This paper investigates the weekly evolution of child skills as measured by unique data from a widely-emulated early childhood home-visiting program developed in Jamaica, adapted to rural China, and applied in different versions worldwide. The design of the study avoids problems of endogeneity of inputs and lack of truly comparable measures of skills across children that plague previous econometric studies of child development. Skills that are nominally classified as the same, in fact, do not appear to share a common unit scale across levels. They are produced by skill-specific, lifecycle-stage-specific technologies. We formulate and estimate a new dynamic stochastic skill production model for multiple skills that is consistent with the evidence. We quantify the dynamics of early life learning. The model explains the “fadeout” of measures of learning by the emergence of new skills not properly measured. We investigate the role of ability in learning. We find important differences in learning patterns between boys and girls.

This paper examines the interactional phenomenon where an institutional complainee initiates a complaint and becomes a co-complainant with their original complainant against a third party that is proposed to have caused grievances to both participants. Institutional complainees initiate their third-party complaints when their complainants repeatedly refuse to affiliate with their attempts to shift responsibility or their proposed solutions. This shift from being the complainee to being a co-complainant is regularly accomplished through practices in which the institutional complainee: 1) produces implicit counter-complaints; 2) partitions complainants and themselves as sharing similar identities; and 3) highlights and upgrades their own grievances. Once complainants affiliate with their complaints, institutional complainees attempt to end the complaint sequences. The interactions end with a sense of solidarity sustained between the participants, even though no satisfying solutions are offered to the original complainants. The findings suggest that institutional actors can make relevant their non-institutional identities and go against what is expected of them as institutional actors to achieve the institutional task of directing blame away from their institutions. Recorded phone conversations between local residents and various institutional actors during COVID-19 lockdowns in China serve as data for this study.

Using proprietary data from China’s interbank bond-pledged repo market, we show that the interest-rate risk and credit risk of the pledged bond are key determinants of repo pricing. From a bond-option perspective, we develop arbitrage-free models that anchor the repo yield curve to the pledged-bond yield curve. The fair repo haircut is interpreted as the per-unit price of a call option on the pledged bond. We extend this framework to incorporate bail-in or bail-out potential, which enhances the model’s empirical performance and provides a novel explanation for systematic repo cheapness and existence of negative haircuts.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

In this paper, we design two chapters to discuss trade dynamics with heterogeneous fluctuations, contributing new insights to macroeconomic issues related to international trade. In the first chapter, we model general exchange rate fluctuations through stochastic processes and analyze the impact of heterogeneous price shocks on export competitiveness. We find that monetary policy and innovation both show positive effects on export trade, while monetary policy stabilizes exchange rate fluctuations to comprehensively boost provincial export competitiveness, innovation reduces its reliance on exchange rate mechanisms. The optimal policy according to exchange rate fluctuations aims to solve the wealth distribution of exporters, and it suggests that optimal policy should promote dynamic transitions in trade patterns rather than maintain existing comparative advantages in heterogeneous trade structures. In the second chapter, we model labor market fluctuations and the ability to utilize production factors through stochastic processes, and we analyze the impact of heterogeneous aggregate production shocks on general international trade. We find that labor market fluctuations only benefit international trade under the cooperation policy. Moreover, for both sanction and cooperation policy scenarios, positive shocks (i.e., shocks where average wage growth in the labor market exceeds unemployment) strengthen their impact on import trade while weakening their impact on export trade, and vice versa. Regarding the theories proposed in these two chapters, we prove them through empirical analyses using the provincial data of China.

Overwork intensity (OI) is a salient issue that directly affects employees’ motivation and productivity. By using a novel dataset of overwork intensity constructed from daily high-resolution nightlight satellite images, we examine whether overwork intensity is a priced risk in the cross-section of stock returns. We show that a zero-investment portfolio that buys the highest OI quintile stocks and shorts the lowest OI quintile stocks earns 0.495% returns per month. This result is robust when controlling for various well-known risk factors. We argue and empirically verify that profftability, corporate governance, investor sentiment and lottery preference are the potential channels that drive the result.

This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

Digital mergers and acquisitions (M&As) are increasingly becoming a critical strategic approach for enterprises to advance digital transformation. This study conceptualizes digital M&As as positive shock events for corporate digital transformation. Using a dataset of digital M&As by Chinese listed companies from 2005 to 2024, this study applies the propensity score matching combined with difference-in-differences (PSM-DID) method to empirically examine the impact of digital M&As on the market value of acquiring firms. The results show that digital M&As significantly enhance acquirers’ market value. Mechanism tests reveal that this effect is driven by digital resource empowerment, operating through increased digital factor inputs and strengthened digital innovation capabilities. Heterogeneity analysis further indicates that the market value enhancement effect of digital M&As is predominantly significant in non-digital firms, non-state-owned enterprises, and firms located in eastern China. This study expands the research scope of the micro-level effects of the digital economy and offers useful references for the Chinese government in refining its digital economy strategies, as well as practical guidance for firms in formulating their own digital investment decisions.

Does digital financial inclusion alleviate poverty? This study investigates this question by integrating the Digital Financial Inclusion Index of Peking University with microdata from the China Family Panel Studies (CFPS) to examine how the expansion of digital financial inclusion affects household multidimensional poverty in China. Anchored in Amartya Sen ’ s capability approach and operationalized through the Alkire–Foster (A–F) framework, the study identifies multidimensional poverty across five key dimensions: income, health, education, insurance, and living standards. Probit models are employed to estimate how digital financial inclusion influences both the likelihood and structure of multidimensional poverty, while instrumental variable techniques are used to address potential endogeneity. Beyond the average effects, the study further explores the mechanisms through which digital financial inclusion contributes to poverty alleviation, focusing on three channels—promoting household consumption, increasing financial investment, and enhancing access to credit. The results reveal that digital financial inclusion significantly mitigates multidimensional poverty, particularly by improving income, living standards, and health outcomes, though its effects on education and insurance are limited. These findings underscore the transformative role of digital finance in fostering inclusive growth, suggesting that policies expanding digital financial infrastructure and literacy can amplify its poverty-reducing effects and advance equitable development.