Mitsuru (Michi) Igami studies strategic industry dynamics. He is an associate professor at Yale Department of Economics and serves as a co-editor of the International Journal of Industrial Organization.

FIELDS: Industrial Organization, Economics of Innovation, International Trade.

CURRENT INTERESTS: (i) innovation, (ii) mergers, (iii) collusion, (iv) high-dimensional data.


Curriculum Vitae: pdf

Research Statement (August 2021): pdf

Upcoming presentations:

KU Leuven "Data & Algorithms for ST&I Studies" conference (9/23/2021)

Düsseldorf (10/5/2021)

Brown (10/7/2021)

Cornell (10/27/2021)

Northwestern Kellogg (11/10/2021)


  1. "Measuring the Incentive to Collude: The Vitamin Cartels, 1990–1999," with Takuo Sugaya (August 18, 2021), accepted, the Review of Economic Studies. Do mergers help or hinder collusion? This paper studies the stability of the vitamin cartels in the 1990s and presents a repeated-games approach to quantify "coordinated effects" of a merger. We use data and direct evidence from American courts and European agencies to show the collusive incentive of the short-lived vitamin C cartel was likely to be negative when it actually collapsed in 1995, whereas the incentives of the long-lived cartels (vitamins A and E, and beta carotene) were unambiguously positive until the prosecution in 1999. Simulations suggest some mergers could have prolonged the vitamin C cartel, but others could have further destabilized it, because both the direction and magnitude of coordinated effects depend not only on the number of firms but also on their cost asymmetry.

  2. "Privatization and Productivity in China," with Yuyu Chen, Masayuki Sawada, and Mo Xiao (April 8, 2021), accepted, the RAND Journal of Economics. We study how ownership affects productivity. Privatization of state-owned enterprises (SOEs) was a major economic reform during China's rapid growth, but its true impact remains controversial. Although private firms seem more productive than SOEs, the government selectively privatized (or liquidated) non-performing SOEs. Because privatization/liquidation takes time to implement, we exploit a lag structure in the timing of ownership changes to address this selection problem. Results suggest private firms are 53% more productive than SOEs on average, but the benefits of privatization take several years to fully materialize. This productivity gap is smaller among larger firms and in economically more liberal times and places; it is larger in consumer-facing and high-tech industries.

  3. "Mergers, Innovation, and Entry-Exit Dynamics: Consolidation of the Hard Disk Drive Industry, 1996–2016," with Kosuke Uetake, the Review of Economic Studies, 87:6 (November, 2020), 2672–2702. How far should an industry be allowed to consolidate when competition and innovation are endogenous? We develop a stochastically alternating-move game of dynamic oligopoly, and estimate it using data from the hard disk drive industry, in which a dozen global players consolidated into only three in the last 20 years. We find plateau-shaped equilibrium relationships between competition and innovation, with heterogeneity across time and productivity. Our counterfactual simulations suggest the current rule-of-thumb policy, which stops mergers when three or fewer firms exist, strikes approximately the right balance between pro-competitive effects and value-destruction side effects in this dynamic welfare tradeoff.

  4. "Artificial Intelligence as Structural Estimation: Deep Blue, Bonanza, and AlphaGo," the Econometrics Journal, 23:3 (September, 2020), S1S24 (Special Issue on the Conference on Dynamic Structural Models and Machine Learning). This article clarifies the connections between certain algorithms to develop artificial intelligence (AI) and the econometrics of dynamic structural models, with concrete examples of three 'game AIs'. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust’s nested fixed-point (NFXP) method. AlphaGo’s 'supervised-learning policy network' is a deep-neural-network implementation of the conditional-choice-probability (CCP) estimation reminiscent of Hotz and Miller's first step; the construction of its 'reinforcement-learning value network' is analogous to their conditional choice simulation (CCS). I then explain the similarities and differences between AI-related methods and structural estimation more generally, and suggest areas of potential cross-fertilization.

  5. "Patent Statistics as an Innovation Indicator? Evidence from the Hard Disk Drive Industry," with Jai Subrahmanyam, the Japanese Economic Review, 70:3 (September 2019), 308–330 (Special Issue on Industrial Organization edited by Reiko Aoki and Luis Cabral). We assess the usefulness of patent statistics as an indicator of innovation, using a direct measure of innovation in the hard disk industry (1976–98). Three findings emerge: (1) Patents "predict" innovations better than a random guess, and a simple refinement makes them more useful; (2) conditional on actually innovating, conglomerates and larger firms patent more than specialized startups and smaller firms; and (3) patent reforms seem to make the patent-innovation relationship nonstationary. These results suggest researchers should use caution when comparing patents of different types of firms and across years.

  6. "Industry Dynamics of Offshoring: The Case of Hard Disk Drives," American Economic Journal: Microeconomics, 10:1 (February 2018), 67–101. [Video by AEA's Diana Schoder]. This paper uncovers a novel pattern of offshoring dynamics in a high-tech industry, and proposes a structural model to explain it. Specifically, the hard disk drive industry (1976–98) witnessed massive waves of entry, exit, and the relocation of manufacturing plants to low-cost countries, in which shakeouts occurred predominantly among home firms and almost all survivors were offshore firms. I build and estimate a dynamic offshoring game with entry/exit to measure the benefits and costs of offshoring, investigate the relationship between offshoring and market structure, and assess the impacts of hypothetical government interventions.

  7. "Estimating the Innovator’s Dilemma: Structural Analysis of Creative Destruction in the Hard Disk Drive Industry, 1981–1998," the Journal of Political Economy, 125:3 (June 2017), 798–847. [SSRN working paper version]. This paper studies strategic industry dynamics of creative destruction in which firms and technologies experience turnover. Theories predict cannibalization between existing and new products delays incumbents’ innovation, whereas preemptive motives accelerate it. Incumbents’ cost (dis)advantage relative to that of entrants would further reinforce these tendencies. To empirically assess these three forces, I develop and estimate a dynamic oligopoly model using a unique panel data set of hard disk drive (HDD) manufacturers (1981–98). The results suggest that despite strong preemptive motives and a substantial cost advantage over entrants, cannibalization makes incumbents reluctant to innovate, which can explain at least 57% of the incumbent-entrant innovation gap. I then assess hypothetical policy interventions concerning broad patents and license fees, and find the industry’s welfare trajectory difficult to outperform.

  8. "Unobserved Heterogeneity in Dynamic Games: Cannibalization and Preemptive Entry of Hamburger Chains in Canada," with Nathan Yang, Quantitative Economics, 7:2 (July 2016), 483–521. We develop a dynamic entry model of multi-store oligopoly with heterogeneous markets, and estimate it using data on hamburger chains in Canada (1970–2005). Because more lucrative markets attract more entry, firms appear to favor the presence of more rivals. Thus unobserved heterogeneity across geographical markets creates an endogeneity problem and poses a methodological challenge in the estimation of dynamic games, which we address by combining the procedures proposed by Kasahara and Shimotsu (2009), Arcidiacono and Miller (2011), and Bajari, Benkard, and Levin (2007), respectively. The results suggest the omission of unobserved market heterogeneity attenuates the estimates of competition, and the tradeoff between cannibalization and preemption is an important factor behind the evolution of market structure.

  9. "Market Power in International Commodity Trade: The Case of Coffee," the Journal of Industrial Economics, 63:2 (June 2015), 225–248. [SSRN working paper version]. This paper studies the impact of market power on international commodity prices. I use a standard oligopoly model and exploit historical variations in the structure of the international coffee bean market to assess the impact of a cartel treaty on coffee prices and its global welfare consequences. The results suggest the International Coffee Agreement (ICA, 1965-89) raised its price by 75% above the Cournot-competitive level, annually transferring approximately $12 billion from consumers to exporting countries, and its lapse in 1989 explains four-fifths of the subsequent price decline, that is, the "coffee crisis."

  10. "Does Big Drive Out Small? - Entry, Exit, and Differentiation in the Supermarket Industry," the Review of Industrial Organization, 38:1 (January 2011), 1-21. This paper measures the impact of the entry of large supermarkets on incumbents of various sizes. Contrary to the conventional notion that big stores drive small rivals out of the market, data from Tokyo in the 1990s show that large supermarkets' entry induces the exit of existing large and medium-size competitors, but improves the survival rate of small supermarkets. These findings highlight the role of store size as an important dimension of product differentiation. Size-based entry regulations would appear to protect big incumbents, at the expense of small incumbents and potential entrants.


  1. "Detecting Edgeworth Cycles," with Timothy Holt and Simon Scheidegger (October 11, 2021). We propose algorithms to detect "Edgeworth cycles," asymmetric price movements that have caused antitrust concerns in many countries. We formalize four existing methods and propose six new methods based on spectral analysis and machine learning. We evaluate their accuracy in station-level gasoline-price data from Western Australia, New South Wales, and Germany. Most methods achieve high accuracy in the first two, but only a few can detect nuanced cycles in the third. Results suggest whether researchers find a positive or negative statistical relationship between cycles and markups, and hence their implications for competition policy, crucially depends on the choice of methods.

  2. "Mapping Firms' Locations in Technological Space: A Topological Analysis of Patent Statistics,'' with Emerson G. Escolar, Yasuaki Hiraoka, and Yasin Ozcan (October 13, 2021), submitted. Where do firms innovate? Mapping their locations and directions in technological space is challenging due to its high dimensionality. We propose a new method to characterize firms’ inventive activities via topological data analysis (TDA) that represents high-dimensional data in a shape graph. Applying this method to 333 major firms’ patents in 1976–2005 reveals substantial heterogeneity: some firms remain undifferentiated; others develop unique portfolios. Firms with unique trajectories, which we define graph-theoretically as “flares” in the Mapper graph, perform better. This association is statistically and economically significant, and continues to hold after we control for portfolio size and firm survivorship. We then compare our approach with existing techniques to further demonstrate its use in data visualization and exploration. [Available on SSRN as well]