Competitive Imperfect Price Discrimination and Market Power

Belleflamme, Paul; Lam, Wing Man Wynne; Vergote, Wouter

doi:10.1287/mksc.2020.1234

Cited by 50 publications

(23 citation statements)

References 31 publications

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“…For instance, Acquisti and Varian (2005) use sequential pricing to analyze intertemporal price discrimination with incomplete information on consumer demand. Jentzsch et al (2013) and Belleflamme et al (2020) also focus on sequential pricing where a higher personalized price is charged to identified consumers after a firm sets a uniform price. Sequential pricing is also common in business practices (see also, Fudenberg and Villas-Boas (2006)).…”

Section: Firmsmentioning

confidence: 99%

Competition and Mergers with Strategic Data Intermediaries

2021

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We analyze competition between data intermediaries collecting information on consumers, which they sell to firms for price discrimination purposes. We show that competition between data intermediaries benefits consumers by increasing competition between firms, and by reducing the amount of consumer data collected. We argue that merger policy guidelines should investigate the effect of the data strategies of large intermediaries on competition and consumer surplus in related markets.

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Section: Firmsmentioning

confidence: 99%

Competition and Mergers with Strategic Data Intermediaries

2021

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“…The malware attack on the Times only threatened computers running Windows. 6 More generally, however, malware can exploit many di¤erent software vulnerabilities about which consumers may have limited or no awareness. Finally, concerns about cyber risks might reduce website tra¢ c. In its own reporting on the malvertising attack, the Times acknowledged that such events can damage a website's reputation and "make its visitors nervous."…”

Section: Introductionmentioning

confidence: 99%

“…Pfeiderer, 1986, 1990;Bergemann et al, 2018), the optimal policy of a monopolist selling data to competing …rms (e.g. Bounie et al, 2018;Belle ‡amme et al, 2019;Montes et al, 2019), and information sharing among competing …rms (e.g. Vives, 1988;Raith, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…David Gallagher , "Times site was victim of a malicious ad swap," New York Times, September 14, 2009, https://bits.blogs.nytimes.com/2009/09/14/times-site-was-victim-of-a-malicious-ad-swap/5 Ashlee Vance, "Times Web Ads Show Security Breach," New York Times, September 14, 2009, https://www.nytimes.com/2009/09/15/technology/internet/15adco.html 6. Riva Richmond, "What to do if you saw an 'antivirus' pop-up ad," New York Times, September 14, 2007, https://gadgetwise.blogs.nytimes.com/2009/09/14/what-to-do-if-you-saw-an-antivirus-pop-up-ad/.7 Op.…”

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confidence: 99%

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Privacy Protection, Security, and Consumer Retention

Lefouili

Riordan

2020

SSRN Journal

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A website monetizes information it collects about its customers by charging third parties for targeted access to them. Allowing for third parties who are well-intentioned, a nuisance, or even malicious, the resulting consumer experiences might be good, bad, or neutral. As consumers learn from experience, the website especially risks losing those customers who su¤er a bad experience. Customer retention thus motivates the website to be cautious about monetization, or to spend resources to screen third parties. We study the website's equilibrium privacy policy, its welfare properties, competition in the market for information, and the impact of regulations improving transparency and consumer control.

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“…Yet, accurately identifying high frequencies of taste over specific ranges can be of great practical importance in policy design. For example, when developing policies relies on sorting or price discrimination (Chen and Iyer 2002; Belleflamme, Lam, and Vergote 2017), one can identify willingness to pay ranges with high frequency of people. Some degree of approximation to multimodal preferences can be achieved by using discrete rather than continuous distributions, as implemented in latent class models.…”

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confidence: 99%

Logit Mixed Logit Under Asymmetry and Multimodality of WTP: A Monte Carlo Evaluation

Scarpa¹,

Franceschinis²,

Thiene³

2020

American J Agri Economics

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The logit‐mixed logit (LML) model advances choice modeling by generalizing previous parametric and semi‐nonparametric specifications and allowing retrieval of flexible taste distributions. Using standard operating conditions in the field, we report results from Monte Carlo experiments designed to assess the finite sample bias‐variance tradeoff for the LML using as a benchmark conventional Mixed logit models (MXL) under asymmetric and multimodal taste distributions. The LML specification always outperforms the MXL in terms of bias, but when the variance around modes is high the mean squared error (MSE) is lower than that of MXL only at sample sizes larger than usual and with some nuances. D‐error minimizing experimental design predicated on multinomial logit significantly reduces MSE, but no clear winner is found between polynomial, step, and spline functions for the multidimensional grid function. Analysis of empirical data from a choice experiment on tap water shows that multimodality emerges only if higher number of node parameters are used in the LML.

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Competitive Imperfect Price Discrimination and Market Power

Cited by 50 publications

References 31 publications

Competition and Mergers with Strategic Data Intermediaries

Competition and Mergers with Strategic Data Intermediaries

Privacy Protection, Security, and Consumer Retention

Logit Mixed Logit Under Asymmetry and Multimodality of WTP: A Monte Carlo Evaluation

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