土地市场扭曲如何影响制造业出口产品质量升级*——基于土地市场交易数据和制造业企业数据的实证分析

地方政府“以地谋发展”的策略在促进各地区制造业大规模集聚和出口贸易快速增长的同时，也势必会给企业出口产品质量带来深刻影响。本文综合利用中国土地市场网城市土地交易数据、中国工业企业数据、中国海关进出口产品数据和中国城市面板数据，实证检验了土地市场扭曲对企业出口产品质量升级的影响，并对其内在机制进行了探讨。研究发现：中国城市建设用地配置存在明显的工业偏向性，进而导致工业用地价格被低估，产生工业用地应得收益大于实际价格的反向扭曲问题。这种反向扭曲可通过抑制技术进步、阻碍产业结构高级化、弱化集聚经济效应等机制显著降低制造业企业出口产品质量。土地市场扭曲对企业出口产品质量升级的影响具有明显的异质性特征。具体而言，土地市场扭曲不利于一般贸易企业与混合贸易企业出口产品质量提升，但对加工贸易企业出口产品质量提升具有促进作用。土地市场扭曲对企业出口产品质量升级的抑制作用由东到西依次递增。土地市场扭曲不利于外资企业和国有企业出口产品质量提升，对集体企业及民营企业的影响不显著。     

Conditional Mandates on Management Earnings Forecasts: The Impact on the Cost of Debt

Exploiting a unique conditional disclosure mandate on management earnings forecasts (MEFs) in China, we examine the differential effects of voluntary and mandatory MEFs on the cost of debt. We find that firms providing voluntary MEFs have lower cost of debt than do mandatory forecasters and nonforecasters. The results of the channel analyses reveal that voluntary forecasters have greater commitment to voluntary MEFs in future periods than do mandatory forecasters and nonforecasters, and the precision, accuracy, and timeliness of MEFs are higher for voluntary forecasters than for mandatory forecasters. Additional analyses show that the differential effects of voluntary and mandatory MEFs on cost of debt are stronger for voluntary forecasters operating in opaque information environments, issuing high-quality and confirming forecasts, controlled by private shareholders, and operating in highly competitive product markets. Overall, our results indicate that, compared with mandatory MEFs, voluntary MEFs are more informative for credit investors, particularly for firms facing greater information risk and operating uncertainty.     

Farm Capital Investment and Deviations from the Optimal Path

We identify farms’ optimal investment path in capital assets and compare it with their actual investment to assess the direction and extent of deviation from the optimal investment. A probit model is further used to investigate the determinants of the probability that a farmer over‐ or under‐invests in capital assets. We use a panel dataset of Dutch dairy farms over the period 2003–2013, and find that most farms under‐invest in capital assets during the study period. Although the number of farms that had over‐invested in capital assets is relatively small, these farms account for the biggest share of total investment in capital assets. The probit results show that liquidity, agricultural support payments, age, land tenure and standard output size are important variables explaining the likelihood of over‐and under‐investment.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

基于生态位理论的浙江省区域农产品质量竞争力评价研究

[目的]把握区域农产品质量竞争力不仅是促进区域农产品富有竞争力的积极方法,也是稳步提升农产品质量安全的重要做法。[方法]文章运用生态位态势理论,构建了以区域为评价主体的农产品质量竞争力评价体系,并从两个层面对浙江省各地级市农产品质量竞争力进行研究。在纵向层面上,测算了各地级市农产品质量竞争力生态位宽度,对各地级市农产品质量竞争力状态进行了评价和排序。在横向层面上,测算了各地级市农产品质量竞争力生态位重叠度,对各地级市农产品质量竞争力的合作程度进行了评价与区分。最后整合运用生态位协同、生态位扩充等理论,提出浙江省各地级市提升农产品质量竞争力发展策略。[结果]通过区域农产品质量竞争力生态位宽度研究发现,杭州和宁波的农产品质量竞争力较强,其生态位宽度分别为0.103 6和0.103 2,而丽水和舟山农产品质量竞争力相对居后,其生态位宽度分别为0.079 0和0.075 6;通过区域农产品质量竞争力生态位重叠度研究发现,可将全省划分为西北平原区域、东南部沿海区域和西南部山地区域等3个区域,各区域内部地级市可通过协同合作提升农产品质量竞争力。[结论]根据各地级市生态位宽度和生态位重叠度的差异,应采取生态位扩充、生态位协同、生态位分离的策略,从而因地制宜提升区域农产品质量竞争力。     

基于可持续生计分析框架的农户宅基地流转意愿研究

[目的]宅基地流转是城乡建设用地优化配置和农民财产性收入增加的重要途径,对解决我国当前土地资源利用的供需矛盾,推动高效用地具有重要意义。文章从家庭生计资产量化入手,探讨不同资产配置类型农户宅基地流转的影响因素,旨在进一步推进宅基地流转进程。[方法]通过农户生计量化法和Logistic回归模型进行实证分析。[结果](1)农户整体宅基地的流转意愿较高,达到69.84%,但资产缺乏型农户的流转意愿非常低,仅有37.93%;(2)非农迁移意愿、宅基地功能和家庭生计资产总值对宅基地流转具有显著影响,年龄、专业技能、住房的满意度、宅基地面积、非农收入比例、非农就业状况6个因素对不同类型农户的影响出现差异。[结论]该文提出建立贫困农户的技能培训机制,制定多样化的宅基地流转制度,从而保证不同类型农户在宅基地流转后,整体福利水平不下降。     

Noise traders,firm-specific uncertainty and technical trading effectiveness

This is the first study to document evidence of technical trading effectiveness at firm level in the Chinese A-share market by investigating the relationship between excess profits of technical trading rules and firm-specific characteristics. Our results reveal that firms with higher excess profits from technical trading have more noise traders and higher institutional ownership and that those firms tend to be growth firms with lower liquidity and higher firm-specific uncertainty. Further analysis shows that the profitability of technical trading rules is unsustainable and the excess profits of the highest technical trading profit quintile portfolio disappear in the following year.     

Competing with complementors: An empirical look at Amazon.com

Research Summary : Platform owners sometimes enter complementors' product spaces and compete against them. Using data from Amazon.com to study Amazon's entry pattern into third‐party sellers' product spaces, we find that Amazon is more likely to target successful product spaces. We also find that Amazon is less likely to enter product spaces that require greater seller efforts to grow, suggesting that complementors' platform‐specific investments influence platform owners' entry decisions. While Amazon's entry discourages affected third‐party sellers from subsequently pursuing growth on the platform, it increases product demand and reduces shipping costs for consumers. We consider the implications of these findings for complementors in platform‐based markets. Managerial Summary : Platform owners can exert considerable influence over their complementors' welfare. Many complementors with successful products are pushed out of markets because platform owners enter their product spaces and compete directly with them. To mitigate such risks, complementors could build their businesses by aggregating nonblockbuster products or focusing on products requiring significant platform‐specific investments to grow. They should also develop capabilities in new product discovery so that they could continually bring innovative products to their platforms.     

Unfolding China’s state-owned corporate empires and mitigating agency hazards: Effects of foreign investments and innovativeness

Integrating agency and institutional perspectives, we describe how China’s socio-political institutions create state-owned corporate empires with unique agency conflicts. We develop a framework demonstrating how economically unjustified firm expansion, i.e. empire building, mediates the relationship between state ownership and performance. We uncover the instrument in empire building and appropriate corporate governance and strategic management remedies. An empirical study on 29,638 Chinese firms evidences that (1) increased state ownership drives higher management expenses and lower firm profitability though empire building; (2) long-term debt is used to finance empire building; and (3) foreign capital investments and innovativeness can mitigate these agency conflicts.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.