arXiv subjects

Bernd Skiera

Publications and source records attributed to Bernd Skiera.

9 recordsLinked to original sources

arXiv2026

The Impact of the General Data Protection Regulation (GDPR) on Online Usage Behavior

Privacy regulations aim to safeguard consumers, but can have unintended consequences on how users interact with websites. This article estimates the causal effect of the EU's General Data Protection Regulation (GDPR) on online usage behavior and decomposes it into usage frequency (unique visitors) and usage intensity (visits per unique visitor). Using a generalized synthetic control estimator across trillions of visits to 6,387 websites in 24 industries and 13 countries-11 months before and 19 months after enforcement-it compares observations subject to the GDPR (EU users or EU websites) with unaffected observations (non-EU users on non-EU websites). Weekly visits decline by 4.88% within 3 months and by 10.02%% after 18 months, with the decline increasingly driven by usage frequency: by 18 months, unique visitors decline by 6.61%, whereas visits per unique visitor decline by only 0.59%. The average conceals offsetting effects consistent with a reallocation of online activity: about one quarter of websites gain significantly, and among websites losing users, the remaining users engage more intensively, whereas intensity falls where user numbers grow. Losses concentrate among hedonic and smaller websites and continue to deepen during the first wave of data-protection enforcement actions rather than being concentrated around the GDPR compliance deadline. Taken together, these patterns are more consistent with restricted data-driven user acquisition and privacy salience, than with consent friction and degraded retention as persistent explanations. Long-horizon, decompositional evaluation thus reveals GDPR's differential effect on usage frequency/intensity and heterogeneous effects not distinguished by the aggregate traffic effects reported in prior work.

econ.GN↗

arXiv2024

The Impact of the General Data Protection Regulation (GDPR) on Online Tracking

This study explores the impact of the General Data Protection Regulation (GDPR), introduced on May 25th, 2018, on online trackers, vital elements in the online advertising ecosystem. Using a difference-in-differences approach with a balanced panel of 294 publishers, we compare publishers subject to the GDPR with those unaffected (the control group). Drawing on data from WhoTracks.me, which spans 32 months from May 2017 to December 2019, we analyze how the number of trackers used by publishers changed before and after the GDPR. The findings reveal that although online tracking increased for both groups, the rise was less significant for EU-based publishers subject to the GDPR. Specifically, the GDPR reduced about four trackers per publisher, equating to a 14.79% decrease compared to the control group. The GDPR was particularly effective in curbing privacy invasive trackers that collect and share personal data, thereby strengthening user privacy. However, it had a limited impact on advertising trackers and only slightly reduced the presence of analytics trackers.

econ.GN↗

arXiv2024

The Economic Value of User Tracking for Publishers

Regulators and browsers increasingly restrict user tracking to protect users' privacy online. In two large-scale empirical studies, we study the economic implications for publishers relying on selling advertising space to finance their content. In our first study, we draw on 42 million ad impressions from 111 publishers covering EU desktop browsing traffic in 2016. In our second study, we use 218 million ad impressions from 10,526 publishers (i.e., apps) covering EU and US mobile in-app browsing traffic in 2023. The two studies differ in the share of trackable users (Study 1: 85%; Study 2: Apple: 17%, Android: 91%). Still, we find similar average ad impression price decreases (Study 1: 18% and Study 2: 23%) when user tracking is unavailable. More than 90% of the publishers realize lower prices when selling ad impressions for untrackable users. Publishers offering content on sports, cars, lifestyle & shopping, and news & information suffer the most. Premium publishers with high-quality edited content and strong reputations, thematic-focused (niche) publishers, and smaller publishers suffer less from the unavailability of user tracking. In contrast, non-premium publishers with non-edited or user-generated content, thematic-broad (general news) publishers, and larger publishers suffer more. The availability of a user ID generates the highest value for publishers, whereas collecting a user's browsing history, perceived as intrusive by most users, generates only a small value for publishers. These results affirm that ensuring user privacy online has substantial costs for online publishers, but those costs differ across publishers and the type of collected data. This article offers suggestions to reduce these costs.

econ.GN↗

arXiv2024

Paying for Privacy: Pay-or-Tracking Walls

Prestigious news publishers, and more recently, Meta, have begun to request that users pay for privacy. Specifically, users receive a notification banner, referred to as a pay-or-tracking wall, that requires them to (i) pay money to avoid being tracked or (ii) consent to being tracked. These walls have invited concerns that privacy might become a luxury. However, little is known about pay-or-tracking walls, which prevents a meaningful discussion about their appropriateness. This paper conducts several empirical studies and finds that top EU publishers use pay-or-tracking walls. Their implementations involve various approaches, including bundling the pay option with advertising-free access or additional content. The price for not being tracked exceeds the advertising revenue that publishers generate from a user who consents to being tracked. Notably, publishers' traffic does not decline when implementing a pay-or-tracking wall and most users consent to being tracked; only a few users pay. In short, pay-or-tracking walls seem to provide the means for expanding the practice of tracking. Publishers profit from pay-or-tracking walls and may observe a revenue increase of 16.4% due to tracking more users than under a cookie consent banner.

econ.GN↗

arXiv2024

Measuring Self-Preferencing on Digital Platforms

Digital platforms use recommendations to facilitate exchanges between platform actors, such as trade between buyers and sellers. Aiming to protect consumers and guarantee fair competition on platforms, legislators increasingly require that recommendations on market-dominating platforms be free from self-preferencing. That is, platforms that also act as sellers (e.g., Amazon) or information providers (e.g., Google) must not prefer their own offers over comparable third-party offers. Yet, successful enforcement of self-preferencing bans -- to the potential benefit of consumers and third-party actors -- requires defining and measuring self-preferencing across a platform. In the context of recommendations through search results, this research contributes by i) conceptualizing a "recommendation" as an offer's level of search engine visibility across an entire platform (instead of its position in specific search queries, as in previous research); ii) discussing two tests for self-preferencing, and iii) implementing them in two empirical studies across three international Amazon marketplaces. Contrary to consumer expectations and emerging literature, our analysis finds almost no evidence for self-preferencing. A survey reveals that even if Amazon were proven to engage in self-preferencing, most consumers would not change their shopping behavior on the platform -- highlighting Amazon's significant market power and suggesting the need for robust protections for sellers and consumers.

econ.GN↗

arXiv2023

Economic Consequences of Online Tracking Restrictions: Evidence from Cookies

In recent years, European regulators have debated restricting the time an online tracker can track a user to protect consumer privacy better. Despite the significance of these debates, there has been a noticeable absence of any comprehensive cost-benefit analysis. This article fills this gap on the cost side by suggesting an approach to estimate the economic consequences of lifetime restrictions on cookies for publishers. The empirical study on cookies of 54,127 users who received 128 million ad impressions over 2.5 years yields an average cookie lifetime of 279 days, with an average value of EUR 2.52 per cookie. Only 13% of all cookies increase their daily value over time, but their average value is about four times larger than the average value of all cookies. Restricting cookies lifetime to one year (two years) decreases their lifetime value by 25% (19%), which represents a decrease in the value of all cookies of 9% (5%). In light of the EUR 10.60 billion cookie-based display ad revenue in Europe, such restrictions would endanger EUR 904 million (EUR 576 million) annually, equivalent to EUR 2.08 (EUR 1.33) per EU internet user. The article discusses these results' marketing strategy challenges and opportunities for advertisers and publishers.

econ.GN↗

arXiv2021

The Impact of Privacy Laws on Online User Behavior

Policymakers worldwide draft privacy laws that require trading-off between safeguarding consumer privacy and preventing economic loss to companies that use consumer data. However, little empirical knowledge exists as to how privacy laws affect companies' performance. Accordingly, this paper empirically quantifies the effects of the enforcement of the EU's General Data Protection Regulation (GDPR) on online user behavior over time, analyzing data from 6,286 websites spanning 24 industries during the 10 months before and 18 months after the GDPR's enforcement in 2018. A panel differences estimator, with a synthetic control group approach, isolates the short- and long-term effects of the GDPR on user behavior. The results show that, on average, the GDPR's effects on user quantity and usage intensity are negative; e.g., the numbers of total visits to a website decrease by 4.9% and 10% due to GDPR in respectively the short- and long-term. These effects could translate into average revenue losses of $7 million for e-commerce websites and almost $2.5 million for ad-based websites 18 months after GDPR. The GDPR's effects vary across websites, with some industries even benefiting from it; moreover, more-popular websites suffer less, suggesting that the GDPR increased market concentration.

econ.GN↗

arXiv2021

How Does the Adoption of Ad Blockers Affect News Consumption?

Ad blockers allow users to browse websites without viewing ads. Online news providers that rely on advertising revenue tend to perceive users adoption of ad blockers purely as a threat to revenue. Yet, this perception ignores the possibility that avoiding ads, which users presumably dislike, may affect users online news consumption behavior in positive ways. Using 3.1 million anonymized visits from 79,856 registered users on a news website, we find that adopting an ad blocker has a robust positive effect on the quantity and variety of articles users consume (21.5% - 43.3% more articles and 13.4% - 29.1% more content categories). An increase in repeat user visits of the news website, rather than the number of page impressions per visit, drives the news consumption. These visits tend to start with direct navigation to the news website, indicating user loyalty. The increase in news consumption is more substantial for users who have less prior experience with the website. We discuss how news publishers could benefit from these findings, including exploring revenue models that consider users desire to avoid ads.

econ.GN↗

arXiv2020

How to Best Predict the Daily Number of New Infections of Covid-19

Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable.

cs.SI↗