132 research outputs found

    Spectrum auctions: designing markets to benefit the public, industry and the economy

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    Access to the radio spectrum is vital for modern digital communication. It is an essential component for smartphone capabilities, the Cloud, the Internet of Things, autonomous vehicles, and multiple other new technologies. Governments use spectrum auctions to decide which companies should use what parts of the radio spectrum. Successful auctions can fuel rapid innovation in products and services, unlock substantial economic benefits, build comparative advantage across all regions, and create billions of dollars of government revenues. Poor auction strategies can leave bandwidth unsold and delay innovation, sell national assets to firms too cheaply, or create uncompetitive markets with high mobile prices and patchy coverage that stifles economic growth. Corporate bidders regularly complain that auctions raise their costs, while government critics argue that insufficient revenues are raised. The cross-national record shows many examples of both highly successful auctions and miserable failures. Drawing on experience from the UK and other countries, senior regulator Geoffrey Myers explains how to optimise the regulatory design of auctions, from initial planning to final implementation. Spectrum Auctions offers unrivalled expertise for regulators and economists engaged in practical auction design or company executives planning bidding strategies. For applied economists, teachers, and advanced students this book provides unrivalled insights in market design and public management. Providing clear analytical frameworks, case studies of auctions, and stage-by-stage advice, it is essential reading for anyone interested in designing public-interested and successful spectrum auctions

    Vulnerabilities of Single-Round Incentive Compatibility in Auto-bidding: Theory and Evidence from ROI-Constrained Online Advertising Markets

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    Most of the work in auction design literature assumes that bidders behave rationally based on the information available for every individual auction, and the revelation principle enables designers to restrict their efforts to incentive compatible (IC) mechanisms. However, in today's online advertising markets, one of the most important real-life applications of auction design, the data and computational power required to bid optimally are only available to the auction designer, and an advertiser can only participate by setting performance objectives and constraints for its proxy auto-bidder provided by the platform. The prevalence of auto-bidding necessitates a review of auction theory. In this paper, we examine properties of auto-bidding markets through the lens of ROI-constrained value-maximizing campaigns, which are widely adopted in many global-scale online advertising platforms. Through theoretical analysis and empirical experiments on both synthetic and realistic data, we find that second price auction exhibits many undesirable properties (equilibrium multiplicity, computational hardness, exploitability by bidders and auctioneers, instability of bidders' utilities, and interference in A/B testing) and loses its dominant theoretical advantages in single-item scenarios. Some of these phenomena have been identified in literature (for budget-constrained auto-bidders) and widely observed in practice, and we show that they are actually deeply rooted in the property of (single-round) incentive compatibility. Although many complex designs have been proposed in literature, first and second price auctions remain popular in industry. We hope that our work could bring new perspectives to the community and benefit practitioners to attain a better grasp of real-world markets

    Learning in rent-seeking contests with payoff risk and foregone payoff Information

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    We test whether deviations from Nash equilibrium in rent-seeking contests can be explained by the slow convergence of payoff-based learning. We identify and eliminate two noise sources that slow down learning: first, opponents are changing their actions across rounds; second, payoffs are probabilistic, which reduces the correlation between expected and realized payoffs. We find that average choices are not significantly different from the risk-neutral Nash equilibrium predictions only when both noise sources are eliminated by supplying foregone payoff information and removing payoff risk. Payoff-based learning can explain these results better than alternative theories. We propose a hybrid learning model that combines reinforcement and belief learning with risk, social and other preferences, and show that it fits data well, mostly because of reinforcement learning

    The effect of wage proposals on efficiency and income distribution

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    Pre-play non-binding communication in organizations is prevalent. We study the implications of pre-play wage proposals and information revelation in a labour relationship in a laboratory experiment. In the baseline, that depicts a typical labour market interaction, the employer makes a wage offer to the worker who may then accept or reject it. In a subsequent treatment, workers, moving first, make private, non-binding, wage proposals to the employer. Our findings suggest that wage proposals promote higher wages, efficiency, and income equality. We run an additional experiment as a robustness check where we make the wage proposals public. We find that most of the results hold. Similar wage proposals are observed in the Public and Private information treatments, while accepted wages in the public treatment are higher than the baseline and significantly lower than under private information. It seems that workers conform to the available information on the wage of their co-worker from the last period when proposals are public. Interestingly, while both benefit over the baseline, public information on wage proposals benefits firms more than workers. We also develop a theoretical model to rationalize our results. The experimental results provide broad support for our hypotheses

    The Effect of Wage Proposals on Efficiency and Income Distribution☆

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    Pre-play non-binding communication in organizations is prevalent. We study the implications of pre-play, private and public, wage proposals in labor markets. To that end, we develop a theoretical model from which we derive certain hypothesis that we test through a laboratory experiment. In the baseline, that depicts a typical labor market interaction, the employer makes a wage offer to the worker who may then accept or reject it. In subsequent treatments, workers, moving first, make private, non-binding, wage proposals to the employer. In a following treatment, the proposals are made public. Our findings suggest that both private and public wage proposals promote higher wages, efficiency, and income equality. Public information on wage proposals benefits firms more than workers while, workers benefit more under private proposals where income inequality is the lowest. We find some support in our data on workers conforming to their co-workers’ wage proposals when these are public. Finally, the gender gap observed in the baseline on acceptance rates and workers’ income vanishes when proposals are present

    Essays on the econometric analysis of treatment assignment rules and altruistic preferences

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    This dissertation has two main themes: treatment assignment rules and altruistic preferences. The first two chapters are about comparing different treatment assignment rules using observational data. The third chapter studies how altruistic preferences are affected by markets and incentives. In Chapter 1, I develop a theoretical framework to compare different treatment assignment rules. A treatment assignment rule is a mapping from observed characteristics to binary treatment status. The welfare difference between two given treatment assignment rules is not point identified in general when data are obtained from an observational study or a randomized experiment with imperfect compliance. I characterize the sharp identified region of the welfare difference and obtain bounds under various assumptions on the unobservables with and without instrumental variables. I conduct estimation and inference of the bounds using orthogonalized moment conditions to deal with the presence of infinite-dimensional nuisance parameters. In Chapter 2, I apply the method I proposed in Chapter 1 to examine two applications in economics. First, I study the problem of assigning individuals to job training programs. I calculate the welfare differences between different hypothetical policies using experimental data from the National Job Training Partnership Act Study. Second, I apply the method to study public health insurance policies. Specifically, I calculate the welfare impact of Medicaid expansion using data from the Oregon Health Insurance Experiment. Chapter 3 (joint with Ching-to Albert Ma and Daniel Wiesen) studies how altruistic preferences are changed by markets and incentives using a laboratory experiment. Subjects are asked to choose health care qualities for hypothetical patients in monopoly, duopoly, and quadropoly. Prices, costs, and patient benefits are experimental incentive parameters. We combine a theoretical model of strategic interaction with a nonparametric estimation method and find that markets tend to reduce altruism

    Mechanism Design with Moral Bidders

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    A rapidly growing literature on lying in behavioral economics and psychology shows that individuals often do not lie even when lying maximizes their utility. In this work, we attempt to incorporate these findings into the theory of mechanism design. We consider players that have a preference for truth-telling and will only lie if their benefit from lying is sufficiently larger than the loss of the others. To accommodate such players, we introduce α\alpha-moral mechanisms, in which the gain of a player from misreporting his true value, comparing to truth-telling, is at most α\alpha times the loss that the others incur due to misreporting. We develop a theory of moral mechanisms in the canonical setting of single-item auctions. We identify similarities and disparities to the standard theory of truthful mechanisms. In particular, we show that the allocation function does not uniquely determine the payments and is unlikely to admit a simple characterization. In contrast, recall that monotonicity characterizes the allocation function of truthful mechanisms. Our main technical effort is invested in determining whether the auctioneer can exploit the preference for truth-telling of the players to extract more revenue comparing to truthful mechanisms. We show that the auctioneer can extract more revenue when the values of the players are correlated, even when there are only two players. However, we show that truthful mechanisms are revenue-maximizing even among moral ones when the values of the players are independently drawn from certain identical distributions. As a by product we get an alternative proof to Myerson's characterization in the settings that we consider. We flesh out this approach by providing an alternative proof to Myerson's characterization that does not involve moral mechanisms whenever the values are independently drawn from regular distributions
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