Queues with batch poisson arrivals and hyper-exponential service time distribution

Summary We consider in this paper the transient behaviour of the queuing system in which (i) the input, following a Poisson distribution, is in batches of variable numbers; (ii) queue discipline is ‘first come first served’, it being assumed that the batches are pre-ordered for service purposes; and (iii) service time distribution is hyper-exponential withn branches. The Laplace transform of the system size distribution is determined by applying the method of generating functions, introduced in queuing theory byBailey [1]. However, assuming steady state conditions to obtain, the problem is completely solved and it is shown that by suitably defining the traffic intensity factor,ϱ, the value,p ₀, of the probability of no delay, remains the same in this case of batch arrivals also as in the case of single arrivals. The Laplace transform of the waiting time distribution is also calculated in steady state case from which the mean waiting time may be calculated. Some of the known results are derived as particular cases.     

Does conditionality in IMF-supported programs promote revenue reform?

This paper studies whether revenue conditionality in Fund programs had any impact on the revenue performance of 126 low- and middle-income countries during 1993–2013. The results indicate that such conditionality had a positive impact on tax revenue, with strongest improvement felt on taxes on goods and services, including the VAT. Revenue conditionality matters more for low-income countries, particularly those where revenue ratios are below the group average. Moreover, revenue conditionality appears to be more effective when targeted to a specific tax. These results hold after controlling for potential endogeneity, sample selection bias, and when revenues are adjusted for economic cycle.     

DO SCANDALS TRIGGER GOVERNANCE CHANGES? EVIDENCE FROM OPTION BACKDATING

We examine whether firms charged with backdating option grants make discernible changes to board structure and activity and whether such changes help recoup value losses from the revelation of option backdating. We find that these firms increased board size, reduced duality, and increased board independence. In addition, the boards and the compensation committees of these firms experienced significant increases in meeting frequency. We also find that firms in the same sectors that had not been identified as backdating option grants experienced similar changes in board activity and some elements of board structure. Additional analysis reveals that increases in board size, chief excutive officer turnover, and the meeting frequency of the audit committee are related to buy‐and‐hold abnormal returns in the postscandal period.     

A Framework for Understanding Corporate Social Responsibility Programs as a Continuum: An Exploratory Study

Corporate Social Responsibility (CSR) programs are increasingly popular corporate marketing strategies. This paper argues that CSR programs can fall along a continuum between two endpoints: Institutionalized programs and Promotional programs. This classification is based on an exploratory study examining the variance of four responses from the consumer stakeholder group toward these two categories of CSR. Institutionalized CSR programs are argued to be most effective at increasing customer loyalty, enhancing attitude toward the company, and decreasing consumer skepticism. Promotional CSR programs are argued to be more effective at generating purchase intent. Ethical and managerial implications of these preliminary findings are discussed. Julie Pirsch, Ph.D., is an Assistant Professor of Marketing at Villanova University. She researches in the areas of cause-related marketing, corporate social responsibility, and new product development. Shruti Gupta, Ph.D., is an Assistant Professor of Management at The Pennsylvania State University at Abington, in Abington, Pennsylvania. Dr. Gupta’s research interests lie in the area of corporate social responsibility, cause-related marketing, environmental consumerism, and social marketing issues. Stacy Landreth, Ph.D., is an Assistant Professor of Marketing at the University of North Texas. She researches in the areas of cause-related marketing and social marketing alliances, as well as advertising source effects.     

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.     

Tax evasion and financial repression

Using a simple overlapping generations framework, calibrated to four Southern European countries, we analyze the relationship between tax evasion, determined endogenously, and financial repression. We show that higher degree of tax evasion within a country, resulting from a higher level of corruption and a lower penalty rate, yields higher degrees of financial repression as a social optimum. However, a higher degree of tax evasion, due to a lower tax rate, reduces the severity of the financial restriction.     

Leveraging Information Across Categories

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products—Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction.     

Misspecified polynomial detrending

This letter shows that substantial errors result in linear detrending of the data when a linear trend is inappropriate. These errors increase without bound with increasing sample size! Also, the reliability of the linear trend is overstated.