The major determinants of logistic performance in a global perspective: evidence from panel data analysis

The global logistics industry has grown significantly and logistics has become an important sector of the business economic system and a major global economic activity in recent years. Logistics activities accelerate economic and productivity growth. Efficient logistics is also important to a country’s competitiveness and source of employment. The purpose of this paper is to uncover and understand the major determinants of logistics performance (LP) to further lift the LP of countries. Using unbalanced panel data of 93 selected countries from 2007 to 2014, the present research attempts to critically investigate the major determinants of LP. In estimating the model, this study prefers to use static panel data approach owing to limited data. The findings of the present study reveal that (a) countries with low level of corruption and stable political environment are likely to yield a high level of LP; (b) improvement in resources supply such as infrastructure, technology, labour, and education also have a significant positive effect on LP. Therefore, institutional reforms and upgrading resources will effectively accelerate LP.     

Using a dual-frame design to improve phone surveys on political attitudes: developing a weighting strategy for limited external information in Hong Kong

Quality & Quantity - In recent years, rapid increases in mobile phone ownership and decreases in landline users have led to potential biases in landline phone survey estimations. Mobile-only...     

Contagion or Inductance? Crisis 1997 Reconsidered

We provide the simple example of a refinancing game with incomplete information, where the lack of transparency is both necessary and sufficient for the propagation of local financial distress across disjoint financial networks. JEL Classification Number: F4.
And the truth shall make you free. John 8: 32     

A Conceptual Model of the Household's Housing Decision–Making Process: The Economic Perspective

At some stage in every household's family life cycle, the household is likely to make certain decisions with regard to its housing environment. Although the household's housing decisions essentially comprise the decision to move, and the selection of a new residence, the process from which these decisions are derived is very complex because varying circumstances could produce a multitude of different housing decisions. Due to the sheer complexity of the household's housing decision–making process, researchers have generally focused on individual decision–making stages rather than on the process as a whole. This paper therefore attempts to conceptualize the entire household housing decision–making process using theoretical concepts from the economic perspective, and then tests the conceptual model with empirical evidence drawn from the moving population within the multi–racial society in Singapore.     

The economics of shortage and problems of reform in Chinese industry

This paper employs the economics of shortage framework to examine post-Mao reforms in Chinese state-owned industry. Performance has been disappointing because reforms through 1985 failed to fundamentally alter economic agent behavior. The “soft” budget constraints at the enterprise and local government levels continue to generate “quantity” and “expansion” drives. The resultant inflationary pressures have necessitated administrative interventions and thwarted reform progress. The Maoist legacy of active participation by local governments in economic management is high-lighted as a major obstacle to the elimination of paternalism in state-enterprise relations.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

Real Investment Implications of Employee Stock Option Exercises

This paper examines a real cost of awarding employee stock options. Based on the observation that managers are extremely concerned about earnings-per-share dilution in equity related compensation, we predict and find that firms experiencing significant employee stock option (ESO) exercises shift resources away from real investments towards the repurchase of their own stocks. We further find weak evidence of a decline in subsequent firm performance (as measured by return on assets) for several years following the cut in discretionary investments as a result of stock option exercises, though this result is sensitive to the metric used to measure performance. Collectively, our findings indicate that ESO exercises potentially impose a real cost on the firm in terms of foregone investment opportunities.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.