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:

What Does a Difs in Difs Estimate Actually Tell Us?

Most differences in differences papers these days have a treatment that turns on and/or off at different times. A specific group can be in the treated category or the control category depending on the year. What does this imply for our interpretation of a differences in differences estimate? Does this matter? Maybe not if a particular policy has one causal impact all the time, no matter what, but if the impact of a policy changes over time (and most things in life do change), then this matters. For a detailed and formal discussion of the issues, see Andrew Goodman-Bacon's NBER working paper, "Difference-in-Differences with Variation in Treatment Timing." For an easy (and fun!) to read explanation, see his twitter thread. 

For all authors of econometrics/econometrics-y papers, I'd love to read more twitter threads like Andrew's describing the main ideas of your work. And if you can insert a gif in between, that just makes everything more enjoyable. 

Cool Data Alert: Minimum Wages

Do you need to get started on your third year paper? Are you interested in studying the impacts of minimum wage changes? Well, have I got the data set for you! This data set tracks effective minimum wage rates across U.S. states from January 1, 2011 to January 1, 2018. Perhaps even cooler, the data set also contains information on the legislative histories behind each change (for example, when it was passed by the legislature and signed by the governor). For details, see this IZA Discussion Paper. Thank you, Michael R. Strain and coauthors, for putting this together.

 

Stata Trick: Are You Doing RD?

Then download this package and you'll have everything you need to do all of the analysis!