An Oldie But Goodie: What Are We Weighting For?

I have recently been struggling with deciding how to use weights in a new project. Sometimes this is straightforward (when using IPUMS data for the 1% sample, use the variable perwt). Sometimes, however, data sets come with multiple weights or you might have to create your own. Weights usually matter for when creating tables of descriptive statistics, but often they do not matter when running regressions. Of course, whether we should use weights should depend on how those weights were created. But before spending too much time reading your data's codebook, it is useful to think about why anyone would want to use weights--ever. For insights, have a look at this summary of the paper, "What are we weighting for?" written by Gary Salon, Steven Haider, and Jeffrey Wooldridge.

From Student to Scholar

This week, students in my labor class are writing proposals for papers due at the end of the semester. This is kind of a big deal since, hopefully, these papers will turn into "third year papers" which (if they're good) will become chapters of their dissertations. It's been quite something to see students go from a broad interest in a topic to a specific question to a specific answerable question to a specific answerable question that hasn't already been answered. I'm reminded that this stuff is hard! But it's so much fun to see them zero in on a topic that they're excited about! Especially when it comes after a long struggle.

In honor of these students, I thought I'd share some of my favorite pieces of advice on how to go from student to scholar. One piece of advice: Don't be boring. Another: Look for ideas in the world, not in journals. But I actually found my favorite piece of advice in a CSWEP newsletter interview of Anna Mikusheva. She writes,

"The main advice is trivial: always work on something. If you have not found a big, exciting project to work on, work on something smaller—a big project will eventually come your way. If you don’t have even a small project, work out some missing details of a paper you’ve recently read or seen presented, and/or try to answer questions that have arisen during the last seminar you attended. Your big idea will come once you have realized enough small projects." 

She also recommends ambition and patience. I would also add that talking about your ideas to people, especially smart people but really anybody who will listen, can be so helpful!

Getting Kinky on Valentine's Day

We all know and love regression discontinuity (RD) designs. We use these to estimate the impact of a policy by comparing outcomes for people just below and just above a policy cutoff. Look here for some RD porn. Well now, there's a "kinkier" version of this becoming more and more popular: the regression kink design (RKD). A really nice description is available here, but let me quote the basic idea:

"The regression discontinuity design exploits a jump or discontinuity in the likelihood of being treated at some threshold point. In the RKD design, there is instead a change in slope at the likelihood of being treated at a kink point, resulting in a discontinuity in the first-derivative of the assignment function...For example, Simonsen et al. (2015) use a kink in the Danish government’s prescription drug reimbursement schedule: the subsidy is based on the total prescription costs the individual has paid during the year – there is 0% subsidy for the first 500 DKK in expenses, then 50% subsidy once you have paid 500 up until you have paid 1200, then 75% subsidy, and eventually an 80% subsidy for expenses above 2800 DKK. The result is that the share of the price paid out of pocket kinks as shown in Figure 1:


Figure 1: Y-axis is the share of the price paid out of pocket. It falls as one approaches 500 DKK since if you have spent 480 and buy something for 50, you get 0% subsidy on 20 DKK and then 50% subsidy on the 30 DKK that is expenditure past the threshold."


I have seen this strategy used for estimating the impact of unemployment insurance payments in the US as well. I'm sure there are many other public policies that can be analyzed using RKD. In honor of Valentine's Day, I suggest you go off to find them!  ;)  

STATA Now Sends Txt Messages

I often joke that STATA is the true love of my life. Always there for me. Night and day, year after year. People sometimes respond that STATA can't bring soup when I'm sick, blah, blah.

Well, unfortunately STATA still can't cook for me, but the good news is that it now sends txt messages. Directly to my phone! :)

This module, developed by a Yale graduate student, will send a message to your phone as soon as your program finishes running. Now you can feel free to leave your desk for a bit--maybe do some laundry, maybe make some soup. STATA will let you know when it's ready for you again. LOVE!  

"It is not in the stars to hold our destiny but in ourselves."

That's a Shakespeare quote. Not sure if this is exactly what he had in mind, but I think it's a great reminder not to get so crazy over small p values that you forget to think about the actual coefficient estimates. Look at the numbers! Do they make sense? Are they believable? Of course, the best case scenario is that the coefficient estimate makes sense and there are a bunch of stars next to it, but when that doesn't happen, read this. Whatever you do, don't stand up in front of reasonably smart people and try to convince them that some crazy, unbelievably big estimate is the right one just because you used an IV. Chances are that the "too big to be believable" estimate is a direct result of using a bad IV.

For now, let me leave with you another quote...

When You Have Multiple Measures of the Same Thing...

The usual problem when doing empirical work is that you don't have an important variable in your data. Very, very occasionally, you have the opposite problem: Lots of different highly correlated variables measuring pretty much the same thing. What to do? How to choose?

I would say that if the variable is typically measured in a specific way in the (economics) literature, maybe go with that one for your main specification unless you have reason to believe you have a better one. Either way, include a table in the appendix showing that your results are robust across measures. Don't freak out if you fail to see a star for one or two of them. This will happen if you have enough different measures and not a large enough sample size.

Another good option to choosing just one for your main specification is to include a summary measure of all of the different measures. Of course, many ways to create summary measures. Have a look here for a nice discussion of this.

And Now For Cool Migration Data

Here you go! Enjoy! Policy data for 45 countries, wow!