房产税能否抑制实体经济"脱实向虚"——来自上海和重庆的经验分析

本文利用我国35个大中城市2006-2018年的平衡面板数据,基于上海和重庆在2011年实施的房产税试点政策,运用合成控制法检验房产税政策能否抑制实体经济"脱实向虚"发展的趋势.研究结果显示,房产税政策能够有效地遏制地区房地产业新增产值占GDP的比重的上升,促使实体企业回归主业,增加实体投资,实现促进实体经济朝着"脱虚返实"方向发展的作用.鉴于此,本文提出应加快推进房产税政策在全国范围内的实施,并在政府补贴、银行贷款等方面采取一系列配套措施鼓励实体企业进行更多的实体投资.     

地方债务治理对银行信贷资金"脱虚向实"的影响——基于国务院"43号文"准自然实验的经验考察

国务院"43号文"的实施,为研究地方债务的治理效果提供了一个准自然实验场景.研究发现:地方债务治理对银行金融资产没有产生影响,但会显著增加银行总贷款、个人贷款和企业贷款,有助于银行信贷资金的"脱虚向实";地方债务治理能显著增加银行实体行业贷款,但不能显著增加银行非实体行业贷款和建筑业贷款.机制检验发现,地方债务治理可以通过降低借款人和借款企业的融资成本,间接性地影响银行信贷资金配置.进一步研究发现,国务院"43号文"实施后,资产质量高的银行倾向于增加企业贷款和实体行业贷款,资产质量低的银行倾向于增加个人贷款;银行规模可以强化地方债务治理对银行贷款增长的促进作用.因此,为促进银行信贷资金的"脱虚向实",应提高地方债务治理水平,强化对银行业务的监管与引导,加强风险管理,保障银行资产质量.     

Can investors time their exposure to private equity?

Private equity performance, both for buyouts and venture capital, has been highly cyclical: periods of high fundraising have been followed by periods of low performance. Despite this seemingly predictable variation, we find modest gains, at best, to pursuing realistic, investable strategies that time capital commitments to private equity. This occurs, in part, because investors can only time their commitments to funds; they cannot time when commitments are called or when investments are exited. There is a high degree of time-series correlation in net cash flows even across commitment strategies that allocate capital in a very different manner over time.     

Go beyond tokens: Number and type both matter

Drawing upon token theory and critical mass theory, this study examines whether all female directors are equally considered as tokens. We argue that inside female directors are less likely than outside female directors to be considered as tokens because they are more powerful and influential. Using a 10-year sample of Chinese publicly listed firms on the Shanghai or Shenzhen Stock Exchanges from 2008 to 2017, we find that even one inside female director can make a significant contribution to corporate social responsibility (CSR), while outside female directors do not have a positive effect on CSR until their number reaches the critical mass (i.e., three). The results suggest that the predictions of token theory and critical mass theory vary across female director types. These findings contribute to the current literature by integrating female director typology with their numerical representation to explain their token status.     

江淮汽车成功开拓哈萨克斯坦市场的经验与启示

产能合作是当前和未来促进中哈两国经济协同发展的核心方式,江淮汽车成功进入哈萨克斯坦,对于推进两国制造业产能合作具有重要意义.充分利用国家政策、对接哈萨克斯坦汽车工业发展战略和市场需求、选择恰当国际市场进入模式和营销策略、培植核心竞争力以及积极应对哈萨克斯坦市场的激烈竞争是江淮汽车成功进入哈萨克斯坦市场的关键因素.我国企业应有效对接哈萨克斯坦经济战略,积极参与中哈产能合作;精准目标市场定位,优化产品结构;选择合适的市场进入模式,积极开展本地化生产;不断提升企业的核心竞争力.     

Close relationship with the supervisor may impede employee creativity by suppressing vertical task conflict

Leader‐member exchange as a form of supportive leadership is expected to foster employee creativity. However, empirical evidence suggested this relationship is complex. This study attempts to address this complexity by proposing and testing a curvilinear mechanism, which attenuates the overall positive effect of LMX on creativity by transmitting a curvilinear indirect effect via vertical task conflict. Results drawn from a sample of 276 supervisor–subordinate dyads provided support for the hypothesized curvilinear indirect effect. Specifically, LMX was found to negatively influence employee creativity by suppressing vertical task conflict for subordinates in high‐quality LMX relationships, while the indirect effect was not significant for subordinates in low‐quality LMX relationships.     

智能制造有利于增加企业技术创新投入吗——基于智能制造试点的准自然实验

智能制造是我国制造业转型升级的主攻方向,而如何准确客观评价智能制造的实施效果成为亟待解决的问题.从技术创新投入视角,将智能制造试点政策实施视为一次准自然实验,采用2011-2019年制造业上市企业面板数据,通过PSM-DID方法研究智能制造对企业技术创新投入的影响.研究结果表明,智能制造显著影响企业技术创新投入,即通过提升客户集成度、供应商集成度进而提高企业技术创新投入,且促进作用仅发生在民营企业和非高新技术行业中.该发现扩展了现有研究外延,同时,为解决当前国际困局和促进智能制造的持续推广提供了重要实践价值.     

The Effect of Fuel Price Changes on Fleet Demand for New Vehicle Fuel Economy

New vehicle purchases by private companies and government agencies, or ‘fleet’ buyers, represent a significant percentage of overall new vehicle sales in the United States. Yet little is known about fleet demand for new vehicle fuel economy including how it responds to fuel price changes. Using unique disaggregated data on fleet and household registrations of new vehicles from 2009 to 2016, we estimate how fleet demand for new vehicle fuel economy responds to fuel price changes. We find that fleet purchases of low fuel economy vehicles fall relative to high fuel economy vehicles when gasoline prices increase, a finding that is consistent with fleet buyers’ taking into account capitalization of fuel costs in the second‐hand market. Our estimates imply that raising gasoline prices by one dollar would increase fuel economy of new vehicles acquired by fleet buyers by 0.33 miles per gallon. We estimate a similar response for household buyers during the same period. This result justifies basing fuel economy responses to fuel cost changes on household data alone, an assumption widely used in the vehicle demand literature and the fuel economy valuation literature. We also find, however, that the response to fuel price changes varies across the types of fleet buyers: rental companies respond strongly to fuel price changes, whereas commercial and government buyers are insensitive. Our estimates imply that an increase in the federal gasoline tax would modestly increase fuel economy of vehicles bought by households and rental companies but would have little to no impact on fuel economy of vehicles bought by non‐rental companies and governments.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.