WAELBROECK Patrick

This article investigates the strategies of a data broker selling information to one or to two competing firms. The data broker combines segments of the consumer demand that allow firms to third-degree price discriminate consumers. We show that the data broker (1) sells information on consumers with the highest willingness to pay. (2) keeps consumers with low willingness to pay unidentified. The data broker strategically chooses to withhold information on consumer demand to soften competition between firms. These results hold under first degree price discrimination, which is a limit case when information is perfect.

Although decision-support algorithms are now part of everyday life and are used in all sectors, especially finance, they are still criticized for their lack of transparency and their biased results. Researchers have studied this issue and make recommendations to reverse the situation.

How much is the timber from public forests worth? How can the Public Forest Service define a fair market price for standing timber lots? What is the cost of low participation in French timber auctions? To estimate the value of a timber lot we adopt the transaction-evidence appraisal approach using data from timber auctions in Lorraine (Eastern France) accounting for the facts that: (i) the seller's reserve prices are secret, (ii) there remain many unsold lots, and (iii) the number of bidders varies from one auction to another. Taking into account the endogenous participation in our hedonic price equation for the highest bid, we estimate that, compared to lots that receive two bids, the highest bid is 22% lower when there is only one bid and 37% higher when there are three or more bids.

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This thesis studies firms that specialize in selling personal information and how their strategies affect competition. In three chapters, we build theoretical models representing the interactions between data brokers specializing in the collection and sale of information, firms buying personal information about their customers, and consumers. We first study the strategies of data brokers in selling information for price targeting of consumers. We show that data brokers do not sell all the information they have, and that a portion of consumers remain unidentified. We then study the strategies of data brokers when they compete in price and quantity of data sold. We show that competition affects consumers in two ways. The more competition there is between brokers, the less information they collect, but the less strategic information retention they engage in. Finally, we consider the reaction of consumers when they can hide from data brokers, and show that the fact that consumers have the ability to hide will induce the broker to sell more consumer data, creating a data externality. These studies help answer important questions about regulating the collection and sale of information, while taking into account a potential active role for consumers in these practices.

We investigate the strategies of a data intermediary selling customized consumer information to firms for price discrimination purpose. We analyze how the mechanism through which the data intermediary sells information influences how much consumer data he will collect and sell to firms, and how it impacts consumer surplus. We consider three selling mechanisms tailored to sell customized consumer information: take it or leave it offers, sequential bargaining, and simultaneous offers. We show that the more data the intermediary collects, the lower consumer surplus. Consumer data collection is minimized, and consumer surplus maximized under the take it or leave it mechanism, which is the least profitable mechanism for the intermediary. We argue that selling mechanisms can be used as a regulatory tool by data protection agencies and competition authorities to limit consumer information collection and increase consumer surplus.

We investigate the strategies of a data intermediary selling customized consumer information to firms for price discrimination purpose. We analyze how the mechanism through which the data intermediary sells information influences how much consumer data he will collect and sell to firms, and how it impacts consumer surplus. We consider three selling mechanisms tailored to sell customized consumer information: take it or leave it offers, sequential bargaining, and simultaneous offers. We show that the more data the intermediary collects, the lower consumer surplus. Consumer data collection is minimized, and consumer surplus maximized under the take it or leave it mechanism, which is the least profitable mechanism for the intermediary. We argue that selling mechanisms can be used as a regulatory tool by data protection agencies and competition authorities to limit consumer information collection and increase consumer surplus.

Algorithms are becoming more and more a part of our daily lives, as in the case of decision support algorithms (recommendation or scoring algorithms), or autonomous algorithms embedded in intelligent machines (autonomous vehicles). Deployed in many sectors and industries for their efficiency, their results are more and more discussed and contested. In particular, they are accused of being black boxes and of leading to discriminatory practices related to gender or ethnicity. The objective of this article is to describe the biases linked to algorithms and to outline ways to remedy them. In particular, we are interested in the results of algorithms in relation to equity objectives, and in their consequences in terms of discrimination. Three questions motivate this article: By what mechanisms can algorithmic biases occur? Can they be avoided? And, finally, can they be corrected or limited? In a first part, we describe how a statistical learning algorithm works. In a second part, we are interested in the origin of these biases, which can be of a cognitive, statistical or economic nature. In a third part, we present some promising statistical or algorithmic approaches to correct biases. We conclude the article by discussing the main societal issues raised by statistical learning algorithms such as interpretability, explainability, transparency, and accountability.

The thesis presents an economic analysis of three new topics related to the economics of online advertising. It uses both empirical and theoretical methods. Chapter 1 presents the background of the thesis as well as the main results developed in the following chapters. In chapter 2, the thesis shows through a theoretical model how transparency in the modes of advertising purchases modifies the market equilibrium. In a context where advertising is considered a nuisance by users, the chapter shows that the introduction of such transparency-enhancing technologies can affect welfare in different ways. Chapter 3 discusses the economic relevance of profiling technologies that allow websites to adapt their level of advertising to the user's sensitivity. The chapter builds a theoretical model and draws the main implications arising from the use of these technologies. In Chapter 4, the thesis highlights how a privacy policy that allows users to opt out of ad targeting can have economic implications in the online advertising market. The chapter presents a new computational methodology and estimates the impact of such a privacy policy on ad prices. The results differ from previous work and show that the impact depends strongly on the characteristics of the website as well as the attractiveness of other sales channels. Finally, Chapter 5 concludes by highlighting how the key points developed throughout the chapters have significant economic weight.

This paper investigates the strategies of a data broker in selling information to one or to two competing firms that can price-discriminate consumers. The data broker can strategically choose any segment of the consumer demand (information structure) to sell to firms that implement third-degree price-discrimination. We show that the equilibrium profits of the data broker are maximized when (1) information identifies the consumers with the highest willingness to pay. (2) consumers with a low willingness to pay remain unidentified. (3) the data broker sells two symmetrical information structures. The data broker therefore strategically sells partial information on consumers in order to soften competition between firms. Extending the baseline model, we prove that these results hold under first-degree price-discrimination.

This paper investigates the strategies of a data broker in selling information to one or to two competing firms that can price-discriminate consumers. The data broker can strategically choose any segment of the consumer demand (information structure) to sell to firms that implement third-degree price-discrimination. We show that the equilibrium profits of the data broker are maximized when (1) information identifies the consumers with the highest willingness to pay. (2) consumers with a low willingness to pay remain unidentified. (3) the data broker sells two symmetrical information structures. The data broker therefore strategically sells partial information on consumers in order to soften competition between firms. Extending the baseline model, we prove that these results hold under first-degree price-discrimination.

This thesis studies the economic consequences of consumers controlling the amount of personal data they are willing to share with firms. This topic is of vital importance to the digital economy as many firms collect and use consumer information afin order to increase their profits. However, these firms may face increasing difficulties in generating profits from personal data. First, more and more consumers are concerned about their online privacy. Second, a growing number of tools are available to control the collection of personal data. We show in the thesis that the use of such tools have a positive effect on consumers' willingness to share their personal information, thus enabling more privacy-friendly data collection. These findings also apply to the topic of financial data. Encouraging the use of non-bank payment methods (and thus allowing consumers to hide information from banks) could be beneficial to online commerce, leading consumers to make more purchases. Infin a context where consumers are concerned about their privacy, banks could benefit from making their screening practices less intrusive, as it would make their credit policies more effective.

In this book, we have addressed the issues raised by a particular sign of trust, the label in the field of personal data, as well as certain paths and trends in the landscape that is currently being built. If the issue of labels is not new in itself, as their presence in the fields of energy or food attests, the specificity of digital goods and their exponential growth do not seem to allow the use of already proven solutions. The increasing circulation of information therefore raises the question of the governance of personal data in a new way.

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In this handbook, we addressed the issues raised by a particular kind of trust sign, data protection seals, and we brought to light certain trends that are emerging in the developing digital landscape. The issue of seals is not a new one - seals have been around for a while in the energy and food industries -, yet digital goods are a specific and skyrocketing market that therefore cannot use solutions already tested elsewhere. The increasing data flows are thus compelling us to reconsider the issue of data protection governance modes in a new light.

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Estimating the value of standing timber is an important topic for the national forest services to set an appropriate withdrawal price and obtain a fair price. The hedonic pricing method is particularly well suited for valuing standing timber lots because many characteristics influence the price. We propose an estimation model based on the results of timber auctions conducted by the Office National des Forêts (ONF). However, many lots remain unsold at the end of these sales. In addition, the withdrawal price is not announced. We correct the bias due to the presence of unsold lots with the Heckman selection model and study the variables that determine the probability of selling a lot as well as the implicit price of the lot characteristics.

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According to the records on "digital trust" from ACSEL, which is an acronym for the French Digital Economy Association, French Internet users used an average of 16.4 digital IDs in 2013, compared to 12.2 in 2009. By digital ID, we mean information - like login and password - needed to activate an account, but also any traces left by an individual due to their activities or technological devices (IP address, photos, types of purchase, etc.). Ninety percent of Internet users use their digital IDs to access e-administration and e-commerce services, 8% for online banking, and 77% for social networks. The same ACSEL study measures the risks perceived by Internet users with regard to their digital ID and observes that 42% of them fear errors with their ID (compared to 34% in 2009) and 40% misuse of their data. It is important to know that the French are victims of 400,000 identity thefts per year on average. ACSEL argues that an Internet user's "breaking point with regards to trust" occurs "when the risks perceived by the user are considered to be greater than the use value of a digital service.

We propose a Bayesian MCMC (Metropolis-Gibbs Monte Carlo Markov Chain) algorithm to estimate the parameters of a selection model in which the selection equation has an endogenous binary explanatory variable, using a model of three simultaneous equations. We apply our methodology to participation in timber auctions in which some lots are not bid (these lots are censored), others receive one bid (no competition), and others receive 2 or more bids. We find that the MCMC algorithm gives stable results for several model specifications, while the Heckman selection procedure does not give reliable results for the coefficient associated with the endogenous binary variable, nor for the correlation coefficient.

Digital identities play a major role in the news, provoking intense work and debate about the evolution of Internet usage and the emergence of new digital services. The lack of control over the technologies used, the increase in identity theft, the desire of Europe and France to regulate the Internet, to protect the privacy of citizens and to develop trustworthy services, are all problems posed by the computerization of the world. This book provides an overview of the current thinking and work on digital identity management in various contexts, such as social networks, cloud computing and the Internet of Things. The technical and legal advances aimed at protecting digital identities are presented, with a particular focus on authentication techniques, identity federation tools and privacy protection techniques In the past four decades, information technology has altered chains of value production, distribution, and information access at a significant rate.

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Recent rapid growth in electronic book sales has raised a critical question for publishers and bookstores: do e-books cannibalize or increase print sales? In this article, we compare the best-selling titles sold on www.Amazon.com in print or electronic Kindle formats during the period from November 2007 to July 2010. Using econometric methods, we find that the cannibalization of print sales by e-books is more likely to occur for superstar titles written by successful authors. However, we find that a new segment of successful electronic titles that are not best-selling in print format emerge. these books would probably have been unpopular without the new Kindle store and therefore this new distribution channel has expanded the market. We refer to these titles as digital outsiders. The latter are characterized not only by lower prices but also by older release dates. They also include titles that are only released in electronic format. We then argue that electronic books increase the market viability of old print releases. Finally we identify a category that we call print-preferred books that are top sellers in print but not as e-books for reasons of color, graphics, or the need to navigate non-linearly, a style to which the current generation of e-book readers are not well adapted.

The music industry is facing the development of online P2P networks, direct download services and other forms of digital piracy, while at the same time the format and promotion of pre-recorded music are undergoing major transformations with the advent of MP3 and new forms of collective promotion, particularly through online communities and social networks. The chapter is organized as follows. First, I analyze how digital piracy and digital music distribution are changing the demand for pre-recorded music. Next, I review new business models and give perspectives on how cloud services will challenge existing offers. I conclude the chapter and discusses the policy implications of digital music distribution.

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In this paper, we use data from a survey of 151 French record companies to test the ‘‘long-tail’’ hypothesis at the level of the firm. More specifically, we test whether, following the ‘‘selling less of more’’ principle coined by Anderson (2006), record companies that have adapted to digitization (at various levels: artists’ scouting, distribution, and promotion) release more new albums without having higher overall sales. We construct a production function in which the output is produced from conventional inputs of labor and capital, as well as inputs that are more specific to the recorded music industry. We consider two types of output: a commercial output (albums sales) and a creative output (number of new albums released). We show that labels that have adapted to digitization are more efficient in respect of creative output, but that there is no effect of adaptation to digitization on the commercial output, which is consistent with the predictions of the long-tail hypothesis.

This thesis is presented in the form of a 134-page volume composed of 5 research articles. The thesis is an empirical contribution to the literature that has developed since the early 2000s on the upheavals introduced by the Internet on the trade of cultural goods. In particular, it examines a set of questions concerning the complementarity or substitution of physical and virtual distribution channels, the "Long Tail" effect and the dispersion of prices on the Internet. The interest of the thesis is to bring empirical evidence to these debates, thanks in particular to the creation of databases obtained by automated capture of observable data on the Internet. The statistical and econometric results of this work detail the specificities of the best-selling books, CDs and DVDs according to the distribution channels (Amazon, Amazon Marketplace, physical stores) but also according to their format (paper books/digital books). On the other hand, the results show a low variability of prices by Amazon Marketplace sellers as well as a low impact of traditional measures of reputation (seller ratings) compared to the size of the sellers' catalogs, questioning the use of ratings as a proxy for reputation.

This thesis in industrial economics seeks to clarify the strategies of firms in protecting their intellectual property, in order to analyze the consequences of these behaviors in terms of social welfare and impact on competition. The first two empirical articles study the protection of intellectual property by innovative firms in France on the basis of the Community Innovation Survey 4. The analysis uses a bivariate probit model and shows that, in the intermediate goods sector, small innovations have a high probability of being patented, while secrecy is more likely to be used to protect a large innovation than a small innovation. The third paper, "Imitation and Intellectual Property Protection: The Strategic Implications of Damage Rules," examines, in a model-based fashion, how the legal framework imposed on the firm influences its IP protection behavior. The fourth article, "Adoption Strategies with an Imperfectly Competitive Technology Market", relaxes the implicit assumption of perfect competition in the technology market present in Fudenberg and Tirole's (1983) technology adoption model. An upstream firm with market power seeks to capture the profits of downstream firms, for example through the fixed part of a dual tariff. We establish that, when the upstream market is constituted by a monopoly, there is no phenomenon of preemption between downstream firms in equilibrium.

This thesis deals with pricing in a dynamic framework. Using an interdisciplinary approach, we present three research papers that focus on the determinants of dynamic pricing. In the first work, we characterize the intertemporal pricing policy of a firm operating in a two-sided market. We identify the conditions for direct externalities to have the same impact on pricing as indirect externalities. In the second work, we study the dynamic pricing of a firm that conducts both product and process innovation policies. We show that process innovation is the primary determinant of pricing policy. In the third work, we conduct an empirical study and measure the dynamics of the reference price in the real estate market for both the seller and the buyer. In particular, our results show that the reference price adjusts twice as fast in a rising market than in a falling market.

The purpose of this research is to analyze the process of formation of innovations, the interactions they generate, and the modalities of diffusion in the economic and social fabric. These three dimensions are intimately linked, and it is to the explicitation of the corresponding links that this work is devoted. The system of interactions includes on the one hand the agents (consumers, firms and public decision-makers) and on the other hand the grouping of these agents on an aggregate scale (industries, consumer associations, political groups, institutions). Interactions can have positive aspects. For example, they can improve the diffusion of information about the characteristics of a new product between informed and uninformed consumers. They can also have negative effects, such as the increase in knowledge required to participate in the productive system, knowledge and mastery that characterize what we often designate under the general term of complexity. In the first part, we study the link between complexity and innovation on an aggregate scale. In the second part, we first review the different methodologies that allow us to test and measure the importance of interactions on innovation formation. We then show that innovative firms weave an information network among themselves. The linear model of innovation where ideas and discoveries are transformed into development projects and then into technological innovations seems to correspond less and less to reality. Finally, in the last part, we determine the role of local interactions on the diffusion of innovation. We adopt a microeconomic analysis framework and propose an overview of the main statistical and economic models of information diffusion concerning the characteristics of a new technology in a social network. We then propose a model in which we specify a local communication network, assuming that only socially close agents can communicate information among themselves through a "word of mouth" mechanism. We study in this context the diffusion of a new technology by a firm producing a good of superior quality.