They incorporate sanity checks and assertions directly into their code. But in econ? Where agreeing to one significant figure is great, and even finding an expected sign is a result? I think you’ll be ok. People will occasionally mutter something vague about ‘ numerical issues’ with Excel, and if you’re doing space science or particle physics that’s fair. Excel is a superb way to prototype analysis. Despite the gasps you’ll hear when you mention linest. The corollary to this is that Excel is not evil. But like foreign languages, the more you know the easier this relearning is.
Whenever I start a project, I have to remind myself of both the syntax and libraries of whatever I’m using. As an economist I’ve used STATA, OxMetrics, SPSS, R and Matlab.
As a computer scientist I’ve used, at various times: C, C++, Java, Basic, COBOL, Python, Perl, Haskell, Prolog, Ruby and various assembly languages. Of course, the downside is that if you use lots of different tools, it’s hard to be an expert in any of them. If that’s Java or C++ then great but if an assortment of unix command line utilities (sort, uniq, grep, awk, sed, wc…) will work, even better. Software engineers and computer scientists, on the other hand, tend to have more of a hacker mindset: use whatever tool will get the job done. Corollary: Excel is not evil.Įconomists tend to be too wedded to particular tools (like STATA), maybe because most undergrad and grad courses teach only one or two econometrics tools (in the graduate classes I taught, I used STATA and Oxmetrics and R - but I’m pretty sure that’s unusual). And since one of my current projects is looking at what new-fangled ‘data scientists’ do, I thought I would assemble here some additional ‘lessons for economists from software engineering’ to complement Gentzkow and Shapiro’s. Social science and computer science is a great combination-it happens to be my background. Though we all write code for a living, few of the economists, political scientists, psychologists, sociologists, or other empirical researchers we know have any formal training in computer science. Via my departmental maillist, this rather useful guide by Matthew Gentzkow and Jesse Shapiro at Chicago: Code and Data for the Social Sciences: A Practitioner’s Guide. Thoughts on: Code and Data for the Social Sciences
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