ミクロ経済学ワークショップ 2022
Microeconomics Workshop

Companion Workshop

Applied Economics Workshop (AEW)

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.

Using a long-panel data set of Japanese firms that contains firm-level sales forecasts, we provide evidence on firm-level uncertainty and imperfect information over their life cycles. We find that firms make nonnegligible and positively correlated forecast errors. However, they make more precise forecasts and less correlated forecast errors when they become more experienced. We then build a model of heterogeneous firms with endogenous entry and exit where firms gradually learn about their demand by using a noisy signal. We present our novel approach to cleanly isolate the learning mechanism from other mechanisms by using expectations data over time. We combine the model with our data to perform a nonparametric decomposition of the age-declining forecast errors and find that learning accounts for between 20% to 40% of the overall decline in forecast errors observed in our data. Our model shows that, within the context of our cross-regional data, productivity gains from removing information frictions range from 6% to 12%.

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.

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.

This study estimates the impact of exposure to the 2015 Nepal Earthquake on intimate partner violence with two rounds of Demographic and Health Surveys data. Using differences-in-differences estimation, we find that exposure to the earthquake lead to a statistically and economically significant increase in intimate partner violence in the urban areas but not in the rural areas. This is possibly due to an increase in the stress felt by the victims. We also offer some evidence that the impact heterogeneity between the urban and rural areas is attributable to the differences in the reconstruction processes and assistance provided.

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.

We estimate valence measures for candidates running in U.S. House elections from data on vote shares. Our estimates control for endogeneity of campaign spending and sample selection of candidates due to endogenous entry. Our identification and estimation strategy builds on ideas developed for estimating production functions. We find that incumbents have substantially higher valence measures than challengers running against them, resulting in about 9.2 percentage-point differences in the vote share, on average. We find that open-seat challengers have higher valence measures than those running against incumbents by about 5.3 percentage points. Our measure of candidate valence can be used to study various substantive questions of political economy. We illustrate its usefulness by studying the source of incumbency advantage in U.S. House elections.

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.

Economic theory suggests that leaders may play a key roles in enabling social movements to overcome collective action problems through a variety of distinct mechanisms. Empirical tests of these theories outside the lab are scarce due to both measurement and identification challenges. We conduct multiple field experiments to test theories of leadership in the context of Myanmar’s burgeoning labor union movement. We collaborate with a confederation of labor unions as it mobilizes garment workers in the run-up to a national minimum wage negotiation. We present three sets of results. First, we document that union leaders differ from union members and non-members along several traits that economists identify as relevant for political selection and that psychologists have associated with ability to influence collective outcomes, respectively. Second, we randomly embed leaders in group discussions on workers’ preferred and expected minimum wage levels. A leader’s presence in the group improves group engagement and increases workers’ consensus around the unions’ preferred minimum wage levels. Third, we conduct a mobilization experiment in which workers are invited to participate in an< unannounced activity that features strategic complementarity in turnout. Leaders influence
participation through both coordination and social pressure mechanisms rather than by simply motivating workers.

We study settings in which, prior to playing an incomplete information game, players observe many draws of private signals about the state from some information structure. Signals are i.i.d. across draws, but may display arbitrary correlation across players. For each information structure, we define a simple learning efficiency index, which only considers the statistical distance between the worst-informed player’s marginal signal distributions in different states. We show, first, that this index characterizes the speed of common learning (Cripps, Ely, Mailath, and Samuelson, 2008): In particular, the speed at which players achieve approximate common knowledge of the state coincides with the slowest player’s speed of individual learning, and does not depend on the correlation across players’ signals. Second, we build on this characterization to provide a ranking over information structures: We show that, with sufficiently many signal draws, information structures with a higher learning efficiency index lead to better equilibrium outcomes, robustly for a rich class of games and objective functions that are “aligned at certainty.” We discuss implications of ourresults for constrained information design in games and for the question when information structures are complements vs. substitutes.

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.