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Research Culture: A survey-based analysis of the academic job market

  1. Jason D Fernandes
  2. Sarvenaz Sarabipour
  3. Christopher T Smith
  4. Natalie M Niemi
  5. Nafisa M Jadavji
  6. Ariangela J Kozik
  7. Alex S Holehouse
  8. Vikas Pejaver
  9. Orsolya Symmons
  10. Alexandre W Bisson Filho
  11. Amanda Haage  Is a corresponding author
  1. Department of Biomolecular Engineering, University of California, Santa Cruz, United States
  2. Institute for Computational Medicine, Johns Hopkins University, United States
  3. Department of Biomedical Engineering, Johns Hopkins University, United States
  4. Office of Postdoctoral Affairs, North Carolina State University Graduate School, United States
  5. Morgridge Institute for Research, United States
  6. Department of Biochemistry, University of Wisconsin-Madison, United States
  7. Department of Biomedical Sciences Midwestern University, United States
  8. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, United States
  9. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, United States
  10. Department of Biomedical Informatics and Medical Education, University of Washington, United States
  11. The eScience Institute, University of Washington, United States
  12. Department of Bioengineering, University of Pennsylvania, United States
  13. Department of Biology, Brandeis University, United States
  14. Rosenstiel Basic Medical Science Research Center, Brandeis University, United States
  15. Department of Biomedical Sciences, University of North Dakota, United States
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Cite this article as: eLife 2020;9:e54097 doi: 10.7554/eLife.54097

Abstract

Many postdoctoral researchers apply for faculty positions knowing relatively little about the hiring process or what is needed to secure a job offer. To address this lack of knowledge about the hiring process we conducted a survey of applicants for faculty positions: the survey ran between May 2018 and May 2019, and received 317 responses. We analyzed the responses to explore the interplay between various scholarly metrics and hiring outcomes. We concluded that, above a certain threshold, the benchmarks traditionally used to measure research success – including funding, number of publications or journals published in – were unable to completely differentiate applicants with and without job offers. Respondents also reported that the hiring process was unnecessarily stressful, time-consuming, and lacking in feedback, irrespective of outcome. Our findings suggest that there is considerable scope to improve the transparency of the hiring process.

Introduction

The number of PhDs awarded in science, technology, engineering and mathematics (STEM) has increased dramatically over the past three decades (Cyranoski et al., 2011; Ghaffarzadegan et al., 2015), but the number of faculty positions available has essentially remained constant (Schillebeeckx et al., 2013). In the US, for instance, the situation has not changed significantly since 2003, when the National Institutes of Health (NIH) received a major budget increase (Alberts et al., 2014). Given the low numbers of faculty positions compared to the numbers of PhDs produced (Larson et al., 2014; Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014), trainees are limited in their job prospects. Many also emerge from academic training feeling underprepared and under-mentored for any other type of job search (McDowell et al., 2015). This leads to a high number of applicants per academic position, many of whom are uncertain about their chances of obtaining a faculty job (Grinstein and Treister, 2018; Sauermann and Roach, 2016).

Cohorts of new PhDs are also both more diverse than before and more diverse than many current hiring committees (Alberts et al., 2014; White, 2019; Bhalla, 2019). Scientific publishing is also faster-paced than it used to be: for example, evolutionary biologists recruited as "junior researchers" in 2013 had published nearly twice as many articles (22 ± 3.4) as those hired in 2005 (12.5 ± 2.4); the same study also found that the length of time between first publication and recruitment as a faculty member had increased from 3.25 (±0.6) to 8.0 (±1.7) years (Brischoux and Angelier, 2015). Longer training periods have been reported repeatedly in many STEM fields, and are perceived as detrimental to both the greater scientific community and individuals in temporary postdoctoral positions (Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014Ahmed, 2019; Rockey, 2012; Acton et al., 2019).

Despite these changes, the academic job search has largely remained the same, resulting in academic hiring being perceived as an opaque process with no clear standards or guidelines. Beyond a requirement for a doctoral degree and possibly postdoctoral training, faculty job advertisements rarely contain specific preferred qualifications. Furthermore, the criteria used to evaluate applicants are typically determined by a small departmental or institutional committee and are neither transparent nor made public. The amount of materials required for faculty job applications is also highly variable among hiring institutions, and often places a heavy burden on both applicants and search committees (Lee, 2014).

Previous studies agree on a need to increase transparency in career outcomes and hiring practices (Golde, 2019; Polka et al., 2015; Wright and Vanderford, 2017). The annual pool of faculty job applicants is large and provides a unique opportunity for examining the application process. We performed an anonymous survey, asking applicants for both common components of research and scholarly activity found on an academic CV, as well as information on their success through the 2018–2019 job cycle. We further performed a small-scale, complementary survey of search committee members. Here we present qualitative and quantitative data on the academic job market, including information on the number of successful off-site and on-site interviews, offers, rejections, and the lack of feedback.

Job applicants start by searching for relevant job postings on a variety of platforms (Supplementary file 1). The initial electronic application generally consists of a cover letter addressing the search committee, a teaching philosophy statement, CV, and a research plan (Figure 1). The length and content of these materials can vary drastically based on the application cycle, region, institution, or particular search committee. In the current system, the expectation is that application materials be tailored for each specific institution and/or department to which the applicant is applying. This includes department-specific cover letters (Fox, 2018a), but may also involve a range of changes to the research, teaching, and diversity statements.

An overview of the academic job search process.

The first column defines common terms in the academic job search; while the second column outlines how the search for an academic job progresses, from a job being posted to an offer being accepted.

The search committee convenes for a few meetings to shortlist the applicants. Applicants are then contacted for interviews somewhere between one to six months after application materials are due. Searches may include an initial off-site (remote) interview, followed by an on-site interview at the hiring university. The on-site interview typically lasts one or two days and consists of a research seminar, possibly a teaching demonstration, and likely a chalk-talk (Rowland, 2016). The on-site interview also usually consists of one-on-one meetings with other faculty members, including a meeting with the hiring department chair, trainees, and the administrative staff.

After the interviews, candidates may be contacted and offered a position, usually in writing. The offer package will include the proposed start date, salary and start-up funds (Macdonald, 2019). The time to offer is also variable, but is usually shorter than the time between application and first contact (based on anecdotal information). Importantly, a single search can result in multiple offers (for instance the department may be able to fund multiple competitive candidates, or the first-choice candidate may decline and the second candidate is given an offer). Searches can also fail if the committee does not find a suitable candidate for their program/department or "go dry" if the applicant(s) deemed qualified by the search committee decline their offer.

Results

We designed a survey for early-career researchers aimed at bringing transparency to the academic job market (see Materials and methods and Supplementary file 41). The survey was distributed via Twitter, the Future PI Slack group, and email listservs of multiple postdoctoral associations, resulting in 322 responses from self-identified early-career researchers who applied for academic positions in the 2018–2019 application cycle. Of these, data from 317 respondents passed simple quality filters and were used for analyses. As all questions were optional, these 317 responses represent the maximum number in our analyses; in cases where respondents chose not to answer the question, we analyzed only the applicant subset with responses and list the number of responses used for each analysis in the appropriate figures and supplementary files.

Demographics of respondents

Respondents reported a large range in the number of submitted applications from a minimum of one to a maximum of 250 (median: 15). The respondent pool was notably enriched in applicants who received at least one off-site interview (70%), at least one on-site interview (78%) and at least one offer (58%); this may represent a significant bias towards successful applicants in our study, as a recent study shows that less than 23% of PhDs eventually secure a tenure-track position (Langin, 2019).

Respondents represented researchers in a wide variety of fields, with 85% from life sciences and related fields, with relatively equal numbers of applications from men and women across this group (Figure 2A). Our survey captured data from an international applicant pool, representing 13 countries (Figure 2B). However, 72% of our respondents reported currently working in the United States, which may reflect the larger circulation of our survey on social media platforms and postdoctoral associations there. Most candidates applied to jobs within the United States (82%), Canada (33%), and the United Kingdom (24%). 96% of respondents entered the job market as postdoctoral researchers (Figure 2C). The applicants spent 1 to 13 years (median: 4 years) in a postdoctoral position. These data are consistent with a recent report suggesting that postdocs in the United States across a variety of fields spend an average of 2.5–3.6 years in their positions (Andalib et al., 2018).

Demographics of academic job applicants.

(A) Distribution of survey respondents by self-identified gender and scientific field (Supplementary file 2). Fields highlighted in green were grouped together as life-science related fields for subsequent analyses. (B) Distribution of countries where respondents were researching at the time of the survey (top, see Supplementary file 3) and the countries in which they applied to faculty jobs (green slices of pie charts, bottom; see Supplementary file 4). (C) Self-reported positions of applicants when applying for faculty jobs (Supplementary file 5). (D) The number of years spent as a postdoctoral researcher ranges from 1 year or fewer (4% of applicants) to eight or more years (9% of applicants; maximum of 13 years, top). Life-science related postdoctoral training (n = 268 respondents) takes significantly longer than in other fields (n = 49 respondents; p=6.5×10−6, bottom; for data see Supplementary file 6; for statistical analysis see Supplementary file 7). (E) Number of postdoctoral positions held by survey applicants (Supplementary file 8). (F) Median values for metrics of research productivity in the applicant pool (Supplementary file 9).

Notably, in our survey population, postdocs in the life sciences spent a median of 5 years in a postdoctoral position, significantly longer than those in other fields, who reported a median postdoc length of 2.75 years prior to applying for a faculty position (Figure 2D), consistent with previous findings on increased training times in the life/biomedical sciences before junior faculty recruitment (Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014; Brischoux and Angelier, 2015; Ahmed, 2019; Powell, 2017; Rockey, 2012). 68% of respondents went on the job market while in their first postdoctoral position (Figure 2E).

Applicants had a large range in their publication records, including number of papers co-authored, h-index, and total citation count. Respondents reported a median of 13 total publications (including co-authorships and lead authorships), with a median of 6 first author papers when entering the job market (Figure 2F).

Publishing metrics by gender

Gender bias in publishing and evaluation is well documented (Aileen Day and Boyle, 2019; Centra and Gaubatz, 2000; Cameron et al., 2016; Witteman et al., 2019). The respondents to our survey were relatively evenly distributed across self-identified genders, with 51% identifying as male, 48% as female, and 1% preferring not to disclose this information (no applicants identified as non-binary; Figure 3A). Men reported significantly more first-author publications, total publications, overall citations, and a higher h-index compared to women (Figure 3B); more men also reported being authors on papers in three journals with high impact factors (Cell, Nature and Science; Figure 3C) than women. The gender differences we observe mirror those seen in other reports on differences in citation counts in STEM fields based on the corresponding author gender (Schiermeier, 2019). Despite popular discussions on a need for papers in Cell, Nature, Science or other journals with a high impact factor (Brock, 2019; McKiernan et al., 2019), 74% of respondents were not authors on a paper in Cell, Nature or Science (CNS), and a greater majority (~84%) did not have a first author publication in these journals (Figure 3C). Of the 51 respondents with papers in these journals, 49 (96%) were in a life science-related field, indicating that the valuation of these journals was highly field-specific (Figure 3C).

Applicant scholarly metrics by gender.

(A) Distribution of gender (male, female, did not disclose) amongst survey respondents (Supplementary file 2, first row). (B) Publication metrics of survey respondents including number of first author papers (top), total publications (middle top), total citations (middle bottom), and h-index (bottom) for male and female respondents. Men in our survey reported more first-authored papers than women (medians of 7 and 5, respectively; p=1.4×10−4), more total publications (medians of 16 and 11; p=3.0×10−3), more overall citations (medians of 343 and 228; p=1.5×10−2), and a statistically significant higher h-index (medians of 9.0 and 7.0; p=5.40×10−3; see Supplementary files 7 and 9). (C) Although most applicants (83.6%) did not have first-author papers in CNS, those in the life sciences had more than applicants in other fields (p=0.012), and men had more than women (p=0.45; see Supplementary files 7 and 11). Note: CNS papers do not include papers in spin-off journals from Cell, Nature or Science. (D) Distribution of funding reported within training period (doctoral fellowship only in blue, postdoctoral fellowship only in red, fellowships during PhD and postdoc in purple, and no fellowship in gray). Females reported significantly more fellowship funding than males (42% of women vs 36% of men for predoctoral fellowships, and 72% of women, 58% of men for postdoctoral fellowships, p=2.40×10−3, χ2 = 12.10, Chi-squared test, df = 2, see Supplementary files 7 and 13). (E) Preprints were posted by 148 of 270 (55%) individual candidates, with an average of 1.57 preprints reported per candidate (top). Number of preprints posted which were not yet accepted for journal publication (bottom) while applying for faculty jobs (see Supplementary file 14).

While 78% of respondents reported having obtained fellowships at some point in their career, this figure was 87% for women and 72% for men (Figure 3D). Women had better success at receiving both doctoral and postdoctoral fellowships. However, the questions in our survey did not distinguish between the types (e.g. government funded versus privately funded, full versus partial salary support) or number of fellowships applied to; many of these factors are likely critical in better understanding gender differences in fellowship support (Figure 3D).

Applications, interviews and offers

The 317 respondents submitted a total of 7644 job applications in the 2018–2019 application cycle, with a median of 15 applications per respondent (Figure 4A). Applicants were invited for a total of 805 off-site interviews (phone, Zoom or Skype; median: 1) and 832 onsite or campus interviews (median: 2), receiving 359 offers (median: 1; Figure 4A). Although many hiring processes consist of an off-site (remote) interview, we found that this was not standard since the typical applicant received more on-site than off-site interviews. In our dataset, 42% of participants received no offers, 33% received one offer, 14% received two offers, 6% received three offers, and 6% received more than three offers. Candidates who received offers typically submitted more applications than those who received no offers, indicating that some candidates may not have submitted enough applications to have a reasonable chance of getting an offer (Figure 4A,D). According to a recent poll on Twitter (which received over 700 responses), most faculty received between one and three offers when they were applying for faculty positions (Whitehead, 2019; Supplementary file 15).

Figure 4 with 1 supplement see all
Job application benchmarks and their impact on success.

(A) Total and median numbers of applications, off-site interviews, on-site interviews and offers recorded in survey responses (Supplementary file 19). (B) Correlations between the total number of applications submitted and off-site interviews (top; R2 = 0.28), onsite interviews (middle) and offers (bottom; R2 = 4.77×10−2). (C) Correlations between the number of interviews completed and offers received (R2 = 0.62). See Figure 4—figure supplement 1 for more details. (D) Total number of off-site interviews (top, p<4.10×10−24, on-site interviews (middle, p=1.20×10−13) and offers (bottom, p=5.0×10−5) for applicants who submitted at least 15 (the median) applications (in red) and less than 15 applications (in blue). (E) Fraction of applications that resulted in offers (offer percentages) for survey respondents who did not apply for jobs outside of faculty positions is significantly higher (p=2.0×10−3, Supplementary file 7) than for those who also applied for both academic and other types of jobs (Supplementary file 14).

Despite the fact that successful candidates submitted more applications, the number of applications per candidate did not correlate with the number of offers, while being only weakly correlated with the number of off-site interviews (Figure 4B). Not surprisingly, the number of on-site interviews strongly correlated with the number of offers received (Figure 4C, bottom). Population medians changed slightly by gender as men submitted slightly more applications, but received slightly fewer off-site interviews. These small differences by gender were not statistically significant (Figure 4A). The median number of offers also did not vary by gender.

We split our population into two groups by application number, one group either at or below the median (<15 applications, n = 162) and the other group above the median (>15 applications, n = 155). These groups had a significant difference in success rates: respondents who submitted more than 15 applications had a significantly higher average number of off-site interviews (Figure 4D). We also asked whether respondents applied for non-faculty positions during this cycle (Supplementary file 16). 71% of applicants did not apply for other jobs and these applicants had a small, but significant increase in offer percentage (Figure 4E).

Taken together, these data seemingly indicate that increasing the number of applications submitted can lead to more interviews, as suggested by others (Jay et al., 2019), with the typical candidate submitting at least 15 applications to achieve one offer. However, the lower correlation between application number and offers (compared to application number and interviews) suggests that while higher application numbers can generate more interview opportunities, other criteria (e.g. the strength of the interview) are important in turning an interview into an offer.

Publication related metrics

The number of papers published, and the impact factors of the journals these papers were published in, can influence the chances of an early-career researcher obtaining an independent position (van Dijk et al., 2014; Powdthavee et al., 2018). As mentioned previously, it is widely believed that you need a paper in Cell, Nature or Science to secure a faculty position in the life sciences (McKiernan et al., 2019; Sheltzer and Smith, 2014; Fox, 2018b). Our data demonstrates that a CNS paper is not essential to an applicant receiving a faculty job offer.

The majority (74%) of our respondents were not an author on a CNS paper (Figure 5A), and yet most participants received at least one offer (58%). However, applicants with a CNS paper did have a higher number of onsite interviews and faculty job offer percentage. Of our respondents, 16% were first author on a CNS paper, and these applicants had a significantly higher percentage of offers per application (p=1.50×10−4, median offer percentages: 11% with a CNS paper and 2% without a CNS paper) and on-site interviews (p=2.70×10−4, median onsite interview percentages: 21% with a CNS paper, and 10% without a CNS paper; Figure 5A).

Figure 5 with 2 supplements see all
Traditional research track record metrics slightly impact job search success.

(A) Pie charts show the fraction of candidates with authorship of any kind on a CNS paper (purple) versus those without (gray), and fraction of candidates who were first author on a CNS paper (purple) versus those who were not (gray). Distributions of off-site interviews (top; p=0.33), onsite interviews (middle; p=2.70×10−4) and offers (bottom; p=1.50×10−4) for applicants without a first-author paper in CNS (gray), and those with one or more first-author papers in CNS (purple; Supplementary files 11, 12, 17). (B) Significant associations were found between offer percentage and the number of first-author papers in CNS (top panel, p=1.70×10−3), career transition awards (second panel, p=2.50×10−2), total citations (third panel, p=2.92×10−2), and years on the job market (fourth panel, p=3.45×10−2). No significant associations were found between offer percentage and having a postdoc fellowship (fifth panel), being above the median in the total number of publications (sixth panel), being an author in any position on a CNS paper (seventh panel), h-index (eighth panel), years as a postdoc (ninth panel), number of first-author papers (tenth panel), number of patents (eleventh panel), or graduate school fellowship status (twelfth panel; Supplementary files 6, 7, 9, 10, 11, 12, 13 and 21). (C) The plots show total citations for those without an offer (blue) and those with one or more offers (gold), for all applicants with one or more first-author papers in CNS (top left); for all applicants without a first-author paper on CNS (bottom left); for all applicants with independent funding (top right); and for all applicants without independent funding (bottom right). In two cases the p value is below 0.05. The bar charts show the offer percentages (gold) for the four possible combinations of career award (yes or no) and first-author paper in CNS (yes or no): for applicants with a first-author paper in CNS, p=0.56, χ2 = 0.34; for applications without, p=0.17, χ2 = 1.92). (D) Summary of significant results testing criteria associated with offer outcomes through Wilcoxon analyses (Supplementary file 7) or logistic regression (Supplementary file 24).

Since the number of on-site interviews and offers are highly correlated (Figure 4C), it is unclear if this increased success simply represents a higher chance at landing more onsite interviews. It is important to note that this effect is correlative and these candidates likely had other attributes that made them appealing to the search committee(s).

We examined several other publication metrics and found no correlation with the number of offers. Specifically, the total number of publications, the number of first author publications, the number of corresponding author publications, and h-index did not significantly correlate with offer percentage (Figure 4—figure supplement 1). When we separated candidates who were above and below the medians for each of these metrics and compared the distribution of offer percentages, only the total number of citations significantly associated with a higher offer percentage (Figure 5B). Although the offer percentage was generally higher for applicants above the median for the other metrics, none of these differences were statistically significant (Figure 5B).

Preprints

Preprints, or manuscripts submitted to an open-access server prior to peer-reviewed publication, are becoming increasingly popular among early-career researchers (Sever et al., 2019), particularly in the life sciences, and can boost article citations and mentions (Sarabipour et al., 2019; Fraser et al., 2019; Abdill and Blekhman, 2019; Conroy, 2019; Fu and Hughey, 2019).

We received 270 applicant responses on the use of preprints; 55% of respondents had posted at least one preprint, and 20% had posted between two and six preprints (Figure 3E, top). At the time of faculty job application, 40% of these respondents had an active preprint that was not yet published in a journal (Figure 3E, bottom), with an average of 0.69 active preprints per person. A number of candidates commented that preprinted research was enormously helpful and served to demonstrate productivity before their paper was published (Supplementary files 17 and 18).

Fellowships and career transition awards

Respondents were highly successful in obtaining fellowship funding during their training (80% received a fellowship of any kind, Figure 3D). Applicants with a postdoctoral fellowship had a greater offer percentage than those without, although the effect was not significant after correcting for multiple comparisons (p=0.17); doctoral fellowships did not appear to influence offer percentage (Figure 5B).

Receiving funding as an early-career researcher is part of a favorable research track record (Eastlack, 2017). A recent study of publicly available data indicates that the proportion of faculty receiving their first large research program grant (an R01 through the NIH) with a history of funding as a trainee (F and K awards through the NIH) is significantly increasing, driven mostly by K awards. Pickett states: "While not a prerequisite, a clear shift is underway that favors biomedical faculty candidates with at least one prior training award" (Pickett, 2019).

Our survey differentiated the types of funding a trainee can receive into predoctoral and postdoctoral fellowships (discussed above), and career transition awards, for which the trainee is listed as the PI and funds can often transition with the trainee to a hiring institute (e.g. the Burroughs Wellcome Fund Career Awards at the Scientific Interface or the NIH K99/R00 Pathway to Independence award). Career transition awards were less frequent, with 25% of respondents receiving awards on which they were PI/co-PI (Supplementary file 20). Respondents with transition funding received a higher percentage of offers (Figure 5B).

Patents

Patents are considered positive metrics of research track record, although their importance and frequency can vary between fields. Only 19% of applicants reported having one or more patents on file from their work when entering the job market (Supplementary file 21). The number of patents held by the applicant did not correlate with the number of offers received (Figure 4—figure supplement 1) and the percentage of offers did not change between those with or without a patent (Figure 5B).

Years on the job market

We also asked how many application cycles they had been involved in. Approximately 55% of our respondents were applying for the first time, and these candidates fared significantly better in terms of offer percentages than those who were applying again (Figure 5B). Additionally, a number of applicants took advantage of resources that provided information about the job application process (Supplementary file 22), and those that did found them helpful (Supplementary file 23).

Analyses such as the work presented here may help applicants refine and present their materials and track record in a manner that might improve success and decrease repeated failed cycles for applicants.

Interplay between metrics

We next examined the relationship between each of the traditional criteria that were significantly associated with an increase in offer percentage. The criteria included being first author on a CNS paper, total citations, and career transition awards.

Overall, we had 241 applicants that fully responded to all of our questions about these metrics. Pairwise testing of each of these criteria found no statistically significant relationships between variables (p=0.45, career transition awards vs CNS; p=0.26 total citations vs CNS; p=0.29 career transition awards versus total citations). Regardless, we plotted subgroups based on offer status and each of these criteria to see if there was evidence for any general trends in our dataset (Figure 5C). Notably, respondents who were first author on a CNS paper and received at least one offer had a greater number of total citations than those who were first author on a CNS paper but did not receive any job offers. Applicants who were first author on a CNS paper or who had a career transition award had higher percentages of securing at least one offer, and those with both had an even greater percentage, although the differences between these groups was not statistically significant.

This analysis suggests that the combination of different criteria holistically influence the ability to obtain an offer. Therefore, we performed logistic regression to examine the relationship between multiple variables/metrics on the successful application outcome of receiving an offer on a subset of applicants (n = 105) who provided answers across all variables. We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables (Supplementary file 24).

This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n = 317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status (Figure 5D). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer (Figure 5—figure supplement 1; Supplementary file 25).

Finally, we extended this analysis to visualize the interplay between all variables in Figure 5B by learning a decision tree automatically from the collected data (Figure 5—figure supplement 2). The algorithm tries to partition the applicants into groups such that each group is entirely composed of individuals with at least one offer or without. A variety of different classifier groups were identified, but no group contained more than ~19% (61 out of 317) of the dataset. In fact, the accuracy of the overall decision tree in distinguishing between candidates with offers and those without was only ~59% (Figure 5—figure supplement 2).

Taken together, these results suggest that there are multiple paths to an offer and that the variables we collected do not sufficiently capture this variability.

Levels of teaching experience

Discussions surrounding the academic job market often center on publications and/or funding, while teaching experience generally receives much less attention. However, the level of teaching experience expected from the applicants can vary, but mostly depends on the type of hiring institution.

We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions (Figure 6A), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus (Clement et al., 2019). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution require a teaching philosophy statement.

Summary of applicant teaching experience and impact on job search success.

(A) Distribution of institution types targeted by survey applicants for faculty positions (PUI only in blue, R1 institutions only in green, or both in red, Supplementary file 26). (B) Distribution of teaching experience reported by applicants as having TA only experience (in purple), beyond TA experience (e.g. teaching certificate, undergraduate and/or graduate course instructorship, guest lectureship and college adjunct teaching, (in orange), or no teaching credentials (in green; Supplementary files 27 and 28). (C) Distribution of teaching experience (TA experience, right, vs. Beyond TA experience, left) for applicants who applied to R1 institutions only (in green), PU institutions only (blue), or both R1 and PUIs (in red), (Supplementary file 27). The degree of teaching experience did not change based on the target institution of the applicant (p=0.56 (ns), χ2 = 0.41; Chi-squared test). (D) Association between offer percentage and teaching experience is not significant (p=0.16; Supplementary files 7, 27 and 28).

Box 1.

Definition of specific terms used in this study.

Early-career researcher (ECR): For the purpose of this study, we define an ECR to be anyone engaged in research who is not recognized as an independent leader/investigator of a research group. This includes graduate and postdoctoral researchers; junior research assistants, research associates, and staff scientists.

Principal Investigator (PI): A scholar recognized as an independent leader of a research group. This includes full professors, group leaders, and tenure-track, non-tenure-track or tenured faculty.

Faculty Job Applicant: An early-career researcher with a PhD (a recent graduate, postdoctoral fellow or research scientist) who seeks to apply for a PI position (see above), usually at the assistant professorship level.

STEM Fields: STEM is an acronym for degrees in fields related to science, technology, engineering, and mathematics. STEM graduates work in a wide variety of fields including the life sciences, the physical sciences, different areas of engineering, mathematics, statistics, psychology, and computer science.

Research Mentor: A research advisor, usually the PI of a lab who mentors graduate and postdoctoral researchers during their academic training in his/her lab.

Adjunct Lecturer: A teacher or post-PhD scholar who teaches on a limited-term contract, often for one semester at a time. This individual is ineligible for tenure.

Teaching Assistant (TA): An individual who assists a course instructor with teaching-related duties in a lecture-based and/or laboratory-based undergraduate or graduate level course.

Doctoral/Graduate and Postdoctoral Fellowships: Funding mechanisms to support the training of a graduate or postdoctoral researcher: the proposal for this is written by the trainee and contains a mentoring/training plan and request for funding to support the trainee salary and/or part of their research expenses such as equipment, lab supplies and travel expenses typically for 1–3 years.

Career Transition Awards: Funding mechanisms facilitating senior trainees towards independent research careers: Includes core/substantial funds to fully support 1–3 years of postdoctoral salary and additional 2–5 years of independent faculty research and staff salaries as well as support for research expenses such as equipment, lab supplies and travel expenses. As a result, some portion of these funds can transition from the training institute to the hiring institute.

R1 University: There are 131 institutions in the United States that are classified as "R1: Doctoral Universities – very high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R2 University: There are 135 institutions in the United States that are classified as “R2: Doctoral Universities – high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R3 University, PUI or Small Liberal Arts College (SLAC): Primarily undergraduate institutions (PUI) are often smaller than large research universities, can be private or public, and offer varying levels of resources for students and faculty. Many faculty at PUIs run a research lab while maintaining significant teaching loads and heavy contact hours with students.

Almost all respondents (99%) had teaching experience (Figure 6B): for roughly half this experience was limited to serving as a Teaching Assistant (TA; Box 1), with the rest reporting experience beyond a TA position, such as serving as an instructor of record (Figure 6B). The degree of teaching experience did not change based on the target institution of the applicant (Figure 6C), nor did the percentage of offers received significantly differ between groups based on teaching experience (Figure 6D).

Research versus teaching-intensive institutions

To our knowledge, there is a lack of systematic evidence describing the process or expected qualifications of a PUI-focused (Box 1) job search (Ramirez, 2016). A subgroup of 25 "PUI Focused" applicants responded to our survey, and, despite this small number, we aimed to describe this important sub-group relative to "R1 Focused" applicants as well as applicants who applied to both types of institutes. The PUI subgroup included a majority of female applicants (60%, Figure 7A) while the R1 subgroup had a majority of male applicants (54%, Figure 7A). Within the PUI subgroup, no differences were seen in the number of first author publications across genders (Figure 7B), although women had a better fellowship history (Figure 7C). The median number of remote interviews, onsite interviews, and offers was also similar to that for the R1 subgroup, although the PUI subgroup submitted fewer applications (Figure 7E). Although both subgroups reported teaching experience (Figure 7D), the PUI subgroup was enriched in adjunct, visiting professor, instructor of record, community college, or contract-based teaching experiences (Figure 7F). Having adjunct experience did not significantly increase the median number of offers received for applicants focused on PUIs, R1s, or both types of institutions (Figure 7G).

PUI focused applicants differ only in teaching experience from the rest of the application pool.

(A) The gender distribution applicants who focused on applying to PUIs (Supplementary file 26). (B) The gender distribution and number of first-author publications of the applicant who focused on applying to PUIs (p=0.88). (C) Summary of the fellowship history by gender for PUI focused applicants (Supplementary file 13). (D) Distribution of teaching experience of PUI focused applicants (Supplementary file 27). (E) The median number of applications, off-site interviews, on-site interviews and offers for PUI focused applicants. (F) Percentage of survey respondents who identified having "adjunct teaching" experience (Figure 1) based on target institution (p=5.0×10−4; χ2 = 27.5, Chi-squared test). (G) The number of offers received segregated by "adjunct teaching" experience in either PUI focused applicants (p=0.55) or R1/both R1 and PUI focused applicants (p=0.98).

A time-consuming and opaque process with little feedback

We asked the applicants to comment on whether any aspect of their training or career was particularly helpful or harmful to their faculty applications (Figure 8A–B). We used word clouds (Supplementary files 27 and 28) to analyze recurrent themes in these open-ended questions. The applicants identified funding as most helpful for their applications, and no-funding as subsequently harmful; this perception agrees with the data presented above (Figure 8A, Figure 5C, Figure 4—figure supplement 1). Additionally, perceptions were also in line with the rest of the data, in that they were unable to largely agree on other measurable aspects of their career that were perceived as helpful. Qualitative aspects that were perceived as particularly helpful included networking and attending/presenting at conferences. Interestingly interdisciplinary-research, which is often highlighted as a strength and encouraged by institutions and funders, was perceived by candidates as a challenge to overcome. Indeed, interdisciplinary candidates may pose an evaluation challenge for committees, given the differences in valuation of research metrics across fields, the extended training time required to master techniques and concepts in multiple fields, as well as valuation of interdisciplinary teams of specialists over interdisciplinary individuals (Eddy, 2005).

Perceptions of the job application process.

Three word clouds summarizing qualitative responses from the job applicant survey respondents to the following questions: A) "What was helpful for your application? " (top; Supplementary file 17), (B) "What was an obstacle for your application? " (middle; Supplementary file 18), and C) "What is your general perception of the entire application process?" (bottom; Supplementary file 31). The size of the word (or short phrase) reflects its frequency in responses (bigger word corresponds to more frequency). Survey respondents were able to provide longer answers to these questions, as shown in Supplementary files 17, 18 and 31. 'CNS-papers' refers to papers in Cell, Nature or Science; 'Pedigree' refers to the applicant’s postdoc lab pedigree or postdoc university pedigree; 'Grant-Writing' refers to the applicant’s grant writing experience with their PhD or postdoctoral mentor; 'Peer-reviewing' refers to the experience of performing peer-reviewing for journals; 'Interdisciplinary-research' refers to comments stating that Interdisciplinary research was underappreciated; 'two-body problem' refers to the challenges that life-partners face when seeking employment in the same vicinity; 'No-Feedback' refers to lack of any feedback from the search committees on the status, quality or outcome of applications.

Notably, many applicants found the amount of time spent on applications and the subsequent lack of feedback from searches frustrating (Figure 8B–C). Most applicants never received any communication regarding their various submissions. For instance, an applicant who applied for 250 positions only received 30 rejections. Overall, our respondents submitted 7644 applications (Figure 4A) and did not hear anything back in 4365 cases (57% of applications), receiving 2920 formal rejection messages (38% of applications; Supplementary file 19). Application rejection messages (if received at all) most often do not include any sort of feedback. Additionally, a considerable amount of time is spent on writing each application and extensive tailoring is expected for competitive materials. Combining these insights, it is therefore unsurprising that almost all applicants, including applicants that received at least one offer (Supplementary file 29), found the process "time-consuming", a "burden on research", and "stressful" (Figure 8B–C).

44% of respondents had applied for faculty jobs for more than one cycle (Supplementary file 30). Though applicants who applied for more than one cycle had significantly lower offer percentages (p=3.45×10−2; Figure 5B), many reported perceived benefits from significant feedback from their current PI through their previous application cycles. Though mentorship was not as often reported as specifically helpful (Supplementary file 17), the lack of mentorship was a commonly cited harmful obstacle (Figure 8B, Supplementary file 18). Lastly, multiple candidates felt that issues pertaining to family, the two-body problem (need for spousal/significant other hire), parental leave, or citizenship status significantly harmed their prospects.

The view from the search committees

To learn more about the characteristics search committees valued in applicants, we performed an exploratory survey of members of such committees. This anonymous survey was distributed in a limited fashion, taking advantage of the professional networks of the authors. Fifteen faculty members responded, with nine having been involved in search committees for over ten years (Figure 9A). As with our survey of applicants, we focused on faculty members at R1 academic centers working in life sciences (14/15 of those polled) and engineering (1/15) within the United States (Figure 9A).

Summary of metrics valued by search committees.

Search committee members were asked on how specific factors were weighted in the decision on which applicant to extend an offer to (Supplementary files 3338). All search committee members surveyed were based at R1 universities (Box 1). (A) Distribution of the fields of study and years of experience for the search committee survey respondents. (B) The median number of faculty job openings, number of applicants per opening, applicants that make the first cut, applicants who are invited for phone/Skype interviews, and offers made. (C) The quantitative rating of search committee faculty on metrics: candidate/applicant research proposal, career transition awards, postdoctoral fellowships, graduate fellowships, PI/mentor reputation (lab pedigree), Cell/Nature/Science journal publications, Impact factor of other journal publications, Teaching experience and value of preprints based on a 5-level Likert scale where 1 = not at all and 5 = heavily. (D) Visual summary of the job applicant perception (from word cloud data) and the results of both surveys (statistical analyses of the applicant survey and criteria weighting from the search committee survey). A number of metrics mentioned in short answer responses were not measured/surveyed across all categories. These missing values are shown in gray.

Two-thirds of respondents replied that the search committees they sat on typically received over 200 applicants per job posting, with one-third receiving 100–199 applications per cycle. Between 5 and 8 applicants were typically invited to interview on-site; one-third of respondents replied that off-site interviews (e.g., via phone or Skype) were not performed (Figure 9B). These statistics help demonstrate the challenges that hiring committees face; the sheer volume of applicants is overwhelming, as mentioned explicitly by several search committee respondents (Supplementary file 32).

We asked what factors search committee members found most important, what their perception of the market was, and how they felt it had changed since they first became involved in hiring. We also asked them to weigh specific application criteria in evaluating an application from 1 (not weighted at all) to 5 (heavily weighted; Figure 9C). Criteria such as transition awards were consistently ranked highly, matching applicant perception; however, committee members also placed substantial emphasis on the research proposal. Two-thirds viewed preprints favorably, although their strength may not yet be equivalent to published peer-reviewed work (Figure 9C). In follow-up questions, a number of respondents emphasized that the future potential of the candidate both as a colleague and a scientist was important.

Since this last point was not prominent in our survey of job applicants, we looked for discrepancies in the two sets of responses (Figure 9D). In general, search committees placed greater emphasis on the future potential and scientific character (research proposal, research impact, collegiality), while applicants focused on publication metrics and funding. However, despite the search committees placing less emphasis on papers in CNS, candidates with papers in these journals were more successful.

We also asked if there were additional factors that search committees wished applicants knew when applying (Figure 10). Several emphasized the quality of the research and papers was the most important factor for assessing prior achievement, but added that a compelling and coherent research proposal was also critical, and was sometimes underdeveloped in otherwise competitive candidates. The importance of departmental fit was also emphasized; interpersonal interactions with faculty members at the interview stage were also mentioned. This last sentiment is consistent with a recent Twitter poll which found that "overall attitude/vibe" was the single most important factor for selection at the interview stage (Tye, 2019). Intriguingly, while one faculty respondent noted that they rarely interview any applicant without a career transition award, such as a K99/R00 Pathway to Independence Award from the NIH (a situation they noted as problematic), another lamented that applicants worried too much about metrics/benchmarks anecdotally perceived to be important, such as receiving these awards. Finally, a majority of respondents noted that it was easy to identify good candidates from their submitted application (11/15), that there were too many good applicants (10/15), and that candidates often underperformed at the interview stage (10/15) (Figure 10, Figure 10—figure supplement 1, Supplementary File 35).

Figure 10 with 1 supplement see all
Search committee perception of the faculty job application process.

Two word clouds representing responses from members of search committees in response to the following questions: A) "What information do you wish more candidates knew when they submit their application?", and B) "Have you noticed any changes in the search process since the first search you were involved in?" The size of the word/phrase reflects its frequency in responses, with larger phrases corresponding to more frequent responses. Search committee faculty members were able to provide long answers to both questions (Supplementary files 38 and 39).

Discussion

Challenges in the academic job market

Currently, there is little systematic evidence for what makes a competitive faculty candidate. As with any opaque, high-pressure environment, an absence of clear guidelines and expectations coupled with anecdotal advice can lead individuals to focus on tangible goals and metrics that they feel will help them stand out in the system. Our findings were consistent with several commonly held notions: the number of applications submitted, career transition awards (e.g. a K99/R00 award), and total citation counts were significantly associated with obtaining offers in our Wilcoxon test and when jointly considering all variables in a logistic regression analysis. Joint academic/industry job searches were negatively associated with obtaining academic offers in both analyses, while the number of years an applicant was on the job market was negatively associated in our Wilcoxon analysis. Papers in CNS were only significantly associated with offers in the Wilcoxon analysis, while postdoc fellowships were only significant in the logistic regression.

Metrics such as career transition awards and postdoctoral fellowships can be broadly categorized as funding metrics and the positive association between these metrics and offer outcomes likely reflects the hiring institute being confident that the candidate will be competitive for future funding for their research program. Indeed, career transition awards essentially provide additional start up funds, while postdoc fellowships provide a track record of funding. Although postdoc fellowships were not significant in our Wilcoxon analyses, this metric was significant in our life science-specific Wilcoxon subgroup analysis (Figure 5—figure supplement 1.) as well as our logistic regression on the whole dataset (Figure 5D). The search committee respondents confirmed the benefit of career transition funding as major strengths for an application.

Association between offers and the number of applications, non-academic job searches, and years on the academic job market requires cautious interpretation. Given that receiving any single faculty offer is a low-probability event, there is value in submitting enough applications to increase the odds of receiving an offer. However, there is likely a balance in ensuring the quality of each application, which requires time and effort to individually tailor to each position. Searching for non-academic jobs might detract from the time available to tailor applications, although the negative association may also reflect other factors such as the typically swifter non-academic hiring timeline, which could cause applicants to remove themselves from a search prior to its conclusion. Likewise, the negative association between repeated years on the job market and offers might reflect fundamental problems with the quality of an application, or more complex factors such as geographical constraints. As we did not collect data that would allow us to determine the quality of application, or the fit of an application to a particular opening, we cannot evaluate these metrics beyond the broad associations found in our dataset. Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring (Clauset et al., 2015). This should be a major consideration for future studies of the academic job market.

When examining publication-related metrics, we found that total citation counts were significantly associated with receiving a job offer in both the Wilcoxon and logistic regression analyses. There was also a significant positive association between being first author on a CNS paper and receiving a job offer in the Wilcoxon analysis, but not in our logistic regression models. Examination of our data also revealed a gender gap in publication metrics, with males reporting more CNS papers and more papers overall, indicating that opportunities for publication are not equally available (Arvanitis and Cho, 2018; Gumpertz et al., 2017). Second, the results of our automated variable selection procedure suggest that being an author in any position on a paper in CNS is an advantage overall (though the result is not significant); however, within the life sciences, being the first author is more of an advantage (again, not significant). Finally, papers in CNS and other journals with high impact factors have been regarded as a major benchmark for trainees in the life sciences (van Dijk et al., 2014), and qualitative comments from our applicant survey conveyed a perception that the absence of a CNS paper is deemed detrimental to offer prospects. Collectively, our data suggest that while being first author on a CNS paper increases the chances of receiving an offer (particularly in life sciences), papers in CNS were neither necessary nor sufficient for securing an offer, as the majority of our respondents received offers without having a paper in CNS.

Consistently, being the author of a CNS paper was not deemed highly important by the search committee members we surveyed. These data may reflect a discordance of priorities for individual faculty members compared to their peers and the system at-large, as recently reported (Niles et al., 2019). This could lead to an unspoken expectation that faculty (especially pre-tenure faculty) see themselves as passive participants in the current academic system, instead of active participants with the authority to realign priorities through search committees (Niles et al., 2019). Future studies with higher numbers of faculty respondents should endeavor to further explore this phenomenon.

Despite challenges in the job market (Larson et al., 2014; Andalib et al., 2018; Kahn and Ginther, 2017), our survey revealed positive outcomes that suggest progress in select areas. Nearly half of the job applicants we surveyed reported posting at least one preprint. Several of the search committee members we surveyed confirmed that while published papers carry the most weight, preprints are generally viewed favorably. Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments (Arvanitis and Cho, 2018; Gumpertz et al., 2017; Ceci and Williams, 2015; Leaper and Starr, 2019), and trail men in publication-related metrics (Figure 3B), our data suggest very few differences in outcomes in the May 2018–May 2019 job cycle. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants (Supplementary file 40).

Overall, the respondents were generally highly qualified according to the metrics we measured, and yet they reported high stress and frustration with their experiences of the faculty job search. In a large number of cases, applicants were not notified of a receipt of their application, nor were they updated on its status, given a final notice of rejection, or informed that the search may have failed. This uncertainty further complicates an already stressful process that can be mitigated by improving practices for a more streamlined application process. Applicants perceived poor mentorship as a major obstacle to their applications. Further, we found that most metrics were differentially valued by candidates and committees. Collectively, these differences in expectations between applicants and hiring institutions, coupled with the opaque requirements for obtaining a faculty position, likely drive the high stress reported by both candidates and committee members alike.

Limitations of this study and measuring outcomes in the academic job market

There are several limitations of this study imposed by both the original survey design and general concerns, such as the anonymity of respondents, and the measurability of various contributing factors. For future data collection we suggest keeping surveys focused on region-specific job markets. Our pool of applicants was largely those seeking a position in North America. We believe these results can be aggregated, but the survey questions may not all be applicable to other large markets (e.g. Europe, China, India). We did not receive a sizable response from applicants looking outside of North America and in fields outside of life sciences to make useful comparisons. A similar survey circulated in each market individually with a similar number of responses would have broader impact.

We purposely did not ask for race, ethnicity, or citizenship demographics, PhD or postdoc institution, and region or institution where offers were received. We believe the addition of these metrics could potentially jeopardize the anonymity of respondents. Despite this, these factors could be significant contributors to the receipt of an academic job offer. Racial inequalities in all STEM fields at all levels exist and need to be addressed (Whittaker et al., 2015), specifically with how they intersect with gender (Gumpertz et al., 2017). As indicated in our open question responses (Figure 8B), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market (Cantwell, 2011). The reputation of a training institution is questionably measurable, but is also often listed in anecdotal advice as important. Recently it was reported that a majority of new faculty are hired from a minority of institutions providing postdoc training (Clauset et al., 2015; Miuccio et al., 2017). It is possible that adding institutional reputation to the other traditional metrics we measured could provide a more complete picture of the current path to a faculty position.

While we measured some of the attributes widely perceived as important in faculty hiring (e.g. funding track record), others are less easily quantified (e.g. the research proposal, lab pedigree, or letters of recommendation that comments from our search committee survey revealed to be important) and data collection on these items would be highly recommended in future surveys. Addressing the quality of application materials is highly context-specific (given the field, search committee, and institutional needs) and can improve (Grinstein and Treister, 2018). Other aspects which are not directly measurable and are often cited as important for applicants in the academic job market are "fit" and "networking" (Wright and Vanderford, 2017). Respondents agreed that networking, conferences, collaborations, and connections were helpful in their job search (Figure 8A). Conference organizers are also starting to offer badges that those searching for faculty jobs can wear at events; exploring the relationship between networking metrics (such as number of conferences and networking events attended) and success on the job market could be a topic for future research. Departmental or institutional "fit" is largely determined by the search committee on an individual basis, and it is likely that we will never be able to measure fit adequately (Saxbe, 2019).

All questions in our survey were optional. We chose this survey design in order to make the survey easier for respondents to complete; however, missing answers represent a source of potential bias as unanswered questions may represent answers that could be negatively perceived and/or zero in value. For example, some individuals may not have felt comfortable indicating they had zero offers, which could lead to the offer percentages we report being inflated. Such bias could also affect the imputations in our logistic regression, and for these reasons we have attempted to provide multiple transparent and qualified analyses of the data. Future surveys may benefit from all questions requiring a response. It is also possible that participation in the survey from the outset suffers from survivorship bias, in that those applicants that had a positive experience are more likely to reflect upon it and complete a survey on the process. Our survey was also likely completed by a highly-engaged group of aspiring future faculty. The Future PI Slack group itself is a space for postdoctoral researchers most interested in obtaining a faculty career to engage with and learn from one another. Thus, the survey data likely reflects a highly motivated and accomplished group and not the full pool of applicants to faculty positions each year. Wider dissemination of future surveys will hopefully be aided by the publication of these results and increased awareness of the survey among trainees in various research communities.

Finally, the data from our survey of job applicants focused on candidates and not the search committees. It is unclear how many individual searches are represented in our dataset. It is likely that as many as ~200–500 committees were represented in our aggregated job applicant data, and different committees may adopt distinct assessment criteria. Our limited search committee survey responses show that the committees represented by our sample favor a holistic assessment of candidates and that decision by universal criteria (especially based solely on career transition awards or papers in CNS) is likely not unilateral, especially across disciplines. Future studies would benefit from surveying a larger pool of search committees to see what major trends and practices dominate, whether the majority of searches adopt a comprehensive evaluation approach, or if there is heterogeneity among committees in how tenure-track hiring assessments are conducted.

Conclusion

The search process for faculty jobs lacks transparency and data regarding what makes a successful applicant. Here, we began to address this deficiency through a survey targeted at the applicants themselves, and including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment about the process, despite the fact that 58% of our participants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the preparation done by applicants and the different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data also show the mental strain on applicants during the hiring process. We propose a number of potential solutions with the understanding that hiring faculty is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the academic job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees (Clement et al., 2019). Further work into the challenges search committees face is needed to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.

Materials and methods

Survey materials

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We designed a survey (the "applicant survey") to collect demographics and metrics that were commonly discussed on Future PI Slack during the 2018–2019 academic job search cycle. The survey was designed to take less than 5 min in order to maximize response rates, and respondents were not required to answer all questions.

After collecting and performing initial analyses of this survey, we designed an additional survey for search committees (the "search committee survey"). The text of both surveys used in this work is included in the Supplementary files 41 and 42. A Google form was used to conduct both surveys.

The applicant survey was distributed on various social media platforms including the Future PI Slack group, Twitter, and Facebook, and by several postdoctoral association mailing lists including in North America, Europe and Asia. The survey was open for approximately six weeks to collect responses.

The search committee survey was distributed to specific network contacts of the various authors. Though this distribution was more targeted, a Google form link was still used to maintain anonymity. The search committee survey was open for approximately three weeks to collect responses. In both cases, respondents to the surveys were asked to self-report, and the information collected was not independently verified. The surveys can be found in Supplementary files 41 and 42.

Data analysis

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Prior to analysis, we manually filtered out five responses in which answers were not interpretable or did not appear to answer the correct questions. Microsoft Excel and RStudio were used to graph the results of both surveys shown in Figures 16 and 8. Specifically, data was filtered and subdivided using the 'tidyverse' collection of R packages, and figure plots were generated using the 'ggplot2' package. Whenever statistical analyses were used, the exact tests, p-values and χ2 values are reported in the appropriate figure or figure legend or caption, results section and Supplementary file 7, and represent the implementations in the basic R 'stats' package.

A p-value of less than 0.05 was considered significant. Where a number of demographics are combined in the reporting throughout this study, any analysis groups with less than five respondents were combined with other similar values instead of the raw n value in an effort to protect the anonymity of participants. Briefly, statistical methods are as follows: in general, the two-tailed Wilcoxon rank sum test (with Holm correction when applicable) or Chi-squared test was used to report p-values (see Supplementary file 7 for detailed breakdown).

The qualitative survey comments were categorized by theme (keywords/context) describing each comment and the frequency of comments pertaining to a particular theme and tabulated (Supplementary files 17, 18, 38 and 39). Word clouds were generated using the WordItOut platform (WordItOut, 2020; Figures 7 and 9). The visual summary heatmap of the job applicant perception and the survey results along with the search committee survey results (Figure 9D) was created by counting the frequency of comments for each metric (i.e. publications, fellowships, preprints) from the respondents to the qualitative (long answer) questions (Supplementary files 17, 18, 38 and 39). The job applicant survey quantitative results were also used to rank metrics based on significance (as determined by Wilcoxon analysis or logistic regression analysis (Supplementary file 7)) and were also incorporated into the heatmap (Figure 9D). A number of metrics were not measured/surveyed as part of our study. These missing values are shown in gray.

Logistic regression analysis was performed in R using the 'glm' function with the 'family' parameter set to 'binomial'. All variables collected in the survey were included as independent variables, except those that were considered to be outcomes (numbers of remote interviews, onsite interviews and offers). The outcome variable was a binary 'Offer' or 'No offer' variable. All continuous variables were z-score normalized to ensure that they were centered and scaled consistently. To reduce collinearity between variables, a forward stepwise variable selection approach was adopted by starting with the variable that was most accurate in predicting offer status when included in a logistic regression model and then iteratively adding a variable to the model to maximize accuracy at every step. Furthermore, at every step, a variable would only be added if it was not correlated (Spearman correlation coefficient ≤0.5) with a variable already included in the model from a previous step. The model with the most accurate variable-combination was used to report coefficients. When multiple independent variables were considered together, missing values accounted for nearly two-thirds of the data, and were therefore imputed by fitting a bagged tree model for each variable (as a function of all the others; 63). Both variations of the analysis (missing data excluded and missing data imputed) were reported. In addition, this entire logistic regression analysis was repeated on a subset, solely comprising of applicants from the life sciences.

In order to visualize the potential paths to an offer, a decision tree was learned automatically from the data using the C5.0 algorithm (Kuhn and Johnson, 2013). All possible combinations of the following parameter settings were evaluated: (Cyranoski et al., 2011) either the tree-based variant or the rule-based variant of the algorithm was run, (Ghaffarzadegan et al., 2015) winnowing of irrelevant variables was set to 'TRUE' or 'FALSE', and (Schillebeeckx et al., 2013) the number of boosting 'trials' was set to 1, 4, 16, 32 or 64. The parameter combination with the best accuracy in predicting offer status in a 10-fold cross-validation experiment (as implemented in the 'caret' package in R) was chosen (Kuhn, 2008). Since decision trees naturally handle missing values and differences in scales, no additional imputation or data normalization was performed before training and testing. The most accurate tree was found to be the one that used the rule-based variant, had no winnowing and no boosting (trials = 1) and was plotted using the 'plot' function in the 'partykit' R package (Hothorn and Zeileis, 2015) and then manually grouped in Illustrator.

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Decision letter

  1. Helena Pérez Valle
    Reviewing Editor; eLife, United Kingdom
  2. Peter Rodgers
    Senior Editor; eLife, United Kingdom
  3. Adriana Bankston
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by two members of the eLife Features Team. The following individuals involved in review of your submission have agreed to reveal their identity: Adriana Bankston (Reviewer #2).

Summary:

This article (which is based on a survey of more than 300 early-career researchers who are/were on the job market) has the potential to be important. However, the data require further analysis and the presentation and discussion need to be improved.

Essential revisions:

1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue).

- Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay.

- Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

3. Men have more first author publications than women particularly in CNS journals - the authors should comment on this.

4. In Figure 4D the authors should identify the factors that differentiate between candidates with one or more offers vs. those who received none.

5. Regarding the logistic regression: rather than conducting an analysis with 16 variables, many of which are collinear, the authors should conduct this analysis one variable at a time to identify the single features that predict job market success. The authors can then test these variables for collinearity and combine uncorrelated variables into models that include two, three, four, etc. independent predictors.

6. 96% of the respondents with CNS publications were in fields in "Biomedical or life sciences" or "Biology (other)" as described in Figure 1A. The authors should therefore repeat several of the key analyses on success predictors on the life science cohort alone, including making a new version of Figure 4 that includes data for the life science cohort alone. The authors should also update the tables S7, S9, S10 and S22 to include statistics about CNS publications done for the life scientists in the cohort alone (alongside the data already presented for the full cohort).

7. The authors conducted a "PUI-only" subgroup analysis (Figure 6), but it would also be informative to conduct an analysis among candidates who only applied to research-intensive positions. The differences between the positions may be noteworthy, and by combining too many unrelated job searches into one analysis, the authors may be missing out on important relationships.

8. The authors should provide some more background on the survey and how it was distributed at the start of the results section. What checks were in place to ensure that only early-career scientists answered it? Similarly, the authors should provide more information in the results section on how the search committee survey was distributed and who answered it.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references:

- Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696.

- Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445.

- Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

10. Another concern is an attempt to make correlations between number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference:

- Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1: e1400005. doi:10.1126/sciadv.1400005

11. Under "Statement of Ethics", the authors write that an IRB exemption was obtained on 08/29/2019. However, the survey itself was conducted in April 2019. I believe that retroactive IRB approval is generally prohibited by federal regulations. Was the IRB aware that the study had already been conducted when they granted the exemption? Can the authors comment on this serious discrepancy?

12. Teaching experience was not assessed either from the applicant or the institution perspective. Applicants self-disclosed their presumed teaching experience and then when a number of them did get job offers at R1s, the statement was made that applicants fulfill the teaching requirements for any university type. That is an oversimplification of findings. The authors' data indicates that the majority of applicants applied to R1s. This should be further discussed by the authors.

13. The authors could comment on whether the number of postdoc positions attained correlated with their ability to obtain faculty positions (in other words, are 3 postdoc appointments more desirable as compared to 1 when applying for a faculty position at a prestigious institution?).

14. The authors could address how the application process might be improved so it is a more positive experience.

https://doi.org/10.7554/eLife.54097.sa1

Author response

[We repeat the reviewers’ points here in italic, and include our replies in Roman.]

Essential revisions:

1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

We have now removed the section below from the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” results, including the corresponding tables:

“A separate Twitter poll indicated that applicants in general, not specifically for this cycle, typically spend more than 3 hours tailoring each application (49) (Table S26 Supplementary File 1). Our pooled applicants at minimum then spent a combined 22,932 hours (7,644 applications x 3 hours preparation each), or 2.62 years, on these applications. Individually, this number amounts to 72 hours for each applicant on average, but does not take into account how long the initial creation of “base” application materials takes, which is often a much longer process. In another follow-up Twitter poll, a majority of respondents felt that time spent on preparing faculty job applications impeded their ability to push other aspects of their career forward (Table S27 Supplementary File 1) (50).”

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue).

- Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay

- Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

We thank the reviewers for this comment, and would like to clarify our position. We agree with the reviewers that the discussion of the importance of CNS publications is a sensitive and well-known issue. The relationship between job market success and CNS publications is complex. We have read the references suggested by the reviewers, and we agree that several studies have concluded that CNS publications are generally held in high regard by search committees. Moreover, we discuss in the text that there is a high perceived importance of CNS papers to applicants. However, our data shows 2 clear findings with regards to CNS: 1) Most applicants who get offers do *not* have a CNS paper and 2) Applicants with a CNS paper appear to have a higher chance of getting an offer than those who do not have a CNS paper. We believe that the demonstration of the first point (most candidates do not have a CNS paper) is an important point for postdocs to consider but does not detract from the 2nd point (CNS papers appear to confer an advantage to an applicant). We note that an analysis of the offer association with CNS publications is shown in Figure 4D. We observed a 7-15% increase in offer rate for candidates which had published in CNS journals. We also note that despite this observation, ~60% of our survey respondents still received at least one offer. Moreover, our data (Figure 4C) suggest that those individuals with CNS authorship who also received a job offer were more highly cited than those with CNS authorship without a job offer, suggesting that assessment of the impact of an applicant’s work is a complex blend of many factors.

Our small survey of faculty search committee members, revealed an attitude towards CNS publications that was discordant with that of the applicants and the conventional expectation. While we acknowledge that we do not have extensive information on the decision making process of every single search committee, the volume of current literature around this issue speaks to the culture shift that is occurring around academic hiring. Formal discussions of CNS as a metric and its impact on equity in the professoriate have resulted in the development of guidelines such as DORA, that reflect the burgeoning culture change. While an in-depth analysis of impact is outside the scope of this work, we look forward to future work on this topic.

3. Men have more first author publications than women particularly in CNS journals - the authors should comment on this.

We note that these results are presented in Figure 2B and 2C, as well as discussed in the “applicant scholarly metrics by gender” results subsection. How these results are in line with previous findings is presented within the introductory paragraph to that results section. We additionally revisit this point in the discussion stating “Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments (52,53,56,57), and trail men in publication-related metrics (Figure 2B), our data suggest very few differences in outcomes in the May 2018-May 2019 female applicant pool relative to their male counterparts. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants (Table S40 Supplementary File 1). We have also now added a further point in the discussion pertaining specifically to the intersection of gender and CNS publications. “First, examination of our data revealed a gender gap in publication metrics, with males reporting more CNS authorship and publications overall, indicating that opportunities for publication are not equally available (52,53). ” Our survey has captured numerous aspects of the academic job market and the factors influencing the success of applicants. We recognize that gender (and other marginalized identities) play a large role. We believe the role of gender is thoroughly discussed in our manuscript while allowing in depth analysis of other factors.

4. In Figure 4D the authors should identify the factors that differentiate between candidates with one or more offers vs. those who received none.

We thank the reviewers for this comment, and for the opportunity to clarify this point within our manuscript. In our study, we performed two different analyses to understand which factors differentiate candidates based on their success in obtaining offers. We performed Wilcoxon tests (presented in Figure 4B and Table S7 Supplementary File 1) comparing the offer percentage (the number of offers/the number of applications) and also performed a logistic regression comparing candidates with offers vs those without (Table S22). The data from these two analyses are summarized in Figure 4D, as noted by the reviewer, but were confusing due to the wording in both the text and the figures. We have clarified this in the text by adding further details regarding the offer status and positive correlations in the following sentence: “When missing values were imputed, significant positive coefficients were observed for having a higher h-index, higher application numbers, career transition awards and identifying as female and obtaining an offer.” Further, we have clarified this in the figure legend for Figure 4D as follows: “Summary of significant results testing criteria associated with 1+ offer (positive) or no offers (negative) with offer outcomes either through Wilcoxon analyses (Table S7 Supplementary File 1) or logistic regression (Table S24 Supplementary File 1) ordered by decreasing effect size.”

5. Regarding the logistic regression: rather than conducting an analysis with 16 variables, many of which are collinear, the authors should conduct this analysis one variable at a time to identify the single features that predict job market success. The authors can then test these variables for collinearity and combine uncorrelated variables into models that include two, three, four, etc. independent predictors.

We thank the reviewer for bringing this to our attention. As suggested by the reviewer, we have undertaken a greedy stepwise variable selection procedure in which we started with the variable that was the most predictive of offer status (1+ offer vs 0 offers) and then incrementally added one variable at a time, selecting for the best pair, triplet, and so on. While doing this we also ensured that no new variable was highly correlated or anticorrelated with a previously included variable (Absolute Spearman correlation coefficient cutoff of 0.5). This resulted in the exclusion of two variables that were highly correlated with citation count: h-index and total number of publications (see correlation plot below).

Author response image 1

Among the remaining 14 variables, the combination of seven variables resulted in the highest accuracy in cross-validation experiments: (Applying to) Other jobs, Application number, Citations, Years on job market, Postdoc fellowships, (transition to independence) Funding and CNS (co-authorship) (see Author response table 1 below).Author response table 1.

We note that while this 7-variable model is more accurate in predicting offer status than the full model presented in the earlier version, there is a possibility of bias typically arising from the subjective decisions made during a variable selection exercise. For instance, the choice of a greedy stepwise approach, the use of Spearman correlation as a measure of collinearity, the correlation coefficient thresholds, the choice of accuracy as the quantity to maximize, among others, are all likely to influence which variables are selected and how many are deemed optimal. Nonetheless, the agreement between the single-variable analyses (Wilcoxon tests) and the more rigorous logistic regression analysis gives us confidence to update the logistic regression analysis in the manuscript to this 7-variables model (see updated Figures 4 and Table S24 Supplementary File 1). We have also modified the text summarizing these results in the “Interplay between metrics” section to read as follows:

"We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables (Table S24 Supplementary File 1) that was tested on a subset of applicants (n=105) who provided answers across all variables. This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n=317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status (Figure 4D). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer (Table S25 Supplementary File 1; Figure 4 – Supplement 1)."

6. 96% of the respondents with CNS publications were in fields in "Biomedical or life sciences" or "Biology (other)" as described in Figure 1A. The authors should therefore repeat several of the key analyses on success predictors on the life science cohort alone, including making a new version of Figure 4 that includes data for the life science cohort alone. The authors should also update the tables S7, S9, S10 and S22 to include statistics about CNS publications done for the life scientists in the cohort alone (alongside the data already presented for the full cohort).

We have updated Table S7 in Supplementary File 1 and added new tables S9 and S11 equivalents for Life/Biomedical Sciences applicants (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) scholarly metrics to the Supplementary file. The new added tables are numbered S10 and S12 in Supplementary File 1. Figure 4 – Supplement 1 shows the same analyses as in Figure 4, but restricted to the life sciences cohort. Table S25 in Supplementary File 1 reports the coefficients, p-values and other related information in the same manner as in Table S24 in Supplementary File 1.

7. The authors conducted a "PUI-only" subgroup analysis (Figure 6), but it would also be informative to conduct an analysis among candidates who only applied to research-intensive positions. The differences between the positions may be noteworthy, and by combining too many unrelated job searches into one analysis, the authors may be missing out on important relationships.

We have updated Figure 6 to include comparisons of candidates who only applied to PUIs (PUI-focused), those who only applied to R1 institutions (R1 Focused) and candidates who applied to both. Our original observations still hold (candidates applying to PUI positions are more likely to have more extensive teaching experience). However the new analysis also reveals some interesting trends (the R1 Focused subgroup is majority male in contrast to the two other groups) that we now comment on in the “Research versus Teaching-intensive institutions” section.

8. The authors should provide some more background on the survey and how it was distributed at the start of the results section. What checks were in place to ensure that only early-career scientists answered it? Similarly, the authors should provide more information in the results section on how the search committee survey was distributed and who answered it.

We have altered the text in two separate places to provide clarity on this point. The first paragraph under “Academic Job Applicant Demographics” has been changed to include statements that participants in the survey self-identified as applicants in the academic job market in 2018-2019. The first paragraph under “Search Committees Value the Future” has been changed to state that the search committee survey was sent to a limited number of faculty, from within the authors’ professional networks, who were known to serve on search committees.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references:

- Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696.

- Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445.

- Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

We thank the reviewer for this comment and note that the complete demographic data of survey participants' country of origin, field of scientific research, and current position is included in Figure 1 and detailed in Tables S2, S3 and S5 in Supplementary File 1. We acknowledge that our survey did not fully capture the difficulties faced by non-citizens in obtaining a US faculty position, though these difficulties have been demonstrated in previous literature. We have added that citizenship status was a common issue thought to hinder applicants’ progress in the last line of the last paragraph of the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” section and highlight its prominence in Figure 7B. We have also added the following text to the discussion of the limitations of our study: “As indicated in our open question responses (Figure 7B), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market (59).” We note that we had previously referenced Sauermann, H. & Roach, M. (2016) in our introduction, it is reference number 9.

10. Another concern is an attempt to make correlations between the number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference:

- Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1:e1400005. doi:10.1126/sciadv.1400005

While we acknowledge that variables such as training institution and prestige of the applicants’ graduate school and postdoctoral mentors undoubtedly influence who obtains faculty job offers, studying this relationship was beyond the scope of the current study. The current study was not designed to explore these variables because of privacy concerns for our respondents. We did not inquire about respondents’ training institutions, advisors’ prestige, or other metrics associated with their “networks” which may have influenced their job search success. Future work to explore these variables are certainly needed. We have cited Clauset et al in our discussion section when we discuss the potential impact of these training/mentoring/network variables we did not assess in the current study. From “Challenges in the Academic Job Market” in the Discussion:

“Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring (51). This should be a major consideration for future studies of the academic job market.”

We would like to add that our open-ended question regarding “What was helpful for your application” (Figure 7A) indicated applicants perceived networking methods and pedigree to be valuable in helping them land faculty jobs. So, there is clearly merit to study these metrics in future work.

11. Under "Statement of Ethics", the authors write that an IRB exemption was obtained on 08/29/2019. However, the survey itself was conducted in April 2019. I believe that retroactive IRB approval is generally prohibited by federal regulations. Was the IRB aware that the study had already been conducted when they granted the exemption? Can the authors comment on this serious discrepancy?

We initially conducted the surveys for the Future PI Slack community only. When we decided on data analysis and wider dissemination of the results, we applied for IRB exemption for use of existing data. We have now corrected our statement of ethics to reflect the exemption criteria for publication of the survey data: “The surveys used in this manuscript were designed and implemented by the authors listed above on a voluntary basis outside of their post-doctoral positions at the time. The authors respect the confidentiality and anonymity of all respondents, and no identifiable private information was collected. Participation in both surveys was voluntary and the respondents could stop responding to the surveys at any time. The use of the data collected in these surveys was determined to meet the exemption criteria for secondary use of existing data [45 CRF 46.104 (d)(4)] by the University of North Dakota Institutional Review Board (IRB) on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage (amanda.haage@und.edu) for further inquiries.”

12. Teaching experience was not assessed either from the applicant or the institution perspective. Applicants self-disclosed their presumed teaching experience and then when a number of them did get job offers at R1s, the statement was made that applicants fulfill the teaching requirements for any university type. That is an oversimplification of findings. The authors' data indicates that the majority of applicants applied to R1s. This should be further discussed by the authors.

We have revised the teaching section within the results section to address the reviewers’ comments about oversimplification of the findings. The statement that applicants fulfill the teaching requirement for any university type has been removed, as requested by reviewers. Below is the revised text.

Levels of teaching experience varied among respondents

Discussion surrounding the academic job market is often centered on applicants' publications and/or funding record, while teaching experience generally receives much less attention. Accordingly, a candidate’s expected teaching credentials and experience vary, largely depending on the type of hiring institution. We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions (Figure 5A), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus (47). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution requires a teaching philosophy statement. The majority (99%) of our survey respondents have teaching experience (Figure 5B), with roughly half of applicants’ experience limited to serving as a Teaching Assistant (TA) (Box 1), and half reporting experience beyond a TA position, such as serving as an instructor of record (Figure 5B). The degree of teaching experience did not change based on the target institution of the applicant (Figure 5C), nor did the percentage of offers received significantly differ between groups based on teaching experience (Figure 5D).”

13. The authors could comment on whether the number of postdoc positions attained correlated with their ability to obtain faculty positions (in other words, are 3 postdoc appointments more desirable as compared to 1 when applying for a faculty position at a prestigious institution?).

We did not look for a correlation between the number of postdoc positions and the ability to obtain faculty positions since the length of postdoctoral positions vary widely in our dataset (i.e. 1 postdoctoral position does not necessarily infer a shorter length of training compared to training spanning across more than one postdoctoral positions) and the majority of our respondents had a single postdoctoral appointment (see Figure 1E). In our survey, we did ask respondents for the total number of years (across all appointments) of postdoc training (Figure 1D) and this further reveals field-specific differences in expectations of postdoc appointments and length. Therefore we believe that the relationship between likelihood of receiving a faculty job offer and training extent is complex and likely requires additional considerations (e.g. quality of training over a training timespan, length of graduate training, etc) and is beyond the scope of the analysis in this paper.

14. The authors could address how the application process might be improved so it is a more positive experience.

We note the reviewer’s desire to provide recommendations for hiring institutions and applicants going on the job market. We have added a paragraph to the conclusion of the paper to address how the application process might be improved, so that it is a more positive experience. Additionally, we plan to share our perspective and recommendations for policy changes in a companion piece currently under preparation that provides opinion on data reported here.

The following text has been added to the conclusion of the manuscript:

Conclusions

The faculty job search process lacks transparency and data regarding what makes a successful applicant. Here, we began to address this need via a job market survey targeted towards the applicants themselves, including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment on the process, despite the fact that 58% of our applicants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the applicant's preparation as well as different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data show that there is a mental strain on applicants during this process, and we propose a number of potential solutions with the understanding that faculty hiring is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees (47). More work is needed to understand the challenges search committees face in order to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.”

https://doi.org/10.7554/eLife.54097.sa2

Article and author information

Author details

  1. Jason D Fernandes

    Jason D Fernandes is in the Department of Biomolecular Engineering, University of California, Santa Cruz, United States and is a member of the eLife Community Ambassadors programme

    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Contributed equally with
    Sarvenaz Sarabipour
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8625-1796
  2. Sarvenaz Sarabipour

    Sarvenaz Sarabipour is in the Institute for Computational Medicine and the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States and is a member of the eLife Early-Career Advisory Group

    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    Jason D Fernandes
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5097-5509
  3. Christopher T Smith

    Christopher T Smith is in the Office of Postdoctoral Affairs, North Carolina State University Graduate School, Raleigh, United States

    Contribution
    Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8212-7886
  4. Natalie M Niemi

    Natalie M Niemi is in the Morgridge Institute for Research, Madison, United States and in the Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

    Contribution
    Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5174-4005
  5. Nafisa M Jadavji

    Nafisa M Jadavji is in the Department of Biomedical Sciences Midwestern University, Glendale, United States and is a member of the eLife Community Ambassadors programme

    Contribution
    Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3557-7307
  6. Ariangela J Kozik

    Ariangela J Kozik is in the Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, United States

    Contribution
    Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2322-4085
  7. Alex S Holehouse

    Alex S Holehouse is in the Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, United States

    Contribution
    Conceptualization, Resources, Data curation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4155-5729
  8. Vikas Pejaver

    Vikas Pejaver is in the Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, United States

    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1943-0284
  9. Orsolya Symmons

    Orsolya Symmons was at the Department of Bioengineering, University of Pennsylvania, Philadelphia, United States. Current address: Max Planck Institute for Biology of Ageing, Cologne, Germany

    Present address
    Max Planck Institute for Biology of Ageing, Cologne, Germany
    Contribution
    Conceptualization, Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2435-4236
  10. Alexandre W Bisson Filho

    Alexandre W Bisson Filho is in the Department of Biology and the Rosenstiel Basic Medical Science Research Center, Brandeis University, Waltham, United States

    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5940-7230
  11. Amanda Haage

    Amanda Haage is in the Department of Biomedical Sciences, University of North Dakota, Grand Forks, United States

    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    amanda.haage@und.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6305-440X

Funding

University of North Dakota (Start-up funds)

  • Amanda Haage

National Institute of General Medical Sciences (F32GM125388)

  • Jason D Fernandes

National Heart, Lung, and Blood Institute (T32HL007749)

  • Ariangela J Kozik

Midwestern University (Start-up funds)

  • Nafisa M Jadavji

Washington Research Foundation (Fund for Innovation in Data-Intensive Discovery)

  • Vikas Pejaver

University of Washington (Moore-Sloan Data Science Environments Project)

  • Vikas Pejaver

Washington University in St. Louis (Start-up funds)

  • Alex S Holehouse

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Carol Greider, Feilim Mac Gabhann, Cori Bargmann, Mark Kunitomi, Needhi Bhalla, Lucia Peixoto, Sarah Stone and Dario Taraborelli for their valuable comments on an earlier version of this manuscript. The authors are members of the Future PI Slack community and would like to thank the entire Future PI Slack community and those who support them in this work.

Ethics

Human subjects: This survey was created by researchers listed as authors on this publication, affiliated with universities in the United States in an effort to promote increased transparency on challenges early career researchers face during the academic job search process. The authors respect the confidentiality and anonymity of all respondents. No identifiable private information has been collected by the surveys presented in this publication. Participation in both surveys has been voluntary and the respondents could choose to stop responding to the surveys at any time. Both 'Job Applicant' and 'Search Committee' survey has been verified by the University of North Dakota Institutional Review Board (IRB) as Exempt according to 45CFR46.101(b)(2): Anonymous Surveys No Risk on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage (amanda.haage@und.edu) for further inquiries.

Senior Editor

  1. Peter Rodgers, eLife, United Kingdom

Reviewing Editor

  1. Helena Pérez Valle, eLife, United Kingdom

Reviewer

  1. Adriana Bankston

Publication history

  1. Received: December 3, 2019
  2. Accepted: June 3, 2020
  3. Accepted Manuscript published: June 12, 2020 (version 1)
  4. Version of Record published: July 14, 2020 (version 2)

Copyright

© 2020, Fernandes et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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