The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

A critical barrier to labor mobility arises from institutional constraints that im-pose discriminatory costs on migrants. Using China’s hukou system as a case study,we construct a novel, outcome-based measure of mobility frictions that infers thesediscriminatory costs. We document a systematic relationship between our frictionmeasure, migrants’ decisions to leave behind families (“partial migration”), remit-tances, and expenditure patterns. Our estimated spatial general equilibrium modelencompasses these features and examines how mobility frictions interact with tradeliberalization to shape migration, inequality, and welfare. Trade-exposed regionsbenefft from attracting migrants, while high-friction regions experience muted laborreallocation and smaller welfare gains.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, we provide evidence that both extreme events (COVID-19 quarantines) and everyday contexts (geographic locations) trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to substantial losses for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which contextual cues affect financial markets.

We document a striking anomaly in international capital flows that we term "sanction arbitrage": U.S. investors exploited the 2014 sanctions on Russia by significantly increasing holdings in Russian equities while Rest-of-World (ROW) investors fled. We rationalize this behavior through a simple game-theoretic model where the sanctioning government faces a trade-off between geopolitical objectives and domestic welfare, effectively creating a protective shield for domestic investors and driving out ROW investors. Empirically, we confirm that pre-sanction U.S flows negatively predicted subsequent sanction designations. Consequently, U.S. investors internalized this protection to act as opportunistic buyers, absorbing fire-sale assets from exiting foreign investors and capturing significant excess returns from Russian stock holdings. These findings reveal that "smart" sanctions designed to preserve market access can inadvertently generate wealth transfers from foreign to domestic agents.

This study investigates the role of contextual cues in memory-based decision-making within high-stakestrading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor’s portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

Background: How to cope with the rapid growth of LTC (long-term care) needs for the old people without activities of daily living (ADL), which is also a serious hazard caused by public health emergencies such as COVID-2019 and SARS (2003), has become an urgent task in China, Germany, Japan, and other aging countries. As a response, the LTCI (longterm care insurance) system has been executed among European countries and piloted in 15 cities of China in 2016. Subsequently, the influence and dilemma of LTCI system have become a hot academic topic in the past 20 years.Methods: The review was carried out to reveal the effects of the LTCI system on different economic entities by reviewing relevantliterature published from January 2008 to September 2019. The quality of 25 quantitative and 24 qualitative articles was evaluated using the JBI and CASP critical evaluation checklist, respectively. Results: The review systematically examines the effects of the LTCI system on different microeconomic entities such as caretakers or their families and macroeconomic entities such as government spending. The results show that the LTCI system has a great impact on social welfare. For example, LTCI has a positive effect on the health and life quality of the disabled elderly. However, the role of LTCI in alleviating the financial burden on families with the disabled elderly may be limited. Conclusion: Implementation of LTCI system not only in reducing the physical and mental health problems of health care recipients and providers, and the economic burden of their families, but also promote the development of health care service industry and further improvement of the health care system. However, the dilemma and sustainable development of the LTCI system is the government needs to focus on in the future due to the sustainability of its funding sources.

Public emergencies reduce social welfare but may paradoxically stimulate corporate innovation through crisis-driven technological adoption. This study establishes a theoretical framework demonstrating that exogenous shocks create asymmetric innovation incentives, with digitally disadvantaged firms exhibiting stronger technological upgrading responses. Empirically, we construct a firm-level digital transformation index through textual analysis using a multi-source media database in China to show that digital transformation can endow firm resilience by boosting capital market performance during public emergencies, especially for those medium-sized enterprises due to the costs and need for digital transformation. This research adds to the evidence that public emergencies can leverage advanced technology adoption.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

This study identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.