This paper studies how dividing candidates in a tournament into separate tracks with differentiated performance criteria affects incentives and aggregate outcomes. We exploit China’s Major Function Oriented Zoning plan, which assigns counties to devel-opment or conservation tracks, with the latter de-emphasizing growth indicators. Using a staggered Difference-in-Differences design, we find that prefectures introducing a con-servation track achieved higher aggregate economic performance despite relaxing growth-based evaluation for part of their subordinate counties. To explain this counterintuitive ef-fect, we develop a stylized Tullock contest model that highlights two institutional features: promotion opportunities remain open to officials in both tracks, and counties within the same prefecture continue to interact across tracks. The model further predicts an inverted U-shaped relationship between the size of the conservation track and overall performance, which is supported by empirical evidence.

The relationship between economic decentralization and regional economic growth has attracted wide academic attention. However, existing literature rarely explores the validity and applicability of decentralization theory from the perspective of location endowments. This study takes China’s county-to-city reform - a decentralization policy - as a point of entry to construct a decentralization model incorporating location endowments. Using county-level panel data from 1992 to 2009 and employing a difference-in-differences (DID) approach, the study evaluates the impact of county-to-city reform under the constraint of location endowments on county-level economic development. The findings reveal that the policy effects of local decentralization are constrained by location endowments. Specifically, in regions with poorer location endowments, county-to-city reform significantly inhibits local economic development. Indeed, mechanism analysis further indicates that in such disadvantaged regions, local decentralization under the county-to-city reform generates a “political resource curse” effect, which hinders economic growth through three channels such as distorted fiscal expenditure structures, corruption, and misallocation of land resources. The conclusions of this study provide a valuable perspective for understanding the conditions under which decentralization theory holds, while also offering rigorous empirical evidence for government agencies to design more targeted decentralization policies.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

中国经济增速自 2012 年起持续下行，学界对其潜在增速的判断长期存在分歧。本文基于2005Q1—2025Q4中国31个省级行政区季度面板数据，构建“双场景 + 双红利 + 双约束”统一分析框架，系统检验不同经济循环模式下的增长约束机制。对比多种计量方法的稳健性后，采用工具变量法、多期 DID、中介效应、滚动窗口门槛回归、反事实模拟及异质性企业 DSGE 模型开展实证分析，得到三项核心结论。第一，中国经济增长呈现清晰的四阶段演化特征，外需主导期与内需主导期的约束机制存在本质差异；仅在外需主导阶段，美元M2增速与GDP增速存在显著倒 U 型关系，7%—10%为最优黄金区间。第二，3.30% 实际融资利率与零边际利润率双重约束严格锁定内需主导期增长，一旦进入“越投越亏”区间，GDP增速将出现断崖式回落。第三，现有研究关于潜在增速的分歧，本质是无条件收敛假说与有条件收敛假说的视角差异。本文拓展了开放条件下的经济收敛理论，构建的双场景双约束分析框架，为理解中国经济增速换挡提供了新的理论视角与实证支撑。

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.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

在实现“双碳”与共同富裕目标并进的背景下，环保规制的公平后果日益受到关注。本文以2013、2014与2016年分批启动的碳排放权交易试点为准自然实验，基于2000—2023年中国区县面板数据，系统评估市场型环保规制的分配效应。结果变量方面，本文使用区县夜间灯光构建的不平等指标（基尼、泰尔、阿特金森）刻画县域经济活动分布，并以县域城乡居民收入对数差距作为补充。识别策略方面，除区县与年份双向固定效应的TWFE-DID外，进一步引入适用于分批采用的更强识别方法：（1）Sun-Abraham分组事件研究用于动态效应检验并规避传统事件回归在异质处理效应下的加权偏误；（2）Callaway-Sant’Anna ATT(g,t)在“尚未受处理/从未受处理”对照组框架下估计分组—时期平均处理效应；（3）合成双重差分（SDID）同时估计单位权重与时间权重，以匹配处理前趋势并降低对严格平行趋势的依赖。研究发现：在TWFE-DID下，试点显著降低了县域夜间灯光基尼与阿特金森指数；更强识别（ATT(g,t)、SDID）在城市层面同样指向夜间灯光泰尔指数的下降，但幅度更为温和。机制检验表明，试点显著降低城市单位GDP能耗，支持“绿色转型—要素再配置—分配格局改善”的作用链条。

Purpose The study investigates the impact of government intervention policy of capital markets (“National Team”) on firms’ sustainable management, i.e., green mergers and acquisitions (GMAs) in China, aiming to understand how such interventions influence corporate investment activities amidst a growing focus on green transition. Design/methodology/approach The research employs a dynamic analysis of quarterly data from Chinese companies (2014 Q1 to 2022 Q4), utilizing identified strategies, such as double machine learning-DID and multiple panel data regressions to assess the effects of government intervention on GMAs, and examines potential economic channels like liquidity, market stabilization, and informativeness. Findings The study finds that increased government intervention via direct stock purchases significantly boosts both the number and amount of GMAs, with economic significance of 23% and 45%, respectively. It identifies liquidity, market stability, and informativeness efficiency as underlying economic channels for this effect. Practical implications The findings suggest that government interventions can enhance corporate investment in green sectors, guiding firms to align strategies with sustainability goals. This can inform policymakers regarding the effectiveness of direct stock purchases in fostering a green economy, especially for large emerging countries. Social implications By promoting GMAs, government interventions contribute to green innovation and energy transition, ultimately benefiting society through enhanced environmental sustainability and compliance with eco-friendly regulations. Originality/value This research uniquely documents the direct effects of government stock purchases on corporate green financial activities, particularly GMAs, in a Chinese context characterized by tight credit, thereby expanding the understanding of government intervention in emerging markets.

This study employs a Triple-Difference (Triple-DID) model, utilizing balanced panel data at the district level from Beijing, Shanghai, Shenzhen, and Hangzhou between 2018 and 2024, to critically evaluate the effectiveness of the Multi-School Zoning Policy (MSZP) in suppressing the capitalization of educational resources into housing prices and promoting educational equity. The research explicitly accounts for spatial and institutional heterogeneity as well as household strategic behavior.The results indicate that: (1) MSZP significantly reduced the average housing price premium associated with elite school districts by 15.2%, with the strongest effect observed in Beijing and the weakest in Hangzhou; (2) The policy's effectiveness diminishes as the spatial concentration of high-quality educational resources increases, highlighting persistent structural inequalities; (3) In areas characterized by resource monopolization and strong institutional inertia, the policy's suppressive effect on educational capitalization and its gains in educational equity are both constrained.The findings suggest that MSZP alone cannot fully overcome the "spatial lock-in" effect of high-quality educational resources. Achieving lasting equity requires complementary deeper institutional innovations, such as robust cross-district teacher rotation, transparent resource allocation mechanisms, and adaptive zoning algorithms. This research offers quantitative evidence for optimizing policy and institutional tools in the pursuit of comprehensive urban education reform.