Abstract Technological innovations can significantly impact market structures and the behavior of actors, necessitating legal changes. However, in the face of technological changes brought by sharing economy, there is a differential in the responsiveness of rule-making institutions across different jurisdictions. This article analyses the rule-making institutions that allow for the regulatory changes procured by Uber and the institutional impediments that may restrict jurisdiction in accommodating this type of crowd-based technological disrupters. Uber’s non-adoption in Hong Kong is compared to its success in the United States to evaluate the limitations of rule-making institutions in responding to the legal change required by sharing economy companies. Changes in the rule-making institutions themselves may be needed to accelerate technological innovations and economic growth. This article suggests that there is a lack of institutional adaptability in Hong Kong, where the rule-making institutions are unable to facilitate potential regulatory reform. While the feasibility of changing a particular jurisdiction’s electoral system and political systems may be low, jurisdictions can improve the accountability of their executive regulators by modifying the incentive structure.

Abstract Ancient law can be analyzed using economic tools. With a renewed interest from the political class to make the ancient Laws of Manu (Manu’sDharmaśāstra) more relevant to modern Indian law, the economic analysis of the Laws of Manu regarding contracts allows us to look at the issue from an efficiency standpoint. The paper will look at both regular contracts and marriage contracts. Surprisingly, many of the ancient laws are like the modern law of contracts especially in areas of contract breach. However, when considering marriage contracts, the bargaining position of men is stronger than women.

Abstract Using a game-theoretic analysis, this study aims to investigate which of the “all or nothing” and “pro rata” methods, regarding the sanction against the breach of duty to disclose in insurance contracts by negligence, is better from two perspectives: one is in terms of how much the sanction increases the policyholder’s probability of effort, while the other is how much it increases the probability of finding the wrong type of policyholder by insurance firms. From the analysis, we find that the desirable method depends on the probability that the policyholders can effortlessly determine their precise risk type as well as their effort cost level.

Abstract This study examines the welfare implications of allowing border carbon adjustments (BCAs) in a globalized economy characterized by international trade and cross-border pollution (CBP). The model predicts that adopting BCAs is a weakly dominant strategy and global welfare is maximized when at least one country adopts BCAs in the presence of CBP, such as global warming. This is because adopting BCAs induces other countries to raise their domestic emission tax rates without concerns such as the excessive shrinkage of domestic production and aggravation of CBP.

Abstract This paper investigates empirically whether firm ownership structures contribute to varying levels of legal compliance, which ultimately influence the likelihood of winning a lawsuit. I hypothesize that private companies are more likely to lose employment lawsuits because the rule of law within the company is rarely established. Using collected 2756 employment judgments decided by district courts in Beijing between 2014 and 2018, I test this hypothesis against three other types of ownership structures in China: state-owned enterprises, wholly foreign-funded companies, and partly foreign-funded companies. The statistical result confirms that private companies are more likely to lose cases, thus supporting the proposed hypothesis. In addition, the company’s scale and the company’s life span also have a significant influence on the employment lawsuit result.

Abstract An optimally designed tax amnesty scheme can serve as a strategic component in a larger tax reform process. Such a reform can particularly assist in the tax collection efforts of developing economies like Pakistan. Pakistan’s tax amnesty schemes in 2018 and 2019 helped grow the tax base substantially. India’s and Indonesia’s schemes in 2016 also showed promise. My study compares the recent tax amnesties adopted by these three countries (Pakistan, India and Indonesia). Based on these experiences, I propose improvements in the composition of Pakistan’s tax amnesty design. An optimal tax policy cannot rely only on wide-spread enforcement, particularly in countries with large underground economies--like Pakistan, India and Indonesia. Instead, it should focus more on the optimal amnesty design alongside targeted enforcement efforts, aimed especially at documenting and taxing large underground economic activities.

Abstract I analyze a model of patent races for COVID-19 vaccines under alternative liability rules. The first inventor of the vaccine gets the monopoly rent, but must assume full liability from its side effects. In this model, firms choose two kinds of investments, one for inventing a vaccine and the other for its safety. I show that firms have an incentive to overinvest in both activities under strict liability. This is contrasted with the established result established that the injurer takes socially optimal accident-preventing precaution under strict liability. This contrast comes from the competition effect. Overinvestment in inventing vaccines due to competition makes a firm overinvest in safety as well. I also argue that it is undesirable for firms to get full or partial exemption from liability, because it would reduce the incentive to invest in safety. Instead, reducing the monopoly rent by regulating the vaccine price resolves both overinvestment problems.

Abstract This paper provides an overview of machine learning models, as compared to traditional economic models. It also lays out emerging issues in law and economics that the machine learning methodology raises. In doing so, Asian contexts are considered. Law and economics scholarship has applied econometric models for statistical inferences, but law as social engineering often requires forward-looking predictions rather than retrospective inferences. Machine learning can be used as an alternative or supplementary tool to improve the accuracy of legal prediction by controlling out-of-sample variance along with in-sample bias and by fitting diverse models to data with non-linear or otherwise complex distribution. In the legal arena, the past experience of using economic models in antitrust and other high-stakes litigation provides a clue as to how to introduce artificial intelligence into the legal decision-making process. Law and economics is also expected to provide useful insights as to how to balance the development of the artificial intelligence technology with fundamental social values such as human rights and autonomy.

Abstract During past two decades government of Pakistan has made prominent attempts to transform its financial system through various reforms. Being chief element of macroeconomic policy, financial reforms are anticipated to fetch considerable economic benefits mostly through efficient utilization of resources and recruitment of national savings. Analyzing the effectiveness of financial sector reforms the findings of this investigation demonstrates considerable and noteworthy effect on the economy of Pakistan, however still the gap of improvement exits. From the analysis it can be suggested that to make the financial sector reforms in more effective way, the information gap between micro-macro prudential should be bridged to ensure the safety and soundness of individual institutions that encompasses all activities aimed at monitoring the exposure of systemic risk and identifying potential threats to financial stability arising from macroeconomic and financial sector reforms.