工资对个人劳动供给的非线性影响：部门市场化程度差异视角

从市场经济体制的建立到经济社会高质量发展，我国不同部门市场化程度有所差异，劳动供给的部门异质性随之体现。基于中国劳动力动态调查数据，结合汉森门槛回归模型和劳动供给模型，依托部门市场化程度的差异性，文章探讨了工资对劳动力个人供给的非线性影响及其部门异质性。主要得出以下研究结论：第一，我国市场部门与公共部门劳动力的个人劳动供给有所差异。第二，工资与个人劳动供给呈现非线性关系并且存在部门异质性。实证分析结果显示，工资对个人劳动供给的影响存在三个"突变点"，工资门槛均使得工资"激励效应"加强并呈现逐步增加的趋势。第三，劳动供给的工资门槛存在性别、城乡和行业异质性。第四，随着年龄增长、人力资本积累，个人劳动时间供给趋于降低，签署书面劳工合同使得市场部门劳动力工作时间显著增加，而政治面貌显著影响公共部门劳动供给。总体来看，无论是市场部门还是公共部门，我国劳动力个人供给曲线均未"向后弯曲"，国民经济高质量发展、改善收入分配格局、提升居民幸福感等议题需持续关注。     

央行沟通与通货膨胀预期

通过合理政策有效引导通货膨胀预期对当前中国经济具有重要现实意义.本文用自回归分布滞后模型和EGARCH模型捕捉央行沟通对通胀预期及其波动的影响.实证结果表明:央行沟通传递的货币政策信息被市场有效捕捉和解读,实现了通胀预期管理目的,增加沟通频率能有效降低通胀预期波动;但央行的"言行偏离"加剧了短期通胀预期波动,降低了央行沟通和货币政策操作的实际效果;通胀预期存在分布厚尾性和右偏斜,市场出现极端预期的概率较大,且更倾向于预期通胀上升,通胀预期波动的群聚特性表明其具有阶段性自我强化特征.研究结论为央行沟通的有效性提供了实证支撑,揭示出的通胀预期分布特征对提高央行预期管理措施的针对性具有参考价值.     

Analytical approximations of local‐Heston volatility model and error analysis

This paper studies the expansion of an option price (with bounded Lipschitz payoff) in a stochastic volatility model including a local volatility component. The stochastic volatility is a square root process, which is widely used for modeling the behavior of the variance process (Heston model). The local volatility part is of general form, requiring only appropriate growth and boundedness assumptions. We rigorously establish tight error estimates of our expansions, using Malliavin calculus. The error analysis, which requires a careful treatment because of the lack of weak differentiability of the model, is interesting on its own. Moreover, in the particular case of call–put options, we also provide expansions of the Black–Scholes implied volatility that allow to obtain very simple formulas that are fast to compute compared to the Monte Carlo approach and maintain a very competitive accuracy.     

Who do you care about? Scientists’ personality traits and perceived impact on beneficiaries

We investigate the roots of scientists' perceptions of the impact of their work by examining stable psychological characteristics such as personality traits. An analysis of personality traits highlights the effects of policies related to gender equality, allocation of research time and skills acquisition. It improves our understanding of the conflicts related to scientists’ perceptions of the impact of their research on beneficiaries. For example, conscientiousness increases the perceived impact on clinical beneficiaries, but reduces the perceived impact on industrial beneficiaries. Organizational scientific freedom increases the effects of personality traits on perceived impact on beneficiaries such that scientists affiliated to a university are less likely than colleagues working in other research settings to perceive the simultaneous impact of their work on both industrial and clinical beneficiaries.     

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.     

Varieties of paternalism and the heterogeneity of utility structures

A principal source of interest in behavioral economics has been its advertised contributions to policies aimed at ‘nudging’ people away from allegedly natural but self-defeating behavior toward patterns of response thought more likely to improve their welfare. This has occasioned controversies among economists and philosophers around the normative limits of paternalism, especially by technical policy advisors. One recent suggestion has been that ‘boosting,’ in which interventions aim to enhance people’s general cognitive skills and representational repertoires instead of manipulating their choice environments behind their backs, avoids the main normative challenges. A limitation in most of this literature is that it has focused on relatively sweeping policy recommendations and consequently on strong polar alternatives of general paternalism and strict laissez faire. We review a real instance, drawn from a consulting project we conducted for an investment bank, of a proposed intervention that is more typical of the kind that economists are more often actually called upon to offer. In this example, the sophistication of current tools for preference attribution, combined with philosophical externalism about the semantics of preferences that makes it less plausible to attribute their literal self-conscious representation to people as propositional attitude content becomes more tightly refined, blocks applicability of the distinction between nudging and boosting. This seems to call for irreducible, context-specific ethical judgment in assessing the appropriateness of the forms of paternalism that economists must actually wrestle with in going about their everyday business.     

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.     

Advertising and brand attitudes: Evidence from 575 brands over five years

Quantitative Marketing and Economics - Little is known about how different types of advertising affect brand attitudes. We investigate the relationships between three brand attitude variables...     

The Impact of Automated Reminders on Credit Outcomes: Results from an Experimental Pilot Program

This study evaluates the impact of an automated reminder program designed to help credit counseling consumers manage their payment obligations and financial goals. Credit counseling consumers were randomly assigned to receive reminders linked to their financial goals and payment obligations for one year after an initial credit counseling session. We find that consumers offered reminders were 21% less likely than the control to experience severe (60+ day) payment delinquencies and were 12% less likely to experience a 30+ day delinquency. At the same time, these consumers saw a 10.5 point increase in credit scores relative to the control group. There were no significant impacts on total credit card balances or installment debt levels. This analysis provides promising evidence that automated reminders can provide an important complement to traditional credit counseling when it comes to improving consumers' credit profiles and does so at a fairly low cost.