DESPERATE TIMES CALL FOR DESPERATE MEASURES: GOVERNMENT SPENDING MULTIPLIERS IN HARD TIMES

We investigate state-dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the U.S. economy is in a slack state. When the endogenized slack state is estimated as the period of the unemployment rate higher than about 12%, the estimated cumulative multipliers are significantly larger during slack periods than nonslack periods and are above unity. We also examine the possibility of time-varying regimes of slackness and find that our empirical results are robust under a more flexible framework. Our estimation results point out the importance of the heterogenous effects of fiscal policy and shed light on the prospect of fiscal policy in response to economic shocks from the current COVID-19 pandemic. (JEL C32, E62, H20, H62)     

历史记忆与族群认同

本文借鉴安东尼.史密斯的族群—象征主义理论,分别从神话、仪式两方面论述历史记忆与族群认同的关系以及族群认同的变动,认为族群认同既是情感—象征的,也是工具—政治—经济的,我们应该看到它们之间的关联以及利益、竞争的分配和规则。     

启发式认知下的会计信息价值相关性研究

通过借鉴行为经济学对投资者启发式认知方式研究的发展,本文发现,目前的会计研究基本忽视了会计信息的价值相关性与投资者在短时间内的决策特征有着密切的联系,我国的一些研究成果认为上市公司的信息披露都存在"好消息早、坏消息晚"的现象,并且提出以报表发布时间的早晚采衡量及时性的观点.对此,本文结合我国特殊的制度背景,探讨了在新信息即将发布前的短时间窗口中市场反应的具体诱因,发现投资者的心态和认知过程对会计信息的价值相关性影响较大,而报表发布时间并不构成会计信息价值相关性的影响因素,我国当前制度仍存在着不利于投资者保护的潜在风险.     

基于SaaS的中小企业信息化模式探讨

我国中小企业的信息化建设迫在眉睫.但是由于受到资金、人才、技术等资源的约束,实现信息化尚存在一定的困难.本文分析了基于SaaS的中小企业信息化模式的内涵和优势,并从中小企业主体的角度出发,指出在实施这一模式时应注意的若干问题.     

战略联盟的范围、治理与稳定性间关系的实证研究

本文从联盟伙伴间的关系视角出发,分析了战略联盟的合作范围和治理类型对伙伴间关系稳定性的影响.由于稳定性是一个抽象且难以用显性指标直接测量的变量,本文把它构建为一个二阶因子结构,并用企业间信任、相互依赖和冲突解决这三个一阶因子来度量它.在此基础上,通过问卷调查获得127家德国联盟企业的数据,并对提出的假说进行了检验,分析结果表明合资企业比非股权契约联盟更有可能享有稳定的伙伴间合作关系,而联盟的范围即联盟协议中所包含的合作活动的多少对联盟稳定性没有显著的影响.     

中国企业创建学习型组织的现状分析及对策研究——以江苏省连云港市企业为例

通过对连云港市企业创建学习型组织现状的实证调研,总结了连云港市企业在创建学习型组织过程中取得的一些经验,剖析了连云港市企业在创建学习型组织中存在的问题.最后,从普遍意义上提出了继续深化中国企业创建学习型组织的对策与建议.     

程序公平和分配公平影响联盟绩效的机理研究

对程序公平和分配公平影响联盟绩效的路径进行了分析,通过理论分析和实证研究发现,承诺是程序公平和分配公平影响联盟绩效的一个显著路径.同时,研究结论也表明:程序公平和分配公平能够提高组织在联盟中的承诺;承诺是提高联盟绩效的一个关键因素.这些结论有助于认识程序公平和分配公平影响联盟绩效的机理,使公平理论能够更好地指导联盟的实践.     

战略联盟模式选择的分析框架：资源、风险与结构模式间关系的概念模型

本文从公司在战略联盟中投入的不同种类资源的风险评价角度,综合考虑资源、风险及战略联盟的结构模式选择间的关系,形成了战略联盟结构模式选择的新的分析框架,提出了揭示三者间关系的概念模型及理论假说。本文认为,公司对战略联盟结构模式选择的偏好会受到管理者对未来公司在战略联盟中的各类风险的主观评价的影响,而公司对战略联盟投入的各类资源及其重要性是影响管理者对风险主观评价的重要因素。因此,应综合考虑战略联盟中所投入的不同种类资源及其所引发的风险大小以选择战略联盟的结构模式。     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.