a field’s prestige hierarchy, and they should not be used to inform or shape expectations of real hiring decisions. In other words, even though there are clear and strong patterns at the system level, the considerable variance in outcomes at the individual level shows that pedigree is not destiny.
One limitation of the present work is that, although doctoral universities were known, doctoral departments were not. Hence, our estimates of self-hiring rates reflect faculty employed by any department at their doctoral university, but not necessarily by their doctoral department. Our analyses therefore estimate only upper bounds on department-level self-hiring. Similarly, our estimates of production and prestige inequalities in individual fields reflect the volume and power of universities placing faculty into those fields, but not necessarily the volume of graduates produced by those fields or the related fields into which they may be hired 26 .
Our data also lack self-identified demographic characteristics and national origin, which limits the conclusions we may draw about the interaction between faculty hiring and representation by race, gender, socioeconomic background and nationality, and any intersectional analyses thereof. For instance, whereas we observe that faculty trained outside the United States constitute 2–19% of US faculty across domains, the fraction of US faculty born outside the United States is considerably higher 42 . Given our identification of markedly higher attrition rates for faculty trained outside the United States, Canada and the United Kingdom, an investigation of attrition by national origin could help identify its causes and address its differential impacts. Our approach also relies on cultural associations between name and binary man–woman genders, leaving the study of self-identified and more expansive identities, as well as intersectional representation more broadly, as open lines of enquiry.
Although our analysis shows that the clear cross-sectional patterns in faculty demographics and hiring networks are shaped by complex and evolving patterns of hiring and attrition alike, our analysis does not causally identify the mechanisms responsible. Our observations of clustered patterns among fields within the same domain suggest a role for domain-level macrocultures 56 . Strong correlations between a university’s ranks across different fields may indicate status signalling 57 , the impacts of elite universities’ resources on individuals’ productivity and prominence 34 , or other factors entirely. And, clear cohort effects—particularly in the representation of women—show non-stationarity in the patterns we observe and in the latent factors that drive them. Critically, future progress in understanding the causal factors shaping the US professoriate must investigate the factors that drive differential attrition, including those related to social identity, doctoral training (both abroad and domestically) and university of employment. Understanding the underlying causes of these differential attrition rates would surely inform efforts and policies aimed at mitigating social inequalities by improving equity and representation, which is likely to shape what discoveries are made and who makes them.
The data used in our analyses are based on a census of the US academic market obtained under a data use agreement with AARC. That unprocessed dataset consisted of the employment records of all tenured or tenure-track faculty at all 392 doctoral-degree-granting universities in the United States for each year between 2011 and 2020, as well as records of those faculty members’ most advanced degree. We cleaned, annotated and preprocessed that unprocessed dataset to ensure consistency and robustness of our measurements, resulting in the data used in our analyses.
Cleaning the original dataset involved nine steps, which were performed sequentially. After cleaning, we augmented the processed dataset with two pieces of extra information to enable further analyses of faculty and universities, by annotating the country of each university and the gender of each professor. The nine preparation steps and two annotation steps are described below.
The first step in preparing the dataset was to de-duplicate degree-granting universities. These universities are in our data either because they were 'employing' universities covered by the AARC sample frame (all tenure-track faculty of US PhD-granting universities) or because they were 'producing' universities at which one or more faculty members in the AARC sample frame obtained their terminal degree (university, degree, year). Producing universities include those based outside the United States and those that do not grant PhDs. Thus, due to the AARC sample frame, all employing universities are US-based and PhD granting, and this set of 392 universities did not require preprocessing. On the other hand, producing universities—those where one or more employed faculty earned a degree—may or may not be PhD granting and may or may not be located in the United States.
Producing universities were cleaned by hand: instances in which single universities were represented in multiple ways ('University of Oxford' and 'Keble College', for example) were de-duplicated and, in the rare instances in which a degree referenced an unidentifiable university ('Medical University, England', for example), the degrees associated with that 'university' were removed but the individuals holding those degrees were not removed.
The second step in preparing the dataset was to clean faculty members' degrees. Terminal degrees are recorded for 98.2% of faculty in the unprocessed data: 5.7% of these degrees are not doctorates (5.3% are Master’s degrees and 0.4% are Bachelor’s degrees). We treated all doctoral degrees as equivalent—for example, we drew no distinction between a PhD and a D.Phil. We note that faculty without doctorates are distributed unevenly throughout academia, with members in the Humanities and Applied Sciences being least likely to have a doctoral degree (Extended Data Fig. 1 ).
Faculty without doctorates were included in analyses of gender. They were also included in the denominators of self-hiring rate calculations but, possessing no doctorates, they were never considered as potentially self-hires, themselves. Faculty without a doctorate were not included in analyses of production and prestige, which were restricted to faculty with doctorates.
The third step in preparing the dataset was to identify and de-duplicate departments. We ensured that no department was represented multiple different ways, by collapsing records due to (1) multiple representations of the same name (for example, 'Computer Science Department' versus 'Department of Computer Science') and (2) departmental renaming (for example, 'USC School of Engineering' versus 'USC Viterbi School of Engineering'). Although rare instances of the dissolution or creation of departments were observed, we restricted analyses that did not consider time to those departments for which data were available for a majority of years between 2011 and 2020, and restricted longitudinal analyses to only those departments for which data were available for all years.
The fourth step in preparing the dataset was to annotate each department according to a two-level taxonomy based on the field (fine scale) and domain (coarse scale) of its focus. This taxonomy allowed us to analyse faculty hiring at both levels, and to compare patterns between levels. Extended Data Table 1 contains a complete list of fields and domains.
Most departments received just one annotation, but some received multiple annotations due to their interdisciplinarity. This choice was intentional, because the composition of faculty in a 'Department of Physics and Astronomy' is relevant to questions focused on the composition of both ('Physics, Natural Sciences') and ('Astronomy, Natural Sciences'). On the basis of this premise, we include both (or all) appropriate annotations for departments. For instance, the above hypothetical department and its faculty would be included in both Physics and Astronomy analyses. The basic unit of data in our analyses is therefore the individual–discipline pair. A focus on the individual would be preferable, but would require taxonomy annotations of individuals rather than departments—information we do not have. Furthermore, many individuals are likely to consider themselves to be members of multiple disciplines.
Whenever a university had multiple departments within the same field, those departments were considered as one unit. To illustrate how this was done, consider the seven departments of Carnegie Mellon’s School of Computer Science. All seven departments were annotated as Computer Science and treated together in analyses of Computer Science.
Some fields have the potential to conceptually belong to multiple domains. For example, Computer Engineering could be reasonably included in the domain of either Formal Sciences (which includes Computer Science) or Engineering (which includes Electrical Engineering). Similarly, Educational Psychology could be reasonably included in the domain of Education or of Social Sciences. In these instances, we associated each such field with the domain that maximized the fraction of faculty whose doctoral university had a department in that domain. In other words, we matched fields with domains using the heuristic that fields are best associated with the domains in which their faculty are most likely to have been trained.
The fifth step in preparing the dataset was to remove inconsistent employment records. Rarely, faculty in the dataset seem to be employed at multiple universities in the same year. These cases represent situations in which a professor made a mid-career move and the university from which they moved failed to remove that professor from their public-facing records. We removed such spurious and residual records for only the conflicting years, and left the records of employment preceding such mid-career moves unaltered. This removed only 0.23% of employment records.
The sixth step in preparing the dataset was to impute missing employment records. Rarely, faculty disappear from the dataset only to later reappear in the department they left. We considered these to be spurious 'departures', and imputed employment records for the missing years using the rank held by the faculty before becoming absent from the data. Employment records were not imputed if they were associated with a department that did not have any employment records in the given year. Imputations affected 1.3% of employment records and 4.7% of faculty.
The seventh step in preparing the dataset was to exclude non-primary appointments such as professors’ associations or courtesy/emeritus appointments with multiple departments. We identified primary appointments by making the following two assumptions. First, if a professor was observed to have just one appointment in a particular year, then that was their primary appointment for that year—as well as for any other year in which they held that appointment (including years with multiple observed appointments). This corresponds to a heuristic that faculty should appear on the roster of their primary unit before appearing on non-primary rosters. Second, if a professor was observed to have appointments in multiple units, and a promotion (for example, from Assistant Professor to Associate Professor) was observed in one unit’s roster but not in another’s, it was assumed that the non-updating unit is not a primary appointment. This corresponds to a heuristic that, if units vary in when they report promotions, it is more likely that the primary unit is updated first and thus units that update more slowly are non-primary.
Primary appointments could not be identified for 1.2% of faculty, and 5.5% of appointments were classified as non-primary. Field- and domain-level analyses were restricted to primary appointments, but analyses of academia included faculty regardless of whether their primary appointment(s) could be identified, under the assumption that employment in a tenure-track position implies having some primary appointment, identifiable or not.
The eighth step in preparing the dataset was to carefully handle employment records with mid-career moves so that each faculty member was associated with only a single employing university. Mid-career moves do not alter a professor’s doctoral university or gender, and so cannot affect measurements such as a discipline’s faculty production Gini coefficient, its gender composition or the fraction of faculty within the discipline that holds a degree from outside the United States. However, mid-career moves have the potential to alter a discipline’s self-hire rate and the steepness of its prestige hierarchy. This raises important questions for how one should treat mid-career moves when performing calculations that average over our decade of observations—should one analyse the appointment before or the appointment after the move(s)?
First we chose to use, whenever possible, the most recent employing university of each professor. In other words, if a professor was employed at multiple universities between 2011 and 2020, only that university where they were most recently employed was considered. Second, we checked that this choice did not meaningfully affect our analyses of self-hiring or prestige, because 6.9% of faculty made a mid-career move within our sample frame. To evaluate the impact of this choice on self-hiring analyses, we first calculated self-hiring rates on the basis of faculty members’ first employing university (that is, their pre-mid-career-move university if they had a mid-career move). We then calculated self-hiring rates on the basis of faculty members’ last employing university (that is, their post-mid-career-move university if they had a mid-career move). Comparing these two estimates we found that, across all 107 fields, eight domains and academia, mid-career moves had no significant effect on our measurements of self-hiring rates (two-sided z -test for proportions, α = 0.05, n = 295,089 faculty in both samples). To evaluate the impact of this choice on prestige hierarchies, we first calculated the upward mobility in rank-sorted faculty hiring networks on the basis of faculty members’ first employing university (that is, their pre-mid-career move university if they had a mid-career move). We then followed the same procedure but on the basis of faculty members’ last employing university (that is, their post-mid-career move university if they had a mid-career move). Comparing these two approaches, we found that mid-career moves did not significantly alter upward mobility in any field or domain (two-sample, two-sided z -test for proportions, Benjamini–Hochberg-corrected α = 0.05; see Extended Data Table 1 for n ). At the academia level, taking the most recent university rather than the first university among mid-career moves resulted in 0.7% more upwardly mobile doctorate-to-faculty transitions (two-sample, two-sided z -test for proportions, Benjamini–Hochberg-corrected P < 0.05, n = 238,281 in both samples).
The ninth and final step in preparing the dataset was to exclude departments that were inconsistently sampled. Not all departments in the unprocessed dataset were recorded by the AARC in all years, for reasons outside the control of the research team. To ensure robustness of results, we restricted our analyses that did not consider time to those departments that appeared in a majority of years between 2011 and 2020. This resulted in the removal of 1.8% of employment records, 3.4% of faculty and 9.1% of departments. Additionally, 24 employing universities (6.1%) were excluded by this criterion, most of which were seminaries.
The country of each producing university was determined by hand. First, Amazon Mechanical Turk was used to gather initial annotations. Each university was annotated by two different annotators. Inter-annotator agreement was >99% and disagreements were readily resolved by hand. To ensure no errors, a second pass was completed by the researchers and resulted in no alterations.
Self-identified gender annotations were provided for 6% of faculty in the unprocessed dataset. To annotate the remaining faculty with gender estimates, we used a two-step process based on first and last names. First, complete names were passed to two offline dictionaries: a hand-annotated list of faculty employed at Business, Computer Science and History departments (corresponding to the data used in ref. 27 ) and the open-source python package gender-guesser 58 . Both dictionaries responded with one of the following classifications: female, male or unable to classify. Second, for cases in which the dictionaries either disagreed or agreed but were unable to assign a gender to the name, we queried Ethnea 59 and used the gender to which they assigned the name (if any). Using this approach we were able to annotate 85% of faculty with man or woman labels. Faculty whose names could not be associated with a gender were excluded from analyses of gender but included in other analyses. This methodology associates names with binary (man/woman) labels because of technical limitations inherent in name-based gendering methodologies, but we recognize that gender is non-binary. The use of these binary gender labels is not intended to reinforce a gender binary.
The prepared and annotated dataset contained 295,089 individuals employed at 368 universities, and was used as the basis of all of our analyses. In some analyses, further inclusion criteria were applied but with the guiding principle that analyses should be as inclusive as possible and reasonable. For example, analyses of the professoriate by gender considered only faculty with a gender annotation but did not require members to hold a doctorate. Analyses of prestige, on the other hand, considered only those faculty with doctorates from US universities but did not require that faculty have a gender annotation. The aim of these inclusion criteria was to ensure the robustness of results while simultaneously being maximally inclusive. When an analysis fell into more than one of the above categories, inclusion criteria for all categories were applied. For example, when analysing changes in US faculty production over time, inclusion criteria for analyses of both US faculty production and over time were applied.
Some fields and domains were excluded from field- or domain-level analyses, either because they were too small or because they were insufficiently self-contained. Faculty in excluded fields were nevertheless included in domain- and academia-level analyses, and those in excluded domains were nevertheless included in academia-level analyses (Extended Data Table 2 ).
Two domains were excluded from domain-level analysis: (1) Public Administration and Policy and (2) Journalism, Media and Communications. These domains were excluded because they employed far fewer faculty than other domains, and because their inclusion made domain-level comparisons difficult.
Fields were included in field-level analyses only if (1) at least 25% of universities had a department in that field or (2) the number of faculty with a primary appointment in that field, and who also earned their doctorate from a university that had a department in that field, was ≥500. These requirements were intended to ensure the coherence of fields for analyses of production and prestige. For information on the number of faculty excluded from field- and domain-level analyses, see Extended Data Table 2 .
Analyses of production and prestige included only faculty who hold a US doctorate. Faculty without a doctorate are a small minority of the population in most fields, and were excluded because their degrees are not directly comparable to doctorates. Faculty with non-US doctorates were excluded because the universities that produced them are outside the sample frame.
For all longitudinal analyses, we required departments to be sampled in all years between 2011 and 2020 to ensure consistency in the sample frame. This resulted in the removal of 5.9% of employment records, 7.2% of faculty and 12.6% of departments for those analyses. Additionally, 15 employing universities (4.1%) were excluded by this criterion.
Some analyses required us to divide faculty into two complementary sets: new hires and existing faculty. For analyses that aggregated faculty over our decade of observation, we labelled faculty as new hires if they earned their degree within 4 years of their first recorded employment as faculty. Thus defined there are 59,007 new faculty, making up 20.0% of the faculty in the dataset. Our longitudinal analyses were more strict, such that faculty were labelled as new only in their first observed year of employment, but were considered as existing faculty for each observed year thereafter.
A professor who leaves academia for any reason constitutes an attrition, including retirement, termination of employment for any reason, acceptance of a position outside our sample frame (for example, in industry, government or a university outside the United States) or death. Our unprocessed data do not allow us to identify reasons for attrition. A professor’s last year of employment is considered the year of their attrition when counting attritions over time. Faculty who change disciplines are not considered to be attritions from disciplines they leave. Because attritions in a given year are identified through comparison with employment records in the next, attrition analyses do not include the final year of the sample frame (2020). Faculty were counted as an attrition at most once; a professor who appeared to leave multiple times was considered an attrition only on exiting for the last time.
Attrition risk is defined, for a given set of faculty in a given year, as the probability that each professor in that set failed to appear in the set in the next year—that is, the proportion of observed leaving events among possible leaving events on an annual basis. Thus, all attrition risks as stated in this study are annual per-capita risks of attrition. Average annual attrition risks were formed by counting all attrition events and dividing by the total person-years at risk.
Faculty hiring networks represent the directed flows of faculty from their doctoral universities to their employing universities. As such, each node in such a network represents a university and each weighted, directed edge represents the number of professors trained at one university and who are employed at the other. For the purposes of the faculty hiring networks analysed here, we restrict the set of nodes to, at most, those employing universities within the AARC sample frame. This means that nodes representing non-US universities are not included, and therefore the edges that would link them to in-sample universities are also not included. Without loss of generality, we now describe in more precise detail the creation of a particular field’s faculty hiring network, but this process applies equivalently for both domains and academia as a whole.
First, universities were included in a field only if they had a unit (for example, a department, or departments) associated with that field. As a result, a university appears in the rankings for a field only if it has a representative unit; without a Department of Botany, a university cannot be ranked in Botany. Second, ranks are identifiable from patterns in faculty hiring only if every unit employs at least one individual in that field who was trained at a unit that also employs faculty in that field. Phrased from the perspective of the faculty hiring network, this requirement amounts to ensuring that the in-degree of every node is at least one. Because the removal of one unit (based on the above requirements) might cause another to fail to meet the requirements, we applied this rule repeatedly until it was satisfied by all units.
The outcome of this network construction process is a weighted, directed multi-graph A ( k ) such that: (1) the set of nodes i = 1,2,... represent universities with a department or unit in field k . (2) The set of edges represent hiring relationships, such that \({A}_{ij}^{(k)}\) is an integer count of the number of faculty in field k who graduated from i and are employed at j . Thus A ( k ) is a positive, integer-weighted, non-symmetric, network adjacency matrix for field k . (3) The out-degree \({d}_{i}^{(k)}={\sum }_{j}{A}_{ij}^{(k)}\) is greater than or equal to one for every node i , meaning that every university has placed at least one graduate in field k . (4) The in-degree \({d}_{j}^{(k)}={\sum }_{i}{A}_{ij}^{(k)}\) is greater than or equal to one for every node j , meaning that every university has hired at least one graduate from field k .
To infer ranks in faculty hiring networks meeting the criteria above, we used the SpringRank algorithm 48 without regularization, producing a scalar embedding of each network’s nodes. Node that embeddings were converted to ordinal rank percentiles. (In principle, embeddings may produce ties requiring a rule for tie-breaking when converting to ordinal ranks. However, no ties in SpringRanks were observed in practice).
To determine whether properties of an empirically observed hierarchy in a faculty hiring network could be ascribed to its in-degree sequence (unit sizes) and out-degree sequence (faculty production counts) alone, we generated an ensemble of n = 1,000 networks with identical in- and out-degrees that were otherwise entirely random, using a degree-preserving null model called the configuration model 46 , 60 . We excluded self-hires (that is, self-loops) from randomization in the configuration model for a subtle but methodologically important reason. We observed that self-hires occur at much higher rates in empirical networks than expected under a configuration model. As a result, were we to treat self-hires as links to be randomized, the process of randomization would, itself, increase the number of inter-university hires from which ranks were inferred. Because of the fact that SpringRank (or an alternative algorithm) infers ranks from inter-university hires, but not self-hires, the act of 'randomizing away' self-hires would thus distort ranks, as well as the number of potential edges aligned with (or aligned against) any inferred hierarchy. In short, randomization of self-hires would, in and of itself, distort the null distribution against which we hope to compare, dashing any hope of valid inferences to be drawn from the exercise. We note, with care, that when computing the fraction of hires violating the direction of the hierarchy, either empirically or in the null model, we nevertheless included self-hires in the total number of hires—that is, the denominator of said fraction. These methodological choices follow the considerations of the configuration model 'graph spaces' introduced by Fosdick et al. 46 .
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
All network data associated with this study and all data contained in Extended Data tables are freely available in machine-readable format at https://doi.org/10.5281/zenodo.6941651 . Explorable visualizations of faculty hiring networks and university ranks are available at https://larremorelab.github.io/us-faculty/ . Source data are provided with this paper.
Open-source code related to this study is available at https://doi.org/10.5281/zenodo.6941612 .
05 july 2023.
A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06379-9
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The authors thank A. Morgan, N. LaBerge and C. J. E. Metcalf for valuable feedback, and acknowledge the BioFrontiers Computing Core at the University of Colorado Boulder for providing High Performance Computing resources supported by BioFrontiers IT. This work was supported by an Air Force Office of Scientific Research Award (no. FA9550-19-10329, all authors), by a National Science Foundation Graduate Research Fellowship Award (no. DGE-2040434, S.Z.) and by a National Science Foundation Alan T. Waterman Award (no. SMA-2226343, D.B.L.).
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K.H.W., A.C. and D.B.L. devised the analysis and wrote the manuscript. K.H.W. performed computational modelling and validated the data. K.H.W. and S.Z. processed the data. D.B.L. supervised the project.
Correspondence to K. Hunter Wapman or Daniel B. Larremore .
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Extended data fig. 1 proportions of faculty without doctoral degrees..
Each transparent circle represents one of 107 fields, coloured and grouped by domain. Filled grey circles represent domain-level estimates. A single blue circle (left) represents U.S. academia overall. Fields for which more than 10% of faculty do not have a doctorate are annotated
Lines are coloured by domain, and show the mean geodesic distance through links in the faculty hiring network from the university at that prestige rank to every other university, divided by the diameter of the network. Smaller values toward the left side indicate that more prestigious universities are more centrally located in each faculty hiring network; less prestigious universities are more peripherally positioned. All universities belong to the network’s strongly connected component by construction (Methods)
Logistic regression coefficients, expressed as change in log-odds of being a self-hire for a one-decile increase in prestige, stratified by domain (colours) or academia (blue), and by four bins of career age as indicated. Circles, significant by two-sided t-test, Benjamini-Hochberg corrected p < .05; crosses, not significant
Logistic regression coefficients, expressed as a change in log-odds of faculty being a self-hire (a), being a non-U.S. faculty (b), or a woman (c) for a one-decile increase in prestige, stratified by domain (colours) and academia (blue), for newly hired faculty (filled symbols) and for existing faculty (hollow symbols) and connected by a line. Circles, significant (two-sided t-test, Benjamini-Hochberg corrected p > 0.05); crosses, not significant. (a) Existing faculty are more likely to be self-hires at more prestigious universities, but this effect attenuates or disappears for new hires, indicating that the positive relationship between self-hiring and prestige is likely driven by attrition. (b) Newly hired faculty are more likely to hold a non-U.S. doctorate than existing faculty. This likely results from higher rates of attrition among faculty with a non-U.S. doctorate (Fig. 1c ). (c) We observe no universal relationship across domains between prestige and gender, but both new and existing faculty are somewhat more likely to be men as prestige increases for academia as a whole
Reporting summary, peer review file, source data fig. 1., source data fig. 2., source data fig. 3., source data fig. 4., source data fig. 5., source data fig. 6., source data extended data fig. 1., source data extended data fig. 2., source data extended data fig. 3., source data extended data fig. 4., rights and permissions.
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Wapman, K.H., Zhang, S., Clauset, A. et al. Quantifying hierarchy and dynamics in US faculty hiring and retention. Nature 610 , 120–127 (2022). https://doi.org/10.1038/s41586-022-05222-x
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The reds have gone 76-81 and have five games left in the regular season.
David Bell’s time in Cincinnati has come to an end.
The Reds fired their longtime manager after six seasons on Sunday, shortly after the team’s 2-0 loss to the Pittsburgh Pirates . The Reds have five games left in the 2024 season, and they will miss the playoffs for a fourth straight year and the fifth time in Bell’s six seasons with the team.
“David provided the kind of steadiness that we needed in our clubhouse over the last few seasons,” Reds president of baseball operations Nick Krall said in a statement. “We felt a change was needed to move the Major League team forward. We have not achieved the success we expected, and we need to begin focusing on 2025.”
Bell, 52, was hired ahead of the 2019 campaign, and he led the franchise to the playoffs during the COVID-19-shortened season in 2020, though the Reds were knocked out in the wild-card round that year. That was the only postseason berth the organization has seen in more than a decade.
Bell finished with a 409-455 career record as the Reds’ manager, which marked his first head job in Major League Baseball. He finished over .500 twice, though his best campaign was an 83-79 finish in 2021, when Cincinnati missed the playoffs.
The Reds are fourth in the NL Central and 13.5 games back from the Milwaukee Brewers, who clinched the division last week. The Reds will wrap up the regular season this week with a two-game series against the Cleveland Guardians and a three-game run against the Chicago Cubs.
Bench coach Freddie Benavides will lead the team the rest of the way.
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He succeeds interim president Dr. Robin Myers and the second system president, Dr. Chuck Welch, who left the system in January after nearly 13 years to become president and CEO of the American Association of State Colleges & Universities (AASCU) in Washington, D.C.
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