Abstract
This paper explores the relationship of Walras’s work to a particularly influential tradition of general equilibrium, that associated with the Cowles Commission for Research in Economics in Colorado in the 1930s and at the University of Chicago from 1939 to 1955, and its successor, the Cowles Foundation, at Yale University from 1955. Irving Fisher introduced general equilibrium analysis into North America with his 1891 Yale dissertation Mathematical Investigations in the Theory of Value and Prices (published 1892) and was responsible in 1892 for the first English translation of a monograph by Walras. Fisher was only able to obtain copies of books by Walras and Edgeworth when his thesis was almost ready for submission, discovering that he had independently reinvented a general equilibrium approach already developed by others, but went beyond Walras in constructing a hydraulic mechanism to simulate computation of general equilibrium and, before Pareto, in using indifference curves. Fisher was closely involved with Alfred Cowles in the Cowles Commission, the Econometric Society and Econometrica in the 1930s, promoting formal mathematical and statistical methods in economics, including drawing attention to the contributions of Walras, Edgeworth and Pareto. The first substantial, systematic work on general equilibrium at the Cowles Commission was in international trade, by Theodore Yntema, research director of the Cowles Commission from 1939 to 1942 and author of A Mathematical Reformulation of the General Theory of International Trade (1932) and by Yntema’s student, Jacob Mosak, author of General Equilibrium Theory in International Trade (1944). A subsequent, much better-known body of work on existence of general equilibrium at Cowles was by Kenneth Arrow and Gerard Debreu (initially independently but leading to a major joint publication) and by Lionel McKenzie, all three associated with the Cowles Commission in Chicago in the early 1950s. After Cowles moved to Yale, the focus of general equilibrium research at the Cowles Foundation was Herbert Scarf’s pioneering work on computable general equilibrium (which he linked to Fisher’s earlier attempt, first presenting his approach in his contribution to Ten Economic Studies in the Tradition of Irving Fisher, 1967). Fisher and then the Cowles Commission were the channel through which Walrasian general equilibrium analysis entered North American economics. This paper is part of a larger history of the Cowles Commission and Foundation, commissioned by the Cowles Foundation.
Presented at the 10th conference of the International Walras Association, University of Lausanne, 13-14 September 2019. I thank Amanar Akhabbar, Annie L. Cot, Cléo Chaussonery-Laïgouche and Harro Maas for helpful comments at the conference, and Daniel Sarech for his presentation which drew my attention to the writings of Firmin Oulès.
A key part of decentralized consensus protocols is a procedure for random selection, which is the source of the majority of miners cost and wasteful energy consumption in Bitcoin. We provide a simple economic model for random selection mechanism and show that any PoW protocol with natural desirable properties is outcome equivalent to the random selection mechanism used in Bitcoin.
Bitcoin’s main innovation lies in allowing a decentralized system that relies on anonymous, profit driven miners who can freely join the system. We formalize these properties in three axioms: anonymity of miners, no incentives for miners to consolidate, and no incentive to assuming multiple fake identities. This novel axiomatic formalization allows us to characterize which other protocols are feasible: Every protocol with these properties must have the same reward scheme as Bitcoin. This implies an impossibility result for risk-averse miners: no protocol satisfies the aforementioned constraints simultaneously without giving miners a strict incentive to merge. Furthermore, any protocol either gives up on some degree of decentralization or its reward scheme is equivalent to Bitcoin’s.
This paper studies, in a two period model, the effects of knowledge spillovers among product market competitors on R&D levels. It argues that when firms' R&D decisions are strategic complements, in industries in which spillovers increase the marginal productivity of a firm's R&D, both incoming and outgoing spillovers spur R&D in equilibrium. Outgoing spillovers can foster innovation even in a homogeneous-product industry. In these industries, the IP law should be such that facilitates knowledge diffusion. If firms have power in deciding the level of knowledge spillovers, we show that a firm will choose to disclose its knowledge to its product market competitors.
We explore conclusions a person draws from observing society when he allows for the possibility that individuals' outcomes are affected by group-level discrimination. Injecting a single non-classical assumption, that the agent is overconfident about himself, we explain key observed patterns in social beliefs, and make a number of additional predictions. First, the agent believes in discrimination against any group he is in more than an outsider does, capturing widely observed self-centered views of discrimination. Second, the more group memberships the agent shares with an individual, the more positively he evaluates the individual. This explains one of the most basic facts about social judgments, in-group bias, as well as "legitimizing myths" that justify an arbitrary social hierarchy through the perceived superiority of the privileged group. Third, biases are sensitive to how the agent divides society into groups when evaluating outcomes. This provides a reason why some ethnically charged questions should not be asked, as well as a potential channel for why nation-building policies might be effective. Fourth, giving the agent more accurate information about himself increases all his biases. Fifth, the agent is prone to substitute biases, implying that the introduction of a new outsider group to focus on creates biases against the new group but lowers biases vis a vis other groups. Sixth, there is a tendency for the agent to agree more with those in the same groups. As a microfoundation for our model, we provide an explanation for why an overconfident agent might allow for potential discrimination in evaluating outcomes, even when he initially did not conceive of this possibility.
The paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople- a “hunter” segment that is more efficient in loan acquisition and a “farmer” segment that is more efficient in loan collection. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation, but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain.
We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market in which firms and consumers can tailor their choices to the demand data. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that can reduce the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.
A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that reduces the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and show that it preserves the privacy of the consumers’ identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows large.