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Profit distribution in sequential innovation is a crucial yet relatively unexplored area of empirical research. With novel cross-section data from the game live-streaming industry, we are the first to assess the contribution shared by initial and follow-on innovators empirically. Unlike the complex innovation paths of patents, the copyright structure within the game live-streaming industry is clear and concise, enabling direct measurement of innovation value. At the industry’s average level, the share stands at 1:2 between game producers and streamers. This measurement remains robust even after controlling for income levels and distinguishing between professionals and amateurs. Nevertheless, significant heterogeneity exists across different game genres. We suggest that a balanced distribution scheme between initial and follow-on innovators should seriously consider their contribution shares.

Blood type is one of the most fundamental phenotypes in biological, medical, and psychological studies. Using a unique dataset of one million Chinese pregnancies, we find strong evidence from a group of statistical tests for assortative mating on blood type. After controlling for anthropometric and socioeconomic confounders, assortative mating remains robust.

In the digital era, short videos have become a significant form of digital copyright, yet the debate over whether stronger copyright protection enhances their creation continues. To contribute to this discourse, we conducted an analysis based on a representative sample of short videos on a prominent Chinese short video platform, Douyin. Capitalizing on an external regulatory intervention, specifically the Campaign against Online Infringement and Piracy (COIP) implemented by the Chinese government, we employed the difference-in-differences (DID) method to assess the impact of reinforced copyright protection on the originality of short videos. Our findings reveal that strengthened copyright protection leads to a significant increase in the originality of short videos. Further research on creator heterogeneity shows that influencers exhibit a significantly more positive response to strengthened copyright protection than amateur creators. Finally, we present evidence explaining how external regulation works by enhancing intra-platform regulation. These results have rich implications for intellectual property protection, digital innovation management, and platform regulation. 在数字时代，短视频已成为数字内容的一种重要形式，但关于加强版权保护是否会促进短视频创作的争论仍在继续。为了解决这一争论，本文利用中国知名短视频平台抖音上的代表性短视频样本进行了分析。借助中国打击网络侵权和盗版专项行动（COIP），本研究采用双重差分法评估了加强版权保护对短视频原创性的影响。研究发现，加强版权保护会显著提高短视频的原创程度。而且异质性研究表明，与业余创作者相比，网红对加强版权保护的反应更明显。最后，本研究发现，外部监管可以通过加强平台内部监管发挥作用。本研究对知识产权保护、数字创新管理和平台监管具有重要的启示。

The fight against online sales of counterfeit goods has received much attention. To the best of our knowledge, existing literature on online counterfeiting lacks a theoretical framework. To fill this gap, this article proposes a two-period model with sellers, buyers, and a platform. We focus on the relationship among platform structure (characterized by the ratio of buyers to sellers), asymmetric information, deterrence strength, and the ratio of counterfeits. Cross-network externalities of platform make platform managers worry about potential exodus of buyers and sellers due to counterfeits. This externality provides platform with strong incentives to fight counterfeits by itself, even without external regulatory requirement. We further show that the higher the ratio of buyers to sellers, the lower the ratio of counterfeits. Moreover, a lower degree of information asymmetry or a higher degree of punishment can reduce the ratio of counterfeits. We suggest that governments and e-commerce platforms work together in the fight against counterfeiting.

Under the stated goal of improving air quality, many cities in China restricted the import of used vehicles from other cities based on tail pipe emission standards. Using detailed data on new and used vehicle registration, we examine the impact of the policy by leveraging the staggered removal of the restriction during 2016–2018. We find that restriction removal led to a sharp increase in cross-city flow of used vehicles but had no significant impact on local air quality. Unilateral removal of the restriction could reduce new vehicle sales in home cities, but universal removal would boost new vehicle sales nationwide.

This research explores and quantifies the downside of technological innovations, especially the negative externality of an innovation interacting with the stock of existing innovations. Using two novel datasets, I make a novel empirical finding that the varieties of innovation-induced risks (e.g. varieties of side effects caused by FDA-approved new drugs) is quadratic in the number of innovations (e.g. number of FDA-approved new drugs) that caused these risks. Based on this new empirical finding, I further develop a Regulatory Growth Theory: a new endogenous growth model with increasing varieties of innovation-induced risks and with a regulator. I model both the innovation-induced risk generating structure and the regulator's endogenous response. This new theory can help to interpret several empirical puzzles beyond the explanatory power of existing models of innovation and growth: (1) skyrocketing expected R&D cost per innovation; (2) decreasing ratio of Qualified Innovations (i.e. Regulator-approved innovations) to the number of total patents and (3) exponentially increasing regulation over time. Greater expenditures on regulation and corporate R&D are required to assess the net benefit of innovation because of \"Risk Externality\": negative interaction effects between innovations. Theoretically, this new \"Risk Externality\" effect counteracts the crucial \"Knowledge Spillover\" effect in the Endogenous Growth models. The rise of regulation versus litigation, and broader implications for regulatory reform are also discussed. Secondly, I propose a new theory of rational \"Rush\", emphasizing the quantity of rational over-investment in contrast to the theory of irrational price \"Bubble\". I illustrate an important friction when financing breakthrough innovations: non-excludability and spillover of uncertain knowledge due to imperfect IPR (Intellectual Property Rights, e.g. patent) protection. Facing a limited supply of new projects with uncertain return, investors make decisions about when and how many projects to invest. Investors' preemption motive will distort their incentives for patient learning about project return, thus inducing them to \"rush in\" to finance uncertain projects massively at a premature stage. A small positive news shock regarding the project return can greatly amplify over-investment and result in large social inefficiency. On the other hand, information externality creates free-rider motive, which can also make under-investment possible. Empirical finding based on sectoral Venture Capital investment shows that weak IPR protection lead to excessively high investment level and more procyclicality. Broader patent rights should be granted when the uncertainty of innovation is high, although the \"Rush\" prevention can induce more patent race at the early R&D stage, i.e. Rush-Race shifting. Finally, I propose a Knowledge Theory of Regulation. Knowledge of innovation-induced risks is a public good. Therefore risk knowledge will be undersupplied if only through courts and private litigations. Compared to courts, regulators have their comparative advantage in acquiring and sharing new risk knowledge. Enlarging risk externality induced by growth and innovation accumulation will lead to faster expansion of regulation thanks to regulators' comparative advantage in risk knowledge acquisition.

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data -- an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers' data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data -- an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers' data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.

We model a dynamic data economy with fully endogenous growth where agents generate data from consumption and share them with innovation and production firms. Different from other productive factors such as labor or capital, data are nonrival in their uses across sectors which affect both the level and growth of economic outputs. Despite the vertical nonrivalry, the innovation sector dominates the production sector in data usage and contribution to growth because (i) data are dynamically nonrival and add to knowledge accumulation, and (ii) innovations \"desensitize\" raw data and enter production as knowledge, which allays consumers' privacy concerns. Data uses in both sectors interact to generate spillover of allocative distortion and exhibit an apparent substitutability due to labor's rivalry and complementarity with data. Consequently, growth rates under a social planner and a decentralized equilibrium differ, which is novel in the literature and has policy implications. Specifically, consumers' failure to fully internalize knowledge spillover when bearing privacy costs, combined with firms' market power, underprice data and inefficiently limit their supply, leading to underemployment in the innovation sector and a suboptimal long-run growth. Improving data usage efficiency is ineffective in mitigating the underutilization of data, but interventions in the data market and direct subsidies hold promises.

We model a dynamic data economy with fully endogenous growth where agents generate data from consumption and share them with innovation and production firms. Different from other productive factors such as labor or capital, data are nonrival in their uses across sectors which affect both the level and growth of economic outputs. Despite the vertical nonrivalry, the innovation sector dominates the production sector in data usage and contribution to growth because (i) data are dynamically nonrival and add to knowledge accumulation, and (ii) innovations \"desensitize\" raw data and enter production as knowledge, which allays consumers' privacy concerns. Data uses in both sectors interact to generate spillover of allocative distortion and exhibit an apparent substitutability due to labor's rivalry and complementarity with data. Consequently, growth rates under a social planner and a decentralized equilibrium differ, which is novel in the literature and has policy implications. Specifically, consumers' failure to fully internalize knowledge spillover when bearing privacy costs, combined with firms' market power, underprice data and inefficiently limit their supply, leading to underemployment in the innovation sector and a suboptimal long-run growth. Improving data usage efficiency is ineffective in mitigating the underutilization of data, but interventions in the data market and direct subsidies hold promises.