RESEARCH ARTICLE

Analyzing academic mobility of U.S. professors
based on ORCID data and the
Carnegie Classification

Erija Yan1

, Yongjun Zhu2

, and Jiangen He3

1College of Computing and Informatics, Drexel University, 费城, PA, 美国.
2Department of Library and Information Science, Sungkyunkwan University, Seoul, 韩国
3School of Information Sciences, 田纳西大学, Knoxville, 美国.

关键词: academic mobility, Carnegie Classification, 性别, ORCID

抽象的
This paper uses two open science data sources—ORCID and the Carnegie Classification of
Institutions of Higher Education (CCIHE)—to identify tenure-track and tenured professors in the
United States who have changed academic affiliations. Through a series of data cleaning and
processing actions, 5,938 professors met the selection criteria of professorship and mobility. 使用
ORCID professor profiles and the Carnegie Classification, this paper reveals patterns of academic
mobility in the United States from the aspects of institution types, locations, 地区, 资金
mechanisms of institutions, and professors’ genders. We find that professors tended to move to
institutions with higher research intensity, such as those with an R1 or R2 designation in the Carnegie
Classification. They also tend to move from rural institutions to urban institutions. 此外,
this paper finds that female professors are more likely to move within the same geographic region
than male professors and that when they move from a less research-intensive institution to a more
research-intensive one, female professors are less likely to retain their rank or attain promotion.

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介绍

Academic moves are a vital component of academic life. The collective patterns of academic
mobility are central to scholarly communication research. Studies of such patterns have impor-
tant implications for developing a better understanding of the higher education landscape and
directly inform assessments of the scientific workforce and the scientific enterprise.

There is already a vast collection of literature that analyzes academic mobility. Current knowl-
edge is ascertained primarily from two levels of mobility: the country level and the affiliation level.
A common approach is first analyzing bibliographic records, then identifying author affiliations,
最后, aggregating affiliations to countries. By following this approach, the current literature
has revealed important facets of academic mobility, including its patterns, its determinants, 和它的
impact on research productivity and career paths. Research has shown that mobile researchers tend
to be more productive and that researchers move to institutions with better opportunities to under-
take their research (Sugimoto, Robinson-Garcia, 等人。, 2017). 因此, 国际
mobility of researchers has exacerbated the “brain drain” effect in which well-trained researchers
are more likely to cluster in places with already abundant human capital (Docquier, Marfouk,
等人。, 2012; Docquier & Rapoport, 2012).

Despite these advances, one major gap remains: The country-level studies have only provided
broad-stroke depictions of academic mobility, and the high-level findings are usually insufficient

开放访问

杂志

引文: 严, E., 朱, Y。, & 他, J. (2020).
Analyzing academic mobility of U.S.
professors based on ORCID data
and the Carnegie Classification.
Quantitative Science Studies, 1(4),
1451–1467. https://doi.org/10.1162
/qss_a_00088

DOI:
https://doi.org/10.1162/qss_a_00088

支持信息:
https://doi.org/10.1162/qss_a_00088

已收到: 31 行进 2020
公认: 16 七月 2020

通讯作者:
Erija Yan
ey86@drexel.edu

处理编辑器:
Ludo Waltman

版权: © 2020 Erija Yan, Yongjun
朱, and Jiangen He. Published under
a Creative Commons Attribution 4.0
国际的 (抄送 4.0) 执照.

麻省理工学院出版社

Analyzing academic mobility of U.S. professors

to understand the impact of academic moves upon individual scientists. 此外, for affiliation-
level studies, there is minimal treatment of authors, and thus we are unable to differentiate between
types of authors such as students, 技术人员, and professors of different ranks. Because authors
are a broad category of personnel in the scientific workforce, they cannot be clearly mapped into
meaningful organizational structures in higher education. The goal of this paper is to accurately
identify U.S. tenure-track and tenured professors via a comprehensive open science data reposi-
保守党 (ORCID) and then link these individuals to institution-level profiles from the Carnegie
Classification of Institutions of Higher Education (CCIHE). The integrated data will allow for clear
and robust examination of academic mobility, institutional stratification, and the role of organi-
zational factors in shaping academic mobility.

Prior literature showed that professors tend to move to institutions with more abundant research
resources and greater human capital, such as their peers’ reputations (Docquier et al., 2012). 这
level of research-related resources can be measured by the research intensity designation of the
Carnegie Classification (Indiana University Center for Postsecondary Research, 2018). 所以,
we hypothesize that professors are more likely to move to more research-intensive institutions (这样的
as those with an R1 designation in the Carnegie Classification). 此外, due to the two-body
问题, finding jobs for a professor’s partner can be quite challenging in rural locations or small
城市. According to a report by the Clayman Institute at Stanford University, 多于 70% 的
professors are in dual-career relationships and about half of them are partnered with another aca-
demic (Schiebinger, Henderson, & Gilmartin, 2008). 所以, it is reasonable to hypothesize that
professors are more likely to move to institutions located in larger metropolitan areas so that their
partners can have better access to job opportunities, and that together, they can have a better quality
生命的. Based on these prior observations, we make the following hypotheses:

(西德:129) H1: 我们. professors tend to move from institutions with lower research intensity to those

with higher research intensity.

(西德:129) H2: 我们. professors tend to move from institutions located in towns or rural regions to

those located in cities.

Gender is an important mediator of academic moves. Prior research found that female re-
searchers tend to move less than male researchers as they advance towards their later career stages
(Hopcroft, 汗, 等人。, 2004; McLean, Morahan, 等人。, 2013). It is also shown that decisions to
move manifest through researchers’ gendered social networks—female researchers are less likely
to move than men when either have adolescents at home (Azoulay, 甘古利, & Zivin, 2017). 作为一个
结果, female researchers may be less likely to make long-distance moves that interrupt their
family life. Based on the prior work, we make the following hypothesis:

(西德:129) H3: female professors in the United States are more likely to move within the same geo-

graphic region than male professors.

By verifying these hypotheses, this paper aims to reveal patterns of academic mobility using a
large sample of U.S. tenure-track and tenured professors. The results illustrate key aspects of
academic mobility along the dimensions of institutional profiles (Carnegie Classifications and
institution locations and regions) as well as professor profiles (gender and rank). 所以, 这
project will be of value to scholarly communication research and contribute to our understanding
of academic mobility within the U.S. science enterprise.

2. LITERATURE REVIEW

2.1. Patterns and Characteristics of Mobility

Quantitative studies have described the characteristics of national inflows and outflows at a
global level (Ioannidis, 2004; Sugimoto et al., 2017; Van Der Wende, 2015). In a study of

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Analyzing academic mobility of U.S. professors

1,523 highly cited scientists, Ioannidis (2004) found that 31.9% did not reside in their country of
birth, but great variability has been shown across developed countries and across different disci-
plines in the proportions of foreign-born scientists. A more recent study of 16 million scientists iden-
tified about 4% as deemed mobile according to the countries of their affiliations in publications
(Sugimoto et al., 2017). The international circulation of scientists is making human capital scarcer
where it is already scarce and more abundant where it is already abundant, thereby contributing to
increasing inequality across countries (Docquier et al., 2012; Docquier & Rapoport, 2012). 这
United States is the top destination country for mobile scientists and is still perceived as a strong
destination for advancing one’s research career (Bland & Van Noorden, 2012; Franzoni, Scellato, &
Stephan, 2012; 甘古利, 2015; Scellato, Franzoni, & Stephan, 2015; Veugelers, Van Bouwel, &
Geuna, 2015). A major reason that individuals came to the United States for educational training
is the prestige of its programs and career prospects (Stephan, Franzoni, & Scellato, 2016). 然而,
foreign-born American scientists are likely to return home when their country develops a strong
infrastructure to support research in their disciplines (Zucker & Darby, 2014).

Elite scientists migrate systematically towards nations with large research expenditure (猎人,
Oswald, 等人。, 2009; Kato & Ando, 2017). Mobility occurs more among potential elite scientists
than among established elite researchers (Laudel, 2005). Elite scientists also tend to move from
places with few peers in their discipline to places with many, which leads to a concentration of
star scientists over time (Zucker & Darby, 2014). Mobility between universities with different levels
of prestige has also been investigated (Allison & 长的, 1987; Debackere & Rappa, 1995). 对于在-
姿态, 随着时间的推移, scientists in the field of neural networks tend to move from more prestigious
universities to less prestigious universities (Debackere & Rappa, 1995). 然而, physicists from
elite institutions are more likely to move to other elite institutions (Deville, 王, 等人。, 2014).
Scientists are more likely to move when their productivity (Azoulay et al., 2017) and scientific
impact (Sugimoto et al., 2017) are high. Emigrants were much more likely to have a foreign
coauthor and to have published in an international journal (甘古利, 2015).

2.2. Determinants of Mobility

Mobility is driven by a variety of reasons that can be academic, job-related, or family-related and
个人的 (Auriol, 2010). Factors related to availability and quality of career opportunities and the
ease of re-entry into the home labor market are critical for return decisions (Ackers & Gill, 2008;
Casey, Mahroum, 等人。, 2001). Personal and professional linkage to the home country can con-
tribute to the probability of return (Baruffaldi & Landoni, 2012): This can include collaborations
with home-country scientific journals, mentoring, visiting, business relationships, 等等. A
reasonable salary level should be guaranteed, but the return decisions of researchers and scien-
tists are primarily shaped by factors such as the quality of the research environment, 专业的
reward structures, and access to state-of-the-art equipment (Thorn & Holm-Nielsen, 2006).
Although the science and technology infrastructure takes precedence over quality of life, 两个都
are influential factors in academics’ mobility decisions (Siekierski, Lima, & Borini, 2018A;
Siekierski, Lima, 等人。, 2018乙).

Gender also plays an important role in mobility. Although more women are involved in inter-
national migration, especially for high-skilled migrants originating from developing countries
(Docquier et al., 2012), female scientists tend to move less than men as they get older or are in
later career stages (Hopcroft et al., 2004; McLean et al., 2013). They also show a more pro-
nounced willingness to follow their spouse than do their male peers (Docquier et al., 2012).
Azoulay et al. (2017) found that elite scientists, particularly those who are female, are less likely
to move when they have recently received NIH funding and appear to be unwilling to move when
their children are in high school.

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Analyzing academic mobility of U.S. professors

2.3. 移动性, 生产率, and Career Advancement

Researchers with foreign work experience tend to publish more articles in high-impact-factor
journals, both in general and as first or last authors, than their counterparts who have not been
国外 (Jonkers & Cruz-Castro, 2013). Migrants perform at a higher level than domestic scientists
with or without prior experience of international mobility (Franzoni, Scellato, & Stephan, 2014;
Halevi, Moed, & Bar-Ilan, 2016). 很多 (2012) argued that the impact of return scholars is likely
to vary according to the quality of the foreign institute to which they have been affiliated.
Countries may also benefit from the mobility: Wagner and Jonkers (2017) found that countries
that welcome international researchers and encourage cross-border collaboration tend to
produce papers with high scientific impact.

For interuniversity mobility, counts of published articles had statistically significant but slight
effects on gains or losses in job prestige (Allison & 长的, 1987). Graduate school prestige was also
a significant determinant of an early entrant’s subsequent academic appointment (D’Aveni, 1996;
Debackere & Rappa, 1995; 磨坊主, Click, & Cardinal, 2005; 威廉森 & 电缆, 2003). 然而,
graduate school prestige has no significant effect on mobility beyond the early career stage (关于
5 年) (Debackere & Rappa, 1995) and cannot stop the downward cascading of affiliation prestige
that results from moving to a university with relatively low prestige from a university with higher
prestige (Miller et al., 2005). Apart from prestige, the moves of elite scientists are partly driven by the
scope for improvement in the quality of their peer environment (Azoulay et al., 2017). 移动的
researchers who changed affiliations during their scientific career tend to have slightly higher
publication and citation rates than other researchers (Aksnes, Rørstad, 等人。, 2013). 然而,
the number of affiliations a researcher moves to, whether two or three, might not make a significant
difference (Halevi et al., 2016). McLean et al. (2013) found a positive relationship between
geographic mobility and advancement in administrative position. Tohmo and Viinikainen
(2017) found that nonfrequent intersectoral mobility was related to higher earnings, 然而
frequent mobility was typically associated with lower subsequent earnings. Mobility from the
university to the private sector may bring economic gains in the natural sciences, whereas in the
social sciences, the earnings returns from mobility are statistically insignificant.

3. DATA AND MATERIALS

3.1.

Identifying Professors with Academic Moves in ORCID

这 2018 version of ORCID data was collected through Figshare (Blackburn, 棕色的, 等人。, 2018).
We limited researchers to U.S. tenure-track or tenured professors, meaning that all affiliations on a
researcher’s profile must be in the United States (or its territories) and any professorship position
needs to be an assistant, associate, or full professorship. The reasons that we only focus on U.S. 亲-
fessors are twofold. 第一的, professorship is the only reliable category of titles in ORCID, and even for
this category, we had to implement a series of heuristics to ensure the accurate grouping of titles and
ranks. For other categories of researchers, it is not reliable to use ORCID to extract their positions.
第二, we restricted ORCID researchers to U.S. professors only for the reason that higher education
systems in different countries have varied norms and expectations. To mitigate the confounding
factors arisen from the differences, we limited the scope to U.S. professors. We also excluded pro-
fessors (n = 3,778) who had both U.S. and outside U.S. affiliations for the same reason outline above.

Because ORCID does not use controlled vocabulary, navigating researcher titles and classi-
fying professor ranks poses a challenge. To ameliorate this, we developed a simple yet effective
rubric to identify tenure-track and tenured professorship:

(西德:129) 第一的, all titles were uppercased, and titles that do not include “PROF” were filtered out.

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(西德:129) 第二, we removed titles that include “visiting,” “adjunct,” “research,” and “clinical.” All
titles were then manually classified into three ranks based on whether they include the
keywords “assistant,” “associate,” and “full.”’ Title abbreviations such as “ASSOC” and
“ASST” also exist. To capture them, we sorted all the titles alphabetically and manually
checked abbreviations (例如, “AST,” “ASSO”) and classified them.

(西德:129) 第三, to address typos such as “ASSSISTANT,” “ASOCIATE,” we developed the following
rubric. “AS” was used to differentiate full professor-related titles from the others, and “SO”
was used to differentiate assistant professors from the others; the rationale being that “AS” is
not likely to be included in the full professor-related titles and assistant professor-related
titles tend to not include “SO.” After applying this rubric, there were still many titles that
could not be categorized into the three ranks because of endowment titles (例如, JAMES B.
DUKE PROFESSOR). We manually reviewed each category and merged endowment titles
into their respective categories.

To ensure data recency, only profiles that were last updated in October 2016 or later were
包括. 总共, 47,044 professors met the criteria; 他们之中, 38,426 did not have a change
in rank or organization and were not included in the current analysis. The remaining 8,618 亲-
fessors made 12,671 changes in rank or organization. These records had a few anomalies that
required treatment.

(西德:129) 第一的, 我们删除了 1,191 records that had the starting year of the second position earlier
than the end year of the first position (first position is defined as the one with the earliest
start year among all positions for a professor).

(西德:129) 第二, 我们删除了 102 records with multiple coaffiliations that could be mistaken for
an organization change (例如, Assistant Professor at Harvard University (自从 2015) 和
Broad Institute [自从 2017]).

(西德:129) 第三, for the remaining 11,378 记录, 我们删除了 28 records with no start year given for
the second position and 456 records with only endowment changes (例如, from professor to
distinguished professor). The intermediate data set contains 10,894 position changes.
(西德:129) 最后的, a gender classifier called genderPredictor1 was applied to identify professors’ gen-
德斯. The classifier is based on a naïve Bayes model that uses the U.S. Social Security
Administration name database as the input training data.

3.2. Linking ORCID with Carnegie Classification of Institutions of Higher Education (CCIHE)

ORCID data itself contains limited metadata about each institution. 所以, it was necessary to
link the data with an external source that provides richer institution information. We identified a
valuable source: the Carnegie Classification of Institutions of Higher Education (CCIHE). CCIHE
provides a set of reliable and comprehensive lists of classifications to more than 4,000 更高
education institutions in the United States The key classification produced by CCIHE is the
Basic Classification (referred to as Carnegie Classification in this paper) that assigns an institution
based on the level of research intensity and the level of degree granted. When linking the ORCID
data with CCIHE, we merged medical schools, hospitals, departments, and affiliated schools
given in the ORCID data into their respective parent universities. If an ORCID record did not
specify the campus of a university, its main campus (flagship campus) was assumed. 有
a small number of records (238) with affiliations that cannot be found in CCIHE, 包括
公司, laboratories, and hospitals that do not have an affiliated university.

1 https://github.com/sholiday/genderPredictor

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We also manually formatted ORCID organization data so that institutions could be properly
matched. 例如, a mention of “Indiana University Bloomington” in an ORCID record
would be formatted as “Indiana University-Bloomington” to facilitate matching in CCIHE. 这
其余的 10,656 records are fully matched with CCIHE; among these, 4,718 had only a rank
change and 5,938 had an organization change (2,278 had both rank and organization changes
和 3,660 had only an organization change) and are included in this analysis. The data is
accessible at figshare (严, 2020). 数字 1 uses histograms to show the distribution of academic
改变: In the subgraph to the left (全部), an academic change can be a promotion, an academic
移动, 或两者; for the subgraph to the right (Moved), an academic change is an academic move.
正如预期的那样, most professors made only one academic move and only 2.5% of the professors
made three or more moves.

In CCIHE, the following variables were collected for each institution: Carnegie Classification
(2018 version), institution locale, institution region, institution sector, and institution minority
serving status (including historically Black colleges and universities, tribal institutions,
Hispanic-serving institution, and other minority-serving institutions). These variables highlight
the characteristics of higher education institutions and allow for more meaningful examinations
of academic move patterns. There are more than 30 Carnegie Classification level codes to mea-
sure the level of research intensity and the level of degree granted (Indiana University Center for
Postsecondary Research, 2018); for ease of presentation and analysis, we merged level codes
1–13 to Associate, 14 and 21–23 to Baccalaureate, 18–20 as Master, and 24–32 as Special
Focus Four-Year, while keeping codes 15 到 17 as separate categories. They are R1 (Doctoral
Universities—Very high research activity), R2 (Doctoral Universities—High research activity),
and R3 (Doctoral/Professional Universities). The full list of all Carnegie Classification codes
can be seen in the Supplementary Information Table S1. For locale types, we kept the three
classes for cities: City Large, City Midsize, and City Small, while merging different town types
to Town, suburban types to Suburban, and rural types to Rural.

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数字 1. Histograms of the distributions of academic change. In the subgraph to the left (全部), 一个
academic change can be a promotion, an academic move, 或两者; for the subgraph to the right
(Moved), an academic change is an academic move.

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Analyzing academic mobility of U.S. professors

We now turn to issues of data representation. ORCID is a self-reporting data repository, 和
the data set used in this study is essentially a sample of academic moves of U.S. tenure-track or
tenured professors. 所以, it is important to recognize potential sampling biases. We examine
data representation issues in the following ways: 第一的, 在里面 10,656 fully matched records, 我们
identified 1,162 distinct institutions from a combination of both move origin and destination
机构. The breakdown of these institutions based on the Carnegie Classification is shown
表中 1.

全部 131 R1 institutions are represented in the current data set; R2 and R3 institutions are also
well represented. About half of the master’s-level institutions and a third of the baccalaureate-
level institutions are represented, whereas associate-level and special focus 4-year institutions
have comparatively inadequate representations. None of the 34 tribal institutions is represented
in the data set. 第二, a third of the public (572 的 1653) and of the private nonprofit institutions
(577 的 1742) in CCIHE are represented in the current data set, whereas only 1% of for-profit
机构 (13 在......之外 929) are represented.

第三, 的 1,162 institutions represented in this data set, CCIHE includes the number of
tenure-track or tenured faculty for 257 机构 (2016 数据), all of which are R1 or R2 institutions.
The top five most represented institutions are Rockefeller University (29% of its tenure-track or
tenured professors are represented in the current data set), 哈佛大学 (16%), Jefferson
(Philadelphia University + Thomas Jefferson University) (15%), Vanderbilt University (15%),
and Iowa State University (14%). A histogram presentation of the distribution of the ratios of pro-
fessors represented in the data set is shown in Supplementary Information Figure S1. 平均而言,
among these 257 机构, 6% of tenure-track or tenured faculty are represented in this data
放. We can safely assert that the data set is skewed towards research-oriented institutions. 最后,
it is important to note that we are not measuring the number of professors that an institution has at
a particular point in history, which is attributable to a slew of factors, but rather institution-level
academic moves. This is a much more meaningful measurement because the total number of
institutions is the same before and after moving, and for any move there must be both an origin

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桌子 1.

Breakdown of institutions based on Carnegie Classifications

Classification
R1 Doctoral Universities Very high

research activity

R2 Doctoral Universities High

research activity

R3 Doctoral/Professional Universities

Master

Baccalaureate

Associate

Special Focus Four-Year

Tribal

全部的

In the data set
131

CCIHE
131

Percentage
100

132

113

381

256

114

1,162

135

151

684

838

1,432

919

4,324

97.8

74.8

55.7

30.6

2.4

12.4

0.0

26.9

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Analyzing academic mobility of U.S. professors

and a destination. 所以, the data set used in this study can be considered as a closed system
in which actors (IE。, professors) make decisions about moving.

4. 结果

在这个部分, we present results on the patterns of 5,938 academic moves that have an organi-
zation change. Among them, 30% of the moves are made by female professors and 70% are by
male professors. 有 11 ORCID records without names or bibliographic data from which to
identify gender by the time we collected the data, so the total number of professors in the
gendered columns is 5,927. For statistics on rank changes for professors without academic
moves, see Supplementary Information Table S2.

4.1. Patterns of Rank Change in Academic Moves

We first report the rank changes after an academic move (桌子 2).

Professors in about 60% of the moves retained their rank, 并在 26% of the moves they
received promotion, either from assistant to associate or from associate to full professor. In about
10% of the moves, they received promotion from assistant to full professor. Demotion only
occurred in 2% of the moves. The percentages for different types of rank changes are similar
for female and male professors.

下一个, we examine the changes in affiliation types defined by the Carnegie Classification of
higher education institutions. The top 10 most common change types are shown in Table 3. A
complete list can be found in the Supplementary Information Table S3.

Half of the time, the new institutions that professors moved to have the same classification as
the old institutions from which they move. 关于 8% 当时的, professors moved from an R2
institution to an R1 institution and 7% for from R1 to R2. Female professors moved from R2 and
master-level institutions to R1 institutions at a slightly higher rate, whereas the percentage in other
affiliation types is similar between female and male professors. We can cross-check institution
type change with rank change (桌子 4) and answer the questions: When did female professors
move to a new institution with a different Carnegie Classification? Did they keep their rank, get
promoted, or be demoted?

When female professors moved from an R1 institution to an R2 institution, they were promoted
3.1% 更多的 (45.8% for female professors vs. 42.7% for male professors) than their male

桌子 2.

Percentage of rank changes after academic moves for female and male professors

Rank change
No change

F
1,110 (61.1%)

中号
2,544 (61.9%)

全部的
3,660 (61.7%)

Up by one rank

483 (26.6%)

1,075 (26.2%)

1,563 (26.3%)

Up by two ranks

174 (9.6%)

418 (10.2%)

592 (10.0%)

Down by one rank

Down by two ranks

40 (2.2%)

9 (0.5%)

62 (1.6%)

12 (0.3%)

102 (1.7%)

21 (0.4%)

笔记: Up by one rank: from assistant to associate or from associate to full; up by two ranks: from assistant to full;
down by one rank: from associate to assistant or from full to associate; down by two ranks: from full to assistant.

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Analyzing academic mobility of U.S. professors

桌子 3.
for female and male professors (顶部 10 based on percentage of change)

Percentage of affiliations’ Carnegie Classification change before and after academic moves

Carnegie Classification change
No change

F
907 (49.9%)

中号
2,201 (53.5%)

全部的
3,116 (52.5%)

R2 to R1

R1 to R2

Special focus 4-year to R1

R1 to Special focus 4-year

Master to R2

Master to R1

R1 to Master

R2 to Master

Baccalaureate to Master

151 (8.3%)

302 (7.4%)

453 (7.6%)

120 (6.6%)

274 (6.7%)

395 (6.7%)

76 (4.2%)

65 (3.6%)

53 (2.9%)

67 (3.7%)

35 (1.9%)

32 (1.8%)

212 (5.2%)

289 (4.9%)

192 (4.7%)

258 (4.3%)

117 (2.9%)

170 (2.9%)

98 (2.4%)

70 (1.7%)

61 (1.5%)

50 (1.2%)

165 (2.8%)

105 (1.8%)

96 (1.6%)

82 (1.4%)

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同行. 同时, when female professors moved from an R2 institution to an
R1 institution, they were demoted at a rate 2.7% 更高 (4.7% for female professors vs. 2%
for male professors) and were promoted 2% 较少的 (28.5% for female professors vs. 30.5% for male
professors) than male professors. The results paint a different picture for female professors who
moved to less research-intensive institutions (R2) and those who moved to more research-
intensive institutions (R1). The results show that when moving to more research-intensive
机构, female professors are less likely to retain their rank or get promoted when compared
with male professors.

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桌子 4.

Rank change vs. Carnegie Classification change for female and male professors

Rank change/Carnegie
Classification change
R1 to R1

Down by
1 rank (%)
中号
F
0.5
0.6

Down by
2 ranks (%)
中号
F
0.1
0.1

No change (%)
中号
F
62.8
61.2

Up by
1 rank (%)
中号
F
25.7
28.2

Up by
2 ranks (%)
中号
F
10.9
10.0

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R1 to R2

R1 to Other

R2 to R1

R2 to R2

R2 to Other

Other to R1

Other to R2

Other to Other

0.0

4.0

5.4

3.7

2.2

5.5

5.6

0.7

0.3

2.0

1.6

1.4

4.0

3.7

3.3

0.0

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0.0

0.5

3.6

0.4

0.0

0.6

0.0

0.5

1.0

54.2

46.0

66.9

60.7

60.5

71.0

59.1

63.0

56.6

52.1

67.6

68.6

52.8

64.0

69.0

60.5

33.3

41.6

19.2

25.0

28.4

21.5

14.6

23.9

30.3

35.2

23.2

22.6

28.9

23.5

20.6

26.2

12.5

11.7

9.3

9.0

7.4

4.9

17.3

7.2

Quantitative Science Studies

12.4

11.8

7.3

16.9

8.0

6.4

9.1

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数字 2. Number of faculty members before and after academic moves for seven types of
Carnegie Classification institutions.

We show the Carnegie Classification for institutions before and after moving (数字 2) 和

pairwise moves between different types of Carnegie Classifications (数字 3).

数字 2 illustrates that professors tend to move from nondoctoral-level institutions
(Baccalaureate-, Master-, and Special Focus Four-Year-institutions) to doctoral-level institutions
(R1, R2, and R3).

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数字 3. An illustration of pairwise moves between different types of institutions based on the
Carnegie Classification.

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数字 3 shows that the most frequent moves are between R1 and R2 and between R1 and
Special focus institutions. Special focus institutions are those specializing in health professions,
工程, 等等. More professors move from baccalaureate- and master-level institutions
to R1 institutions than from R1 institutions to the two types of institutions, as shown by the thickness
of the arcs. This result, in conjunction with the observations made in Figure 2, supports our first
假设.

4.2. Geographic Patterns of Academic Moves

We examine the changes in affiliation locale defined by CCIHE. The top 10 most common
change types are shown in Table 5. A complete list can be found in the Supplementary
Information Table S4.

For more than a quarter of the time, there is no change of locale type for the new institutions
that professors moved to. The most popular locale change is from large cities to midsize cities,
followed by from midsize cities to large cities. Moves between large cities and suburbs and
between small cities and large cities are also popular choices. The movement pattern for female
professors is quantitatively similar to that of male professors. To get a clearer picture of the rela-
tionship between levels of institution research intensity and levels of urbanization, we kept R1
and R2 designations and merged all other Carnegie Class types into Other Class while merging
three city types into City and merging all other local types as Other Local. In Supplementary
Information Table S5, we separately report the move types based on institution research intensity
and locals. When professors move from less research-intensive institutions to more research-
intensive institutions (Other Class-R1), 9% of the moves are from urban to nonurban regions
(City-Other Local) 和 27% of the moves are from nonurban to urban regions. 反过来, 什么时候
professors move from more research-intensive institutions to less research-intensive institutions
(R1-Other Class), 22% of the moves are from urban to nonurban regions (City-Other Local) 和
仅有的 12% of the moves are from nonurban to urban regions.

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桌子 5.
male professors (顶部 10 based on percentage of change)

Percentage of affiliations’ locale change before and after academic moves for female and

Locale change
No change

F
493 (27.2%)

中号
1,163 (28.3%)

全部的
1,657 (27.9%)

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City Large to City Midsize

146 (8.0%)

City Midsize to City Large

113 (6.2%)

City Large to Suburb

Suburb to City Large

111 (6.1%)

City Small to City Large

103 (5.7%)

City Large to City Small

99 (5.5%)

City Small to City Midsize

83 (4.6%)

City Midsize to City Small

70 (3.9%)

Suburb to City Midsize

57 (3.1%)

321 (7.8%)

316 (7.7%)

239 (5.8%)

227 (5.5%)

225 (5.5%)

212 (5.2%)

156 (3.8%)

146 (3.6%)

131 (3.2%)

469 (7.9%)

431 (7.3%)

351 (5.9%)

339 (5.7%)

329 (5.5%)

312 (5.3%)

239 (4.0%)

217 (3.7%)

188 (3.2%)

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数字 4. Number of faculty members before and after academic moves for six institution location
类型.

We show the change of institution locales before and after moving (数字 4) and the pairwise

moves between institutions of different location types (数字 5).

数字 4 shows that all three types of cities (大的, midsize, and small) are popular destina-
tions for professors. All three city sizes show increases in the number of professors who moved
to these locales, whereas professors tend to move away from town-based institutions.

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数字 5. An illustration of pairwise moves between institutions of different location types.

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桌子 6.
male professors (顶部 10 based on percentage of change)

Percentage of affiliations’ region change before and after academic moves for female and

Region change
No change

Mid East–Southeast

Great Lakes–Southeast

Southeast–Mid East

Southeast–Great Lakes

Southeast–Southwest

Southwest–Southeast

Mid East–Great Lakes

Great Lakes–Mid East

Southeast–Plains

F
593 (32.7%)

中号
1,136 (27.6%)

全部的
1,731 (29.2%)

52 (2.9%)

60 (3.3%)

56 (3.1%)

52 (2.9%)

50 (2.8%)

29 (1.6%)

43 (2.4%)

48 (2.6%)

44 (2.4%)

150 (3.7%)

141 (3.4%)

131 (3.2%)

129 (3.1%)

112 (2.7%)

113 (2.8%)

96 (2.3%)

82 (2.0%)

81 (2.0%)

202 (3.4%)

201 (3.4%)

188 (3.2%)

181 (3.1%)

162 (2.7%)

142 (2.4%)

140 (2.4%)

131 (2.2%)

125 (2.1%)

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笔记: New England: CT ME MA NH RI VT; Mid East: DE DC MD NJ NY PA; Great Lakes: IL IN MI OH WI;
Plains: IA KS MN MO NE ND SD; Southeast: AL AR FL GA KY LA MS NC SC TN VA WV; Southwest: AZ NM
OK TX; Rocky Mountains: CO ID MT UT WY; Far West: AK CA HI NV OR WA; and Outlying areas: AS FM GU
MH MP PR PW VI.

A visual inspection of the arcs in Figure 5 shows that there are more moves from institutions
in towns to institutions in suburbs and cities of different sizes than moves from city and sub-
urban institutions to town institutions. The results from Figures 4 和 5 support our second
假设.

CCIHE classifies all U.S. states into 10 团体. The classification can be seen in the note of
桌子 6. We examine the changes in affiliation regions and show the top 10 most common
change types.

数字 6. Number of faculty members before and after academic moves for 10 institution region types.

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Analyzing academic mobility of U.S. professors

数字 7. Map that shows the change of number of faculty for 48 我们. continental states.2

One key finding from Table 6 is that female professors are less likely to move from their
existing region when moving to a new institution (33% stayed in the same region vs. 28% 为了
male professors). This result supports our fourth hypothesis. The Southeast region experienced
high levels of effluxes (to Mid East, Great Lakes, Southwest, and Plains) and influxes of pro-
fessors (from Mid East, Great Lakes, and Southwest).

We show the change of institution regions before and after moving (数字 6). Regions that
gained professors include Southwest, Rocky Mountains, and Far West whereas institutions in
New England, Great Lakes, and Plains lost professors. The Mid East and Southeast experienced
slight changes and the other two regions (我们. Service Schools and Outlying Areas) 报道
low numbers.

We report the state-level academic move in Supplementary Information Table S6. The top five
states with the highest exodus of professors are North Dakota (−65%), Mississippi (−53%),
Louisiana (−46%), Alaska (−44%), Wyoming (−41%). The top five states/territories with the high-
est influx of professors are Montana (80%), Oregon (78%), 华盛顿, 直流 (49%), Alabama
(48%), and Arizona (37%). The top five states with the highest number of professors postmove
are Texas (499), 加利福尼亚州 (474), 纽约 (400), 宾夕法尼亚州 (367), and Illinois (270).

We visualize the changing numbers of faculty for the 48 我们. continental states in Figure 7.
Arcs in the maps represent the faculty moves between two states, with origination colored red
and destination colored green. The width of an arc represents the number of faculty members
moving from one state to another. Only moves with at least 10 faculty members are shown in
this visualization. The sizes of red, 蓝色的, and green circles represent the number of faculty
members moving out of, 之内, or into a state respectively. Using the U.S. census regions as

2 An interactive and complete visualization can be accessed by http://jiangenhe.com/facutly_mobility/.

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Analyzing academic mobility of U.S. professors

桌子 7.
female and male professors

Percentage of affiliations’ minority serving status before and after academic moves for

Minority serving institutions (MSI)
Non-MSI to non-MSI

F
1,479 (81.4%)

中号
3,364 (81.8%)

全部的
4,852 (81.7%)

Non-MSI to MSI

MSI to non-MSI

MSI to MSI

159 (8.8%)

386 (9.4%)

547 (9.2%)

137 (7.6%)

310 (7.5%)

447 (7.5%)

41 (2.3%)

51 (1.2%)

92 (1.6%)

a reference, except for Wyoming, all other states in the West region gained faculty. 随着
exceptions of Michigan and Ohio in the Midwest and Pennsylvania and Delaware in the
Northeast, all other states in the two regions lost faculty. States in the South region exhibit
diverse patterns, with Texas, Tennessee, North Carolina, Alabama, and Florida gaining faculty
as others in this region lost faculty. 全面的, the west and south witnessed an influx of profes-
sors and the north and east of the United States witnessed an efflux of professors.

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4.3.

Institution Types and Academic Moves

Using CCIHE’s classification of minority serving institutions (MSI), we first measure the per-
centage of professors employed in MSI. Before moving, 7% of professors in the data set were
employed in MSI and 9% after moving. There is a slight decrease of female professors in MSI,
从 33% 到 31%; 然而, both numbers are higher than the percentage of female professors
for all institution types (30%). 桌子 7 shows the movement between MSI and non-MSI.

The majority of moves are within non-MSI (81%). Male professors are slightly more likely to
move from a non-MSI to an MSI (9.4% for men vs. 8.8% for women) and female professors are
more likely to move from one MSI to another MSI (2.3% for women vs. 1.2% for men).

最后, we report results on academic moves between public and private institutions

(桌子 8).

About half of the moves are within public institutions (51% for women vs. 49% for men).
There is a decrease of both female and male professors in private institutions, 从 33% 到
29% for female professors and from 34% 到 31% for male professors. The results suggest that
female professors are slightly more likely to be employed in a public institution than male
professors, both before and after moving.

桌子 8.
and male professors

Percentage of affiliations’ funding type change before and after academic moves for female

Institution type
Public to public

F
930 (51.2%)

中号
2,014 (50.0%)

全部的
2,947 (49.6%)

Private to public

343 (18.9%)

829 (20.2%)

1,175 (19.8%)

Public to private

282 (15.5%)

Private to private

251 (13.8%)

692 (16.8%)

564 (13.7%)

976 (16.4%)

818 (13.8%)

笔记: 10 moves by female professors and 12 moves by male professors involve a for-profit private institution as
the move origin or destination. These moves are not tabulated in Table 8.

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Analyzing academic mobility of U.S. professors

5. DISCUSSION AND CONCLUSIONS

This paper used two open science data sources (ORCID and CCIHE) and identified 5,938 mo-
bile tenure-track and tenured professors in the United States. Using the Carnegie Classification
and professor profiles, we revealed patterns of academic mobility in the United States from the
aspects of institution types, locations, 地区, funding mechanisms, and professors’ genders.
We found that professors tended to move to institutions with higher research intensity such as
those with a R1 or R2 designation in the Carnegie Classification. They are also more likely to
move to institutions located in cities from those located in towns or rural areas. The one pro-
fessor-level attribute showed that female professors tend to move within the same geographic
region at a higher rate than male professors, likely a result of them preferring moves that are
less disruptive to their social networks. This paper also found that female professors are less
likely than their male colleagues to retain their rank or get promotion when they move from a
less research-intensive institution to a more research-intensive one.

Future research will benefit from measuring the productivity and impact of professors before and
after moving. We plan to link professors’ ORCID profiles with the Web of Science data to obtain
publication and citation data. The data will also be used in regression models to establish premove
baselines and quantify the effect of academic moves on postmove productivity and impact.

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作者贡献
Erija Yan: 概念化, 方法, 项目管理, 可视化, Writing—
original draft, Writing—review and editing. Yongjun Zhu: 方法, Writing—original draft,
Writing—review and editing. Jiangen He: 可视化, Writing—original draft, Writing—
review and editing.

COMPETING INTERESTS

The authors have no competing interests.

资金信息

No funding has been received for this research.

DATA AVAILABILITY

The data used in this paper is freely assessable via figshare at https://doi.org/10.6084/m9
.figshare.12642623.v1.

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