Eight Facts about Informal Collaboration in Financial...

Eight Facts about Informal

Collaboration in Financial Economics

Co-Pierre Georg∗, and Michael E. Rose†

August 23, 2015

WORK IN PROGRESS - DO NOT CIRCULATE

Collaboration is increasingly important for successful innovation and in

the production of knowledge. Much collaboration relies on social interactions

and takes on informal forms like feedback provided by colleagues as well as

presentations in seminars and at conferences. We document the intensive and

extensive margin of informal collaboration in financial economics, using more

than 2782 published research articles. We also construct a social network to

analyze the information flow prior to publication.

Keywords: Formal collaboration, informal collaboration, social network,

acknowledgements

JEL Classification: A14, D83, G00

∗University of Cape Town and Deutsche Bundesbank. E-Mail: [email protected].†University of Cape Town. E-Mail: [email protected]

1

[email protected]

[email protected]

1. Introduction

Innovation has become an increasingly collaborative effort over past decades, in partic-

ular in scientific research as a special form of innovation (Wuchty et al., 2007; Jones,

2009). The most visible type of collaboration in science is co-authorship, and the rise

of co-authorship has been documented and examined in a number of studies (McDowell

and Melvin, 1983; Barnett et al., 1988; Goyal et al., 2006). The literature expands to

questions on how co-authorship links emerge (Freeman and Huang, 2015; Fafchamps and

van der Leij, 2006); how authors benefit from network linkages (Azoulay et al., 2010;

Ductor et al., 2014); and whether teams are more productive than solo authors (Wuchty

et al., 2007; Ductor, 2015).

Most collaboration, however, is much less formally organized (Laband and Tollison,

2000): in science, researchers engage in discussions over lunch, around watercoolers,

or at academic seminars and conferences. In a joint editorial, the editors of the three

major finance journals, encourage authors actively to collaborate informally in order to

improve their papers: "So how should authors maximize the value of the journal review

process? They should circulate their papers and give seminars to colleagues to receive

constructive criticism before submitting to a journal." (Green et al., 2002). Informal

collaboration is not observable in most settings. The advantage of scientific research in

economics and finance is that it is a common norm to acknowledge helpful colleagues,

seminars, and conferences in the acknowledgements section of a paper (Laband and Tol-

lison, 2000). Cronin and Franks (2006) documents informal collaboration in life sciences

(in the journal Cell) document an increase in find all forms of informal collaboration

(except manuscript preparation).

This form of collaboration, however, has been addressed by only few studies, with the

notable exception of Laband and Tollison (2000) and Oettl (2012). Laband and Tollison

(2000) compare formal versus informal intellectual collaboration in academic publishing

in economics and biology. They find a comment to increase expected citation by 1.13

citations. The benefit of an acknowledged comment grows with the commenters’ citation

stock. Hence, it matters whom authors approach for help and critique. Oettl (2012)

2

examines the effect of the unexpected deaths of 149 life scientists on their former co-

authors. These dead scientists are mapped along two dimensions, namely productivity -

measured as publication output - and helpfulness, where helpfulness is defined as above

average frequent occurrence in others’ acknowledgement sections. His finding is striking:

Former co-authors experience a 14% drop in the quality of their work if the dead scholar

is very helpful and 16% if the dead scholar is also very productive. Co-authors of scholars

that are above average productive but below average helpful experience no drop in their

research quality.

In this paper, we study various aspects of informal collaboration in scientific research

in the fields of financial economics. We construct a novel and unique dataset using

the acknowledgement sections of 2782 publications in six major finance journals (the

three general interest journals The Journal of Finance, Journal of Financial Economics,

and The Review of Financial Studies, and three field journals, Journal of Money, Credit,

and Banking, Journal of Financial Intermediation, and Journal of Banking and Finance)

during two sample periods (1998-2000 and 2008-2011).1 Using acknowledgement sections

from these papers, we construct two networks. In the network of formal collaboration,

scientists are connected whenever they coauthor an article. In the network of informal

collaboration, scientists are connected whenever one acknowledges the other. In case of

author teams, we assume a link between every commenter and every author.

Though there are number of studies investigating co-author networks, none of them

includes social ties occurring informally, that is via the provision of comments. Most

notably is the study by Goyal et al. (2006), who confirm the expectation of a evolving

small network in formal collaboration as technology lowered the cost of such collabora-

tion. Including connections between authors visible from their comments allows us to

analyze other ways of informal flow. For example, there are more than 10 times as many

connections between researchers in the network of informal collaboration than in the

network of formal collaboration (for the 2009-2011 period), but only about 2.5 as many

1In the early sample there are 887 publications out of which 503 (384 ) are in general interest (field)journals. In the late sample there are 1895 publications out of which 910 (985 ) are in generalinterest (field) journals.

3

nodes. As a result, the social distance between scholars decreases: The average short-

est path length between two authors in the co-author network is more than 11, while

it is only about 4.5 in the acknowledgements network: It only takes four interactions

(i.e. informal collaborations) to communicate a new idea between any two academics

in the acknowledgements network, while the same communication would require eleven

interactions (i.e. formal collaborations) in the co-author network.

We identify eight stylized facts. First, informal collaboration has always been preva-

lent. The share of articles that do not acknowledge helpful input was never higher than

15% colleagues. Though today a slightly smaller share of articles report informal collabo-

ration, they report more informal collaboration (that is, acknowledge more commenters,

seminars and conferences). And there are more about 1.5 as many commenters as there

are authors involved in the production of knowledge. Second, there is more informal

collaboration with articles that publish in general interest journals. The median gen-

eral interest paper published between 2009 and 2011 acknowledges 11 commenters, 5

seminars and 3 conferences, but the median field paper acknowledges 4 commenters, 0

seminars and 1 conference.

Third, we can observe a democratization effect: More authors from lower ranked

universities collaborate with authors from top universities and more papers are being

presented at lower ranked universities. At the same time, the journals in our set become

more polarized: Field journal articles are presented at better ranked universities than

general interest publications but the rank of their author’s main affiliations is signifi-

cantly lower.

Fourth, the average distance between authors of papers published in general interest

journals today is much larger than the average distance of their field journal counterparts.

In 1998 however, authors from general interest journals were geographically somewhat

closer together than authors of field journals. Fifth, authors today travel on average 20%

to present seminars, while authors of general interest journals travel about 50% longer

than authors of field journals.

Fifth, reciprocity is rare and has becoming more so. In the early timeframe about

4

24% of all potential commenter-links are reciprocal, meaning that about a quarter of

all authors also comment on the work of their commenters. A potential reciprocal

commenter-link emerges when the commenter published at least one paper without the

author under consideration. Again, the share declined by nearly a third to 17% in the

2009-2011 period. The same holds for commenting co-authors: Only about 16% of all

authors in the 1998-2011 period are acknowledged for helpful comments by their other

feasible co-authors. A co-author is said to be feasible if she publishes another paper

without the author in consideration. The share has declined by a quarter to 12% in the

2009-2011 period.

Sixth, the network of formal collaboration today has grown and became more in-

clusive. This is mainly due to a larger number of co-authoring teams. Seventh, the

social network of informal collaboration is somewhat less connected today although it

expanded massively. This indicates that information today spreads at a slightly slower

pace in the 2009-2011 timeframe than a decade before. The social network consisting of

the three general interest journal only is (and was) however much denser than the field

journals counterpart.

Eight, central authors (in the late timeframe) are not necessarily central commenters.

Most Pearson correlations between centralities for authors and commenters are less than

0.50. The only exception are eigenvector central nodes are tend to be betweenness central

commenters (Pearson correlation: 0.69).

The remainder of this paper is organized as follows. Section 2 introduces our dataset

and explains the construction of the social network and key variables. Section 3 briefly

introduces some graph theory nomenclature. Section 4 discusses all stylized facts and

Section 5 concludes.

2. Data

We use data from three sources. First, we collect published research papers from six

journals from the journals’ websites. Second, we collect geographical coordinates of

5

universities, central bank, think tanks and private banks from the English Wikipedia.

Third, we use the Tilburg Ranking of the Tilburg University to rank author affiliations

and seminar venues.

Our main data source is research articles that have been published in six financial

journals: The Journal of Finance (JF), the Journal of Financial Economics (JFE), The

Review of Economic Studies (RFS), the Journal of Financial Intermediation (JFI), the

Journal of Money, Credit, & Banking (JMCB), and the Journal of Banking and Finance

(JBF). The first three journals are commonly regarded as general interest journals,

while the remaining three journals are considered to be field journals. The set of general

interest journals for example is identical to the set used in the study of Borokhovich

et al. (2000).

We look at articles published during two sample periods: the early sample consists of

articles published between 1998 - 2000 and the late sample those published between 2009

- 2011 (Figure 1). The idea is to account for effects of organizational and technological

change on the academic publication process (Ding et al., 2010). We restrict our analysis

to what the publishers Wiley (JF and JMCB), Oxford Journals (RFS) and Ohio State

University Press (JMCB) call Articles and what Elsevier (JBF, JFE, JFI) calls Original

Research Articles. Additionally, we omit notes, discussions, shorter articles, conference

announcements, minutes, policymakers roundtable, etc. This gives us a total of 2782

(early: 887 ; late: 1895 ) articles.

The sample is very homogenous: 91% of all articles listing JEL codes2 belong to JEL

letter G (Financial Economics). Additional 6% list E (Macroeconomics and Monetary

Economics), but not G. The six journals are not only homogenous in their research

focus, but also in their editorial process: All journals except the JMCB used single blind

referee process throughout the sample period and only the JFE reveals the names of the

referees upon publication if they agree. Three journals (RFS, JFE and JBF) explicitly

encourage authors to acknowledge helpful individuals on the paper.

For each paper we collect standard bibliometric information. This include the title,

2Articles published in the JF never lists JEL codes, while the RFS introduced JEL codes in Winter2006 only.

6

each author, each affiliation for each author, listed Journal of Economic Literature (JEL)

codes and the number of pages.

FIGURE 1 ABOUT HERE

Our main contribution is to examine the acknowledgement section which is typi-

cally on the frontpage or the end of an article (Green et al., 2002). In this section,

authors commonly acknowledge helpful input by colleagues and state where a paper

has been presented. Funding sources and hospitality during visiting positions are often

acknowledged, too. From the acknowledgement section, we collect: which seminars and

conferences the article has been presented, visiting (and former) faculty positions and

the names of colleagues that have provided input.

Like in other disciplines (e.g. life science, see Cronin and Franks (2006)), authors

acknowledge for different reasons. We distinguish between the following groups of ac-

knowledged individuals: (1) editors, (2) referees, (3) discussants, (4) PhD advisors and

committee members, (5) colleagues that have provided comments (commentators), (6)

colleagues that have provided data, (7) research assistants, and (8) non-academic per-

sonnel from banks and industry. In what follows, we restrict our analysis categories (3)

till (5) categories and omit the rest. There are several reasons for our focus. First, there

is no flow of academic information involved in the last three categories. Second, the vast

majority of articles acknowledges the editor of the respective journal. If we calculate an

editor’s structural embeddedness, we are likely to find biases towards frequently pub-

lishing journals. The more articles a journal publishes, the better its editors perceived

centrality in the data; which is what we strive to avoid. The same is true for referees,

but they are rarely known by name.3

A consolidation procedure was necessary because the same name is frequently spelled

in different ways. The Journal of Finance’s longtime editor Campbell R. Harvey is be-

ing acknowledged as Cam Harvey, Campbell Harvey, Campbell R. Harvey, and Campell

3Due to the Journal of Financial Economics’ journal policy, referees may choose to reveal themselvesupon publication. For some JFE publications, the referees is therefore publicly known, because theauthors acknowledge her. Other journals don’t list referees by name.

7

Harvey (with a typo). An additional problem arises for example with different naming

conventions e.g. for Asian names (first name, last name vs. last name, first name). To

account for all these effects, we conduct an internet search for all authors and acknowl-

edged individuals to find their full and proper name. After the consolidation process

we are left with 3455 (7527 ) names in the early (late) timeframe. Some researchers of

course occur in both timeframes, which is why there is a total of 9474 persons in our

dataset.

Similarly, we consolidated the names of affiliations. For example, the MIT’s Sloan

School of Management is being acknowledged as MIT (Sloan), MIT Sloan, and MIT

Sloan School of Management. We consolidate affiliations on the university (or central

bank, bank, think tank) level. This allows us to augment our database with the Tilburg

ranking.4 The Tilburg ranking ranks economics department worldwide based on their

unweighted publication output in 70 journals over the preceding five years.5

To examine traveling distances of authors, we augment our dataset with geographic

coordinates for affiliations of authors as well as places where seminars were given. For

731 universities, central banks and private firms we receive geographic coordinates from

then English Wikipedia6

Using bibliometric and acknowledgement information we construct two types of so-

cial networks. In the network of formal collaboration (or network of co-authorship)

academics are connected by an undirected and unweighted link whenever they have

co-authored a paper. While co-authorship, which is a formal and strong way of collab-

oration, is relatively rare in our sample, informal collaboration in the form of feedback

on a paper is relatively common. In the network of informal collaboration (or network

of acknowledgements) two academics are connected by a directed and unweighted link

whenever one acknowledges the other. Consequently, an author shows up in both net-

works, but with different neighbors: in the network of formal collaboration with her

4Data downloaded on 7 March 2014.5By using the Tilburg ranking we undererstimate the ranking of authors affiliated with a central bankor business school. Given the large sample size we have, this error will be small, however. As of yet,there is no research ranking of central banks available.

6Data downloaded in June 2015.

8

co-authors as network neighbors, but in the network of informal collaboration with her

commentators as neighbors. The network of formal collaboration (or co-author network)

is shown in Figure (6) for two subsamples of the early/late sample, while the network of

informal collaboration (or acknowledgements network) is shown for the same subsamples

in Figure (7).

Both networks account for different dimensions of scientific work. A node with many

ties in the formal network is someone that is likely to publish and collaborate often.

Therefore, the formal network captures the productivity dimension. The informal net-

work on the other hand captures a dimension that Oettl (2012) called helpfulness. This

is plausible, because a node with many ties in the network of informal collaboration

represents a researcher that comments (discusses, advises) often. A very productive

researcher must not necessarily be a very helpful researcher in the sense that she helps

others during the publication process.

3. Network nomenclature

We introduce some graph theory in order to examine the structure of (social) networks.

Formally, let Pt be the number of articles in t ∈ early, late. To each paper p ∈ P , there

is a set of authors κp as well as a set of commentators ιp. Also, let N = {1, 2, . . . , n}

be the set of academics in the social network. N c and Na denote academics in the

co-author and acknowledgement network, respectively. A link between two academics

i, j ∈ N c in the co-author network is denoted gcij and takes the values {0, 1, . . . lc} where

lc is the maximum number of papers academics have co-authored. The set of all gcijforms the symmetric adjacency matrix of the co-author network Gc. Similarly, a link

between two academics i, j ∈ Na in the acknowledgement network is denoted gaij and

takes values {0, 1, . . . , la}. The adjacency matrix of the acknowledgement network is

non-symmetric as acknowledgements are directed from one academic to another and gaijdoes not necessarily equal gaji (i.e. academic i acknowledging academic j does not imply

that academic j acknowledges academic i, while co-authorship is always bi-directional).

9

The set of co-authors of academic i in the co-author network Gc is denoted Ni(Gc) =

{j|gcij ≥ 0}. The number of co-authors of academic i, also denoted academic i’s degree

is given by |Ni(Gc)|. In the network of acknowledgements the set of academics j that

academic i acknowledges are called i’s successors N si (Ga) = {j|gaij > 0} and the number

of successors |N si (Ga)| is called the out-degree of academic i.

Using the number of links each node node has, we can characterize network density.

Density is defined as the share of realized paths E on the potential paths given the

network size:

density =2E

n(n− 1)(1)

Density measures the network’s efficiency in information transmission. The higher the

number, the more potential connections are realized and thus the faster the transmission.

Formally, two nodes belong to the same network component if there exists a path

between them. A component is connected if every node is reachable from every other

node. A component of a directed graph is said to be weakly connected if there is a path

between any two nodes in the component when the directionality is ignored. The size of

a component is the number of nodes (i.e. the number of academics) it contains. If a node

i belongs to a small network component, all other nodes are fairly close and CE(i; g)

would thus be large. In contrast, a node in a large network might have a potentially

much smaller centrality because many other nodes are far away. Being part of a small

(and therefore isolated) component is at odds with our notion of being central in the

network.

Within the same component we can compute network centralities. We present four

centrality measure: count of (direct) neighbors or degree, count of indirect neighbors,

betweenness centrality and eigenvector centrality.

Denote the shortest path between j and k in network g as σjk(g) and the number of

shortest paths between j and k that contain node i as σjk(i|g). Betweenness centrality

CB(i; g) of node i in network g is then given as:

10

CG(i; g) =∑j,k∈N

σjk(i|g)

σjk(g)(2)

Betweenness centrality is thus the probability that node i is on a shortest path in

component g. Recall, that each link resembles a social relationship, i.e. the exchange of

information, either formally in the co-author network or informally in the acknowledge-

ment network. A high betweenness centrality indicates that the academic is relevant

for most communication processes inside the network. In this case, the academic can

excel power by simply deterring or preventing information flows. It also suggests that

communication flows will dry out if these nodes were removed from the network.

The eigenvector centrality is our fourth and most important centrality measure. It

weights the adjacent nodes with their respective eigenvector centrality (Bonacich, 1986).

Typically, high eigenvector central nodes are clustered together meaning that eigenvector

centrality points to the best connected nodes in a network. For academic i it is defined

as:

CE(i; g) =1

λ

∑t

gi,j · eigenvector(j), (3)

where λ 6= 0 is a constant. 1λadditionally normalizes the measure. Written in matrix

notation, the formula yields

λ · eigenvector = eigenvector ·A (4)

which is precisely the definition of eigenvalues (in this case the eigenvalue is λ). Put

differently, the vector containing the eigenvector centralities for all the nodes is the

eigenvector associated with matrix A’s eigenvalue λ. By virtue of the Perron-Frobenius

Theorem, the equality of λ and A’s largest eigenvalue λmax ensures a unique and positive

solution. Eigenvector centrality is high when an academic knows many academic or few

academic in central places or both.

FIGURE 5 ABOUT HERE

11

Though all the centrality measures are related, they focus on different aspects. Fig-

ure 5 compares all four centrality measures that we use on the same (example) network.

Nodes with many neighbors (5a) are likely to be very active but the measure remains

silent about local influence or brokering potential. The same holds true for the very

similar count of second neighbors (5b). To broker between groups requires a high be-

tweenness centrality (5c). Very eigenvector central nodes (5d) are likely to be the center

of the best-connected clique in a network. The centrality decreases evenly in concentric

rings from the most eigenvector central node. This is a main difference to the count of

second neighbors.

4. Facts

4.1. Informal Collaboration is prevalent

The vast majority of published research articles acknowledgements informal input by

colleagues. Of all the 2782 articles in our dataset, 490 (11%) articles do not acknowledge

any commenter.7 Nearly half of which were published in the ’Journal of Banking and

Finance’ (JBF) in the late subset. In the early (late) subset there are 754 (1538 ) articles

with acknowledgements. In total, the share of articles reporting informal collaboration

(the extensive margin) has remained very stable over time. Figure 1 gives evidence of

this development.

FIGURE 1 ABOUT HERE

But not only the extensive margin of informal collaboration has remained remarkably

stable over the years, the intensive margin (the number of acknowledged commenters,

reported seminars and conferences) has done so as well. This comes as a surprise, because

one might hypothesize that declining transport and communication costs have made it

easier to exchange ideas and drafts. Table 1, panel A lists for each year the average

7These articles may however acknowledge comments by editors, referees, funding, data exchange andresearch assistance. We are however only interested in informal collaboration prior to publication.

12

number of authors, of acknowledged commenters, of reported seminars and of reported

conferences. While the average team size has increased from 1.9 in 1998 to 2.2 in 2011,

the number of acknowledged individuals has only increased from 8.4 in 1998 to 9.4 in

2011 (with a peak of 10.2 commenters in 2009). Also seminars and conferences remain

very stable with a mean of about 5 and 2, respectively. When however we calculate

the number of commenters, seminars and conferences per author, the pattern becomes

even more striking: In every year, there has been about 5 commenters, 3 seminars and 1

conference per author. The yearly mean values deviate by less than 5% from the overall

mean (only the mean of normalized conferences deviates by at most 10% from the overall

mean).

TABLE 1 ABOUT HERE

Figure ?? is a boxplot comparing the distribution of the number of all acknowledged

individuals for the early timeframe (1998-2000) and the late timeframe (2009-2011). It

shows that, while the median number of acknowledged individuals remained stable at 6

persons, today there are more articles that acknowledge many commenters. The record

is held by Spamann and Holger (2010): ’The “Antidirector Rights Index” Revisited’,

The Review of Financial Studies 23(2), 467-486, which acknowledges as many as 53

persons. For both timeframes it holds that 25% of all articles acknowledge less than 2

commenters.

FIGURE ?? ABOUT HERE

When we normalize the number of commenters with the number of authors to get

the number of commenters per author, we find a very striking pattern: In every year

the number of commenters per author was between 4.13 (2010) and 4.8 (1998), while

the median was 3 commenters per author in every year. If we exclude those articles

not acknowledging anyone, the numbers rise by approximately one person. That is,

the median number of commenters per author, given that the authors report informal

collaboration, is 4, while the mean number fluctuates between 4.9 (2011) and 5.4 (2009).

13

When we compare all individuals that author with those that appear in acknowledge-

ment sections, we find find that by far not every author is acknowledged in other articles’

acknowledgement section. On the other had, not every acknowledged commenter is an

author herself. Out of 3920 authors, 1863 (about 47%) do not appear as acknowledged

person. Of 7611 acknowledged researchers, 5554 (about 72%) are not publishing in the

set of our journals. These relationships hold for every subset, regardless of field journals

or general interest journal, or early or late subsample. Obviously a lot more researchers

are involved in the generation of knowledge than those actually publishing.

4.2. More informal collaboration in general interest journals

Figure 2 shows the average number of authors, commenters, seminars and conferences,

grouped by year and journal. Papers published in general interest journals acknowledge

benefit from more informal collaboration: They acknowledge almost twice as many

colleagues than papers published in a field journal (top right figure), and are presented

more than twice as often on seminars and conferences (bottom left and bottom right

figure). Only in terms of formal collaboration, all papers evolve similarly (top left figure).

FIGURE 2 ABOUT HERE

Another striking fact is how well journal ranking is reflected by the amount of infor-

mal collaboration. Starting with commenters, all general interest journals were relatively

close together in 1998 with an average number of around 10 commenters. The same holds

for the three field journals, whose average paper had about half as many commenters.

Until 2011 however, the JFI evolved remarkably, more resembling a general interest jour-

nal in terms of the average number of acknowledged commenters. An average JFI paper

acknowledges about 10 colleagues, while the three general interest journals acknowledge

about two commenters more. They have become more homogenous than 13 years before.

The number of acknowledged commenters of average paper published in the JMCB and

the JBF remained approximately constant.

14

In 1998, all journals were relatively diverse, with RFS having the most seminars per

paper (about 5) and JMCB and JBF having less than one. The JFI, as third field journal,

ranks fourth with more than 2 seminars per paper. The general tendency for all journals

is an increase in the average number of seminars, but with some years contracting the

trend. For example, in 2000 an average JFE paper lists less than 4 seminars, but in

1999 it was about 5 seminars. Looking at 2011, we see again how the journal rank is

reflected in the ranking of seminars per paper: JF papers have been presented at nearly

8 seminars (an increase to 1998 by almost 100%) and papers published in the JMCB or

the JBF at one to two seminars. Again, JFI papers are very similar to papers published

in general interest journals.

The average number of conference per article tells a similar story, albeit more clear: In

1998, all general interest journal and the JFI cluster together, listing slightly more than

one conference. They evolved strikingly similar, reporting now about three conference.

The other two field journals, JBF and JMCB, however, were presented on 0.5 conferences

on average in 1998, evolved similar, and list now 1 conference more.

Tables 2 and 3 contain summary statistics for some key figures for the early and the

late subset respectively. We separate the samples by publication status, that is for the

the general interest (JF, JFE, RFS) and field journals (JFI, JMCB, JBF), respectively.

We show the mean, standard deviation, median, minimum and maximum for each of the

following variable: three year citations, the number of authors, their average affiliation

rank, the number of acknowledged commenters, the number of seminars and conferences

a paper has been presented and the count of pages.

[TABLE 2 ABOUT HERE]

[TABLE 3 ABOUT HERE]

Comparing summary statistics of the full samples (panel A) first, we not that nearly

all numbers increase: More citations, more authors, more seminars, conferences and

acknowledged commenters in 2009-2011 than a decade earlier. Even average affiliation

rank increases in real terms, which means that more authors are affiliated with lower

15

ranked universities. Only the average paper length decreased by four pages to 22. For

all variables, standard deviation increased as well, suggesting that the increase in the

mean value is driven by the upper end.

4.3. Democratization in access to information, but not journal

space

The Tilburg ranking ranks universities according their (unweighted) research output.

Using this source, we are able to compare the rank of author’s main affiliations as well as

seminar venues. General evidence suggests that lower ranked universities now participate

more in the production of knowledge than at the end of the 2000s. This is due to two

channels: The first channel is via authors, and mainly occurs in field journals: Authors

from lower ranked universities now publish more often than a decade before. The second

channel is via seminars and occurs almost exclusively in general interest journals: There

are more presentations at lower ranked universities than a decade before.

TABLE 4 ABOUT HERE

Table 4 lists some key figures regarding the distribution of the average rank of main

affiliations of all authors. It shows the total number of articles, for which Tilburg ranking

ranks at least one main affiliation. Below is the share of articles of which at least one

author is affiliated with a top 30 university. The remaining four rows in table 4 show

median values per subset for the highest affiliation rank of an article, the mean rank over

all affiliations of an article, the range between the highest and the lowest rank affiliation

and the lowest affiliation rank of an article.

The general tendency is: Author groups are becoming more diverse, in the sense

that today authors from higher ranked and lower ranked affiliations work together to a

larger extent than 10 years before. The mean rank increased from 67 to 104, the rank

range increased from 37 ranks to 55 and the median lowest rank increased from 84 to

138. This tendency is similar for both journal groups, albeit somewhat stronger for field

journal publications.

16

The divergence between the two journal groups in terms of the median value for the

highest affiliation rank and the share of articles written by authors with top-30 affiliation

however points to a polarization of the journals: It seems to be harder now for author

groups whose best ranked main affiliation is too low. In the early timeframe, the share

of papers written by at least one top-30 affiliated author was about equal for papers

published in general interest journals and field journals (47% and 42% respectively).

Ten years later however, there are twice as many papers written by top-30 affiliated

authors in general interest journals than in field journals. In the same time, the median

value for the highest rank improved from 39 to 22 for general interest journals, but it

worsened for field journal publications (from 46 to 97).

TABLE 5 ABOUT HERE

Table 5 lists similar figures but for seminar venues rather than main affiliations. A

polarization between the journal groups is also visible, yet differently than for main

affiliations. On average, papers that are published in field journals are presented at

higher ranked universities: Both the highest rank of the median paper and the mean

rank of the median paper indicate higher ranks for field journal publications than for

general interest journal publications. The difference between the two journal groups

has grown over time. Also the share of articles that have been presented at top-30

universities is higher: It remained constant at 61% for field journal publications, but

declined for general interest publications from 55% in to 43%.

Another trend however reversed over time: General interest publications used to be

presented at a smaller range of universities in terms of their ranking, with the lowest

ranked university still being higher ranked than seminar venues of the median field

journal (125 compared to 182). In the late timeframe however, the median of the lowest

rank of general interest publications is lower than for the median field journal publication

(211 compared to 174) while the rank for both groups is equal at 182.

17

4.4. Academics tend to travel more today

Using geographic coordinates of affiliations and seminar venues, we compute the spherical

distance to each other. Using a simple string match procedure, we were able to compute

the distances between 731 affiliations. We then calculate the average distance between

the main affiliations of each author. We also calculate the average distance between

an author’s main affiliation and the university (central bank, think tank) where she

presented her work at a seminar. If there are more than two main affiliations (multiple

authors from different universities), we choose the one closest to the seminar venue.

Table 6 presents the result.

We find that the average distance between authors has increased from 1351 km in

1998-2000 to 1689 km in 2009-2011. Again, the pattern is different for different journals:

Authors that publish in general interest journals between 1998 and 2000 were on average

80 km closer to each other than authors of papers published in field journals. In the late

timeframe however authors from general interest journals are on average 600 km further

apart than their field journal counterparts.

TABLE 6 ABOUT HERE

The average distance to seminar venues tells a slightly different story. Unlike in the

average distance between authors, papers published in top journals have always been

presented further away than field journal articles. Papers published in top journals have

traveled on average 1173 km in the early timeframe, and 1264 km in the late timeframe.

Field journal publications however travel less today than a decade before: While in

the early timeframe they have been presented at universities at an average distance of

1074 km, the value decreased by about 11% to 957 km. This explains why, averaged

over all journals, the average distance between the closest author and the seminar venue

remained stable at about 1110 km in both timeframes.

18

4.5. The social network of formal collaboration became more

inclusive

The rise of co-authorship in scientific work has been well documented (McDowell and

Melvin, 1983). What is less explored is the connectedness of authors in response to this

increase and the more diverse landscape. Table 7 gives and overview of key figures of

the network of formal collaboration. To highlight differences between the journals, we

also build networks using subsets by journal status. That is, one network compromising

information from general interest journals (JF, JFE, RFS) and one using field journals

(JFI, JMCB, JBF) only.

TABLE 7 ABOUT HERE

Between the early and the late timeframe, the size by number of authors increased

by 123%, while the number of links between nodes (each link represents a unique co-

authorship) by 205%. Due to the higher number of co-author links, the network is

better connected in 2009-2011 than it was a decade earlier. For example, the number

of unconnected components decreased and the size of the giant component increased

dramatically. This is also visible in figure 6, which shows the co-author network for both

timepoints.

FIGURE 6 ABOUT HERE

As another result of the massive increase, density (equation 1) declined. Average

path length and diameter are related measures that additionally illustrate a network’s

connectedness. An average path length of 5.1 means that it takes on average five steps to

go from one individual to any other. The lower the number the more efficient information

is transmitted. It increased remarkably during the time of our observation, and so did

diameter. Diameter is simply the longest of all shortest paths. Note that both measures

are calculated for the giant component only. In total, the network of formal collaboration

has expanded remarkably in these 10 years albeit the network is less connected.

19

Comparing two subsets by journal quality provides evidence that in all dimensions the

formal network for general interest journals is better connected than the network build

of field journal publications only. While there were more authors publishing in general

interest than in field journals in the early sample, the reverse is the case for the late

sample: the number of authors publishing in general interest journals has doubled from

the late to the early sample, but at the same time the number of authors publishing

in field journals has more than tripled. The rise in co-authorship documented in the

literature can be seen in the increase in the number of edges (each edge indicating a

unique co-author relationship). There are more than twice as many edges for publications

in general interest journals in the late sample than in the early sample and over four

times as many in the field journal sample. The expansion of the network from the early

to the late sample did not lead to an increase of the shortest average path length for the

field journal sample, while it increased three-fold for the network of formal collaboration

among general interest journals.

4.6. The social network of informal collaboration is not more

connected today

Table 8 highlights some key figures describing the network of informal collaboration.

The network of informal collaboration expanded massively due to the larger number of

commenters, but proportionally more than the increase in the number of publication

would suggest. While the number of publications doubled between the early and the

late timeframe, the number of nodes in the network of informal collaboration more than

doubled and the number of social connections nearly tripled. Most increase occurs due

to field journals, where the number of nodes and edges more than tripled. Figure 7

makes this trend evident.

TABLE 8 ABOUT HERE

FIGURE 7 ABOUT HERE

20

The network however has not become more connected generally. The number of

components increased, entirely caused by publications in field journals. The giant com-

ponent was already very large in the early timeframe, connecting more than 95% of all

academics, and has grown only slightly to 98%. Density remained at values close to 0

and the average shortest path length remained broadly constant at 4.5. Diameter, being

the longest of all shortest paths, even increased by two nodes, meaning that information

today travels longer through the social network of informal collaboration.

4.7. Reciprocity is rare

We first examine whether acknowledged commenters (links in the network of informal

collaboration Ga) are also co-authors (links in the network of formal collaboration Gc.

We denote the number by Θ and define it for each timeframe t as:

Θt =P∑p

κp∑a

ιp∑c

{gaca|gaca = gcca = 1}. (5)

gaca = 1 if commenter c is acknowledged by author a, while gcca = 1 if c and a have

coauthored a paper. Θearly is 193, while Θlate equals 538. In order to normalize these

numbers, we divide by the number of possible links Ξ. We define Ξt for each t as:

Ξt =P∑p

κp∑i

Ni,t(Gc) \ κp, (6)

where Ni,t(Gc) is the set of neighbors of author i in the network of formal collaboration

Gc in timeframe t. In the early (late) timeframe there are 1180 (4664) possible links of

commenting co-authors. Hence, between 1998 and 2000, (193/1180)∗100% ≈ 16% of all

(feasible) authors commented on their co-authors other work, while between 2009 and

2011 the respective ratio reduced to (538/4664) ∗ 100% ≈ 12%. It remains subject to

speculation why over time less authors read the drafts of their co-authors.

Next, we examine reciprocity, which we define as gaij = gaji = 1 for author i and

commenter j. The count of reciprocal links in the early timeframe equals 698 and 1364

21

in the late timeframe. In order to normalize these numbers, we divide by the number of

possible reciprocal links φ. There are two necessary conditions for a reciprocal link: (I)

j is an author herself and (II) there is at least one paper where j is not a coauthor of i.

In the early timeframe there are 2912 such links, while in the late timeframe there are

8117 links that could possible be reciprocal. Hence, between 24% (early) and 17% (late)

of all possible feasible links are reciprocal links. Again, it is a puzzle while the share of

reciprocal author-commenter links declined.

One caveat is in order, as we only observe commenting ties within our dataset con-

sisting of financial economics journals. This is not necessarily the natural domain of all

commenters, given their field of expertise. For example, 2014’s Nobel price laureate Lars

Peter Hansen has been acknowledged by more than 20 articles in our dataset, while he

didn’t author a single article in our dataset.

4.8. Central authors are not necessarily central commenters

To explore the position of individuals in the network, individual centralities provide

interesting insights (Jackson, 2014). Centralities allow us to analyze the role individual

academics play in the transmission of information via formal and informal channels. We

choose a set of four complementing network measures which identify best very central

and hence influential nodes. We conducted the network analysis with version 1.9.1 of

the python package NetworkX (Hagberg et al., 2008).

We focus our analysis around four definitions of centrality, namely the count of

neighbors, the count of second neighbors, betweenness centrality (see equation 2) and

finally eigenvector centrality (see equation 4). The later two centralities rely on links

between nodes. Hence, they are defined for the network component of the focal node

only. We thus compute centrality on the largest (weakly) connected component only and

ignore all other nodes. The only locally statistic we can compute irrespective of whether

the local network is a giant component or not, is the simple number of neighbors.

TABLE 9 ABOUT HERE

22

Table 9 shows Spearman correlations for all centralities. Since Spearman correlation

compares ranks, we can explore whether central authors are central commenters, and

vice versa. Since we compute centralities only for the respective giant components, we

can only compute correlation coefficients for researchers that belong to both the giant

component of the co-author network as well as the acknowledgement network. The

upper panel in table 9 depicts correlation coefficients for the early subsample, with only

53 authors belonging to the giant component of the network of formal collaboration.

Hence the figures have limited explanatory power. The late subsample, shown in panel

B in 9, however rests on more than 600 researchers. For this timeframe, we see relatively

small but positive correlations (i.e. usually below 0.5). That means that author with

many direct or indirect neighbors are not often very central in the network of informal

collaboration. The only exception are eigenvector central authors, which appear to be

relatively more central in the network of informal collaboration. Highest correlation is

0.69 with betweenness centrality, meaning that eigenvector central nodes are relatively

often betweenness central commenters.

To put more reality in our analysis, we list the 10 most central nodes according each

of the four centrality measures listed above in tables 10 (for the early timeframe) and

11 (for the late timeframe). Panel A of each table shows lists most central authors

while Panel B of each table lists most central scientists from the network of informal

collaboration. Since we only look at the network’s giant components, lists from panel A

should be digested carefully.

TABLE 10 ABOUT HERE

TABLE 11 ABOUT HERE

The results speak for itself: Clearly great financial economists of our time appear

in the list. Two features are worth noting: First, there is a relatively small overlap

between the most central authors and the most central nodes in the network of informal

collaboration. Secondly, the order varies among centralities, especially when comparing

eigenvector centrality with the simple counts of neighbors.

23

5. Concluding remarks

In this paper we present evidence on some key trends in informal collaboration in the

way we produce knowledge.

These findings may help understanding differences in (perceived) quality among jour-

nals, but also among papers and author.

24

A. Appendix

A.1. Tables

Table 1: Yearly average for selected forms of reported collaboration.

Panel A: Yearly unconditional average.

1998 1999 2000 2009 2010 2011 All

Authors 1.90 1.95 2.00 2.29 2.30 2.30 2.19Commenters 8.39 8.42 8.00 10.21 9.89 9.59 9.36norm. Commenters 5.39 5.15 5.10 5.45 5.08 4.96 5.17Conferences 2.19 2.23 2.25 2.84 3.02 3.25 2.84norm. Conferences 1.25 1.23 1.25 1.36 1.44 1.51 1.39Seminars 5.22 5.36 5.41 6.12 6.27 5.89 5.87norm. Seminars 3.19 3.23 3.23 3.13 3.01 2.94 3.08

Panel B: Yearly conditional average.

1998 1999 2000 2009 2010 2011 All

Authors 1.90 1.95 2.00 2.29 2.30 2.30 2.19Commenters 7.41 6.86 6.86 8.49 8.11 8.06 7.84norm. Commenters 4.76 4.19 4.37 4.54 4.17 4.17 4.33Conferences 1.04 1.11 1.12 1.84 1.99 2.21 1.72norm. Conferences 0.59 0.61 0.62 0.88 0.95 1.03 0.84Seminars 2.64 2.89 2.92 3.93 3.70 3.59 3.45norm. Seminars 1.61 1.74 1.75 2.01 1.78 1.79 1.81

Note: We report the number of authors, commentators, conferences and seminars perpaper averaged over all papers published in that year. norm. Commentators, norm.Conferences and norm. Seminars are the number of commentators, conferences andseminars paper author. Panel A reports the unconditional average where non-reportedinstances of collaboration are treated as zero. Panel B reports the conditional averagewhere non-reported instances of collaboration are excluded.

25

Table 2: Summary statistics for paper-related characteristics in the early sample.

Panel A: Full early sample (887 articles).

Statistic Mean Median St. Dev. Min Max

Number of authors 1.95 2 0.81 1 5Avg. affiliation rank 117.71 67.00 148.93 1.00 936.00Number of commenters 7.03 6 6.03 0 34Number of seminars 2.82 1 3.94 0 21Number of conferences 1.09 0 1.50 0 10

Panel B: General interest journals only (503 articles).



Panel C: Field journals only (384 articles).



Note: Sample contains all articles published between 1998 and 2000. The sample canbe divided into a sample for the publications in general interest or field journals.

26

Table 3: Summary statistics for paper-related characteristics in the late sample.

Panel A: Full late sample (1895 articles).



Panel B: General interest journals only (910 articles).



Panel C: Field journals only (985 articles).



Note: Sample contains all articles published between 2009 and 2011. The sample canbe divided into a sample for the publications in general interest or field journals.

27

Table 4: Rank distribution of main affiliations for various subsets.

Early (1998-200) Late (2009-2011)All Gen. interest Field All Gen. interest Field

Total 697.00 440.00 257.00 1488.00 808.00 680.00Top-30 0.44 0.53 0.28 0.37 0.55 0.16Highest rank 43.00 21.00 89.00 54.00 22.00 136.00Mean rank 67.00 46.00 112.00 104.00 55.00 203.00Rank range 37.00 36.00 49.00 55.00 44.00 76.00Lowest rank 84.00 64.00 127.00 138.00 77.00 254.00

Note: Gen. interest contains only the general interest journals JF, JFE, and RFS.Field contains the field journals JFI, JMCB, and JBF. Total shows the total number ofarticles listing at least one ranked main affiliation. Top-30 is the share of articles withat least one author affiliated with a top-30 university. The remaining variables aremedian values for the respective subset. Only affiliations that are ranked in theTilburg ranking considered.

Table 5: Rank distribution of seminars for various subsets.

Early (1998-200) Late (2009-2011)All Gen. interest Field All Gen. interest Field

Total 380.00 298.00 82.0 906.00 638.00 268.00Top-30 0.58 0.62 0.4 0.53 0.63 0.27Highest rank 17.00 14.00 42.0 26.00 16.00 82.00Mean rank 77.00 74.00 92.0 97.00 85.00 167.00Rank range 150.00 161.00 116.0 182.00 178.00 204.00Lowest rank 145.00 153.00 120.0 184.00 170.00 249.00

Note: Gen. interest contains only the general interest journals JF, JFE, and RFS.Field contains the field journals JFI, JMCB, and JBF. Total shows the total number ofarticles listing at least one ranked seminar venue. Top-30 is the share of articles thathave been presented at at least one top-30 university. The remaining variables aremedian values for the respective subset. Only seminar venues that are ranked in theTilburg ranking considered.

Table 6: Average distance between authors’ main affiliations and to seminar venues

Avg. distance Early (1998-200) Late (2009-2011)All Field Gen. interest All Field Gen. interest

... between main affiliations 1351 1454 1203 1688 2000 1376

... to seminars 1114 1168 950 1109 1294 764

Note: All distances in kilometers. First row shows the average distance between allunique main affiliations of a paper. Second row shows the average distance between allseminar venues and the closest main affiliation.

28

Table 7: Global network measures for the network of formal collaboration

temp Early (1998-200) Late (2009-2011)All General interest Field All General interest Field

Nodes 1265.00 722.00 618.00 3109.00 1423.00 1864.00Edges 1025.00 622.00 420.00 3229.00 1666.00 1597.00Components 502.00 264.00 299.00 903.00 310.00 730.00Giant component 53.00 27.00 20.00 601.00 398.00 45.00Density 0.06 0.11 0.16 0.01 0.01 0.06Avg. path length 5.16 4.37 2.66 10.76 10.10 4.31Diameter 13.00 10.00 5.00 27.00 24.00 9.00

Note: Gen. interest contains only the general interest journals JF, JFE, and RFS.Field contains the field journals JFI, JMCB, and JBF. The variables “Avg. pathlength” and “Diameter” were calculated using the giant component only.

Table 8: Global network measures for the network of informal collaboration

temp Early (1998-200) Late (2009-2011)All General interest Field All General interest Field

Nodes 3461.0 2259.00 1659.00 7578.00 4394.00 4511.00Edges 11247.0 8438.00 2853.00 33311.00 24370.00 9108.00Components 160.0 41.00 168.00 505.00 26.00 583.00Giant component 3151.0 2205.00 1196.00 6875.00 4355.00 3547.00Density 0.0 0.00 0.00 0.00 0.00 0.00Avg. path length 4.6 4.05 5.82 4.51 3.81 6.22Diameter 13.0 8.00 14.00 15.00 8.00 16.00

Note: Gen. interest contains only the general interest journals JF, JFE, and RFS.Field contains the field journals JFI, JMCB, and JBF. The variables “Avg. pathlength” and “Diameter” were calculated using the giant component only.

29

Table 9: Spearman correlation for different centralities in both networks.

Panel A: Early sample (1998-2000).

Author propertiesDir. neighborsIndir. neighbors 0.44Betweenness 0.77 0.19Eigenvector 0.36 0.70 0.20Papers 0.44 0.29 0.80 0.26

Commenter propertiesDir. neighbors 0.20 0.33 0.50 0.10 0.51Indir. neighbors 0.28 0.42 0.47 -0.08 0.47 0.68Betweenness 0.03 0.02 0.50 0.08 0.46 0.91 0.67Eigenvector 0.31 0.38 0.43 -0.03 0.46 0.74 0.59 0.62Occurences 0.23 0.27 0.61 0.21 0.60 0.69 0.47 0.68 0.62

Panel B: Late sample (2009-2011).

Author propertiesDir. neighborsIndir. neighbors 0.48Betweenness 0.80 0.39Eigenvector 0.25 0.52 0.20Papers 0.57 0.39 0.88 0.16

Commenter propertiesDir. neighbors 0.29 0.44 0.65 0.18 0.50Indir. neighbors 0.29 0.47 0.55 0.21 0.46 0.69Betweenness 0.18 0.14 0.70 0.13 0.48 0.90 0.68Eigenvector 0.31 0.44 0.56 0.22 0.44 0.71 0.57 0.56Occurences 0.33 0.37 0.61 0.12 0.59 0.73 0.48 0.70 0.62

Note: Correlation coefficients were computed for individuals that are in the giantcomponent of both the network of formal collaboration and informal collaborationonly. Papers is the count of an author’s published research articles and Occurences isthe number of acknowledgments a scholar was listed in.

30

Table 10: 10 most central nodes in early subsample (1998-2000).

Panel A: Network of formal collaboration.

top Dir. neighbors Indir. neighbors Betweenness Eigenvector Papers

1 Allen N Berger 13 Joseph M Scalise 18 Anthony Saunders 0.6583 Allen N Berger 0.5497 Anthony Saunders 82 Anthony Saunders 11 Gregory F Udell 18 Allen N Berger 0.5904 Anthony Saunders 0.3740 Arnoud W A Boot 63 Michael J Barclay 7 Mark J Flannery 14 Kose John 0.4615 Joseph M Scalise 0.2382 Allen N Berger 64 Michael S Weisbach 7 Anthony Saunders 14 Rangarajan K Sundaram 0.4343 Gregory F Udell 0.2382 Michael S Weisbach 55 Avanidhar Subrahmanyam 7 Allen N Berger 13 David L Yermack 0.4193 Diana Hancock 0.2166 Paul H Schultz 56 Rene M Stulz 7 Seth D Bonime 12 Anil Shivdasani 0.3401 J David Cummins 0.2166 Maureen O’hara 57 Jeffry M Netter 6 Daniel M Covitz 12 Jun-koo Kang 0.2918 Mary A Weiss 0.2166 Avanidhar Subrahmanyam 58 Larry Hp Lang 6 Rebecca S Demsetz 12 Rene M Stulz 0.2450 Hongmin Zi 0.2015 Eugene F Fama 59 William L Megginson 6 Sally M Davies 12 Mark J Flannery 0.2081 Seth D Bonime 0.1976 Paul A Gompers 510 Wayne E Ferson 6 Philip E Strahan 11 Joel F Houston 0.1802 Daniel M Covitz 0.1976 S Viswanathan 4

Panel B: Network of informal collaboration.

top Dir. neighbors Indir. neighbors Betweenness Eigenvector Occurences

1 Allen N Berger 125 Alon Brav 355 Allen N Berger 0.0595 Jay R Ritter 0.2039 Jay R Ritter 442 Jay R Ritter 114 Rene M Stulz 346 Jay R Ritter 0.0506 Sheridan Titman 0.1944 Sheridan Titman 413 Sheridan Titman 103 Avanidhar Subrahmanyam 328 Raghuram G Rajan 0.0462 Raghuram G Rajan 0.1681 Raghuram G Rajan 364 Mark J Flannery 97 William L Megginson 323 Sheridan Titman 0.0438 Luigi Zingales 0.1665 Allen N Berger 325 Luigi Zingales 90 Jay R Ritter 305 Mark J Flannery 0.0401 Eugene F Fama 0.1508 Andrei Shleifer 306 Raghuram G Rajan 86 Michael S Weisbach 303 Luigi Zingales 0.0353 Rene M Stulz 0.1485 Mark J Flannery 307 Avanidhar Subrahmanyam 83 John R Graham 282 Rene M Stulz 0.0334 Avanidhar Subrahmanyam 0.1474 Rene M Stulz 288 Rene M Stulz 83 Luigi Zingales 279 Wayne E Ferson 0.0306 Andrei Shleifer 0.1182 Wayne E Ferson 289 Andrei Shleifer 79 Jeffry M Netter 270 Andrei Shleifer 0.0287 Kenneth R French 0.1176 Luigi Zingales 2710 William L Megginson 70 Paul A Gompers 267 Gary B Gorton 0.0253 Robert W Vishny 0.1069 Eugene F Fama 26

31

Table 11: 10 most central nodes in late subsample (2009-2011).

Panel A: Network of formal collaboration.

top Dir. neighbors Indir. neighbors Betweenness Eigenvector Papers

1 Rene M Stulz 16 Kose John 29 Itay Goldstein 0.3617 Chen Lin 0.4467 Rene M Stulz 112 Chen Lin 13 Vinay B Nair 24 Kose John 0.3589 Yue Ma 0.3149 Thomas J Chemmanur 93 Viral V Acharya 12 John R Graham 24 Lucian A Bebchuk 0.3343 Murillo Campello 0.2945 Viral V Acharya 84 Thomas J Chemmanur 12 Campbell R Harvey 24 K J Martijn Cremers 0.3263 Ping Lin 0.2266 Alex Edmans 85 Massimo Massa 12 Murillo Campello 23 Alon Brav 0.3112 Hong Zou 0.2201 Richard Roll 86 Shane A Johnson 12 Craig Doidge 23 Vinay B Nair 0.2509 Rene M Stulz 0.2113 Chen Lin 87 Geert Bekaert 11 Rudiger Fahlenbrach 22 John R Graham 0.2164 Paul H Malatesta 0.1855 Massimo Massa 88 Allen N Berger 11 Karl V Lins 22 Viral V Acharya 0.2136 Joel F Houston 0.1802 Allen N Berger 89 Anthony Saunders 10 Darius P Miller 22 Iftekhar Hasan 0.1900 Yuhai Xuan 0.1727 Robin Greenwood 810 David A Hirshleifer 10 G Andrew Karolyi 21 Michael W Brandt 0.1755 Micah S Officer 0.1507 Philip E Strahan 7

Panel B: Network of informal collaboration.

top Dir. neighbors Indir. neighbors Betweenness Eigenvector Occurences

1 Rene M Stulz 195 Massimo Massa 1051 Viral V Acharya 0.0291 Viral V Acharya 0.1465 Jeremy C Stein 692 Viral V Acharya 194 Viral V Acharya 958 Rene M Stulz 0.0262 Rene M Stulz 0.1368 Rene M Stulz 553 Alex Edmans 182 Alex Edmans 952 Alex Edmans 0.0207 Jeremy C Stein 0.1316 Michael R Roberts 484 Avanidhar Subrahmanyam 161 Amiyatosh K Purnanandam 940 Hans Degryse 0.0206 Alex Edmans 0.1171 Mitchell A Petersen 445 Yakov Amihud 141 Rene M Stulz 868 G Andrew Karolyi 0.0196 Michael R Roberts 0.1125 John Y Campbell 446 G Andrew Karolyi 138 Sudheer Chava 854 Steven Ongena 0.0191 Yakov Amihud 0.1108 Andrei Shleifer 437 Jeremy C Stein 138 K J Martijn Cremers 842 Allen N Berger 0.0190 Amir Sufi 0.1045 J Darrell Duffie 428 Raman Uppal 132 Alok Kumar 815 John Y Campbell 0.0184 Amiyatosh K Purnanandam 0.1020 Sheridan Titman 419 Massimo Massa 125 S Viswanathan 804 Yakov Amihud 0.0184 Andrei Shleifer 0.1018 Douglas W Diamond 4110 Michael R Roberts 124 Bruce I Carlin 771 Avanidhar Subrahmanyam 0.0178 Massimo Massa 0.1018 Yakov Amihud 41

32

A.2. Figures

Figure 1: Share of articles with and without acknowledgements.

1998 1999 2000 2009 2010 2011

0.15

0.25

0.50

0.75

0.90

with without with without with without with without with without with without

Journal JF RFS JFE JFI JMCB JBF

Figure 2: Number of authors, commentators, seminars and conferences over time perjournal.

● ●●

● ●●

● ●

●

● ●

●

0.0

0.5

1.0

1.5

2.0

2.5

1998 1999 2000 2009 2010 2011

Aut

hors

●● ●

●● ●

●

●

●●

●

●

0

5

10

1998 1999 2000 2009 2010 2011

Com

men

ters

●●

●●

●

●

●

●

●●

●

●

0

2

4

6

8

1998 1999 2000 2009 2010 2011

Sem

inar

s

●

●

●●

●

●

●●

●

●●

●

0

1

2

3

1998 1999 2000 2009 2010 2011

Con

fere

nces

Journal

●● JF

RFS

JFE

JFI

JMCB

JBF

33

Figure 3: Number of commenters, seminars and conferences acknowledged on a paperper timepoint and journalgroup.

●

●

●

●

●

●●

●

●●

●●

●

●

●

●●

●

●●

●

●●

●

●●●●

●

●

●

●●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

0

33

34

53

early late

Commenters

●

●

●

●

●

●●

●●●●

●

●

●

●

●

●

●●

●

●●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●●

●

●●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

000

21

27

early late

Seminars

●

●

●

●●●●●

●

●

●

●●●●

●

●

●●

●

●

●

●●●

●●●

●●

●

●

●●

●

●

●

●●

● ●●●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●●

●

●●●

●

●

●

●●

●

●●

●

●●

●●●●●

●

●

●

●

●

001

10

23

early late

Conferences

Journalgroup field generalinterest

Note: Violinplot showing the distribution with a boxplot highlighting quantiles and outliers inside.

34

Figure 4: Distance between authors and distance to seminar venues by journalgroup andtimepoint.

(a) Distance between authors’ main affiliation

●

● field

general

interest

●

● field

general

interest

1203

1454

1376

2000

early late

(b) Distance from closest main affiliation andseminar venue

●

● field

general

interest

●

● field

general

interest

950

1168

764

1294

early late

Note: Spherical distance between geographic coordinates of university (central bank, think tank), mea-sured in kilometers.

35

Figure 5: Centrality measures

(a) Number of first neighbors (b) Number of second neighbors

(c) Betweenness centrality (d) Eigenvector centrality

Note: Illustration of four centrality measures for the same network example with 256 nodes. Redindicates nodes with the highest values and blue indicates nodes with the lowest values.

36

Figure 6: Co-Author network (or network of formal collaboration) by timepoint

(a) Early (1998-2000)

(b) Late (2009-2011)

Note: A link is drawn between every co-author of a published research article. Red links indicatethat the research article was published in a general interest journal, while blue indicates a field journalpublication (overlapping links remain red). Giant component in the middle.

37

Figure 7: Commenter network (or network of informal collaboration) by timepoint

(a) Early (1998-2000)

(b) Late (2009-2011)

Note: A link is drawn between an acknowledged commenter and every author of a published researcharticle. Red links indicate that the research article was published in a general interest journal, whileblue indicates a field journal publication (overlapping links remain red). Only the giant component isshown.

38

References

Azoulay, P., Graff Zivin, J.S., Wang, J., 2010. Superstar Extinction. The Quarterly

Journal of Economics 125, 549–589.

Barnett, A.H., Ault, R.W., Kaserman, D.L., 1988. The Rising Incidence of Co-

authorship in Economics: Further Evidence. The Review of Economics and Statistics

70, 539–43.

Bonacich, P., 1986. Power and Centrality: A Family of Measures. American Journal of

Sociology 92, 1170–1182.

Borokhovich, K.A., Bricker, R.J., Simkins, B.J., 2000. An Analysis of Finance Journal

Impact Factors. The Journal of Finance 55, 1457–1469.

Cronin, B., Franks, S., 2006. Trading Cultures: Resource Mobilization and Service Ren-

dering in the Life Sciences as Revealed in the Journal ArticleâĂŹs Paratext. Journal

of the American Society for Information Science and Technology 57, 1909–1918.

Ding, W.W., Levin, S.G., Stephan, P.E., Winkler, A.E., 2010. The Impact of Informa-

tion Technology on Academic ScientistsâĂŹ Productivity and Collaboration Patterns.

Management Science 56, 1439–1461.

Ductor, L., 2015. Does co-authorship lead to higher academic productivity? Oxford

Bulletin of Economics and Statistics 77, 385–407.

Ductor, L., Fafchamps, M., Goyal, S., Leij, M.J.V.D., 2014. Social Networks and Re-

search Output. The Review of Economics and Statistics forthcoming.

Fafchamps, M., van der Leij, M.J., 2006. Scientific Networks and Co-authorship. Uni-

versity of Oxford Economics Series Working Papers .

Freeman, R.B., Huang, W., 2015. Collaborating with People Like Me: Ethnic Co-

authorship within the US. Journal of Labor Economics 33, S289–S318.

39

Goyal, S., van der Leij, M.J., Maria-Gozalez, J.L., 2006. Economics: An Emerging Small

World. Journal of Political Economy 114, 403–412.

Green, R., OâĂŹHara, M., Schwert, G.W., 2002. Joint editorial. The Journal of Finance

57, 1031–1032.

Hagberg, A.A., Schult, D.A., Swart, P.J., 2008. Exploring network structure, dynamics,

and function using NetworkX, in: Proceedings of the 7th Python in Science Conference

(SciPy2008), Pasadena, CA USA. pp. 11–15.

Jackson, M.O., 2014. Networks in the Understanding of Economic Behaviors. Journal

of Economic Perspectives , 3–22.

Jones, B.F., 2009. The Burden of Knowledge and the "Death of the Renaissance Man":

Is Innovation Getting Harder? Review of Economic Studies 76, 283–317.

Laband, D.N., Tollison, R.D., 2000. Intellectual Collaboration. Journal of Political

Economy 108, 632–661.

McDowell, J.M., Melvin, M., 1983. The Determinants of Co-Authorship: An Analysis

of the Economics Literature. The Review of Economics and Statistics 65, 155–160.

Oettl, A., 2012. Reconceptualizing Stars: Scientist Helpfulness and Peer Performance.

Management Science 58, 1122–1140.

Wuchty, S., Jones, B.F., Uzzi, B., 2007. The Increasing Dominance of Teams in Produc-

tion of Knowledge. Science 316, 1036–1039.

40

Eight Facts about Informal Collaboration in Financial...

Documents

Transcript of Eight Facts about Informal Collaboration in Financial...