Hitotsubashi University
Institute of Innovation Research

Author ordering in scientific research: evidence fromscientists survey in the US and Japan

Sadao Nagaoka
Hideo Owan

IIR Working Paper WP#13-23

Feb. 2014

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Author ordering in scientific research: evidence from scientists survey in the US andJapan1
Sadao Nagaoka
Institute of Innovation Research, Hitotsubashi University,
Hideo Owan
Institute of Social Science, University of Tokyo
February 2014
Abstract
This paper examines what drives author ordering in scientific research. We first discussatheoreticalframework

for

the

choicebetweenalphabeticalordering

andrelative-contribution-based ordering and develop hypotheses, focusing on the nature ofresearch, in particular, the importance of collaboration in the context of incomplete
contract, the measurement cost of contribution-based ordering and the role of aprincipal investigator (PI). Our empirical examinations, based on the new large scaleoriginal scientists’ surveys in the US and Japan, show the supporting results. In
particular, an alphabetical ordering is more likely to be used when the research istheoretical and has less empirical component and when the team size is large and notco-located. The variation of research method goes a long way in explaining the variationof the use of alphabetic ordering across fields (mathematics and economics vs. theothers) as well as its variation within a field. We also find that PI or Co-PIs are more
likely to exist when the project uses more resources as well as when the team is moreheterogeneous. Finally, we confirm that author ordering sends two signals incontribution based ordering, the first author for the largest research contribution andthe last author for the PI ( or Co-PI).
Key words:

alphabetical ordering; contribution; science; incomplete contract; help;

principal investigator
JEL Code:O32; D23

We would like to thank for the support from Sloan Foundation through the project of
Economics of Knowledge Contribution and Distribution led by Joshua Gans. We would
like to thank the comments received at the “Economics of Knowledge Contribution andDistribution” workshop at Georgia Institute of Technology, November 2013.

1

1

1.

Introduction

Scientific research has become increasingly a team work (Stephan (2012) for acomprehensive review). As the share of multi-authored papers surges, it has become an
important issue how the contribution of each author is recognized and how therecognition motivates researchers. In particular, their ordering could be an important
instrument for recognizing contributions.
There are significant variations of name ordering across scientific disciplines
and the reasons are still not yet well understood. In early contribution, Zuckerman(1968) studied name ordering of the papers in physics, chemistry and biology. It reportsthat the percentage of the papers with alphabetized (or lexicographic) name ordervaries significantly across three fields and it is the highest in physics. Furthermore, it
points out that Nobel laureates are more likely to be the last authors than the firstauthors. It is also found that alphabetical (or lexicographic) ordering is more extensively
used in economics than in natural science (Engers, Gans, Grant and King (1999) andLaband and Tollison (2000)). Laband and Tollison (2006) also show that the
preponderance of non-alphabetized papers in agricultural economics, as compared toeconomics, within economics field.
There is no established view on when and why alphabetical ordering is usedrather than contribution-based ordering. The question of why the author name ordering
practices differ significantly across disciplines remain a major puzzle, as pointed out by
Merton (1973) decades ago. However, we have at least three theories explaining the
choice of alphabetical ordering. Engers, Gans, Grant and King (1999) propose a model
based on ex-post Nash bargaining with no side payment, which can potentially explain
the prevalence of alphabetical (or lexicographic) ordering in economics. In their model,alphabetical ordering is chosen with some positive probability ex-post to achieve Nashsolution at the expense of ex-ante incentive for efficiency. It is even possible thatalphabetical ordering appears as the market norm in the unique equilibrium. On theother hand, Laband and Tollison (2000) suggests that informal collaboration (“helping”)
is more important in economics than in biology, as shown by more “thanks” expressed inacknowledgements, and therefore a pay compression by alphabetical (or lexicographic)ordering is more used in economics to facilitate helping among authors. In this view,alphabetical ordering is chosen ex-ante for efficiency reason. Furthermore, Joseph,Laband, and Patil (2005) present a simulation result where we observe alphabeticalordering only when each of two authors performs very well. In this case, alphabeticalordering emerges ex-post even if they work hard to obtain priority in listings. Their
model offers one potential explanation of why high-quality papers tend to have2alphabetical ordering.
This paper aims at contributing to our understanding of the determinants of
the author ordering in scientific research, based on novel survey data covering morethan 4,000 US and Japanese science projects. We think that there are three major
contributions of this paper. First, we find that the nature of research, in particular, theresearch method used goes a long way in explaining the variation of the use ofalphabetic ordering across fields (mathematics and economics vs. the others) as well as
its variation within a field. We argue that such result reflects the importance ofcollaboration in the context of incomplete contract, that is, an incomplete division oflabor and the importance of unplanned help among the authors makes weak incentive
more desirable. We point out the importance of “help” among the authors, but not social
nature of research as pointed out by Laband and Tollison (2000).
Second, we show that the measurement cost of contribution based ordering is
also a major reason for the choice of alphabetical ordering. While an individual
incentive under alphabetical ordering declines sharply with the number of authors due
to free-riding, the measurement cost saved by alphabetical ordering increases with thenumber of authors, as pointed out based on interviews by Zuckerman (1968). Suchcombination allows the possibility of a U shaped pattern between the incidence ofalphabetical ordering and the team size. We also expect that collocated team will adopt
more a contribution based ordering, controlling for the nature of research, due to lower
measurement cost. We have corroborated this hypothesis.
Third, we show how the choice of the governance form in the research teaminteracts with the choice of name ordering. Having a PI or co-PIs who manage theproject including monitoring each member’s progress is associated with therelative-contribution-based ordering even after controlling for factors affecting the two.
PI has an incentive to measure members’ contributions fairly because he typically is

assumed to be the last author (thus has less conflicts of interest with othermembers) and presumably cares his reputation as a PI. The rest of the paper isorganized as follow. Section 2 provides theoretical background and hypotheses. Section
3 explains data. Section 4 and 5 explains the estimation models and results. Section 6
concludes.
2. Theoretical Background
While a number of factors are likely to affect the types of author ordering (See Figure 1),
we focus here on the nature of research, the role of Principal Investigator (PI) and themeasurement cost.
32.1 Nature of research- importance of collaboration in the context of incomplete contract
in researchFirst, the nature of research determines the importance of the incompleteness of theinitial “contract” (i.e. agreement) on what should be done and how the tasks should beallocated and the importance of unplanned mutual help along the course of conducting
research. When the research project seeks unknown and uncodified fundamental
concepts and mechanisms, efforts should cover numerous trials and explorations of new
ideas. Then, it would be impossible to define the division of labor ex-ante among the
researchers, and getting new idea, different perspectives and feedbacks from thecoauthors would become very important. Furthermore, verifying these efforts, many ofwhich may not be embodied in the final research output, can be very difficult and costly.
In such sciences, alphabetical ordering is likely to be efficient because team pay systemwith equal sharing will encourage “helping” behaviors, while ordering based on relative
contributions hinder such cooperation, as analyzed in Lazear (1989) and Itoh (1992).2
In contrast, when the key concepts are codified and the methodology is well established,
the research project will be focused on exploiting existing idea and defining the divisionof labor ex-ante is easy. In this case, contribution-based ordering could be optimal due tothe shift in the tradeoff between cooperative and competitive efforts, because the valueof unmeasured helping is rather limited.
The importance of unplanned and unobservable collaborations from co-authors
can be highlighted in a comparison between pure theoretical researches vs. empiricalresearches. In pure theoretical researches where the exploration of new perspectives
and mechanisms is valuable, unplanned “helps” one member offers to the otherco-authors could lead to path-breaking results but the assessment of such contribution
could be very different across co-authors because of redundancies (i.e. one already hadsuggested idea), cryptomnesia (i.e. reappearance of once-forgotten memory of suggested
idea as if it were one’s own idea), and simple forgetting. Another aspect of abstract
theoretical research is that they involve numerous trials most of which end up in
failures and are hard to describe in an explicitly codified manner. This is different fromempirical and experimental researches where failed trials are shared among teammembers in the form of computer data log files or laboratory notes. If one’s contribution
was conducting many trials which all turned out to be failures but not necessarily
reported to other co-authors, it is not clear how much recognition should be given to
him/her even though crossing out failure cases is necessary process in science. Given
Itoh (1992) further implies that encouraging teamwork through alphabetical ordering
is even more desirable if help from a co-author is complementary with one’s own efforts.
2

4

that assessments of one’s contribution are not perfectly correlated among co-authors,compressed pay embodied in alphabetical ordering is likely to be optimal (MacLeod
2003). When there is clear division of labor (and thus helping each other is not sofrequent or indispensable) and investigating numerous ideas and approaches are lessnecessary, co-authors are more likely to have equal assessment of each other’scontribution.
2.2.

Principal investigator (PI) as an organizer and monitor

The principal investigator (PI) organizes activities in the research team and monitors
each member’s work and progress as well as defines and implements the attribution
rule for the authors.3 The existence of a PI enables the exploitation of the scale andscope in research and therefore the formation of a large team. It could also change therelative

efficiency

of

the

alphabeticalandcontribution-basedordering

completely—more likely to expand the scope of relative contribution basedordering—for the following reasons:First, the PI has a strong incentive to monitor co-authors by frequently
checking the progress and asking them to record results in experiment notebooks
because of the credit given to him as the PI. The PI also arranges for meetings andworkshops to facilitate collaborations among the team members. These roles indicatethat he may observe, at least partially, the full dimension of the efforts and the outputsof the team members. Therefore, the PI may be able to use relative contributions basedordering more effectively–encourage competition without harming cooperation among
the team members.
Second, the PI typically has a control of essential assets for the project such as
facility, equipment, and scarce materials used in experiments. Therefore, he/she has themuch stronger bargaining power than other co-authors. This means that he can design
an incentive contract and may make a leave-it-or-take-it offer to other co-authors.
Furthermore, since he works on multiple projects with many authors, he also has astrong incentive to accumulate reputational capital as depicted in the literature ofmultimarket reputation (Kreps and Wilson 1982, Milgrom and Roberts 1982). Therefore,
the PI has capacity to commit to pre-determined author ordering policy to avoid theinefficiency as analyzed by Engers, Gans, Grant and King (1999), where the actual
contributions may end up with almost equal share, even if contribution based ordering

The principal investigator is often the person who put forward a successful research
proposal for financial resources for the project, develops a research execution plan,
recruits the team members and manages the project in staffing and budgeting.
3

5

is more efficient. This effect also tends to expand the scope of relative contribution
based ordering in some cases4.
Thirdly, the PI can minimize the conflict of interests by avoiding directcompetition with his co-authors. In fact, PI often becomes the last author, while theother authors are ordered according to the relative contributions. Then, PI has a little
incentive to “steal“ the credit from the other team members.
To summarize, the existence of the PI reduces the cost of measuring theindividual performance and enforcing the ordering of the authors according to therelative contributions, while the incentive problem for the PI can be solved by providing
him the last position in author ordering.
2.3

Cost of monitoring and evaluations

One disadvantage of contribution based ordering is the cost of monitoring andevaluating individual contributions—necessary for the determination of relative
contributions—including the cost of haggling and conflicts. Such transaction cost isabsent for alphabetical ordering. This consideration suggests that alphabetical ordering
is chosen if the number of author size is very large and the potential risk of conflict islarge.
Similarly, author location could affect author ordering through its
impacts on the observability of co-author contributions. If all authors work in the same
institution, there will be more frequent communications, more interactions, and higher
expectation of long-term collaboration. Therefore, relative contributions are more likely
to be jointly observed and the efforts to make fair assessment of each other’scontribution will be high.5 This makers ordering based on relative contributions more
The commitment power of the PI also can also expand the scope of alphabeticalordering. Co-authors may feel it unfair to rank them using differences especially whenthe market gives them very different credits based on the order, so that the alphabeticalordering may become ex-post infeasible. The existence of PI may be able to prevent suchrenegotiation.
5 Note that author location could have a countervailing effect when efforts are4

observable but not verifiable. If all authors work in the same institution, implicit
agreements are more likely to be honored because any deviation may be easily punished.
This means that if members agree to choose alphabetical ordering from the efficiency
reason, they are more likely to commit to it. It is also likely that members become morelenient toward a colleague who did not contribute to the project from the expectationthat he will contribute to the next project if they share the expectation of repeatedcollaboration. These factors make alphabetical ordering more likely. We examine this6

likelyy.

(Figure 1)

Field norm
2.4. F
Anotther factor thatt
we beliieve is impoortant is fielld norm. If mostmresearrch teams inn thefield choose alphhabetical orrdering, chooosing relatiive contribu
ution based ordering will
w
e
which
h in turn disscourages teams from cchoosing thhehavee a stronger signaling effect,
latter unless thee inequality
y in contribu
utions is larrge enough. Similarly, if a majoritty ofuthor teamss in the fieldd choose rellative contriibution based orderingg, its signaliing
co-au
effectt becomes weaker,w
enco
ouraging mmany more to choose thee ordering tto avoid theebiaseed nature off alphabeticcal orderingg. Proving th
he existence
e of field noorm and its
possiibility in ou
ur analysis.

7empirical assessment is difficult because it only reinforces the influence of other factors.
Therefore, we do not examine any hypothesis involving the role of field norm.
2.5 Hypotheses
Given these theoretical grounds, we test the following hypotheses with respect to the
choice of alphabetical ordering.
First, nature of research, in particular, the incompleteness of co-authorshipagreement—frequent encounter to unforeseen contingencies—and the importance of
unplanned help (collaborations) required in such contingencies significantly raise thebenefit of weak or no relative performance based incentive. We expect that contract
incompleteness and importance of help are high in theoretical research and in the
research pursuing fundamental understanding. It is difficult to define ex-ante divisionof labor for a theoretical research because parallel exploration is often desirable andspontaneous help (collaborations) is indispensable. In contrast, ex-ante division of labor
is relatively easier in empirical research and in the research pursuing a specific realworld problem, given that the target is more clearly specified.
Hypothesis 1 (Effects of nature of research):
Theoretical research tends to adopt an alphabetical ordering, while an empiricalresearch tends to adopt a contribution based ordering. Similarly, a research pursuing afundamental understanding tends to adopt an alphabetical ordering, while that
pursuing a solution to a specific real world problem tends to adopt a contribution basedordering.
Second, the measurement cost of contribution-based ordering increases with
the number of authors, and it is also high when the authors are not collocated. At thesame time, the number of authors also affects the incentive provided under alphabeticalordering, which declines sharply with the number of authors

due to free-riding (i.e.

1/N problem), while the incentive effect of contribution-based ordering, at least for the
first author, does not decline as sharply with the number of authors.
Hypothesis 2 (Effects of size of research team): The relationship between the size of
project team and the choice of author ordering is likely to be non-linear and can exhibit
the U-shape with respect to the probability of alphabetical ordering—a small and a verylarge teams are more likely to adopt alphabetical ordering.
8Assuming that the nature of the research project is controlled, collocation of theauthors would reduce the measurement cost of a contribution-based ordering, so thatthe collocated team would use more contribution-based ordering and the team whose
authors are not collocated will use alphabetical ordering.
Hypothesis 3 (Effects of author location): When all authors are not affiliated with thesame institution, the ordering is more likely to be alphabetical, since the measurement
cost of using contribution-based ordering is high.
There are two distinct roles of PI: one for project design and resource
mobilization and the other for project management. For the first role (i.e. project design
and funding), there are economies of scale and large-scale mobilization of resources(large team size, use of external research materials etc.) requires someone specializingin organizing the activities—thus, PIs are likely to exist. In addition, when relative
contribution based ordering is effective, a monitor is necessary. Thus, the nature ofresearch and team composition that make alphabetical ordering more likely as stated in
the above hypotheses will make PI less likely to exist. At the same time, the exogenous
reason for the existence of PI such the resource mobilization reason may makealphabetical ordering less likely to be adopted, since a monitor is more likely to exist.
For an example, large budget indicates that the research requires some physical assets
or scarce resources. They make PIs more likely to exist and, since PIs have incentives
and capability to monitor team members, the ordering based on relative contributions ismore likely to be adopted.
Hypothesis 4 (Size of resource mobilization, the existence of PI and its effects): Projects
that have large budget and mobilize the other large resources are more likely to havePIs, which in turn make alphabetical ordering less likely. In addition, the factors whichmake alphabetical ordering less likely as stated in the above hypotheses 1 and 3(research nature and co-location) will make PI more likely to exist. The existence of PI
in turn facilities the use of contribution based ordering.
PIs also enjoy separate recognitions as the last author and are motivated toaccumulate reputational capital for fair evaluations and commitment.
Hypothesis 5 (Two types of signals from contribution based author ordering): The
9author ordering in the case of non-alphabetical ordering sends the two importantsignals: one for signaling the relative contributions of researchers and the other for
signaling the contribution in terms of initiating and managing the research project.

3. Data sources and descriptive statistics
3.1 Data sources and the distribution of the ordering types
We use the Hitotsubashi Univ-NISTEP-Georgia Tech scientists’ survey in Japanand the US (see “Knowledge creation process in science: Key comparative findings
from the Hitotsubashi-NISTEP-Georgia Tech scientists’ survey in Japan and the US”(jointly with Igami, Walsh and Ijichi )6.

This collected 2,100 responses in Japanand 2,300 responses in the US (the response rate was 27% in Japan and 26% in theUS), with one third of the population from top 1% highly cited papers (referred to by
H papers), and the rest of the paper referred to by N papers. The survey covers
comprehensively

theprojectcharacteristics

which

producedsuch

paper:

motivations of the research projects, knowledge sources that inspired the project,uncertainty in the knowledge creation process, research competition, composition of
the research team, sources of funds and research outputs, e.g., research papers,
students, patents, etc. It covers all sciences, including social science.
In addition, the survey specifically asked the order of the authors: (1)contribution-based; (2) alphabetical; (3) senior first; (4) senior last; and (5) other.7
Figure 2 shows the distribution of the types of ordering (% of each type of the orderof the authors) across four categories of the papers (H papers and N papers in Japanand in the US). In each category of the papers, ordering by relative contribution
accounts for the most (more than 60%), followed by senior last ordering,alphabetical ordering and then senior first ordering. In both countries, contribution
based ordering is less used in H papers, and alphabetical and senior last ordering ismore used in H papers. Contribution based ordering is more used in Japan than in
the US.
(Figure 2)
We shows the distribution of the types of ordering by 21 scientific fields inJapan and the US in Figure 3. Although contribution based ordering accounts formore than a half of the papers in most sectors, there exist significant variations
across fields. Alphabetical ordering has a near half to a dominant portion only in
http://www.iir.hit-u.ac.jp/iir-w3/file/WP11-09NagaokaIgamiWalshIjichi.pdf
The respondents were asked to choose only one, although there was a response
category (“other”). We treated “other” as a separate category which cover those cases
where none of the four ordering rule dominated.

6
7

10

mathematics, and economics and business. It accounts for two thirds of the papers
in mathematics and for more than 40 % of the papers in economics and business inthe two countries. The ordering of “senior author last” also has a significant portion
and accounts for more than 40% of the papers in biology & biochemistry,
immunology,

molecular

biology,neuroscience&behavioral

biology,andpharmacology & toxicology in the US. The share of this ordering is less dependent
on the fields in Japan.
(Figure 3)
3.2 Nature of research
There is very strong correlation between the research method employed
and the incidence of the alphabetical ordering across sectors. Figure 4 shows thepercentages of the intensive use of theoretical analysis, and experiments andobservations–results from the question of how intensively the team of scientists
used three research methods (theoretical analysis, experiments and observations,and computations), measured by the Likert scale from no-use (0) to highly intensiveuse (5). Figure 4 uses the response of “highly intensive use”. Since the question was
included only in the Japanese survey, the figure shows only the data for Japan. As isshown, mathematics, and economics and business where the alphabetical ordering
is highly prevalent are outstanding in terms of very high proportion of the intensive
use of theoretical analysis. It exceeds 65%, followed by Physics where only 33% of
the projects use theoretical analysis intensively. Economics and business issurprisingly close to mathematics in terms of the intensive use of theoreticalanalysis. We can also observe a relatively intensive use of theoretical analysis inphysics where alphabetical ordering is prevalent, after mathematics, and economics
and business.
(Figure 4)
Mathematics, and economics and business are also distinguished by the low
percentage of intensive use of experiment and observations (5.1% and 7.7%), whichis less than half of the third lowest field (computer science) in terms of the use of
these research methods, although physics has a relatively high proportion of the
projects using experiment and observations. The variation of the intensity of thetwo research methods goes a long way in explaining the variation of the use ofalphabetic ordering across fields (mathematics and economics vs. the others), as
shown in Figure 5. In this Figure only the Share of alphabetic ordering is based on
the average of the US and Japanese responses. The importance of theoretical11

analysis relative to empirical analysis is given by the difference of the share of very
intensive use of theoretical analysis and the share of very intensive use ofobservations and empirical analysis for each field. In our statistical analysis, wewill further examine whether the project intensively using the theoretical analysis
and using less the empirical analysis is more likely to use alphabetical ordering
within each scientific field, controlling for the other attributes of the projects,
including a number of authors.
(Figure 5)
Another important characterization of the research project is theobjectives: the importance of seeking fundamental understanding and that ofsolving specific issues in real life as the objective of the research project. We would
expect that alphabetical ordering will become more important as the importance offundamental understanding as the objective of the research increases and that it
becomes less important as the importance of providing a solution increases. We do
not find strong cross section correlations between the research objectives and theincidence of alphabetical ordering among the fields, as we saw for the researchmethods and the incidence of alphabetical ordering. However, across projects, as
shown in Figure 6, the share of alphabetical ordering increases with the importanceof fundamental understanding as research objective, in both US and Japan. The
senior author last ordering also tends to increase. More strongly, the share ofalphabetical ordering decreases with the importance of solving a specific real world
problem as the research objective increases, as shown in Figure 7.
(Figure 6 and 7)
The fact that solution orientation matters even in mathematics is indicatedby Figure 8. This figure shows that the alphabetical ordering declines with theimportance of solution orientation in mathematics (In mathematics, 84% of thepapers regards seeking fundamental understanding as very important, so that thevariation in this respect is small). The results also suggest that there exists a
general principle operating across scientific fields.
(Figure 8)
3.3 Author size and locations
Figure 9 shows the distribution of the author ordering type by the author size. In
both Japan and in the US, the share of an alphabetical order becomes initiallysmaller with the increase of author size from 2 authors per paper and then12

increases again. Thus, it follows a U-shaped pattern.
(Figure 9)
Figure 10 shows the distribution by the type of institutional affiliations of
the authors. In both Japan and the US, the alphabetical ordering is less prevalent
when all the authors are co-located in a domestic single institution. On the other
hand, the ordering of “senior author last” becomes more prevalent with co-locationand the share of ordering based on contributions is lower for international
collaborations than domestic collaborations. These results suggest that distancemakes monitoring less effective and/or less intensive, resulting more frequently inalphabetical ordering.
(Figure 10)
3.4 Research management as seen from corresponding authors
Figure 11 shows the relationship between the research management and the author
ordering. The question we asked on management in the survey was whether the
respondent (corresponding author) was PI or Co-PI, whether he played somemanagement less than the role of PI or Co-PI, or whether he played no managementrole as well as whether the research management was unnecessary for the project.
Out survey mainly went to the corresponding author. 80% (60%) of the respondents
were PI or Co-PI in the US (Japan). No research management was necessary for 8%of the cases in Japan and 5% in the US. As shown in this Figure, research
management existed significantly less in the papers with alphabetical ordering in
the two countries.
(Figure 11)
Figure 2 Author ordering by sample (H: highly cited papers, N: Normal papers)(JP, the total sample size =1943, in which 546 papers are H papers)

13

(US
S, the total sample siz
ze=2058, in which 731
1 papers aree H papers))

14

Figu
ure 3.

Author orderinng by sciencce fields (authors>=2))(JP,, sample sizze=1943)(U
US, sample size =2058))

15

Figure4.

F
Frequencyof the us e of theorretical analysis and observations &expeeriments byy scientific fields (Japaan,% , “ veryv
frequeent use” )16

Figure 5.

A
Alphabetica
l orderingg and natu
ure of reseearch (the importance oftheorretical ana
alysis relatiive to empirrical analysis) across 21 science fields(Note) The Sha
are of alphaabetic orderring is baseed on the av
verage of th
he US and
Japa
anese respoonses. The importanceie of theorettical analys
sis relative to empiricaalanalysis is giveen by the diifference off the share of very inteensive use of theoreticcal
analysis and th
he share of very intenssive use of observation
ns and emp
pirical anallysis
for each field.

17

Figure 6

A
Authororrdering byy the objjective of research (Fundam
mentalundeerstanding,, 1 not at alll, 5 very immportant)
(JP)
00%
10

Other

9
90%

Senior lla

880%

Senior
first
Alphabeeti

7
70%
6
60%
5
50%
4
40%
30%
3
20%
10%
0%
9)
1(19

2(77)

3(148)

4(727)(US))

18

5(971)

Fundamentaal
understandiingFigure 7.

Auuthor ordering by the objective ofo research (Solution oof a specificc realworld problem, 1 not at alll, 5 very immportant)
(Jap
pan)

(US
S)

Figure 8 Autthor orderinng by the oobjective of research inn mathemaatics (Solutiion ofa speecific real worldw
probllem, 1 not a
at all, 5 verry importan
nt)

Noote: the num
mber in thee bracket inndicates th
he author siize.
19Figure 9.

A
Authorsize and the auuthor order

(Jap
pan)

(US
S)

Notte: the num
mber in the bracket ind
dicates thee author size.

20

Figure 10. Insstitutional affiliations and authoor ordering
(Jap
pan)

(US
S)

21

Figure 11.

Research ma
anagementt and the auuthor orderr

(Jap
pan)

(US
S)

Notee: The first three choicces are thosse by the reesponding author.
a22

4. Estimation models
4.1 Choice of alphabetical ordering
We examine Hypotheses 1 to 3 by examining the determinants of the choice ofalphabetical ordering at project level. The dependent variable is the dummy foralphabetical ordering: 1 for alphabetical ordering and 0 otherwise.
Σ

Σ

fa
(1)
where the summation Σ indicates that
, and

,

,

are multi-dimensional vectors ofdummies for the corresponding categorical variables by abusing notation.
In order to characterize the nature of research we use the information on
the objectives for the research project and the research method employed. We
hypothesize that the measurement cost of relative contribution based ordering islarger when the number of authors (lnauthors) is large, and when the authors are
not co-located and belong to different institutions (not a single institution). In orderto reflect potential nonlinearity, we also introduce the square of lnauthors:lnauthors_sq. For identifying the co -location, we use a dummy (co_location) which
is set to 1 if all authors belong to a single domestic institution, and set to 0otherwise.
We also control for the team structure because it is quite possible thatheterogeneous team members expect their contributions unequal among themembers from the beginning and thus find it difficult to agree with alphabeticalordering. One measure of heterogeneity is the existence of non-research authors
who provided only research inputs other than research work, such as researchmaterials, access to external research equipment, software or database or money(nonresearch_author which is a dummy). We also use two dummies to indicate
whether all authors are senior (no assistant professors, post-doctoral fellows nor
students, a dummy homogneous_o)and whether all authors are junior(undergraduates, graduate students, postdoctoral fellows, or assistant professor, adummy homogneous_y).
We also introduce a dummy on whether the research management wasnecessary or not (mnt =0 if unnecessary, 1 otherwise) to assess the existence of a PI.
We also introduce the sample type (citedness, H papers or N papers) to account forthe sample selection effect, including the effect as analyzed in Joseph, Laband, and23

Patil (2005)—high-quality papers have disproportionally high share of the papers withalphabetical order ex-post simply because authors are more likely to agree withalphabetical order when all members succeed in contributing to the project. Twenty one

science field dummies and the publication year are included as further controls. We
use the entire sample separately for the US and Japan.
4.2 Existence of research management
We are interested in under what circumstances a PI who specializes in research
management exists. One problem is that we do not have the exact information
about whether the research team has a PI or not unless the correspondence author
is a PI or co-PI. As the second best, we use the same indicator used in the above
equation (1)–whether the research management was necessary or not (mnt =1 ifmanagement was necessary, 0 otherwise). Since it is quite possible that somemanagement role is shared among members without a PI, we use the following two
samples to examine the robustness of results: the whole sample and the sample
with either no management or with the corresponding author being either PI or
co-PI. The specification is given by the following equation, with the dummy for the
existence of a management or PI:
ΣΣ

Research methods
fa

Research resources(2)

where the summation Σ indicates that
, and

,

,

are multi-dimensional vectors ofdummies for the corresponding categorical variables. Except for the variables forresearch resources, we use the same set of explanatory variables as equation (1). As
the measures of research resources, we introduce a budget size (lnres_fund) and thenumberof

non-author

researchers,

such

as

techniciansandstudents(ln1non_authors). Since PI mobilizes resources as well as manages the project, we
expect that PI is more likely to exit, if the project has a large budget, and a large
scale of non-author researches (such as technicians and students), as stated in
Hypothesis 4. Furthermore, we expect that nature of research, author co-locations andthe team structure will have similar (although the opposite signs) effects on theexistence of research management as in the analysis in Section 4.1. The number ofauthors would positively affect the existence of research management as it reflects thescale of resource mobilization.
24

4.3 Last author as a signal of managerial contribution and the first author as asignal of the largest research contribution
When non-alphabetical order is chosen, the order of the authors is used to signal
contributions. Our analytical discussions (Hypothesis 5) suggest that the authors
may wish to send two different contributions by the authors: one for signaling thelargest contribution of a researcher in the research tasks and the other for signaling
the contribution in terms of initiating and managing the research project (PI task).
Thus, we test whether the first author is used for signaling the largest researchcontribution and the last author for signaling the management contribution. Thus,
the dependent variables are the dummies indicating whether the corresponding
author is the first author (First author) or the last author (Last author). The
estimation model is given by



,
Ex

knowledge contributions

Σ(3)

We use the sample where the respondent chose ordering other than alphabeticalordering rule, given that author ordering is essentially random in the case ofalphabetical ordering. The focal explanatory variables are the dummy indicating
whether the corresponding author contributed most to the research which led to thepaper (res_role_d), the dummy indicating whether the corresponding author was PI
or Co-PI (mnt_role_d), and the dummy indicating whether he/she played the bothroles (both_res_mnt_d). For this, we collected information from the survey onwhether the corresponding author made the largest contribution in research among
the researchers as well as whether he was a PI or a Co-PI.
We introduce the following control variables. The first one is author size
(lnauthors), which tends to reduce the probability of any author becoming the firstor the last author. However, if the role of corresponding author is more likely to bedelegated to those other than PIs in larger teams and the focal point is the firstauthor who benefits most from the name ordering, the chance that the first author
becomes the corresponding author may be increasing in the size of the research
team.

If the latter effect is important, the corresponding author is more likely tobe the first author when the team size is large. If there exist important external
ex-ante knowledge contributions that helped initiating the project, the relative roleof the corresponding author in generating the research outcome will decline. We
recognize the knowledge embodied in humans (cncpt_hum, for example, visiting
25

scholar, advisor in a graduate program, or corporate partner) and that embodied inliterature (cncpt_lit). We also control for the rank of the corresponding author. A
lower ranked corresponding author may be more likely to be the first author, sincesuch credit is more important for young scholars in the labor market. The othercontrols are the type of the sample, and 10 science field dummies.
We use liner probability model for estimations.
5. Estimation Results
5.1 Alphabetical ordering
Table 1 presents the results of econometric estimations based on a linear
probability model for a pooled sample for each of Japan and the U.S. and Table 2
shows the results for Japan separately estimated for the two fields wherealphabetical ordering is prevalent (mathematics, and economics and business) andfor all the other fields. We start by discussing Table 1. Model 2 and 4 in Table 1
introduces the dummy for the existence of a research manager (mnt). Consistent
with Hypothesis 1, papers from research projects oriented to finding a solution to a
specific real world problem and employing experimental method are less likely tofollowalphabeticalordering,

while

those

employingtheoreticalanalysisintensively are more likely to use alphabetical ordering.
More specifically, the results suggest that when the importance of finding asolution to a specific practical problem as the research objective increases by 4
points from 1 to 5 in the Likert Scale, the probability that the paper follows
alphabetical ordering declines by 6 percentage points in Japan and 4 percentage
points in the US (see Model 1 and 3). Very consistent with these results, according
to the result from a Japanese sample (Model 1), the research project using thetheoretical method very intensively is 6 percentage points more likely to usealphabetical ordering, compared to the research project which does not use atheoretical method. Furthermore, the research project using the experimental
method very intensively is 10 percentage points more likely to use contribution
based ordering, compared to the research project which does not use an
experimental method. The use of computation method has a similar effect,
although the size of the effect is around a half. As for the US sample, the research
project which significantly led to the improved facilities is less likely to usealphabetical ordering (2.5 percentage points if yes). These effects are very
substantial and strongly support Hypothesis 1. On the other hand, it is found thatthe score of fundamental understanding objective does not significantly affect the26

choice, controlling for the other variables characterizing the nature of research.
(See Table 1)
The number of authors has a clearly U-shaped relationship with the choiceof alphabetical order. Its share initially declines and then increases with thenumber of authors in both countries, supporting Hypothesis 2. The minimum isachieved at 5 authors in Japan (Model 1) and at 7 authors in the US (Model 3). The
initial decline would suggest the decline of an incentive effect of an alphabeticalordering and the latter increase would reflect the benefit of saving the cost ofmonitoring and evaluation in a big team. A U-shaped relationship between theauthor size and the choice of alphabetical order is not affected much even if wecontrol for the necessity of project management (compare Model (1) and (2) as well
as Model (3) and (4). This may indicate that the research management variablelargely captures the necessity of resource allocation and task coordination rather
than that of monitoring.
When the authors are co-located (all affiliated with one institution), thepaper is significantly more likely to be non-alphabetical in both US and Japan. The
coefficients are larger in the US. The results signify the negative effect of distanceon the cost of implementing contributions based ordering, thus supporting
Hypothesis 3.
The team structure does not have significant coefficients, except for the
homogeneous young scholars dummy in the US. If the team is exclusively made up
of junior researchers, they tend to adopt alphabetical ordering in the US, consistent
with our expectation. On the other hand, nonresearch_author is found to have nosignificant coefficient. The coefficient of research management (mnt) is negative but
only weakly significant at 10 % level (model 2 and 4), suggesting that the existenceof a research manager such as a PI reduces the use of alphabetical ordering. This
result provides some support for Hypothesis 4.
Finally, Table 2 presents the results for two subsamples: mathematics andeconomics/business, and the other sciences using only Japanese data. The
specification is parsimonious, reflecting a small sample for the first subsample.
Among four variables for nature of research (use, theoretical research, experimentand observation, and computation), except for experiment and observation in Model(5), have highly significant coefficients with the same signs for both subsamples.
However, the size and significance of the estimated coefficient are much larger formathematics and economics/business—roughly 10 times greater coefficients.

Thus,

for an example, one point increase in the intensity of the use of theoretical analysis
27

increases the incidence of alphabetical ordering by 12% for mathematics andeconomics/business but only 1% for the rest of the fields. One interpretation of thisstark difference is that the degree of collaboration required in theoretical analysis
may differ significantly across fields. Another interpretation is that a field norm
may augment or weaken the impact of the nature of research because the signaling
effect of contribution-based ordering could differ across fields depending on thenorm. These results together with Figure 5 imply that the nature of research cansignificantly explain both the variation within a field as well as that across fields ofthe incidence of alphabetical ordering.
(Table 2)
5.2 Existence of a PI or research management
The estimation results in Table 3 show that the project is significantly more likely
to have research management, when the research budget is large in the twocountries. The semi-log elasticity coefficient is significantly larger in Japan: 0.02 vs.
0.008. The number of the authors has positive and similar sized coefficients in bothcountries and it is significantly positive in the US. These results support Hypothesis
4 that the mobilization of large research resources makes managerial control
important.
Co-location makes research management more likely to exist in bothcountries (5% significant), consistent with the results on hypothesis 3. Ourinterpretation is that co-location makes the cost of management lower so that a PI
is more likely to exist. Heterogeneity of a research team is also associated with theincidence of PI or someone taking a managerial role. When a research teams
consists of all senior members, it requires less research management and thus
unlikely to have a PI. The existence of non-research coauthors is also significantly
positive in Japan, but it is not in the US.
Unlike the choice of alphabetical ordering relative to contribution basedordering, the nature of research does not significantly affect the existence of
research management, except that the use of experiment, as measured by the
improvement of facilities, has a significantly positive coefficient in the US and theuse of a computational method has a positive and weakly significant coefficient inJapan.
5.3 Signals from the first author and the last author
The Estimation results reported in Table 4 (linear probability model) suggest that28

the first author and the last author do clearly represent different types ofcontributions of the authors when non-alphabetical ordering is chosen. The author
with the largest research contributor is likely to become the first author and not thelast author in both countries. At the same time, the PI/Co-PI author is likely to
become the last author in both countries. These results strongly support Hypothesis
5. If the same author performs both, he/she tends to become the first author.
In addition, when the number of authors is large, a corresponding author issignificantly less likely to become the last author in the two countries. But it is more
likely to become the first author in the US (10% significance). This contrast mightreflect a difference between the two countries in the way that managerial tasks areshared within research teams. As Figure 11 shows, the corresponding author ismore likely to be the one who played some management role in Japan than in the
U.S.. If managerial tasks are more likely to be shared among multiple co-authors
rather than one PI, the role of corresponding author may be more likely to be
assigned to one of them rather than the first author.

Finally, corresponding author

with a junior position is more likely to be the first author to a similar degree in the
US and Japan.
(Table 4)
6. Conclusions
This paper has examined empirically what drives author ordering in scientific research.
While there is substantive existing literature, they do not explain well the reasons why
the alphabetical ordering is significantly used only in a limited number of fields andwhy there are significant variations across fields. We hope that this paper madecontributions for deepening our understanding.
We first discussed a theoretical framework for the choice between
relative-contribution-based ordering and alphabetical ordering, focusing on nature ofresearch, in particular, the importance of collaboration in the context of incomplete
contract, measurement cost and the role of a principal investigator (PI). Based on this,
we hypothesize that (1) alphabetical ordering is more used in theoretical research or one
pursuing a fundamental understanding, relative to empirical research or one pursuinga solution to a specific real world problem, (2) it is also more used when themeasurement cost of contribution-based ordering is large (a large team and distantlocations), and (3) a PI exists to mobilize financial resources and enables the use ofcontribution-based ordering by monitoring the members’ outputs.
Our empirical examinations, based on the new large scale original scientists’29

surveys in the US and Japan, show the results consistent with these hypotheses. In
particular, an alphabetical ordering is more used when the research is theoretical andhas less empirical component and when the team size is large and not co-located. In
particular, the variation of research method goes a long way in explaining the variationof the incidence of alphabetical name ordering across fields (mathematics andeconomics vs. the other fields) as well as a significant variation within a field.
We also find that PI or Co-PIs are more likely to exist when the project uses
more resources but also when the team is more heterogeneous. We find that author
ordering sends two signals in contribution based ordering, the first author for thelargest research contribution and the last author for the PI or Co-PI.

There exists a number of remaining research issues. One is to examine theeffects of author ordering on performance, which will further clarify the efficiency
ground for the choice of author ordering. The past literature suggests that the
papers with alphabetical ordering are often highly cited. Our results reported in
Figure 2 also suggest that highly cited papers are more likely to use alphabeticalordering in both countries. However such tendency disappears in Japan once wecontrol for the size and nature of the project, as reported in Table 1.

30

Table 1

Choice of alphabetical (dependent variable order_alpha_adj : 1 ifalphabetical and 0 otherwise,

Linear Probability Model,

All sample andauthors>=2)JP

USModel (1)jp_theoretical
Research methods (6points Likert scale in jp_experimentJapan and a dummy (imprv_facilities US)
in the US)
jp_computation(imprv_comput US)
lnauthorsNumber of authors

lnauthors_sq

Author co-locations co_location( domestic single
institution)
Team structure
Only senior
Only young
Research
management
Sample (H type)

Model (3)Model (4)withmanagement

withmanagement

0.004710.00496(0.00320)(0.00322)-0.0166*** -0.0163***(0.00350)(0.00349)
0.0118*** 0.0118***(0.00254)(0.00253)-0.0198*** -0.0194***(0.00379)(0.00378)-0.0101*** -0.00973***(0.00319)(0.00314)-0.190*** -0.188***(0.0227)(0.0227)
0.0581*** 0.0579***(0.00628)(0.00626)

0.003210.00359(0.00381)(0.00380)-0.0110*** -0.0109***(0.00395)(0.00395)-0.0252*** -0.0234**(0.00923)(0.00920)
0.00274
0.00268(0.00971)(0.00965)-0.152*** -0.148***(0.0351)(0.0352)
0.0399*** 0.0393***(0.0103)(0.0104)-0.0149**-0.0393*** -0.0379***

VARIABLES
fundamental
Research objective(5 point Likert scale) use

Model (2)(0.00717)-0.00768(0.00591)-0.00486(0.00766)
0.0906(0.109)-0.0136*(0.00714)
nonresearch_author-0.00719(0.00591)
homogeneous_o-0.00644(0.00761)
homogeneous_y
0.0859(0.105)mnt-0.0361*(0.0186)citedness
0.01470.0147(0.00904)(0.00901)
Observations
1,931
1,931
R-squared
0.428
0.431
Adjusted R-squared
0.418
0.421
RMSE
0.145
0.144
Log Likelihood
1012
1016
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.10(0.00925)-0.00821(0.00907)-0.0123(0.0121)0.113**(0.0504)

0.0244**(0.00998)
1,754
0.375
0.363
0.180
537.8

(0.00917)-0.00998(0.00905)-0.0131(0.0121)0.116**(0.0502)-0.0587*(0.0337)
0.0235**(0.0100)
1,748
0.384
0.372
0.179
545.6

Note. 21 field dummies (see Table 3 for the results from 3 fields) and year effect notdisplayed.

31

Table 2.

Choice of alphabetical (mathematics & economics/business vs. the otherfields, dependent variable order_alpha_adj, Linear Probability Model, Japan
sample and authors>=2)

VARIABLES
Research objective (5 point
Likert scale)use
jp_theoretical

Research methods (6 points
Likert scale in Japan and adummy in the US)

jp_experimentjp_computation
lnauthorsNumber of authors

lnauthors_sq

Author co-locations ( domestic co_location
single institution)
Sample (H type)

Note.

citednessJP
Model (5)Model (6)Mathematics &Economics/Busi Other fields
ness-0.121***-0.0146***(0.0420)(0.00333)
0.124**
0.00959***(0.0464)(0.00252)-0.0259-0.0183***(0.0407)(0.00369)-0.114***-0.00724**(0.0243)(0.00311)-0.933-0.183***(1.403)(0.0226)0.320
0.0567***(0.650)(0.00630)-0.0969-0.00954(0.126)(0.00650)
0.256
0.0126(0.155)Observations
41
R-squared
0.710
Adjusted R-squared
0.613
RMSE
0.303
Log Likelihood-2.875
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.10(0.00891)
1,899
0.276
0.266
0.133
1148

21 field dummies (see Table 3 for the results from 2 fields) and year effect

not displayed.

32

Table 3.

Existence of a PI or research management (Linear Probability Model)
JP
Model (7)fundamental

0.00267

0.00406

0.004880.00603(0.00675)
0.00306(0.00532)
0.000501(0.00453)-0.000200(0.00485)
0.00796*(0.00424)
0.0423(0.0271)-0.00702(0.00493)
0.0335***(0.00788)
0.00275(0.00596)
0.00147(0.00489)
0.00161(0.00564)
0.00851*(0.00464)
0.0287(0.0298)-0.00521(0.00547)
0.0351**(0.00606)
0.00306(0.00404)(0.00640)
0.00311(0.00424)

0.0217**(0.0101)
0.00484(0.0120)0.0465**(0.0212)-0.00519(0.00449)
0.0214**0.0249**(0.0108)0.00431(0.0127)0.0479**(0.0227)-0.00550(0.00480)
0.0236**(0.0129)(0.0141)(0.0107)(0.0113)jp_theoretical
Research methods (6jp_experimentpoints Likert scale in Japan(imprv_facilities US)
and a dummy in the US)
jp_computation(imprv_comput US)
lnauthors

Team structure

lnauthors_sq
co_location

nonresearch_author

0.0208*0.0279**(0.0114)(0.0127)
Only seniorhomogeneous_o-0.0403*** -0.0443***(0.0144)(0.0161)
Only young
homogeneous_y-0.0763-0.196(0.157)(0.248)lnres_fund
0.0200*** 0.0233***
Resources (fund, nonauthor(0.00361)(0.00399)
researchers)
ln1non_authors
0.008760.0110*(0.00565)(0.00622)
citedness-0.0287** -0.0373**
Sample (H type)(0.0133)(0.0152)
Observations
1,872
1,669
R-squared
0.091
0.109
Adjusted R-squared
0.07350.0899
RMSE
0.246
0.258
Log Likelihood-16.49-85.86
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.10

Note. 21 field dummies and year effect not displayed.

33

all

Model (10)part (PI or
CoPI vs. nomanagement)

all

use

Author co-locations (domestic single institution)

US
Model (9)VARIABLES

Research objective (5point Likert scale)

Number of authors

Model (8)part (PI or
CoPI vs. nomanagement)-0.0128-0.0145(0.0109)(0.0114)-0.0264*-0.0286*(0.0156)(0.0165)0.03920.0403(0.0281)(0.0323)0.00828*** 0.00910***(0.00252)(0.00268)
0.003420.00304(0.00462)(0.00488)-0.0135-0.0124(0.0109)(0.0114)
1,733
1,644
0.100
0.108
0.08150.0892
0.196
0.200
380.2
327.1

Table 4

What does the first and the last authors signal? ( linear probability model,

authors>=2, sample excluding alphabetic ordering)

VARIABLES

(1)

(2)

(3)

(4)

JP

US

JP

US

First author
0.550***(0.0470)0.0415(0.0380)-0.148***(0.0519)0.0349*(0.0179)
0.0111(0.00939)
0.000453(0.0142)-0.0260(0.0215)-0.113***(0.0292)
0.0909***(0.0324)-0.0297(0.0391)-0.0437**(0.0171)
0.00542(0.00777)-0.00836(0.00993)
0.00397(0.0208)-0.205***(0.0479)0.186***(0.0387)-0.113**(0.0529)-0.131***(0.0183)-0.0320***(0.00957)
0.0214(0.0145)0.0653***(0.0219)

Associate professor 0.210***0.0801***-0.0870***-0.0637**(0.0228)0.314***(0.0271)
Post doctoral fellow 0.332***(0.0492)
0.320**
Rank of correspoinding author (Base: Technicians(0.142)
professor)
PhD
0.361***(0.0576)
Master
0.320**(0.128)Other
0.273**(0.108)
Unknown
0.412(0.396)
Observations
1,875
R-squared
0.385
Log Likelihood-904.9Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(0.0279)
0.136***(0.0302)0.323***(0.0413)-0.00793(0.121)
0.326***(0.0423)
0.189*(0.102)0.220***(0.0519)0.127(0.0924)
1,665
0.380
-806.1(0.0218)-0.140***(0.0260)-0.148***(0.0472)-0.190(0.136)-0.132**(0.0553)-0.112(0.123)-0.200*(0.104)-0.209(0.380)1,875
0.077-826.8(0.0284)-0.116***(0.0308)-0.367***(0.0422)-0.157(0.123)-0.344***(0.0431)-0.319***(0.104)-0.215***(0.0530)-0.221**(0.0943)
1,665
0.324
-839.2Largest contribution in research
PI or Co-PI

res_role_d
mnt_role_dBoth largest research contribution
and PI/CoPI, additional effectAuthor size

both_res_mnt_d

Ex-ante knowledge contribution(embodied in humans)

cncpt_hum

Ex-ane knowledge (literature)Sample (H papers)

0.580***(0.0305)
0.0216(0.0338)-0.180***(0.0408)-0.0210(0.0179)-0.0306***(0.00810)
0.0121(0.0104)-0.0752***(0.0216)

Last author

lnauthorscncpt_lit
citedness

Lecturer/assistantNote. 10 science field dummies and year effect not displayed.

34

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36

Appendix.

Descriptive statistics

JP
Variableorder_alpha_adj
fundamental
use
jp_theoreticaljp_experimental

Total
Contribution
Alphabetical
Senior last
Obs
Mean
Std. Dev. Min Max Obs
Mean
Obs
Mean
Obs
Mean
1943
0.04
0.19
0
1
1471
0
73
1
248
0
1958
4.31
0.85
1
5
1471
4.27
73
4.73
247
4.42
1957
3.51
1.28
1
5
1470
3.58
73
2.05
247
3.55
1957
3.57
1.69
1
6
1470
3.51
73
5.53
247
3.42
1958
5.02
1.65
1
6
1471
5.09
73
2.26
247
5.34

jp_computational
authorsco_location
nonresearch_authors
homogeneous_o

1958
1959
1959
1959
1959

3.24
6.71
0.35
0.39
0.32

1.86
15.82
0.48
0.49
0.47

1
2
0
0
0

6
327
1
1
1

1471
1471
1471
1471
1471

3.26
5.87
0.35
0.41
0.33

73
73
73
73
73

3.41
29.45
0.14
0.23
0.51

247
248
248
248
248

3.07
4.83
0.48
0.38
0.21

homogeneous_ylnres_fund
ln1non_authors
citedness
mnt

1949
1883
1959
1959
1959

0.00
11.58
1.03
0.28
0.92

0.07
2.20
0.99
0.45
0.27

0
4.6
0.0
0
0

1
19.5
6.4
1
1

1466
1417
1471
1471
1471

0.00
11.48
1.03
0.26
0.92

72
66
73
73
73

0.06
11.12
0.79
0.47
0.73

246
243
248
248
248

0.00
12.01
1.06
0.30
0.96

corre_firstcorre_last
res_role_d
mnt_role_dboth_res_mnt_d

1957
1957
1957
1959
1957

0.50
0.20
0.67
0.59
0.45

0.50
0.40
0.47
0.49
0.50

0
0
0
0
0

1
1
1
1
1

1471
1471
1470
1471
1470

0.58
0.17
0.68
0.58
0.45

73
73
73
73
73

0.32
0.38
0.63
0.38
0.33

248
248
247
248
247

0.25
0.23
0.66
0.72
0.50

cncpt_hum
cncpt_lit
publication_year

1954
0.78
1.19
0
6
1956
0.81
0.92
0
4
1959 2003.454 1.695343 2000 2007

1466
0.77
1468
0.80
1471 2003.41

73
73
73

0.86
0.99
2003.5

248
248
248

0.80
0.82
2003.6

US
Variableorder_alpha_adj
fundamentaluse
imprv_facilities
imprv_comput

Total
Contribution
Alphabetical
Senior last
Obs
Mean
Std. Dev. Min Max Obs
Mean
Obs
Mean
Obs
Mean
2058
0.06
0.23
0
1
1313
0
119
1
485
0
2091
4.55
0.84
1
5
1305
4.52
119
4.69
483
4.63
2052
3.74
1.35
1
5
1288
3.84
113
3.00
470
3.64
1989
0.35
0.48
0
1
1240
0.35
102
0.16
470
0.39
2010
0.42
0.49
0
1
1249
0.45
108
0.43
476
0.33

authorsco_location
nonresearch_authors
homogeneous_ohomogeneous_y

2101
2101
2101
2101
1931

5.57
0.37
0.31
0.22
0.02

10.95
0.48
0.46
0.41
0.15

2
0
0
0
0

375
1
1
1
1

1313
1313
1313
1313
1210

5.29
0.37
0.31
0.24
0.02

119
119
119
119
109

7.04
0.18
0.24
0.45
0.06

485
485
485
485
458

5.40
0.44
0.30
0.10
0.02

lnres_fund
ln1non_authors
citednessmntcorre_first

2026
2101
2101
2095
1873

11.74
0.59
0.36
0.95
0.46

2.06
0.87
0.48
0.22
0.50

8.52 18.42
0.00 5.80
0
1
0
1
0
1

1267
1313
1313
1308
1174

11.70
0.63
0.33
0.95
0.56

114
119
119
119
111

10.63
0.34
0.44
0.78
0.56

473
485
485
485
440

12.04
0.51
0.40
0.99
0.15

corre_last
res_role_d
mnt_role_dboth_res_mnt_d
cncpt_hum

1873
2093
2095
2087
2051

0.39
0.59
0.80
0.50
0.67

0.49
0.49
0.40
0.50
1.08

1174
1309
1308
1304
1283

0.29
0.65
0.79
0.53
0.66

111
116
119
116
115

0.34
0.55
0.62
0.45
0.68

440
485
485
485
472

0.70
0.45
0.84
0.39
0.66

cncpt_lit
publication_year

2024
0.66
0.72
0
4
2101 2003.429 1.726076 1999 2007

1269
0.67
1313 2003.39

115
119

0.79
2003.3

462
485

0.65
2003.6

0
0
0
0
0

37

1
1
1
1
7