Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods

This paper has been replaced with http://digitalcommons.ilr.cornell.edu/ldi/37. We consider the problem of the public release of statistical information about a population–explicitly accounting for the public-good properties of both data accuracy and privacy loss. We first consider the implications of adding the public-good component to recently published models of private data publication under differential privacy guarantees using a Vickery-Clark-Groves mechanism and a Lindahl mechanism. We show that data quality will be inefficiently under-supplied. Next, we develop a standard social planner’s problem using the technology set implied by (ε, δ)-differential privacy with (α, β)-accuracy for the Private Multiplicative Weights query release mechanism to study the properties of optimal provision of data accuracy and privacy loss when both are public goods. Using the production possibilities frontier implied by this technology, explicitly parameterized interdependent preferences, and the social welfare function, we display properties of the solution to the social planner’s problem. Our results directly quantify the optimal choice of data accuracy and privacy loss as functions of the technology and preference parameters. Some of these properties can be quantified using population statistics on marginal preferences and correlations between income, data accuracy preferences, and privacy loss preferences that are available from survey data. Our results show that government data custodians should publish more accurate statistics with weaker privacy guarantees than would occur with purely private data publishing. Our statistical results using the General Social Survey and the Cornell National Social Survey indicate that the welfare losses from under-providing data accuracy while over-providing privacy protection can be substantial

Getting Time Off: Access to Leave Among Working Parents

"Mommy, I don't feel good." "Honey, it's time to go to the hospital." When working parents, or parents-to-be, hear these phrases, their anxiety levels often increase. Not only because their children are sick or their partners are in labor, but also because they will have to find a way to keep their jobs while tending to their families' needs. Maternity and paternity leave, along with vacation, sick leave, and personal leave, help workers balance their responsibilities at home and at the office. This brief uses new data from the National Survey of America's Families (NSAF) to portray which working parents have access to paid and maternity/paternity leave.Although federal law guarantees job-protected, unpaid family leave to many workers, only three in five American workers are eligible to take this leave (Cantor et al. 2001). Moreover, no state or federal legislation requires employers to provide paid leave of any kind. Because access to leave is not universal, some caregivers do not fully realize the benefits of job-protected leave, namely job security and some flexibility to care for children.This analysis examines whether access to leave differs by socioeconomic characteristics. The data suggest that the majority of working parents can take maternity or paternity leave from their jobs. Although access to maternity/paternity leave varies with measures of economic well-being, it is much more equal than access to paid leave. Most poor workers, working welfare recipients, and working recent welfare leavers cannot take paid leave from their jobs. And those who can take paid leave typically have fewer days of paid leave than nonpoor workers or workers with no recent welfare experience. The relatively even distribution of access to maternity and paternity leave, compared with the uneven access to paid leave, could be an effect of the Family and Medical Leave Act of 1993 (FMLA)

Equilibrium and Learning in Fixed-Price Data Markets with Externality

We propose modeling real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers they please, as a simultaneous-move game between the buyers. A key component of this model is the negative externality buyers induce on one another due to purchasing similar data, a phenomenon exacerbated by its easy replicability. In the complete-information setting, where all buyers know their valuations, we characterize both the existence and the quality (with respect to optimal social welfare) of the pure-strategy Nash equilibrium under various models of buyer externality. While this picture is bleak without any market intervention, reinforcing the inadequacy of modern data markets, we prove that for a broad class of externality functions, market intervention in the form of a revenue-neutral transaction cost can lead to a pure-strategy equilibrium with strong welfare guarantees. We further show that this intervention is amenable to the more realistic setting where buyers start with unknown valuations and learn them over time through repeated market interactions. For such a setting, we provide an online learning algorithm for each buyer that achieves low regret guarantees with respect to both individual buyers' strategy and social welfare optimal. Our work paves the way for considering simple intervention strategies for existing fixed-price data markets to address their shortcoming and the unique challenges put forth by data products

Animal welfare and human ethics: a personality study

We elicit concern for animal welfare in an incentivized, direct and real setup that allows us to separate genuine interest in animal welfare from confounding factors like advertisement, replacement arguments or image concerns. Subjects choose between intensive farming and organic living conditions for a laying hen. Opting for better living conditions is costly, but guarantees better food, daylight, and more space to the hen. Hence subjects have to trade off a selfish benefit (money) against the welfare of a hen. Our data shed light on a long-standing philosophical debate about the relationship between animal welfare and human ethics. We confirm that subjects with higher interests in the hen's well-being exhibit higher moral standards towards humans. Supporters of intensive farming are significantly less prosocial and open-minded, and more Machiavellian than others

Bicriteria Multidimensional Mechanism Design with Side Information

We develop a versatile new methodology for multidimensional mechanism design that incorporates side information about agent types with the bicriteria goal of generating high social welfare and high revenue simultaneously. Side information can come from a variety of sources -- examples include advice from a domain expert, predictions from a machine-learning model trained on historical agent data, or even the mechanism designer's own gut instinct -- and in practice such sources are abundant. In this paper we adopt a prior-free perspective that makes no assumptions on the correctness, accuracy, or source of the side information. First, we design a meta-mechanism that integrates input side information with an improvement of the classical VCG mechanism. The welfare, revenue, and incentive properties of our meta-mechanism are characterized by a number of novel constructions we introduce based on the notion of a weakest competitor, which is an agent that has the smallest impact on welfare. We then show that our meta-mechanism -- when carefully instantiated -- simultaneously achieves strong welfare and revenue guarantees that are parameterized by errors in the side information. When the side information is highly informative and accurate, our mechanism achieves welfare and revenue competitive with the total social surplus, and its performance decays continuously and gradually as the quality of the side information decreases. Finally, we apply our meta-mechanism to a setting where each agent's type is determined by a constant number of parameters. Specifically, agent types lie on constant-dimensional subspaces (of the potentially high-dimensional ambient type space) that are known to the mechanism designer. We use our meta-mechanism to obtain the first known welfare and revenue guarantees in this setting

The Combinatorial World (of Auctions) According to GARP

Revealed preference techniques are used to test whether a data set is compatible with rational behaviour. They are also incorporated as constraints in mechanism design to encourage truthful behaviour in applications such as combinatorial auctions. In the auction setting, we present an efficient combinatorial algorithm to find a virtual valuation function with the optimal (additive) rationality guarantee. Moreover, we show that there exists such a valuation function that both is individually rational and is minimum (that is, it is component-wise dominated by any other individually rational, virtual valuation function that approximately fits the data). Similarly, given upper bound constraints on the valuation function, we show how to fit the maximum virtual valuation function with the optimal additive rationality guarantee. In practice, revealed preference bidding constraints are very demanding. We explain how approximate rationality can be used to create relaxed revealed preference constraints in an auction. We then show how combinatorial methods can be used to implement these relaxed constraints. Worst/best-case welfare guarantees that result from the use of such mechanisms can be quantified via the minimum/maximum virtual valuation function

YIELD GUARANTEES AND THE PRODUCER WELFARE BENEFITS OF CROP INSURANCE

Crop yield and revenue insurance products with coverage based on actual production history (APH) yields dominate the U.S. Federal Crop Insurance Program. The APH yield, which plays a critical role in determining the coverage offered to producers, is based on a small sample of historical yields for the insured unit. The properties of this yield measure are critical in determining the value of the insurance to producers. Sampling error in APH yields has the potential to lead to over-insurance in some years and under-insurance in other years. Premiums, which are in part determined by the ratio of the APH yield to the county reference yield, are also affected by variations in APH yields. Congress has enacted two measures, yield substitution and yield floors, that are intended to limit the degree to which sampling error can reduce the insurance guarantee and producer welfare. We examine the impact of sampling error and related policy provisions for Texas cotton, Kansas wheat, and Illinois corn. The analysis is conducted using county level yield data from the National Agricultural Statistics Service and individual insured-unit-level yield data obtained from the Risk Management Agency’s insurance database. Our findings indicate that sampling error in APH yields has the potential to reduce producer welfare and that the magnitude of this effect differs substantially across crops. The yield substitution and yield floor provisions reduce the negative impact of sampling error but also bias guarantees upward, leading to increased government cost of the insurance programs.Actual Production History, Crop Insurance, Sampling Error, Yield Guarantee, Production Economics, Risk and Uncertainty,

Approximately Optimal Mechanism Design: Motivation, Examples, and Lessons Learned

Optimal mechanism design enjoys a beautiful and well-developed theory, and also a number of killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major search engines auction off online advertising. There are, however, some basic problems for which the traditional optimal mechanism design approach is ill-suited --- either because it makes overly strong assumptions, or because it advocates overly complex designs. The thesis of this paper is that approximately optimal mechanisms allow us to reason about fundamental questions that seem out of reach of the traditional theory. This survey has three main parts. The first part describes the approximately optimal mechanism design paradigm --- how it works, and what we aim to learn by applying it. The second and third parts of the survey cover two case studies, where we instantiate the general design paradigm to investigate two basic questions. In the first example, we consider revenue maximization in a single-item auction with heterogeneous bidders. Our goal is to understand if complexity --- in the sense of detailed distributional knowledge --- is an essential feature of good auctions for this problem, or alternatively if there are simpler auctions that are near-optimal. The second example considers welfare maximization with multiple items. Our goal here is similar in spirit: when is complexity --- in the form of high-dimensional bid spaces --- an essential feature of every auction that guarantees reasonable welfare? Are there interesting cases where low-dimensional bid spaces suffice?Comment: Based on a talk given by the author at the 15th ACM Conference on Economics and Computation (EC), June 201

Privacy-Preserving Public Information for Sequential Games

In settings with incomplete information, players can find it difficult to coordinate to find states with good social welfare. For example, in financial settings, if a collection of financial firms have limited information about each other's strategies, some large number of them may choose the same high-risk investment in hopes of high returns. While this might be acceptable in some cases, the economy can be hurt badly if many firms make investments in the same risky market segment and it fails. One reason why many firms might end up choosing the same segment is that they do not have information about other firms' investments (imperfect information may lead to `bad' game states). Directly reporting all players' investments, however, raises confidentiality concerns for both individuals and institutions. In this paper, we explore whether information about the game-state can be publicly announced in a manner that maintains the privacy of the actions of the players, and still suffices to deter players from reaching bad game-states. We show that in many games of interest, it is possible for players to avoid these bad states with the help of privacy-preserving, publicly-announced information. We model behavior of players in this imperfect information setting in two ways -- greedy and undominated strategic behaviours, and we prove guarantees on social welfare that certain kinds of privacy-preserving information can help attain. Furthermore, we design a counter with improved privacy guarantees under continual observation