Asymptotic consequences of neglected dynamics in individual effects models*

We assess the asymptotic consequences of estimating static models based on cross-section or panel data, when in reality the data are generated by a dynamic relationship, involving lagged dependent and current and lagged exogenous variables as well as individual effects. If the exogenous variable follows a stationary process, then the static estimators usually underestimate its long-run effect. This inconsistency is less severe, the higher the autocorrelation of the exogenous variable. If the exogenous variable follows a random walk with or without individual-specific drift, then the estimators are found to be consistent for the long-run effect.     

Effective HR implementation: the impact of supervisor support for policy use on employee perceptions and attitudes

Abstract

Effective implementation of human resource (HR) policies appears to depend on supervisors’ active involvement in the intervention process. Following recommendations of a recent intervention evaluation framework, we examine how perceived supervisor support during the implementation of a work-life intervention helps to change participants’ perceptions of organizational family supportiveness, and how this ultimately changes participants’ engagement and turnover intentions. A three-wave longitudinal study in a professional services firm (N = 434) that has formally involved supervisors in the process of a work-life intervention showed support for our study hypotheses. Supervisor support for policy use influenced employees’ positive work–home culture perceptions, which in turn strengthened employees’ work engagement and diminished their turnover intentions over time. We discuss practical implications and give recommendations on the future design of HR interventions and related policies.     

Strictness of leniency programs and asymmetric punishment effect

We analyze the impact of leniency programs on the behavior of firms participating in illegal cartel agreements in a two-stage repeated game model. Our approach takes into account asymmetric punishment effect and allows to discuss the design of leniency programs in the setting with asymmetries. The main contribution of the paper is that we consider heterogeneous firms. This heterogeneity results in additional costs in case of disclosure of the cartel, which are caused by asymmetric punishments. Next, following current antitrust rules, we analyze effects of the strictness of leniency programs, which reflects the likelihood of getting a complete exemption from fine even in case many firms self-report simultaneously. Our main conclusion is that leniency programs work better for small companies, since a lower rate of law enforcement is needed in order to induce self-reporting by smaller firms, while big firms are less likely to start a cartel in the first place given the possibility of self-reporting in future. Finally, we analyze optimal enforcement strategies of the antitrust authority and conclude that the authority with limited resources should implement more generous leniency rules the more cartelized the economy is.     

Estimating structural equation models with non-normal variables by using transformations

We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution-free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non-normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.     

Strongly stable networks

We analyze the formation of networks among individuals. In particular, we examine the existence of networks that are stable against changes in links by any coalition of individuals. We show that to investigate the existence of such strongly stable networks one can restrict focus on a component-wise egalitarian allocation of value. We show that when such strongly stable networks exist they coincide with the set of efficient networks (those maximizing the total productive value). We show that the existence of strongly stable networks is equivalent to core existence in a derived cooperative game and use that result to characterize the class of value functions for which there exist strongly stable networks via a “top convexity” condition on the value function on networks. We also consider a variation on strong stability where players can make side payments, and examine situations where value functions may be non-anonymous—depending on player labels.     

Are you really doing good things in your boss's eyes? Interactive effects of employee innovative work behavior and leader–member exchange on supervisory performance ratings

Organizations increasingly depend on employee efforts to innovate. However, the quality of relationships between leaders and employees may affect the recognition that employees receive for their innovative work behaviors. Drawing from a social cognition perspective, we tested a model in which leader–member exchange (LMX) moderates the impact of employee innovative work behavior on supervisory ratings of employee performance. Results from two multisource studies combining self, colleague, and supervisor ratings consistently showed that employees receive more favorable performance ratings by engaging in innovative work behavior when they have high‐quality LMX relationships. Moreover, we found that this interactive relationship was mediated by leader perceptions of innovative employee efforts, providing support for a moderated mediation model. Implications for the literatures on performance appraisal, LMX, and innovation are discussed.     

Making the ‘Invisible’ Visible: Redevelopment‐induced Displacement of Migrants in Shenzhen,China

Gentrification in China is intertwined with urban redevelopment, which causes the large‐scale displacement of rural–urban migrants from ‘villages in the city’ (ViCs). Because of the informality of ViCs, migrant renters have very insecure tenancy and during redevelopment they are treated as a negligible (‘invisible’) social group. As they are very difficult to locate after displacement, they are also literally invisible to researchers. To make the invisible visible, this study traced a sample of displaced migrants from Huangbeiling village in Shenzhen. The focus was on the displacement process and on identifying the consequences for the displaced. We found various forms of displacement during the redevelopment process. Nearby ViCs were prioritized by displaced migrants to minimize as much utility loss as possible. However, they generally suffer from decreased proximity, increased living costs, and the loss of social networks and job opportunities. Remarkably, some choose to return to the gentrifying village, enduring displacement in situ caused by increasing rents, drastic physical neighbourhood changes and declining liveability, in exchange for retaining their original social and economic networks. Large‐scale urban redevelopment is causing the rapid shrinkage of informal housing. Recognizing and addressing the housing needs of this impoverished social group is a matter of urgency.     

Measuring volatility with the realized range

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson [1980. The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 61–65] we replace each squared intra-day return by the high–low range for that period to create a novel and more efficient estimator called the realized range. In addition, we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level of the daily range. Simulation experiments demonstrate that for plausible levels of non-trading and bid–ask bounce the realized range has a lower mean-squared error than the realized variance, including variants thereof that are robust to microstructure noise. Empirical analysis of the S&P500 index-futures and the S&P100 constituents confirms the potential of the realized range.