We co-host workshops for researchers with the Center for International Research on the Japanese Economy (CIRJE).

Workshop information:

・The workshops are held online using Zoom (pre-registration required). Some workshops are held both in-person and online.
・All times are Japan Standard Time (JST).
・Unless otherwise mentioned, presentations are in ENGLISH.
・For details and how to participate, please see Center for International Research on the Japanese Economy (CIRJE) Microeconomics Workshop 2022.

Schedule

Future Workshops

[Dec. 2, 2022] Shoya Ishimaru (Hitotsubashi University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/12/2 (Fri) 10:25-12:10

Title: Geographic Mobility of Youth and Spatial Gaps inLocal College and Labor Market Opportunities

Abstract: This paper examines the importance of college and labor market opportunities for thecausal link between childhood locations and adulthood economic outcomes. I develop andestimate a dynamic model of individual choice of whether and where to attend college andwhere to work, accounting for home preferences, spatial search friction, and moving costs. Theestimated model suggests that substantial gaps in college attendance rates and the expectedwages among children from different counties in the US would arise from imperfect mobility incollege and locational decisions, even in the absence of spatial gaps in family background andchildhood neighborhood quality.

[Dec. 12, 2022] Kota Saito (California Institute of Technology)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/12/12 (Mon) 10:25-12:10

Title: Approximating Choice Data by Discrete Choice Models

Abstract: We obtain a necessary and sufficient condition under which random-coefficient discrete choice models such as the mixed logit models are rich enough to approximate any nonparametric random utility models across choice sets. The condition turns out to be very simple and tractable. When the condition is not satisfied and, hence, there exists a random utility model that cannot be approximated by any random-coefficient discrete choice model, we provide algorithms to measure the approximation errors. After applying our theoretical results and the algorithms to real data, we find that the approximation errors can be large in practice.

[Dec. 13, 2022] William Fuchs (The University of Texas at Austin McCombs School of Business and Universidad Carlos III)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/12/13 (Tue) 10:25-12:10

Title: Time Trumps Quantity in the Market for Lemons

Abstract: We consider a dynamic adverse selection model where privately informed sellers of divisible assets can choose how much of their asset to sell at each point in time to competitive buyers. With commitment, delay and lower quantities are equivalent ways to signal higher quality. Only the discounted quantity traded is pinned down in equilibrium. With spot contracts and observable past trades, there is a unique and fully separating path of trades in equilibrium. Irrespective of the horizon and the frequency of trades, the same welfare is attained by each seller type as in the commitment case. When trades can take place continuously over time, each type trades all of its assets at a unique point in time. Thus, only delay is used to signal higher quality. When past trades are not observable, the equilibrium only coincides with the one with public histories when trading can take place continuously over time.

[Dec. 19, 2022] Mihai Manea (Stony Brook University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 2 on the 1st floor of the Kojima Hall

Date & Time: 2022/12/19 (Mon) 10:30-12:00

Title: Bargaining and Exclusion with Multiple Buyers (with Dilip Abreu)

[Dec. 27, 2022] Yuta Toyama (Waseda University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/12/27 (Tue) 10:25-12:10

Title: Welfare Effects of Nonlinear Electricity Pricing with Misperception: A Case of Free Electricity Policy (joint with Ngawang Dendup)

Abstract: This paper evaluates the welfare effects of nonlinear electricity pricing when consumers may have a misperception about the pricing schedule. We focus on a unique electricity subsidy program in Bhutan where electricity is provided for free up to 100 kWh per month. Using administrative billing data from the universe of retail customers, we find a distinctive bunching of consumption at 100 kWh after the introduction of the program. To interpret this observation and derive welfare implications of the policy, we construct and estimate a model of electricity demand for consumers with different types of misperceptions. We identify the composition of consumer types by exploiting the differential behavior of each type near the threshold and the variation of tariff schedule across regions and over time. We then conduct a simulation analysis to evaluate the free electricity policy. The current subsidy scheme benefits households with higher electricity consumption. The welfare loss due to the misperception is about 1%-5% of monthly electricity expenditure. Finally, we investigate the optimal tariff schedule and its welfare and distributional implications.

Past Workshops

[Nov. 8, 2022] Susumu Sato (Hitotsubashi University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/11/8 (Tue) 10:25-12:10

Title: Competition in Two-Sided Markets: an Aggregative-Games Approach

Abstract: This article develops an aggregative-games framework for studying asymmetric platform oligopoly in two-sided markets. Using a model of platform choice that has a unique stable consumption equilibrium, I derive an IIA demand system for two-sided platforms that generalizes multinomial logit models. Then, I represent platform competition as an aggregative game and apply it to three competition analyses: platform dominance, platform mergers, and long-run equilibrium with fringe entry. The dominance of a large platform is associated with a higher consumer surplus on one side only when the consumers benefit from both network effects and two-sided pricing. The merger analysis demonstrates that network effects serve as a synergy but also make large mergers harmful to consumers, and the pre-merger price structure provides useful information on the effects of two-sided pricing. In an equilibrium with fringe entry, any change in competitive environments that benefits consumers on one side hurts consumers on the other side.

[Nov. 1, 2022] Hiroko Okudaira (Doshisha University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/11/1 (Tue) 10:25-12:10

Title: Parental Investment after Adverse Event: Evidence from the Great East Japan Earthquake

Abstract: Parents often increase private investment in their children when they fear the negative effects of an adverse event. However, such an endogenous response makes it difficult to identify the cost of the adverse event and those disadvantaged by the shock. This study investigates the nature of an adverse shock that leads to endogenous responses by parents. Relying on the types of damage caused by the Great East Japan Earthquake, we find that parents exposed to intense ground motion increased their investment in children’s cognitive skills. This positive response survives or becomes even larger after accounting for physical destruction and radioactive contamination.

[Oct. 25, 2022] Makoto Yokoo (Kyushu University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/10/25 (Tue) 10:25-12:10

Title: Matching Market Design with Constraints

Abstract: Two-sided matching deals with finding a desirable combination of two parties, e.g., students and colleges, workers and companies, and medical residents to hospitals. Beautiful theoretical results on two-sided matching have been obtained, i.e., the celebrated Deferred Acceptance mechanism is strategyproof for students, and obtains the student optimal matching among all stable matchings. However, these results are applicable only for the standard model, where only distributional constraints are the maximum quota (capacity limit) of each college. In many real application domains, various distributional constraints are imposed due to social requirements. For example, a college needs a certain number of students to operate, or some medical residents must be assigned to a rural hospital.

In this talk, I represent a simple and general abstract model, and introduce a few representative constraints that can be formalized using this model. In this model, distributional constraints are defined over a set of allocation vectors, each of which describes the number of students allocated to each college. Then, I present two general mechanisms. One is the generalized DA, which works when distributional constraints satisfy two conditions: hereditary and an M-natural-convex set [1]. More specifically, the generalized DA is strategyproof, and finds the student optimal matching among all matchings that satisfy some stability requirement. The other is the adaptive DA [2], which works when distributional constraints satisfy hereditary condition. It is strategyproof and nonwasteful.

[1] Kojima, F., Tamura, A., Yokoo, M.: Designing matching mechanisms under constraints: An approach from discrete convex analysis, Journal of Economic Theory, 176 (2018)
[2] Goto, M., Kurata, R., Kojima, F., Kurata, R., Tamura, A, Yokoo, M.: Designing Matching Mechanisms under General Distributional Constraints, American Economic Journal: Microeconomics, 9 (2):226-62, (2017).

[Oct. 18, 2022] Aniko Öry (Yale School of Management)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/10/18 (Tue) 10:25-12:10

Title: Dynamic Price Competition: Theory and Empirical Evidence From Airline Markets

Abstract: We introduce a model of oligopoly dynamic pricing where firms with limited capacity face a sales deadline. We establish conditions under which the equilibrium is unique and converges to a system of differential quations. Using unique and comprehensive pricing and bookings data for competing U.S. airlines, we estimate our model and find that dynamic pricing results in higher output but lower welfare than under uniform pricing. Our theoretical and empirical findings run counter to standard results in single-firm settings due to the strategic role of competitor scarcity. Pricing heuristics commonly used by airlines increase welfare relative to estimated equilibrium predictions.

[Oct. 11, 2022] Daniel Quint (University of Wisconsin-Madison)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/10/11 (Tue) 10:25-12:10

Title: “Bid Shopping” in Procurement Auctions with Subcontracting 

Abstract: We analyze the equilibrium effects of “bid shopping” – a contractor soliciting a subcontractor bid for part of a project prior to a procurement auction, then showing that bid to a competing subcontractor in an attempt to secure a lower price. Such conduct is widely criticized as unethical by professional organizations, and has been the target of legislation at both the federal and state level, but is widespread in procurement auctions in many places. Our baseline model suggests that bid shopping that brings in new subcontractors after the auction – rather than diverting some existing subcontractors to post-auction competition – always increases social surplus, benefitting the procurer at the expense of the existing subcontractors. Bid shopping that causes some subcontractors to wait to bid until after the auction tends to decease total surplus when subcontractors’ bid preparation costs are low, but may increase total surplus when bid preparation costs are high.

[Oct. 4, 2022] Yingni Guo (Northwestern University)

Date & Time: 2022/10/4 (Tue) 10:25-12:10

Title: Robust Monopoly Regulation

Abstract: We study the regulation of a monopolistic firm using a non-Bayesian approach. We derive the policy that minimizes the regulator’s worst-case regret, where regret is the difference between the regulator’s complete-information payoff and his realized payoff. When the regulator’s payoff is consumers’ surplus, he imposes a price cap. When his payoff is the total surplus of both consumers and the firm, he offers a capped piecerate subsidy. For intermediate cases, the regulator uses both a price cap and a capped piece-rate subsidy. The optimal policy balances three goals: giving more surplus to consumers, mitigating underproduction, and mitigating overproduction. 

[July 26, 2022] Nina Bobkova (Rice University)

Date & Time: 2022/7/26 (Tue) 10:25-12:10

Title: Information Choice in Auctions

Abstract: The choice of an auction mechanism influences which object characteristics bidders learn about and whether the object is allocated efficiently. Some object characteristics are valued equally by all bidders and thus are inconsequential for the efficient allocation. Others matter only to certain bidders, and thus determine the bidder with the highest object value. I show that the efficient auction is the second-price auction: it induces bidders to learn exclusively about object characteristics which matter only to them. An independent private value framework arises endogenously.

[July 5, 2022] Michael Zierhut (Humboldt University)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/7/5 (Tue) 10:25-12:10

Title: Dynamic Inconsistency and Inefficiency of Equilibrium under Knightian Uncertainty

Abstract: This paper extends the theory of general equilibrium with Knightian uncertainty to economies with more than two dates. Agents have incomplete preferences with multiple priors `a la Bewley. These priors are updated in light of new information. Contrary to the two-date model, the market outcomes varies with choice of updating rule. We document two phenomena: First, unless agents apply the full Bayesian rule, consumption decisions may be dynamically inconsistent. Second, unless they apply the maximum-likelihood rule, ambiguous probability mass may dilate, which causes price fluctuations. Either phenomenon results in Pareto inefficient allocations. We ask whether it is possible to design one updating rule that prevent both phenomena. The answer is negative: No such rule exists. Efficiency can be restored by restricting priors: Full Bayesian and maximum-likelihood updates agree when priors are rectangular, and when ambiguity is sufficiently large, all equilibria are Pareto efficient, even if prices and allocations change over time. 

[June 28, 2022] Michihiro Kandori (The University of Tokyo)

  • This seminar was held in-person and online.
    venue (in-person): Seminar Room 1 on the 1st floor of the Kojima Hall

Date & Time: 2022/6/28 (Tue) 10:25-12:10

Title: Machine Learning Approach to Uncover How Players Choose Mixed Strategies (joint work with T. Hirasawa and A. Matsushita)

Abstract: How do humans behave in a situation where (i) one needs to make one’s own behavior unpredictable and (ii) one needs to predict the opponent’s behavior? Such a situation can be formulated as a game with a mixed strategy equilibrium. If humans are put in such a situation, it should be obvious that, rather than calculating and following the mixed equilibrium, they use their intuition, hunch, and some heuristics to achieve the above-mentioned goals (i) and (ii). Exactly what kind of mechanisms are employed has not been fully understood. By using a unique big experimental data set we have collected about a game with mixed strategy equilibrium, which has more than 75,000 observations, we compare conventional behavior economics models and some leading machine learning models to uncover how human behavior is determined in such a situation. Our big data enabled us to obtain a reliable comparison of the prediction powers of those models, and we found that machine learning models, most notably a version of the deep learning model LSTM, substantially outperform the leading behavioral model (EWA). Finally, we try to improve the EWA model by incorporating the insights gained by the machine learning models.

[June 21, 2022] YingHua He (Rice University)

Date & Time: 2022/6/21 (Tue) 10:25-12:10

Title: Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice (joint work with Yeon-Koo Che and Dong Woo Hahm)

Abstract: Recent evidence suggests that market participants make mistakes even in a strategically straightforward environment but seldom with significant payoff consequences. We explore the implications of such payoff-insignificant mistakes for inferring students’ preferences from school-choice data. Uncertainties arise from the use of lotteries or other sources in a typical school choice setting; they make certain mistakes more costly than others, thus making some preferences—those whose misrepresentation would be more costly and would thus be avoided by students—more reliably inferable than others. We propose a novel method of exploiting the structure of the uncertainties present in a matching environment to robustly infer student preferences under the Deferred-Acceptance mechanism. We then apply our methods to estimate student preferences through a Monte Carlo analysis capturing canonical school choice environments with single tie-breaking lotteries, and also to New York City’s high school assignment data. We then evaluate the effects of an affirmative action policy on disadvantaged and non-disadvantaged students.

[June 14, 2022] Hongyao Ma (Columbia Business School)

Date & Time: 2022/6/14 (Tue) 10:25-12:10

Title: Randomized FIFO Mechanisms (joint work with Francisco Castro, Hamid Nazerzadeh and Chiwei Yan)

Abstract: We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders’ waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

[June 7, 2022] Jonathan Libgober (University of Southern California)

Date & Time: 2022/6/7 (Tue) 10:25-12:10

Title: Learning Underspecified Models (joint work with In-Koo Cho)

Abstract: This paper considers optimal pricing with a seller who does not possess a complete description of how actions translate into payoffs. We refer to a problem with this property as underspecified. To save computational costs, she delegates the pricing decision to an algorithm, in every period over an infinite horizon. Not knowing the true demand curve, the algorithm is tasked with ensuring that the optimal price emerges with sufficiently high probability, at a rate that is uniform over the set of possible demand curves. The monopolist views the complexity-profit tradeoff lexicographically, seeking an algorithm with a minimum number of parameters subject to achieving the same long run average payoff. For a large class of feasible demand curves, the optimum is achieved by an algorithms that assumes demand is linear even if it is not. Though misspecified, this saves on computational cost, and still achieves an attractive worst-case learning rate.

[May 31, 2022] Jacob Leshno (The University of Chicago Booth School of Business)

Date & Time: 2022/5/31 (Tue) 10:25-12:10

Title: Price Discovery in Waiting Lists: A Connection to Stochastic Gradient Descent (joint work with Itai Ashlagi, Pengyu Qian, and Amin Saberi)

Abstract: Waiting lists offer agents a choice among types of items and associated non-monetary prices given by required waiting times. These non-monetary prices are endogenously determined by a tâtonnement-like price discovery process: an item’s price increases when an agent queues for it, and decreases when an item arrives and a queuing agent is assigned. By drawing a connection between price adjustments in waiting lists and the stochastic gradient descent optimization algorithm, we show that the waiting list mechanism achieves allocative efficiency minus a loss due to price fluctuations that is bounded by the granularity of price changes. We further consider a price discovery process inspired by the waiting list mechanism and show that this simple price discovery process performs well if the granularity of price changes is chosen to appropriately trade-off the speed of price adaptation and loss from price fluctuations.

[May 24, 2022] Eric Budish (The University of Chicago Booth School of Business)

Date & Time: 2022/5/24 (Tue) 9:00-10:00 

Title: The Economic Limits of Bitcoin and Anonymous, Decentralized Trust on the Blockchain

Abstract: Satoshi Nakamoto invented a new form of trust. This paper presents a three equation argument that Nakamoto’s new form of trust, while undeniably ingenious, is extremely expensive: the recurring, “flow” payments to the anonymous, decentralized compute power that maintains the trust must be large relative to the one-off, “stock” benefits of attacking the trust. This result also implies that the cost of securing the trust grows linearly with the potential value of attack — e.g., securing against a $1bn attack is 1000 times more expensive than securing against a $1m attack. Thus, if Bitcoin is to become significantly more economically useful than it is today, then the cost of maintaining Bitcoin must grow commensurately as well for it to remain trustworthy. A way out of this flow-stock argument is if both (i) the compute power used to maintain the trust is non-repurposable (as has been true for Bitcoin since mid-2013), and (ii) a successful attack would cause the economic value of the trust to collapse. However, vulnerability to economic collapse is itself a serious problem, and the model points to specific collapse scenarios. The analysis thus suggests a “pick your poison” economic critique of Bitcoin and its novel form of trust.

Cancelled [May 10, 2022] Xuan LI (The Hong Kong University of Science and Technology (HKUST))

Date & Time: 2022/5/10 (Tue) 10:25-12:10

Title: TBA

Abstract: TBA

[Apr. 28, 2022] Peng Shi (University of South California)

Date & Time: 2022/4/28 (Thu) 9:00-10:30

Title: Optimal Matchmaking Strategy in Two-Sided Marketplaces

Abstract: Online platforms that match customers with suitable service providers utilize a wide variety of matchmaking strategies: some create a searchable directory of one side of the market (i.e., Airbnb, Google Local Finder); some allow both sides of the market to search and initiate contact (i.e., Care.com, Upwork); others implement centralized matching (i.e., Amazon Home Services, TaskRabbit). This paper compares these strategies in terms of their efficiency of matchmaking, as proxied by the amount of communication needed to facilitate a good market outcome. The paper finds that the relative performance of the above matchmaking strategies is driven by whether the preferences of agents on each side of the market are easy to describe. Here, “easy to describe” means that the preferences can be inferred with sufficient accuracy based on responses to standardized questionnaires. For markets with suitable characteristics, each of the above matchmaking strategies can provide near-optimal performance guarantees according to an analysis based on information theory. The analysis provides prescriptive insights for online platforms.

[Apr. 26, 2022] Youjin Hahn (Yonsei University)

Date & Time: 2022/4/26 (Tue) 10:25-12:10

Title: Can STEM Learning Opportunities Reshape Gender Attitudes for Girls?: Field Evidence from Tanzania (joint work with So Yoon Ahn and Semee Yoon)

Abstract: We study how educational opportunities change adolescents’ gender attitudes in Tanzania, using an experiential education program focused on STEM subjects. After the intervention, girls’ gender attitudes became more progressive by 0.29 standard deviations, but boys’ gender attitudes did not change. Perceived improvement in the labor market opportunities appears to be an important channel to explain the result. The intervention also increased girls’ weekly study hours and boosted their interests in STEM-related subjects and occupations. Our results show that providing STEM-related educational opportunities to girls in developing countries can be an effective way of improving their gender attitudes.

[Apr. 12, 2022] Inga Deimen (University of Arizona)

Date & Time: 2022/4/12 (Tue) 10:25-12:10

Title: Communication in the Shadow of Catastrophe (joint work with Dezsö Szalay)

Abstract: We study the role of risk in strategic information transmission. We show that an increased likelihood of extreme states – heavier tails – decreases the amount of information transmission and makes it optimal to alter the mode of decision-making from communication to simple delegation. Moreover, the worst-case losses under communication increase relative to the worst-case losses under delegation when the tails get heavier.

[Apr. 5, 2022] Giovanni Compiani (The University of Chicago Booth School of Business)

Date & Time: 2022/4/5 (Tue) 10:25-12:10

Title: A Method to Estimate Discrete Choice Models that is Robust to Consumer Search

Abstract: We state suffcient conditions under which choice data suffices to identify preferences when consumers are not fully informed about attributes of goods. Standard estimators undervalue hidden attributes, as consumers will be unresponsive to some variation in those attributes. If consumers search goods in order of the component of utility observable to them without search, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds under both full information and a range of search models and is thus robust to what consumers know when they choose. Our approach can be used to recover preferences from choices made by imperfectly informed consumers, to test for full information, and to forecast how consumers will respond to information. We verify in a lab experiment that our approach succeeds in forecasting the response to new information and assessing the value of that information when consumers engage in costly search. In data from Expedia, our method identifies which attribute was not immediately visible to consumers in search results, and we then use the model to compute the value of information about the hidden attribute.