Alexander MacKay is an assistant professor of business administration in the Strategy Unit. His research focuses on matters of competition, including pricing, demand, and market structure. Previously, he was a postdoctoral scholar at Harvard Business School and at Harvard Kennedy School.
Alexander MacKay is an assistant professor of business administration in the Strategy Unit. His research focuses on matters of competition, including pricing, demand, and market structure. Previously, he was a postdoctoral scholar at Harvard Business School and at Harvard Kennedy School.
Professor MacKay earned his Ph.D. in economics from the University of Chicago. Prior to his doctoral degree, he worked at a consulting firm that specialized in the design and analysis of business experiments. He has a B.A. in economics from the University of Virginia.
The optimal duration of a supply contract balances the costs of reselecting a supplier against the costs of being matched to an inefficient supplier when the contract lasts too long. I develop a structural model of contract duration that captures this tradeoff and provide an empirical strategy for quantifying (unobserved) transaction costs. I estimate the model using federal supply contracts for a standardized product, where suppliers are selected by procurement auctions. The estimated transaction costs are substantially greater than consumer switching costs and a significant portion of total buyer costs. Counterfactuals illustrate the importance of accounting for the duration margin.
We study the dynamics of residential electricity demand by exploiting a natural experiment that produced large and long-lasting price changes in over 250 Illinois communities. Using a flexible difference-in-differences matching approach, we estimate that the price elasticity of demand grows from -0.09 in the first six months to -0.27 two years later. We find similar results with a dynamic model in which usage is a function of past and future prices. Our findings highlight the importance of accounting for consumption dynamics when evaluating energy policy.
This article discusses the empirical challenges that researchers face when demonstrating the existence and effects of resale price maintenance (RPM). We outline three approaches for finding price effects of RPM and the corresponding hurdles in data and methodology. We show that the quantity test that was suggested by Posner (1977; 1981) does not identify the change to welfare when demand-enhancing effects are considered generally. Finally, we present some solutions to the challenge of identifying welfare effects, and we suggest guidelines for future research.
We show that, in general, consistent estimates of cost pass-through are not obtained from reduced-form regressions of price on cost. We derive a formal approximation for the bias that arises even under standard orthogonality conditions. We provide guidance on the conditions under which bias may frustrate inference.
We use a revealed-preference approach to estimate investor expectations of stock market returns. Using data on demand for index funds that follow the S&P 500, we develop and estimate a model of investor choice to flexibly recover the time-varying distribution of expected returns. Our analysis is facilitated by the prevalence of “leveraged” funds that track the same underlying asset: by choosing between higher and lower leverage, investors trade off higher return against less risk. Although generated from a different method (realized choices) and a different population, our quarterly estimates of investor expectations are positively and significantly correlated with the leading surveys used to measure stock market expectations. Our estimates suggest that investor expectations are heterogeneous, extrapolative, and persistent. Following a downturn, investors become more pessimistic on average, but there is also an increase in disagreement among participating investors.
Increasingly, retailers have access to better pricing technology, especially in online markets. Firms employ automated pricing algorithms that allow for high-frequency price changes. What are the implications for price competition? We develop a model of price competition where firms can differ in pricing frequency and choose algorithms that autonomously react to rivals' prices. We demonstrate that pricing technology with these features can increase prices in competitive equilibrium, relative to the standard simultaneous price-setting model. Using high-frequency data from major online retailers, we document asymmetric pricing frequency and pricing patterns consistent with the model. A simple counterfactual simulation implies that pricing algorithms lead to meaningful increases in markups, especially for firms with superior pricing technology.
We study the pricing decision of firms in the presence of consumer inertia. Inertia can arise from habit formation, brand loyalty, switching costs, or search, and it has important implications for the interpretation of equilibrium outcomes and counterfactual analysis. In particular, consumer inertia affects the scope of market power. We show that the effects of competition on prices and profits are non-monotonic in the degree of inertia. Further, a model that omits consumer inertia tends to overstate the marginal effect of competition on price, relative to a benchmark that accounts for consumer dynamics. We develop an empirical model to estimate consumer inertia using aggregate, market-level data. We apply the model to a hypothetical merger of two major retail gasoline companies, and we find that a static model predicts price increases greater than the price increases predicted when accounting for dynamics.
We consider the identification of empirical models of supply and demand. As is well known, a supply-side instrument can resolve price endogeneity in demand estimation. We show that, under common assumptions, two other approaches also yield consistent estimates of the joint model: (i) a demand-side instrument or (ii) a covariance restriction between unobserved demand and cost shocks. The covariance restriction approach can obtain identification even the absence of instruments. Further, supply and demand assumptions alone may bound the structural parameters. We develop an estimator for the covariance restriction approach that is constructed from the output of ordinary least squares regression and performs well in small samples. We illustrate the methodology with applications to ready-to-eat cereal, cement, and airlines.
At the end of 2016, the leadership team of Commonwealth Joe Coffee Roasters—Robert Peck, Chase Damiano, and Jeremy Martin—had begun an ambitious retail expansion strategy in the Washington, D.C. metropolitan area for their specialty coffee business. That October, they had opened their first custom-designed store in a brand-new luxury apartment building. The new store was an immediate success, even though a Starbucks had recently opened across the street. At the same time, a separate business line—selling cold brew coffee in kegs to office customers—had grown unexpectedly. Many new office accounts were added, even as the team promoted the new retail store. Now, as Peck, Damiano, and Martin prepared for the next capital raise, targeted for February 2017, they evaluated their strategy going forward. Should Commonwealth Joe stay with retail, or should they shift their strategy to focus on the office business?
This case describes Uber’s global market entry strategy and responses by regulators and local competitors. It details Uber’s entry into New York City (United States), Bogotá (Colombia), Delhi (India), Shanghai (China), Accra (Ghana), and London (United Kingdom). In each instance, the case includes information about Uber’s strategy in that market, existing regulations on taxis and transportation in each market, the reactions of competitors and regulators, and regional information. The case allows for instruction related to competitive strategy, global expansion, nonmarket strategy, regulation, market economics, supply restrictions, and related topics.
MacKay, Alexander J., Amram Migdal, and John Masko. "Uber: Competing Globally." Harvard Business School Case 720-404, April 2020. (Revised June 2020.)
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Two esports entrepreneurs must choose on which business model to focus their time and money. After successfully launching an online esports coaching platform, a number of new opportunities emerge in the rapidly growing esports space that now has close to one billion gamers and an online audience that is greater than the Superbowl. Should they focus on a B2C business, a B2B business? Develop an analytics capability to support "sabremetrics" for esports or a content creation capability? How can they best exploit ownership of the world's top team in the "Hearthstone" league. The choice will depend on the future industry structure and how the two can most effectively "follow the money."
At the end of 2016, the leadership team of Commonwealth Joe Coffee Roasters—Robert Peck, Chase Damiano, and Jeremy Martin—had begun an ambitious retail expansion strategy in the Washington, D.C. metropolitan area for their specialty coffee business. That October, they had opened their first custom-designed store in a brand-new luxury apartment building. The new store was an immediate success, even though a Starbucks had recently opened across the street. At the same time, a separate business line—selling cold brew coffee in kegs to office customers—had grown unexpectedly. Many new office accounts were added, even as the team promoted the new retail store. Now, as Peck, Damiano, and Martin prepared for the next capital raise, targeted for February 2017, they evaluated their strategy going forward. Should Commonwealth Joe stay with retail, or should they shift their strategy to focus on the office business?
