Copyright for Blockheads: An Empirical Study of Market Incentive and Intrinsic Motivation

Copyright law is widely perceived as the means to promote social welfare by providing a necessary incentive for intellectual creation. However, there has been little clarity in copyright literature on how artists actually respond to copyright incentives: What factors motivate artists to create works? How do artists perceive the usefulness of copyright protection? Would artists continue their artistic careers in a world without copyright law? This Article contains a systematic study regarding copyright incentives, based on industrial statistics and extensive interviews from the music industry in China—a virtually copyright-free environment featuring one of the highest piracy rates in the world, which has caused a dramatic transformation of the music business. The empirical research indicates three seemingly paradoxical phenomena: (1) while 17.9% of all the musicians in the sample referred to economic benefits as at least part of their motivations for music creation, 97.4% specifically recognized money as being important and helpful for music creation; (2) while 56.4% alleged that copyright piracy did not affect their creative motivations, 72% agreed that copyright piracy does affect music creation and (3) while 53.8% explicitly admitted that they had little awareness or knowledge of copyright, 92.3% indicated that the current level of copyright protection is insufficient and 71.8% suggested that copyright law should provide strong incentives for music creation. The empirical evidence itself provides compelling explanations for such paradoxes: Even though musicians seem to primarily create music for music’s sake, copyright law could still supply powerful incentives for music production in a way that not only caters to market demand, but also allows for broader artistic freedom. Copyright piracy that does not necessarily affect musicians’ intrinsic motivations could nevertheless affect music creation in terms of the time spent on music creation, the volume of investment in music creation and, ultimately, the quality of music creation. Most importantly, copyright incentives do not function as a reward that musicians consciously bargain for and chase after, but as a mechanism that preserves market conditions for gifted musicians to prosper, including a decent standard of living, sufficient income to cover production costs and maximum artistic autonomy during the creative process

Why Is Betamax an Anachronism in the Digital Age?

This Article aims to examine whether, as some courts indicate, the Sony doctrine is largely irrelevant in cyberspace. If the answer is no, how should courts properly apply the Sony doctrine to protect copyright holders\u27 legitimate interests and further the innovation and prosperity of Internet technologies? This Article argues that the Sony doctrine should be given the widest application possible and not be subject to any preconceived formula. In the digital age, the test of capable of substantial noninfringing uses is still well suited to advance the ultimate objective of copyright law contemplated by the Supreme Court as well as by the Constitution: promot[ing] the Progress of Science and useful Arts

Blockchain Copyright Exchange – A Prototype

The copyright market for creative works such as music and movies traditionally involves a complex web of licensing transactions and exorbitant transaction costs. Out of every dollar that consumers pay, an artist who writes, performs, and produces her own work may receive less than fifteen cents while the rest are diverted to cover the costs of financing new production, marketing new works, and distributing royalties. Although artists are typically scheduled to receive royalties on a quarterly basis, a payment may lag as far as two years after users paid. Furthermore, if a collecting society is unable to identify the rightful owner for a royalty payment, it routinely allocates the royalty among its existing members. This Article proposes a blockchain copyright exchange (“BCE”) that dramatically improves efficiency and accuracy in copyright transactions by hardcoding thousands of copyright rules and license terms in blockchain-based smart contracts. First, BCE allows artists to earn a royalty per stream potentially sixteen times larger than Spotify offers and eighty times larger than YouTube offers. Artists receive payments at a speed millions of times faster, in a matter of seconds instead of months, with zero administrative charges and zero dollars falling through the cracks. Second, BCE allows artists to launch crowdfunding campaigns inviting fans to securely finance creative works in return for a share of copyright ownership in the form of a non-fungible token (“NFT”) or a fungible token (“FT”). It significantly diversifies the investment risks for artists and labels alike. Third, BCE cultivates a healthy ecosystem among artists and users by mobilizing users to mine BCE tokens through distribution and promotion of licensed works. These powerful incentives, together with BCE’s innovative enforcement mechanisms, may effectively eliminate the breeding ground for copyright piracy

Copyright Complements and Piracy-Induced Deadweight Loss

Conventional wisdom suggests that copyright piracy may in effect reduce the deadweight loss resulting from copyright protection because it allows the public unlimited access to information goods at a price closer to marginal cost. It has been further contended that lower copyright protection would benefit society as a whole, as long as authors continue to receive sufficient incentives from alternative revenue streams in ancillary markets, for example, touring, advertising, and merchandizing. By evaluating the empirical evidence from the music, performance, and video game markets, this Article highlights a counterintuitive yet important point: copyright piracy, while decreasing the deadweight loss in the music market, could simultaneously increase the deadweight loss in ancillary markets via the interaction between complementary goods. The deadweight loss in ancillary markets tends to become dominant if a substantial portion of relevant consumers have high valuation but low frequency in music consumption, are risk averse toward up-front payment with uncertain demand, or discount future value at a high rate. Additionally, this Article’s findings shed new light on the current debates over several competing propositions to reform indirect copyright liabilities in the digital age

Copyright Complements and Piracy-Induced Deadweight Loss

Conventional wisdom suggests that copyright piracy may in effect reduce the deadweight loss resulting from copyright protection because it allows the public unlimited access to information goods at a price closer to marginal cost. It has been further contended that lower copyright protection would benefit society as a whole, as long as authors continue to receive sufficient incentives from alternative revenue streams in ancillary markets, for example, touring, advertising, and merchandizing. By evaluating the empirical evidence from the music, performance, and video game markets, this Article highlights a counterintuitive yet important point: copyright piracy, while decreasing the deadweight loss in the music market, could simultaneously increase the deadweight loss in ancillary markets via the interaction between complementary goods. The deadweight loss in ancillary markets tends to become dominant if a substantial portion of relevant consumers have high valuation but low frequency in music consumption, are risk averse toward up-front payment with uncertain demand, or discount future value at a high rate. Additionally, this Article’s findings shed new light on the current debates over several competing propositions to reform indirect copyright liabilities in the digital age

Is the Market Smart Enough to Identify Superior Analysts and Follow their Recommendations?

In this article we investigate whether there is persistency in analysts forecasting ability and if it exists, whether the market has the ability to identify these differences in abilities. Our results reveal that the forecasting ability of analysts is persistent. We then investigate whether investors identify superior analysts’ ability by analyzing market reaction to their recommendations compared to the reaction to other analysts. Our findings suggest that the twoday returns after the analysts’ reports’ are strongly positively correlated with analysts’ recommendations and there is a significant difference in reaction between high and low quality analysts. We conclude that the market is smart enough to identify different types of analysts and follow their recommendations respectively

Corporate Lobbying and ESG Reports: Patterns among US Companies, 1999–2017

To lobby legislators, it is important for interest groups to signal their ability to help legislators win elections and provide them with policy-relevant information. We explore for-profit companies’ use of environmental, social, and governance (ESG) reports as a signaling device to promote their reputation to legislators and convey their ability to provide electoral and policymaking support, which is valuable for lobbying. To this end, we create a panel dataset by combining ESG reports issued by US companies and the same companies’ lobbying and campaign contribution records from 1999 to 2017. We expect companies to issue more ESG reports, as well as reports containing more quantitative content, when they lobby. The data conform to our expectations. We also reason that lobbying may be more strongly related to ESG reporting when it is coupled with campaign contributions made by affiliated corporate political action committees, but the data do not support this expectation

Bias Amplification Enhances Minority Group Performance

Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels. Previous approaches based on worst-group loss minimization (e.g. Group-DRO) are effective in improving worse-group accuracy but require expensive group annotations for all the training samples. In this paper, we focus on the more challenging and realistic setting where group annotations are only available on a small validation set or are not available at all. We propose BAM, a novel two-stage training algorithm: in the first stage, the model is trained using a bias amplification scheme via introducing a learnable auxiliary variable for each training sample; in the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset. Empirically, BAM achieves competitive performance compared with existing methods evaluated on spurious correlation benchmarks in computer vision and natural language processing. Moreover, we find a simple stopping criterion based on minimum class accuracy difference that can remove the need for group annotations, with little or no loss in worst-group accuracy. We perform extensive analyses and ablations to verify the effectiveness and robustness of our algorithm in varying class and group imbalance ratios.Comment: 21 pages, 14 figure