We study how expanding access to intellectual property (IP)-backed credit affects firm innovation and aggregate total factor productivity (TFP). Exploiting China's IP-backed financing pilot as a quasi-natural experiment, we use difference-indifferences estimates to show that the policy increases R&D participation, raises firm-level productivity, and reduces capital misallocation. To interpret these effects and quantify their aggregate implications, we develop a dynamic model of heterogeneous firms that borrow subject to collateral constraints backed by tangible assets and IP, and choose R&D investment and the accumulation of wealth. R&D raises future productivity and creates collateralizable IP. We estimate the model by the method of simulated moments, using the policy-induced shift in R&D participation to identify the change in IP pledgeability. The estimated model implies that a nationwide adoption of IP-backed financing would raise long-run TFP by 14%, with roughly two-thirds coming from static gains due to improved capital allocation and the remainder from dynamic gains driven by higher innovation that accumulate over time. Relative to a comparable expansion of lending backed by tangible collateral, IP-backed financing delivers larger productivity gains by easing constraints for high-productivity but low-wealth firms and by strengthening incentives to accumulate IP as collateral for future borrowing.

This paper explores the impact of the Integrated Government Service Platform (IGSP) reform, a large-scale digital regulatory initiative in China, on firms’ investment efficiency. Using a firm-level panel dataset of Chinese listed firms, we find that the IGSP reform significantly enhances firms’ investment efficiency. Mechanism analyses indicate that the IGSP enhances firms’ investment efficiency by improving governance service quality and convergence of governance services across cities within a province, thereby reducing economic policy uncertainty and reinforcing market expectations. The heterogeneity analyses reveal that the effect is more pronounced for firms without political connections, those operating in high business volatility and high-tech industries, and regions with stronger informal institutions. Finally, the IGSP encourages firms to expand their investment activities to other cities within the same province. This finding is fully consistent with the underlying mechanisms identified in this study. Our findings provide novel evidence on the economic consequences of digital regulatory integration and offer policy implications for advancing service-oriented and efficiency-driven governance in developing economies.

This paper studies the role of the soft budget constraint in shaping firm dynamics and long-run economic growth. We develop a Schumpeterian model with two sectors: consumption and investment, and two types of firms: state-owned enterprises (SOEs) and privately owned enterprises (POEs). Within this framework, multi-product firms require fixed inputs of investment goods to operate — a resource that is also crucial for firm expansion, and the soft budget constraint allows subsidized firms to continue operating on loss-making product lines. This distortion raises the price of investment goods, suppresses firm dynamics, and dampens aggregate growth. To quantify the mechanism, the model is calibrated using Chinese firm-level data. Our quantitative exercise shows that eliminating the SOE soft budget constraint friction reduces the price of investment goods by 9.82 percent, increases the overall TFP growth rate by 0.12 percentage points, and generates a welfare gain of 4.94 percent.

Enhancing the performance of corporate green supply chain management (GSCM) represents a critical enabler for delivering on the central imperatives of the global low-carbon transition. Using panel data of Chinese listed companies from 2003 to 2022, this study employs a staggered difference-in-differences (DID) approach to examine the impact of the Low-Carbon City Pilot (LCCP) policy on firms' GSCM performance. The results show that the LCCP policy significantly improves firms' GSCM performance, and this effect remains robust across a range of sensitivity tests. Mechanism analyses reveal that the policy operates primarily by stimulating green innovation and increasing investment in environmental protection equipment. Furthermore, financial constraints play a significant moderating role: the positive effect of the policy on GSCM performance is stronger when firms face fewer financing constraints. Heterogeneity analysis indicates that the policy effects are more pronounced among non-heavy-polluting enterprises, those located in resource-dependent cities, and ISO 14001-certified enterprises. This study provides novel micro-level evidence on the environmental governance effects of place-based climate policies, and offers key policy implications for scaling up low-carbon city pilots and promoting corporate supply chain decarbonization.

In recent years, despite China’s sustained expansion of public investment in education, pronounced regional disparities in educational development persist, revealing the practical constraints of the existing fiscal sharing structure in advancing equity objectives. In particular, the mechanism through which the allocation of fiscal responsibilities between central and local governments influences educational equity performance remains insufficiently examined in a systematic and empirical manner. From the perspective of fiscal structure, this study seeks to identify and explicate the evolutionary trajectory of educational fiscal equity in China, as well as its underlying institutional determinants. To this end, the study constructs a fiscal performance evaluation framework comprising seven core indicators. Objective weights are assigned using the entropy method, and the TOPSIS model is subsequently employed to conduct a comparative ranking analysis of national education fiscal performance over the period from 2013 to 2023. The results indicate that educational fiscal equity has exhibited an overall upward trend during the study period; however, its evolution displays clear stage-specific characteristics. In the initial phase, improvements in equity were primarily driven by fiscal expansion, leading to rapid gains. In the subsequent period, progress became increasingly constrained by structural optimization bottlenecks, with fiscal performance gradually stabilizing at a plateau in more recent years. Further analysis reveals that the persistently heavy fiscal burden borne by local governments, coupled with the relatively limited regulatory and equalization capacity of the central government, constitutes a key structural impediment to the sustained improvement of educational equity. By elucidating the internal mechanism through which the allocation of fiscal responsibilities shapes educational equity performance, this study provides both theoretical grounding and quantitative support for deepening fiscal system reform and optimizing policies related to the allocation of educational resources.

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.

Firm characteristics help estimate conditional factor loadings. Larger libraries contain new and overlapping information. We use the cross-sectional distribution of firm characteristics as a ruler to evaluate IPCA loading spaces. With factor counts fixed, expanding from 36 to 153 characteristics can improve in-sample fit while representing momentum and size positions less fully. Reallocating shared exposures changes attribution without changing joint fitted returns. With coefficients fixed, exposure references alter holdings, trading, and subsequent risk at the same characteristic target. Statistical factor models should be evaluated not only by return fit, but also by the exposures they represent and the investment decisions they support.

This paper asks whether a personally experienced bubble crash leaves a lasting imprint on investment style. We study this in China’s stock market, where two technology-growth booms —the 2014–2015 Internet boom and the 2025–2026 AI hardware boom—share a similar narrative but are separated by a decade of generational turnover. Following managers across the mutual funds they run from 2013Q1 to 2026Q1, we compare those who lived through the 2015 crash with otherwise similar managers who entered afterwards. We find that the caution of veteran managers reflects a scar rather than a symptom of ageing. Conditional on age and career length, managers who lived through the 2015 crash hold more value exposure and chase technology stocks less when prices have been rising, and this reluctance deepens with how much the crash hurt them—crash-window tenure, performance shortfalls, redemption pressure—rather than with age. These demand differences reach asset prices as well: stocks held disproportionately by managers without bubble memories carry significantly higher subsequent left-tail risk, whereas crash-experienced ownership stabilises the stock, particularly when ownership is broad. Heterogeneous institutional beliefs rooted in career experience therefore shape not only how managers invest but also the crash risk of the assets they hold.

Can fully agentic artificial intelligence predict equity returns in real time? We build a novel dataset of daily AI-generated attractiveness scores spanning the entire Russell 1000 universe, continuously updated since April 2025. Each trading day, the model freely searches the live internet and produces quantitative scores for every constituent stock before the next market open, eliminating both look-ahead bias and attribution bias. A daily value-weighted portfolio of the 20 highest-ranked stocks earns a Fama-French six-factor alpha of 16.5 basis points per day (41.5% per annum), with an average bid-ask spread of 1.92 basis points. Predictability is sharply asymmetric: the model reliably identifies future outperformers but not future underperformers. The evidence points to stock picking rather than market timing: performance is strongest on down-market days, and the alpha does not come from betting on aggregate market direction. The AI’s reasoning traces help explain this predictability. Alpha is concentrated in traces with richer live-web evidence and stronger analytical structure. In the theme attribution, most active alpha reflects firm selection under an investment lens, not broad theme choice.

Firms frequently overstate digital transformation in public disclosures while committing less in observable investment, a phenomenon known as digital catering. We examine whether digital public financial infrastructure can discipline such symbolic behavior. Using a staggered difference-in-differences design on Chinese A-share listed firms over 2014--2024, we find that exposure to China's e-CNY pilot reduces digital catering by roughly 14% relative to the sample mean. The decline is driven almost entirely by reduced rhetorical digital transformation, while balance-sheet-based digital commitment rises only marginally: the e-CNY pilot is associated with substantially less digital talk, but not with a commensurate increase in digital walk in the short run. The effect operates through lower agency costs and reduced information asymmetry, and follows an amplification--substitution pattern---stronger among digital-sector and accounting-opaque firms, weaker where internal controls and institutional ownership are already strong. The evidence identifies financial-transaction verifiability as a novel source of discipline over corporate cheap talk: CBDC-related infrastructure constrains unsupported symbolic claims without immediately accelerating substantive digital investment.