NBER WORKING PAPER SERIESINCENTIVES AND THE EFFECTS OF PUBLICATION LAGS ON LIFE CYCLERESEARCH PRODUCTIVITY IN ECONOMICSJohn P. Conley
Mario J. Crucini
Robert A. Driskill
Ali Sina Onder
Working Paper 17043
http://www.nber.org/papers/w17043
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
May 2011

We wish to thank John Siegfried and the AEA for making JEL publication data available to us, Jonathan
Lee and Peter Kozciuk for excellent research assistance. We thank Andreea Mitrut, Laurent Simula,and seminar participants at Vanderbilt University, Uppsala University and University of Gothenburg
for helpful comments. We take responsibility for any errors that remain. The views expressed herein
are those of the authors and do not necessarily reflect the views of the National Bureau of Economic
Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official
NBER publications.
© 2011 by John P. Conley, Mario J. Crucini, Robert A. Driskill, and Ali Sina Onder. All rights reserved.
Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided
that full credit, including © notice, is given to the source.

Incentives and the Effects of Publication Lags on Life Cycle Research Productivity in Economics
John P. Conley, Mario J. Crucini, Robert A. Driskill, and Ali Sina Onder
NBER Working Paper No. 17043
May 2011, Revised November 2011
JEL No. A11,J0,J11,J24
ABSTRACT
We investigate how increases in publication delays have affected the life-cycle of publications of recent
Ph.D. graduates in economics. We construct a panel dataset of 14,271 individuals who were awarded
Ph.D.s between 1986 and 2000 in US and Canadian economics departments. For this population ofscholars, we amass complete records of publications in peer reviewed journals listed in the JEL (atotal of 368,672 observations). We find evidence of significantly diminished productivity in recent
relative to earlier cohorts when productivity of an individual is measured by the number of AER equivalentpublications. Diminished productivity is less evident when number of AER equivalent pages is usedinstead. Our findings are consistent with earlier empirical findings of increasing editorial delays, decreasing
acceptance rates at journals, and a trend toward longer manuscripts. This decline in productivity isevident in both graduates of top thirty and non-top thirty ranked economics departments and may haveimportant implications for what should constitute a tenurable record. We also find that the researchrankings of the faculty do not line up with the research quality of their students in many cases.
John P. Conley
Department of Economics
Vanderbilt University
401 Calhoun Hall
Nashville, TN 27235
j.p.conley@vanderbilt.edu
Mario J. Crucini
Department of Economics
Vanderbilt University
Box 1819 Station B
Nashville, TN 37235-1819
and NBER
mario.j.crucini@vanderbilt.edu

Robert A. Driskill
Vanderbilt University
robert.a.driskill@vanderbilt.edu
Ali Sina Onder
Department of Economics andUppsala Center for Fiscal Studies
Uppsala University
alisina.onder@nek.uu.se

1Introduction

Ellison (2002) documents that the time an economics paper typically spends at a given journal between
submission and publication has more than doubled over the last thirty or so years. As Ellison notes, thishas important implications:“The change in the publication process aﬀects the economics profession in a number of ways: it
aﬀects the timeliness of journals, the readability and completeness of papers, the evaluation ofjunior faculty, and so forth.” (Ellison 2002 p. 948)
While all of these eﬀects are important, the stakes may be highest when it comes to the evaluationof junior faculty. One would expect that, ceteris paribus, increased publication lags would make it morediﬃcult for members of recent cohorts to produce as long a curriculum vitae in six years as earlier cohorts(the usual time that tenure decision is made). If institutions do not internalize the eﬀect of the new publishingenvironment, then fewer junior faculty will receive tenure than in the past. At an individual level, the cost of
not gaining tenure is obviously significant. However, the costs are also significant for the profession at large.
Failure to promote qualified scholars leads to more frequent, costly searches by departments for new faculty
and the discouragement and exit of qualified scholars who would otherwise enrich the stock of economic
research.
This notwithstanding, gaining tenure is a powerful incentive. It certainly might be the case that more
recent Ph.D.s respond to the new environment by working harder or smarter. While this would impose
costs on junior faculty, the increased eﬀort might partly or wholly oﬀset longer publication lags. Thus, thequestion of whether or not there is an “Ellison Eﬀect” on CVs is ultimately empirical.
In this paper, we begin by demonstrating the plausibility and potential magnitude of the Ellison Eﬀect
in a simple model of research production and publication calibrated with what we believe are plausible
parameter values. We find that increasing publication delay from one period to two periods has substantial
eﬀects of the expected length of a vita at the end of six periods. These eﬀects are magnified if we also
include lower acceptance rates and longer paper lengths, which also seem to be part of the new publishingenvironment.
Next, we explore the existence of the Ellison Eﬀect empirically using data from various sources to reconstruct the JEL-listed journal-publication record of almost every graduate of a U.S. or Canadian Ph.D.granting economics department from 1986 to 2000. Our central approach is to document research productivity (in this context measured by the quantity and quality of publications) among successive cohorts of
new Ph.D.s using diﬀerent controls to assure the robustness of the finding. This documentation leads to anumber of findings about the heterogeneity of our sample that inform our eﬀorts to investigate existence andstrength of the Ellison Eﬀect.
First, we find that approximately half of the graduates never publish at all, at least in those journals
found in EconLit1 . Of those graduates who do publish, however, the proportion of journal publications
produced by each productivity-percentile of a cohort is remarkably stable: the publication “Lorenz curve”
for each cohort is practically identical. Roughly speaking, among the publishing members of each cohort,
80 per cent of the output is produced by the top 20 per cent of the researchers while the top one percent ofresearchers produces approximately 14 percent of all publications.
1 This percentage is also found for a small sample of graduates from 1997 by Stock and Siegfried (2006), for a sample ofgraduates from 1969-1988 by Hutchinson and Zivney (1995), and for a sample of European economists by Combes and Linnermer(2003).

2

Second, the institution from which students receive their Ph.D’.s has a significant impact on the quality
and quantity of their published research. Publishing graduates of top thirty departments produce morethan three times as many AER equivalent pages and papers than do their counterparts from non-top thirty
departments2 . Furthermore, for all cohorts, the average quality of each published paper and page is about
three times better for graduates of the top programs compared to the non-top programs.
In light of this, we divide our data in three ways. First, we restrict attention to Ph.D.s who produce
at least one published paper in a JEL indexed journal within six years after graduation on the grounds
that this “publishing population” is the only part of the sample for which research is an objective. For
this subsample, we make a distinction between graduates of “top thirty” and “non-top thirty” departments
based on representative research rankings. One might think of this as an “ex ante” predictor of publication
success3 . We also make a distinction based on the publication productivity percentile of researchers as
calculated by the length of the vita at the sixth year after graduation to test if there are diﬀerences between
scholars with diﬀerent “ex post” publication success.
Our major finding is that there is evidence of an Ellison Eﬀect. The strength of this evidence, however,depends on whether we use AER equivalent pages or AER equivalent publications as measure of research
productivity. It also varies depending on whether we look at graduates of top thirty or non-top thirty
institutions, and whether we look at the ex post more-productive or less-productive scholars.
In particular, the longer “time to build” process documented by Ellison (2002) has a measurable, but not
uniformly dramatic, eﬀect on publication success of all cohorts in terms of AER equivalent pages publishedby graduates of the top thirty programs. For these scholars, the productivity (that is, publishing success)of older cohorts is on average higher than the productivity of mid cohorts, and the productivity of the
middle cohorts higher than that of the youngest cohorts. In contrast, there is no such pattern of declining
productivity for the non-top thirty departments using AER equivalent pages as a measure. In both of thesecases a distinct humped-shaped pattern of life cycle research productivity extends across and within cohorts:
annual productivity rises until about the sixth year after graduation and then falls fairly quickly to about
60 % of the peak.
When we look at the number of AER equivalent publications instead of pages published at the end of six
years, we find large and statistically significant declines in productivity over time for graduates of both the
top and non-top thirty departments. By this measure for graduates of the top thirty programs, the oldest
cohort is 45% more productive than the middle cohorts and 65% more productive than the youngest. The
middle cohorts in turn, are 13% more productive than the youngest cohorts. For non-top thirty departments,
the oldest cohort is 19% more productive than the middle and 58% more productive than the youngest, while
the middle cohorts are 33% more productive than the youngest cohorts.
For the ex post measure of publication success using productivity percentiles, the eﬀects are even more
pronounced: as we compare higher quintile ranges across cohorts, the dominance of older cohorts over younger
cohorts, and the chronological ordering of cohorts in research productivity become more evident. This meansthat top performers of each cohort have been hit more severely by the publication bottleneck.
This, in conjunction with the finding that pages published are similar across cohorts (at least for the top
thirty departments), is consistent with Ellison’s (2002) documentation of the increasing length and decreasing
number of published papers, and suggests two things. First, it exposes a significant methodological question
2 We use the ranking from Coupe (2003) to determine the top thirty U.S. and Canadian economics departments. His ranking
appears highly correlated with other popular rankings.
3 Later in the paper we go into more detail about the rankings we used and the various robustness checks we carried out
with respect to this.

3

about the best way to measure productivity of departments, graduate programs, and individual scholars.
Productivity patterns over time look diﬀerent depending on whether papers or pages are chosen as a basis ofcomparison. We argue that what the profession values when granting tenure, giving raises, or making seniorhires is the number of lines on a CV and the quality of the research papers on those lines. It is much harder
to distill this into the number of AER-quality weighted pages, and we suspect that this is seldom attemptedin practice. If this speculation is true, then it is important to look at AER equivalent papers rather thanAER equivalent pages. Second, when AER equivalent papers are used as the productivity metric, there isa significant drop-oﬀ in the weighted quality of the CVs of Ph.D. graduates over time. Thus, unless webelieve that recent graduates are fundamentally of poorer quality, the same quality of tenure candidate issignificantly less productive today than 10 or 15 years ago. We will explore the robustness of this finding
and its implications below.
While our primary focus in this paper to investigate the existence of the Ellison Eﬀect, these data allow us
to investigate the relative performance of graduate programs in terms of the research output of their Ph.D.s.
This allows us to construct a new type of departmental ranking system that can be compared with other,more traditional systems which focus on the publications of faculty members at a particular department. We
find that MIT, Princeton, Harvard and Rochester do best by this quality measure and more generally thatthe rankings of other departments does not entirely agree with more traditional measures that use faculty
output.
In the remainder of the paper we first develop a simple model using specific, plausible parameter values
and show how the change in “time to build” documented by Ellison (2002) aﬀects the time-profile of anindividual’s vita. We then describe our data and document within and across cohort research productivity patterns and changes. Finally we investigate life cycle eﬀects and cohort eﬀects on Ph.D.s’ research
productivity and discuss what they imply for the existance of the Ellison Eﬀect.

2

An Illustrative Model

Our purpose here is not to develop a general model of lifetime production and consumption, but rather
to focus on a simple partial-equilibrium model that highlights the eﬀects of a change in the time between
submission and acceptance of a manuscript. The focus is on the period of time between entry into theacademic workforce and the time of decision on tenure, namely six years. It is undoubtedly consistent witha more general model such as found in Levin and Stephan (1991), in which individuals optimize over their
lifetimes by choosing their allocation of time between labor and leisure.

2.1

Model Parameters and Solutions

We construct a model in which there are five exogenous parameters:• : the length of a manuscript4 ;• 0 : an individual’s stock of unpublished papers at the time he receives a Ph.D. (thus 0 is the numberof manuscripts initially submitted to journals);• : the individual’s production of manuscript pages (per-year);
4 Ellison (2002) documents that  has increased over time at a greater rate than the number of high-quality journal pages
published. While he speculates on the causes of this change, he does not oﬀer conclusive evidence of a particular cause. Thus,
we treat this as exogenous.

4• ∆ : the “time to build” lag between when an individual’s stock of unpublished manuscripts is submitted
and when a decision on acceptance is received;•  : the percentage of the stock of an individual’s unpublished manuscripts that were newly-submitted
at  − ∆, that are accepted for publication at time .
Of course, in a more complete model, these exogenous variables would be endogenous and would reflect
optimal choices of individual producers and the supply of journal pages available.
An individual’s number of newly-submitted manuscripts at any time  is denoted by  , and the numberof newly-submitted pages is denoted by  . Thus, given that manuscripts have  pages, ( ) =  .
To summarize, we assume that an individual arrives in the profession with a stock 0 = (0 ) of
manuscripts. Each year after that, the individual writes  =  manuscripts, where  and  are exogenous
constants. Each year, all individuals submit every one of their existing unpublished manuscripts that are notin the evaluation process. Then, after a specified period of time, a percentage  of these newly-submitted
manuscripts are accepted.
To capture the change in the time between submission and publication emphasized by Ellison (2002), we
consider two scenarios. In the first scenario, the exogenous percentage  of newly-submitted manuscripts are
accepted for publication the year following submission. Thus, the time to build is one period (∆ = 1). In thesecond scenario, the exogenous percentage  of newly-submitted manuscripts are accepted for publication
two periods after submission. Thus, the time to build is two periods (∆ = 2).To distinguish between thesetwo cases, we denote parameters and variables associated with the one-period lag between submissions andacceptances by putting a “tilde” over the symbol. That is, in the first scenario the number of newly-submitted
manuscripts at time  is denoted as e and so forth.
With a one-period submission-acceptance lag, the number of newly-submitted manuscripts at time evolves according to the following first-order diﬀerence equation:e = ̃ + e−1 − e−1 = ̃ + (1 − )e−1(1)This says that the number of newly-submitted papers at time  equals the previous period’s submissions
plus new additions ̃ minus accepted submissions from the previous period e−1 . With the exogenously
given original stock of unpublished papers 0 , the solution to this diﬀerence equation is found by well-known
methods to be:
e =

−1
X
=0

̃ (1 − ̃)+ (1 − ̃) e0 =

Ã

̃
̃

!

The number of acceptances per year, denoted by ̃ , is the

+

Ã

e0 −

Ã

̃
̃

!!

(1 − ̃)

(2)

̃ = e−1(3)̃ = ̃−1 + ̃(4)

The length of an individual’s vita at any time t, denoted by ̃ , is thus given as

Now consider our second scenario, in which the time between acceptance and publication is two periods.
In this case, the number of newly-submitted manuscripts evolves according to the following second-order5

diﬀerence equation:
 =  + −2 − −2 =  + (1 − ) −2

(5)

The key diﬀerence here from the first scenario is that newly-submitted manuscripts have to wait for two
periods for a decision. As a result, submitted manuscripts at time  will still be waiting for a decision attime  + 1; hence those manuscripts will be contained neither among new journal submissions at time  + 1,
nor among accepted manuscripts at time  + 1, denoted by +1 .
There are two initial conditions that apply to this problem: 0 is given and 1 =  . Thus, the solution
to (5) will be =+ 1 1 + 2 2 (6)where 1 and 2 satisfy the two boundary conditions= 0 

+ 1 1 + 2 2= 

1 + 2 +and√ = ± 1 −  ,  ∈ {1 2}

(7)

(8)

Alternatively, we can write the solution as


 =

2 −1
X

=0



 (1 − ) + (1 − ) 2 0 =

µ




¶

µ
µ ¶¶


+ 0 −(1 − ) 2


(9)

¶³
´
+1
1− (1 − ) 2

(10)

when  is even, and
−3

 =

2
X

=0



 (1 − ) + (1 − )

−1
2

1 =

µ




when  is odd.
The number of acceptances per year, denoted in this scenario as  , are = −2(11)Thus, the length of an individual’s vita in this scenario, denoted as  , is = −1 + 

2.2

(12)

Calibrated examples

To give some sense of the quantitative implications of the model, we calibrate the model based on plausible
values of the parameters from the work of Ellison (2002). We explore three separate changes to the publishingenvironment that Ellison discusses: an increase in publication lags, a decrease in acceptance rates, andincrease in the length of manuscripts.

6First, we consider a base case meant to represent the historical publishing environment. We take abenchmark of a 20% acceptance rate, one new paper per year as flow of production, an initial stock of threepapers at graduation and a one year “Time to Build”:(1) ̃ = 020 ̃ = 1 and ̃0 = 3
Second, we consider the eﬀects of increased delays. We change the time to build to two years, but keep thesame set of parameter values otherwise:(2)  = 020  = 1 and 0 = 3
Third, we go back to a one period lag, but we consider the eﬀect of increasing manuscript length by one
third so that both initial stock and flow of new manuscripts decreases to 75% of the two cases above:5(3) ̃ = 020 ̃ = 075 and ̃0 = 225
Finally, we consider a one period lag and no increase in manuscript length, but decrease the acceptance rate
to 12%:6(4) ̃ = 012 ̃ = 1 and ̃0 = 3
The results are shown in Table 1, and they reveal that new Ph.D.s trying to publish in the historicalregime (case 1) are significantly more productive than new Ph.D.s facing any one of the three changes
considered above. Agents publishing under the historical regime were 75% more productive than agentsfacing a two period time to build, 33% more productive than agents who must publish longer manuscripts,
and 42% more productive than agents facing lower acceptance rates.

Table 1. The Eﬀect of Lags, Acceptance Rate and Manuscript Length on CVs
(1)
Year

One year delay

1
2
3
4
5
6

0.60
1.28
2.03
2.18
3.65
4.52

Length of Vitae(2)(3)
LongerTwo year delay Manuscript
0
0.60
0.80
1.48
1.84
2.58

(4)
Low Acceptance
Rate

0.45
0.96
1.52
2.11
2.74
3.40

0.36
0.80
1.30
1.87
2.50
3.18

These drops are substantial, and would be even more so if we subjected new Ph.D.s to all three changes atonce as we do in the real world. Of course, new Ph.D.s may be aware of the current publication environment
5 Ellison finds that articles increased in page length about 33%, from about 18 pages to 24.
6 Note that the increase in manuscript length that Ellison documents implies that if the annual page budgets of journals has
not increased (which is substantially the case at least with the top journals) then the acceptance rate, , should fall. Add tothis the number of submissions seems to be going up each year, and that for the few journals that actually report acceptance
rates, the rates have gone down, and there is strong reason to believe that we see lower acceptance rates in general at economics
journals in more recent periods.

7and may be responding. For example, they may submit more papers while a graduate student or stay ingraduate school longer in order to have a better chance at tenure.

3

Data

The panel dataset we construct consists of two parts: a census of Ph.D. recipients from academic institutionsin the US and Canada who received their economics Ph.D.s between 1986 and 2000 and a complete recordof the journal publications of these individuals for the years 1985 to 2006 in EconLit listed journals.

3.1

Economics Ph.D. Holders

The record of economics Ph.D. recipients was constructed from two sources. The definitive source is the list
provided by the American Economic Association (AEA), based upon an annual survey of economic departments. We use the Economics Ph.D. list of the AEA that records economics Ph.D. recipients beginning in
1985, and we supplement the data with information from the “2003-2004 Prentice Hall Guide to Economics
Faculty by Hasselback”. This directory contains information about current faculty members of all economics
departments in the US, and of well-known Canadian and European research universities. Using the information about faculty members’ graduation year and Ph.D. granting institution found in the Hasselback, a morecomprehensive dataset of records of economics Ph.D. recipients from 1986 to 2000 was created. The numberof Ph.D. recipients listed in both the AEA economics Ph.D. list, and in Hasselback is shown in Table A.1
in the appendix. The total number of Ph.D. recipients in a given year is not simply the sum of the entries
from each source as there is considerable overlap between them. From 1988 to 2000 the number of Ph.D.s
granted is fairly stable at about 1,000 per year. The significant growth from 1986 to 1988 may be due toless comprehensive coverage of Ph.D.s early in the AEA survey. Pooling all years, the panel contains 14,271
economics Ph.D.s.

3.2

Journal Publications

The record of journal publications is obtained from two diﬀerent sources. Our main source is the EconLit
Publication Database. The number of publications for the years 1985 to 2006 recorded in EconLit is listed
in Table A.2 in the appendix. The number of papers has grown from about 9,500 in 1984 to almost 26,000
in 2005. Pooling all years, the panel of publications contains 368,672 peer-review papers. Of course, only afraction of these are coauthored by Ph.D.s in our sample7 .
The International Bibliography of Social Sciences (IBSS) database was used to obtain additional information on journal publications that have more than three authors. The reason for this is that EconLit reports
only the first author’s name, if a journal publication has more than three authors. There were 1,125 suchoccurrences in the EconLit journal publication database, and 558 of those were in top 25 journals.

3.3

Supplemental data

Raw counts of publications are imperfect measures of the research productivity of individual scholars becauseof the variation in the quality of those publications. The journal rankings and journal quality indexes from
Kalaitzidakis, Mamuenas and Stengos (2003) are used to account for this variation. We convert their journal
7 Fafchamps, van der Leij and Goyal (2006) and Goyal. van der Leij and Moraga-Gonzalez, (2006) construct a similar sample
of published papers, but focus on the nature of the coauthor network rather than changes in publication rates across cohorts.

8

quality indexes into American Economic Review (AER) equivalence, meaning that we express quality ofeach journal as a fraction of AER quality and use these weights in calculating numbers of AER equivalentpublications and AER equivalent pages in our analysis. Ph.D.s in our database published in 992 diﬀerent
journals that are indexed in EconLit. The top 65 of these journals carry weights for AER equivalence aslisted in Table A.3 in the appendix. We assign each of the remaining journals a weight of 0.012. That is,according to the impact-adjusted ranking of peer-reviewed journals in economics, 12 pages in the AER (arelatively short paper) are equivalent to 1000 pages in the 66th ranked journal.
One might worry that our results might be highly dependent on the specific quality index employed
here. As a robustness check we use a discrete ranking for journal quality provided by Combes and Linnemer(2010), where journals are not assigned a unique quality index but grouped into quality categories. Top 5
journals form the top quality group, denoted by AAA in Combes and Linnemer (2010), and we assign thesejournals a conversion rate of 1 to the AER. The next 15 journals form the second quality group (denoted by
AA), and we assign these journals a conversion rate of 23 to the AER. The next 82 journals form the third
quality group (denoted by A), and we assign these journals a conversion rate of 13 to the AER.8 Fortunately,
the qualitative results obtained are similar regardless of which approach is used to ranking journals.9 .
By matching manuscripts by authors in our Ph.D. panel with the indices of journal quality, we calculate
the number of AER equivalent pages for article  in journal  as:
AER Pages =(raw pages) (journal index)
(authors)

(13)

where “raw pages” is the length of the manuscript, “journal index” is the quality weight converting thenumber of pages in journal  into an equivalent number of pages of the AER, and “authors” is the numberof authors of the manuscript. Dividing by number of authors we assign each author an equal share of credit
for the research output. We also analyze how productivity measures change when we give each author fullcredit for the research output, and in this case we don’t divide by the number of authors. Taking the sumof this index over all publications by an individual Ph.D. recipient in a specific year, gives the publication
quality index for this individual in that year.
Similarly, the number of AER equivalent publications for article  in journal  is calculated as:
AER Publications =(journal index)
(authors)

(14)

The focus of our analysis is the impact of the ongoing slowdown in the publication process on the
productivity of vintages of Ph.D.s indexed by their year of graduation. Since the variation in productivity
across individuals at a point in time is immense, we also examine subsets of the panel for evidence of aslowdown. This entails controlling for life cycle patterns of productivity as well as conditioning on the
ranking of the institution the Ph.D. received her degree.
8 Combes and Linnemer (2010) group journals into six quality groups: AAA, AA, A, B, C, and D. We use the AER conversion
rate of 0.12 for all journals in categories B, C, and D.
9 Whenever we refer to AER equivalent pages or AER equivalent publications in this paper, the AER equivalence is obtainedby employing indices provided by Kalaitzidakis et al. (2003) unless otherwise noted.

94Research Productivity of Cohorts by Flows and Stocks of Publications

The main goal of our empirical study is to evaluate the consequences of the slowdown in the publication
process across cohorts of new Ph.D.s. As one might expect, there is considerable variation in the publication
records of the approximately 1,000 individuals who receive Ph.D. in U.S. and Canada each year. For example,
in most cohorts and in most departments, about 40 to 60 percent of graduates fail to publish a single paper
in an EconLit listed journal in their first six years after graduation. These graduates are eliminated fromthe sample on the grounds that we wish to focus attention on graduates with scholarly research ambitions.
Thus, when we use the term “graduates” in what follows it should be taken to mean “graduates who havepublished at least one paper in an EconLit listed journal within their first six year of graduation.” Ph.D.s
are organized into five cohorts, each pooling three consecutive years of Ph.D. graduates. For example, the1987 cohort consists of individuals who had their Ph.D. conferred in either 1986, 1987 or 1988.10

4.1

Productivity Distribution within Cohorts

An interesting way to characterize within cohort heterogeneity is to use an “intellectual Lorenz curve” toquantify the inequity of contributions in each cohort to the aggregate flow of peer-review publications. Table
2 shows the cumulative distribution of all AER equivalent pages and publications in our panel of peer-review
publications as a function of the productivity ranking of Ph.D.s at their sixth year after graduation.

Table 2. Intellectual Lorenz Curve

Top 1%
Top 5%
Top 10%Top 20%1987

Percent AER Pages
1990 1993 1996

1999

Percent AER Publications
1987 1990 1993 1996 1999

13.3
42.7
61.7
81.6

14.5
45.7
64.4
82.5

15.7
45.5
63.7
82.7

11.9
37.5
56.6
78.1

16.2
44.2
62.8
81.7

13.7
43.1
62.9
82.1

13.2
39.4
58.0
78.4

14.1
39.6
57.5
78.1

12.7
39.2
58.1
78.7

12.9
40.1
58.2
79.0

The top 1% of producers generates about 13 to 16% of all AER equivalent pages. The top 10% produces
more than 60% of all published pages and the top 20% produces more than 80% of the output. These
proportions are robust across all cohorts and does not change significantly when AER equivalent publications
are used in place of AER equivalent pages, especially when top 1% and top 5% of Ph.D.s are considered.
Thus the distribution of research productivity within each cohort is very skewed, however it is consistentacross cohorts whether we consider AER equivalent pages or AER equivalent publications.

4.2

Graduates from Top Thirty and Non-Top Thirty Institutions

As documented in the prior section, approximately half of Ph.D.s never publish, and even restricting attention
to those Ph.D.s who publish, we observe a very skewed productivity distribution. Thus, our data confirms
the conventional wisdom that a very small but highly productive group of Ph.D.s create a disproportionately
large share of total publications. Although there are always exceptions and outliers, the best performers
1 0 Ph.D. cohorts defined in this way are also used in subsequent parts of the paper, except for the regression analysis.

10are mostly graduates of top research universities. We therefore separate all the Ph.D.s in our dataset into
two groups: graduates of top thirty economics departments and graduates of non-top thirty economics
departments. We use economics departments’ ranking of Coupe (2003)11 for our analysis.
Which departments are “top thirty” is open to question, of course, and could be calculated in several
diﬀerent ways. Given the length of time covered by this study (fourteen years) it is doubtless the case thatdepartments have moved in and out of this group. It is not our purpose to make any definitive judgment
on which departments deserve this recognition. In our view, it would be better to view our division of
departments into two groups as an eﬀort to study how a representative set of “top departments” performs
against “non-top” departments.
Table A.4 in the appendix provides two rankings of the top thirty departments in U.S. and Canada. In
the left column in Table A.4 department ranking of Coupe (2003) is reproduced, where departments areranked by the productivity of their faculty members. Alternatively, one can argue that a department’s true
quality is measured by its graduates’ productivity12 . Following this idea, department ranking presented in
the right column in Table A4 is based on our own calculations. We rank departments according to their
Ph.D.s’ productivity, which is measured as the average number of AER equivalent publications accumulated
at the sixth year after graduation. Figure 1 shows the distribution of AER equivalent publications per
graduate of each of the top thirty departments aggregated across all cohorts.
It is interesting to see that the Coupe ranking that focuses on faculty research quality does not line
up particularly well with this measure which instead focuses on the research quality of graduates of these
programs. Potential graduate students might do well to consider the implied ranking of departments given
here when deciding where to apply for graduate training.

4.3

Life Cycle Productivity Measured by Flows of Publications

We begin by exploring the annual productivity of graduates of the top thirty departments and non-top
thirty departments by various measures. The first measure of productivity is the annual number of raw
pages published by graduates of the top thirty economics department and the remaining departments in ourdataset. Young scholars at top and non-top thirty departments share a very similar career-cycle pattern ofproductivity. Annual productivity steadily rises from the year before the Ph.D is granted to a peak at about
the fifth year. Productivity then drops oﬀ at a decreasing rate for the remaining years in our sample to about
60% of the peak value. This qualitative pattern also holds for all cohorts in both top and non-top thirty
departments. It is certainly possible that productivity rises while new Ph.D.s are “eating their yolk”. As
they exhaust the stock of research questions they studied in graduate school, they become less productive.
However, it is at least a little suspicious that this capital stock happens to start declining exactly at the pointthat tenure decisions are made. Also note that the gradual decline from the fifth year on is consistent withthe presence of an overhang of completed and submitted work done before the tenure decision that only is
accepted in subsequent years. Thus, we find evidence that is at least broadly consistent with the remarkable
hypothesis that incentives seem to work in the economics profession.
Figures 2 and 3 show life cycle productivity of graduates of top thirty and non-top thirty departments,
respectively, in terms of number of published pages. Graduates of the top thirty departments peak at about
1 1 Coupe (2003) ranks economics departments worldwide by the productivity of their faculty. There are only two economics
departments within Coupe’s top thirty that are outside U.S. and Canada: the London School of Economics and the Universityof Oxford. Since our dataset consists of U.S. and Canadian economics departments’ Ph.D.s, we drop the London School of
Economics and the University of Oxford and include Coupe’s numbers 31 and 32 (both in the U.S.) instead.
1 2 See for example Collins et al. (2000).

1111 raw pages published in their fifth years, and then slowly decline to about 7 pages. Graduates of the
non-top thirty departments, in contrast, peak at about 6-7 pages published per year and then decline toabout 4 pages per year. There seems to be no obvious chronological ranking of the productivity graphs bycohorts based on annual number of published pages.
Although published pages seems to be the standard way of measuring productivity in similar studies13
our view is that it does not capture the true productivity incentives faced by new members of the profession.
Our experience is that lines on a CV (as opposed to pages published) is more valued by the profession fortenure, promotion, raises, and so on. There are at least two reasons to use manuscripts as the unit of account.
First, each paper contains a self-contained research idea and some require more pages to fully articulate thanothers even when their inherent scientific value is quite similar. Second, there are significant variances in thelength of research manuscripts across sub-fields of economics having to do with the content of the topic andthe norms of exposition. This renders the page metric a somewhat dubious unit of account. We therefore
consider the annual number of raw publications (that is, papers) published by members of our sample.
When we measure productivity in terms of number of published papers, we see a similar pattern as above
for all cohorts with productivity peaking at about year five and then declining. Graduates of the top thirty
departments peak at about 0.5 to 0.7 papers per year (see Figure 4), while non-top thirty peak at about 0.3
to 0.5 papers per year. Chronological ranking of the productivity across cohorts that was not apparent inpublished pages becomes now apparent revealing a ranking of cohorts with the oldest cohorts publishing themost and the youngest the least.
These first two measures are still limited in that they take no account of where graduates publish theirpapers. We therefore produce comparable figures but weight publications according to journal quality to
get AER equivalent pages and papers published annually. As mentioned above, our journal quality index
is based on Kalaitzidakis et al (2003). Considering AER equivalent pages, we continue to see the familiar
productivity peak at about five years. Graduates of the top thirty departments peak at about 1.5 to 2 AER
pages per year, and this is shown in Figure 5, while non-top thirty peak at about 0.4 to 0.5 pages. Thus, we
see that top thirty graduates are more than 3 times as productive as non-top thirty graduates compared attheir peak annual productivity levels.
Figure 6 shows an alternative version of Figure 5, where number of AER equivalent pages is based on thediscrete quality index provided by Combes and Linnemer (2010). Graduates of the top thirty departments
peak at about 4 to 4.5 AER pages per year, while non-top thirty graduates peak at about 1.4 to 1.7 pages.
Comparing peak levels of their annual productivities, we see that top thirty graduates are about 3 times
more productive than non-top thirty graduates.
Moving to AER equivalent publications (again using Kalaitzidakis et al (2003) as this is our default
quality index), graduates of the top thirty departments peak at about 0.08 to 0.13 AER publications per
year compared to 0.02 to 0.03 for non-top thirty departments. Again we see that top thirty graduates are
more than three times as productive as non-top thirty graduates compared at their peak annual productivity
levels.

4.4Life Cycle Productivity and Coauthorship

It is typical to divide the credit evenly over the authors of a paper, as we have done in the data reported
above. Thus, if there are two authors on a ten page AER paper, they are each credited with producing five
AER pages. It is debatable whether or not this is fair. It surely takes more than half the time to write
1 3 See for example Combes and Linnemer (2003) and Rauber (2008).

12

a coauthored paper than an equivalent single authored paper (as all the authors of the current paper will
surely attest). Again, our experience suggests that the profession tends to look at lines on a CV and only
make some smaller discount for coauthorship. Thus, one might wonder what would happen if we gave all
authors full credit for a paper.
We find the same pattern of productivity peaking at five years and the same slight tendency of the older
cohorts to be more productive than the younger cohorts when we do not discount for coauthorship. However,it seems that the drop oﬀ after five years is not as steep. This might reflect increased coauthorship with
graduate students or more opportunities for collaboration as a scholar becomes better known. The overall
eﬀect is to raise the peak productivity in terms of AER equivalent pages to a range from 2.4 to 3.2 for top
thirty graduates and to a range from .6 to .8 for non-top thirty graduates. This compares to 1.5 to 2 AER
pages per year for top thirty graduates, and .4 to .5 for non-top thirty graduates with coauthor discounts.
A related question is whether there have been secular changes in the pattern of coauthorship over theyears. Based on our extensive panel, coauthorship does appear to be on the rise. This may be due to theincreasing importance of the Internet and email which lowers the transactions cost of working with authors
outside one’s department. We find that the average number of authors per publication in all EconLit journals
rises from 1.35 authors in 1984 to 1.6 authors 2001, while for the top twenty journals, it goes from about 1.5
to 1.8 (see Figure 7).
This secular trend of increased coauthorship is in line with the observation that younger cohorts have on
average more coauthors in their publications. We see a very interesting life-cycle pattern in coauthorshipas well. Figure 8 shows the average number of authors of publications that are aﬃliated with at least one
member of the respective cohort.
We observe a U-shaped life cycle for coauthorship across cohorts. This is a similar observation to that
presented by Rauber and Ursprung (2008) about German academic economists. Coauthorship tends to bemuch less frequent between 1st and 4th years after graduation. This pattern remains consistent over all
cohorts. As we move from older to younger cohorts, we observe that the U-shape is preserved and graduallyshifted upwards. This indicates that although the coauthorship pattern for younger cohorts is similar to thatfor older cohorts, younger cohorts collaborate with larger numbers of coauthors compared to older cohorts
over their life cycle. This might be a rational response to increased publication lags, because increased
coauthorship would allow Ph.D.s to produce more manuscripts. More manuscripts may or may not lead tomore publications (especially after controlling for quality), and we will investigate this in the next section.

4.5

Life Cycle Productivity Measured by Stocks of Publications

We now turn to cumulative productivity measures to investigate how diﬀerent cohorts compare to each otherat the same point in time, namely at the end of the sixth year after graduation. We decide to focus on thesixth year for cumulative productivity analysis since tenure or promotion decisions are mostly based on theevaluation of cumulative productivity around this time.
We rank Ph.D.s in each cohort based on their cumulative productivity at their sixth year after graduation.
For each cohort, total number of AER equivalent pages and papers produced by Ph.D.s at 99th, 95th, 90th,
85th, 80th, 75th and 50th (median) percentiles are reported in Table 3.

13

Table 3. Performance of Various Percentiles
Percentiles
99th
95th
90th
85th
80th
75th
MedianAER Equivalent Pages
1987 1990 1993 1996 1999

AER Equivalent Publications
1987 1990 1993 1996 1999

70.0
33.9
20.5
13.6
8.4
6.2
1.1

3.87
2.00
1.34
0.99
0.62
0.45
0.08

57.2
28.0
14.5
9.4
6.2
4.0
0.9

69.6
27.1
15.9
10.6
7.3
5.3
1.0

57.3
26.7
15.0
9.4
6.2
4.0
0.9

65.1
24.3
15.0
9.7
6.3
4.3
0.9

3.06
1.48
0.98
0.62
0.43
0.31
0.06

3.23
1.33
0.85
0.61
0.44
0.30
0.06

2.45
1.28
0.76
0.52
0.37
0.26
0.06

2.48
1.22
0.73
0.51
0.37
0.26
0.05

Comparing diﬀerent percentiles of productivity distribution within any cohort reveals that productivity
is very skewed. Based on AER equivalent pages, a Ph.D. ranked at 99th percentile is more than twice as
productive as a Ph.D. ranked at 95th percentile and a remarkable 60 to 70 times more productive than the
median Ph.D.. One obtains similar results if one looks at AER equivalent publications instead of pages.
Comparing, on the other hand, a given percentile across cohorts, AER equivalent pages don’t show aclear pattern, whereas AER equivalent publications do. Especially when we aggregate cohorts 1990 and 1993
into one large cohort, and cohorts 1996 and 1999 into another, the downward trend as measured in AER
equivalent publications becomes evident at all percentiles reported here. These findings may have crucial
implications for the tenure evaluation process of younger cohorts. Two Ph.D.s in diﬀerent cohorts may beranked at exactly the same percentile within their respective cohorts and yet the younger cohort member
has fewer AER equivalent publications than the member of older cohort.
Table 4 provides an overview of various productivity measures attained by an average member of each
respective cohort at the end of their sixth year after graduation. We provide two measures for the numberof total publications: one which splits credit for a publication equally between coauthors, and one which
gives full credit to each of the coauthors. Similarly we provide two versions of AER equivalent publications
and AER equivalent pages: The first one is calculated as explained above, splitting credit equally between
coauthors and using a continuous journal quality index. The second one is calculated by giving full credit
to each of the coauthors.

14

Table 4. Per capita output at the end of the sixth year after Ph.D.
Ph.D.s from Top Thirty
Equal Credit to each coauthor

1987

1990

1993

1996

1999

Total Pages

58.0

52.8

56.7

54.2

51.7

Total Publications

3.58

3.04

2.99

2.76

2.47

Pages per publication

16.2

17.4

19.0

19.6

20.9

AER Pages

9.95

7.56

8.14

7.32

8.04

AER Publications

0.61

0.43

0.41

0.36

0.37

Total Pages

82.0

76.5

82.9

81.5

82.3

Total Publications

4.94

4.33

4.27

4.07

3.84

Full credit to each coauthor

Pages per publication

16.6

17.7

19.4

20.0

21.4

AER Pages

14.9

10.9

12.2

11.4

13.6

AER Publications

0.89

0.62

0.6

0.56

0.61

AER Pages

1.50

1.44

1.50

1.56

1.69

AER Publications

1.46

1.44

1.46

1.56

1.65

Ratio of ‘Full’ to ‘Equal’ Credit

Ph.D.s from Non-Top Thirty
Equal Credit to each coauthor

1987

1990

1993

1996

1999

Total Pages

33.8

34.2

36.3

33.2

38.8

Total Publications

2.57

2.38

2.36

2.0

2.16

Pages per publication

13.2

14.4

15.4

16.6

18.0

AER Pages

2.42

2.29

2.33

1.88

2.07

AER Publications

0.19

0.17

0.14

0.11

0.12

Total Pages

51.0

51.2

55.3

51.4

63.6

Total Publications

3.83

3.57

3.54

3.09

3.49

Pages per publication

13.3

14.3

15.6

16.6

18.2

AER Pages

4.04

3.7

3.9

3.04

3.55

AER Publications

0.3

0.26

0.23

0.18

0.2

AER Pages

1.67

1.62

1.67

1.62

1.71

AER Publications

1.58

1.53

1.64

1.64

1.67

Full credit to each coauthor

Ratio of ‘Full’ to ‘Equal’ Credit

At the end of six years, cumulative productivity of graduates of the top thirty departments is consistent
with the hypothesis that productivity is decreasing for younger cohorts. Based on total number of raw
publications (see second row in Table 4 as well as Figure 9), the 1987 cohort is 45% more productive than
the 1999 cohort. This ratio drops to 29% when we assign full credit for coauthored publications, however
this is still quite substantial, and would certainly aﬀect a tenure committee’s view of a tenure candidate. To
be more precise, at the end of six years, we test for each cohort pair the null hypothesis that cohort meansare equal against the alternative hypothesis that older cohort outperforms the younger one. Comparing 1987
cohort to 1990 cohort, 1990 cohort to 1993 cohort, 1993 cohort to 1996 cohort, and 1996 cohort to 1999

15

cohort, we obtain p-values of 0.0004, 0.35, 0.035, and 0.0039, respectively. For non-top thirty graduates,
the story is less clear, because cohorts’ productivity ranking does not follow a strictly decreasing trend,and cohorts do not line up as hypothesized from the old to the young, when compared at six years after
graduation.
We get more definitive results for productivity when we look at AER equivalent publications for graduatesof top thirty as well as non-top thirty departments. One can see that the 1990 and 1993 cohorts and the1996 and 1999 cohorts, respectively have very similar productivity patterns (see Figure 10). Thus, we will
aggregate these for both the top and non-top thirty departments. At the end of six years, the top thirty
1987, 1990+1993, and 1996+1999 cohorts published 0.61, 0.42, 0.37 AER equivalent publications over all,respectively. We see a very striking decline in productivity over time: the middle cohorts are 13% moreproductive than the youngest cohorts while the oldest cohort is 45% more productive than the middle and
65% more productive than the youngest. We test the null hypothesis that cohort means are equal against thealternative hypothesis that the older cohort outperforms the younger one. We obtain the following p-values:
0 for 1987 and 1990+1993, 0 for 1987 and 1996+1999, 0.0089 for 1990+1993 and 1996+1999.
For non-top thirty schools we find at the end of six years, the 1987, 1990+1993, and 1996+1999 cohorts
published 0.19, 0.16, 0.12 AER equivalent publications, respectively. We see an overall trend in which
the middle cohorts are 33% more productive than youngest cohorts while the oldest cohort is 19% moreproductive than the middle and 58% more productive than the youngest. We test the null hypothesis thatcohort means are equal against the alternative hypothesis that older cohorts outperforms the younger one.
We obtain the following p-values: 0.079 for 1987 and 1990+1993, 0.0003 for 1987 and 1996+1999, 0.0003 for1990+1993 and 1996+1999.
The strong trend of individual cohorts lining up chronologically in decreasing productivity (with 1999
cohort being only slightly more productive than the 1996 cohort) does not hold, however, if we switch our
productivity measure from AER equivalent publications to AER equivalent pages. As shown in Table 4,1987 cohort is the most productive cohort, however other cohorts do not line up chronologically: 1993 cohort
achieves higher productivity than 1990 cohort, and 1999 cohort outperforms 1996 cohort. Considering Ph.D.sfrom top thirty departments, although 1990 and 1993 cohorts together have higher average productivity than1996 and 1999 cohorts together, this is not the case anymore when we assign full credit to each coauthor.
Moreover, comparing diﬀerent cohorts of top thirty departments using diﬀerent policies to discount forcoauthortship (namely equal credit and full credit), we see that the ratio of coauthor-not-discounted tocoauthor-discounted AER equivalence measures, which is denoted as the “ratio of full to equal credit” inTable 4, decreases from 1987 cohort to 1990 cohort, but then increases from older to younger cohorts. Thus
we find quality discounted rates of coauthorship increasing as cohorts get younger. This is in line with our
discussion of coauthorship patterns over the life cycle of cohorts in the previous subsection.
This suggests a very important methodological and policy question about what is the right measure of
productivity: AER equivalent publications or AER equivalent pages? Moreover, should coauthors share thecredit or should they be assigned full credit? Our position is that tenure committees look at lines on a CV,
first and page counts second, if at all. If this is true, then younger scholars look significantly less productive
than their older colleagues. In part, this is because papers seem to have gotten longer on average over theyears. This leads to a potential double whammy for assistant professors seeking tenure. First, by counting
publications instead of pages, more recent tenure candidates will appear unfairly less productive than theircolleagues who got tenure in the past. Second, a department that set a standard that could be met by say
the top twenty percent of new tenure candidates who graduated in 1987 will find that it can be met perhaps

16

by only eleven percent of those who graduated in 1999. This may be good or bad, but at least tenurecommittees should be aware of the implications of not adjusting tenure standards that reflect the current
publishing environment of longer papers, lower acceptance rates and longer delays.
Another usefulg way to think about how publishing patterns have changed is to look at the ratio of
AER equivalent publications to total publications. This is a measure of average publication quality acrosscohorts. One can think of this as a “signal to noise ratio” as it indicates what fraction of an AER-quality
publication is contained in a given publication. As publication quality of a cohort decreases, the ratio of
AER equivalent publications to total publications will decrease. Table 5 shows total number of publications
within six years after obtaining Ph.D., number of AER equivalent publications, and the percentage of AER
equivalent publications in total publications (hence the signal to noise ratio) across cohorts. We calculate
AER equivalent publications using indices provided by Kalaitzidakis et al. (2003) as well as by Combes andLinnemer (2010). AER equivalent publications obtained by two diﬀerent indices are very highly correlated,thus yielding very similar trends.

Table 5. Aggregate cohort output
Total publication output
1987 1990 1993 1996 1999
Top 30 Ph.D.s
Total Publications
AER (Combes)
AER (Kalaitzidakis)
Non Top 30 Ph.D.s
Total Publications
AER (Combes)
AER (Kalaitzidakis)

1987‘Signal to noise’ ratio
1990 1993 1996 1999

1988

2531

2792

2468

2075

772

877

964

843

756

0.388

0.347

0.345

0.342

0.364

340

359

387

325

313

0.171

0.142

0.139

0.132

0.151

1004

1494

1527

1228

1549

250

377

358

271

352

0.249

0.252

0.234

0.221

0.227

72

105

93

68

86

0.072

0.070

0.061

0.055

0.056

Top 30 Ph.D.s relative to Non Top 30 Ph.D.s
Total Publications
1.98
1.69
1.83
2.01
AER (Combes)
3.09
2.33
2.69
3.11
AER (Kalaitzidakis) 4.72
3.42
4.16
4.78

1.34
2.15
3.64

Notes: The rows labelled Combes and Kalaitzidakis use AER equivalent weights from theCombes and Linnemer (2010) and the Kalaitzidakis et al. (2003) studies to aggregate
publications in each cohort.

While it seems clear that the 1987 cohort of top thirty departments’ graduates had higher quality publications on average by any measure, the signal to noise ratio decreases from the 1987 cohort to the 1996
cohort. However 1999 cohort performs better on quality than the previous three cohorts. Overall there is aclear trend of declining overall average publication quality of top thirty graduates except for the youngest
cohort. Turning to cohorts of non-top thirty departments’ graduates we find it diﬃcult comparing 1987

17

cohort to 1990 cohort: whether 1987 cohort outperforms 1990 cohort or not depends on the specific quality index, which can be interpreted as suggesting that the two cohorts don’t diﬀer much at all in quality.
Younger cohorts perform worse than the oldest two cohorts, and 1999 cohort performs slightly better than1996 cohort. Thus, we can say that signal to noise ratio has worsened across cohorts graduating from mid
80s until late 90s. This partially confirms the conventional wisdom that the enormous growth of new journals
and publication outlets has led to a decline in overall average publication quality14 .

5Life Cycle and Cohort Eﬀects

Our analysis in preceding sections has shown that the hypothesized gradual downward shift in productivity
across cohorts is not entirely obvious when only annual productivity is considered. We detect, however,that older cohorts do outperform younger cohorts if we look at cumulative instead of annual productivity.
Comparing AER equivalent publications accumulated at the end of six years after graduation, we observea gradual downward shift across cohorts. In this section we focus again on annual productivity of Ph.D.s.
Our aim is to formally flesh out productivity diﬀerences across cohorts which manifest themselves clearly
at a cumulative level but which are not as obvious at the annual level when only descriptive tools are used.
The goal is to distinguish life cycle and cohort eﬀects in the annual productivity measures. As Figures 2 to
6 reveal, an average Ph.D.’s annual publication productivity follows a distinct hump-shaped life cycle. The
number of AER equivalent publications achieved at any given time is aﬀected both by the location on life
cycle after graduation and by cohort specific eﬀects.

5.1

Estimation Results and the Ellison Eﬀect

We estimate a pooled Tobit model of annual research productivity where we treat the dependent variable
as a latent variable. Our dependent variable is the annual research output measured as the number of AER
equivalent publications for a given individual at a given time (which is zero for nearly sixty percent of allobservations in the raw data). Explanatory variables include time polynomials, dummy variables for thegraduation year and a dummy variable to indicate whether the graduate is from a top 30 department. The
dummy variables for the graduation year are the key variables of interest since they allow us to test the null
hypothesis that younger cohorts are less productive than older ones.
Annual research productivity of individual  at time  is the dependent variable, with time measured
as “years after graduation”. A third degree polynomial in time captures the life cycle pattern of research
productivity. All individuals’ publication records from the first until sixth year after graduation are covered
because our aim is to compare cohorts within their “tenure-track” period which corresponds to approximately
the first six years after graduation. This leaves us with a total of 42,924 observations15 .
Cohort eﬀects are captured by dummy variables for graduation years that span from 1986 to 1999,
and individuals with the same year of graduation are described as belonging to the cohort for that year.
Observations extend to graduates in the year 2000 but the last dummy is dropped to avoid collinearity
1 4 One should note that graduating cohorts have had approximately the same size from 1989 to 2000. The suggested increaseof publications by younger cohorts in lower quality outlets might have been a consequence of three things: being subject toincreased publications lags, facing increased supply of economics Ph.D.s from non-U.S. and non-Canadian institutions (hence
increased competition from the rest of the world), or a mix of the two.
1 5 Publication records for most cohorts extend beyond six years. If we use all available years for each cohort in our pooled
Tobit model, we would be using 81,051 observations. Estimating our model using these observations and correcting for the loss
in time dummies’ eﬃciency due to unbalanced panel, we obtain qualitatively the same results as with only six years as far as
significance of time dummies and their signs are concerned.

18

between the cohort dummies. Thus, the marginal eﬀect of a given cohort dummy variable shows how therespective cohort performs relative to the graduates of 2000 cohort. The cohort dummies are not interactedwith time polynomials: we are assuming the year of graduation aﬀects the level of the life cycle and not its
slope at diﬀerent points in the life-cycle. If a slowdown in the publication process is occurring over time,
the coeﬃcients on the cohort dummies should decrease in value as we move in time from 1986 to 1999. Our
hypothesis is that the coeﬃcients on the graduation year dummy, i.e. cohort eﬀect, will be highest in theinitial year and decline over time as publication lags continue their upward trend. The specification also
includes,  30, a dummy variable that takes on the value 1 if individual  is a graduate of a top thirty PhD
program. This just shifts the conditional mean of the flow of publications but does not alter the shape ofthe life-cycle profile or the within-group year eﬀects.
Marginal eﬀects for coeﬃcients from four diﬀerent Tobit models are reported in Table 6. The four Tobit
models diﬀer by their dependend variables. Column (1) reports marginal eﬀects from regressing annual
number of AER-equivalent publications adjusted for coauthorship (where credit from publication is equallyshared among all coauthors) on life cycle eﬀects, cohort eﬀects, and a dummy variable capturing the rankingof the graudate institution. We use the significance of point estimates for cohort eﬀects to determine whether
a cohort outperforms another cohort in terms of research productivity.
Since one might claim that the coauthor-adjusted number of AER-equivalent publications is the conventional measure for research productivity that is used in most cases, and since we have been focusing on thisparticular productivity measure in the previous sections of the paper, we put this measure under the spotin this section. However we run the same Tobit model for three other productivity measures: we use theannual number of AER-equivalent publications without adjustment for coauthorship where each coauthor
gets full credit for the publication (column (2)), the annual number of AER-equivalent publications where
AER-equivalence is based on a discrete ranking16 of journals (column (3)), and the total number of annual
publications without any quality weights (column (4)).
1 6 Discrete ranking of journals is obtained from Combes and Linnemer (2010), as in the previous section.

19

Table 6. Pooled Tobit Model- Marginal Eﬀects
(1)

(2)

(3)

(4)

Top30

0.0208***0.0306***

0.0375***0.058***

Life cycle eﬀects

2
30.0359***-0.0054***0.000220.0484***-0.0066***
0.000230.0771***-0.0116***0.0005*0.2152***-0.0319***0.0014*

Cohort eﬀects
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

0.0218***0.0158***0.0121***0.004
0.0059**0.0088***0.0061**
0.003
0.0041*0.00032
0.0005-0.0023-0.00080.00150.031***
0.0185***0.0125***0.00070.00490.0092**0.0056-0.0001
0.0031-0.002-0.001-0.0064-0.00210.00090.0454***0.0361***0.0282***0.011**
0.0189***0.0208***0.0164***0.0105**0.0139***0.00420.0064-0.00030.00130.0084*0.1168***0.093***
0.0846***
0.041***
0.0645***0.0672***0.06***
0.044***
0.0509***0.0242*0.0325**0.00930.0136
0.0336*** () ( |    0)
Observations

0.04720.186542924

0.07190.275942924

0.12760.354242924

0.406
0.973742924* significant at 10%; ** significant at 5%; *** significant at 1%

Evidently, cohort eﬀects decrease from older to younger cohorts, which means that the predicted diﬀerencein annual number of publications is higher between 2000 cohort and any cohort from late 1980s than between
2000 cohort and any cohort from late 1990s. This observation is robust across all four diﬀerent measuresof research productivity that we use in Table 6. In order to better visualize the pattern that is revealed bycohort eﬀects of graduates over fourteen years, Figure 11 plots the cohort eﬀects from columns (1), (2), and(3) in Table 6. All three of them show very similar trends. Diﬀerences in productivity between 2000 cohortand other cohorts are more pronounced when we assign full credit to each coauthor instead of discounting forcoauthorship. It is evident that there is a downward shift that begins with sharp declines in the late 1980s
but which seems to settle into a steady-state pattern in the late 1990s. In the last few years, the annual

20flow diﬀerences across cohorts are too small numerically to be statistically distinguishable. Thus, most of
the evidence of the slowdown comes in the first half of the cohort sample. The year 1986 seems somewhatof an outlier, so we discount that evidence, but subsequent nearby years are higher than later years, by astatistically significant amount.
Marginal eﬀects of the Tobit model are evaluated around the conditional mean of the dependent variable
and show how the conditional mean is aﬀected by the life cycle eﬀects, cohort eﬀects, or the institution
dummy. In order to give a better understanding for the magnitude of the marginal eﬀects, we provide
conditional and unconditional means of the respective productivity measure at the bottom of Table 6. Dueto the nonlinearity of the Tobit model and our extensive use of dummy variables in this estimation, only
limited intuition can be gained from studying the numerical values of marginal eﬀects. Instead of analyzing
quantitative aspects of these marginal eﬀects, we focus on the sign and significance of them. The dummy
variable for graduates of top thirty departments is found to be highly significant which confirms our earlier
discussions and descriptive findings about the performance diﬀerences between graduates of top thirty andnon-top thirty departments.
Results in Table 6 show that diﬀerent ways of measuring research productivity yield diﬀerent resultsin terms of statistical significance: 2000 cohort’s research productivity cannot be statistically significantly
distinguished from that of any cohort after 1991 when we use “the annual number of AER-equivalent publications with full credit to each coauthor” (column (2)) as a measure for the research productivity. On theother hand, when we use “the annual number of total publications” (column (4)) instead, then the picturechanges: in this case, 2000 cohort is significantly outperformed by almost all other cohorts, except for the
1997 and 1998 cohorts.
In order to compare cohorts’ research productivity pairwise, we make use of significance levels of cohort
eﬀects. This can be done by comparing the point estimate of the marginal eﬀect of a graduation year dummy
to the 95% confidence interval of another graduation year dummy’s marginal eﬀect. If the point estimate
lies to the right of the 95% confidence interval of the other cohort’s marginal eﬀect, then we say that thelatter cohort is outperformed by the former one. A more rigorous method, which we employ in our analysis
below, is to run separate Tobit models, each of which take one of the fifteen cohorts as base. Then thesigns and significance levels of cohort eﬀects will reveal whether the base cohort is outperformed by othercohorts or outperforms other cohorts and at what significance level exactly. For this analysis we measureresearch productivity in terms of the annual number of AER-equivalent publications first, then we discuss
other alternatives.
Results of the pairwise comparisons of cohorts’ research productivity are summarized in Table 7. A ‘+’
sign indicates that members of cohorts belonging to the year indicated in the row statistically significantly
outperform members of the cohorts indicated in the columns in terms of research productivity.

21

Table 7. Comparison across Cohorts: AER-Equivalent Publications with Equal Credit
1987
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+

+
+
+

+
+
+

+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

(+)

+
+
+

+
+
+
(+)
+
+
+
(+)
+

+
+
+

+
+
+

+
+
+
+
+
+
+
+
+

(+)
+
(+)

+
+
+

(+)

+

(+)

(+)

Note: "+" indicates the row cohort out-performed the column cohort at the 5% level of significance, and"(+)" indicates the row cohort out-performed the column cohort at the 10% level of significance.

If older cohorts outperform younger cohorts, the ‘+’ signs should be found above the diagonal of thematrix in Table 7. We see that this is true of almost all comparisons across cohorts on average. An average
member of the 1986 cohort outperforms average members of all subsequent cohorts, the 1990, 1991 and1992 graduates, on average, consistently outperform graduates from later years, whereas the 1989 graduates
outperform only 1997 and 1998 graduates on average. There are only two cases where a younger cohort
outperforms an older one: 1991 outperforms 1989, and 1999 outperforms 1997, both at a significance levelof 10%. Thus, after controlling for life cycle eﬀects, we reach the following conclusion: there is a significant
decrease in Ph.D.s’ annual publication productivity from the late 1980s until the mid-1990s. Performance of
graduates from 1995 to 2000 cannot be statistically distinguished, except that 1999 statistically significantly
outperforms 1997.
As we mentioned above, whether a cohort statistically significantly outperforms another cohort depends
on how we measure the annual research productivity. To this end, we run the same procedure as in Table
7 using the other three research productivity measures mentioned above. Tables A.5, A.6, and A.7 in
the appendix document pairwise comparisons of cohorts when we use AER-equivalent publications withfull credit, AER-equivalent publications with discrete quality ranking, and number of publications without
quality adjustment as measures for research productivity, respectively. As Tobit estimates in Table 6 alreadysuggest, diﬀerent productivity measures yield diﬀerent, however encompassing results. In all three cases
documented in Tables A.5, A.6, and A.7, the ‘+’ signs are found almost always above the diagonal ofthe matrix in the respective table. Hence using diﬀerent productivity measures doesn’t change our basic
conclusions from analyzing Table 7: Ph.D.s’ annual publication productivity from the late 1980s until the
mid-1990s drops significantly, and research productivities of cohorts from 1995 to 2000 cannot be statisticallydistinguished, except for only a few cases.

22

Research productivity diﬀerences across cohorts become obvious when we use the number of total publications to measure research productivity, as shown in Table A.7. However we doubt that this is a reliablemeasure, because quality is an essential part of measuring and evaluating research output. Our other three
productivity measures integrate publication quality and thus establish an AER-equivalence for annual research productivity. Comparing these three measures, diﬀerences across cohorts are more pronounced when
we use the annual number of AER-equivalent publications calculated using a discrete journal ranking. Sincejournals are grouped into diﬀerent tiers in this ranking, a shift of publications across tiers would generate agreater, and more importantly, a discontinuous downward shift in research productivity as opposed to what
would happen when using a continuous journal ranking. Results shown in Table A.6 reveal that younger
cohorts might be publishing significantly less in the top-tier and more in the second-tier or third-tier journals
compared to older cohorts. Results shown in Table 7 are based on the annual number of AER-equivalentpublications that are calculated using a continuous ranking of journals, and they yield fewer amount ofstatistically significant productivity diﬀerences in the pairwise comparison of cohorts compared to the one
using discrete rankings. Finally, when we don’t discount for coauthorship and assign each coauthor fullcredit for a publication while using a continuous ranking, then diﬀerences across cohorts become even lesspronounced. This is in line with what we have already shown in earlier sections, that younger cohorts tend
to coauthor more than older cohorts. This may simply reflect a shift in social norms in the profession, or it
may be a strategy to get around the Ellison Eﬀect. Nevertheless it does not seem to work to save younger
cohorts from being outperformed by older ones, as shown in Table A.5.

5.2

Research Productivity within and across Quintiles

As shown in section 4.1, distribution of research productivity of Ph.D.s is extremely skewed: the top 5%
of Ph.D.s produce about 40%, and the top 20% of Ph.D.s produce about 80% of all publications in theirrespective cohorts. Similar skewedness shows up across all cohorts. Our analysis so far has compared cohorts
based on their average performance. The extreme skewedness of productivity distribution within cohorts,
however, suggests that comparisons of the output of diﬀerent productivity percentiles across cohorts would
be interesting. To this end, we rank all Ph.D.s within a given cohort based on the number of AER equivalentpublications17 they achieve at the end of sixth year after graduation. We then compare annual productivityof Ph.D.s across cohorts who are in the third quintile range (between 40th and 60th percentiles), and thenthose who are in the second quintile range (between 60th and 80th percentiles), and finally those in the top
quintile range. We run pooled Tobit estimation with the same specification as above on these three quintile
ranges18 . Marginal eﬀects for the top three quintile ranges are reported in Table 8.
1 7 These are calculated using continuous journal rankings and assigning equal share to each coauthor.
1 8 We determine quintile ranges based on cumulative productivity at the end of sixth year after graduation, because annual
productivity is highly volatile. If we look at annual productivity, a highly productive graduate may be found in the lowest
quintile range at some years due to this volatility. As a result, determining graduates’ productivity quintiles in the above
manner and comparing the same quintile range across cohorts using marginal eﬀects from the pooled Tobit regression proves
to be a more sensible method than using quantile regression on annual productivity. (See e.g. Koenker (2004)).

23

Table 8. Marginal Eﬀects for Top Three Quintiles
P40-P60

P60—P80

P80-P100Top30-0.0004618

0.0003573

0.0201221***

Life cycle eﬀects

2
3

0.0075388***-0.0014535***0.0000899**

0.0230354***-0.0036719**
0.0001701

0.0923817***-0.0106398
0.0001321

Cohort eﬀects
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

0.0034581***0.0048398***
0.0039493***0.0015597**
0.0026034***
0.0025138***
0.0028776***0.0016973**
0.0024074***0.0016029**
0.0022369***0,0007187
0,0002142
0.0018777***

0.0138791***
0.0146142***0.0113483***
0,003843
0.0064763**
0.0064635**
0.005512**
0,002337
0.0073632***
0,0004858
0,0033282-0,0005396
0,0008649
0,0026219

0.1060625***
0.0588261***
0.046402***0,0173163
0.0239718**
0.0373514***0.0287218**
0,0104932
0.020059**
0,0065441
0,004813-0,0068057-0,000829-0,0029431* significant at 10%; ** significant at 5%; *** significant at 1%

Interestingly, the positive impact of having graduated from a top thirty department, which is statisticallysignificant for average members of cohorts (see Table 6), has a statistically significant positive impact only forgraduates in the top quintile range. Its marginal eﬀect is insignificant for the second and third quintile ranges.
This suggests that lower quality graduates of top places are not significantly diﬀerent from typical graduatesof any Ph.D. program. For all three quintile ranges, we observe that cohort eﬀects yield a decreasing trend
as we move from older to younger cohorts. Pairwise comparisons of cohorts for the third, the second andthe first quintile ranges are reported in Tables 9, 10, and 11, respectively.

24

Table 9. Publication comparisons across cohorts (P40-P60)
1987

1988

1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1989
+
+
+

1990
+

1991
+

1992

1993

+

+
+
+

1994

1995

+
+

+
+
+

1996

1997

1998

1999

2000

+
+

+
+
+

+
+
+
+
+
+
+
+
+
+
+

+
+
+

+
+
+
+
+
+
+
+
+
+
+

+
+
+
+
+

+

Note: A "+" indicates the row cohort out-performed the column cohort at the 5% level of significance.

Table 10. Publication comparisons across cohorts (P60-P80)
1987
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+
+
+

+
+

+
+

+
+
+

+
+
+

+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+

+

+

+

Note: A "+" indicates the row cohort out-performed the column cohort at the 5% level of significance.

25

Table 11. Publication comparisons across cohorts (P80-P100)
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+

+

+
+
+

+
+
+

+

+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+

+
+
+

+
+
+
+
+
+
+

+
+
+

+
+
+

+
+
+
+
+
+
+
+

+

+

+

+

+
+
+

Note: A "+" indicates the row cohort out-performed the column cohort at the 5% level of significance.

Although pairwise comparisons of cohorts for the top three quintile ranges yield more or less similar
results as pairwise comparison of cohorts’ average performers (see Table 7), they do reveal an interesting
pattern. Comparing the third quintile ranges across cohorts (see Table 9), we observe that 1986, 1987, and1988 cohorts outperform most of the younger cohorts, and 1997, 1998, and 2000 cohorts are outperformedby most of the older cohorts. As we move to higher quintile ranges, the upper diagonal of the ‘pairwise
comparison matrix’ displays more ‘+’ signs. That is, as we compare higher quintile ranges across cohorts,
the dominance of older cohorts over younger cohorts, and the chronological ordering of cohorts in publication
productivity become more evident. This means that top performers of each cohort are hit more severely
by the publication bottleneck, namely the Ellison Eﬀect. This in turn suggests that the top performers of
the youngest generation are likely to fall most short of productivity expectations formed on the basis of thishistorical publishing environment, while middle and lower performers may not look much diﬀerent than theirpredecessors. Top departments especially should be aware of these facts when evaluating junior faculty fortenure.
We plot cohort eﬀects across percentiles in Figure 12, which shows how cohort eﬀect for a given cohortchanges across diﬀerent percentile ranges19 . Cohort eﬀects are hard to distinguish in lower percentile ranges,
especially up to the range of 40th to 50th percentile, and they don’t line up following a chronologicalordering. There exists, however, a much clearer pattern for higher percentile ranges, and they line up
following chronological ordering, except that 1999 cohort’s top percentile range performs poorer than thatof the 2000 cohort. One should keep in mind that there will necessarily be more variation in productivity of
Ph.D.s in higher percentile ranges, because they produce larger amounts of publications than those in lower
1 9 We group Ph.D.s in each cohort into percentile ranges of ten increments (range of 90th to 100th percentile, range of 80th
to 90th percentile etc) based on their cumulative performance at the end of sixth year after graduation and estimate cohort
eﬀects across percentile ranges as we did with quintile ranges. We plot only cohorts 1988, 1991, 1994, and 1999 for illustrative
purposes.

26

percentile ranges. This means that the confidence intervals around cohort eﬀects are also widening as cohort
eﬀects diverge in high percentile ranges, so that some of this divergence may be insignificant. Tables 9 to
11 show that although not all of the divergence in cohort eﬀects in high percentile ranges is significant, aconsiderable amount of it is significant so that the upper diagonal of the ‘pairwise comparison matrix’ getsfilled with more ‘+’ signs as we consider higher percentile ranges.

6

Conclusion

Ellison (2002) documents how most journals require today more than double the time they required thirty
years ago to evaluate a submitted paper. It is only natural to wonder how this longer “time to build” production process for published manuscripts aﬀects younger Ph.D.s.. It is particularly important to investigate
whether younger cohorts perform significantly more poorly than older cohorts in terms of research output.
Promotions, job oﬀers and tenure decisions in academia are based on an individual’s publication record.
This publication record is not only compared to his/her cohort peers, but also to older cohorts, because
institutions may be worried about keeping their established standards. Assuming that younger cohorts are
not less smart or working less diligently than older cohorts, a downward trend in publication records ascohorts get younger must have important policy implications for the economics Ph.D. job market.
Reconstructing the journal publication record of 14,271 graduates from U.S. and Canadian Ph.D.-granting
economics departments from 1986 to 2000, we obtain strong evidence of productivity decrease as we compare
younger to older cohorts. It is evident that there is a downward shift that begins with sharp declines in
the late 1980s and seems to settle into a steady-state pattern in the late 1990s. In the last few years, theannual flow diﬀerences across cohorts are too small numerically to be statistically distinguishable. Looking
at the cumulative number of AER equivalent publications reached at the end of six years after graduation,
we see convincing evidence of the expected productivity decline for both top and non-top thirty departments.
Thus, unless we believe that recent graduates are fundamentally of poorer quality, the same quality of tenurecandidate is significantly less productive today than 10 or 15 years ago.
If we use the number of AER equivalent pages as a measure of productivity, then the above mentioned dropoﬀ in productivity is not obvious. This is consistent with Ellison’s (2002) documentation of the increasing
length and decreasing number of published papers. This exposes a significant methodological question
about the best way to measure productivity of departments, graduate programs and individual scholars.
Productivity patterns over time look diﬀerent depending on whether number of papers or pages is chosen
as a basis of comparison. We argue that what the profession values when granting tenure, giving raises, ormaking senior hires is the number of lines on a CV and the quality of the research papers on those lines. It
is much harder to distill this into the number of AER-quality weighted pages, and we suspect that this isseldom attempted in practice.

27

References[1] Combes, Pierre-Philippe, and Laurent Linnemer. 2003. “Where are the Economists who Publish?
Publication Concentration and Rankings in Europe Based on Cumulative Publications.” Journal of theEuropean Economic Association, 1(6): 1250-1308.
[2] Combes, Pierre-Philippe, and Laurent Linnemer. 2010. “Inferring Missing Citations, A Quantitative Multi-Criteria Ranking of all Journals in Economics.” GREQAM Working Paper, no. 2010-28.
[3] Collins, Jeﬀery T., Richard G. Cox, and Victor Stango. 2000. “The Publishing Patterns ofRecent Economics Ph.D. Recipients.” Economic Inquiry, 38(2): 358-367.
[4] Coupe, Tom. 2003. “Revealed Performances: Worldwide Rankings of Economists and Economics
Departments, 1990-2000.” Journal of the European Economic Association, 1: 1309-1345.
[5] Davis, Joe C., and Debra Moore Patterson. 2001. “Determinants of Variations in Journal Publication Rates of Economists.” The American Economist, 45(1): 86-91.
[6] Ellison, Glenn. 2002. “The Slowdown of the Economics Publishing Process.” Journal of Political
Economy, 110(5): 947-993.
[7] Fafchamps, Marcel, Marco J. van der Leij, and Sanjeev Goyal. 2006. “Scientific Networks And
Co-Authorship.” University of Oxford Economics Discussion Paper no.256.
[8] Goodwin, Thomas H., and Raymond D. Sauer. 1995. “Life Cycle Productivity in Academic
Research: Evidence from Cumulative Publications Histories of Academic Economists.” Southern Economics Journal, 61: 728-743.
[9] Goyal, Sanjeev, Marco J. van der Leij, and José Luis Moraga-Gonzalez. 2006. “Economics:
An Emerging Small World.” Journal of Political Economy, 114(2): 403-432.
[10] Hutchinson, E. Bruce, and Terry L. Zivney. 1995. “The Publication Profile of Economists.”
Journal of Economic Education, 26(1): 59-79.
[11] Kalaitzidakis, Pantelis, Theofanis P. Mamuneas, and Thanasis Stengos. 2003. “Rankings ofAcademic Journals and Institutions in Economics.” Journal of the European Economic Association,
1(6): 1346-1366.
[12] Koenker, Roger. 2004. “Quantile Regression for Longitudinal Data.” Journal of Multivariate Analysis,
91: 74-89.
[13] Levin, Sharon G., and Paula E. Stephan. 1991. “Research Productivity Over the Life Cycle:
Evidence for Academic Scientists.” American Economic Review, 81(1): 114-131.
[14] Rauber, Michael, and Heinrich W. Ursprung. 2008. “Life Cycle and Cohort Productivity in
Economic Research: The Case of Germany.” German Economic Review, 9(1): 431-456.
[15] Stock, Wendy A., T. Aldrich Finegan, and John J. Siegfried. 2009. “Completing an Economics
Ph.D. in Five Years.” American Economic Review, 99(2): 624-629.

28

[16] Stock, Wendy A., and John J. Siegfried. 2006. “Where Are They Now? Tracking the Ph.D. Classof 1997.” Southern Economic Journal, 73(2): 472-488.
[17] Stock, Wendy A., and John J. Siegfried. 2006. “Time-to-Degree for the Economics Ph.D. Class of
2001-2002.” American Economic Review, 96(2): 467-474.

29

7

Appendix
Table A.1. Number of Ph.D.s in Economics by Data Source
Year

AEA

Hasselback

Overlap

1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000

264
597
787
953
947
905
928
1,074
1,021
1,025
955
935
981
866
969

227
216
196
230
164
178
155
173
182
170
155
167
178
182
181

61
95
94
147
107
122
106
110
122
109
104
107
113
106
110

Total
425
714
883
1,035
1,001
956
970
1,132
1,077
1,078
1,002
990
1,040
936
1,032

Note: The overlap indicates the number of PhDs common
to the AEA and Hasselback data.

30

Table A.2. Number of Publications
Year

Number

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006

9,918
9,872
9,918
10,552
10,767
11,254
11,905
13,10813,492
14,374
15,825
17,692
18,385
19,86920,81821,83522,271
21,991
23,51025,61825,976
19,72231

Table A.3. Journal Weights Relative to the American Economic Review
JournalIndex

JournalIndex

1. American Economic Review

1.000

36. IJGT

0.061

2. Econometrica

0.968

37. Economic Inquiry

0.060

3. Journal of Political Economy

0.652

38. World Bank Economic Review

0.057

4. Journal of Economic Theory

0.588

39. Journal of Risk and Uncertainty

0.056

5. Quarterly Journal of Economics

0.581

40. Journal of Development Economics

0.055

6. Journal of Econometrics

0.549

41. Land Economics

0.051

7. Econometric Theory

0.459

42. IMF Staﬀ Papers

0.051

8. Review of Economic Studies

0.452

43. Canadian Journal of Economics

0.051

9. JBES

0.384

44. Public Choice

0.050

10. Journal of Monetary Economics

0.364

45. Theory and Decision

0.049

11. Games and Economic Behavior

0.355

46. Economica

0.046

12. Journal of Economic Perspectives

0.343

47. Journal of Urban Economics

0.044

13. Review of Economics and Statistics

0.280

48. IJIO

0.043

14. European Economic Review

0.238

49. JLEO

0.041

15. JEEA

0.238

50. Journal of Law and Economics

0.039

16. International Economic Review

0.230

51. National Tax Journal

0.039

17. Economic Theory

0.224

52. Journal of Industrial Economics

0.039

18. Journal of Human Resources

0.213

53. Journal of Economic History

0.038

19. Economic Journal

0.207

54. Oxford Economic Papers

0.037

20. Journal of Public Economics

0.198

55. Journal of Comparative Economics

0.034

21. Journal of Economic Literature

0.188

56. World Development

0.032

22. Economics Letters

0.187

57. Southern Economic Journal

0.031

23. Journal of Applied Econometrics

0.166

58. Explorations in Economic History

0.030

24. JEDC

0.145

59. Economic Record

0.029

25. Journal of Labor Economics

0.128

60. Journal of Banking and Finance

0.026

26. JEEM

0.119

61. Contemporary Economic Policy

0.024

27. RAND Journal of Economics

0.114

62. Journal of Population Economics

0.024

28. Scandinavian Journal of Economics

0.107

63. JFQA

0.021

29. Journal of Financial Economics

0.099

64. JITE

0.020

30 OBES

0.084

65. Applied Economics

0.020

31. Journal of International Economics
32. Journal of Mathematical Economics
33. JEBO
34. Social Choice and Welfare
35. AJAE

0.078
0.076
0.071
0.069
0.062

Note: Journal of Business and Economic Statistics (JBES), Journal of the European Economic
Association (JEEA), Journal of Environmental Economics and Management (JEEM), Oxford
Bulletin of Economics and Statistics (OBES), Journal of Economic Behavior and Organization (JEBO)
American Journal of Agricultural Economics (AJAE,), International Journal of Game Theory (IJGT)International Journal of Industrial Organization (IJIO) ,Journal of Law, Economics, and Organization(JLEO), Journal of Financial and Quantitative Analysis (JFQA), Journal of Institutional andTheoretical Economics (JITE)

32

Table A.4. Top Thirty Economics Departments in US and Canada
Rank

Ordered by faculty productivity

Ordered by productivity of Ph.D.s

Index∗

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.

Harvard University
University of Chicago
University of Pennsylvania
Stanford University
MIT
UC-Berkeley
Northwestern University
Yale University
University of Michigan
Columbia University
Princeton University
UCLA
New York University
Cornell University
University of Wisconsin
Duke University
Ohio State University
University of Maryland
University of Rochester
University of Texas, Austin
University of Minnesota
University of Illinois
UC-Davis
University of Toronto
University of British Columbia
UC-San Diego
University of Southern California
Boston University
Pennsylvania State University
Carnegie Mellon University

MIT
Princeton University
Harvard University
University of Rochester
California Institute of Technology
Yale University
Northwestern University
Carnegie Mellon University
University of Chicago
UC-San Diego
University of Pennsylvania
Stanford University
University of Toronto
University of Western Ontario
University of Minnesota
Brown University
University of British Columbia
Columbia University
SUNY, Stony Brook
UCLA
University of Iowa
UC-Berkeley
University of Virginia
Duke University
Queen’s University
University of Wisconsin
University of Michigan
Johns Hopkins University
New York University
McMaster University

0,801

Note: Departments that are ranked top-30 in ONLY one of the two rankings are in italics.
∗

Index is the average value of AER equivalent publications per research-active

graduates at the end of six years after graduation.

33

0,741
0,694
0,674
0,602
0,596
0,578
0,544
0,522
0,502
0,487
0,477
0,421
0,401
0,38420,3841
0,354
0,353
0,329
0,323
0,317
0,316
0,296
0,290
0,271
0,270
0,265
0,262
0,260
0,251

Table A.5. Comparison across Cohorts: AER-Equivalent Publications with Full Credit
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+

+

+
+
+

+
+
(+)

+
+

+
+
(+)

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+

(+)

(+)
+
+

(+)
+
(+)

+
+
+
+
+
+
+
(+)
+

+
+
+

+

+

(+)

+

(+)

Note: "+" indicates the row cohort out-performed the column cohort at the 5% level of significance, and"(+)" indicates the row cohort out-performed the column cohort at the 10% level of significance.

Table A.6. Comparison across Cohorts: AER-Equivalent Publications (Discrete Quality Ranking)
1987
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+

+
+
+

+
+
(+)

+
+

+
+
+

+
+
+

+
+
+

+
+
+

+
+
+
+
+
+

+
+
+
+
+
+
+
+
+

+
+
+

+
+
+

+
+
+
+
+
+
+
+
+

+
+
+
+
+
+
+
+
+

(+)

(+)
+

+

(+)

+
+

+

Note: "+" indicates the row cohort out-performed the column cohort at the 5% level of significance, and"(+)" indicates the row cohort out-performed the column cohort at the 10% level of significance.

34

Table A.7. Comparison across Cohorts: Number of Publications (Equal Credit without Quality Weights)
1987
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

+
+
+

+
(+)

+

+
+

+
+
+

+
+
+

+
+
+

+
+
+
+
+
+

+
+
+
+
+
+
+
+
+

+
+
+

+
+
+

+
+
+
+
+
+
+
+
+
(+)

+
+
+
+
+
+
+
+
+
(+)
+

(+)

+

(+)
(+)

(+)

+

+
+
(+)

Note: "+" indicates the row cohort out-performed the column cohort at the 5% level of significance, and"(+)" indicates the row cohort out-performed the column cohort at the 10% level of significance.

35

FIGURE 1. PRODUCTIVITY BY DEGREE-GRANTING INSTITUTION
AER EQUIVALENT PUBLICATIONS
0,90

0,80

AER
R equivalent publications

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,00

FIGURE 2. LIFE-CYCLE PRODUCTIVITY OF GRADUATES OF TOP 30 PROGRAMSANNUAL RAW PAGES
14

Raw paages publishedd per year

12

10

8

1987
1990
1993

6

1996
1999
4

2

0
-1

0

1

2

3

4

5

6

7

8

9

Years After Graduation

10

11

12

13

14

15

FIGURE 3. LIFE-CYCLE PRODUCTIVITY OF GRADUATES OF NON-TOP 30 PROGRAMSRAW ANNUAL PAGES
80
8,0

7,0

Raw pages published pper year

6,0

5,0
,
1987
4,0

1990
1993
1996

3,0

1999
2,0

1,0

0,0
-1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 4. LIFE-CYCLE PRODUCTIVITY OF GRADUATES OF TOP 30 PROGRAMSRAW ANNUAL PUBLICATIONS
0 80
0,80

0,70

Raw puublicaitons perr year

0,60

0,50
,
1987
0,40

1990
1993
1996

0,30

1999
0,20

0,10

0,00
-1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

FIGURE 5. LIFE-CYCLE PRODUCTIVITY OF GRADUATES OF TOP 30 PROGRAMSAER EQUIVALENT PAGES
2,5

AER equiivalent pages pper year

2

15
1,5

1987
1990
1993
1

1996
1999

0,5

0
‐1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 6. LIFE-CYCLE PRODUCTIVITY OF GRADUATES OF TOP 30 PROGRAMSAER EQUIVALENT PAGES BASED ON DISCRETE QUALITY INDEX
54,5

AER equiivalent pages pper year

4
3,5
3
1987
2,5

1990
1993

2

1996
1999

1,5
1
0,5
0
-1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 7: COAUTHORSHIP IN ECONLIT JOURNALS
2,00
1,90

Authors pper Paper

1,80
1,70
1,60
1,50
1,40
1,30
1,20
1,10
1,00

All EconLit Journals

Top 20 EconLit Journals

FIGURE 8. LIFE-CYCLE PATTERN OF COAUTHORSHIP

2,4

Number of authors pper paper

22
2,2

2
1987
1,8

1990
1993
1996

1,6

1999
1,4

1,2

1
-1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 9. CUMMULATIVE NUMBER OF PUBLICATIONS OVER THE LIFE CYCLE(AVERAGE PRODUCTIVITY OF TOP 30 DEPARTMENTS' GRADUATES BY COHORT)
9

8

Cummullative publications

7

6

5

1987
1990

4

1993
1996
1999

3

2

1

0
‐1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 10. CUMMULATIVE AER EQUIVALENT PUBLICATIONS OVER THE LIFE CYCLE(AVERAGE PRODUCTIVITY OF TOP 30 DEPARTMENTS' GRADUATES BY COHORT)
1,4

Cummuulative AER puublications

1,2

1

08
0,8

1987
1990
1993

0,6

1996
1999
0,4

0,2

0
‐1

0

1

2

3

4

5

6

7

8

Years After Graduation

9

10

11

12

13

14

15

FIGURE 11. COHORT FIXED EFFECT COEFFICIENTS(RELATIVE TO THE 2000 COHORT)
0,05000,04000,03000,02000,01000,0000
1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

‐0,0100
AER‐Equiv.

AER‐Equiv., full credit

AER‐Equiv., discrete ranking

1997

1998

1999

FIGURE 12. COHORT FIXED EFFECTS OF SELECTED COHORTS ACROSS PERCENTILE RANGES
0,08

0,07

0,06

0,05

0,04

Cohortfixed
effects

1988
1991
1994

0,03
,

1999

0,02

0,01

0
20‐30
‐0,01

30‐40

40‐50

50‐60

60‐70

Percentile Ranges

70‐80

80‐90

90‐100