Suggested New Year's Resolution for Economists...

Be better seminar participants by following these suggestions about questions during seminars, summarized on twitter by Leah Boustan in this way:

Clarifying: Always
Deeper: Wait until results presented
Anticipatory: Never

Happy New Year Everybody!

Economic Demography Workshop

Many of you know that the PAA (Population Association of America) meetings are a great place to present your demography-related work to other economists as well as sociologists, demographers, anthropologists, etc. What you may now know is that on the Wednesday afternoon before the PAAs, the economists studying population issues gather for the Economic Demography Workshop. Presentations are about a half hour long and are followed by excellent discussions. You can have a look at past programs here. The deadline for submission is in two days, but even if you do not have a paper ready for submission, check back in a few months to see the program. Attend the workshop if you will be in town, but even you can't be in Denver this year, be sure to browse through the papers on the program. They're always really good!  And maybe they'll be especially good this year since I'm the chair of the organizing committee. ;) 

Is It Ever Too LATE?

This is just a friendly reminder that even the very best IV analyses can only get us local average treatment effects (LATEs). OLS estimates, on the other hand, are average treatment effects (ATEs). This makes it very difficult to compare the two. See this post by Marc Bellemare for a nice description of the issue. My thoughts: Sure, if you have reason to believe that responses to the treatment will be the same for everyone..or if you believe that everyone in your sample is a "complier," sure, they are comparable. Keep in mind though that either of these instances are likely to be rare. I personally do like to see OLS estimates even in IV papers. But I would urge you to just be careful when making comparisons. The fact that an IV estimate is different from an OLS estimate does NOT necessarily imply that the OLS estimate suffers from endogeneity bias. 

AEJ: Applied vs. AEJ: Policy

What's the difference? Where should you submit? Matt Notowidigdo, AEJ: Policy co-editor, very generously gives us the scoop on twitter. So helpful! I had no idea that AEJ: Policy competes more with AEJ: Macro than AEJ: Applied these days. 

Thank you, Matt, for all of this! 

And now a plea to the editors of other journals: I would be so, so grateful if you could do the same for your journals. I even promise to blog about it. ;) 

Careful with IVs...

You know when you just have a feeling about something. Maybe it's a gut instinct that comes from nowhere, but more often, the feeling comes from your experiences. Anyway, for years I have had that type of feeling about instrumental variables (IV) analyses. Yes, I understand the math behind how IV techniques can work wonders in terms of correcting bias. But after playing with IVs in various contexts, using various data sources, for various papers, etc., I have gotten a sense that IV results are just not as robust as I would like. 

Well, it turns out that my feeling is probably right, and Alwyn Young, an economist at LSE, has shown this formally in a recent paper about inference in IV models. Yes, you should read the paper, but since you're probably busy with writing or grading final exams these days, have a quick look at Marc Bellemare's post about the article. These are some of the most important points, as described in Marc's post: 

1. Conventional tests tend to overreject the null hypothesis that the 2SLS coefficient is equal to zero.

2. 2SLS estimates are falsely declared significant one third to one half of the time, depending on the method used for bootstrapping.

3. The 99-percent confidence intervals (CIs) of those 2SLS estimates include the OLS point estimate over 90 of the time. They include the full OLS 99-percent CI over 75 percent of the time.

4. 2SLS estimates are extremely sensitive to outliers.

So what to do? Does this mean we should abandon IV analyses and just report OLS results? Absolutely not. A good exogenous IV can correct some pretty major problems in the OLS. I guess what I would say is just to be careful. And to always report both the IV and OLS results!

In the meantime, a fun activity: come up with new slogans for OLS! Check out some recent candidates from the world of twitter!  

More Insight on Where to Send Short Papers

Remember I blogged about where to send short papers? That new AER journal specifically for short papers has arrived! Read all about the new journal, American Economic Review: Insights here. 

Short papers or long papers, I am happy that there will be another journal of AER quality! 

Cool Data Alert: Distances Between Zip Codes

Here it is. Thank you to the NBER for putting it together and especially to Jean Roth for creating the Stata files. 

Twitter, Now a Source of Data Not Just Procrastination

If you have Stata 15, you can now very easily import Twitter data directly into Stata using the command twitter2stata. More details here. Tweet that!

Need a Dissertation Topic in Applied Micro?

Browse through this website, Our World in Data. Not only can you check out some really fascinating relationships in the world, but you will get some sense for the data sets that exist out there and what types of information they contain. Anything spark your interest? 

Just In Time for Practice Job Talk Season....

Here is an excellent presentation put together by Rachel Meager on public speaking for academic economists. 

Look through it before giving a practice job talk, a real job talk, or any talk. I am giving a talk on Wednesday, and I will certainly try to incorporate the suggestions. 

Some of my favorites: 

"Really good ideas in economics are often obvious ex-post." 

This is exactly why the introduction and motivation parts of the talk are so important. Right from the beginning, tell the audience why your question is important and why the answer is not obvious. Then explain how you answered it. But be careful...

"..don’t try to show how hard you worked. This is boring for the audience which makes them think you don’t respect their time."

For sure, don't list all of the mistakes you have made and how you didn't get what you had hoped. More eloquently, 

"Present the work you have, not the work you wish you had."

But my very favorite piece of advice applies not only to giving talks, but pretty much all of life. 

"You can’t control the audience nor the outcome of the talk. You can only control what you do. Do not attempt to micromanage reality."

Go forth and do good work. Then show it to the world..in a way they can understand and appreciate it!

Are We Any Better Than the Social Psychologists?

The NYTimes ran an article last week explaining the ordeal with power poses and Amy Cuddy. The article describes the big issues with all empirical work: the scarcity of replications studies and p-hacking. I read the article just before bed one evening after spending the afternoon tinkering with data. I wouldn't be lying if I told you that it kept me up that night. 

I really do believe that we are learning about the world by doing research (however imperfect the process is), but can we as researchers do better? Would we all be better off if the incentives were changed to reward more replication (even if it comes at the expense of new studies)? And what about papers with non-results? Would the world be better off if these papers were ever published in good journals? 

I suppose it's difficult for me to single-handedly change the reward system within economics, but I do have control over the types of projects I start (and continue). I also have a tiny bit of control over what gets published were via refereeing. I have blogged in the past about how the referee process has changed over the years (papers have gotten longer, so many appendix tables, etc). On that sleepless night last week, I decided that maybe all the robustness checks aren't such a bad idea after all. In a way, they are mini-replication studies. They also make p-hacking a lot more difficult! I think we have come a long way in the field, and we are getting even better. I am so happy that more and more journals are requiring data and code to be available to readers. But I still think we can do better. 

Anyway, see Chris Blattman's thoughts on this. 

And Dan Hammermesh's recent paper on replication studies within economics. 

So You Don't ALWAYS Have to Cluster..In Fact, Often You Shouldn't

I have been meaning to blog about this new paper all weekend and just never got around to it---I wonder if that happens often with econometrics papers. Anyway, my procrastination paid off this time because David McKenzie just posted a truly excellent description of the paper. 

My summary of David's summary: There are times when we all know we have to cluster, and we know exactly the level at which we should cluster. In those times, cluster! There are other times when it isn't clear that we have to cluster, but a referee or a seminar participant can always suggest a new, completely not obvious potential way to cluster. In those times, read the Abadie, Athey, Imbens and Wooldridge paper, then read David's description, and then really think about whether you should be clustering in your particular context.  

Shakespeare had it all wrong! The real question is, "To cluster or not to cluster..."  But now, we have an answer!

Cool Data Alert: How to Get Administrative Data

J-Pal has put together a catalog of administrative data sets. Not only can you see what exists, but there is information on how to access the data. So helpful! Definitely worth at least a browse!

Woa!!! Even Macroeconomists are Worrying about Identification! ;)

Hehehe. Sorry, couldn't resist!

Here's the paper for all of you macroeconomists reading my blog.  ;)

Some of My Favorite Topics within Labor Economics

I hadn't really thought about the fact that Larry Katz has been involved in so many of my very favorite literatures....until reading this interview. A big take-away:

Region: Thank you. I’ve learned so much today, and we’ve touched just a fraction of your work. I don’t know where you find the time to produce so much!

Katz: One thing is trying to leverage teams in research, which is a big trend in economics, a healthy trend. Most projects are not just done on your own; they involve a team of scholars working with large data sets, setting up the field experiment, collaborating with others.

Unanticipated Cool Part of the Job..

Having journalists write about your research in a way that makes it sound even more exciting than you thought it was!

Read this.

And then this.

I told you writing is important!

PS
From xkcd.com



https://xkcd.com/1895/

Who Should Submit the Paper?

Answer: Probably doesn't matter. But maybe the senior person.

See David Evan's discussion.

More Advice on How to Write

David Eli has some advice. All good advice. But now my advice which I guess is a shortened version of what he says: 1. Pay attention to good (and bad) writing when reading it. 2. Write. 3. Rewrite. 4. Rewrite again.

Are you still reading this? Get back to your writing!

I Have Been Traveling...Back to Blogging Soon

From PhDComics.com:

Even Smaller P-Values?

Have you ever had a brilliant idea for a paper---one with an interesting and important question and a believable identification strategy? After a lot of work, you acquire and manage to clean up the perfect data set. You run that regression. Maybe you even get the expected sign and a reasonable estimate of a coefficient.....but the standard errors are just too big. You run a few more regressions and no matter what you try, you just can't get that p-value below .05 and so you end up putting that project in the filing cabinet. There just isn't enough variation in the data to identify anything. <sigh>

Well, it seems like this actually isn't happening as often as it should. Too many papers are being published that cannot be reproduced. Daniel Benjamin, a behavioral economist at USC, and 71 coauthors from a variety of fields have just published a paper with this one sentence summary: 

We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

This would certainly require larger sample sizes if we wanted to keep publishing the same number of papers with "significant" results. My personal view: To start, why not do away with the norm of reporting just standard errors with the little stars? Why not instead publish p values? That way readers could easily and quickly distinguish between a p-value of .049 and .0049.  I wonder if this would a make a difference in terms of which papers get published in the different journals. 

And now, because I have no shame, I will share the song that I can't get out of my head as I am writing about p's. Have a listen to this and share with your kids.  :)  

(h/t David McKenzie, again)

"Relax. Nothing is Under Control."--Adi Da

That quote seems particularly fitting for my life right now, but since this is a blog about doing applied micro, let me write again about control variables (hehe, I know corny). Marc Bellman has a new blog post about the sensitivity of results to the use of particular controls. On the one hand, we should expect results to be sensitive. That's why we include those controls! On the other hand, if researchers are playing with different specifications including many different combinations of controls and only reporting those generating significant results, ...well, you know. 

Marc's post discusses a recent working paper by Lenz and San showing that in about 40% of the observational studies analyzed in the journal, American Journal of Political Science, researchers obtain statistical significance of their estimate of interest by tinkering with the covariates included. 

Yikes! Would you expect similar numbers in an economics journal? 

In past blog entries, I've written about how economics papers have gotten longer and longer over the years and how referees often help write the paper instead of just 'refereeing'. But now that I see that 40% figure, I think maybe these are not such horrible developments. If you have only one specification to tinker with, it's not so hard to get that statistical significance, but if you have many suggested by referees, it's not impossible but certainly a lot harder. 

Economists have been worried about the issue of control variables recently. I really like Marc's description of two recent papers:

(1) y = a + bX + cD + e

"The issue of what goes on the RHS of equation (1) is getting a lot of attention in the applied literature. Two prominent examples are Emily Oster’s forthcoming JBES article “Unobserved Selection and Coefficient Stability: Theory and Evidence” and Pei, Pischke, and Schwandt’s (2017) NBER working paper titled “Poorly Measured Confounders are More Useful on the Left than on the Right.”

Oster provides a method to assess just how much coefficient (as in coefficient c in equation 1) stability tells us about selection on unobservables. Pei et al. develop a test of identifying assumptions that treats putative additional controls as dependent variables in equation (1).

I expect both methods to become part of the applied econometrician’s toolkit over the next five to 10 years. At the very least, I expect a bare-bone regression of y on D alone to become something that has to be included in a paper, along with a discussion of why the controls that were included on the RHS of equation (1) were retained for analysis."

(h/t David McKenzie) 

How to Choose a Title

Confession: I often don't really think about titles until it's time to submit the paper to a conference. I have been known to quickly come up with, say, four potential titles and then ask my friends to vote, but that's about it. This blog entry makes the very excellent point that paper titles are really, really important. Not only that, but Patrick Dunleavy goes through both how to write a good title and how to write a terrible one. Both lists are so helpful! I really like the suggestion to do a google search of the potential title to see what else comes up. I'm also a fan of the full narrative title idea, but I can imagine that might be really tricky to do well. 

My addition to all of this: Pay attention to titles when you read papers. Which titles do you like? Which do you hate? Which are completely uninformative? I think this process alone will help you come up with better titles for your own papers. 

Inspiration for this blog post: this David Evans blog entry. 

Yeah, This is Exactly How I Feel Sometimes

From SAS...

Cool Data Alert: Health Surveys

We all know about the National Health Interview Survey (NHIS). We also (hopefully) know that the harmonized version is easy to download from our friends at IPUMS. What you may not know is that sample sizes are large enough to do good research on the U.S. immigrant population. Hooray! But I have more good news. This past weekend at the iHEA World Congress in Boston, I learned about the California Health Interview Survey (CHIS). OK, it is only for California, but it's a large data set and there are lots of questions specifically pertaining to immigrants--including parents country of birth and visa status! UConn grad students, if you're interested in looking into this for your dissertation, come see me. 

For even more data sets related to health, see here. 

Where to Send Short Papers

The average length of an economics paper (1.5-spacing, 12-point font, and 1-inch margins) grew from 16 pages in the early 1970s to 45.5 pages in 2011–12, a nearly 300 percent increase (see this Card and DellaVigna article in the Journal of Economic Literature for more details or this Vox summary of the article). That said, as David Evans puts it in a recent blog entry: "Good ideas don’t have to be long ideas." So where should we send shorter papers? Should we bother writing them at all? Read David Evans blog for the official statistics, but I will emphasize that it really depends on (1) how fast you can write the short paper, (2) whether it's even possible to write a longer paper with existing data, and (3) whether the short paper would make an important contribution to the literature. 

If you do decide that a short paper makes sense, look here for ideas about where to send it. As you can see in the comments, the Review of Economics and Statistics also sometimes publishes short papers. You may also be able to submit papers for possible inclusion in the American Economic Review Papers and Proceedings issue by submitting your paper to the AEA Meetings. In fact, if you submit to a CSWEP session, your changes may be a bit higher. 

Or you can just wait a while. Word on the street is that there may be a brand new journal in the works for short papers....let's see what happens. 

What Makes A Good Economist?

This Lindau video is getting old but remains excellent. Don't sell your soul! Work on important topics! So fun to see my friend, Alex Teytelboym, in the video. Speaking of Alex, you should definitely look at his cool work on refugee placement, but also insightful is this visual depiction of what often happens when you add a new factor to a model that I found linked to on his webpage....  :). 

My Advice: Take the Advice of Nobel Laureates

Yikes! I've been delinquent with my blogging lately. While I try to make progress on papers, referee reports, and car-buying, let me leave you with this advice for young economists by the masters themselves. It's hard for me to accept the "get your nose out of the data" advice (I need to work on that one myself), but I love the focus on "energy, effort, and passion."

Tyler Cowen Interviews Raj Chetty

I'm a big fan of all of Raj Chetty's work and loved hearing about it in his own words, but my favorite part of the interview was absolutely his discussion of the "Raj Chetty Production Function." What is his secret recipe? Yes, he answers important questions with new data and then describes these results in very clear ways, but it's certainly not as easy as it sounds.

Raj says, "It’s very easy — students often have this reaction, that all I need to do is get access to this big dataset, and then I’m going to be all set for my thesis. And what you end up finding is that that is often not the case. It’s very easy to write a paper that is not that good, even with cutting-edge data and modern techniques. So one of the things that I try to do — and the easiest way to see this is if you internally, within our research group, see the iterations of the papers we’ve been working on — where we start out is often very far away from the papers that people see as the finished product. We work hard to try to write a paper that ex post seems extremely simple: 'Oh, it’s obvious that that’s the set of calculations you should have done.'"

I've actually just started exploring a new data set for a brand new project and those words were quite inspirational. 

Speaking of inspiration, I was happy to learn that Raj is also a fan of the Piano Guys! Here's one of my favorite songs of theirs. Oh, but I like this one, too.

More on Machine Learning

From our friends at xkcd.com:

Call Your Mother...

...and tell her about your research! 

Listen to this Vox talk by Deidre McCloskey on how to communicate your ideas to others. Her main advice: explain your work to your mother in everyday language. Hints: Give examples! Be concrete! Tell stories! 

I also liked this piece of advice she gives to graduate students: You should be able to explain your results in words, in diagrams, and with equations. 

The Job Market: Speed Dating for Economists

This post is for graduate students. Anyone who has already finished the PhD knows exactly what this is like. I hadn't realized that economists were the only ones that did the job market like we do the job market. Anyway, NPR thought it was interesting enough to do a story. Click here. 

How to Respond to Referee Reports

The people at Phdcomics.com have some helpful pointers:

Calling All Econometrics Instructors

Please, please, please read this. 

It is an essay written by Josh Angrist and Jörn‐Steffen Pischke (the creators of Mostly Harmless Econometrics) calling for a paradigm shift in econometric teaching. I think that if we changed the way we taught econometrics, students at all levels would be better equipped to write good papers and maybe even more importantly, better equipped to judge the quality of different papers long after they have graduated. Maybe most importantly, those of us teaching applied micro courses could spend less time teaching econometrics and more time on content...ok, maybe that's just me being lazy.  ;)  

But seriously, I'd love to hear from folks teaching these courses. Do you believe that because we have changed "how we use econometrics we need to change the way we teach econometrics"? Why should students spend so much time thinking about "functional form, whether error terms are independent and identically distributed, and how to correct for serial correlation and heteroskedasticity" while researchers spend very little time thinking about them? Is econometrics "better taught by example than abstraction"? (Quotes are taken from the article.)
 

How to Succeed in Academia: Be Curious

Well, here it is. Lasse Pedersen, from NYU, has produced a definitive guide to pretty much every aspect of my job. 

Favorite pieces of advice: 

1. "It’s about the same amount of time to write an unimportant paper as an important one." (p. 3)  This is very true. I can't tell you how often I have started "a quick little paper" only to spend a significant amount of time working on it. It's important to be choosy about topics. That said, better to work on something rather than nothing. Lots of times what I thought would be quick and easy but unimportant turned out to be complicated but more interesting. 

2. "Become a world expert on a literature." (p. 5)  Because that's just cool. Also, it's good for your letter writers to be able to say that you're the world's expert on X. But mostly, it's just cool. 

3. "The best papers often occur where theory meets empirics (i.e., they relate to both even if they only contribute to one of the dimensions)" (p. 7) 

4. "Talk about your research with lots of people." (p. 10)  I would add that talking about your research with experts will give you a better idea of where your research fits in the literature, talking with policy makers may give you a better sense of what is important and doable, and talking with your grandmother will give you practice explaining your ideas in easy to understand language. All of these are critical. 

5. On the importance of writing: "If a paper with an interesting idea gets rejected, it is often because it is so badly written that the editor/referee fear that its execution will be sub-standard even after several costly rounds of revision." (p. 17). Yes! Yes! Yes! 

6. "During seminars, draw the right questions by making your contribution clear." (p. 24) 

7. Discussing a paper at a conference: "A discussion is a service to the audience, not to the author and not an evaluation."  Yes! Yes! Yes! Of course, give the author all of your comments, but not necessarily in front of everyone. 

And my very favorite piece of advice: "Be Curious!" (p. 35) I think it's really, really excellent to have a job that that is the main overarching guide towards success (in the long run).  Thank you, Lasse, for reminding me of that. 

Completely New To Stata? Start here.

Do you need to write a paper for a class and don't know how to start the data work? I recommend going through these modules provided by UCLA. If you find better ones, let me know, but remember that Stata is mostly about learning by doing. Start coding!

Stata Tip: Never Ever Copy-Paste, Use Outreg2 and Tabstat To Their Fullest

Yes, we all know about outreg2 (I hope), but most of us don't use it to its fullest potential (I think). My graduate student sent me this handy little PowerPoint presentation on outreg2 (thank you, Sam). I had no idea you could use outreg2 to make nice tables of descriptive statistics. 

I have been using the tabstat command to make tables of descriptive statistics. Only recently, I learned that it becomes so much more useful when using the longstub option along with it. Have a look at the tabstat help for more details. 

My favorite outreg2 option? addstat! See the outreg2 help for more details, but I will leave you with one useful thing you can do with it: Imagine you have a regression table showing the results of your analysis conducted on several different samples (sample1, sample2, etc.)  You probably want to show the mean of your dependent variable in the different samples. What to do? 

regress y x if sample=1

sum y if sample=1

outreg2 using TableName, addstat(Dependent Variable, rmean)

Then do the same for the other samples. Voila! The mean of the dependent variable shows up just below the R-squared! 

Structural vs. Quasi-Experimental Econometrics: Does it Need to be Winner Take All?

My blog focuses on quasi-experimental econometrics because, well, that's what I do and so that's what I can (hopefully) say something useful about. Yes, I'm a huge fan of "Mostly Harmless"-style analyses, but those techniques are not the only ways we can learn about the world using data. 

In a recent blog entry, Noah Smith provides a really nice description of the structural and quasi-experimental styles of analysis. After going through the pluses and minuses of each, he concludes: "So why not do both things? Do quasi-experimental studies. Make structural models. Make sure the structural models agree with the findings of the quasi-experiments. Make policy predictions using both the complex structural models and the simple linearized models, and show how the predictions differ."

I think that's the right advice for the profession, but what if you're a first or second year PhD student? Well, then I think you do have to make a decision. Let's say, for example, you're a graduate student at UConn, and you want to write a dissertation on the impact of immigration policies on natives. Should you work mostly with me or with Hyun Lee? I think the answer depends on which techniques you'd like to learn. Regardless of the path you take, you are absolutely responsible for knowing the limitations of your analyses. 

How to Write an Economics Paper, Step by Step

By this time in the semester, students in my labor class should be making real progress on their research papers. While trying to think of ways to help them, I came across this fabulously detailed guide, written by Plamen Nikolov, on how to write papers. It's funny that a lot of these things I don't even think about anymore. I guess they just come naturally (well, sometimes) after years of reading papers. But the students in class need to write a paper now, and so these tips will hopefully be useful. 

Some favorites: 

1. Keep sentences short. Monosyllabic words are best. 

2. It is not necessary to cite every single paper in the literature. The main point is to set your paper off against the 4-5 most closely related current papers. 

3. Leave policy implications to the introduction and conclusion. 

4. It is better to acknowledge shortcomings than to make overly broad unsupported statements. 

Colleagues, what are the most annoying/funny mistakes your students make when writing their first paper?

Ah, Saturdays...

I just took my car to two different body shops and both were closed. I never realized mechanics don't work on Saturdays. When do people with non-academic jobs fix their cars? 

More Valentine's Day Kinkiness

I hereby declare Valentine's Day to be Regression Kink Design (RKD) Day.  David Card and coauthors provide the latest practical advice on how to use RKD here.

Now go out there and get a little kinky! Happy Valentine's..I mean RKD..Day!

"A referee report is not a mind-dump about the paper."

That is my favorite line in a recent Journal Economic Perspectives paper on how to write good referee reports. Some of my personal thoughts on the recommendations: 

1. Most importantly, I am so happy to get any guidance on how to write a referee report.  Yes, we can talk about the not-so-ideal incentives referees may have when writing reports, but I think a bigger problem is that we, the referees, are not exactly sure what editors would like to see in referee reports. Until now, most of my thoughts on this have come from reading reports on my own papers, but if everyone just does this, norms determine everything. 
2. I do like to see a good, healthy number of robustness tests in a paper, and I kind of like extensions. I think these additional analyses should be done. The question then is how many need to be done by the original authors vs. other researchers in other papers published in less prestigious journals. I don't know the answer to that. 
3. The most important piece of practical advice in the paper is to separate comments by whether they are essential or suggested. I think all authors want to make their paper the best it can be, and it's nice for referees to share their impressions on how to do that. That said, the authors have often thought about the issues for many years while referees have thought about them for..a week? a day? Given this, I don't think the best way to produce good research is for the authors to mindlessly follow the whims of referees---even though that is the often the easiest and least risky approach to getting a paper published. I think it's fine for referees to share their whims (sometimes whims result in great ideas) as long as they are clearly labeled as such.  
4. More useful practical advice: Comments should be numbered and the whole thing should be 2-3 pages.
5. The hardest part of refereeing is making a call on whether the topic of the paper is "sufficiently broad interest" or whether the paper has"made a sufficient leap over the existing literature." I would have love to have more guidance on how to make those calls, but I guess in the end, it's just about our opinion. Ah, but this brings me to my favorite piece of advice for my graduate students writing papers: Do take the time in your introduction to explain the contribution of your paper, making it very clear how the paper contributes to the existing literature. 

Ok, time to get back to writing a referee report. Or a paper. 

Data Alert: Another Database on Immigration Policies

I haven't looked at this carefully enough to see how it differs from the other migration policy data set I blogged about, but I think they're both worth exploring. Let me copy-paste a description: 

The International Migration Policy And Law Analysis (IMPALA) Database is a cross-national, cross-institutional, cross-disciplinary project on comparative immigration policy.The database, which will be used for both qualitative and quantitative research across a range of disciplines, improves existing databases on policy and captures trends in immigration selection policy, naturalization policy, illegal immigration policy and bilateral agreements across 20 OECD countries, across time. It opens up possibilities for a range of comparative qualitative and quantitative research on the determinants of migration policy and the effect of migration policy on social, economic, demographic and political trends.

You can read more about it in this journal article. 

For Those of You Giving Job Talks These Days

Think, think, think about how to explain things as clearly as possible. Good luck and enjoy!

Watch This Before Making Tenure Decisions: The Curse of the Top 5

I started this blog for graduate students, but I'm hoping that by now some tenured professors who vote on tenure cases have a look every now and then. This one is for you guys.

Tenure or no tenure, what is one of our favorite topics of conversation? That new AER/QJE/JPE/etc. paper that shouldn't have been published there because it's not so good. Or that other paper (usually our own or our friend's) that for sure should've been published in a top 5 but was desk rejected in five minutes. Yes, this is all fun and good chatter at lunch--actually, I think it's great to be talking about papers period--but when it comes time for tenure decisions, this stuff is really, really important. These decisions have a huge impact on our colleagues' lives as well as the future of our departments. For this reason, I'm really happy to see important economists talking about these issues in a public way.

Have a look at this webcast of a panel discussion at the recent AEA meetings called, "Publishing and Promotion in Economics: The Curse of the Top Five." James Heckman, George Akerlof, Angus Deaton, Drew Fudenberg, and Lars Hansen talk about the problems arising when departments place too much emphasis on Top 5's when making tenure and hiring decisions. Many excellent points were brought up. I was especially impressed by the notion that different Top 5's are relatively more influential in different fields within economics. Some suggestions were made for improving things, but what I got out of the talk was mostly that this is a difficult problem. What is the alternative to relying on publications for making tenure decisions? Yes, we could theoretically read the papers, but even if we understood all of our colleagues' papers perfectly, it's very difficult to have a sense for the contribution of the paper in its subfield, especially given how specialized economics has become. Should we rely more on algorithms? Maybe, but we don't have much by way of data for new researchers. What to do? I don't know, but at the very least, we should be thinking about these things very carefully when making decisions.

Happy New Year (And Blog Anniversary)!

This is my new year wish for all of you (from Shit Academics Say):

May your words be plenty, your typos be few, your ideas accepted, and manuscripts too.