The 2024 Nobel Prize in Economics will be announced this coming Monday, October 14.
What is your prediction for the winner(s)? And what odds would you give to that outcome?
Alex MacKay
360 posts
Alex MacKay
@_amackay
Economics professor at @UVA. I study pricing capabilities (e.g., algorithms, contracts, etc.), competition, and dynamic consumer behavior.
Joined November 2009
- Some news: This fall, I'll be joining the economics department at the University of Virginia. It is a great pleasure to return "home." I am so thankful for this period, for the wonderful people who work at HBS, and for the broader Boston community.
- New working paper: @HendrikDoepper, @NateHMiller, and @JoelStiebale, and I examine whether product-level markups are increasing over time. We estimate IO-style models with flexible consumer preferences, allowing us to look at potential drivers of change in both supply and demand.
- Consider a market with dominant firm that has superior pricing technology/algorithms. The firm's algorithms allows it to quickly undercut any price change made by rivals. Though this practice may appear "competitive," it could raise prices for all firms. Paper now accepted!Forthcoming in AEJ: Microeconomics: "Competition in Pricing Algorithms" by Zach Y. Brown and Alexander MacKay. aeaweb.org/articles?id=10…
- Coase (1937) observes that a buyer might choose longer contracts over shorter ones to avoid the costs associated with new contracts. These costs may include identifying potential sellers, administering an auction or other mechanism, and drawing up the contract.Forthcoming in AEJ: Microeconomics: "Contract Duration and the Costs of Market Transactions" by Alexander MacKay. aeaweb.org/articles?id=10…
- Pricing algorithms affect the nature of competition in online markets. Using high-frequency data, we show that firms in the same market have different pricing technologies. Firms with superior technology change prices more frequently and react to price changes by slower rivals...
- When estimating demand, the price coefficient may be biased because firms adjust prices in response to demand shocks. In this (forthcoming) paper, @NateHMiller and I show how to resolve this source of endogeneity by invoking the supply side in standard empirical models.Forthcoming in AEJ: Microeconomics: "Estimating Models of Supply and Demand: Instruments and Covariance Restrictions" by Alexander MacKay and Nathan H. Miller. aeaweb.org/articles?id=10…
- New working paper! We examine the adoption of a dynamic pricing algorithm and high-frequency data to address two questions: (1) How do consumers respond to dynamic pricing? and (2) What is the potential for dynamic pricing to reduce costs? ssrn.com/abstract=41642…
- Our paper (with Mark Egan and Hanbin Yang), “Recovering Investor Expectations from Demand for Index Funds,” is accepted at @RevEconStud. We use an IO-style demand estimation approach to recover beliefs about the performance of the stock market and other assets. An overview:
- Are online prices higher because of pricing algorithms? Here we provide a high-level summary of the recent economic literature (see footnotes for papers):
- Announcing a postdoc position at the new Pricing Lab at @HarvardHBS! Open for AY 23-24 to those with (and without) AP offers. You can work with industry data alongside Alberto Cavallo (macro side of the lab) and myself. Here is the microeconomics posting: academicpositions.harvard.edu/postings/12090
- 🚨We are hiring two postdocs for AY 24-25 at the Pricing Lab at @HarvardHBS. The position is open to those with and without AP offers. You can work with industry data alongside @albertocavallo (macro side of the lab) and myself. Link for microeconomics: academicpositions.harvard.edu/postings/12981
- Big thanks to @jeff_gortmaker and @conlon_chris for including the implementation of the covariance restrictions identification strategy in PyBLP. @NateHMiller and I would be happy to hear of any use cases and ways that we can add functionality.If you want to try out this approach in your own BLP estimation, PyBLP has pretty good support! Thanks to @_amackay for helping with getting it added. Adding covariance restrictions was fairly straightforward, with one sort of obscure technical challenge. 1/6
- Total markups that consumers paid (over production costs) were stable from 2019 to 2023, despite rising prices, for a large global consumer product manufacturer. @albertocavallo summarizes our new working paper (w/ @SEAlvarezBlaser and @Paolo_Mengano )Replying to @albertocavallo3/ Key Finding 1: Total markups faced by consumers (retail prices relative to production costs) were stable during the recent inflation surge. Contrary to claims of "greedflation," we do not see any discrete change in total markups, suggesting that the increase in retail prices
