企业通过跨国并购在全球配置创新资源,并通过协同创新带动技术进步,这成为经济全球化背景下适应科技创新交叉融合模式的重要路径之一。文章构建了创新活动突破企业边界的跨国并购理论模型,并使用双重差分方法实证分析了中国企业跨国并购对协同创新的影响。结果表明,跨国并购促进了企业与其他研发主体之间的协同创新,这种影响主要通过企业内部增加研发投入、外部拓展海外市场和建立战略共享渠道这三个途径得以实现。此外,跨国并购的协同创新效应会受到企业内部特征、外部环境和并购交易行业特征等因素的影响。文章的研究为中国企业通过国际化战略来构建协同创新发展模式,以及双循环新发展格局下借助外循环来提升国内创新能力提供了新的思路。

近年来,美国以国家安全为由大幅加强对华出口管制,滥用实体清单打压中国科技企业,以期遏制中国科技创新发展势头,那么美国能实现其目的吗？鉴于此,文章整理了美国出口管制工具中的实体清单数据和商业管制清单数据,以 2013—2021 年中国 A 股制造业上市公司为研究样本,计量分析了美国对华出口管制对中国企业创新的影响。研究发现：首先,美国对华出口管制对中国企业创新具有双重作用。一方面,其抑制了中国企业创新产出,但该抑制效应会随时间逐渐减弱；另一方面,其促进了中国企业创新投入,该促进效应不仅会即刻显现且具有持续性,在考虑“树大招风”和产业关联的情况下,上述效应仍成立。其次,机制分析表明,美国对华出口管制通过阻碍创新知识流动抑制中国企业创新产出,通过提升企业创新动力促进中国企业增加创新投入。最后,拓展分析显示,美国对华出口管制会促使中国企业转向“内生创新”和减少低质量创新产出,研发国际化、龙头企业带头创新、政府创新驱动都可以帮助企业应对美国对华出口管制。文章的研究既拓展了出口管制对企业创新的研究边界,对探索中国特色自主创新之路也有一定启示。

要实现国内消费的长期增长,需制度的改革和完善。流动人口子女的教育问题受到了广泛关注,自 2001 年中央政府颁布“两为主”政策以来,流动儿童在父母流入城市的义务教育入学问题有了显著的改善,但异地中考和高考的问题日益突出,绝大多数省份的异地高考政策都要求随迁子女在当地拥有三年高中学籍,能否在流入地参加中考是更为前置的问题。据此,文章试图从随迁子女异地中考政策的视角,基于城区人口 100 万以上城市的随迁子女升学政策进行实证检验,分析影响流动人口家庭在流入城市消费的制度性约束。结果显示,异地中考政策的限制造成了大城市的流动人口在流入地“留居不消费”现象加剧,当子女年级越接近中考时,在异地中考门槛越高的城市,流动人口家庭在流入城市的消费越低,且这一抑制作用对中等学历、城镇户口、男孩家庭和流入超大、特大城市的家庭作用更大。机制检验表明,异地中考政策主要通过降低流动人口家庭的家庭教育期望和改变子女留守选择这两个机制来影响消费。综上,文章认为,应继续推进随迁子女升学政策的改革,进一步保障随迁子女的受教育权利,激发流动人口消费的热情与活力,这对缓解经济下行叠加复杂多变的国际政治经济环境等多重负面影响下的需求收缩,刺激城市经济复苏具有重要作用。

This paper aims to empirically explore the cyclical attributes of dynamic correlation shifts between the stock and real estate market, and the factors that influence this correlation during different periods of the economic cycle. Our research uncovers a significant structural shift in the correlation towards the end of 2012. By taking into account macroeconomic growth, regulatory policies, financial market conditions, and developments within both the stock and real estate markets, we investigate the time-varying characteristics of these factors' influence. The results highlight the pronounced cyclical asymmetry of these influential factors. Currently, the wealth effect in China's stock and real estate markets has significantly diminished, and the credit-price effect has vanished. A marked seesaw relationship is evident between the two markets. This outcome supports that various restrictions imposed on the real estate market have reduced its investment appeal.

We investigate the impact of government environmental credit ratings on bond credit spreads based on a sample of Chinese A-share listed companies from 2014 to 2022. Empirical results demonstrate that a favourable environmental credit rating significantly reduces bond credit spreads, highlighting the incentivising effect of environmental credit ratings. Mechanism testing reveals that a good environmental credit rating diminishes information asymmetry and enhances an enterprise’s resource acquisition capabilities, reducing bond credit spreads. Furthermore, subgroup analyses suggest the reduction effect is more pronounced in enterprises with low debt and tax credit ratings.

We analyze the latent factors for the Chinese market through the recently proposed risk premium principal component analysis (RP-PCA). Our empirical research covers 95 firm characteristics. We demonstrate that the RP-PCA on the Chinese market can identify factors that capture co-movements and explain pricing. Compared to the traditional PCA approach, it explains a larger proportion of return variation in both double-sorted and single-sorted portfolios. The Sharpe ratios of the tangency portfolios are significantly higher than those of the standard PCA. Additionally, we show that the RP-PCA loadings are more closely associated with factor returns.

This study introduces a novel factor model based on self-attention mechanisms. This model effectively captures the non-linearity, heterogeneity, and interconnection between stocks inherent in cross-sectional pricing problems. The empirical results from the Chinese stock market reveal compelling ffndings, surpassing other benchmarks in terms of profftability and prediction accuracy measures, including average return, Sharpe ratio, and out-of-sample R2. Moreover, this model demonstrates both practical applicability and robustness. These results provide valuable evidence supporting the existence of the three aforementioned properties in crosssectional pricing problems from a theoretical standpoint, and this model offers a powerful tool for implementing profftable long-short strategies.

The Chinese stock market is known for high synchronicity and the market portfolio represents a prominent risk factor to investors in the Chinese stock market. We conjecture that as a result, stocks with high exposure to market risk in China earn higher returns. Indeed, we find that CAPM beta is positively related to daily and monthly stock returns in the Chinese stock market. To substantiate our argument, we further show that the betareturn relation is stronger during periods when market risk is high. Moreover, we find that market risk is priced only during the day but not overnight in the Chinese stock market. We explore the effect of several unique trading rules in China and show evidence that the “T+1” trading rule is likely the cause.

Regional financial development has greatly supported the rapid growth of Chinese municipal corporate bonds. This study introduces the concept of regional financial resources and constructs an informative measure of regional financial development by using principal component analysis (PCA), incorporating 13 indicators from three primary financial industries, including bank, security and insurance. Using a sample of municipal corporate bonds (MCBs) issued in China from 2009 to 2019, we find that an increase in regional financial development is associated with significant MCB credit spreads narrowing. This effect can be realized by improving fiscal stability and debt sustainability. Additionally, this narrowing varies among cities and provinces with different fiscal conditions and economic development. The results are also verified through a series of robustness tests. This study proposes possible policy suggestions for improving the Chinese fiscal management and MCBs market.

This study investigates the impact of the land rental market (LRM) on labor productivity in rural China. Particular attention is given to farm and non-farm labor productivity. Using 2012 household-level data and a multinomial endogenous switching treatment regression (MESTR) technique, we find that rural households renting-in farmland increased labor productivity in the farm sector by about 55%, while labor productivity in the non-farm sector decreased by about 6%. We also find that rural households renting-out farmland had lower labor productivity in both the farm and non-farm sectors by 13% and 9%, respectively. More family labor transferred from the farm to the non-farm sector after renting-out land.