Growing privacy concerns have provided important background for the introduction of personal data protection laws, such as the General Data Protection Regulation (GDPR) in the EU. However, empirical evidence on the relationship between privacy concerns and individual behavior remains limited. To fill this gap, this paper uses national-level panel survey data that directly measure individuals' privacy concerns alongside a wide range of digital behaviors. Two empirical approaches—individual fixed-effects regressions and a difference-in-differences specification exploiting a major data-breach disclosure—yield consistent patterns: higher privacy concerns are associated with lower engagement in internet communities, reduced online content contribution, and lower digital consumption such as app purchases. These results suggest that data protection laws may yield welfare benefits if they successfully mitigate privacy concerns.

Using a comprehensive dataset of posts from a major platform for anime- and manga-style artwork, we study the impact of the launch of a prominent text-to-image generative AI. Focusing on the majority of incumbent creators who do not adopt AI as a primary tool, we show that the AI launch led to a significant decline in post uploads by illustrators, whereas comic artists were less affected due to comics' need for tight stylistic alignment across sequential images. We present empirical evidence for two underlying mechanisms: (1) illustration posts experience a loss of viewer attention—measured by bookmarks—following the AI launch, which can significantly harm creators' business models; (2) direct competition from AI-generated content plays a role: illustrators who work on intellectual properties (IPs; e.g., Pokémon) that are more heavily invaded by AI reduce their uploads disproportionately more. We further examine creators' responses and show that illustrators who are highly exposed to AI avoid using tags favored by AI-generated content after the AI launch and broaden the range of IPs they work on, consistent with a risk-hedging response to AI invasion.

This paper presents a structural model to assess the effects of seller reward programs on digital platforms. The empirical context is a Korean livestreaming platform where sellers (streamers) broadcast content, earn revenue from viewer tips, and receive commission discount rewards through performance-based monthly tournaments. I collect streamer-time-level microdata on effort and revenue from streamer tracking websites and estimate a dynamic game to capture how reward program design affects forward-looking streamers' behavior and, in turn, platform revenue. Counterfactual simulations show that commission discount rewards motivate streamers—especially more profitable ones—to stream more and increase total revenue, but cause the platform to retain a substantially smaller share of that revenue. Because the latter effect quantitatively dominates, the current program is not optimal, and reducing the discount rate can improve platform revenue. These results highlight a central trade-off in platform design between incentivizing seller effort and preserving platform revenue.

In this article, we develop a command, xthenreg, that implements the first-differenced generalized method of moments estimation of the dynamic panel threshold model that Seo and Shin (2016, Journal of Econometrics 195: 169–186) proposed. Furthermore, we derive the asymptotic variance formula for a kink-constrained generalized method of moments estimator of the dynamic threshold model and provide an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. We illustrate the use of xthenreg through a Monte Carlo simulation and an economic application.