What publishes in top-5 economics journals?

Part I: agricultural economics, lab experiments, field experiments & economics of education

Most of us have a sense that it is more difficult to get certain topics published in the top 5 economics journals (American Economic Review, Econometrica, Journal of Political Economy, Quarterly Journal of Economics, and Review of Economic Studies), but there is not much hard data on this. And if a particular topic appears infrequently in top journals, it may simply be because it’s a relatively rare topic overall.

To get more evidence on this issue, I used Academic Sequitur data, which covers the majority of widely-read journals in economics. The dataset I used contains articles from 139 economics journals and spans the years 2000-2019. On average, 6 percent of the papers in the dataset were published in a top 5 journal.

I classified papers into topics based on the presence of certain keywords in the abstract and title.* I chose the keywords carefully, aiming to both minimize the share of irrelevant articles and to minimize the omission of relevant ones. While there is certainly some measurement error, it should not bias the results. (Though readers should think of this as a “fun-level” analysis rather than a “rigorously peer-reviewed” analysis.)

I chose topics based on suggestions in response to an earlier Tweet of mine. To keep things manageable, I’m going to focus on a few topics at a time. To start off, I looked at agricultural economics (5.3% of articles in the dataset), field experiments (1.0% of articles), lab experiments (1.9% of articles), and education (1.8% of articles). I chose these to have some topic diversity and also because these topics were relatively easy to identify.** I then ran a simple OLS regression of a “top 5” indicator on each topic indicator (separately).***

The results are plotted in a graph below. Field experiments are much more likely to publish in a top 5 journal than in the other 134 journals (about 5 percentage points more likely!), while lab experiments are much less likely. Education doesn’t seem to be favored one way or the other, while agriculture is penalized about as much as field experiments are rewarded. Moral of the story: if you want to publish an ag paper in a top 5, make it a field experiment!

Now you might be saying, “I can’t even name 139 economics journals, so maybe this isn’t the relevant sample on which to run this regression.” Fair point (though see here for a way way longer list of econ journals). To address this, I restricted the set of journals to the 20 best-known general-interest journals—including the top 5—and re-generated the results.**** With the exception of lab experiments, the picture now looks quite different: both field experiments and education research are penalized by the top 5 journals, but agriculture is not.

Combining the two sets of results together, we can conclude that the top 5 penalize agricultural economics research but so do the other good general-interest journals. The top 5 journals also penalize field experiments relative to other good general-interest journals, but top general-interest journals as a whole rewards field experiments relative to other journals. Finally, top 5 journals penalize education relative to other good general-interest journals, but not relative to the field as a whole.

The second set of results is obviously sensitive to the set of journals considered. If I were to add field journals like the American Journal of Agricultural Economics, things would again look much worse for ag. And how much worse they look for a particular topic depends on how many articles the field journal publishes. So I prefer the most inclusive set of journals, but I welcome suggestions about which set of journals to use in future analyses! Would also love to hear everyone’s thoughts on this exercise in general, so please leave a comment.

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Endnotes

*I did not use JEL codes because many journals do not require or publish these and we therefore do not collect them. JEL codes are also easier to select strategically than the words in the title and abstract.

** An article falls into the category of agricultural economics if it contains any of the following words/phrases in the abstract or title (not case-sensitive, partial word matches count): “farm”, “crop insurance”, “crop yield”, “cash crop”, “crop production”, “crops production”, “meat processing”, “dairy processing”, “grain market”, “crop management”, “agribusiness”, “beef”, “poultry”, “hog price”, “cattle industry”, “rice cultivation”, “wheat cultivation”, “grain cultivation”, “grain yield”, “crop diversity”, “soil conditions”, “dairy sector”, “hectare”, “sugar mill”, “corn seed”, “soybean seed”, “maize production”, “soil quality” “agricultural chemical use”, “forest”. Field experiment: “field experiment”, “experiment in the field”. Lab experiment: “lab experiment”, “laboratory experiment”, “experimental data”, “randomized subject”, “online experiment”. Education: “return to education”, “returns to education”, “college graduate”, “schooling complet”, “teacher”, “kindergarten”, “preschool”, “community college”, “academic achievement”, “academic performance”, “postsecondary”, “educational spending”, “student performance”, “student achievement”, “student outcome”, “student learning”, “higher education” “educational choice”, “student academic progress”, “public education”, “school facilit”, “education system”, “school voucher” “private school”, “school district”, “education intervention”. Articles may fall into multiple categories.

*** Standard errors are heteroskedasticity-robust

**** The 15 additional journals are (in alphabetical order): American Economic Journal: Applied Economics, American Economic Journal: Economic Policy, American Economic Journal: Macroeconomics, American Economic Journal: Microeconomics, American Economic Review: Insights, Economic Journal, Economic Policy, Economica, European Economic Review, Journal of the European Economic Association, Oxford Economic Papers, Quantitative Economics, RAND Journal of Economics, Review of Economics and Statistics, Scandinavian Journal of Economics.

How to write a good referee report

Given the centrality of peer review in academic publishing, it might astonish some to learn that peer review training is not a formal component of any PhD program. Academics largely learn how to do peer review by osmosis: through seeing reports written by their advisors and colleagues, through being on the receiving end of them, and through experience. The result is perhaps predictable: lots of disgruntled researchers and the formation of such groups as “Reviewer 2 must be stopped” on Facebook.

This post is my attempt to make the world a better place by giving some advice on peer review. I have written over 100 reports, and I would like to think I do a good and efficient job (then again, I also mostly learned through osmosis, so you be the judge). Some of my advice is based on a great paper by Berk, Harvey, and Hirshleifer: “How to Write an Effective Referee Report and Improve the Scientific Review Process” (Journal of Economic Perspectives, 2017).

  1. As a reviewer, your job is to decide whether the paper is publishable in its current form and what would make it publishable if it is not. This is a distinct role from that of a copyeditor, whose job is to scrutinize every word and sentence, or a coauthor, whose job is to improve the contribution and substance of the paper. A reviewer’s goal is not to improve the paper, but to evaluate it, even though in the process of evaluating it, he may make suggestions that improve it. Of course, it is difficult for people to completely separate their own opinions from objective facts, but the harder we strive to play the right role, the fairer and smoother the review process will be.
  2. Your explanation of the paper’s strengths and weaknesses is more important than your recommendation. Many of us agonize over whether to recommend rejection or revise-and-resubmit. But reviewers do not know how many other submissions the journal receives or what their quality is. Even if you think the paper is great, it may be rejected because there are many papers that are even better. And a mediocre paper may make the cut if the other submissions are inferior to it. So the biggest service you can do for the editor is to help her rank the paper against the other submissions she is handling. Thus, you should aim to explain to the editor of what’s most impressive about the paper and what is lacking. The recommendation itself is secondary. When I recommend a rejection, I use the letter to the editor to outline the issues that make the paper unpublishable (there are usually 1-3), and why I don’t think they can be fixed by the authors.
  3. In case of rejection, make it clear to the authors what the deal-breakers are. The most frustrating and confusing reports to get are ones that raise seemingly addressable issues but are accompanied by a rejection recommendation. It may seem easier to save the “worst” for the letter to the editor, but it will leave the authors trying to guess why exactly the paper was rejected. Anecdotally, the most likely conclusion they will come to is “The reviewer just didn’t like the paper and then looked for reasons to reject it”, which is how Reviewer 2 groups get formed. Of course, you should use professional and courteous language in your reports. But don’t hide your ultimate opinion about the paper from the authors.
  4. In case of a revise-and-resubmit, make it clear to the authors what the must-dos and nice-to-dos are. Point 1 does not mean you should avoid suggestions that wouldn’t make or break publication. Many of my papers were improved by suggestions that weren’t central to the revision (for example, a reviewer suggested a great title change once). So if you have a good idea for improving the paper, by all means share it with the authors. But keep in mind that they will have at least one or maybe two-three other reviewers to satisfy, and the “to do” list can quickly spiral out of control. Sometimes the editor will tell the authors which reviewer comments to address and which to ignore. But sometimes the editor will pass on the comments to the authors as is. By separating your comments into those you think are indispensable and those that are optional, you’ll be doing the authors a big favor.
  5. Don’t spend a lot of time on a paper that you’re sure you’re going to reject. This is perhaps the most controversial piece of advice (see this Tweet & subsequent discussions) because some authors view the review process as a “peer feedback” system. But it is not (see point 1). And, at least in economics, many of us are overwhelmed with review requests and editors sometimes have a hard time finding available reviewers. Treating the review process as “peer feedback” exacerbates this problem. If you think the authors’ basic premise is fundamentally flawed or the data are so problematic that no answer obtained from them would be credible, you should not feel obligated to give comments on other parts of the paper. This does not mean that you should not be thorough – there are few things more frustrating than a reviewer complaining about something that was explicitly addressed by the authors. But in such cases you do not need to give feedback on parts of the paper that did not affect your decision.

Finally, I’d like to wrap up with an outline of how I actually do the review. First, I print out a physical copy of the paper and read it, highlighting/underlining and making notes in the margins or on a piece of paper. Second, I write a summary of the paper in my own words (it is useful for the editor to get an objective summary of the paper, and the authors can make sure I was on the same page as them). Third, I go through my handwritten comments and type the most relevant ones up, elaborating as needed. Fourth, I number my comments (helpful for referencing them in later stages, if applicable), order them from most to least important, and separate the deal-breakers or must-dos from the nice-to-dos. Fifth, I highlight the deal breakers (if rejecting) or must-dos (if suggesting revisions) in the letter to the editor. Finally, regardless of my recommendation, I try to say something nice about the paper both in the editor letter and in the report. Regardless of its quality, most papers have something good about them, and authors might be just a tad happier if their hard work was acknowledged more often.

A new way of ranking journals 2.0 – journal connectedness

A few weeks ago, I proposed that one could rank journals based on what percent of a journal’s authors have also published in a top journal. I calculated this statistic for economics and for finance, using the top 5/top 3 journals as a reference point.

Of course, one does not have to give top journals such an out-sized influence. One beauty of this statistic is that it can be calculated for any pair of journals. That is, we can ask, what percent of authors that publish in journal X have also published in journal Y? This “journal connectedness” measure can also be used to infer quality. If you think journal X is good and you want to know whether Y or Z is better, you can see which of these two journals has a higher percentage of authors from X publishing there. Of course, with the additional flexibility of this ranking come more caveats. First, this metric is most relevant for comparing journals from the same field or general-interest journals. If X and Y are development journals and Z is a theory journal, then this metric will not be very informative. Additionally, it’s helpful to be sure that both Y and Z are worse than X. Otherwise, a low percentage in Z may just reflect more competition.

With those caveats out of the way, I again used Academic Sequitur‘s database and calculated this connectedness measure for 52 economics journals, using all articles since 2010. Posting the full matrix as data would be overkill (here’s a csv if you’re interested though), so I made a heat map. The square colors reflect what percent of authors that published in journal X have also published in journal Y. I omitted observations where X=Y to maximize the relevance of the scale.

A few interesting patterns emerge. First, the overall percentages are generally low, mostly under 10 percent. The median value in the plot above is 3 percent and the average is 4.3 percent, but only 361 out of 2,652 squares are <0.5 percent. That means that a typical journal’s authors’ articles are dispersed across other journals rather than concentrated in some other journal. This makes sense if the typical journal is very disciplinary or if there are many equal-quality journals (eyeballing the raw matrix, it seems like a bit of both is going on, but I’ll let you explore that for yourself).

There are some notable exceptions. For example, 41% of those who have published in JAERE have published in JEEM, 54% of those who published in Theoretical Economics have published in JET, and 35% of those who have published in Quantitative Economics have published in the Journal of Econometrics. These relationships are highly asymmetric: only 13% of those who have published in JEEM have published in JAERE, only 16% of those who have published in JET have published in Theoretical Economics, and only 4% of those who have published in the Journal of Econometrics have published in Quantitative Economics.

There is also another important statistic contained in this map: horizontal lines with many green and light blue squares indicate journals that people seem to be systematically attracted to across the board. And then there’s that green cluster at the bottom left, with some yellows thrown in. Which journals are these?

I had the benefit of knowing what the data looked like before I made these heat maps, so I deliberately assigned ids 1-5 to the top 5 journals (the rest are in alphabetical order). So one pattern this exercise reveals is that authors from across the board are flocking to the top 5s (an alternative interpretation is that people with top 5s are dominating other journals’ publications). And people who publish in a top 5 tend to publish in other top 5s – that’s the bottom left corner. In fact, if you omitted the top 5s, as the next graph does, the picture would look a lot less colorful.

But even without the top 5, we see some prominent light blue/green horizontal lines, indicating “attractive” journals. The most line-like of these are: Journal of Public Economics, Journal of the European Economics Association, Review of Economics and Statistics, Economics Letters, and JEBO. Although JEBO was a bit surprising to me, overall it looks like this giant correlation matrix can be used to identify good general-interest journals. By contrast, the AEJs don’t show the same general attractiveness.

Finally, this matrix illustrates why Academic Sequitur is so useful. Most authors’ articles are published in more than just a few journals. Thus, to really follow someone’s work, one needs to either constantly check their webpage/Google Scholar profile, go to lots of conferences, or subscribe to many journals’ ToCs and filter them for relevant articles. Some of these strategies are perfectly feasible if one wants to follow just a few people. But most of us can think of way more people than that whose work we’re interested in. Personally, I follow 132 authors (here’s a list if you’re interested), and I’m sure I’ll be continuing to add to this list. Without an information aggregator, this would be a daunting task, but Academic Sequitur makes it easy. Self-promotion over!

If you think of anything else that can be gleaned from this matrix, please comment.

Ranking finance journals

Last week, I tried out a new way of “ranking” economics journals, based on the percent of 2018-2019 authors who have also published in one of the top 5 economics journals anytime since 2000. This week, I decided to take a look at finance journals (political science is next in line, as well as some extensions and robustness checks for econ journals).

The top 3 finance journals are generally agreed to be Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. How do other finance journals stack up against them according to this metric? For fun and fairness, I threw in the top 5 econ journals into the mix, as well as Management Science.

Here are the “top 10” journals according to this metric (not counting the reference top 3, of course). The first numerical column gives the percent of authors that published in the journal specified in the row in 2018-2019 who have also published an article in any of the top 3 finance journals at some point since 2000. The next three columns give journal-specific percentages.

Because this is not my field, I have less to say about the reasonableness of this ranking, but perhaps finance readers can comment on whether this lines up with their perception of quality. Compared to the econ rankings, the raw percentage differences between journals appear larger, at least at the very top. And the overall frequency of publishing in the top 3 is lower. Management Science makes the top 5, but the top econ journals do not (
JPE and ReStud do make the top 10). To me, this makes sense, since it’s pretty clear that this ranking picks up connectedness as well as quality. Anecdotally, finance departments seem to value Management Science and the top 5 econ journals no more and perhaps less than the top 3 finance journals.

Here are the rest of the journals I ranked (as before, if a journal is not on the list, it doesn’t mean it’s ranked lower, it means I didn’t rank it). Here, we can clearly see that not many people who publish in JF, JFE, and RFS publish in AER, QJE, or Econometrica.

If there’s another journal you’d like to see ranked in reference to the top 3 finance ones, please comment!