DISCUSSION PAPER SERIESIZA DP No. 14643

Publishing Economics:
How Slow? Why Slow? Is Slow
Productive? Fixing Slow?
Aboozar Hadavand
Daniel S. Hamermesh
Wesley W. Wilson

AUGUST 2021

DISCUSSION PAPER SERIESIZA DP No. 14643

Publishing Economics:
How Slow? Why Slow? Is Slow
Productive? Fixing Slow?
Aboozar Hadavand
Minerva University

Daniel S. Hamermesh
Barnard College and IZA

Wesley W. Wilson
University of Oregon

AUGUST 2021

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IZA DP No. 14643

AUGUST 2021

ABSTRACT
Publishing Economics:
How Slow? Why Slow? Is Slow
Productive? Fixing Slow?*
Publishing in economics proceeds much more slowly on average than in the naturalsciences, and more slowly than in other social sciences and finance. It is even relatively
slower at the extremes. We demonstrate that much of the lag, especially at the extremes,arises from authors’ dilatory behavior in revising their work. The marginal product of anadditional round of re-submission at the top economics journals is productive of additional
subsequent citations; but conditional on re-submission, journals taking more time is notproductive, and authors spending more time is associated with reduced scholarly impact.
We offer several proposals to speed up the publication process. These include no-revisions
policies, such as Economic Inquiry’s; limits on authors’ time revising articles, and limits on
editors waiting for dilatory referees.
JEL Classification:

A11, B20

Keywords:

slowness, sociology of economics, top journals, procrastination,
editorial behavior

Corresponding author:
Daniel S. Hamermesh
Department of Economics
Barnard College3009 Broadway
New York, NY 10027
USA
E-mail: hamermes@eco.utexas.edu* We thank Amy Ando, Belinda Archbong, Garry Barrett, George Borjas, André Burgstaller, Colin Cameron, Steven
Deller, Co-Pierre Georg, Sarah Hamersma, Campbell Harvey, Van Kolpin, Kevin Lang, Andrew Leigh, Derek Neal,
Andrew Oswald, Glen Waddell, and participants in seminars at several universities for helpful comments. We also
thank the many authors who responded to email surveys, the people who provided salary information, and theeditorial staffs of the APSR and especially Economic Inquiry. Most important, we thank the editors of the three “TopFive” journals, whose data underlie much of the analysis, for approving this project, and their editorial assistants
who compiled and provided the data. Under confidentiality agreements with the journals, most of the data are notavailable publicly.

I. Introduction
The slowness of publishing in economics was pointed out by Ellison (2002), although scholars who
had been active in the profession for at least a quarter century were by that date very aware of the changes
that had occurred in the publishing process. Today the difficulties are well known and have been discussed
by many editors (discussions in Szenberg and Ramrattan, 2014). In this study we first provide evidence on
how the publishing process

in terms of the lags involved

compares to that in the hard sciences (verymuch slower) and in other social sciences (substantially slower).
There are many components that might explain the slowness of economics journals. Culprits might
be dilatory editors/referees, authors dela ing responding to initial encouraging editorial responses, or lags
bet een a stud s acceptance and its publication. We cannot elucidate the underlying causes of each ofthese possible contributing factors. There is no a to infer h editors or referees might sit on a paper,
why authors might hesitate to revise their work quickly, or why economics differs from other disciplines.
All e can do is document the magnitude of each factor s contribution to slo publication by providing thefirst evidence on this issue, one novel contribution of this study.
The central part of the empirical work here examines the productivity of slowness. We measureproductivity by the professional attention that a study receives from other scholars

its post-publication(both online and in-print) citations. If a longer publication process increases the scholarly productivity of
economic research, perhaps we should view these benefits as justifying the cost

although evidence

suggests (Conley et al., 2013) that a slow process reduces the quantity of publications, as measured by
pages written. We cannot discover whether slowness in economics publishing makes it more productive
than research in other disciplines, nor can we even analyze whether increasing slowness in economics has
made economic research more productive. We can, however, analyze whether at a point in time publishedresearch with a longer gestation period has a greater impact, providing the first objective evidence on thiscrucial outcome of the publication process (although Laband, 1990, provided very useful subjective
evidence on one aspect, the role of referees).

1Answers to these questions require positive analysis. Making normative suggestions about how the
publication process in economics might be speeded up with no loss of quality constitutes the second major
section of this study. Although basically suggestive, it too has some positive bases, as we examine data
describing publications in journals that have experimented with alternatives to standard practices in
economics publishing.
II.

Characteristics of Slowness in Economics Publishing

Much of the analysis in this section is based on a set of data collected from leading economics
journals. We asked the editors of each of the Top Five journals in the field for details on each article,
excluding Nobel/presidential addresses, comments, replies, etc., that was published in 2012 and 2013. The
details for each paper include: Its initial submission date; date of the initial editorial response; the date offirst re-submission, etc., until date of acceptance.1 We use articles published in those two years to allow
time for each article to influence subsequent research. Three of the five editors provided the data, showing
these outcomes for each of 241 published articles and thus allowing charting how each article flowed
through the editorial process.
A. Slowness in Economics—and Other Fields
Along with other, publicly available data, we use these data to examine the speed of publishing ineconomics, political science and psychology, and the natural sciences, both in 2012-13 and recently ( 2020).
The Review of Economics and Statistics (arguably the most-cited general journal outside the Top Five ),along with one of the two Top Five journals that did not provide complete information, do publish
submission, acceptance (and obviously) publication dates with each article. Adding this information to thatof the three journals in our main data set, we compare the process among them to that in three other social
science journals: The American Political Science Review (APSR), leading in its discipline; and the Journal

1

We vie the Top Five as the American Economic Review, Econometrica, the Journal of Political Economy, theQuarterly Journal of Economics, and the Review of Economic Studies. We are very aware of differences in thesejournals average impacts, of the tremendous heterogeneity of impacts of articles within each journal (Hamermesh,2018), and of the possibly deleterious effects of over-reliance on publication in these outlets (Heckman and Moktan,
2020). Nonetheless, we follow convention in bibliometric analyses and restrict this part of the study to these journals.

2

of Applied Psychology and the Journal of Personality and Social Psychology, t o of a probable Top Fivein psychology, which also publish this information with each article. The same information is included witheach article in Nature, one of the two most widely cited scholarly journals, and in the Proceedings of the
National Academy of Sciences (PNAS), which has a five-year impact factor higher than all but one of theeconomics Top Five. 2
The upper panel in Table 1 presents statistics describing the distribution of times from initialsubmission to acceptance and then to publication among articles published in 2012-13.3 Time to acceptanceis crucial for young scholars seeking tenured positions and for more senior ones seeking new positions,since with an acceptance they can include the publication on their CVs. It may also be important foreconomists in obtaining public recognition of their work, as journalists often ask whether a study has been
peer-reviewed. It is also crucial to establishing bona fides in expert testimony or in providing policy advice
generally. Time to publication used to be an important indicator of how long it took from the time whenauthors viewed their research as complete to when others could see and use the finished product. Today,
however, this measure seems less important, since many journals include widely available online final
versions of articles shortly after acceptance.
By any measure, the record in economics is, at best, poor, perhaps epitomized by Figure 1. The
mean time from submission to acceptance of articles published in these journals (the REStat and four of the
Top Five ) in 2012-13 was slightly more than two years. This outcome compares to slightly more than2

Care is required comparing impact factors across disciplines, since scholars in different fields differ in their propensity
to cite other studies. The average article in the Top Five referenced 56 items in 2019, almost the same as in the
APSR. Articles in the two natural science journals averaged 42 references to other studies. At the other extreme,
articles in the two psychology journals averaged 106 references per article; and the two leading sociology journals,
the American Journal of Sociology (AJS) and the American Sociological Review (ASR), with impact factors of 5.9 and
8.2, averaged 110 references in each article (Clarivate Analytics, 2020).
3

The means are simple averages of the average times in each journal in each group. Regrettably, several attempts toelicit this information from the leading sociology journals AJS and ASR, in a discipline arguably most comparable toeconomics, failed.

3

one year in the three other social science journals, and only six months in the two science journals.4 If theaverage time suggests problems, the extreme times can only be characterized as disgraceful.5 Even at the75th percentile the time from submission to acceptance is twice as long in economics as in the other social
sciences, and four times as long as in the science journals. Moreover, the uncertainty faced by economic
researchers is greater: The 90-50 (90/10) ratios of time to acceptance are 2.0 (4.2) in economics, 1.8 (3.7)
in the social science journals and 1.8 (3.0) in the natural science journals.
While we do not inquire why economics differs so greatly from other disciplines, it is worth notingthat the acceptance rate in the Top Five journals currently averages six percent, compared to ten percent
in the three social science journals, and to fifteen percent in the two natural science journals. At least in thenatural science journals, the papers are shorter, and the supply of journal space is greater (with the PNAS
publishing nearly weekly). This is an important difference; but why the very low acceptance rates in the
Top Five could generate much longer publishing lags is unclear.6
One might argue that these figures reflect ancient history, and that the situation has improved
greatly over the past decade.7 That argument would be wrong, as shown in the bottom panel of Table 1.
Despite now-universal online submission procedures at these journals, the change over the decade was verysmall, with the mean time to acceptance rising slightly and, worse still, with an increase in the mass in the4Upon seeing this Table, one distinguished economist remarked, If Watson and Crick had to deal with economics
publishing, their article would have been 70 pages long and taken three years to get into print. Watson and Crick(1953) was published eight weeks after the discovery was announced; it was one page long.
5

The 90th percentile statistics are bad enough. The maximum durations in the sample were 7 years 5 months from
submission to acceptance, and 9 years 5 months from submission to publication.
6

One is reminded of George Stigler s perhaps apocryphal response to the then-Editor of the AER, who complained ofhaving so man good papers to choose among, Wh not publish one occasionall ?
7

One of the editors who kindly supplied the data underlying most of the work in this Section questioned our request
for 2012-13 data, stating that the journal s process ma have been slo in the past but as no longer slo . We
explained that we needed data from those ears to e amine articles impacts. We have not had the heart to note that,while the mean submit-to-accept time at that journal has speeded up slightly, the mass in the right tail has increased.

4

upper tail of the distribution.8 Of the five economics journals, the median time from submission toacceptance increased in two, fell in two and was unchanged in one. The duration at the 90th percentile
increased in four and fell in one. Similar increases in the mass in the upper tails of the distributions ofacceptance times in the other social science journals and in the natural science journals also occurred; but
their average speed and the speed of the slowest publications remained far more rapid than in economics.
To summarize toda s situation succinctly, an economics article that is at the 50th percentile of time toacceptance would be at the 85th percentile of times to acceptance in the other social science journals and at
the 97th percentile in the two natural science journals.9
The possible harm from slow publishing is not greatly mitigated by the ever-growing, at both theextensive and intensive margins, series of discussion/working papers. These are not peer-reviewed, andthus lack the bona fides of journal articles, in the eyes of other scholars, including university administrators,
and the media. Moreover, the plethora of such papers creates congestion externalities, even in the mostvisible such series (Lusher et al., 2021), making it difficult to keep up with what trusted experts view asimportant.

B. Contributors to Slow Publishing
There are many plausible explanations for the slowness in getting an economics paper accepted.
These include the number of times a paper is resubmitted, the amount of time that it spends with editors
and referees (denoted here b time in journal s hands), and the length of time that it spends in author(s)revisions (time in author(s) hands). Here e e amine the relative contributions of each of these to the lagsin publication and consider the characteristics of the papers and their authors in relation to these outcomes.

8

The failure of submit-to-accept times to fall in the economic journals could not have been due to Covid-19 induced
delays. Only 13 percent of the papers tabulated were accepted after April 1, 2020. Given the rapid turn-around in thenatural science journals, however, Covid might explain their (small) increase in submit-to-acceptance times.
9

Without one of the Top Five journals, the statistics in Table 1 are incomplete. We cannot solve this problem for2012-13 for this journal, but we can piece together a good estimate for articles in 2020 using some in-publication
information and an email survey of authors. The data suggest that its mean submit-to-publication time was 30 months,
with a mean submit-to-accept time of 23 months. If these estimates were included in the statistics shown in the Table,
they would reduce the mean times by one month each.

5

Using the descriptions of each stage of the submission/review process for each of the 241 articles
published in the three Top Five journals in 2012-13, we calculated the number of rounds of submission/resubmission/re-re-submission/re-re-re-submission that each went through. We denote this number by 2 if
the second editorial response

the response after the first re-submission

was an acceptance, 3 or 4 if thethird or fourth was an acceptance.10 We can decompose the total time from submission to publication into
three parts: Time spent in the journal s hands, time in the author(s) hands, and time bet een acceptanceand in-print publication.
In addition to these descriptors of the editorial process itself, we gathered other information on eacharticle about: Its Web of Science citations in each year from the year of publication up through 2020 (thusnine years of citations to articles published in 2012, eight years to those published in 2013); the cumulative
number of Google Scholar citations the article had received as of March 2021; its length in pages; 11 thenumber of references included; the number of authors; and the sub-field in which the article might beclassified (theory; empirical with administrative data; other empirical, including calibration; experimental;
econometric theor ). Characteri ing the articles authors, e obtained the Web of Science citations of eachauthor in the year the article was submitted, used to construct the citations of the most-cited author; thepost-Ph.D. experience of the most-cited author, and the number of female authors.
Table 2 presents descriptive statistics of all these variables e cept times in the journal s andauthor(s) hands, hich e e amine in detail belo . The average or median published paper goes throughthree rounds: It is submitted, re-submitted, and then re-submitted again, when it is then accepted; but nearly
one-fourth of all articles went through a fourth round. (For the empirical articles, we consider acceptanceas the date when an acceptance email was sent, i.e., thus earlier than the final submission that includes a
documented dataset.)10

Two of the articles went through a fifth round, although in one case the elapsed time was less than one month.

11

These are calculated based on the average number of characters per page in each journal, with the number normalized
to the journal with the most characters per page.

6

Fifty-three percent of articles are empirical (11 percent using administrative data, 42 percent using
other data), with pure theory accounting for 35 percent of the publications, and experiments andeconometric theory accounting for the remaining 12 percent. The average article has slightly above two
authors, but nearly ten percent have four or more authors, reflecting the stretching of the right tail of the
distribution of authors/article noted by Hudson (1996), Ellison (2002), Card and DellaVigna (2013),
Hamermesh (2013), and Jones (2021). The average article contains nearly 29 printed pages, not including
the ubiquitous and often voluminous on-line appendices. There is substantial variation in the number ofreferences included, and its correlation ith article length is onl +0.30. Of the articles authors, 22 percent
were women, with the incidence of female authors rising as the total number of authors increases.
The average article received 70 Web of Science citations in its first eight or nine years in print with,as is always the case in citations, substantial skewness in this measure (Hamermesh, 2018). The skewness
is equally pronounced in the distribution of cumulative Google Scholar citations. Even more skewness
exists in the distribution of citations at the time of submission to each article s most cited author. The
average most-cited (on each article) author received over 270 citations in the year of submission, about
average among tenured faculty members in economics departments that might be viewed as Top 30; but
the median most-cited author of an article was cited only one-third as often. The post-Ph.D. experience ofthe most-cited authors averaged around 15 years

typical of a relatively young full professor and consistent

with evidence on the age distribution of authors in leading economics journals (Hamermesh, 2013).
With our focus on the process by which articles are handled, we examine the contributions of thethree components of time from submission to publication. Their distributions are presented in Figure 2,
containing decompositions of the average time from submission to publication, measured in months on thevertical axis and shown within each of five deciles. (Each journal is weighted as one-third of the total.)
Several aspects of the Figure are striking: 1) The main proximate determinant of inter-decile differences in
speed of publication is the huge rise in the amount of time spent in author(s) hands (the sum of times
between receiving a response from a journal to re-submission) as the total time to acceptance andpublication rises. Among papers in the middle decile, this is 10 months; among those in the slowest decile,7

26 months are spent in author(s) hands; 2) While the amount of time spent at journals increases

ithslowness of publication, moving from the middle to the slowest decile increases that duration much less,from 10 to 18 months; 3) Lengthier submit to-publication-times are essentially unrelated to changes in thetime between acceptance and publication.
Figure 2 aggregates across the three journals and does not reflect the role of the heterogeneity ofjournals in the total publication lag. To examine how these contributing factors differ across the threejournals, Table 3 shows the means and variances of the submit-to-acceptance lags among the 241 articles
in total and for each journal separately, and it decomposes the variance into its two sources and theircovariance. Most interesting in this Table are: 1) The substantial heterogeneity in the length of time articles
are in process

the variance is quite large even within a journal. 2) The heterogeneity across the journals:

Journal 1 handles the papers somewhat more quickly than the other two, but, most important, there is almost
no variation in the amount of time a paper spends at the journal; and 3) Consistent with the evidence inFigure 2, over half the variation in lags in acceptance arises from authors spending more time on revisions.
The covariance bet een the time in a journal s and author(s) hands is positive in Table 3, but inno case does it account for even a third of the total variance in the submit-to-acceptance time. Even thislow correlation is due mainly to the fact that articles that go through more rounds necessarily take moretime of both authors and editors/referees. The correlations between editor/refereeing time and author(s)response times are shown for each round separately in Table 4, both for all papers handled in the round,and for those completed in that round. The correlations at each article s final round average +0.30. Thus,
those articles that take more editor/referee time to handle are associated with authors spending more time,
but the relationship is weak.
Various characteristics of the articles might cause them to go through more rounds of resubmissions at a journal; and they might lead editors and referees to spend more time handling the paper.
The same characteristics might lead authors to take longer re-submitting an article that has received an
encouraging initial editorial response. To examine the first issue, in Column (1) of Table 5 we present leastsquares regressions of the number of rounds through which a paper travels at a journal as a function of all
8the article/author characteristics on which we have information (except the number of references included,
which may be partly affected by the number of rounds and time spent refereeing/revising). Column (2)presents the same regressions with journal indicators added to account for the heterogeneity demonstrated
in Table 3. The estimates treat each article equally

the observations are unweighted.12

Authors characteristics are unrelated to the number of rounds an article goes through: Neither how
well-cited an author is, nor his/her post-Ph.D. experience, nor the gender of authors is related to thisoutcome. Characteristics of the article do, however, affect the number of rounds: Theory papers are handled
in significantly fewer rounds, with differences across the other sub-fields being small and statisticallyinsignificant. Papers with fewer authors are handled more rapidly. Articles that are longer when publishedare handled in no more rounds than shorter articles.
Columns (3) and (4) present estimates of the correlates of the length of time that the journals taketo handle a submission. The clearest result is that theory papers are dealt with significantly and substantiallymore quickly (2-1/2 months on a mean of 10 months) at each journal (again with only small differences
across the other sub-fields). Weaker evidence shows that having multiple authors is associated with more
rapid treatment by the journals, perhaps because co-authors help iron out problems that might otherwise
lead editors and referees to spend more time handling the article. There is weak evidence that better-citedauthors receive somewhat faster treatment and that, conditional on an author s prior scholarl recognition,
more senior authors submissions are handled more slo l , other things equal. These last t o results are
consistent

ith the observation that one s prior impact on the profession matters much more than one s

longevity in determining how one is treated. Other than these effects, none of an article s characteristics
affects the time that it spends at a journal.

12

Using weights that are inversely proportional to the number of articles in each journal in the sample produces only
minute changes in the estimates. Similarly, while ordered-probit estimation is more appropriate than least-squares, its
implications differ little from those of the results in the Table. Similarl , replacing the variable an female author
with indicators of the number of female authors and replacing t o or more authors ith indicators of their number
do not change the qualitative conclusions about the effects of these measures.

9The final two columns of Table 5 describe the determinants of the time that authors spend revising
their papers in response to a requested re-submission. The only correlates whose relation to this outcome
are even marginally significant are the presence of a female author (2-1/2 months extra on an average of 12months) and the negative effect of an article classified as theoretical.13 There is no evidence that authors
whose prior work has had a greater scholarly impact, or those who are more senior, are differentially slow
in handling requests for revision. Here and throughout this table, variations in the length of articles haveessentially no effect on the outcomes. The results demonstrate that most of the variation in authors behavior
is idiosyncratic.14
C. The Productivity of Slowness
The most important question in judging whether the uniquely lengthy publication process ineconomics is worthwhile is its effect on the scholarly impact of the research that survives this very lengthy
treatment at these major outlets. We recognize that research published in these top journals often has
important influences beyond those on other scholars, for example, on debates about policy or on inchoate
popular feelings about economic issues. Nonetheless, economic research, indeed, any scholarly research is
judged at least in part by the extent to which it influences subsequent work. We therefore answer thisquestion by measuring the impact of the outcomes examined in Tables 3 and 5 on the annual patterns of(Web of Science) citations up through 2020 to the articles published in 2012 and 2013.15
Table 6 lists the results. Each observation is an article/year, necessitating clustering standard errors
on the individual articles. In addition to the regressors included in Table 5, we add the number of references

13

Less time is spent revising theory articles at each round of the publication process. There are no significant
differences across the other sub-fields. Thus, articles using administrative data take no longer than other non-theory
articles to revise, and similarly for articles based on experiments.
14

Here and in the next sub-section we also experiment with a measure of heterogeneity the standard deviation ofcitations across co-authors. This measure is uncorrelated with the time co-authors spend revising, and its inclusion has
minute effects on the estimated impacts of the other regressors.
15Checchi et al. (2021) show that there is a remarkably high correlation between this objective bibliometric measureand subjective peer-based evaluations of individual research products, suggesting that a subjective approach tomeasuring impact would yield results that would arguably be similar.

10included in each article, since additional references might, for scholarly or invidious reasons, generate more
subsequent citations to the article. Column (1) shows the least-squares estimates of the relationship ofcitations to the number of rounds the article has gone through and to various control variables. With averageannual citations of about eight, the productivity of additional authors is low (within this set of studies inthese leading journals), although not much different from that found in other studies (Hollis, 2001; Medoff,
2003; Bosquet and Combes, 2013; Hamermesh, 2018). Having a female author on a study has a substantial
but not quite statistically significant positive effect on the scholarly impact of the article, larger than foundin other studies (Laband, 1987; Ferber and Brün, 2011; Hamermesh, 2018) perhaps because of withinsubfield differences by gender in the topics on which economists work, or perhaps because these are better
articles.16 Lengthier articles have no greater impacts than shorter ones, perhaps due to the relatively narrow
range of page lengths in the sample. A one standard-deviation increase in the number of references increases
citations to the article by a statistically significant 0.06 standard deviations.
Theory papers on average receive roughly half as many citations per post-publication year as dootherwise identical other articles, a result consistent with evidence comparing across leading specialized
journals in different sub-fields.17 Articles by authors whose prior work has been more heavily cited receive
more attention; but conditional on that measure, more senior authors

ork is cited less. As ith the impactof these measures on the time that journals spend handling the paper, this juxtaposition suggests an
autocorrelation of scholarl impacts of one s ork, and that those

ho have not made it earlier in theircareers ill not make it even ith ork published in a leading outlet.
The central variables of interest indicate the number of rounds at the journals. The results suggest,other things equal, that the 51 percent of articles that require a third round (two re-submissions) have greater

16

The articles cited on this issue, which is quite secondary to the crucial points of this study, are part of a burgeoning
and now voluminous literature.
17

The average five-year impact factor among the Journal of Development Economics, Journal of Econometrics,
Journal of International Economics, Journal of Labor Economics, Journal of Monetary Economics, and Journal of
Public Economics was 3.67 in 2019. The average five-year impact factor of Games and Economic Behavior and
Journal of Economic Theory was 1.49 (computed from Clarivate Analytics, 2020).

11

subsequent scholarly impacts than the 27 percent of papers that go through only two rounds (that are
accepted after the first re-submission), the excluded category in Table 6. On the other hand, the marginal
gain in scholarly impact from the fourth round (the 22 percent of articles that are re-submitted, re-submitted
again, and then accepted after yet another re-submission) is smaller although positive.
One might think that greater editorial attention or more time that authors spend revising before resubmission(s) would improve the quality of the article in terms of its subsequent impact. The specificationin Column (2) thus adds measures of time spent at the journal and with author(s). Given the number ofrounds an article goes through, greater lags in the process reduce citations to the article.18 These effects are
statistically significant, are not huge, but not small either: A one standard-deviation increase in the time ata journal reduces subsequent citations by 0.11 standard deviations. A one standard-deviation increase in thetime that authors spend revising reduces them by 0.13 standard deviations.
These estimates ignore the tremendous heterogeneity across journals in the kinds of articles andhow they are treated. This difficulty is accounted for in the estimates shown in Columns (3) and (4) of theTable by the inclusion of journal indicators. The major comparisons to the results presented in the first twocolumns are: 1) Not surprisingly, given the heterogeneity shown in Table 3, the negative impact on
subsequent citations of the time that an article spends with the journal disappears; 2) The estimated marginal
productivity of a fourth round at a journal is reduced but becomes about equal to that of a third round; and
3) Most important, the negative effect on citations of additional time spent in author(s) hands remains
essentially unchanged. 4) None of the estimated impacts of the controls is altered in any important way.
As the statistics in Table 2 demonstrate, citations to the articles in this sample are highly skewed,
as are prior citations received by their authors. The regressions in Columns (1)-(4) describe the averageexperience of these published articles; but given the skewness in these variables, they do not describe what18

To account for citations to articles pre-publication, we re-estimate the equations here and in Table 7 using cumulative
Google Scholar citations (through 2020) instead of annual Web of Science citations. This re-specification does not
qualitatively alter any of the inferences. With cumulative Google Scholar citations equaling roughly 35 times annual
Web of Science citations in this sample, the coefficient estimates are almost proportionately smaller. The measuresthat are significantly related in Table 6 remain significant.

12the median author faces. To infer that, Columns (5) and (6) present least absolute deviation (LAD)
estimates, with the same specifications as in Columns (3) and (4) and including journal indicators. While
the parameter estimates of the control variables are smaller than in the OLS estimates, they are qualitatively
quite similar. The estimated impacts of the number of rounds through which an article goes and the amountof time spent in editor/referees or author(s) hands are also smaller; but the basic inference remains thesame. The marginal impact of another round at a journal is positive, but, conditional on the number ofrounds, authors slowness in revising their work has a significant negative relation to its citations.
Table 6 shows that there are differences in the scholarly impacts of theory and other articles, while
Table 5 showed that journals spend sharply different amounts of time dealing with them and that authorsof theory articles spend less time revising them in response to re-submission requests. Perhaps this isbecause upon submission a theory paper is clearly correct or incorrect, with fewer inherent possibilities forrevision and the main issue being whether the result is sufficiently important. Regardless, to examine thetheory-other sub-field distinction further, Table 7 presents estimates of equations specified like those in
Columns (4) and (6) of Table 6, but with the articles separated into sub-samples of theory and other papers.
For each type of article, the first column shows OLS estimates, the second LAD estimates.
Depending upon the type of article, the crucial variables the marginal impact of an extra roundof re-submission and the time spent at the journal and with author(s)have different relations to scholarly

impact. The conclusions from Table 6 apply mainly to non-theory articles: For them, the marginal productof a third re-submission (a fourth round) is positive; most important, additional time that authors spend
revising is associated with a lesser scholarly impact. Among theory articles, the time spent either at thejournal or by authors is unrelated to the paper s impact, although there is evidence that the marginal productof a second re-submission (a third round) is positive, while the marginal product of a fourth round is not.
The analysis of this sample leads to the conclusion that multiple rounds of editing/handling at thesejournals ma be productive (in terms of articles scholarl impacts). Publishing longer papers ( ithin therange of full-length articles included in the sample) is, however, unproductive of scholarly impact. The

13

strongest conclusion is that authors spending more time responding to requested re-submissions is
unproductiveIII.

indeed, those papers over which authors kvetch longer have lesser impacts.
Solutions to Slowness

The results in Sections II.B. and II.C. do not reflect ex ante random assignment of papers to quicker
or longer processes; nor were there random assignments of articles to differing amounts of time spent byeditors/referees or by authors. (We do not see how such randomness could be ethical, although randomly
nudging some submitting authors to choose faster decision routes might work.) Without a true experiment,
we cannot be sure that articles that went through more rounds were not inferior to others ab initio andrequired extra attention to bring them up to par. Similarly, articles on which authors spent more time,
conditional on the number of rounds, might have needed that time to rise to the quality level of the journal,even though the author(s) efforts ere insufficient to put their eventual impacts above average. The former
caveat may be important, although we saw that additional rounds of handling were productive; the latterdoes not seem credible, especially given the low correlation of the time a journal takes to generate a firstrevise-resubmit request and the author(s) time responding to it.
The findings in the previous section suggest three margins along which the publishing process
might be improved with no loss of quality. Additional back-and-forth between authors and editorsrounds with a journal

more

has some scholarly value. Even with a positive marginal product of third revisions,however, that gain must be traded off against the cost to ( ounger) scholars careers, in that additional backand-forth with journals postpones their ability to demonstrate their scholarly prowess. A second margin is
in the time authors sit on their papers after hearing back from journals, time that our results suggest is
unproductive. The final margin is the time that editors and referees spend handling papers, time that is notproductive at the margin.

14

A. Fast-track Publication: The Economic Inquiry Experiment19
In response to the lengthening publication process and to referees effectively becoming anonymous
co-authors, in 2007 Economic Inquiry (EI) introduced a two-track process. Submitting authors could choose
between a fast track, in which the article receives a simple yes or no; or a regular track, which might leadto acceptance with minor revisions, to one or more revise/re-submit responses with subsequent additionalrefereeing, or to rejection (McAfee, 2010) (https://weai.org/view/EI-No-Revisions). Several journals (e.g.,
American Economic Review: Insights) now have instituted a similar quick turnaround policy, although none
appears to offer authors a choice of tracks or a definite no-revision track. This policy change is obviously
not a randomized experiment: Authors may nonrandomly self-select into the treatment (fast-track) group.
To examine how this experiment worked out, we collected data on the 935 articles published in EI
between 2009 and 2018 inclusive, yielding a usable sample of 835 articles that were not invited and wereat least ten pages long. In addition to all published articles, we have information on the track used for 5,178
rejected articles. We obtained information that allowed the construction of variables that are similar mutatis
mutandis to those used in Section II.
Fast-track papers were only slightly more likely to be accepted for publication than those submitted
through the regular track (an acceptance probability of 0.159, s.e. = 0.004, versus 0.149, s.e. = 0.002). Thereis little difference in the time between submission and first decision among accepted papers along the two
tracks. Rejection times are also similarly distributed across tracks. The difference between the tracks arises
from the lag between initial response and final acceptance on regular-track papers. Figures 4 show the
distributions of submission to acceptance times along the two tracks, with kernel density estimates imposedon the histograms. There is a very long tail among regular-track papers, with a 90th percentile of 17 months(compared to 7.5 months among fast-track papers). Aside from the obvious risk of rejection, submission
along the regular track carries a small risk of involvement in a dragged-out process, less arduous than at
Top Five journals but still quite long.

19

This sub-section is a very much shortened version of Hadavand et al. (2020).

15

Certain characteristics of authors generated predictable differences in the track chosen. As the
probit derivatives in Column (1) of Table 8 suggest, more successful (in terms of prior scholarly impact)
and more senior authors were more likely to choose the fast track. Most interesting, we searched overvarious ranges of the post-Ph.D. experience of authors to find where the likelihood function was maximized.
This occurred using the closed interval [5, 10] years post-Ph.D. If any author was in this experience range,
the probability of a fast-track submission was significantly higher. With only nineteen percent of publishedarticles submitted along this track, the parameter estimate implies that this choice is over one-third more
likely if an author is in this range of experience

presumably facing an impending job-security and/or

promotion decision.
Fifty-one percent of accepted submissions along the regular track went through more than two
rounds of submissions, far below the 73 percent among Top Five journals. Paralleling the analysis in
Section II.C., we estimate the determinants of annual citations to each of the 835 usable articles. As wasdone there, each article is included as an observation in each post-publication year. Column (2) of Table 8
presents a simple model, including only the track chosen; if regular track, whether an article went throughonl

two rounds of submission, and two variables that mechanically alter the number of citations. The

third column adds the length of time (in years) from submission to acceptance, the post-Ph.D. experienceand prior citations of the most cited author, and controls for: Number of pages; JEL category, aggregatedinto 10 groups; number of authors, and whether at least one author was female.
The least-squares estimates in Table 8 demonstrate that, whether we include covariates or not, fasttrack papers are cited significantly more than articles submitted through the regular process. 20 Two-roundregular-track papers receive statistically insignificantly more citations than those published papers that wererefereed multiple times, demonstrating that the marginal product of an extra revision here is zero.

20

Re-estimating the equations in Columns (2) and (3) using Poisson estimation to account for the count nature of thedependent variable, which contains many zeros, yields essentially the same conclusions as the Table.

16

We stress that these estimates do not account for the possible endogeneity of the choice of track;
they merely show adjusted estimates of the subsequent scholarly impacts of articles that may differ inquality in ways for which we could not adjust. We have not shown that the same paper will have greater
impact if submitted along the fast track; but this sample provides no evidence that fast-track handling
reduced an article s subsequent impact.21
The apparent zero marginal product of third or higher-order rounds of refereeing contrasts with theresults for the third round in Section II. The difference may, of course, simply result from sampling
differences or underlying unmeasurable quality differences between these articles and those published in
Top 5 journals. A substantive explanation is that refereeing at the top journals may be of higher quality
than at EI, with the difference in quality being more pronounced on later-round referee reports.
Some articles published in Top Five journals will have been refereed and rejected at other(usuall other Top Five journals). That is much more likel for publications submitted to journals outside
the elite group, including EI. It is thus possible that fast-track publications had received more input fromreferees before submission than regular-track articles and ere thus more polishedhen submitted to EI.

To examine this possibility, we conducted an email survey of authors of all articles published through thefast track in 2015-18 and took two equal-sized samples of regular-track articles, either with two or multiple
rounds of refereeing, that were published in those years. Each of 188 authors as asked to list: 1) The total
number of prior submissions; 2) The equal or smaller number of submissions on which you received refereereport(s); and 3) The total number of referee reports received from these journals.
In Table 9 we present the results of this survey, which generated useable responses from 62 percent
of authors. Only nine percent of publications in this journal were not previously submitted elsewhere. Fasttrack articles had been submitted to fewer journals, however, and there was little difference between fastand regular-track articles in having received no prior referee reports. They had, however, received more

21

Instrumenting for fast-track using an indicator of whether any author of the article was in the five to ten-year rangeof experience reduced the impact of fast-track on citations somewhat; but, with the instrument quite weak, as impliedby the estimates in Column (1) in Table 8, it is not clear what this search for exogeneity tells us.

17

reports from more journals, a marginally significant difference that is generated by a few extremely
extensively refereed articles. While not conclusive, the survey evidence suggests that fast-track articles hadreceived little more refereeing input before submission than other papers.22 Together with the results inTable 8, these findings suggest that providing an up-or-down editorial process does not reduce the qualityof publications, at least in a journal outside an elite group.
B. Limiting Revision TimeIn the data set describing Top Five articles published in 2012-13, the time between receipt of thefirst decision and the first re-submission exceeded six months on 56 percent of the 241 articles; and it
exceeded one year among 25 percent. Of the 176 articles that went through three or four rounds, 15 percent
spent more than six months in the author(s) hands between the second response and the second resubmission. Most surprising is that nine of the 52 papers that went through four rounds were worked on for
more than three months between the third editorial response and the final re-submission. While the times
to re-submission decrease with the number of re-submissions, they remain long.
With the demonstration that additional time spent re-submitting is at best unproductive, thequestion arises as to why. One reason for this apparent negative marginal product may be thatprocrastinating authors produce lower-quality research, other things equal. Yet another may be that they
are too busy to devote the real time necessary for producing high-quality revisions (although the estimatesin Table 4 showed that these lags are unrelated to authors characteristics that might indicate that they arebusier). An alternative explanation is that some authors may use the submission process to obtain comments
on a paper that was not well-polished and was submitted prematurely, with revision time needed to bring
the paper up to a minimally acceptable level. Yet a fourth possibility consistent with slowness on the secondand subsequent re-submissions is that the feedback received in response to the first re-submission is of
reduced quality because the authors and, especially, the editors and referees failed to remember all thenuances of a subject that they handled many months, or even years before. Regardless, the evidence

22

Estimating equations describing the number of prior reports and including the large vectors of covariates that wereused in Column (3) of Table 8 does not alter this conclusion.

18

suggested that allowing authors free rein to delay re-submission does not add to their articles scholarl
value.
Requiring rapid re-submission is standard in the natural sciences but very rare in economics. One
journal, the American Journal of Agricultural Economics (AJAE), the oldest and most distinguished in its
sub-field, does not impose a deadline but does include in its revise-resubmit letter, please submit therevised manuscript and separate responses to the reviewers

within six months of receiving this letter. 23

It had a median submit-to-acceptance time of 10 months in 2020, with the 90th percentile being 22 months.24
Both statistics are unsurprisingly far below the comparable statistics shown in Table 1 and well below thoseof the six leading specialized applied journals. Of those six, none had a median submit-to-accept time as
low as the AJAE, and in only one was the 90th percentile of the distribution at least as fast.
We cannot tell whether turnaround times in this journal are relatively rapid because of the moral
suasion in its revise/re-submit letters, because for some reason the ethos that generates publishing lags in
the Top Five has not infected it as much as other specialized journals, or something else unique to its subspecialty. Regardless, this admonition might be included in all revise/re-submit letters. By providing at leasta soft deadline, journals might take advantage of incentives that induce collaborators to move together more
quickly (Bonatti and Hörner, 2011). Going further, the evidence in this study of the negative relation
between subsequent citations and lags in authors revisions suggests that imposing and enforcing a sixmonth limit on time spent revising would not be harmful to their eventual scholarly impact. If nothing else,
it would help pull the right tail in the distribution of submit-to-acceptance times to the left.
An objection to this proposal is that authors are busy. Of course, they are. But for most authors
publishing an article in these journals is a jackpot prize, one that merits putting an invited re-submission on
the front burner of activities. Very few, if any, requested revisions take more than six months of actual

23

Email communication, Amy Ando, Co-editor, AJAE, March 19, 2021.

24

The AJAE has an impact factor of 3.44, almost identical to the average of the six journals specialized non-theory
journals listed in Footnote 17.

19

work; more likely is that the delays simply result from author(s) procrastination. Given the rewards,
procrastination is difficult to explain; and it can be costly.25
While some journals specify length limits on submissions, published versions of accepted articles
suggest that those limits are often violated. Aside from the flouting of these limits incentivizing journals
and authors to drag out the decision process, it also sacrifices journal space that might be devoted to otherauthors work. Raising the remarkably low acceptance rates (compared to other fields) at top economics
journals would be a beneficial result of limiting page counts. The well-documented page-creep in
economics journal publishing may accompany the increasing lags in editorial decisions, lags that might be
reduced if page limits were enforced both ab initio and throughout the refereeing process.
C. Limiting Refereeing/Editing TimeThe evidence in Section II made it clear that editor/refereeing lags are not the more important
contributor to the e cessive times from submission to acceptance and publication at the Top Five
economics journals. It also demonstrated, however, that conditional on the number of rounds of back-andforth, additional time spent by referees and editors has little positive effect on an article s eventual scholarly
impact. This suggests that there is room for marginal improvements along this dimension too.
While the data used here cannot distinguish between the contributions to publication lags of dilatory
editors and the referees whom they assign, we do know (Hamermesh, 1994) that most referees whocomplete their assigned task do so quickly. The difficulty is that a small fraction take a very long time ormore commonly refuse (5 percent) or decline (15 percent) a refereeing request. The theory ofprocrastination (see, e.g., Akerlof, 1991) suggests that people backload completion of tasks until just before
a deadline. While there are deadlines in requests to referees, they are not enforced: Referees can backload
indefinitely. Monetary incentives merely shift a few delayed reports across the margin to qualify for thepayment (Hamermesh, 1994); non-monetary incentives, for examples, the American Economic Review s or
Journal of Political Economy s lists of referees, or free journal subscriptions to reward rapid refereeing, are25

In at least one case, an author delayed 18 months in responding to a revise-resubmit request from a Top Fivejournal; the eventual re-submission was quickly rejected by the new editor who was uninterested in the topic.

20

unlikely to provide much motivation to overcome procrastination. Public shaming of delinquent referees is
a possibility, but journals may be unwilling to engage in it; and, in any case, it is unclear whether suchshaming would reduce delinquents misbehavior.
Many referees are simply unreliable; since refereeing deadlines are not enforceable, journal editors
may feel stuck ith delinquents. There is a solution: Fire the delinquent after some short period of nonresponse. If an article is so narrowly focused that only two or three scholars can provide usefulcomments/recommendations to the editor, it probably does not belong in a top general-interest journal. A
reasonable argument is thus that no referee should be allowed more than two months to handle an article (apolicy that is currently implicit and tightly enforced by the Quarterly Journal of Economics). If a referee
fails to respond within that time limit, the editor should immediately request a report from another referee.
We showed in Section II that 22 percent of articles at Top Five journals

ent through four

submissions/re-submissions, back-and-forth with the journal. While these additional rounds did generate
positive marginal products in terms of additional subsequent citations, is that worth the delay in making
research more visible and in authors improving their CVs? Perhaps journal editors should not plan on
soliciting more than two re-submissions, ith the second requesting onl

cleaning-up and polishing. If

implemented, this would also reduce the incidence of multiple rounds of re-submissions that end in
rejection. This recommendation requires that editors exercise judgment when soliciting the first resubmission, being clear that, as one former Top 5 editor suggested, an initial re-submission will only besought if the additional ork is doable and can be handled by the author(s) in a reasonable length of time,
as recommended in the previous sub-section.
The editors of most Top Five journals are paid for their ork, ith substantial time released fromteaching and/or monetarily and often quite lucratively. (One Top Five journal pa s its editors $51,500
per annum; another pays $32,000with $64,000 to the Editor-in-Chief.) They should be well-paid

theirwork is important and time-consuming. Asking that they abide by the dictum that they only solicit resubmissions on papers on which there is a clear path to publication is not unreasonable. Moreover, given

21

their remuneration, sitting on a paper longer than t o weeks upon submission/re-submission or uponreceipt of a sufficient number of referee reports is inexcusable.

IV.

Escaping the Low-level Equilibrium

The economics profession is in a low-level equilibrium trap, with much longer decision times thanany other discipline that communicates ideas through scholarly journals. Toda the lags bet een an article s
acceptance and publication are unimportant. Online publication often occurs within a few weeks of anarticle s acceptance; and most articles published in Top Five and other journals have long circulated inworking-paper form. Even ignoring the now technologically irrelevant lag between acceptance andpublication, however, economics publishing remains woefully slower than that in other disciplines.
The long lags hurt the profession and, as we have shown, are at least partly unproductive. They
have especially severe negative impacts on younger scholars facing tenure/job-security decisions, withcases where Ph.D. essays are hanging in the balance at a journal even hen a person s tenure case is being
considered. In many institutions junior economists are compared to peers in other disciplines, even in othersocial sciences, whose research oeuvres do not suffer the same lags in acceptance/publication. Economists
making decisions about their colleagues future understand this problem, but higher-level administrators
often do not, creating needless stumbling blocks to tenure for active junior economists.
We have outlined several steps that might reduce the time bet een an article s submission and its
acceptance. While the evidence supporting these recommendations comes from data describing Top Fivejournals, they are equally valid at lower-level journals (whose decision process, as shown in Section III.B,
is also distinctly slow). The initiatives of a few journals, such as the AER: Insights and Economic Inquiry,
are laudable, but their examples are unlikely to become widespread until the leading journals improve theirturnaround times.
In all these journals, the burden of improving the situationon the same footing as other disciplinesof putting the economics profession

rests on their editors. They need to change their behavior, to insist

that referees behave as gatekeepers rather than co-authors, and to be sure that authors respond reasonably

22rapidl to editors requests for re-submissions. The low-level equilibrium trap developed because editors
let it develop. We will not escape it until editors change how they deal with referees and authors.

23

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25

Table 1. Acceptance and Publication Lags (in Months), Economics, Other Social Sciences, and“Hard” Sciences, 2012-13 and 2020*
Weighted
Percentile
Mean Std. Error
10

25

50

75

90

2012 -13
Four “Top 5” + REStat (535)**
Submission to acceptance
Submission to publication

24.72
33.15

(0.63)(0.66)

10
17

14
25

21
35

32
43

42
52

APSR, JApplPsy, JPersSocPsy (371)
Submission to acceptance
Submission to publication

12.84
18.05

(0.36)

6

8

11

13

16
22

22

(0.37)

1217

38

Nature, PNAS (195)
Submission to acceptance
Submission to publication

5.77
7.80

(0.22)(0.24)

3
5

4
5

5
7

7
9

9
11

2020
Four “Top 5” + REStat (308)**
Submission to acceptance
Submission to publication

26.38
34.31

(1.00)(0.93)

10
18

15
25

22
32

35
44

50
59

Submission to acceptance
Submission to publication

14.37
22.99

(0.52)(0.56)

6
14

8
17

13
21

17
26

24
32

Nature, PNAS (183)
Submission to acceptance
Submission to publication

7.16
9.35

(0.41)(0.41)

3
5

4
6

6
8

8
11

13
15

APSR, JApplPsy, JPersSocPsy (212)*Number of articles in parentheses. Means weighted by the inverses of the numbers of articles from the journal in thesamples. Several articles (fewer than five in each case) accepted within a month of submission were deleted from thesamples of psychology journals.
**In addition to the REStat and the three Top 5 journals that provided us ith confidential information,
included published information from one of the t o Top 5 journals that did not provide such data.

1e alsoTable 2. Descriptive Statistics from the Three-Journal Sample, Articles 2012-13 (N=241)
Variable means:
Number of rounds:
2
0.27
3
0.51
4
0.22

Field:
Theory0.35
Admin. data
0.11
Other data
0.42
Experiment
0.09
Econometric theory 0.03

Number of authors
1
0.21
2
0.43
3
0.27
4+
0.09

(fraction with any female):(0.12)(0.18)(0.28)(0.39)

Percentile
Variable:

Mean

Std. Error

10

25

50

75

90

Page equivalents*

29.18

0.41

21

25

29

33

37

References included

46.08

1.18

26

33

44

55

67

Cumulative Web of Science citations

69.52

5.15

10 20

44

86

168

Cumulative Google Scholar citations

299.24

21.67

42 78

168

402

739

Web of Science citations of mostcited author during year of submission

271.81

36.53

7

28

97

284

701

Post-PhD experience of most-citedauthor

16.41

0.66

4

8

14

24

32

*Pages standardized to the journal with the densest format.

2

Table 3. Decomposition of Variance of the Submit-to-Acceptance Lag (in months)
Mean

Variance

Due toAuthor(s)Due toJournal2*
Covariance

All journals

22.20

16.68

9.24

4.32

3.12

Journal 1

15.36

12.12

10.44

0.24

1.44

Journal 2

24.60

14.16

3.72

6.36

4.08

Journal 3

26.28

17.52

10.32

2.88

4.32

3

Table 4. Correlations of Time in Journals and Author(s) Hands
All Papers
Round

Completed in RoundCorrelation

N=

Correlation

N=

2

0.023

241

0.255

65

3

0.260

176

0.256

124

4

0.364

52

0.364

52

4

Table 5. Determinants of the Editorial Production Process, OLS Estimates, N=241
No. of rounds

Months at journalMonths with author(s)Ind. Var.
Citations to most- citedauthor/100

0.009

0.005

-0.256-0.129-0.161-0.121(0.008)(0.008)(0.086)(0.070)(0.127)(0.126)

Experience of
most-cited author-0.006-0.006

0.072

0.071

0.074

0.079

(0.005)(0.005)(0.049)(0.039)(0.073)(0.071)

Any female author

0.044

0.028

-0.902-0.413

2.467

2.570

(0.112)(0.109)(1.145)(0.920)(1.696)(1.659)-0.171-0.162-1.577-1.700-0.0130.078(0.115)(0.118)(1.190)(0.955)(1.762)(1.722)-0.004-0.005

0.121

0.039

0.043

-0.074(0.007)(0.007)(0.075)(0.064)(0.112)(0.115)

Two or more authors

Equivalent pages

TheoryAdj. R2-0.238-0.205-1.070-2.235-1.808-2.142(0.098)(0.096)(1.000)(0.810)(1.481)(1.461)

0.018

0.081

0.041

0.383

0.006

0.052

*Standard errors in parentheses. Columns (2), (4), and (6) contain journal indicators. The correlation of the residuals
in Columns (3) and (5) is 0.24, between the residuals in Columns (4) and (6) is 0.31.

5

Table 6. Determinants of Annual Post-publication Citations (N=241 articles, 2,049 citation-years)*
OLS

LAD

Ind. Var:

3 rounds

4 rounds

Journal hands

2.541

3.261

1.314

1.827

0.815

1.020

(0.956)(1.038)(0.924)(1.033)(0.323)(0.357)

3.066

5.909

2.349

3.987

0.977

1.295

(1.671)(2.145)(1.645)(2.209)(0.382)(0.552)-0.145-------

0.010--------------(0.071)

Author(s) hands-------(0.086)-0.147-------(0.057)

Citations to mostcited author/100
Experience of
most-cited author
Any female author

Two or more authors

Equivalent pages

Number of references

Theory-0.1400.259(0.306)-------(0.062)-0.662(0.170)

0.508

0.447

0.463

0.446

0.407

0.409

(0.091)(0.085)(0.088)(0.086)(0.057)(0.079)-0.114-0.091-0.118-0.106-0.103-0.095(0.041)(0.042)(0.040)(0.041)(0.013)(0.016)

2.953

3.064

2.696

3.011

0.755

0.918

(1.695)(1.638)(1.610)(1.600)(0.372)(0.376)

1.206

1.301

1.139

1.360

0.494

0.429

(1.108)(1.111)(1.097)(1.104)(0.322)(0.344)-0.099-0.068-0.027-0.037

0.005

0.011

(0.078)(0.076)(0.082)(0.083)(0.022)(0.023)

0.072

0.065

0.058

0.061

0.029

0.029

(0.026)(0.025)(0.025)(0.025)(0.007)(0.007)-4.087-4.224-3.7343.859-1.969-2.036(0.898)(0.913)(0.937)(0.967)(0.2570.266*Standard errors in parentheses, clustered on the articles. Columns (3)-(6) contain journal indicators. Each equation
also includes a vector of indicators of year post-publication.

6

Table 7. Determinants of Post-Publication Citations, Theory Articles vs. Others*Ind. Var.:

3 rounds

4 rounds

Journal hands

Author(s) hands

R2
N (articles, observations)Non-theory

Theory

OLS

LAD

OLS

LAD

0.916

-0.206

1.578

0.727

(1.670)(1.048)(1.283)(0.637)

3.628

0.559

1.082

0.314

(3.255)(1.368)(1.406)(0.922)

0.099

0.069

0.025

0.033

(0.124)(0.063)(0.075)(0.042)-0.193-0.083-0.004-0.004(0.076)(0.038)(0.068)(0.030)

0.288

0.273

0.315

0.270

(158, 1,345)(83, 704)*Standard errors in parentheses, clustered on the articles. Each equation also includes a vector of indicators of year
post-publication. Also included in each equation are journal indicators and all the other independent variables shownin the estimates of Table 6.

7

Table 8. Selection Equation, and Determinants of Citations, Economic InquiryInd. Var.:

Dep. Var.:

Any author [5-10]
years post-Ph.D.
Years post-Ph.D.
of most-cited author
Five prior years citationsof most-cited author (/1000)

Fast track*

Citations**

0.085(0.029)0.00230.0087(0.0014)(0.0057)

0.03450.380(0.0159)(0.125)

Fast track

Two-roundregular

0.510

0.419

(0.188)(0.203)

0.147

0.103

(0.138)(0.155)

Years from submission toacceptance

0.051(0.116)

Year post-publication (9)Issue number (4)N pages
JEL category (10)N authors (3)Any female author
Pseudo-R2 or R2
N=

0.020
835

*Also includes year of initial submission.
**Standard errors in parentheses, clustered on articles.

8

X
X

X
X
X
X
X
X

0.087
3889

0.117
3889

Table 9. Prior Submissions of EI Articles Published 2015-18: Survey Results*

Track:
Fast

RegularN=

Prior
submissions

Prior
submissions
with reports

N reports

37

2.14

1.67

3.64

(1.52)(1.24)(2.73)

78

t-statistic on
difference
Regular track:
2 rounds

3+ rounds

t-statistic on
difference

37

41

2.38

1.47

2.95

(1.70)(1.21)(2.69)

-1.15

1.18

1.91

2.49

1.32

2.70

(1.95)(1.11)(2.63)

2.29

1.61

3.17

(1.45)(1.30)(2.75)

0.69

-1.46

-1.07

*

Standard deviations in parentheses.

9Figure 1. Depiction of Publishing Lags in Economics**Rafael Pereira @UrbanDemog

10Figure 2. Contributions to Slowness, by Decile of Speed
60

Speed in months

48

36

24

12

0
Fastest decile

3rd decile

JournalMiddle 10 percent

Author(s)11

8th decile

Accept to Publication

Slowest decile

Figure 3. Average Citations/year by Rounds of Submissions
16

14

12

Citations/year

10

8

6

4

2

0
0

1

2

3

4

Year post-publication
2 Rounds

3 Rounds

12

4 Rounds

5

6

7

Figure 4a. Distribution of Times from Submission to Acceptance, Fast Track (N=160)

Figure 4b. Distribution of Times from Submission to Acceptance, Regular Track (N=675)

13