Despite strong GDP growth and a favourable demographic profile, India faces an impending jobs crisis. A large share of the workforce remains employed in lowproductivity agriculture, while many new labour market entrants are absorbed into the persistently large informal sector. By contrast, China's rapid ascent was driven by manufacturing-led, export-oriented industrialisation, underpinned by large inflows of foreign direct investment and sustained technology transfer. India's manufacturing base remains modest in contrast. The bulk of well-paid, formal employment continues to be concentrated in the high-skill services sector. This paper contrasts the development trajectories of these two economies and identifies several underutilised jobs-growth levers in India: manufacturing, goods exports, manufacturing-oriented foreign direct investment and innovation. All of these remain underdeveloped, yet together they offer a pathway to more labour-absorbing, durable growth. Leveraging them effectively would be central to achieving India's 'Viksit Bharat 2047' ambition of attaining high-income status. The scale of India's challenge to employ eight to ten million labour-market entrants per year implies that job creation must become an explicit policy priority. This calls for greater trade openness, particularly with Asia and Europe, to integrate India into Asia-centric global supply chains as an alternative to China. Labour market reform is equally critical, making the effective implementation of the new labour codes essential. Strengthening innovation ecosystems and realigning education and skills policies to support industrialisation are also key. Without these structural shifts, India's current pattern of jobless growth risks transforming its demographic dividend into a long-term liability.

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, I show that an everyday contextual cue—geographic location—triggers the recall of previously traded stocks: when an investor returns to a city she has visited before, her probability of retrading stocks associated with that city rises by 2.1 percentage points under the most saturated specification (up to 12.2 percentage points under less saturated ones). The effect displays the signatures of similarity-based recall: it is diluted by intervening trading experiences, decays with the age of the memory trace, survives when the city is not the investor's home, is not explained by mechanical recency, and is present for orders submitted via both smartphones and PCs. I then examine an extreme-event cue, COVID-19 quarantine, and find a smaller and less robust effect; the interference and decay signatures are absent precisely for these strongly encoded traces, as the memory account predicts. Memory-cued transactions underperform: stocks bought upon the cue earn about 50 basis points less per week than stocks bought for other reasons on the same day. The study provides causal field evidence for memory-based theories, particularly similarity-based recall, in real-world trading.

Entry of arbitrageurs need not increase with larger mispricing. Arbitrageurs trade mispriced assets and close positions once mispricing is corrected, seeking to minimize carry cost (shadow value of trading capital). More deployed arbitrage capital shortens the wait, lowering carry costs but also payoffs. Discretionary entry is profitable only in a “Goldilocks” region, where deployed capital is neither too low nor too high: enough capital is deployed to accelerate correction, while profits remain. The region’s upper bound follows from the condition that every sequence of entrants has a last entrant who cannot rely on subsequent discretionary entry.

Less informative strategic advice can produce more informative prices. We study an informed adviser who both trades on his own account and receives compensation linked to the post-report price. Holding private information fixed, we show how promotion can restrain manipulation motivated by trading profits. At sufficiently high reputation, raising compensation enough to eliminate false bad news used to buy cheaply improves market price informativeness in every equilibrium, even though the strategic adviser’s report becomes uninformative. Under further sufficient conditions, always-favourable strategic reporting yields the most informative current prices among equilibria at every interior reputation. We also derive analytical sufficient conditions under which higher reputation lowers the adviser’s expected payoff and worsens prices within the same equilibrium. Financial influencers motivate the joint incentives: a reward for higher prices can counteract the incentive to depress prices before buying, while greater credibility need not benefit its holder.

We examine the relationship between government data opening and corporate leverage manipulation. Leveraging the staggered establishment of province-level open government data platforms in China as a quasi-natural experiment, we find that government data opening significantly reduces corporate leverage manipulation. This policy effect is more pronounced among firms with low capital intensity, firms with limited analyst coverage, and those located in regions with low banking competition. Mechanism analysis suggests that government data opening affects the cost-benefit tradeoff of corporate leverage manipulation by alleviating financing constraints and enhancing corporate governance, thereby mitigating leverage manipulation practices. Our research contributes to the literature on government data opening, highlighting its role in reducing leverage manipulation and offering valuable implications for mitigating significant financial risks.

This paper studies whether and how policies that promote a currency’s use in trade invoicing can also strengthen its role as an anchor currency in other economies’ exchange rate baskets. We exploit the establishment of RMB swap lines by the People’s Bank of China as a natural setting to examine this mechanism. We first estimate countries’ implicit currency baskets and the weights assigned to the RMB, and then use a staggered difference-in-differences design to assess the effect of initiating swap line agreements on the RMB’s weights in these baskets. Our results indicate that RMB weights increase by approximately 5% about nine months after a swap line is introduced and remain persistently higher thereafter. Finally, we develop a three-country DSGE model to interpret these findings, showing that by promoting RMB invoicing, swap lines reinforce the RMB’s role as an anchor currency for exchange rate stabilization.

Partially convertible economies face a market-design problem: trade integration, cross-border investment, and domestic balance-sheet exposure increase the demand for currency hedging before full financial integration is complete. China adopted a distinctive architecture for this problem by fostering a deliverable offshore Renminbi market (CNH) alongside the segmented onshore market (CNY), rather than relying only on non-deliverable forwards. This creates two venues for closely related claims on the same currency. Spot prices are tightly linked, yet CNY and CNH forwards display a persistent and economically large discrepancy. We study that discrepancy in a joint equilibrium model for spot and forward trading with transaction costs and segmented supply. In the benchmark case with common constant supply and deterministic costs, spot parity implies a forward differential with the wrong sign relative to the data. Random offshore stress, modeled as a jump in trading costs, overturns this benchmark while preserving tight spot parity. The model yields a semi-explicit representation in the CNY/CNH application and a calibration of the observed forward discrepancy in terms of the market-implied likelihood and severity of offshore liquidity stress.

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].

This paper examines the spillover effects of China’s Carbon Emissions Trading Scheme (CETS) on non-regulated firms’ bank loans. Using a sample of Chinese A-share listed firms and a staggered difference-in-differences design, we find that suppliers experience a significant decline in bank loans when their customers are included in the CETS. This effect is driven by reductions in firms’ cash flow and customer concentration. The negative effect of downstream CETS on suppliers’ bank loans is attenuated for suppliers with better environmental performance, more comprehensive carbon disclosure, and closer geographic proximity to customers. We also find that, in response to reduced bank credit, firms rely more heavily on trade credit. Overall, this study sheds new light on the unintended financial consequences of CETS policy on non-regulated firms.

Examining a cross section of seven regional Emission Trading System (ETS) and thenational ETS in China, we explore the interplay between firms and governments. We find heterogeneous adaptation among firms. Firms in regions anticipating stringentpolicies reduce emissions and invest in decarbonization technology, whereas expecta-tions of lenient policies lead to increased emissions. Meanwhile, governments set upmore stringent carbon policies when firms decarbonize more proactively. The resultsare robust to allowance allocation policies, such as cap-and-trade or tradable perfor-mance standards. Our findings underscore the importance of strategic interactionsbetween firms and governments in decarbonization.