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Is University Prestige Really That Important?

Yarilet Perez is an experienced multimedia journalist and fact-checker with a Master of Science in Journalism. She has worked in multiple cities covering breaking news, politics, education, and more. Her expertise is in personal finance and investing, and real estate.

Many students look forward to the day they'll graduate high school and move on to college or university . But there's now a great deal of debate as to whether the experience and, more importantly, the value of getting a post-secondary education is worth it. While a degree from a prestigious university might look great on the wall, does it actually offer any real advantage in life? The answer to that question might once have seemed obvious. But researchers started to take a serious look at the evidence, with some surprising results.

Key Takeaways

• Research shows that it's less about which college you go to when it comes to success and happiness.
• Many of the CEOs of the top 10 largest Fortune 500 companies did not attend Ivy League schools.
• Reports indicate that factors other than university prestige, such as mentorship and career advice at university are more important.

Losing Faith

Is college or university really worth it? Does it matter if you go to a prestigious university? That question can only be answered by the people who attend these schools. And timing is affecting how people feel.

According to a 2015 Gallup-Purdue poll, only half of the 29,000 people surveyed said they were adamant that their college education was worth it. Employment status, personal income levels, student loan debt , and personal relationships were some of the factors that affected how people responded to the survey.

But these sentiments seem to be changing. The value of a college or university degree appears to be dropping. In fact, a Wall Street Journal -NORC survey showed that 56% of respondents feel that a degree isn't worth it anymore, with those aged 18 to 34 being the most doubtful. Compare this to 53% of Americans who felt positive about college degrees in 2013.

Experts say graduation rates and high student debt are among the reasons why people are losing confidence in the value of a post-secondary degree. Getting a college education is expensive. The average cost of attending a post-secondary institution in the U.S. is $36,436 each year per student, according to Education Data Initiative. The price tag, of course, depends on where you go:

• Attending an in-state public college for a four-year degree costs $26,027 per year.
• Going to a private non-profit school (Ivy League schools fall under this category) averages about $54,501 per year for a four-year degree.

Who Went Where

In his 2015 book "Where You Go Is Not Who You’ll Be: An Antidote to the College Admissions Mania," The New York Times columnist Frank Bruni suggested that getting into a prestigious school is a goal for most individuals—not a challenge, especially if it comes from an institution like Amherst, Dartmouth, or Duke.

Bruni marshaled evidence from a wide assortment of fields to show that a degree from a highly selective university is neither a prerequisite to success nor a guarantee of it. He noted that the chief executive officers (CEOs) of the 10 biggest companies in the Fortune 500 mostly attended state schools for their undergraduate degrees. Consider the following:

• Walmart: Doug McMillion, University of Arkansas
• Amazon: Andrew R. Jassy, Harvard University
• Apple: Tim Cook, Auburn University
• CVS Health: Karen S. Lynch, Boston University
• UnitedHealth Group: Andrew P. Witty, University of Nottingham (United Kingdom)
• Exxon Mobil: Darren Woods, Texas A&M University
• Berkshire Hathaway: Warren Buffett, University of Pennsylvania
• Alphabet: Sundar Pichai, Indian Institute of Technology (Kharagpur, India)
• McKesson: Brian S. Tyler, University of California, Santa Cruz
• AmerisourceBergen: Steven H. Collis, University of Witwatersrand

Smaller, more entrepreneurial outfits are no different. Only two CEOs (Keith Cooper, CEO of Revolutionary Clinics, and Colin Walsh, CEO of Varo Bank) of 10 high-ranking companies attended Ivy League colleges as undergraduates .

The Grading Game

So what's the best way to rank schools? There are a number of outlets that do the job for you. U.S. News may be the most prominent arbiter of the nation’s universities, but it hardly has the field to itself. Other magazines, including Money and Forbes , plus an assortment of websites, also rank schools on various measures. 

Payscale calculates the 20-year net ROI for over 2,000 colleges and universities in the U.S. based on the salaries reported by its website visitors. Net return on investment (ROI) refers to the difference in median earnings over 20 years between someone who graduated from that college and someone who only finished high school, minus the school’s total four-year cost .

Perhaps not surprisingly, its list favors schools with high concentrations of majors in well-paying fields, such as engineering. MIT ranks in the second spot on the lists published by both Payscale and U.S. News .

The founders of many of the most successful companies of the past 20 years dropped out of university, such as Bill Gates , Steve Jobs , and Mark Zuckerberg .

But the U.S. Military Academy, SUNY Maritime College, and Colorado School of Mines, all in the top 10 on Payscale's ROI list, might come as a surprise to anyone familiar with the U.S. News ratings where only the Colorado School of Mines makes the list of Best National Universities in the 89th spot.

The highest-rated Ivy League school on Payscale’s list is Princeton, which comes in 16th with Harvard following at number 17. Payscale also allows visitors to sort by major and learn where an art major can expect to get the best ROI for their four years.  

What Matters More

To many critics within academia and those in the business world, almost any type of rating misses the point. Many argue that the effort a student puts into their time is more important than a school’s prestige.

This includes taking advantage of opportunities such as internships and study-abroad programs and getting to know (and becoming known by) the right faculty members. A motivated student can get a great education at a supposedly so-so school. An unmotivated student, on the other hand, is more likely to get a so-so education even at a highly selective one. 

Still, many parents remain convinced that getting into a top-tier school is essential to their children’s success in life, especially on the career front. And they’re willing to do or spend whatever it takes to make that happen; hence the booming industry of SAT tutors and college admissions consultants.

The 2018 Strada-Gallup Alumni Survey report (previously the Gallup-Purdue Index report) highlights meaningful mentorship, career advice, and academic challenge during a student's time at school as measures of success after graduation.

This desire is perhaps best seen through the 2019 college admissions bribery scandal that revealed many wealthy individuals, including celebrities, paid into a scheme that bribed admissions officials at universities in return for accepting their children.

A Gallup and Lumina Foundation poll from 2013 illustrated the disconnect between perception and the actual working world. When American adults were asked how important they thought a job candidate’s alma mater is to hiring managers, 80% said it was either very or somewhat important. 

But when Gallup put the same question to business leaders, the people who are actually in a position to offer graduates jobs, the results were strikingly different. A majority of them, 54%, said it was not very important or not important at all.

What Makes a University Prestigious?

Prestige is a factor with no specific metrics. However, a university can generally be deemed prestigious when several attributes are present. Perhaps the most important is reputation. A university with a good reputation historically and consistently receives accolades in research and academics and produces high-performing graduates. Another important component of prestige is how restrictive the university or college is at selecting its student body. Prestigious universities typically receive more applications than available seats, and applicants with the best GPAs and admission test scores are selected. In addition to training the brightest minds, a prestigious university also has the best of the best faculty to train them.

How Much Does University Prestige Matter?

That depends on who you ask as people's views are changing. In 2015, half of the respondents of a Gallup-Purdue poll said they believed their college education was worth it. But a Gallup-Lumina Foundation survey from 2013 shows that about 54% of business leaders do not believe that university prestige matters when considering an applicant for employment. Meanwhile, a Wall Street Journal -NORC poll found that more than half of people surveyed are no longer confident in the value of post-secondary education.

What Are the Prestigious Universities Called?

In the United States, the most prestigious universities are part of the Ivy League, which is situated in the Northeast. This group is comprised of eight private universities: Princeton University, Columbia University, Harvard University, Yale University, University of Pennsylvania, Dartmouth College, Brown University, and Cornell University.

Although they are highly regarded for their academics and for churning out some of the brightest minds, the league wasn't formed on the basis of academics. Rather, it was established because of their outstanding sports performance.

Keep in mind that prestige isn't exclusive to Ivy League members, as there are many other highly-regarded institutions like Stanford and MIT.

The Bottom Line

For many students, a degree from a prestigious university is no longer a ticket to success and happiness, if, indeed, it ever was. Numerous, less vaunted schools can prepare them just as well for their careers and lives. While a degree from a top school may be a shortcut, students at any school who play an active role in the process and take full advantage of the opportunities those four years can provide have a leg up on those whose best effort ends at acceptance.

Gallup. " GALLUP-PURDUE INDEX 2015 REPORT ," (Download), Page 5.

The Wall Street Journal. " Americans Are Losing Faith in College Education, WSJ-NORC Poll Finds ."

Education Data Initiative. " Average Cost of College & Tuition ."

Frank Bruni. "Where You Go Is Not Who You'll Be." Grand Central Publishing, 2015.

Forbes. " Doug McMillon ."

NSCAI. " Commissioner Bio ."

Apple. " Apple Leadership - Time Cook Chief Executive Officer ."

CVS Health. " Karen S. Lynch President and Chief Executive Officer, CVS Health ."

The Medicine Maker. " The Power List 2015 Sir Andrew Witty ."

Exxon Mobil. " Management Committee ."

Forbes. " Warren Buffett ."

Britannica. " Sundar Pichai ."

McKesson. " Brian S. Tyler - Chief Executive Officer, McKesson Corporation ."

AmerisourceBergen. " Steven H. Collins ."

Inc. " Inc. 5000 2021 Introducing the Inc. 5000 Fastest-Growing Private Companies in America ."

Payscale. " Best Value College ."

U.S. News & World Report. " Best National University Rankings ."

Gallup. " 2018 Strada-Gallup Alumni Survey ."

Lumina Foundation. " What America Needs To Know About Higher Education ," Pages 18 and 29.

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Gre prep online guides and tips, do graduate school rankings really matter.

If you’re thinking about applying to grad school, you might be curious about how the programs you’re considering compare to each other. So why not look up graduate school rankings? Unfortunately, there’s no master list that ranks every single graduate program, and in fact, some types of programs don’t have grad school rankings at all.

While Ph.D. and master’s degree rankings can certainly be a useful tool when choosing which schools to apply to, how much they really matter depends on what kind of program you’re pursuing. Read on to learn more about what goes into grad school rankings and why they might be important for you.

Feature image credit: Gianluca Annicchiarico /Flickr

What Are Graduate School Rankings?

When you were applying to colleges for undergrad, you likely used a college rankings list or website to help figure out which colleges to apply to. You may have had to check a couple of different lists (e.g. for liberal arts programs vs large universities), but overall, it was probably pretty easy to compare different schools and get a sense of how they stacked up against each other.

For grad school, rankings work in a completely different way, because there’s no one list that ranks all grad schools across different programs and degrees. While it’s possible to say “the best college is Princeton” and have that be meaningful for potential students, it’s not possible “the best grad school is Princeton” and leave it at that without being more specific. Instead, each different field of study has its own rankings system that accounts for only those programs that fall into that category.

The reason for this difference in ranking methods is that grad programs are much more autonomous within an institution than undergrad programs are. Often there’s the perception that no matter what you study as an undergraduate, the quality will be relatively consistent across different subject areas, even if there are different schools within your university. By contrast, for grad school it’s more generally accepted and understood that programs for different fields of study might vary widely in quality and reputation within a single institution.

Furthermore, while there are graduate school rankings lists created by some of the same groups that publish undergrad rankings, the lists aren’t universal for all fields by any means. Some of the areas of study and degrees not included in these lists are ranked elsewhere, but other programs (like journalism) don’t have any formal grad school rankings at all.

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There are two key characteristics of graduate school rankings:

• They’re subject-specific . Different lists exist for law schools, med schools, computer science programs, and so on; there may even be lists for the different specialties within a field, like public interest vs. corporate law.
• They don’t exist for every subject.  Some subjects simply don’t have graduate school rankings. For example, there are no journalism master’s degree rankings.

How Are Grad School Rankings Determined?

The main reason graduate school rankings aren’t really comparable is that different metrics are valued differently for different fields. For instance, MBA rankings will likely weight grad outcomes like starting salary more and undergraduate GPAs less than master’s or Ph.D. rankings for academic fields like Psychology.

Grad school rankings are usually calculated using a mix of subjective and objective criteria. Subjective measures that might factor into rankings include peer, employer, and recruiter assessments of program quality. Objective criteria used for ranking include information about students as they enter the program (standardized test scores, undergraduate GPAs) and data from after they’ve graduated (starting salaries, percent of grads employed immediately after graduation).

In general, the following points are considered (with varying weighting) when calculating graduate school rankings:

#1: Acceptance Rates, Standardized Test Scores, and GPAs

Grad school acceptance rates often factor into graduate program rankings as an indication of selectivity: the harder a school is to get into, the more highly ranked it will be . However, acceptance rate doesn’t play as big a role in grad school rankings as other selectivity factors because of its close relation to program size; many grad programs end up being highly competitive simply as a function of only having a couple of slots per year, rather than because the program is particularly elite.

Chief among the other selectivity factors used in grad school ranking are student scores on standardized tests like the GRE, LSAT, MCAT, and GMAT.  The higher the relevant test scores of the students accepted to the program, the better ranked it will be.

The same general rule goes for the undergraduate GPAs of a grad program’s accepted students. Schools who accept students with higher undergraduate GPAS will be ranked more highly.

Both high test scores and high undergraduate GPAs are included in grad school rankings because they can be a good indicator of the academic prowess and focus you’d see from peers in the program. Higher test scores and GPAs imply that students in that program value academic achievement and learning and will take their studies seriously.

Having peers who are committed to the same level of academic achievement as you are is particularly important in schools that require regular collaboration between students as part of the degree. You don’t want to end up in a program where you’re the only one who does the work and everyone else coasts on your efforts!

#2: Program Outcomes

The second category of data used to inform graduate program rankings are items that show what students get out of the program. Examples of these program outcome measurements include number of degrees awarded per year and graduation rate.

Grad school rankings usually further break down graduation rate into both the overall rate of graduation for the program and the percentage of students who graduate within a specified number of years . These two ways of looking at grad program graduation data are important because not only do you want to avoid programs with low graduation rates, but you’ll also want to be wary of schools with longer-than-average programs.

Let’s say that you’re looking at applying to Ph.D. programs in artificial intelligence, where the average program length is around five years, and you come across a program where the average student takes eight years to get her doctorate. The unusually long program length could just be a sign that there are a lot of part-time students at the program. However, it could also be a sign that the program isn’t supportive of its students and prefers to keep them on as cheap labor for the department rather than pushing them to achieve their degree goals.

Two other program outcomes captured in grad school rankings are the rate of student employment after graduation and the salaries of the new grads . If students coming out of a program aren’t finding jobs, or are getting relatively low-paying jobs in the field, that reflects poorly on the education and training they received in the program. Low rates of hiring for graduates can also be an indicator that the program doesn’t have a good placement system or lacks connections with businesses, organizations, and other schools that would help students find employment more easily.

There are a few additional measures relating to program outcomes used in grad school rankings for certain fields of study. Law school rankings include information about the rate of recent grads who pass the bar exam. Master’s degree rankings may include information about the number students matriculating into Ph.D. programs and the selectivity of the Ph.D. programs students are accepted into.

#3: Faculty Quality

Compared to the previous two factors, the quality of a grad program’s faculty is somewhat harder to assess. Because faculty quality is relatively subjective (as compared to test scores and graduation rates), different grad school ranking lists rate faculty quality using different criteria. Here we’ll discuss the two most commonly cited ways to assess grad schools’ faculty quality, which are the amount of research done by faculty members and student-faculty ratios.

As a general rule, faculty quality is most often judged in terms of original research carried out and not in terms of teaching ability. For research-intensive Ph.D. programs, this measure of faculty quality is an extremely important factor because you’ll want to choose a school with faculty doing high-quality research in areas you’re interested in.

The student-faculty ratio is the other main way grad school rankings rate faculty quality. Meant to assess how much attention and time students at a particular school are likely to receive from faculty members , student-faculty ratios can also indirectly serve as an indicator of how likely it is students will be able to build personal relationships with their professors that may benefit them both during and after grad school. For instance, schools with smaller student-to-faculty ratios give students more of a chance to get to know professors well enough to get stellar letters of recommendation for internships and jobs.

#4: Prestige

The final factor accounted for in graduate school rankings is the perceived reputation of a graduate program in the eyes of others.

In the eyes of the world and those not in your particular field, a degree from Yale will be impressive no matter what the subject, even if there are other schools that are technically better in that area. Prestige and reputation assessments, however, take into account the overall quality of the program and the capabilities of its graduates as rated by others in the field, rather than brand-recognition by the world at large. If you’re planning on pursuing positions in your field after graduation, then studying at a school with a good reputation in your specific field (and with a professor who is highly regarded) is essential.

For instance, a Ph.D. from the University of Washington in focus in artificial intelligence would be more impressive to potential schools than a Ph.D. in the same area from Yale, even though UWash is a state school and Yale is an Ivy League school.

Prestige assessments used in grad school rankings are done by peers at other schools or employers in the field (e.g. lawyers at top firms and judges for law school grads, or school superintendents for education grads). The reason peers and employers rank schools highly is based on their personal experience with graduates of the programs; if the programs graduate incompetent or uneducated students, then the reputation of the program will suffer, no matter how prestigious the larger university it is part of.

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Do Graduate School Rankings Matter For You?

Whether or not graduate program rankings matter for you depends on two important factors: your field of study and what you want to do with your degree.

Factor 1: Field of Study

For areas like law and business , grad program rankings matter quite a bit. Law school grads who attended top-tier schools are much more likely to be hired at top firms or for competitive public interest positions. Business school grads from top b-schools are more likely to get the internships and jobs that they want. In these cases, attending a highly ranked school can make up for a lower GPA or class rank.

Graduate school rankings don’t matter as much in other fields, especially ones that are extremely specialized . For instance, if you’re applying for a degree in a highly specific subject like the psychology of human sexuality, the ranking of the school is less important than choosing a school with a department or professor doing research or other work you’re interested in.

A good rule of thumb: if it’s difficult to find a list of rankings for your particular subject area online, then rankings probably don’t matter as much.

Factor 2: Personal Goals

What you want to do with your degree also affects how much program rankings matter. If you’re planning to use your master’s degree to further your career as a middle school music teacher, for instance, then graduate school rankings can play less of a role in your decision-making process. Potential employers will be more concerned with whether or not you have a master’s degree and what you have to show for it than with what the rank of the school you got it from is.

For those with a post-graduate trajectory to academia (tenure track professor, post-doc, or other research positions), however, grad school and Ph.D. rankings will be integral to your decision of which schools to apply to . Hiring committees will assuredly be very aware of what the top programs in the field are and prioritize candidates who attended competitive graduate programs.

How to Use Grad School Rankings

Now that you have a better understanding what graduate school rankings measure and why they might matter, you need to figure out how heavily rankings should inform which programs you apply to.

Think about what you really want to get out of grad school and choose schools that are ranked highly along that dimension. For example, if you’re thinking about applying to law school but are highly concerned with how soon after you graduate you’ll be able to get a job to start paying off law school loans, then you’ll want to research which schools have the best employment outcome for graduates and highest prestige (which increases likelihood of swift employment) and figure out the best way to meet their standards for admission.

You can also use the information from rankings to help you figure out which schools are going to suit your needs best while you’re a student there. When I was applying to grad schools for music, I was way more concerned with the quality of composition and computer music faculty than I was with standardized test scores and GPAs of enrolled students, particularly since several of the programs did not even require GRE scores. As a result, I eliminated any schools that didn’t have strong composition and computer music programs from my list of schools to apply to.

When you form your list of grad schools to apply to, there will be many factors to consider, from your degree’s value to the school’s location to which professors you want to work with for the next five-plus years. Use grad school rankings as another source of information to help you decide which schools are the best fit for you.

What’s Next?

Graduate degrees can enhance your earning potential and boost your career in many (but not all) fields. Find out if grad school is worth it for you with this guide .

Decided on grad school, but worried about getting in? Read these articles to find more about grad school acceptance rates and how to get into grad school .

Need help figuring out how to wow grad schools with what you’ve already accomplished? Learn how to write a grad school CV and how to get grad school recommendation letters .

Ready to improve your GRE score by 7 points?

Author: Laura Staffaroni

Laura graduated magna cum laude from Wellesley College with a BA in Music and Psychology, and earned a Master's degree in Composition from the Longy School of Music of Bard College. She scored 99 percentile scores on the SAT and GRE and loves advising students on how to excel and fulfill their college and grad school dreams. View all posts by Laura Staffaroni

Regardless of an elite graduate school degree, undergraduate prestige greatly impacts salary

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Nov 13, 2014, 12:00 AM

New research finds that no matter where you earn your graduate degree, the prestige of your undergraduate institution continues to affect earnings. In fact, college graduates who earn their undergraduate degree from a less prestigious university and a graduate degree from an elite university earn much less than those who attend both an elite undergraduate and graduate school. And it is unlikely their salary will ever catch up.

“Status of the graduate degree-granting institution should have a more important relation to earnings than status of the undergraduate institution,” said Joni Hersch , professor of law and economics at Vanderbilt Law School . “[But] even high ability students who attend nonselective institutions for their bachelor’s degrees are, on average, unable to overcome their initial placement by moving up to an elite graduate or professional school for an advanced degree.”

Hersch is author of, “ Catching Up Is Hard to Do: Undergraduate Prestige, Elite Graduate Programs, and the Earnings Premium .”

Hersch used data from the 2003 and 2010 National Survey of College Graduates, which provides information on a representative sample of almost 178,000 college graduates, including more than 83,000 students earning graduate degrees.

Hersch compared the Carnegie Classifications to selectivity categories from Barron’s Profiles of American Colleges to divide schools into categories. Hersch also used various classifications with assistance from the  and to divide schools into categories. Roughly speaking, Tier 1 schools are top private research institutions. Tier 2 schools are selective private liberal arts colleges. Tier 3 are top public research universities and Tier 4 are remaining four-year universities and colleges. Because ability influences whether a student is accepted to a selective institution, Hersch accounted for ability in her study by examining graduates of similarly selective graduate programs.

Elite earnings

Hersch’s research found that graduates of Tier 1 schools earn a lot more than everyone else.

But even when looking at those with graduate degrees from elite schools, the students who went to a lower tier undergraduate school earn considerably less.

“Earning an elite graduate degree does little to reduce the pay gap associated with an elite undergraduate degree,” writes Hersch.

Among those who earned a graduate degree from Tier 1 – Tier 3 institutions, men from Tier 1 undergraduate schools earn 39 percent more than men with undergraduate degrees from Tier 4 schools. Among women with top graduate degrees, those from Tier 1 undergraduate schools earn 44 percent more than women with undergraduate degrees from Tier 4 schools.

Moving up to graduate school

Hersch finds that where a student earns his or her bachelor’s degree is closely related to whether that student even goes to graduate school.

Among Tier 4 graduates, the highest degree is a bachelor’s degree for 69 percent of men and 68 percent of women. In contrast, more than 50 percent of men from Tier 1 and Tier 2 schools and almost half of women from Tiers 1 and 2 schools will earn a graduate degree.

And of the small percentage of students from lower tier institutions who earn a graduate degree, a tiny number move up to a top tier graduate school.

“The odds of a Tier 4 student having a graduate degree from a Tier 1 institution are quite small with 3 percent among men and 2 percent among women,” writes Hersch.

Medical and legal

The type of graduate degree a student earns is also strongly related to prestige of the undergraduate institution. Hersch finds that among male Tier 1 graduates, nearly 9 percent have medical degrees and nearly 11 percent have law degrees. In contrast, among male Tier 4 graduates, fewer than 2 percent have medical degrees and fewer than 3 percent have law degrees.

Among female Tier 1 graduates, 5 percent have medical degrees and nearly 8 percent have law degrees. But less than 1 percent of female Tier 4 graduates have medical degrees and only slightly more than 1 percent have law degrees.

Tier levels

To identify schools considered elite and to put these schools into tier levels, Hersch used both the Carnegie classification and the Barron’s Profiles of American Colleges. The Barron’s Profiles look at quality indicators of each year’s entering class (SAT or ACT, high school GPA and high school class rank, and percent of applicants accepted).  Barron’s then places colleges into seven categories: most competitive, highly competitive, very competitive, competitive, less competitive, noncompetitive, and special.

The Carnegie classifications are based on factors such as the highest degree awarded; the number, type, and field diversity of post-baccalaureate degrees awarded annually; and federal research support. For example, Research I universities offer a full range of baccalaureate programs through the doctorate, give high priority to research, award fifty or more doctoral degrees each year, and receive annually $40 million or more in federal support.

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November 13, 2010

Are You Satisfied? PhD Education and Faculty Taste for Prestige-Limits of the Prestige Value System

This paper empirically evaluates Caplow and McGee’s (The academic marketplace, 1958) model of academia as a prestige value system (PVS) by testing several hypotheses about the relationship between prestige of faculty appointment and job satisfaction. Using logistic regression models to predict satisfaction with several job domains in a sample of more than 1,000 recent social science PhD graduates who hold tenure-track or tenured faculty positions, we find that the relationship between prestige of faculty appointment and job satisfaction is modified by PhD program prestige. Graduates of high prestige PhD programs value prestige more highly and graduates of low prestige programs value salary more highly. We explain our findings by incorporating reference group theory and a theory of taste formation into our model of the academic PVS, which identifies PhD programs as sites of socialization to different tastes for prestige (a process of cultural transmission) in addition to their well recognized role in transmission of human and social capital. We discuss practical and theoretical implications of our findings in relation to efforts to measure PhD program quality and to understand the structure of academic labor markets.

Morrison, E., Rudd, E., Picciano, J., & Nerad, M. (2010). Are You Satisfied? PhD Education and Faculty Taste for Prestige-Limits of the Prestige Value System.  Research in Higher Education 52 (1),  pp. 24-46.

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July 10, 2020

PhD Program Selection: Does School Ranking Matter?

Because tenure-track jobs are scarce, selecting the right PhD program is an extremely important decision that can have a dramatic impact on your future career. When it comes to making this important choice, many students often rely on school rankings to determine which PhD program is best for them, in hopes that banking on a school’s reputation will help them in the future. But is relying on rankings to decide on a PhD program the best way to proceed? Does a PhD from a top 10 institution automatically optimize your chances of securing the position of your dreams, post-graduation? In other words, when it comes to picking a PhD program, does school ranking really matter? 

Not as much as you would think. 

How PhD rankings help, and how they don’t

While it may be true that top 10 schools might have a great department in your field, it doesn’t necessarily mean that earning a PhD from one of these schools is the best decision for your future. Why? For the simple reason that the department might not have the right faculty to support you in your research goals. 

If you’re considering enrolling in a PhD program , it’s important to keep in mind that the research world abides by specific rules. While the name attached to an undergraduate degree or an MBA can have a direct effect on career outcomes, the reality is different for doctorates. A PhD from a highly ranked school doesn’t automatically guarantee a higher starting salary after graduation, or that you will suddenly be put at the top of the pile of interviewees for a tenure-track position. In fact, when hiring committees look at freshly minted PhDs to fill a tenure-track position, they mainly look at the relevance and quality of a candidate’s research and how well this research fits with the needs of their department, rather than focus on the competitive selection process used by the school that issued the diploma. 

Unfortunately, many students let rankings influence their decision when they come up with a list of PhD programs to apply to, and they buy into a name, rather than focusing on choosing a research environment where they can thrive. They often first create a list of programs based on a school’s ranking or reputation and then settle for faculty members on staff who “fit the bill” and who list their topic of choice among their areas of interest.

Prioritizing the faculty “fit factor”

If you find yourself at the stage of selecting a program and you want to find a program that will provide you with the best career options, it’s important to understand that your first priority should be to find the best expert in your field. You should be looking to work with someone who is a specialist in your area of interest and whose specialty is in direct alignment with yours. 

Don’t settle for a faculty member who lists your topic as a secondary interest. Since your marketability in the job market as a recent PhD graduate will be determined by the people who have guided and supported you throughout your doctoral research, not picking the right research team can have a disastrous impact on your future career. This is why it’s important to take the right approach when selecting a PhD program. 

How to proceed in creating your list of best PhD programs for YOU

Instead of conducting research on the top 10 programs in your field according to rankings, start by conducting research to find who the top 10 experts in your area of interest are. You don’t know who they are? Put your research skills to work! 

• Start by looking at the most recent articles and books written on your topic or your area of interest and make note of their authors. Don’t forget to look at their bibliographies and keep track of the authors they cite, too. 
• Rank them by importance. Which names are the most-often cited? Who seems to be the most important researcher on topics that interest you? 
• Once you have 10 to 15 names, find out where they teach. Make a list of these schools. These are the schools where you should be thinking of applying. 

Next steps: Department investigation

Once you have your preliminary list of schools, the next step should be to investigate the department where they work. Ask yourself:

• In addition to this potential advisor , are there any other faculty members who could possibly sit on your dissertation committee? 
• How many people in the department have expertise that could benefit your potential research projects? 
• What does student life look like? 

Since your future in research will depend on the quality of the work you produce in graduate school, explore the resources offered by the department to support you throughout your doctoral studies. 

Finally, modify your list by giving precedence to schools that give you the best options and eliminate schools that don’t. This should be your final list of schools.

Widen your net beyond the Ivies

Let’s face it: Most recognized experts do not work at an Ivy League school. In fact, they work for different tiers of schools all over the country, and they often work at institutions that might not have a shiny name but are very reputable and produce top research at the international level. By taking a route that puts an emphasis on the tools you need to conduct relevant research and positions you on the cutting-edge of your field, you will have taken the right steps to make sure that you have a list of PhD programs that will make your career options optimal after graduation. 

By keeping in mind that the reputation of your advisor and research committee supersedes the importance of the school’s ranking, your final list of schools will guarantee that you make the right choice when the time comes to enroll in a program. You will be choosing the environment where you’ll be able to conduct research that will really make you stand out and put you on the path for a promising career in research!

Do you need help choosing the best PhD programs for you? Do you need guidance with any other element of the PhD application process? We’re here for you every step of the way. Explore our PhD Admission Services and work one-on-one with an expert advisor who will help you get ACCEPTED.

Related Resources:

• Plotting Your Way to a PhD , a free guide
• How to Write About Your Research Interests
• How to Be a Competitive PhD Applicant and Apply to the Best Programs for You

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The Role of Early-Career University Prestige Stratification on the Future Academic Performance of Scholars

• Open access
• Published: 30 April 2022
• Volume 64 , pages 58–94, ( 2023 )

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• Mario González-Sauri   ORCID: orcid.org/0000-0003-1614-6031 1 &
• Giulia Rossello 1 , 2  

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This paper investigates the effect of university prestige stratification on scholars’ career achievements. We focus on 766 STEM PhD graduates hired by Mexican universities between 1992 and 2016. We rank university according to their prestige based on the pairwise assessment of quality contained in the PhD hiring networks. Further, we use a quasi-experimental design matching pairs of individuals with the same characteristics, PhD training or first job experience. Our results challenge the positive association between prestige and academic performance as predicted by the ‘Matthew effect’. Scholars hired internally sustain higher performance over their careers in comparison to those who move up or down the prestige hierarchy. Further, we find a positive (negative) relation between downward (upward) prestige mobility and performance that relates to the “big-fish-little-pond” effect (BFLPE). The evidence of a BFLPE-like effect has policy implications because hinders the knowledge flows throughout the science system and individual achievements.

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Introduction

For universities, forming and hiring PhD graduates is essential for their competitiveness as young scholars will become lecturers, senior researchers and professors in their faculties. However, for early-career scholars, the transition from PhD and first job is competitive, stressful and can have consequences for their future careers (Bazeley, 2003 ). At the same time, the academic labor market is well-known for exhibiting a Matthew effect, where prestigious positions in hierarchical networks give advantages to early-career scholars across their careers (Bol et al., 2018 ; Teplitskiy et al., 2020 ; Horta et al., 2018 ).

The Matthew effect predicts that ‘early career’ gains in prestige confer researchers advantages that over time render higher research performance. The mechanism behind this success is a process of cumulative advantage in which early career prestige attracts resources (Merton, 1968 ; Long, 1978 ; Merton, 1988 ), increases visibility (de Solla Price, 1965 ; Wang, 2014 ; Farys and Wolbring, 2021 ) and collaborators (Perc, 2014 ). This positive feedback loop reinforces the prestige of the authors (as well as universities) and leads to higher research productivity (Allison and Stewart, 1974 ). This thesis has been tested extensively from sociology, economics, and studies of science and technology, presenting mixed results. One group of studies asserts that university prestige is positively associated with individual performance (Fox, 1983 ; Headworth and Freese, 2016 ; Su, 2011 ). For instance, the study of Bedeian and Feild ( 1980 ) concludes that institutional prestige from the PhD is more important than publication productivity prior to the first job appointment in the academic labor market. However, when the endogeneity of university prestige is accounted, the literature shows mixed results (Allison and Long, 1987 ; Williamson and Cable, 2003 ; Bair, 2003 ; Miller et al., 2005 ; Laurance et al., 2013 ; Appelt et al., 2015 ), leaving several gaps that are areas of opportunity for the present study.

The first gap that we address is the potential selection bias in the estimation of prestige and research performance. Most studies, disregard, that researchers are sorted into prestigious appointments by self-selection. To address this problem, we take advantage of a quasi-experimental design. Our treatment is the change in prestige that naturally occurs to scholars in the transition between PhD graduation and first job appointment. This change in prestige is a partially random assignment, given the exogenous choice of hiring committees. This shock allocates scholars into three groups depending on their change in prestige (treatment condition). One group exhibit a positive change or upward prestige mobility when moves from a less prestigious PhD university to a more prestigious first job appointment. Conversely, a second group displays a negative change in prestige (downward mobility) and the last group experience no change in prestige as this group is hired by their faculty. Footnote 1 . We address the selection bias, by matching early career researchers in these three groups keeping constant their individual characteristics, PhD training or first job experience. Fixing these determinants of research performance allows to observe the effect of early-prestige in the short, medium and long-run career.

A second gap in the literature is that results are typically bias towards top-tier institutions located mostly in North America and Western Europe (Clauset et al., 2015 ). Those universities usually have a long history and are well known and integrated internationally (Demeter and Toth, 2020 ). In contrast, less mature higher education systems are younger, less consolidated and operate under resource constraints. Therefore, this paper examines the subject in a large emerging economy, namely Mexico.

More importantly, the prestige of academic institutions in those contexts is less known and can be more volatile compared to more mature university systems. In developing university systems, prestige may change according to modes, tastes and new information. For example, reputation can decrease if frauds and misbehavior are found within a department. In the same way, if a department wins a large National grant, its reputation will improve. Similarly, the academic mobility of star scholars may change substantially the prestige and output of an entire department. Indeed, most of the US-based literature on prestige and hiring has found at the departmental level, that the correlation between hiring centrality and survey based measures of prestige is higher than the correlation between bibliometric production and survey based measures of prestige (Clauset et al., 2015 ; Burris, 2004 ). Additionally, in the case of an emerging economy many institutions are not listed in formal university rankings Footnote 2 and non-English literature is not present in bibliometric databases. Thus, all the mentioned reasons make the use of survey-based or bibiometric-based measures not only unsuitable for measuring prestige in this context but also practically harder. A third gap to address is then to develop a measurement of prestige suitable for less developed university systems, robust against prestige variation over time, taking into account the job market dynamics, (see Oyer 2006 ).

To overcome the aforementioned problem of measurement error, we estimate the institutional prestige using a ranking algorithm based on university hiring networks of PhDs. The university hiring network has been extensively studied and contains explicit hiring flows from one university to the other (Barnett et al., 2010 ; Clauset et al., 2015 ; Lang et al., 2019 ). Past research on these flows indicates that university hiring networks contains an implicit hierarchical order of prestige among institutions (Barnett et al., 2010 ; Mai et al., 2015 ). This hierarchy of prestige comes into existence because hiring decisions in the academic labor market are pairwise evaluations of “quality” between candidates and universities. We use the information of each pairwise assessment of quality to measure university prestige, applying the algorithm developed in our previous study (Cowan and Rossello, 2018 ). We argue that this measurement of prestige based on a pairwise peer review assessments goes beyond university ranks based on bibliometric indicators and subjective surveys. In particular, in this paper, we propose a dynamic estimation of the algorithm that overcomes the potential volatility of prestige in less mature higher education systems.

Using our dynamic measurement of prestige, this paper asks how university prestige stratification affects academic performance and the labor market mobility of scholars in Mexico. We resolve the endogeneity of university prestige on academic performance from several sources. To overcome the problem of reverse causality, we assess how early-career institutional prestige affects future academic performance of scholars with longitudinal data. In line with the literature, changes in prestige during early-career affect future research performance, given that initial conditions in academia confers cumulative advantages/disadvantages with long-run consequences (Bazeley, 2003 ; Bol et al., 2018 ; Lee, 2019 ). To overcome the problem of bias from omitted variables correlated with individual capabilities, we take advantage of a quasi-experimental design and a re-sampling technique. For our three treatment groups (Upward, Downward, or Unchanged prestige change) we control for the PhD training, the postdoctoral experience and the individual characteristics because these variables also affect academic performance. Our re-sampling technique matches pairs of scholars with similar characteristics assigned to different treatments. Firstly, we compare pairs with similar characteristics that receive the training and prestige from the same university and graduate window. Secondly, we compare pairs with equivalent characteristics, similar networks, experience and prestige from their first job appointment. By holding constant these initial conditions for similar individuals allows us to compare the effect of changing prestige (treatment) on future research performance.

Our results are based on a sample of 766 Mexican PhD graduates in STEM between 1992 and 2016 and show a highly stratified university system. At the macro level, a selected group of 10 institutions graduate and hire the vast majority of early career scholars. This stratification is consistent with more mature university systems (Burris, 2004 ; Barnett et al., 2010 ; Clauset et al., 2015 ). This centralization of research could promote higher levels of specialization and targeted allocation of resources. But also can reveal a structural problem of “lock-in”, hindering mobility and the flows of knowledge through the national science system.

Focusing on the individual level, our results reveal a more nuanced and complex pattern than the one predicted by the Matthew effect. We find that moving up the prestige ladder does not necessarily correlate with a higher academic performance. Controlling for the PhD training and the experience gained from their first job, our results confirm that scholars (with similar characteristics) hired internally exhibit on average higher levels of academic performance. Interestingly, early-career scholars with similar characteristic and initial conditions who experience downward prestige mobility perform on average better than scholars who exhibit upward prestige mobility. We explain our results using the analogy from the big-fish-little-pond effect (BFLPE), where individual performance relates to the average performance of her/his peers. The effect that prestige has on individual performance may relate to the “profile of prestige” an individual is accustomed to. Agents might perform “better” (“worse”) in an environment with a “lower” (“higher”) competition where the perceived average ability of peers is “lower” (“higher”). Testing these psychological mechanisms is beyond the scope of this work, but we highlight a non-linear association between prestige and performance that can be conductive of future research.

Prestige, Mobility and Performance

University prestige.

The main approaches for ranking universities by prestige are input-output (Debackere and Rappa, 1995 ; Chan et al., 2002 ; Kalaitzidakis et al., 2003 ; Oyer, 2008 ; Buela-Casal et al., 2012 ), survey (Abbott and Barlow, 1972 ; Cyrenne and Grant, 2009 ; Moodie, 2009 ; Olcay and Bulu, 2017 ), and network-based measures (Barnett et al., 2010 ; Cowan, and Rossello 2018 ; Zhu and Yan 2017 ; Nevin 2019 ). Some of the most popular input-output based measures use bibliometric indicators. For instance, Debackere and Rappa ( 1995 ) use bibliographic records to calculate the prestige rankings for universities in the top-twenty departments of neuroscience by adding their citations. Similarly, Oyer ( 2008 ) uses the methodology proposed by Kalaitzidakis et al. ( 2003 ) to rank universities based on the contribution of universities to the top thirty journals in economics selected by their normalized citation index. Chan et al. ( 2002 ) take a similar approach and use the number of pages produced by a university faculty in top journals to rank universities. However, bibliometric based measures focus on research outputs, disregarding inputs such as graduates production and other relevant measurements of academic excellence. Buela-Casal et al. ( 2012 ) assess the higher education system in Spain using a mix of input-output indicators including journal publications, number of full-time researchers, number of R&D projects, PhD graduates, scholarships and patents.

Another way to rank universities is through surveys. This method aims at capturing the peers’ perception of academic prestige that includes niches within a discipline. Abbott and Barlow ( 1972 ) use a survey of graduate faculty to rank universities in 29 disciplines, with an ordinal response scale of five levels. For Fogarty and Saftner ( 1993 ) the main drawbacks of survey methods are that they assume that scholars are unbiased of their current and previous affiliations. An additional issue with survey measures is that scholars tend to have sticky and localized information about other universities—they know better institutions of similar status that are those which are in direct competition with them (Cowan and Rossello, 2018 ). For instance, institutions that are closer in rank are likely to compete for the same resources (projects and grants) and publish articles in similarly ranked academic journals.

Network-based measures are a new framework to rank universities that examine a social process in which scholars recognize quality in their work. This evaluation is cross-validated by the interactions between institutions. Following these lines, the core idea of our ranking approach is that the PhD hiring networks contain information about how scholars evaluate each-other quality (Clauset et al., 2015 ). Finally, the methodology of Cowan and Rossello ( 2018 ), applied in the university system of South Africa, exploits the information contained in the movements in the labor market to approximate the distribution of prestige. The advantage of their method is that it directly uses pairwise assessments of quality between PhD graduates and hiring committees to rank universities. As a further expansion of their methodology, our ranking algorithm is dynamic across time. Thus, we consider the job market of variable sizes that consequentially changes the prestige of the institutions involved. A dynamic perspective to rank universities is more suited in a large, developing setting where universities are young and the distribution of prestige is potentially more volatile.

The Link Between Mobility and Performance

The literature studies the relationship between university prestige and mobility to understand how the prestige of the PhD-granting institution affects the placement and subsequent labor market outcomes. In the PhD job market, both individuals and universities have incentives to make accurate choices. On the one hand, for the job market candidate, the transition from the PhD and to her/his first academic job might affect future career perspectives (Laudel and Gläser, 2008 ). On the other hand, for the university, each new hire is a strategic asset that influences its human capital and competitiveness (Cowan and Rossello, 2018 ). However, hiring PhD graduates could be a difficult task, since early-career scientists often have few research records. In such a case, universities have little information about the inherent ability of young candidates, especially when they graduated in other universities.

Related to this, Oyer ( 2006 ) examines the job market for economists in the US 1979–2004 and finds that the job market conditions, that influence the first job placement of scholars, contains a large element of randomness. In particular, he finds that the initial job market ‘luck’ (i.e. favorable market conditions) affects top positions in academia that later drive research productivity between neighboring cohorts of graduates. The role of job market luck in affecting placement operates due to the asymmetry of information. The latter makes the prestige of the PhD-granting institution a signal for the unobserved skills and abilities of applicants. Indeed, past literature shows a positive relationship between the prestige of the PhD granting institution and future employment (Crane, 1970 ; Debackere and Rappa, 1995 ; Oyer, 2006 ; Bedeian et al., 2010 ; Appelt et al., 2015 ; Pinheiro et al., 2017 ; Headworth and Freese, 2016 ). PhD graduates from prestigious institutions tend to get “better” job compared to graduates from lower-tier universities. Similar results suggest that a prestigious PhD is as a key mechanism to alleviate the asymmetry of information between candidates and hiring faculties. Moreover, since a “good” affiliation provides opportunities, networks, and resources, this first sorting of the job market has potential long-run consequences on career achievements (Oyer, 2008 ; Bedeian et al., 2010 ). A similar early-career advantage is potentially problematic when quality and prestige do not entirely overlap. In this respect, some studies argue that institutional prestige from the PhD granting institution is more important than researchers “quality” for obtaining a first academic job (Long et al., 1979 ; Allison and Long, 1990 ; Baldi, 1995 ; Gerhards et al., 2018 ).

Another group of studies pays attention to how changes of institutional prestige relate to academic performance and academic achievement of scholars. Oyer ( 2008 ) use a longitudinal sample to estimate how changes in institutional prestige affect the academic performance of economists. He shows that, even after controlling for proxies of individual-level ability, that early academic prestige positively correlates with academic performance measured by publication productivity. Moreover, he shows that scholars generally move down the prestige ranking over their careers. He argues that this is because high ranked universities produce a significant percentage of the total graduates that later move to lower-ranked universities. Chan et al. ( 2002 ) examine the mobility of scholars publishing in 16 top financial journals. They find that upward ranking mobility is rare and that scholars who experienced it produce twice as many publications compared with average production of scholars from destination universities. They furthers show that after controlling for ability using publication productivity, the rank of the PhD grading institution predicts upward ranking mobility through their academic careers. Azoulay et al. ( 2014 ) take an alternative approach, comparing academic performance of scholars before and after upward mobility given by a prestigious academic recognition. They find that gains from upward ranking mobility have a lower effect on scholars who have above average citations than on scholars with low or below average citations. In general, these studies suggest that upward ranking mobility is associated with higher academic performance, but this is not always the case. Cowan and Rossello ( 2018 ) offer a closer look at how prestige differentials from the PhD to the first job relate to academic performance. They use a quasi-experimental methodology based on matching pairs and examine a sample of 1011 South African job market candidates in STEM. They show that scholars hired internally (maintaining their place in the prestige hierarchy) exhibit on average higher performance compared with scholars who move up in the prestige rank. Their work underlines that the link between prestige changes and performance might be complex.

The BFLPE and Similar Mechanisms

The complex relation between prestige changes and performance can relate with the relation that individuals have with their new peers and the working environment. When individuals change institution they compare themselves with new peers, therefore, their view of themselves may be odd because they have little information on the new environment.

Psychology of education examines how the social comparison affects individual performance by looking at how the average achievement of peers affects the individual academic self-concept (Marsh and Hau, 2003 ; Marsh et al., 2008 ). The main hypothesis in this literature is the big-fish-little-pond effect (BFLPE). The hypothesis suggests that a student will have a lower academic self-concept (and thus performance) in an academically selective school, where the average achievement of peers is high, than in a non-selective one (Astin, 1969 ; Marsh and Hau, 2003 ; Marsh et al., 2008 ; Salchegger, 2016 ; Rosman et al., 2020 ; Keyserlingk et al., 2020 ).

The empirical evidence on the BFLPE shows mixed results and focuses on children or adolescents at school age (Salchegger, 2016 ). Two recent contributions test the BFLPE looking at first-year university students. Rosman et al. ( 2020 ) find no support for the BFLPE examining 115 first-year undergraduate psychology students at the Leibniz Institute in Germany. In contrast, in a larger and more representative study, Keyserlingk et al. ( 2020 ) find strong support for the BFLPE in a sample of German students in the transition from high school to universities. However, in higher education, competition and the need of collaborating with peers is higher. In similar circumstances, additional mechanism of social comparison might influence beliefs and achievements. The additional mechanisms in the literature are peer effects and the what does not kill me makes me stronger effect.

In general terms, the literature on peer effects in academia studies whether the social comparison generates learning that in turn affects performance. Most of this literature focuses on positive peer effects. For example, Slavova et al. ( 2016 ) study whether hiring a new scientist affects the scientific performance of the incumbents in the hiring department. They examine 94 U.S. chemical engineering departments, finding that a new hire generates positive peer effects in the performance of colleagues with a recent tenure. As a limitation, their analysis does not consider which is the impact of changing department for the newcomer. Related to this, past research suggests that, depending on the level of the competition, peer effect can also be negative (Stapel and Koomen, 2005 ). When resources are scarce, the level of competition to access them increases and in extreme cases a newcomer can be perceived by the group as a treat. In this case, the effect of the social comparison may negatively affect the performance. An extreme case of thereof are bullying episodes or misbehavior in academia (McKay et al., 2008 ; Keashly and Neuman, 2010 ; Giorgi, 2012 ). Footnote 3

The way in which people are integrated or promoted in a new workplace can affect individual self-esteem and academic self-concept as well. In general, the literature associates high self-esteem and self-concept to high career outcomes. However, there are cases where this association appears to be negative (Whelpley and McDaniel, 2016 ; Sherf and Morrison, 2019 ; Li et al., 2020 ; Weiss and Knight, 1980 ).

For example, in a recent contribution, Wang et al. ( 2019 ) find support for the what does not kill me makes me stronger effect. They compare publication and citation records of 561 narrow wins and 623 near miss scientists who applied for the NIH grant Footnote 4 They find that despite an early setback, individuals with near miss proposal systematically outperform those with narrow wins in the longer run. This result could be consistent with a BFLPE in early career scientists, where an initial promotion or confirmation of abilities may be counter-productive for their future performance. The complex mechanisms associating individual performance and the comparison with peers motivates us to study how prestige changes affect research performance in the transition into the first academic job.

Data originates from the Mexican National Council of Science and Technology (CONACYT). Data were collected through the most extensive science policy of the country, the National System of Researchers (NSR), whose aim is to increase the productivity, quality and competitiveness of Mexican researchers (Gras, 2018 ). NSR was implemented in 1985 when the primary motivation behind the policy was the raising concern about technological capabilities and performance of the Mexican science system under the threat of inflation and budget cuts. Reyes and Suriñach ( 2015 ) describe how the policy evolved across the years, but in general, its structure is substantially unchanged.

We focus on STEM graduates, excluding from the analysis those in Social Sciences and Humanities to reduce the potential influence that schools of thought have on the PhD job market. Footnote 5 Our sample spans 25 years representing 766 PhD job market candidates hired in a Mexican university between 1992 and 2016. We present the summary statistics of the panel in Table  1 . We include 36 Mexican institutions Footnote 6 Footnote 7 and longitudinal records for each scholar of academic performance ( NSR rating ) and individual level controls as gender , discipline , graduation year and evaluation year .

Our dependent variable is the NSR rating , which measures the academic performance of researchers using 5 ordered categories. The general framework of the NSR rating process is summarized in Fig.  1 and works as follows. Each researcher applies to the NSR submitting her/his curriculum vitae and publications. The submitted publications comprise not only scientific articles, but also books, chapters in books, patents, and technological developments and transfers. Each application is assigned to one of seven different research disciplines. Every three years, for each discipline, the NSR forms the evaluation commission which comprises 14 prominent researchers called to rate the applications. The evaluation commission works as follows. Each member of the commission evaluates the performance of the applicants evaluating all the submitted material, this following a peer-review process that ends with a grade. The CONACYT authorities supervise the quality and independence of the evaluation commissions (more details are in “ NSR Disciplines and Evaluation Procedure ”). Footnote 8 In contrast to bibliometric measures, a peer-review evaluation has the advantage of including a holistic evaluation taking into account the validation practices of each discipline within the country. In particular, a similar assessment of research performance considers seniority, the quality of publications, the individual contribution to co-authored works, and above all (sub-)field differences. The evaluation process ends with a rating that systematizes the academic performance of researchers in 5 ordered categories. In the paper, we use those categories to measure the academic performance of individuals.

NRS Rating system. Details of the evaluation procedure

Interactive Prestige Ranking

In this section, we describe the variation of the prestige ranking algorithm developed by Cowan and Rossello ( 2018 ) for a dynamic setting. The algorithm assumes that that movements in the academic job market contain information about how universities and PhD graduates perceive each other’s quality. The input of the algorithm is the hiring network defined as \(G=(V, E)\) . The vertices V of the network are the universities participating in the job market and edges E represent movements of PhD graduates from one university to another. The network G is represented by a weighted directed adjacency matrix A that captures the flows of graduates from PhD to their first job institution. A ’s off diagonal elements ( \(a_{ij}\) with \(i \ne j\) )) show the number of scholars that graduated from university i and were hired by university j within 5 years after doctoral graduation. Conversely, the diagonal elements ( \(a_{ii}\) ) are the PhD graduates hired internally (trained and hired by their faculty).

The ranking algorithm is based on two key assumptions. The first concerns universities, that is, they try to improve their status and quality and in pursuing it they try to hire from universities “better” than themselves. The second assumption considers scholars, that is, they want to be hired by the most prominent institution. If both academics and institutions satisfy those desires fully, in A exists a unique order of university names (rows/columns names) such that PhD graduates only move down the hierarchy. In other words, under this assumption, rows and columns of the adjacency matrix A can be rearranged in an upper triangular matrix such that all entries below the diagonal are equal to zero. We define this unique order \(o^{*}\) , where the sum of rows have a global maximum score equal to \(s^{*}\) .

Geographic location and other recruiting criteria imply that the PhD job market often departs from this strict assumptions. Thus, empirically the order is not unique, and A it is not a perfect upper triangular matrix . However, since prestige is an important selection criteria for university and scholars, we apply the heuristic algorithm proposed in Cowan and Rossello ( 2018 ) to find the set of orders that gets as closer as possible to \(s^{*}\) and have the minimum number of violations from an upper triangular matrix configuration. To approach the underlying prestige hierarchy, the algorithm works as follows. The algorithm for \(k=10000\) times starts assigning to A a random order of rows, then it computes the score \(s_k\) such that

For 100 times the algorithm tries to improve the score \(s_k\) in the following way. For each iteration, two nodes (both rows and columns) are randomly selected and swapped. If the swap does not decrease the score, we keep it, otherwise we reject it. After this 100 searches, the obtained order \(o_k\) and score \(s_k\) are recorded, obtaining a set of n-tuple \(O=\{o_1, o_2, \ldots , o_k\}\) orders and their associated scores \(S=\{s_1, s_2, \ldots , s_k \}\) .

From these two sets O and S , the algorithm selects the set \(Q=\{o_m \in O \mid s_m \approx s^{*} \}\) of orders that reach the highest score. Each n-tuple of the set \(Q=\{q_1,\ldots ,q_m\}\) contains a possible university rank \(R(v)=\{r_1, r_2, \ldots , r_m \}\) Then for each university the algorithm computes its prestige score according to the formula

which is in other words the mean of its ranks in the set of orders with maximum score Q . Footnote 9 The prestige score of each university provides a natural ordering or ranking of universities that is our measure of prestige.

A key assumption of this algorithm is that a single adjacency matrix A captures the underlying hierarchy of prestige. This implies that all universities (nodes) and scholars participate in the labor market and the size of the market is fixed. However, movements between universities over an interval of time can be constrained by various forces.

We relax the assumption that the hiring network is fixed across time, adopting a dynamic computation of the algorithm. The proposed variation iterates the previous algorithm over closed intervals, \(t=[y-\Delta , y+\Delta ]\) , of time centered around the PhD graduation year y , with fixed windows of \(\Delta =3\) years. This implies that the hiring network and the scholars and universities involved are different for each time window. Footnote 10 However, not all institutions are present across t intervals, for instance, more recent universities are not listed in the early years of the sample. Hence, the final scores of our Interactive Prestige Ranking is the average score of each university i over t intervals of time.

After the computation of the ranking, we distinguish between three groups of scholars, Up , Down and Stay , that exhibited different changes in the prestige during the transition between PhD graduation and first job in the following way. For each researcher in the sample, we calculate the difference between the PhD prestige rank and the one of her/his first job institution. The difference is positive (negative) for the group of scholars who move Up ( Down ) which experience upward (downward) prestige mobility—they are hired by a university more (less) prestigious than their PhD. The difference is equal to zero for scholars who Stay experiencing internal hiring—those hired by their PhD institution.

Prestige Ranking Results

Table  2 shows the ranking of Mexican universities using the dynamic ranking with a 3 years time window. The rank follows from the average of the university rank computed by our algorithm across all time windows. The lower the average indicates that the university has occupied the higher positions more often across time periods.

Table  3 shows the stratification of prestige in the Mexican university system. Looking at the movements in the prestige hierarchy, Table  3 highlights a high level of stratification in the Mexican university system. Where the 10 most prestigious Mexican universities produce the 68% of PhD graduates and nearly half of them are hired as a first job in those institutions. This stratification is also geographical, as mostly all top universities are located near Mexico City. Thus Mexican academia operates in a highly stratified system where public and private research funds are mostly centralized.

Descriptive Statistics

Table  4 presents the correlation between the dynamic ( d ) and static ( s ) prestige ranking (Pr) with individual level variables. In addition of the measurement of productivity from the NSR we compute bibliometric indicators using Science Citation Index data of Web Of Science (WOS) for the period 1992–2016. First, we compare the stability of prestige over time by comparing the Spearman correlation coefficient from the static and dynamic ranks. The high correlation between the prestige from PhD and first job, 0.64 and 0.83 respectively, indicate that prestige does vary over time but not largely. The difference in prestige ( \(\Delta \) Pr) is positive (0.182) and negatively ( \(-0.649\) ) correlated with the prestige from the PhD and first job, respectively. This pattern shows that moving up the ranking (positive difference) is associated with lower prestige from PhD university (higher in rank). Conversely, moving down the ranking (negative difference) is associated with a lower prestige from first job.

Next, we draw attention to the peer-review research productivity variable from NSR and the prestige variables. The results show that static ( s ) and dynamic ( d ) measures of prestige are negatively correlated with productivity (NSR). This is expected, since lower rank signifies higher university prestige. Interestingly, the correlation between \(\Delta \) Pr, and all the measurements of productivity is close to zero but positive and significant. A difference is equal to zero, indicates that scholars were hired by university after PhD graduation (stayed). Lastly, we show the correlation between the NSR rating and other bibliometric variables. In general, the NSR rating is positively correlated with bibliometric measurements of productivity. However, this correlation is not higher than 0.40, which suggest that the NSR performance variable takes into account local research and other products of research such as patents (See “ NSR Disciplines and Evaluation Procedure ” and the previous section for a description of the NSR rating ).

Quasi-Experimental Design

In this section, we examine how movements in the prestige hierarchy in the transition from the PhD to the first job affect scholars’ academic performance. We take advantage of a quasi-experimental design that naturally occurs in the academic labor market of early-career scholars. After PhD graduation, early-career researchers are self-selected into academic positions. However, the choice of hiring committees is a quasi-random assignment that naturally clusters PhD graduates into three groups according to prestige differentials: Up , Down , and Stay . The allocation is a partially random assignment given that early-career researchers typically have thin publication records after PhD graduation, such that ability and research skills are mostly unobserved. Given the asymmetric information in the labor market and its dynamics, the choice of hiring committees evaluating early-career researchers contains a large element of randomness (Oyer, 2006 ). In this setting, PhD prestige plays an important role as a signal mechanism of candidates’ quality.

To deal with the endogeneity of university prestige related to training, experience and individual characteristics, we use a bootstrap matching pairs technique. To assess the effect of early career university prestige on future academic performance, we compare the research performance of the bootstrap matches pairs of individuals with similar characteristics but different treatment ( Up , Down , Stay ). When individuals are paired holding constant their PhD institution, we test how changes in prestige relate to academic performance irrespective of training. Similarly, the comparison done matching scholars by their first job examines how prestige movements affect scholars performance for agents with the same first academic job. The Matthew effect of the academic labor market may amplify the role of early-career prestige on long-run academic performance. Therefore, we replicate our analysis comparing the performance of matched pairs scholars in the short (up to 2 years), medium (3–5 years) and long-run (6–25 years) after PhD graduation.

Our quasi-experimental design follows Cowan and Rossello ( 2018 ) and Way et al. ( 2019 ). The basic idea is that career movements from the PhD to the first job is a quasi-random assignment made by the job market. Individuals with the same PhD training are placed in different institutions, likewise, individuals with different training (PhD) are placed in the same first job. Thus, our strategy compares matched couples that either received the same training (PhD) or are exposed by the same working environment (same first job) but experienced different prestige movements (treatments groups). The additional variables on which we match the pairs are: gender, age, discipline, and graduation year.

However, we should remark two potential limitations and the scope for future research. The first limitation is that individuals with the same PhD training (or first job) might have slightly different productivity or performance (broadly defined). Unfortunately, we do not have prior students’ data to control for that. Besides this limitation, we must highlight that our method partially implicitly controls for performance. Each PhD program has rules and quality standards with minimum requirements, both in admission and in promotion decisions. Thus, all graduates from a PhD program met at least those minimum requirements. Internal rules at universities make the performance of individuals in the same PhD batch comparable. The same reasoning applies to the first job. Internal university rules make hiring committees accountable for their decisions. This implies that job candidates must meet minimum “quality” requirements to be considered for the job. The latter makes new hires of a university similar in terms of prior performance. Thus, our strategy might mitigate this potential data limitation.

A second limitation is that our technique discretized prestige movements in three treatment groups, rather than considering them as a numeric variable. Our method might have the disadvantage of considering movements from the first to the second ranked institution as movements from the first to the bottom ranked one. Besides this potential drawback, we should remark that the way in which we operationalized our ranking algorithm limits this possibility. The situation in the example above is very unlikely. Most hiring patterns from one university to other are stratifies and clustered around universities of comparable prestige. Very distant prestige movements represents a strong violation of the basic assumptions of our ranking algorithm. The ranking algorithm therefore minimise that possibility. Even if it is still possible for an individual to move from the first to the bottom ranked institution, this situation is rare, since a university that has many individuals moving this way will be penalised by the ranking algorithm. The rank of a university is higher as much more students it is able to place in better ranked first job.

For each comparison Up vs. Stay , Down vs. Stay , and Up vs. Down we generate \(n=10000\) bootstrap samples of the group on the left-hand side (the smaller) of its same size s . For each of the 10000 samples of size s , we create matched pairs of scholars matching on gender, age, discipline, graduation year and PhD (or first job) university. In order to compare their performance, in each sample we estimate the proportion of pairs, \(p^{*}=(p_1, p_2, \ldots , p_n)\) , in which one group \(g^\alpha \) have higher performance than the other (group) \(g^\beta \) . Such as

Where academic performance g is the individual NSR research rating. Footnote 11 For each group in the comparisons Up vs. Stay , Down vs. Stay and Up vs. Down , we estimate the two \(p^{*}\) and construct their \(F(p^{*})\) cumulative empirical distribution function (CEDF) . To assess the performance of one group over the other, we test for first order stochastic dominance (Levy, 1992 ). This test implies higher performance of \(g^\alpha \) over \(g^\beta \) if \(F(p^\alpha ) \le F(p^\beta )\) for all \(p^{*}\) . Footnote 12 We compare the two CEDFs running a two-sided and a one-sided Kolmogorov-Smirnov test ( KS test ).

The null hypothesis of a two-sided test is \(H_{01}:F(p^\alpha ) = F(p^\beta )\) —the two CEDF are drawn from the same distribution. Rejecting the null hypothesis \(H_{01}\) implies that the academic performance is statistically different between the two groups. The null hypothesis of the one-sided test is \(H_{02}:F(p^\alpha ) \ge F(p^\beta )\) . Rejecting the null hypothesis implies that \(F(p^\alpha )\) stochastically dominates \(F(p^\beta )\) , in other words, that the increase in academic performance associated with a change of prestige from group \(g^\alpha \) is statistically different and greater than \(g^\beta \) .

Matched Pairs Results

In this section, we compare scholars performance of Up vs. Stay , Down vs. Stay , and Up vs. Down in the short, medium and long-run. In particular, we examine the CEDF of the proportion in which one group received a higher NSR rating than the other. Up , Down , and Stay represent prestige changes from the PhD to the first job where prestige is measured with the dynamic ranking algorithm with a moving time window of 3 years. Footnote 13 Footnote 14 Results of the KS-tests of \(H_{01}\) and \(H_{02}\) in Table 5 show for every comparison that the CEDFs are different and one group stochastically dominates the other.

Figure  2 compares the NSR research performance of matched pairs of scholars who Stay and move Up the hierarchy. In all figures, scholars match if they have the same gender, age, discipline , and graduation year . Additionally, figures on the left match scholars with the same PhD while those on the right-hand side match those with the first job institution. The matching procedure allows us to compare scholar with same PhD (or first-job) and characteristics but experiencing different prestige movements. In both cases, the CEDF of \(Stay>Up\) (solid lines), is located below that of \(Up>Stay\) (dashed lines) implying that the Stay group stochastically dominates the Up group. The implication of the results is the following. On the one hand, looking at scholars with the same (PhD) training (left-plots) we find that those hired internally have on average a better research performance than those experiencing upward prestige mobility (hired into a university more prestigious than their PhD). On the other hand, comparing scholars with the same first job (right-plots) but different training (PhD) we find that internal hired perform better than those coming from less prestigious PhDs ( Up ). These results suggest that scholars who manage to secure positions at their faculty after graduation demonstrate higher NSR levels of performance than those who migrate to upper ranked institutions.

Up versus Stay comparison. The solid curves are CEDFs of the proportion of pairs in which \(R_{Stay}> R_{Up}\) . Dotted curves are CEDFs for \(R_{Up}> R_{Stay}\) . Pairs matched by gender, age, discipline, graduation years, and same PhD university (left), or same first job university (right). From top to bottom: short-run (Up to 2 years), medium-run (3–5 years), and long-run (6–25 years) after PhD graduation

Results for the comparison between the Down and Stay groups are in Fig.  3 . In this case, we compare scholars who take academic positions in their faculties after graduation and PhD graduates experiencing downward prestige mobility. Results are the same matching the pairs on the PhD or the first job institution—the CEDF \(Stay>Down\) stochastically dominates \(Down>Stay\) . In line with our previous results, plots on the left-hand side show that scholars with the same PhD training moving ( Down ) to a less prestigious institution in their first job tend to have a lower NSR rating than those hired internally ( Stay ). Similarly, plots on the right-hand side compare scholars with the same first job and indicate that those hired internally ( Stay ) have a higher performance than those moving down the hierarchy.

Down versus Stay comparison. The solid curves are CEDFs of the proportion of pairs in which \(R_{Down}> R_{Stay}\) . Dotted curves are CEDFs for \(R_{Stay}> R_{Down}\) . Pairs matched by gender, age, discipline, graduation years, and same PhD university (left), or same first job university (right). From top to bottom: short-run (Up to 2 years), medium-run (3–5 years), and long-run (6–25 years) after PhD graduation

The last comparison in Fig.  4 examines performance differences between scholars who experience upward and downward prestige mobility. Results show that the CEDF of the proportion of pairs of scholars where the performance of \(Down>Up\) stochastically dominates \(Up>Down\) . The stochastic dominance of one over the other implies that scholars who experience downward prestige mobility sustain higher performance over their career than those experiencing upward mobility in their early career. These results are consistent both matching pairs, keeping fixed the (PhD) training (left-plots) or the first job (right-plots) institution. In the first case, comparing scholars with the same (PhD) training, we find that those moving down the hierarchy have higher performance than those moving up to more prestigious first job institutions. In the second, pairing scholars with the same first job but different PhDs institution, we find that those coming from more prestigious PhDs ( Down ) have a higher NSR rating on average than those moving up from less prestigious PhD institutions.

These results seem counter-intuitive at first glance, since most studies have associated upward ranking mobility with higher academic performance. Footnote 15 In particular, the first result of a negative impact of upward prestige mobility comparing scholar with the same training contradicts the previous results of Chan et al. ( 2002 ). However, their analysis is slightly different. They use a longitudinal analysis in one sub-field of economics, and their sample is limited to scholars with publications in 16 top journals in finance. In contrast to us, they find that scholars who experience upward prestige mobility publish twice as many as their colleagues. What is most interesting is that what we found is a pattern through the career of scholars and for both dynamic (Fig.  4 ) and static ranking estimations (Appendix Fig. 9 ). Nevertheless, these findings require further research that we discuss in the “ Discussion ” section.

Robustness Checks

Our methodology might be prone to potential weaknesses that we discuss in this section, providing additional robustness checks.

Static and Dynamic Ranking Results Comparison

The first potential drawback stands in the different time-frame between the categorization of prestige movements ( Up, Down, Stay ) which follows from the dynamic prestige ranking and the dependent variable NSR rating . This might mean that people move to the hierarchy in the PhD to the first job transition, and at the same time universities might change their ranking position relative to other institutions. A simple way to overcome this limitation is to run the same analysis using the static prestige ranking. In this case university prestige is assumed to be constant over the period of analysis and thus movements in prestige are computed on the basis of this aggregation of the data. Results using the static prestige ranking, in Appendix Figs. 7 , 8 , 9 , are consistent with the previous. Additionally, we should remark the issue above does not apply to the short run estimation (top panels of Figures 1 , 2 and 3 ). Since the dynamic computation of the ranking is done using windows of times that overlaps with the comparison of the research performance. In particular, the evaluation of the NSR ratings (our dependent variable) overlaps with the period of the transition up/down/stay from the PhD to the first job. In more general terms, comparing the results using the static and the dynamic ranking. The main results show consistency.

Regression Analysis

In this sub-section, we further assess the robustness of our previous results using an Ordinal Logistic Regression model. In Table  6 the dependent variable is the NSR rating of research performance with five ordered categories (See Section “ Data ”). We include the control variables that we use in the stochastic pair analysis.

Models 1 to 3, on Table  6 , explore the effect of university prestige from PhD, first job and change in prestige (Pr-PhD, Pr-Job, \(\Delta \) Pr). As expected, the models show negative coefficients on prestige of PhD and first job given that higher rank (lower prestige) decreases the likelihood of achieving higher academic performance. These results are also consistent in Model 4, that shows a similar effect of prestige from PhD and first job. Model 3 and Model 5 incorporate the continous change in prestige ( \(\Delta \) Pr), which is significant but closer to zero. Similarly to the stochastic pair analysis, these results suggest that researches who are hired by their faculty ( \(\Delta \) Pr \(=0\) ) have higher odds of achieving higher research performance. To better understand the average change of moving Up , Down or Stay in the ranking we estimate Models 6 and 7 using the categorical variable of change in prestige similar to the previous section. We argue that the discrete change in prestige is more suitable to analyze the BFLPE, as it captures the shock in prestige internalized by researchers.

As shown in Table  1 , scholars move on average only 6 places ( \(\Delta \) Pr). Henceforth, it is more likely that a graduate move up or down of few position in the rank. For example, A graduate who moved from the most prestigious university in the country to the second most prestigious is more common than moving from the most prestigious university to the least prestigious for their first job.

However, both could be labelled by their peers as researchers moving down the ladder and, so, they can also internalize their experience psychologically thinking that they have been “downgraded” and perceive a sense of failure (Waters and Leung, 2017 ).

Indeed, scholars are typically only aware of the universities and departments from whom they work and compete. Thus, they operate with asymmetric information and are myopic with respect to their precise place in the distribution of prestige. Henceforth, moving up/down within their league (average 6), has other potential costs and psychological effects (See “ Discussion ” section). And, the effect of changing prestige may be internalized irrespectively of the change in the number of places in the prestige ladder.

Model 6, estimates the average effect of moving Down and Stay with respect to the baseline category Up . The results are consistent with our previous results, Model 6 shows that the researches who experience downward mobility reported 115% ( exp (0.144)) odds of achieving higher levels of academic performance in comparison to the Up group. In the same model, the group of researchers hired by their faculty after PhD graduation reports 158% higher odds of achieving higher performance rates than the group who stay. Model 7, has the group Stay as baseline and henceforth the log-odds of moving Up or Down are negative. Figures  5 and 6 , reports the predicted probability for each group, clearly the probability of achieving higher levels of research performance is more loaded in the stay group. The predicted probability of achieving an NSR level II or higher is approximately 10% larger for the group that stayed in comparison to the group that went up. These results are in line with the previous section.

Marginal effect Model 6 Table  6

Predicted probability Model 6 Table  6

Our main findings underline a more complex relation between prestige movements and performance than the common positive association. Our first result is that scholars experiencing downward prestige mobility show higher performance compared with colleagues with similar characteristics moving upward in the prestige hierarchy. This result challenges past studies that often associates upward prestige movements with above average academic performance (Chan et al., 2002 ). We interpret this finding in the light of the literature of the BFLPE and related mechanisms presented in “ The BFLPE and Similar Mechanisms ” section.

Becoming a researcher in academia has career phases where the social comparison might matter. The BFLPE hypothesis in psychology claims that individual performance can be affected by how individuals perceive themselves in comparison with their peers. The transition from the PhD to the first academic job is a stressful event in a scholar career, where the social comparison might matter more. Indeed, the psychological literature predicts that the BFLPE mechanism takes place when individuals change their environment. Footnote 16 When individuals change institution, or advance in their career, their peers also changes. For instance, after PhD graduation, mobile scholars change their place in the prestige hierarchy. A scholar moving down the hierarchy has higher prestige and academic self concept relative to their incumbent peers with lower prestige. The higher prestige and academic self-concept translates into higher competitiveness, visibility and resources than scholars moving upward.

Following the BFLPE mechanism, we can interpret the results of the Mexican PhD job market as follows. On the one hand, scholars who move down might think they are “ big shots ” relative to their new peers, and this is beneficial to their performance. Or in terms of positive peer effects, their new colleagues might think they have made “ a catch ” and give them more resources. In addition, an initial “ failure ”—moving down in the hierarchy—might lead to an effort to “ regain the previous prestige ”. Our results might also relate to how co-workers see the new hired, and this might generate different peer effects. Depending on how a new researcher is integrated in the new department, the peer effect can positively or negatively affect individual performance.

The explanation mentioned above is consistent with the apparently paradoxical result in this paper. To accurately test the BFLPE mechanism will require psychological tests on self-esteem or academic self-concept. However, the latter is out of the scope of our work and should motivate further studies. Still, we consider our results to be relevant for the policy debate of prestige stratification and mobility in the academic market, specially in developing settings. In light of the consistency of our findings, further studies should operationalize a standardised psychological test aimed at measuring the change in academic self-concept.

Our second result suggests a positive association between internal hiring and research performance in comparison to scholars moving in the hierarchy. This result, in conjunction with our analysis of the stratification of the Mexican system, suggests that PhD graduates are not moving to the peripheral areas of Mexico. The market is operating quite efficiently, however, identifying talented PhDs that are hired internally. This also suggests a negative effect of mobility, as internal hiring is associated with higher research performance.

The negative association between early career mobility and performance is not surprising. Past literature on academic inbreeding Footnote 17 finds that its relationship with individual performance is ambiguous and depends on the sample, country, era, field, career stages, and measures which are used (Gorelova and Yudkevich, 2015 ; Capponi and Frenken, 2021 ). For example, Cruz-Castro and Sanz-Menéndez ( 2010 ), controlling for the type of mobility experienced by researchers, find that in Spain inbreeding scholars obtain a tenure earlier and with a higher number of publications than mobile colleagues. While, Horta et al. ( 2010 ) with Mexican data find a negative relationship between inbreeding and the number of publications. However, our work differs to theirs in many respects. First, we use a more nuanced measure of performance, the NSR rating which includes the local knowledge production and the quality evaluation of independent scholars specialized in the fields. Second, we give a more narrow definition of mobility focusing on the transition from the PhD to the first job and excluding international mobility.

The positive relation between inbreeding and performance in early-career is not surprising in Mexico, where inbreeding is pretty common. The Mexican university system is highly stratified and geographically centralized. The centralization of science and research is a well-known problem in Mexico, that is changing slowly through investing in research infrastructure in peripheral areas (Lopez-Olmedo et al., 2017 ). However, our results suggest that science policies in Mexico should continue working on increasing the flows of specialized human capital to aid the development of regional capabilities. With these lenses, our results might highlight that inbreed scholars specialized in areas germane to their faculty. A similar thing might be beneficial for the career of individuals but not necessarily good for the system as a whole.

More generally, Oyer ( 2006 ) highlights that the dynamics of the job market might impact the career in the long run. Possible mechanisms are university-specific capabilities and norms, co-worker behavior, and university turnover. The latter in particular implies that the time of graduation and information about the job market might give individuals an advantage/disadvantage depending on the job market conditions. In a system with few resources and low mobility, two main mechanisms might explain why those internally hired are more successful than the others. On the one hand, resource constraints make the job market less predictable because available positions at universities might be subjected to economic fluctuations and budget cuts. On the other hand, low mobility levels make that information about universities and their job market sticky and localized. In a similar case, those able to secure their initial positions at universities will have better information of the job dynamics as well as about norms and routines that might help them to become more germane to their institutions compared to those trained elsewhere.

We should remark a limitation of our analysis, our work considers Mexico as a closed system. We do not have data on foreign PhD graduates returning to the country. However, given the ties between Mexico and North America, it is likely that many Mexican trained abroad return to their country. For example, Finn ( 2010 ) estimates that only 40% of Mexican PhD graduates, trained in the US, are hired in a US university. Footnote 18 In the same line, Rivero and Peña ( 2020 ) show that repatriation policies have contributed to keeping the rate of return of Mexican researchers to 60% and 83% from Europe and the US, respectively. This suggests that a large proportion of PhD graduates returns to Mexico. This sub-sample of foreign-trained PhDs might behave differently following different career paths. However, besides this limitation, we concentrate on the Mexican university system, and we consider our findings relevant for science policy in emerging contexts.

This work is the first studying how prestige stratification affects scientific performance of early career scientists in the Mexican higher education system. The majority of comparable analyses looked at university systems in developed economies, especially in North America, where mobility after the PhD tends to be high and systems are more integrated. In the U.S. for example, there are often hiring practices that prevent universities from hiring their own graduates immediately after the PhD. Studying these mechanisms in less mature settings with higher resource constrains has policy implication because the gap between prestige and performance can be larger than in other settings.

Our findings in general suggest that there is a negative relation between mobility during early-career and academic performance. Moreover, when we decompose mobility looking at prestige differentials between PhD and first job institution, we find that scholars who Stay or move Down the hierarchy remain mostly in first-tier (top 10) institutions. Footnote 19 Our results of the matched pair analysis provide evidence of the same association of prestige movements and performance in the short-, medium- and long-run. Further, comparing those moving up with those moving down the hierarchy, we find that those moving down have sustained higher performance than those moving up.

The reasons why promising scholars experiencing upward prestige mobility have lower performance than their colleagues (with the same PhD) that stay or move downwards requires further investigation, as highlighted in the previous section. Similarly to the higher education system in the U.S. and other developed economies, we find a large stratification in the Mexican university system but low mobility (around 50% of PhD graduates are hired by their faculty), with a few prestigious institutions (around 10) producing the majority of PhD graduates that are subsequently mostly hired in these same institutions. A high concentration of prominent scholars in a few academic institutions reveals large inequalities in the distribution of prestige. On the one hand, the stratification of higher education could promote higher levels of specialization with a targeted allocation of resources. On the other hand, it can also reveal a structural problem in the science system, a “lock-in”, where researchers are trained and hired by elite institutions and flows of knowledge are reduced throughout the national science system. In the case of Mexico, a structural “lock-in” could be additionally reinforced by the negative association between mobility (upward prestige mobility in particular) and performance.

Sometimes called in the literature Academic Inbreeding (Horta et al., 2010 ).

As for example Times Higher Education and Shanghai University Ranking.

See Henning et al. ( 2017 ) for a review on the topic. However, most of this research is limited to voluntary surveys and small samples.

NIH (National Institutes of Health) is the largest public founder of biomedical research in the world.

CONACYT Research Areas included are I, II, III, VI and VII. Respectively, Physics-Mathematics and Earth Sciences, Biology Chemistry and Life Sciences, Medicine and Health Sciences, Biotechnology and Agricultural Sciences, and Engineering. See Appendix “ NSR Disciplines and Evaluation Procedure ”.

The list of institutions is presented in the Appendix “ A. Faculty Hiring Matrix Names ”.

The sample comprises the 36 Mexican universities who provide doctoral education in STEM subjects. In Mexico, universities specialized in teaching do not grant doctorates. In Mexico, there are 1250 institutions of Higher Education, including public universities, technological institutes, technological universities, private institutions, teacher training colleges, and other public institutions. Among those, universities are 213. However, 50% of the research and 58% of the students concentrates in only 45 public universities which are mostly located near Mexico City and in other large cities.

For more details on the NSR system see (Gras, 2018 ).

Further details of the procedure are described in the Appendix.

The number of universities is very small in early years of the sample, their analogous adjacency matrices yield trivial orders of small length. To overcome this, we aggregated the first years of the sample up to the year 2000.

NSR rating is one of 5 ordered categories.

Graphically, this is inspected if \(F(p^\alpha )\) lies below and to the right of \(F(p^\beta )\) .

Details on the dynamic rank computation are in “ Interactive Prestige Ranking ” section.

We execute the algorithm with a time window of \(\Delta =3\) and run robustness checks using other time ranges ( \(\Delta =5\) and \(\Delta =8\) ) and our results are consistent.

Moreover, we should consider that those that start high in the hierarchy have few options to move up the ranking, and they are more likely to move down, while the opposite is true for those low in the prestige hierarchy. This implies that those moving up (down) are more likely to have PhDs in less (more) prestigious institutions.

See Marsh, 1991 for a discussion.

Academic inbreeding focuses on the other side of the coin with respect to mobility: immobility and inertia. Academic inbreeding is defined when universities hire their own PhD graduates (Gorelova and Yudkevich, 2015 ).

See https://orise.orau.gov/stem/reports/stay-rates-foreign-doctorate-recipients-2011.pdf ; Last access December 2020.

Those moving Up the hierarchy, get their PhD degree mostly from second-tier (bottom 30) institutions, but move to first-tier institutions with their first job. This is an expected result, in the light that those graduating from prestigious universities have fewer possibilities to move higher in the hierarchy.

Applicants can only receive this distinction one time.

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A. Faculty Hiring Matrix Names

NSR Disciplines and Evaluation Procedure

Disciplines

Levels of Rewards linked to the NSR ratings

SNI Candidates Footnote 20 : Granted for 3 years, with the possibility of 2 years of extension.

Level I: Granted for 3 years the first time, and every 4 years in the following periods.

Level II: Granted for 4 years the first time, and every 5 years in the following periods.

Level III: Granted for 5 years the first and second time, and every 10 years in the following periods.

Emeritus Professors: Candidates must have 65 years of more, and have accumulated at least three periods of level III distinction (15 years) without interruption.

NSR Rating and Evaluation

We detail here the NSR rating process. The NSR ratting is a peer review evaluation of research performance. Scholars submit their CVs with their scientific production. The evaluation of the CVs takes into consideration primarily the research output and linkages with industry and the public sector and to a less extent human capital formation of research groups Footnote 21 . The research output include: Scientific Articles, Books, Book Chapters, Patents, Technological Developments, Innovations and Transfers of Technology. These research products contain not only publications indexed in Web Of Science or Scopus but also local research in Spanish which is relevant for the country.

The “Evaluation Commission” reviews the quantity and the quality of the aforementioned research products in each CV (application). All the members of the commission participate in the revision of applications, but the evaluation is the direct responsibility of two members of the commission. The Evaluation Commission is comprised by a heterogeneous group of 14 prominent scholars from different institutions, changed every 3 years Footnote 22 . The rotation of the members and evaluation process is overlooked by the “Council of Approval” and several CONACYT authorities. Their job is to eradicate any personal bias and discrimination to ensure a meritocratic evaluation. Footnote 23

There are multiple commissions for each of the corresponding research areas described in Table  7 . The commission evaluates and assigns researchers to one level of research performance. The levels of research performance (NSR ratings) are ‘SNI Candidate’, ‘Level I’, ‘Level II’, ‘Level III’ (ordered from low to high performance). There is a special category called ‘Emeritus’ excluded from our analysis because this recognition is uncommon. An Emeritus recognition is an honorary recognition to professors at the end of a career. Every NSR rate has an associated economic reward that increases linearly. Belonging to the NSR, implies high recognition of the quality and academic prestige of the researcher, the result of a scientific production of considerable importance at the national level and, in some cases, also at the international level (Reyes and Suriñach, 2013 ). Thus, our dependent variable, NSR ratings, is not a productivity measure only, but it measures research performance in a broader sense. In contrast with bibliometric measures, our measure of research performance accounts for ‘standard’ of quality from the national context within each discipline.

Results Static Rank

Figures ( 7 , 8 and 9 ).

Stochastic Analysis of Up vs Stay . The solid curves are CEDFs of the proportion of pairs in which \(R_{Stay}> R_{Up}\) . Dotted curves are CEDFs for \(R_{Up}> R_{Stay}\) . Pairs matched by gender, age, discipline, graduation years, and same PhD university (left), or same first job university (right). From top to bottom: short-run (Up to 2 years), medium-run (3–5 years), and long-run (6–25 years) after PhD graduation

Stochastic analysis of Stay vs Down . The solid curves are CEDFs of the proportion of pairs in which \(R_{Down}> R_{Stay}\) . Dotted curves are CEDFs for \(R_{Stay}> R_{Down}\) . Pairs matched by gender, age, discipline, graduation years, and same PhD university (left), or same first job university (right). From top to bottom: short-run (Up to 2 years), medium-run (3–5 years), and long-run (6–25 years) after PhD graduation

Stochastic analysis of Up vs Down . The solid curves are CEDFs of the proportion of pairs in which \(R_{Down}> R_{Stay}\) . Dotted curves are CEDFs for \(R_{Stay}> R_{Down}\) . Pairs matched by gender, age, discipline, graduation years, and same PhD university (left), or same first job university (right). From top to bottom: short-run (Up to 2 years), medium-run (3–5 years), and long-run (6–25 years) after PhD graduation

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González-Sauri, M., Rossello, G. The Role of Early-Career University Prestige Stratification on the Future Academic Performance of Scholars. Res High Educ 64 , 58–94 (2023). https://doi.org/10.1007/s11162-022-09679-7

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Race and Ethnicity

Why college prestige matters and why it shouldn't, for the wealthy, college admissions have become an ever-escalating arms race..

Posted January 25, 2021 | Reviewed by Lybi Ma

Three years after graduating from my alma mater, I found myself reliving the rat race of college admission, but this time, I was watching from the interviewer's perspective. In thirty minutes, I had to figure out whether my seventeen- and eighteen-year-old interviewees could become future graduates that my college would be proud to claim as its own.

These teenagers were undoubtedly talented—concertmasters, club presidents, varsity team captains, and champions of various extracurricular activities. But I remembered the applicants not by their accomplishments, but by how they carried themselves during the interview. Some impressed me with their introspection, eloquence, and self-assurance ; others I associated with their nervousness. They fidgeted with their coffee cups, failed to meet my gaze, or struggled to express themselves.

I wanted to comfort these jittery applicants by letting them know that everything would be okay. Still, I held back because I didn’t know whether that was entirely true. Of course, the college they attended wouldn’t define these applicants' success; only they could. But coming from a well-regarded college could certainly give these students a leg up. As a first-generation immigrant and a first-generation college graduate, my university opened doors that my high school self could have never imagined.

For instance, if I had attended my state school, would I have been awarded a research internship and a year-long fellowship at the National Institutes of Health, despite my then-lackluster biology grades? Would I have nabbed a nonfiction book deal as a college senior? Probably not.

Prestige begets prestige. My classmates also benefited from the opportunities provided by our alma mater. Consulting companies and financial firms actively recruited students through our career center. Our college also had various scholarships for students to fund their passion projects and internships. Successful alumni lent their advice and support to help underclassmen who were decades their junior. Now, only seven years after graduation, my former classmates include countless lawyers, physicians, engineers, consultants, investment bankers, writers, artists, academics, multiple Olympians, an Emmy Award-winning reporter, and more.

Many graduates from prestigious colleges come from privileged backgrounds and continue to benefit from the perks of their institutional affiliations. This may be why that the average Ivy League graduate earns more than twice as much as the typical college graduate ten years after matriculation, according to a 2015 article published in the Washington Post . The result is also evident in the disproportionate number of Fortune 500 CEOs, politicians, and other changemakers hailing from highly-selective institutions.

In fact, in a rare break, Joe Biden and Kamala Harris became the first president-vice president pair without an Ivy League degree since Walter Mondale and Jimmy Carter from 1977 to 1981. Moreover, Amy Coney Barrett, former President Donald Trump 's last Supreme Court nominee, is the only current justice without a Harvard or Yale law degree.

Some (perhaps the graduates themselves) may argue that the large pay gap reflects the continuous drive and hard work enabling students of prestigious institutions to be admitted in the first place. The problem with this reasoning is that it neglects the role of parental wealth in success in high school and college admissions. According to the Equality of Opportunity Project , at selective colleges, "more students come from families in the top 1 percent of the income distribution than the bottom half of the income distribution," and yet low-income students at these institutions have "nearly the same odds of reaching the top fifth of the income distribution as their peers from higher-income families."

For the well-to-do, elite college admissions have become an ever-escalating arms race for interesting extra-curricular activities. Those who have the means to do so can create and fund opportunities to help them stick out in the minds of the admissions committee. In the most extreme cases, I've seen parents utilize their networks to create impressive-sounding internships for their children, add their sons and daughters as co-authors in complex research papers, and help their kids set up companies or charities soliciting thousands of dollars in donations from wealthy family friends. In all, the nature of college admissions and the perks associated with an elite education make these institutions an incubation chamber for amplifying economic inequality.

Given the disparate benefits of attending a prestigious institution, then, at the very least, admissions should be more fair and equitable. This would require getting rid of legacy admissions—institutional preference given to applicants on the basis of family relations—and opening up more spots per class. However, neither is realistic. In the United States, alumni make up a vital source of donations, and many donors give with the hope that their children will be looked upon favorably when they apply for colleges. Also, adding more seats per class will increase the acceptance rate, which, in many college rankings, is inversely associated with perceived prestige.

The most realistic solution to bolster equity in higher education lies upon deliberate changes in how applicants are assessed. For one, admissions offices can place a greater weight on “distance traveled”—the relative difference between a student's starting point and the progress they have made—instead of the absolute value of an applicant's achievements. To whom much is given, much more should be expected. Doing so will select the students who made—and will continue to make—the most of their opportunities.

Another, more modest means of increasing equity would be for alumni of prestigious institutions to use their own privilege to level the playing field. I love my alma mater, and I also know that my affiliations grant me more credibility and opportunities than others with similar or even greater aptitude. Therefore, at this point in my career, I've sought to utilize my resources for others by mentoring pre-medical students from underserved backgrounds. As a first-generation college graduate, to give back means not pulling the ladder up behind me; instead, it means using my resources to add a few rungs below so that others may ascend.

Yoo Jung Kim, M.D. , is a physician at an academic hospital in Chicago and the co-author of What Every Science Student Should Know . Yoo Eun Kim is an MBA Candidate at Stanford Graduate School of Business and a former middle-school teacher.

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Does Program Prestige Matter?

By That Research Lady February 17, 2017 in Psychology Forum

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When graduating with a PhD in clinical psychology, does the prestige of your graduate program effect your career in the short and long term? My career goals are centered around academic research with an interest in consulting. I'm wondering if the national reputation of a program (ie. graduating from a school in the top ten vs. top 50) is a significant factor in the trajectory of your career.

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My understanding is that graduating from a more prestigious program will give you an advantage when it comes to securing coveted faculty positions . Academia is very hierarchical, unfortunately. However, I would personally weight other factors (e.g., fit with advisor and program) more heavily if you're making a decision about which school to attend.

42 minutes ago, St0chastic said: My understanding is that graduating from a more prestigious program will give you an advantage when it comes to securing coveted faculty positions . Academia is very hierarchical, unfortunately. However, I would personally weight other factors (e.g., fit with advisor and program) more heavily if you're making a decision about which school to attend.  

Thanks for the response! It's a lot to weigh so I thought getting some clarity on this would help.

As I understand it, yes typically for Academic positions the prestige of a program would be considered. However, one thing I've been told is that the "family tree" of mentors can be surprisingly important as well. One of the faculty where I work now has a poster in her office that traces back the history of mentors and their students quite a ways. If people have worked with your mentor, or if they've also been a student of your mentor (or even mentored your mentor), then that seems to give a small advantage as well. 

• C is for Caps Locks , That Research Lady and t_ruth

1 hour ago, JacobW83 said: As I understand it, yes typically for Academic positions the prestige of a program would be considered. However, one thing I've been told is that the "family tree" of mentors can be surprisingly important as well. One of the faculty where I work now has a poster in her office that traces back the history of mentors and their students quite a ways. If people have worked with your mentor, or if they've also been a student of your mentor (or even mentored your mentor), then that seems to give a small advantage as well. 

Yeah, that's definitely true. All else being equal, it's better to work for a well-regarded PI at a less renowned institution than a less well-regarded PI at a top 10 institution. That said, chances are faculty who are at a top 10 institution are probably well known in their field.

Also, the prestige hierarchy will vary depending on your specific field or even subfield and is probably only loosely correlated with undergraduate rankings. A lot of public schools that are ranked in the 20s or lower, for example, are top 10s in certain research areas (e.g., UCLA, UC Berkeley, UNC, etc.). 

• That Research Lady and C is for Caps Locks

I'm weighing a newer faculty member at a top ten in clinical psyc vs. a more established person in the field at a top 50. I'd be able to work with both, in theory, if I was at either institution since they often work together, but of course I would have limited exposure to the advisor at the other institution. I'm leaning towards the established faculty member since our interests are better aligned but I'm not sure if I am giving up a major opportunity to have a more successful career by not attending the top ten. Thank you for your responses so far!

psychpride9

What exactly are prestige and rankings determined by? I feel like a lot of people just take rankings at face value without questioning who determines them (i.e., which people have enough power to determine rankings, that the field of psychology is still dominated by white men who are biased when selecting which grants get funded, etc.) Because I'm considering a super less well-known school over a top 5 program - it looks like I'd be able to publish about the same amount, the faculty at this less well-known school are super respected in their field, etc. And this lesser known school is extremely competent in terms of diversity and social justice compared to all the other programs I applied to, including this top 5 one. Though, as OP touches on, people have told me that going to this top 5 program would help me get a job after graduation - but at this point I'm not sure it's the type of job I would even want (though this program was great when I interviewed, just not as good a fit for me I think). I'd be curious to hear what other people think, because after going on some interviews I feel like basing your decision on fit is the way to go.

• JungAndNotAFreud

1 minute ago, psychpride9 said: What exactly are prestige and rankings determined by? I feel like a lot of people just take rankings at face value without questioning who determines them (i.e., which people have enough power to determine rankings, that the field of psychology is still dominated by white men who are biased when selecting which grants get funded, etc.) Because I'm considering a super less well-known school over a top 5 program - it looks like I'd be able to publish about the same amount, the faculty at this less well-known school are super respected in their field, etc. And this lesser known school is extremely competent in terms of diversity and social justice compared to all the other programs I applied to, including this top 5 one. Though, as OP touches on, people have told me that going to this top 5 program would help me get a job after graduation - but at this point I'm not sure it's the type of job I would even want (though this program was great when I interviewed, just not as good a fit for me I think). I'd be curious to hear what other people think, because after going on some interviews I feel like basing your decision on fit is the way to go.

I am in the same boat. My top two schools are both counseling programs. One is pretty well known for counseling but both are no where near as prestigious at the clinical program I interviewed at. And honestly? I was not nearly as impressed with the clinical program as I was with the counseling. I just think "fit" is SO important. I didn't feel the fit with the clinical program like I did the other two, and if you're going to be there for 4-6y yrs I think it's better to choose where you think you'll thrive the most. Just my two cents though.

Dondante_MMJ

I may be way off here, or maybe it is just my outlook on life, but I think that where I obtain my PhD. will not determine where I end up in the long run. Maybe in the short term prestigious universities can help. My thinking is that no matter where I land my first job, if I have well thought out research that is relative to advancing knowledge and my field, obtain grants for said research, and publish, I will be able to obtain tenure track positions at great universities. I understand the it's not what you know it's who you know to a certain extent. I also understand that who I know has little to do with the work and effort I plan on putting in to my future. To me the best fitting school will be one that truly encourages growth. I would much work under someone at a "less prestigious," university that truly understands and pushes me than someone at a "higher rated," university that doesn't quite fit who I am

• 1|]010ls10o

8BitJourney

I'm wondering this as well. Aside from juggernauts like UNC chapel hill, berkley, ucla, and some others how can people really tell what's prestigious vs what's not?

Clinapp2017

From working with the DCT at my undergrad institution and seeing some hiring processes, fit appears to be  at least  as important as program prestige. You can go to Harvard, SDSU/UCSD, UCLA, (insert strong program here in your sub field) and work with an awful advisor and not publish anything. You could go to a less "prestigious" school and have a good match and publish some great pubs and be set. 

Idk it's such a toss for me between some of my programs and match will be the most important thing. That AND potential to be on some high impact pubs. 

C is for Caps Locks

I think prestige always matters, but as others have said there are other considerations that can often be more important.

So in an ideal situation you find a prestigious program where you have a great supervisor that fits your research/goals well, but if not I think you just have to consider what matters most in the long run based on your goals (probably ask faculty you work with about this).

1 hour ago, psychpride9 said: What exactly are prestige and rankings determined by?...I'd be curious to hear what other people think, because after going on some interviews I feel like basing your decision on fit is the way to go.

This is obviously a very personal decision, so there's no blanket statement that I can give that will apply to all people and all situations. Generally, I agree that fit should trump (ooops can't use that word anymore) program prestige for the reasons you list.

As for who determines prestige, I would argue that prestige is determined by faculty who are in charge of hiring as well as funding agencies. They are the gatekeepers that determine who gets to be a part of academia or not.

• 4 weeks later...

caffeinated-runner

I'm not sure if this thread is still active, but I had a similar debate between two programs, so I'll weigh in. 

Program 1: Ranked #2, offered a little over a $20k stipend for the first year, + guaranteed funding for 5 years. PI extremely well-known, established, & respected.

Program 2: Unranked*, offered a little over a $20k stipend for the first year, + funding for 4 years if I stay in good standing. PI much younger, not as well-known, but certainly well-respected. 

Cost of living is similar in both places, perhaps slightly higher at Program 1, but it's also a bigger city with more to do, etc.

*To be fair, I've only found rankings of 1-10 for this sub-field in psych. 

I decided on Program 2 for a number of reasons. Looking at the numbers above, that might seem a little crazy. I sometimes worry that it might have been crazy, too (and will especially wonder if I end up there for a 5th year without funding), but here are some of the other factors I considered: 

• Faculty fit. Both possible mentors would undoubtedly have been great, but their mentorship styles differed in unexpected ways. The PI at program 1 was very quiet and professional. Past students indicated that he is a great academic mentor, but that you shouldn't expect more than that (e.g., he's also very private, and the relationship will stay particularly formal even after several years). The PI at program 2 (whom I had worked with for a year in the past) is also very professional, but he made a point to tell me that he would provide mentorship both academically and professionally. Having known both him and one of his previous grad students, I know that he will also provide appropriate support and guidance personally, and I've seen him demonstrate a work-family-life balance that is important to me. 
• Research fit. The research at Program 1 would have fascinated me, and certainly would have helped me to stand out in the future. There seemed to be a lot of independence in the lab (and ample resources to let you do nearly anything you wanted), but the project I had initially been interested in was a side-project that the PI had no intentions of continuing on. He might have considered it, but it certainly would not have been a key focus unless I initiated it. At Program 2, the research fit is phenomenally close. I've also had the benefit of working in this lab previously, and on the project that I intend to continue on in graduate school. Interviewing at other programs and exploring other potential lines of research helped me realize how passionate I am about this specific niche area, and that it really is the area that I want to focus in. Research in this program will focus on that specific line of work, and challenge me to incorporate other viewpoints and interdisciplinary applications of the work. While I would of course be given independence to do my own thing as well, I wouldn't be working in a silo in this lab: others have overlapping interests that will supplement and challenge my own. 
• Prestige matters, but you can establish yourself as competitive or "prestigious" in other ways. Publishing, of course, helps, as does presenting. At Program 2, I already have a year of work in, and my PI indicated that there would be opportunities to publish, and perhaps in more prestigious journals, more quickly. You can make yourself stand out in other ways, rather than through the reputation of the university alone. 
• Program culture/ diversity. While there were no red flags at Program 1, and the school itself is very diverse and has lots of programs to support women, POC, and the LGBTQ+ community, I interviewed exclusively with white men. I didn't notice it at first, but in retrospect I feel like I was invited into a very privileged circle, both in that it was a prestigious school, but also that it was a bit of an old boy's club.  At Program 2, I interviewed with as many female professors as male, many of whom held equal or more prominent positions within the department as their male peers. One of the male professors specifically addressed how invaluable it has been to have a female head of the department, and how it has shaped the culture of the program. I only applied to programs that had a pretty even split of male-female faculty, so I know that Program 1 does also have female professors, but during the interview weekend, I met them only when they were the ones coordinating the event, handing out t-shirts, organizing breakfast, etc. I'm sure this wasn't intentional, and they may simply have been the ones who volunteered for those responsibilities, but it says something. I've handled my share of gender discrimination in STEM (both subtly and extremely overtly), and it's something that I'm very conscious of. I don't want to work my way into the boy's club, i want there to  not be  a boy's club in the first place. 

TL;DR:  Go with your gut feeling, choose the program with the mentor and research fit that suits you. Prestige matters, but not that much. Make the decision that's right for you. 

Also: When I turned down Program 1, the PI was incredibly kind and understanding, which just goes to show: they want you to make the right decision for you, too! So while yes, it's stressful, and scary, trust yourself. You know what's right for you. 

Good luck! 

• olka and That Research Lady
• 3 years later...

@caffeinated-runner. what a great post! how are you feeling about your choice 3 years later? 

1 hour ago, olka said: @caffeinated-runner. what a great post! how are you feeling about your choice 3 years later? 

wow, I completely forgot that I had commented on this thread! Glad I had notifications forwarding to email, as I'm fairly inactive here altogether. 

I'm now in my fourth year at program #2. I've finished a master's thesis/ degree along the way, passed my comprehensive exams, and now am now into dissertation work (how has that much time passed?!!). The process of applying to and deciding on programs seems far, far away. While I've thought of program #1 often over the years, I don't regret my decision to attend program #2. Most of what I said previously holds true, but I do now have a little more insight, now that I've been here for a few years. Here are some extra tidbits: 

First, I think I would have been just as happy at program #1 as I am at program #2. Both were excellent opportunities, and I've often wondered how my graduate career would be different if I'd chose oppositely. That said, I have zero regrets. I love my program, my advisor, my lab, and my research. I just have a little FOMO regarding the other lab because I have a feeling I'd have loved it there, too. But I also know I'd have FOMO about my current lab if i'd not chosen it. It was a hard choice. There really wasn't a wrong decision. I had a gut feeling to go with program #1, so I did. Trust your gut. Also - and I can't emphasize this enough - don't burn bridges. You're about to build a career in your field and you may end up working with the PI from the other program later on! As an example, my lab now has a post-doc who interviewed with our PI as a graduate student (but ultimately earned their PhD elsewhere) several years ago! I, too, would happily do a post-doc at program #1. The right people will respect your decision, regardless of what you choose. 

Second, I stand by my opinion that prestige isn't that important in the longterm. My current advisor was tenure-track when I joined, and earned tenure last year. They're young. The other faculty member has been in the game for longer, and has a lot of name recognition. Prestige would have been an added bonus with this person, who also checked all the boxes for what I was looking for in a mentor.  However, rather than "prestige" per se, consider respect. Is the person you are considering working with producing science that is well-respected? Are they respected by their peers? Do they respect their peers? Do they respect their employees (research technicians, post-docs, etc.)? Do they respect current graduate students? Undergraduates? Importantly, do they respect you? In my case, the answer to all of these questions was a resounding yes for both professors, but be mindful of this. Don't trade respect for prestige. Also, as I'm writing this I realize that I now think more in terms of prestige of an individual scientist than I do of the university. In my experience, the PI matters far more than the school. tl;dr:  Definitely don't choose a program based on the prestige of the school alone. 

Third, money matters, but in my experience, PhD programs are almost always fully funded unless there's a bigger issue (like not enough department funds, or a PI not having grant funding, or a PI not getting tenure). Program #1 guaranteed funding for five years in print, while program #2 technically only guaranteed a semester at a time in writing. That seemed like a big difference at the time, but it hasn't been. My PI (program #2) verbally promised funding for five years, and was very transparent about the grant funding in the lab as well as department funding that would support me. That made me feel safe in my decision at the time. Now, I know that my funding was - for all intents and purposes - guaranteed just as much as it was in the other program. Ask about department funding, university support, grants, tenure status, etc. to get a feel for how well supported you - and your work - will be. 

Fourth, the broader community and graduate student supports matter.  Both programs I considered have a graduate union, which I didn't know much about at the time, but appreciate a lot now that I'm here. This protects our pay & working hours, plus organizes to ensure that we have healthcare benefits and the option for vision and dental insurance as well. There have been a lot of pay cuts and furloughs at the university due to COVID, but my salary has not yet been affected, likely due in part to our strong union. Consider the broader community, too. Contrary to popular belief, you're a whole human being while you're in graduate school. Will you be able to build a community or identity outside of graduate school? Is that something that will make or break your decision? 

Fifth, be ready for the unexpected. Choose a program where you feel that you'll be supported and able to handle whatever life throws at you. I never would have anticipated something like COVID-19 happening during my graduate career, but it did. Thankfully, I'm in a lab and an environment with values and expectations that align well with my own, meaning we've been able to work through this crisis empathetically and about as well as can be expected. It's been hard, of course, but I've had a whole team of quality people going through it with me. That's important to me.  It can be very hard to predict what might happen when you're in a program, but how a particular professor/ lab - or their university at large -  responds to situations like COVID-19 can also be very telling. It's something that I'll consider when I do ultimately look for post-docs, so I'd include it as you consider graduate programs. Even aside from big things like COVID-19, there will always be unexpected hurdles in graduate school. Choose a mentor who you trust to guide you through them in a way that aligns with your expectations/ needs, or at the very least is someone who you feel comfortable communicating those expectations and needs to. 

On 2/17/2017 at 7:12 AM, That Research Lady said: When graduating with a PhD in clinical psychology, does the prestige of your graduate program effect your career in the short and long term? My career goals are centered around academic research with an interest in consulting. I'm wondering if the national reputation of a program (ie. graduating from a school in the top ten vs. top 50) is a significant factor in the trajectory of your career.

Yes, it does.

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Take me where I want to go: Institutional prestige, advisor sponsorship, and academic career placement preferences

Diogo l. pinheiro.

1 Department of Sociology, Savannah State University, Savannah, GA, United States of America

Julia Melkers

2 School of Public Policy, Georgia Institute of Technology, Atlanta, GA, United States of America

Sunni Newton

3 Center for Education Integrating Science, Mathematics and Computing (CEISMC), Georgia Institute of Technology, Atlanta, GA, United States of America

• Conceptualization: DLP JM SN.
• Data curation: DLP JM.
• Formal analysis: DLP JM SN.
• Funding acquisition: JM.
• Investigation: DLP JM.
• Methodology: DLP JM SN.
• Project administration: DLP JM.
• Supervision: JM.
• Validation: DLP JM.
• Visualization: DLP JM.
• Writing – original draft: DLP JM.
• Writing – review & editing: DLP JM SN.

Associated Data

The authors confirm that, for Georgia Institute of Technology IRB approval related reasons, some access restrictions apply to the data on which this article is based. The data used in this project are based on detailed survey data that include specific career placement and degree institutional details that could allow for individual identification. IRB rules prohibit the distribution of data to individuals not included on the project protocol when identification of subjects is possible from the raw data. This is particularly an issue for underrepresented groups in our sample. Individual identity of survey respondents is confidential. Data are from the NETWISE study. Access to the limited, de-identified data may be available by contacting Melanie Clarke in the GT IRB Office at [email protected] or the corresponding author and study PI, Dr. Julia Melkers, at ude.hcetag@sreklemj . Some IRB restrictions may apply.

Placement in prestigious research institutions for STEM (science, technology, engineering, and mathematics) PhD recipients is generally considered to be optimal. Yet some doctoral recipients are not interested in intensive research careers and instead seek alternative careers, outside but also within academe (for example teaching positions in Liberal Arts Schools). Recent attention to non-academic pathways has expanded our understanding of alternative PhD careers. However, career preferences and placements are also nuanced along the academic pathway. Existing research on academic careers (mostly research-centric) has found that certain factors have a significant impact on the prestige of both the institutional placement and the salary of PhD recipients. We understand less, however, about the functioning of career preferences and related placements outside of the top academic research institutions. Our work builds on prior studies of academic career placement to explore the impact that prestige of PhD-granting institution, advisor involvement, and cultural capital have on the extent to which STEM PhDs are placed in their preferred academic institution types. What determines whether an individual with a preference for research oriented institutions works at a Research Extensive university? Or whether an individual with a preference for teaching works at a Liberal Arts college? Using survey data from a nationally representative sample of faculty in biology, biochemistry, civil engineering and mathematics at four different Carnegie Classified institution types (Research Extensive, Research Intensive, Master’s I & II, and Liberal Arts Colleges), we examine the relative weight of different individual and institutional characteristics on institutional type placement. We find that doctoral institutional prestige plays a significant role in matching individuals with their preferred institutional type, but that advisor involvement only has an impact on those with a preference for research oriented institutions. Gender effects are also observed, particularly in the role of the advisor in affecting preferred career placement.

Introduction

The traditional pathways for PhD scientists and engineers have expanded considerably, and accordingly, individual’s career preferences have become more varied. Recent attention has been paid to the preparation and support of PhD recipients in STEM (science, technology, engineering, and mathematics) for non-academic careers, given growing interests and opportunities in government and industry, coupled with evidence of a growing PhD workforce without a corresponding growth in academic jobs [ 1 – 3 ]. Yet while there has been a growing body of research addressing non-academic pathways, there has been less attention paid to the nuanced career interests and opportunities within academe. Most notably, not all PhD scientists with aspirations for an academic career are interested in intensive research careers; some STEM PhD recipients prefer teaching focused careers, and such preferences are driven by a variety of personal and professional factors [ 2 , 4 – 10 ]. In considering these varied career path options, a question of interest is as follows: what factors determine an individual’s ability to realize their career goals within the academic career path, particularly with respect to obtaining a more research or teaching centric academic position? Does preference alone determine career placement?

Studies of academic job placement would suggest otherwise. First and foremost, multiple studies have found that departmental or institutional prestige of PhD-granting institution is a better predictor of job placement post-PhD than any individual measures [ 5 , 11 – 18 ]. Complicating this job search and placement process is the support of advisors, or lack thereof, in advisees’ pursuit of their preferred career paths. Evidence suggests that the extent to which an advisor encourages and assists an advisee in the job search process for non-faculty research positions varies considerably, often on the basis of the type of career the advisee seeks [ 1 , 3 ].

The aim of our research is to examine how individual academic career placement is affected by individual preference, doctoral institution prestige, and advisor support. Further, how do these factors work when a STEM PhD recipient pursues a path other than toward the most prestigious/competitive institutions? For all the attention that factors related to departmental prestige and advisors have received, the bulk of this attention has been concentrated on a relatively small set (~150) of the top research institutions. Yet the academic workforce is actually employed in more than 4,500 post-secondary universities and colleges nationwide [ 19 ]. To effectively investigate academic career preferences, researchers must look beyond the relatively narrow band of highly prestigious and research-centric career paths in the top research institutions. Our work is based on a large, National Science Foundation funded research study of STEM faculty members in four disciplines from 487 post-secondary institutions, including a range of teaching versus research-centric institutions at varying levels of prestige, in the United States. The results of our analysis show important effects of preferences, prestige and advisor support in academic placement, including important gender effects.

Preferences and choice in the STEM academic job market

Employment outcomes in any labor market are dependent on two key factors: opportunity and choice, where opportunity refers to the jobs that are made available by employers, and choice entails how the workers select from among those opportunities[ 20 ]. There has been considerable work addressing the opportunity aspect of the academic labor market, with studies estimating the impacts of ascriptive (e.g., gender, race, nationality, institutional prestige) or achieved (e.g., publications, grants, awards) characteristics on career placement and outcomes. Previously investigated academic career outcomes include institutional prestige [ 11 , 13 ], salary [ 21 ], early career productivity[ 22 ], and rank/advancement [ 23 ].

Attention to career preferences, or choice , has been more recently motivated by a challenging and restricted PhD job market [ 24 ], coupled with expanding interest of STEM doctoral recipients in non-research careers. A growing body of work, as well as the development of policy initiatives, addressing these preferences has mostly focused on non-academic careers [ 3 , 25 – 28 ]. However, comparably less attention has been paid to career choices within academe, including interests in particular types of institutions and preferences for careers with a teaching vs. a research focus. The academic workplace is broad and marked by a significant contrast in expectations and focus of faculty work between research and teaching institutions, large and small colleges and universities, and liberal arts colleges. Evidence from myriad studies indicates that work-family balance, individual’s undergraduate experience, teaching interests, spousal-employment constraints, dual academic couple career challenges, and/or a desire to “give back” to the community shape preferences for academic positions outside of the most prestigious and research intensive institutions [ 2 , 4 – 10 ]. These various considerations driving individual’s career preferences, combined with the increasingly varied academic career options, present a potentially fruitful area of inquiry into opportunity and choice in the academic labor market.

Much of what has been learned about shifting career preferences in the contemporary academic labor market has focused on doctoral students as they refine their aspirations and enter the active job market. Observations of “branching” of interests into varied career paths is increasingly the norm, particularly in some STEM disciplines [ 25 ], and shifts in career preferences for or against academia have been observed during PhD pursuit [ 29 ]. Individual demographic characteristics have also been linked to academic career preferences, changes in these preferences, and factors driving these preferences and changes in them, over the course of graduate students’ progressions through graduate school[ 30 – 34 ]. This is important for many reasons, including the fact that women and underrepresented minorities in STEM disciplines are disproportionally represented in non-doctoral-serving institutions [ 35 ], and women are represented at higher rates at teaching-focused as compared to research-focused schools [ 36 ].

Placement, preferences and prestige

Existing research on academic prestige indeed finds that the prestige of PhD-granting institution is the main force shaping the academic labor market [ 5 , 11 , 18 , 37 , 38 ]. Long, Allison and McGinnis [ 5 ], for example, tracked over 200 male PhDs in biochemistry and reported that prestige of PhD granting institution had a significant and substantial effect on prestige of the institutions where candidates were subsequently employed. More importantly, their research also found that these effects were independent of any pre-employment productivity, and that pre-employment productivity had no significant impact on a candidate’s position within the “prestige hierarchy.” Similar results have been found for faculty hires in mathematics, chemistry, biology, physics, sociology, and several other disciplines [ 12 , 15 , 39 , 40 ]. Recent work in the field of sociology [ 13 ] showed that the accumulation of resources and opportunities coupled with prestige has market advantages, and provides evidence that institutional prestige is especially important in determining employment opportunities at more prestigious schools, net of key individual characteristics. Here, symbolic capital (i.e., the prestige of institutions within the field of academia) then plays a role in the development of prestige hierarchies.

If we consider academic prestige as a type of capital, partly symbolic and partly social [ 41 ], we might expect that individuals with a teaching preference from more prestigious departments may be more likely to work at teaching-focus institutions, and individuals with a research preference from prestigious departments may be more likely to work at research-focused institutions. If so, this may mean that the effect of prestige might be even more significant than previously thought, given that faculty with their degrees from prestigious doctoral departments but who hold less prestigious positions may have followed such a path largely due to their personal preference rather than limited employment options. Prestige may interact with career path preferences, where for example, individuals from more prestigious departments with a teaching preference may be more likely to work at Liberal Arts colleges (prestigious teaching institutions) than any other type of institution. And, individuals from prestigious departments with a research preference may be more likely to work at Research Extensive institutions (which are the most prestigious research-centric) than at any other type of institution. Given this expectation, we hypothesize:

Hypothesis 1: Individuals from more prestigious departments are more likely to work in institutional types that best match their (research or teaching) preferences.

Advisors and career trajectories

The relationship between a graduate student and his/her advisor plays a prominent role in shaping the students’ graduate school experience. In fact, some scholars argue that this relationship is the single largest determining factor in a graduate student’s overall experience and persistence in a PhD program [ 42 – 44 ]. Observing how the role of one’s PhD advisor shapes an individual’s perceptions of a certain career path, and may impact their career preferences [ 45 ]. Further, student-reported quality of relationship with advisor has been shown to be a significant predictor of reported preference for a faculty position at a research university [ 1 , 46 ].

The academic workplace has been frequently described as being shaped by “sponsored mobility” [ 15 , 47 , 48 ], where “mobility is like entry into a private club where each candidate must be ‘sponsored’ by one or more of the members”[ 48 ]. Advisors and other faculty act as these “sponsors”, opening doors, or failing to do so, for different types of opportunities. Doctoral advisor visibility [ 15 ], productivity [ 49 ], and co-authorships with their students[ 22 , 50 ] have each been found to have significant and frequently long-lasting effects on academic careers. These findings generally point to a dual process through which advisors affect scientific careers. On one hand, there is the ascriptive aspect of sponsorship, where advisor visibility raises the profile of individual candidates, thus increasing their success on the academic market. On the other hand, having a more involved and productive advisor has been shown to facilitate socialization into academic careers. For example, Fuerstman and Lavertu [ 51 ] have found that search committees consistently rank recommendation letters as one of the most important factors influencing hiring decisions, and Ladner, Bolyard, Apul and Whelton [ 52 ] stressed the importance of direct advice on application and negotiation strategies for academic engineers.

While advisor relationships may shape career preferences, our interest is focused on the tangible engagement of the advisor in the job search process itself. Our expectation here is that advisor involvement in a candidate’s job search process will lead to a better matching of candidate’s preferences and employment. Advisor involvement should result in better market outcomes, which in turn should provide individuals with more opportunities to choose a suitable institution that matches their preferences. With this in mind, we propose the following hypothesis:

Hypothesis 2: Individuals who were more actively sponsored by their advisors are more likely to have a job at an institution that matches their preferences with regards to teaching or research orientations.

Our overall model, depicted in Fig 1 , illustrates these expected relationships and factors that explain institutional placement, together with a number of disciplinary, demographic and other factors that are relevant to these outcomes.

The purpose of this research was to examine how institutional prestige, coupled with individual placement preferences and other factors relevant to the search process, matters for academic job placement type. To contextualize our analysis, we first present a descriptive summary of the initial academic career preferences and the extent of job mismatch in our data. We then present the statistical models used to test the hypotheses specified above. Details on methods and these models are at the end of this article.

Descriptive analysis: Academic career placement preferences

When asked about their top choice for their first post-PhD career placement, over half of respondents (52%) indicated a research intensive environment, with fewer (34%) preferring a teaching intensive position. Men were more heavily represented in the group of respondents who preferred a research intensive environment (62% male and 38% female), while respondents who preferred a teaching intensive environment were almost evenly split by gender (51% men and 49% for women). As shown in Fig 2 , job search strategies generally reflected these preferences, with about 75% of respondents in either preference group primarily targeting institutions with that emphasis. However, there were differences in the number of applications submitted, with those with a primary research interest submitting statistically significantly more job applications (25.8 versus 21.5) than did those with a teaching preference. Further, these individuals may be casting a broader net in the job search process, while those with a teaching preference may be focusing more tightly on institutions consistent with their interests. Our results show that individuals with a research preference applied at a slightly higher rate to teaching intensive institutions, while those with a teaching preferences did not show the same pattern in applying to research-intensive schools.

Mismatch and academic career placement preferences

To what extent do faculty experience a career placement mismatch, landing in an academic position that is not in line with their teaching or research preferences? To address this, we examine anyone who had a teaching preference and is currently employed at a research institution and vice versa. As shown in Fig 3 , while only about one-third of respondents appear to have a mismatch in career placement, there are interesting differences in terms of the type of mismatch . The descriptive results show that men are substantially more likely to report being mismatched by having a research preference and working at a teaching oriented institution, while women report similar rates of mismatch across preferences.

What factors explain this mismatch? As shown in Table 1 , only a few variables seem to consistently predict mismatches. Respondents with doctorates from prestigious institutions are about one-third (0.751) less likely to be mismatched, and dissertation award winners are more than 50% less likely to be mismatched (0.439). Both are expected and suggest the influence of recognition in the career placement process.

*** p<0.01

** p<0.05

* p<0.1

Faculty with a teaching preference, however, are also more likely to be mismatched than those with a research preference, even when coming from prestigious institutions. Further, disciplinary effects are also observed–male biochemistry faculty are less likely to be mismatched, although results for women in biochemistry are not significant. Notably, there were no differences in terms of gender or level of advisor sponsorship in relation to likelihood of being mismatched.

Overall, prestige matters. At low levels of doctoral prestige ( Fig 4 ), at least some individuals are more likely to be mismatched in their academic career placement regardless of their preference. For faculty with doctorates from more prestigious institutions, individuals with research preferences are unlikely to be mismatched, but individuals with teaching preferences remain statistically more likely to be mismatched. This seems to be consistent with previous findings [ 6 ] that professors from prestigious backgrounds who have a teaching preference often experienced higher levels of dissatisfaction in research oriented jobs.

The role of prestige and advisors in career placement

Turning to our primary explanatory models, we examine the likelihood of placement in either a research or teaching oriented institution, and then specifically at placement in specific Carnegie Classified institutional types. We first conduct our analysis on our full sample, and then partition the data to run the models separately for men and women. We present marginal effects for ease of interpretation here: each presented coefficient represents the percent change in likelihood of a particular outcome given a unit change in the independent variable . Since these are marginal effects, the results of interaction terms are already shown in terms of their joint impact and significance (see supplemental materials for tables with relative risk ratios and separate terms for each of the variables that affect the interaction, plus interaction terms). For the interaction between doctoral prestige and preferences and the interaction between advisor sponsorship and preferences, the marginal effects are presented for respondents based on whether their initial academic career placement was research or teaching focused.

Table 2 presents the average marginal effects of the multinomial logit model applied to the full sample of faculty in the four disciplines included in our study. Our results show that teaching preference affects career placement in some cases. Consistent with the job search strategies noted earlier, it is not surprising that individuals with a teaching preference are 45% less likely to hold a position in a Research Extensive institution ( b = -0.0456). Within teaching institutions, those with a teaching preference are almost twice as likely to be placed in a Master’s institution rather than a liberal arts college (30% versus 15%) ( b = 0.312 and b = 0.157 respectively). Note that the liberal arts colleges included in our sample are among the most competitive in the nation (Oberlin 50).

* p<0.05

** p<0.01

*** p<0.001

Results also show that the demographic patterns in career placement are generally reflective of national statistics, and are consistent with findings from prior studies. Female faculty in our sample are about 3% ( b = 0.031) more likely to be employed at Liberal Arts institutions than any other institutional types [ 35 ], and 6% ( b = -0.060) less likely to be employed at Master’s Institutions. Notably, there are no significant statistical gender differences with regards to likelihood of employment at research institutions. Regarding race and ethnicity, there are no significant differences regarding the likelihood of employment at different institutional types, with the exception of Hispanics being slightly less likely to be at Research Intensive institutions. Having children of dependent age at time of PhD is similarly associated with no significant differences in employment patterns.

Year of PhD, on the other hand, has a significant impact on the type of institution in which tenure track faculty are employed. An increase of one year in terms of PhD graduation date leads to an increase of 0.5% in terms of probability of working at a Master’s institution, and a 0.5% decrease in the probability of working at a Research Extensive institution. This indicates that younger faculty are substantially more likely to work at Master’s rather than Research Extensive institutions. With regards to family background, being a first generation college graduate significantly impacts where an individual will work. First generation college graduates are less likely to work at Liberal Arts colleges than at the other institutional types noted, regardless of preference.

In the job search process, individual accomplishments show visible importance. Results highlight the importance of the visibility of research done as a graduate student with regard to institutional placement: having won a best dissertation award increases the probability of working at a Research Extensive institution by nearly 16%. This underscores other findings about how an early and successful start in research activity is important to career research productivity [ 22 ].

Regarding our key variables of interest, results show that institutional prestige and advisor sponsorship reveal some interesting relationships. To understand how preference interacts with the range of doctoral advisor involvement in the job search process, as well as with institutional prestige, these relationships were captured through four interactive variables. When preference (research vs teaching) is interacted with institutional prestige and advisor sponsorship, results are significant. Each additional standard deviation in terms of advisor support increases the probability of working at a Research Extensive institution by almost 4% for those with a research preference (significant at the 0.05 level). Among those with a research preference, individuals who received a maximum score for advisor support are nearly 14% more likely to work at Research Extensive institutions than those who received the average advisor support. In other words, having an advisor who more broadly “sponsors” an individual makes it more likely that that individual will work at a Research Extensive institution if he/she preferred a research-focused institution.

Importantly, when considering the interaction of preference and advisor support, the marginal effects are not significant for those with teaching preferences. This indicates that advisor involvement is particularly important for advisee placement at Research Extensive institutions, but individuals who prefer a teaching oriented institution see no change in the odds of landing at their preferred institutional type based on advisor sponsorship. This result, while somewhat surprising, has a possible explanation. Since advisors are, almost by definition, faculty at research oriented institutions, they may simply lack the connections or resources to help their students with teaching preferences land at teaching institutions. This lack of connections at teaching oriented institutions was conveyed to the authors of this project in a number of informal interviews with faculty at teaching oriented institutions in preparation for this project, and is supported by our research findings.

If advisor sponsorship is only crucial for those who seek employment at Research Extensive institutions, doctoral institutional prestige appears to behave much more as a form of capital. The results suggest that individuals from prestigious universities seem to be able to “use” that prestige to gain positions at the most prestigious of the research-oriented institutions, as illustrated by the significant interaction between research preference and institutional prestige. For those with a preference for research oriented institutions, an increase of one standard deviation in institutional prestige increases the probability of gaining a position at a Research Extensive institution by a little over 7%, while decreasing the probability of working at a Research Intensive or Master’s institution by approximately 3 and 4%, respectively. Given the high variation in our institutional prestige measure, this means that someone from a top ranked institution is about 20% more likely to land at a Research Extensive institution when compared to the average. Meanwhile, for those with a teaching preference, an increase in one standard deviation in PhD institutional prestige leads to an increase of about 3% in the probability of landing at a Liberal Arts college.

Gender and academic career placement

These results point to certain factors that affect an individual faculty member’s ability to gain a position consistent with their career preferences. A point for further consideration is the extent to which this varies across individuals. Given gender disparities in academic science [ 29 , 53 , 54 ], and to address potential endogeneity of preferences and gender effects, we partitioned the data and ran these models separately for male and female faculty. Results for each subsample are provided in Tables ​ Tables3 3 and ​ and4 4 .

When the models are run separately for male and female faculty, results reveal that advisor sponsorship seems to function differently by gender. For faculty who prefer a research intensive environment, advisor sponsorship has a statistically significant and positive (4%) effect in placement in a Research Extensive institution for men, but has no effect for women . For faculty with a teaching preference, however, results are considerably different, particularly by gender. For female faculty who reported a teaching preference, advisor sponsorship appropriately reduces the probability of landing at a Research Extensive university by about 5% for each additional standard deviation, and increases the probability of landing at a Master’s institution (lower prestige teaching institution) by slightly more than 5%. Yet, advisor sponsorship has no effect for male faculty with a teaching preference. However, it has no effect on placement in the highly prestigious (teaching oriented) liberal arts colleges for either men or women. These results reveal a gendered component to advisor sponsorship, where it has a positive effect for men with research preferences, but a somewhat mixed effect for women with teaching preferences.

There is also a difference between male and female faculty in how the prestige of their doctoral institution aligns with preferences to impact placement. While doctoral prestige functions similarly for both men and women with a research preference in increasing the likelihood of their placement in a Research Extensive institution (and decreasing the likelihood of their placement in a Master’s institution), for women it has a bigger impact in terms of affecting the probability of landing a job at a liberal arts college, regardless of preference. Results show that even those female faculty who reported a preference for research oriented institutions are more likely to be employed at a liberal arts college if they come from a prestigious doctoral program. Not surprisingly, the effect is slightly stronger for those with a teaching preference.

Notably, and consistent with the full model, results show no gender effects in how individual preferences for a teaching intensive environment affect institutional type placement. Individual accomplishments, in the form of a dissertation award, also function similarly for men and women, in impacting the likelihood of placement in a Research Extensive institution. Taken together, these results collectively indicate a world where doctoral prestige and advisor involvement work through mechanisms that are gendered, supporting placement for female faculty in teaching oriented institutions (but not high prestige liberal arts institutions) and male faculty in research oriented ones.

Advisor involvement and endogeneity

One possibility that might explain our results with regard to the impact of advisor involvement is the possibility of endogeneity in terms of advisor support. t is, advisor support may be conditioned on career preferences. While we cannot definitely rule that possibility out, evidence suggests that this is not the case. A t-test reveals no statistically significant difference in terms of advisor sponsorship across preferences. Likewise, if we estimate a regression analysis using all the variables from the full model with advisor sponsorship as a dependent variable, the results show an insignificant coefficient for teaching preference. There are, however, gender differences, and women report receiving less advisor sponsorship than men, which is consistent with other research that has found advisors being less willing to collaborate with female advisees, for example [ 22 ].

While there may not have been differences in terms of advisor sponsorship across preferences, we do have evidence of differences in terms of advice provided to respondents, as shown in Table 5 .

Advisors are more likely to suggest research intensive positions, and rarely advise seeking teaching intensive positions, to those with a stated preference for research oriented jobs. For those interested in teaching positions, however, the advice is evenly split. That is, for those with a stated teaching preference, the advisor is just as likely to recommend a teaching oriented position as they are to recommend a research oriented position. The result here is that while there may not be significant differences in terms of reported things (letters, calls, etc.) provided by an advisor during their job search, there are differences in terms of what types of positions advisors recommend. Interestingly enough, and unlike our full models, there are very few differences in terms of gender. The only one above to be significant in a t-test is related to advisors suggesting more competitive positions than the respondent was interested in. Women with a teaching preference are more likely to report that their advisor advised them to seek a more competitive position than men with a teaching preference.

As a result, gender differences in terms of actual employment outcomes and preferences seem to be based not on reported levels of advisor sponsorship or advisor reactions to those preferences, but on factors beyond the scope of our study. For example, there may be qualitative differences in terms of advisor sponsorship (e.g., advisors may be providing less glowing letters of recommendation for women seeking research positions than for men with similar preferences) or to external perception of that sponsorship (e.g., search committees may be less trusting of letters of recommendation for women seeking research positions or men seeking teaching positions). We cannot discern between these possible causes, though recent research [ 55 ] provides some evidence for the latter hypothesis.

The main contribution of our work is that it highlights factors that may result in a career mismatch in the academic marketplace, some of which vary by gender. As we have noted, not all PhD scientists are interested in intensive academic research careers. A range of personal and professional factors shape academic career preferences. Our results provide meaningful progress towards addressing our research questions: What factors determine an individual’s ability to realize their academic career goals, particularly in the ability to select a more research or teaching centric academic position? Does preference alone determine career placement?

Understanding the factors that matter in achieving one’s preferred career placement is relevant to investigating inequities in the workforce and placement process, but also extends to other career-related issues. The general occurrence of a mismatch between one’s job and a variety of individual characteristics has been explored extensively. Such mismatches can occur between a job and an employee’s education level, educational background, preferred work schedule, skills, and/or interests. The fit of an individual to their position relates to several positive work-related outcomes, including self-efficacy, job satisfaction, and attraction to the organization and intentions to accept a job offer within the context of the job application process [ 56 – 58 ]. Specifically in the academic workplace, a host of negative consequences has been associated with such mismatches, including reduced income, increased turnover, and reduced job satisfaction [ 59 , 60 ].

The meaningful role that doctoral advisors play in the academic job search of a newly minted PhD is expected. Our results show that doctoral institutional prestige and advisor involvement in the job search process are substantially important in academic careers, but they are important in very different ways. Advisors play a key role for those wanting to pursue careers at research oriented universities. This seems to be in part because of a strong relationship between advisor involvement and preference (i.e., there seems to be a degree of endogeneity between the type of support received from advisors and preference). But even taking this into account, advisor support is still important when we look only at those with a research preference. This provides evidence that while advisors play an important part as “sponsors” within academia, their contribution is restricted to the institutional type that they themselves are familiar with. This is also consistent with studies of PhD recipients with interest in non-academic careers, which also note a lack of advisor support [ 1 , 25 ]. Advisor influence seems to disappear when we look at those individuals who would prefer a teaching oriented career, even for those who graduate from the most prestigious teaching institutions.

If advisor influence is limited to those who want to pursue a research oriented career, doctoral prestige behaves much more like a form of capital [ 41 ]. When Bourdieu discussed the forms of capital, he used the capital analogy as something that could be deployed to achieve a certain social status. Within academia, prestige seems to work that way: its effect is contingent on how the individual “employs” it. Those who prefer a teaching oriented career are able to transform that prestige into a greater likelihood of teaching at Liberal Arts colleges, where our data have shown that individuals are more likely to report satisfaction with their teaching obligations (see Materials and Methods section). And for those with research aspirations, prestige opens doors at the more resource rich Research Extensive institutions. Besides differentiating between the impact of advisors and institutions, this finding is remarkable because it indicates that previous research has actually underestimated the importance of prestige in the academic labor market. As noted previously, most research on academic prestige has found that within Research Extensive institutions there is a caste [ 12 ] that tends to come from the same elite institutions. We have shown that those from highly prestigious institutions who have a teaching preference are more likely to work at Liberal Arts colleges. Alumni from elite departments that land at places other than top ranked research institutions may do so by following their preferences. If not for those preferences, we could imagine a much more significant dominance of these elite departments within research oriented universities and colleges.

A few caveats are in order. As is the case in most existing research on academic careers, our study suffers from survival bias. While our sample goes beyond many previous studies by including different institutional types, it is still limited by the fact that we only have data on tenured and tenure track faculty. Research on individuals who move into non-tenure track academic positions (e.g. research scientists), whether by choice or necessity, is sorely needed in order to understand the full breadth of how preferences and various support mechanisms play a role in the academic marketplace. It may very well be the case that the impacts of prestige and advisor sponsorship are even greater than estimated here if we consider those who involuntarily leave academia. Our results are hampered by our lack of data on those who exit academia all together, or do not choose it in the first place. Given this, our results are specific to those who persist in the academic workforce.

Related, we also lack data on the institutional culture and support mechanisms for career choices in our respondent doctoral institutions. While advisor support may be important, other factors such as support for non-research careers from other faculty or career/teaching related resources on campus may impact career preferences and direction. Given this, we are not able to examine the cultural and other factors that matter in early career placement.

Another important caveat is that we only have data from one point in time, and the information we have on preferences is based on individual recollection. To the extent that it is possible, we have tried to address that by confirming our results with different samples. While we presented results from a sample with a full range of career stages, our results are substantively consistent if we focus only on junior faculty or on those who are on their first tenure track appointment. Still, there is always the possibility that people may remember their initial career preferences inaccurately. Thus, some caution should be observed in our interpretation of results. More comprehensive qualitative studies might be able to shed some light on these issues.

Materials and methods

Statistical methods.

To contextualize our research question and subsequent analysis, we first provide a descriptive analysis of the initial career preferences and current placement of our survey respondents. Using frequencies and a comparison of means we examine the extent to which a mismatch of preferences and placement exists. We also use a descriptive model which allows us to control for various demographic and other background factors in explaining mismatch.

Next, to test the hypotheses noted above, we use a multinomial logit model[ 61 ] to address how career preferences, institutional prestige and advisor support explain career placement. Multinomial logit models are among the most popular methods for analyzing issues where discrete choices are at play. By using this method, we can estimate how different variables affect the probability of a given outcome for each observation. This approach is ideal for our purposes, given that our outcomes exist in the form of a nominal variable with four mutually exclusive possibilities (i.e., employment at a Research Extensive, Research Intensive, Liberal Arts or Master’s institution). Our models are weighted by sampling probability, as discussed below. To deal with the issue of research versus teaching career placement preference, we ran three different models. One model included the full sample, and had teaching preference as an independent variable, which was also interacted with the key independent variables. The other two were restricted by gender, to understand the ways in which different factors affect men and women. We are interested in gender differences given overall disparities in STEM careers, including that women faculty are employed in Research Intensive Institutions at a slightly lower rate than are men [ 35 ].

Data and variables

Our data come from a large National Science Foundation-funded project (NETWISE II). The primary data collection for this project involved the implementation of an extensive survey of STEM faculty in the United States. A significant concern of the project was to address academic career distinctions by gender and race/ethnicity. Given this, four STEM fields were selected for inclusion: biology, biochemistry (high female representation), civil engineering (transitioning female representation), and mathematics (lower levels of female representation). Another purpose of the project was to understand career variations across the broader academic STEM workforce. Therefore, the population included all tenured/tenure-track faculty in our selected disciplines from not only the research-centric institutions (Carnegie Classified Research Extensive and Research Intensive institutions), but also teaching-centric (Historically Black Colleges and Universities (HBCUs), a cluster sample of Master’s I/II and Hispanic Serving Institutions (HSIs), Women’s Colleges, and the Oberlin 50 baccalaureate) institutions offering degrees in the four target disciplines. The institutional types included here account for nearly 28% of all institutions of higher learning in the United States, and nearly 75% of all 4 year institutions. The survey and protocol were approved by the Institutional Review Board as part of Human Subjects protection. Once the clustered institutional samples were selected, we conducted a sampling procedure in order to stratify across institutional type, faculty rank and discipline, and oversample for gender, resulting in a final sample of 9,925 (38% of the original sampling frame).

The survey addressed a broad set of items relevant to the study of academic careers in STEM. Sections included individual background, job search experiences, early career preferences, relationship with advisor and mentors, positions held and other advancements, research, teaching and professional activities, and other professional experiences. The survey was implemented online and had a total unweighted response rate of 42%, with 4,195 completed or partially completed surveys submitted. Because we are interested in career placement issues and career preferences, we are focusing on individuals who reported having an initial preference for academic careers (as opposed to industry or government) with either a research oriented or teaching oriented focus, for a total of 2,670 respondents used in our analysis. To account for the weighting procedure described above with regards to the sample, all our models use appropriate sample weights. Table 6 presents the number of institutions with at least one respondent included in our sample, by institutional type.

NOTE: Frequencies do not reflect changes made after 01/30/2001.

Source: Carnegie, 2004

Dependent variable

Institutional type.

The dependent variable in our models is the institutional type at which the respondent is currently employed. Using the 2000 Carnegie Classification system, our sample includes Carnegie Classified Research Extensive, Research Intensive, Master’s I & II and Liberal Arts colleges [ 19 ]. We code Research Extensive and Research Intensive to be “research oriented” institutions, while Master’s and Liberal Arts institutions are coded as “teaching oriented”. (Note that the Liberal Arts schools include not only the Oberlin 50 but also HBCU and Women’s Colleges from our sample). The rationale here is that the target disciplines in the Research Extensive and Research Intensive institutions are typically doctoral degree granting and research focused, whereas the Master’s I & II and Liberal Arts institutions are more teaching focused. To illustrate these assumptions, Table 7 includes basic descriptive statistics from the survey data regarding teaching and research involvement at each institutional type. Respondents in Research Extensive institutions report spending more time on research and less time on teaching as compared to their peers in Research Intensive institutions; furthermore, faculty from both of these research-centric institutions provide reports that are notably different from those of the Master’s I & II and Liberal Arts faculty. These descriptive data demonstrate varied expectation and also satisfaction with research and teaching loads across this set of schools.

Independent variables

Doctoral institutional prestige.

Existing research has demonstrated that the key determinant of prestige rankings within academia is the centrality of the institution within hiring networks (e.g.,[ 12 , 40 ]). High prestige institutions are institutions that hire and place their graduate students at other similarly central institutions. We measure doctoral prestige by estimating a department's eigenvector centrality in hiring networks. To do this, we created a network that linked PhD institutions with hiring institutions, and estimated eigenvector centrality using social network analysis software. PhD institution was reported by respondents in the survey, and current institution was identified in our population development via internet search, and verified by survey respondents. Centrality is measured as the reciprocal of the average shortest distance between one institutional node and all others, where the smaller the average shortest distance, the higher the eigenvector centrality. We use eigenvector centrality because this measure is highly correlated with existing survey-based prestige measures [ 40 ]. We use undirected networks to take into account that a substantial number of institutions involved in our sample do not have graduate programs, as it allows us to assign a greater degree of centrality if they frequently hire from other high prestige places.

We use this approach in order to solve the challenges presented by our institutionally and disciplinarily diverse data set. Survey based measures of departmental prestige (i.e., measures where prestige values are determined through a survey of academics in the field), such as the 1995 NRC rankings, the current US News and World report rankings or the QS World University Rankings will frequently not cover all institutions, nor all fields. Using these measures would not allow us to compare across the various disciplines in our study, as well as many of our sampled institutions, thereby severely impacting our usable sample.

Our choice of institutional centrality as a measure of prestige does not appear to produce any different results than alternative measures would provide. Our measure of centrality is highly correlated with existing field and institutional-type specific rankings, such as the National Research Council’s (NRC) 1993 survey of doctoral program prestige, published in 1995 [ 62 ]. To check for any potential bias from our selected approach, we analyzed the NRC and other rankings against our eigenvector centrality measure, and found them to be highly correlated ( Table 8 ). Further, all our results are consistent in terms of size and significance of effects, regardless of whether we use eigenvector centrality measures or NRC measures.

As the correlation results show, our measure of institutional centrality correlates with existing prestige measures between 0.65 and 0.735. Detailed results on this analysis may be found in the S1 – S3 Appendices . These include the average marginal effects for the full models using each of the different prestige measures mentioned above (1995 NRC rankings, current US News and World Report rankings and QS World University Rankings).

As an example, our measure finds that the most central (and as such most prestigious) places in our sample are UC-Berkeley for research institutions, California Polytechnic State University-San Luis Obispo for Master’s institutions, and Bucknell, Denison and Swarthmore (tied) for baccalaureate colleges, which are fairly consistent with published rankings.

Teaching (career placement) preference

To account for individual career placement preferences, we include a teaching preference dummy variable. This variable is based on the survey question: “As you were finishing your PhD, what was your preferred career choice?” The choices were mutually exclusive, and included “tenure track position at a research intensive institution” and “tenure track position at a teaching intensive institution,” "position in industry," "position in government," or "non-tenure track academic position." The teaching preference dummy is based on the latter. Given that the question asks specifically about preferences when the respondent first went on the market, it is entirely possible that some respondents have changed their preferences since that time, or that there is some other form of recall bias, introducing some bias or noise in our models given that the dependent variable is about current employment. Nonetheless, we are confident that our results are robust: our results remain consistent even if we reduce our sample to only pre-tenure faculty, or to only faculty who are still in their first tenure track appointment.

Advisor sponsorship

Regarding advisor sponsorship, we focus on active and tangible ways that advisors supported respondents in their job search. While co-authorship with early career researchers has been demonstrated to be important in later career productivity[ 22 ], other activities demonstrate active engagement in the job search process. We focus on the relational characteristics of the advisor-advisee relationship, which capture the extent to which an advisor is invested in a particular candidate. Our variable is based on respondents’ indication that their advisors did the following for them in their initial job search:

• Wrote recommendation letters
• Made phone calls on their behalf
• Defended career choice with others
• Gave advice on how to negotiate

We transformed these four variables into a single measure that captures the extent to which these resources were or were not provided. To do this, we conducted a principal component factor analysis of the 4 binary items, and created a single standardized measure of advisor sponsorship (Eigenvalue = 1.45). We used polychoric correlations (PCA)[ 63 ], as is appropriate for combining discrete and binary variables into a measure that captures a concept as a single measure. Unlike a simple summative variables, PCA allows variables to be weighted differently, acknowledging the variation in importance of these different factors. The principal component estimated here explains 0.53, or about 53%, of the variation in the 4 items. Details on the scoring of the principal factor are available in S1 – S3 Appendices.

To guard against the possibility of recall bias regarding advisor involvement given the variation in time lapsed since our respondents would have had this interaction, we took two different strategies. First, we compared our measure of advisor involvement to a question regarding co-authorship with one's advisor (less likely to be forgotten as an event). Correlations between our measure of advisor involvement and a variable regarding advisor co-authorship are not statistically significantly different for respondents with pre-1995 PhDs and post-1995 PhDs. Additionally, our results with regards to advisor involvement are substantially similar if we restrict our sample to assistant and associate professors, with the only difference being that for male faculty with a research preference the coefficient becomes insignificant, though still with the same sign. For female faculty, the results are substantially the same.

Control variables

Our models include a number of additional control variables. We include basic demographic information such as race, ethnicity, gender and whether the respondent had a child of dependent age at the time of their PhD (though we have no information on custody). Additionally, we include discipline-specific dummy variables to control for any field variations. We also control for year of PhD, in order to control for seniority and stage of one’s career. To account for individual strengths which may matter in the job market, we also include a dummy variable that indicates whether the respondent has received a dissertation award. This item is included as a way of controlling for respondent research visibility as a graduate student, and separates ascriptive measures such as departmental prestige and advisor involvement from personal qualifications. Finally, we also introduce a dummy variable meant to capture cultural capital, or the “cultural competence” that individuals accumulate as a result of their affiliation or experience [ 41 ]. Cultural capital has been found to have some impact on advisor relationships; for example, Pinheiro, Melkers and Youtie [ 22 ], found that individuals with faculty parents are more likely to collaborate with their advisors on publications. Thus we included a measure of whether the respondent is a first generation college graduate. The variables included in our models are summarized in Table 9 . One potentially important factor that we are not able to take into consideration is marital status at the time that the respondent was on the job market. While we do know marital status at the time of the survey, it is not possible to address this important possible constraint on job seeking behavior. While not shown here, earlier in our analysis we did include the binary variable “never married” (8% of our sample), which was not significant across all our models.

Supporting information

S1 appendix, s2 appendix, s3 appendix, acknowledgments.

This research was supported by the National Science Foundation (NSF Grant # DRL-0910191). We thank the scientists who took the time to provide data for this study, and the anonymous reviewers who provided constructive and useful feedback.

Funding Statement

This work comes from the U.S. National Science Foundation supported project: NETWISE II: Empirical Research: Breaking through the Reputational Ceiling: Professional Networks as a Determinant of Advancement, Mobility, and Career Outcomes for Women and Minorities in STEM, U.S. National Science Foundation Grant # DRL-0910191 to JM ( http://www.nsf.gov/div/index.jsp?div=DRL ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability