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。     

The Benefit of Frequent Corporate Philanthropy

Applying behavioural economic theory, we argue that the frequency of philanthropic activity is important in determining corporate philanthropy's economic outcome. Using Chinese data from 2003 to 2016, we find that firms with more frequent philanthropic activities obtain more government subsidies than firms that only engage in one-off charitable donations. Firms with better corporate governance, such as higher management ownership and more independent directors, are more likely to adopt a strategy of frequent donation. Furthermore, firms are more likely to be frequent donors when management or local government officers have a long tenure. Our findings provide insight into the giving process and suggest that firms can maximize the benefit of corporate philanthropy if they strategically consider the donation frequency and donation amount simultaneously.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

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.     

Debt Overhang and Deleveraging in the US Household Sector: Gauging the Impact on Consumption

Using a novel data set for the U.S. states, this paper examines whether household debt and the protracted debt deleveraging help explain the dismal performance of U.S. consumption since 2007 in the aftermath of the housing bubble. By separating the concepts of deleveraging and debt overhang—a flow and a stock effect—we find that excessive indebtedness exerted a meaningful drag on consumption over and beyond wealth and income effects. The overall effect, however, is modest—‐around one sixth of the slowdown in consumption between 2000–06 and 2007–12—and mostly driven by states with particularly large imbalances in their household sector. This might be indicative of non‐linearities, whereby indebtedness begins to bite only when misalignments from sustainable debt dynamics become excessive.     

Situating the Asian Infrastructure Investment Bank in the context of global economic governance

The creation of the Asian Infrastructure Investment Bank (AIIB)was welcomed by the World Bank but opposed by the Obamaadministration. The paper explains China’s positive relationshipwith the Bank and the Organization for Economic Cooperation andDevelopment (OECD) in terms of the mission of the Bank, sharedby the OECD, to develop and deepen the global economy. The AIIBand the related Belt and Road initiative promise to do this throughinvestment in infrastructure and connectivity in and around thepoorly integrated Eurasian landmass. But while the current Chineseleadership has supported an inclusive global economy based uponfree trade and supported by multilateral institutions, China’s controlof resources outside the multilateral framework and adherence topractices that challenge liberal principles prompt suspicions thatthese commitments are either disingenuous or anyway subjectto reversal. In itself, therefore, the AIIB provides no conclusiveevidence either way on China’s future course.     

Positive Feedback Trading and Investor Sentiment

This article examines how investor sentiment affects positive feedback trading behavior. By analyzing the daily closing total return of CSI 300 index and its individual returns of stocks, we find that relatively high or low sentiment induces active positive feedback trading. With a specific indicator of sentiment, we explain the microstructure setting of the relationship between positive feedback trading and sentiment. We adopt the classical feedback model from Sentana and Wadhwani (1992) to measure positive feedback trading behavior. By adding sentiment factor to the model, we successfully explain how sentiment influences the behavior of both feedback traders and rational investors. The empirical findings suggest that positive feedback traders are more likely to trade when the prices of most securities move forward together. When the sentiment of feedback traders is at an intermediate level, the feedback trading behavior is insignificant.     

Review: Chinese public administration and finance—a call for a new theory,research and dialogue

In implementing its national strategy to achieve global leadership, China needs a new public administration theory that integrates political administration, economic management and social regulation. Even so, China has already started to promote its brand of political and economic development abroad. Therefore new comparative research and dialogue is called to explore the universality of Chinese and Western public administration ideas.     

Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications

Recent methodological developments provide a way to incorporate the temporal dimension when accounting for spatial effects in hedonic pricing. Weight matrices should decompose the spatial effects into two distinct components: bidirectional contemporaneous spatial connections; and unidirectional spatio-temporal effects from past transactions. Our iterative estimation approach explicitly analyses the role of time in price determination. The results show that both spatio-temporal components should be included in model specification; past transaction information stops contributing to price determination after eight months; and limited temporal friction is exhibited within this period. These findings highlight the decidedly non-linear temporal patterns of such information effects.     

Flow Signals: Evidence from Patent and Alliance Portfolios in the US Biopharmaceutical Industry

Integrating signalling theory and the portfolio diversity literature, we theorize that diversity in a firm's patent and alliance portfolios sends contrasting flow signals impacting its market value in a nuanced way. Diversity in an alliance portfolio mediates the patent portfolio diversity – market value relationship by suppressing the negative effect of patent portfolio diversity creating an overall positive effect. We test our mediation model on a longitudinal set of 225 US biopharmaceutical firms that were awarded 17,078 patents and participated in 37,744 alliances between 1990 and 2006. Our theory and findings contribute three novel insights. First, we demonstrate the value of a temporal lens in explaining why diversity in a firm's patent and alliance portfolios send flow signals that establish expectations among market observers and have performance implications. Second, establishing that patent and alliance portfolio diversity are temporally sequenced provides compelling evidence for the value of studying multiple types of portfolios, their temporal relationships and effects on firm outcomes. Third, since diversity in a firm's portfolios can send contrasting flow signals conditioned on the cognitive demands and proximity involved in interpreting the signals, firms that do not maintain a ‘signalling fit’ with market observers increase the probability of unintentional negative signalling effects.