Best Stata Command Ever! ..at Least the Most Motivating!

You're in a slump. You have no idea how to respond to a referee. You're not sure whether you should completely abandon the project. Maybe the entire career plan. What should you do? Open up Stata (if it's not already open). Type this:

ssc install motivate
motivate

After it's installed, you can just type "motivate" into Stata on an as needed basis.

Thank you, Kabira Namit, for bringing this to the world.

How to Answer Questions During Seminar or Job Talk

This is an excellent twitter thread written by Michael Kraus, a psychologist at Yale. 

I will summarize the basic idea (and of course adding my own opinions while I'm at it). The ability to answer questions during a job talk is very important. The best way to prepare to answer questions is to..make sure you know the answers to the questions. Give as many practice job talks as you can to many different people so that the questions do not come as a surprise. Some questions come up often. Consider answering those within the talk. If that isn't possible, be sure to have a well-prepared answer to those (maybe a link in your slides to the answer). Also, know the background of  your topic. If you're studying the impact of a particular policy, know the details of that policy. Have a quick look at the most closely related papers on the night before your talk so you know the literature. 

Even if you do all of these things perfectly, there will still be questions you can't answer.  My favorite piece of advice from the thread: 

..it is okay to say you don't have an answer to a question--your data can't possibly be comprehensive enough to answer every important question. But, don't stop with "my data can't speak to that." Add what data you'd need, the analysis you might do, or the study you'd design.

Career Advice for Graduate Students and Folks on the Tenure Track

This PowerPoint was written by Amy Catalinac to show women how to overcome barriers in political science departments. The advice is excellent for women and men in economics. Many of the things she mentions I have heard before but always good to be reminded over and over again. 

My favorite advice (this one is on how to get good letters of recommendation): 

Jump at every opportunity offered, if it increases exposure to you/your work or involves an experience that advisors can write about. Comes at a bad time? The quicker you get used to that, the better! Feel you’re not capable? Then you must say yes. People who only do things they’re comfortable doing won’t reach their potential. 

I also really like the advice about framing the paper. Many graduate students believe that the hard work is doing the data analysis and preparing those tables. Yes, that is absolutely important, but it also takes A LOT of time, energy, and thought to understand the significance of the numbers in those tables. It is your job to make that significance "obvious" to the readers. The tables are not enough. 

My advice for third year paper writers at UConn: Do not wait until the week before the deadline to start writing up results. Writing is hard work. You can always do more data analysis after you have a draft of the introduction written.  My bet is that the process of writing will inspire really nice ideas for further data analysis. 

Measurement Error and Attenuation Bias

I know, I know. It's been a while since I've posted anything. Crazy-busy semester. Again. I'll be back in action soon, but in the meantime have a look at this amazing animation showing why (classical) measurement error leads to attenuation bias. It looks like you can even make the animation yourself in R. Thank you, Lionel Page, for tweeting about this and Maarten van Smeden for making it. 

It always made sense to me: Random noise has zero correlation with anything. As you add more noise to a variable, you'll get closer to that zero correlation. But to see it in an animation, that's just really cool. 

PhD Economists in Tech

Last week I had a student in my office telling me that Amazon may be his dream job. I told another student to at least look at the non-academic job postings because there can be some really interesting work outside of academia. Definitely worth taking a peek. 

It struck me, however, that I am really not sure how students should prepare for non-academic jobs in general and tech jobs in particular...well, besides writing an amazing dissertation. Good news: Susan Athey and Michael Luca just put out a new NBER working paper explaining the whole thing. My big takeaway: there is a wide variety of really interesting work done by PhD economists at tech companies. In terms of how to prepare, I will copy-paste from the article: 

"While PhD economists are well suited to tech careers in many ways, we also see areas for the field to improve the preparation of PhD economists for working with or in tech companies. First, with the importance of prediction, targeting, and precise estimates in tech companies, machine learning plays an important role in tech companies. While the field of economics has long been a leader in causal inference, the field is still in the process of incorporating machine learning into its standard toolkit. Second, economists have historically received less training, relative to computer scientists, at coding and at optimizing code to run statistical algorithms at large scale. Investing in these skills (and incorporating them into the PhD curriculum) can help to prepare economists to work in this area. At the same time, it remains important that economists have a strong conceptual understanding of economic issues like incentives and equilibrium effects, as well as strong empirical skills in the areas such as causal inference that we have described in this paper." (Athey and Luca 2018).

You might also want to have a look at this list of the 25 best companies for perks and benefits. 

Also, write an amazing job market paper. 

How Many Papers Should You Be Working On?

Answer: 6.

In case you don't believe me, here is the discussion on AEA's discussion board Econspark. My additional advice: You should probably devote more time to your most promising papers, but another thing to keep in mind is that if you've started a project that someone else is likely to also work on (because, for example, it is an obvious next step in the literature and the data is freely available), then you may want to get that paper through to the draft stage fairly quickly. 

H/T: David Mackenzie. Again. 

What Does Your Functional Form Say About You?

The folks at xkcd spell it out for us: