Why do technology companies hire economists, and what is their contribution? What kinds of problems do they work on? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights.
Answer by Susan Athey, Economics of Technology Professor, Stanford GSB; Boards: Expedia, Ripple, on Quora:
This is a great time for economists in tech companies -- the most interesting firms in Silicon Valley are hiring chief economists as well as economic teams at a very rapid clip. Every week, I am contacted to help fill a position, or I hear about a new hire by firms like AirBnb, Netflix, Pandora, Uber, etc. Each tech company, and each chief economist, is different, but there are several main categories.
First are microeconomic issues involved in pricing and product design. Hal Varian was one of the first tech firm chief economists, and by his account, he worked on the AdWords auction in the early days. At Yahoo! and Microsoft, economists (including myself) worked a lot with online advertising marketplaces.
Personally, I was involved in many aspects of marketplace design for search advertising, from changes in auction design, to the design of metrics and measures that better reflect the platform's long term objectives, to strategy. Generally, technology firms have fairly novel and complex pricing and product design issues. A nice research paper that illustrates some of the issues involved is by Dinerstein, Einav, Levin, and Sundaresan. Here, the way that eBay ranks products in response to a consumer search affects which sellers get more business as well as the extent of price competition. One of my papers (with Denis Nekipelov) models advertiser behavior and looks at the impact of algorithm changes on welfare. Other interesting microeconomic issues arise in the context of on-demand marketplaces like AirBnB and Uber. Former Stanford students Andrey Fradkin (MIT), Chiara Farronato (HBS), and Zoe Cullen (graduating this year) have written interesting research papers that highlight some of the market design issues encountered by tech firms at AirBnB and TaskRabbit, including the role of search frictions for both buyers and sellers; Mike Luca at HBS has analyzed a number of issues with Yelp, and he works closely with them.
Second is corporate strategy. I worked on the Microsoft-Yahoo! search alliance, including understanding the effect of combining the user base and advertising platform. Microeconomists are generally experts at understanding strategy as well as questions about market equilibrium. We can provide a framework for understanding what industry structures may be sustainable, versus not, and think through incentives for acquisitions and vertical integration.
Third is public policy. Tech firms are interested in issues like broadband access. Simon Wilkie, former chief economist at FCC and professor at USC, works on this issue at Microsoft. Policies around intellectual property, privacy, data security, etc. all have a great role for economic analysis and an understanding of costs and benefits. Many public policy forums include economists and lawyers in decision-making, and economic research can inform these issues as well.
Fourth, and closely related, are direct legal and regulatory challenges -- antitrust/competition policy issues and regulatory investigations. There have been numerous high-profile antitrust investigations against Google in every part of the world; the e-Book case is another big one. In-house economists can directly inform regulators and also help outside economic experts learn about the institutional facts, access data, and become informed.
More junior economists have a wide variety of roles in tech firms. They can take traditional data science roles, be product managers, work in corporate strategy, or on policy teams. They would typically do a lot of empirical work.
In terms of complementing existing non-economist workers, I have found that economists bring some unique skills to the table.
First of all, machine learning or traditional data scientists often don't have a lot of expertise in using observational data or designing experiments to answer business questions. Did an advertising campaign work? What would have happened if we hadn't released the low end version of a product? Should we change the auction design? Machine learning is better at prediction, but less at analyzing "counter-factuals," or what-if questions. (I'm currently doing a lot of research on modifying machine learning methods to make them more suitable for causal inference -- you can search for some of my papers on arxiv.org).
Secondly, economists are trained to think about equilibrium or feedback effects. There are many decisions in marketplace or platform businesses where the short term and long term effects of a change go in opposite directions. If you stop charging people to post pictures on eBay, conversion rates and customer satisfaction and transaction volume rise -- but that requires sacrificing a large revenue stream. If you tighten your criteria for how well a search ad matches a user query, you lose money in the short run, but in the long run the advertisers should bid more for the increased conversion rates of the ad clicks they do receive. Economists often focus on these types of problems which can be ignored or minimized by engineers.
Overall adding one economist to a team can bring a really valuable alternative perspective, and I'm not at all surprised that all the top tech firms are hiring them!
Economists do face challenges, however. Since they are often in the minority, they have to learn to speak the "native" language (engineering, MBA-speak, legalese), and in tech firms the fact that an idea is well-accepted or standard in economics doesn't get you anywhere. Economists have to make their ideas convincing from first principles, and they also need to learn how to operate in a technical environment to carry out empirical work. For me, that meant learning large-scale computing, engineering platforms and development tools, machine learning, and all of the nitty gritty around developing metrics and A/B testing platforms. It was fascinating, but required a large investment, and not everyone can succeed in that environment. I certainly loved every minute of what I learned, and indeed it sparked not just one but multiple new research agendas for me.