教育驱动的县域城镇化与规划政策反思

县域是完成农民就近城镇化、实现城乡要素跨界配置、促进一二三产融合发展最为基础的空间单元.在一些户籍人口规模大的县域,县城公共服务设施的提标扩面是保障城镇化质量的关键,而教育设施至关重要.通过剖取皖北太和县重点研究,发现该类县城呈现出非常明显的基础教育驱动人口集聚的城镇化景观,现象背后折射出农业转移人口以家庭为单位用时空成本换代际流动的诉求,即该类城镇化的动力机制.农村学生家庭在乡城迁移过程中发生了从"半耕半工"向"半工伴读"的分工模式转变,家庭成员因就近陪读和远距离务工相结合而呈现横跨乡村—本地县城—异地大城市的时空离散.以教育为媒介,上一代在付出巨大时空成本的同时,下一代进行着"城市性"的生活方式、思维模式的社会习得,积累融入社会阶层体系的文化资本,乃至以考学作为通道,实现基于代际之上的城镇化.研究对于具体规划实践中,认识县域城镇化的特征趋势、扩大县城公共服务设施布局中教育资源的供给,提供了更为坚实的实证基础.     

The cross‐cultural study of LMX and individual employee voice: The moderating role of conflict avoidance

This article examines the role of national culture, measured by conflict avoidance, on the relationship between leader–member exchange (LMX) and individual employee voice. Using data collected from automotive industry employees in the United States and Korea, the findings show that conflict avoidance is negatively related to employee voice and also moderates the relationship between LMX and employee voice in the Korean sample. In particular, the relationship between LMX and voice becomes less positive when conflict avoidance is high. On the other hand, conflict avoidance does not have a direct effect on employee voice as well as an interactive effect with LMX on employee voice in the U.S. sample. This study, therefore, highlights the importance of the national culture in the comparative study of employee voice.     

Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.     

Investor behaviour and reaching for yield: Evidence from the sterling corporate bond market

We provide evidence on how corporate bond investors react to a change in yields, and how this behaviour differs in times of market‐wide stress. We also investigate ‘reaching for yield’ across investor types, as well as providing insights into the structure of the corporate bond market. Using proprietary sterling corporate bond transaction data, we show that insurance companies, hedge funds and asset managers are typically net buyers when corporate bond yields rise. Dealer banks clear the market by being net sellers. However, we find evidence for this behaviour reversing in times of stress for some investors. During the 2013 ‘taper tantrum’, asset managers were net sellers of corporate bonds in response to a sharp rise in yields, potentially amplifying price changes. At the same time, dealer banks were net buyers. Finally, we provide evidence that insurers, hedge funds and asset managers tilt their portfolios towards higher risk bonds, consistent with ‘reaching for yield’ behaviour.     

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.     

State‐Dependent Forward Guidance and the Problem of Inconsistent Announcements

Forward guidance can be provided as an unconditional promise, i.e. commitment to a specific low policy rate. Alternatively, the promise may include an escape clause, i.e. a condition defining the state of the economy under which the central bank would not keep such a low rate and, instead, it would revert to setting policy under discretion. The escape clause can be expressed as a threshold in terms of a specific variable. The present paper shows that, when such a threshold is expressed in terms of an endogenous variable (e.g. output, inflation), there are cases where it becomes impossible for the central bank to act in a way that is consistent with its promise. Consistency imposes limits on the policy rate that can be set since reverting immediately to the optimal discretionary rate can be incompatible with exceeding the threshold.     

企业社会责任报告与公开债务融资

本文以2016—2018年沪深两市A股非金融类上市公司为样本,研究企业社会责任报告与公开债务融资的关系.研究发现,披露企业社会责任报告显著提高了企业公开债务融资的成功率和规模;企业社会责任报告质量越高,对企业公开债务融资选择、规模的正向影响越大;相较于民营企业而言,国有企业披露社会责任报告对公开债务融资的影响更为明显.进一步研究发现,相较于应规披露、未披露企业社会责任报告的上市公司而言,自愿披露对公开债务融资具有显著正向影响;市场化水平较高的地区,披露企业社会责任报告对公开债务融资具有显著促进作用,市场化水平较低的地区则不具有明显影响.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.