On Comparing Alternative Advertising Policies of Pulsation*

In this paper, five alternative advertising policies that belong to the advertising pulsation class are compared analytically for linear and concave response functions using a modified version of the Vidale-Wolfe model. The results of the research show that (1) For both linear and concave response functions, advertising pulsing/maintenance policy dominates advertising pulsing policy but is dominated by the Uniform Advertising Policy. For convex response functions, the order of dominance is reversed. (2) For linear response functions, uniform advertising policy dominates the impulse advertising policy but is dominated by the chattering advertising policy. (3) For concave response functions, uniform advertising policy dominates both the impulse advertising policy and the chattering advertising policy. (4) For convex response functions, chattering advertising policy dominates both the advertising pulsing policy and the impulse advertising policy. The Vidale-Wolfe model is estimated using the well-known Lydia Pinkham data. Optimality analysis shows that the company was overadvertising about half of the time studied. Overadvertising seems to have produced appreciable gain in sales and created significant barriers to competitive entry at a little cost in terms of foregone profits.     

Faculty Performance Evaluation: Modeling to Improve Personnel Decisions*

Based on likely future changes in faculty evaluation practices, this paper examines how institutions of higher education might operationalize performance evaluation as related to research, teaching, and service. A model is developed that allows coupling performance evaluation and relevant market considerations with merit pay, tenure, and promotion decisions. The approach is specifically applicable to tenure-track faculty members in colleges and universities.     

Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.     

Disagreement over the immigration of low-income earners in a welfare state

This paper studies natives’ economically motivated preferences over different levels of immigration of low-income earners. Immigration affects natives through both intra- and intergenerational redistribution programmes and in the labour market. Our analysis suggests, in a welfare state that looks after the poor and the aged, economic motivation does not necessarily lead a native to have an extreme opinion on the preferable level of immigration, although it causes disagreement among natives. We find, regardless of parameter values, high-income earners prefer at least as much immigration as low-income earners who, in turn, prefer at least as much immigration as pensioners. The median voter is then likely to be a low-income native.

Olympic rankings based on objective weighting schemes

In this paper, we propose an objective principal components weighting scheme for all-time Winter Olympic gold, silver and bronze medals based solely on the number of medals won. Our results suggest that the approximately equal weights be assigned (or the total medal counts be used regardless of color) if all of the three medal types are retained for ranking purposes. When the proposed methodology is tested against five alternative weighting schemes that have been suggested in the literature using the results for the 2010 Vancouver Winter Olympics, we find a significant agreement in the country rankings. Furthermore, our implementation of principal components variable reduction strategy results in the identification of silver as the best representative medal count for parsimonious Winter Olympics rankings.KEYWORDS: Olympic rankings, principal components analysis, variable reduction strategy, medal counts, objective weighting schemeJEL Classifications: C18, C38, C43     

Use of Poset Theory with Big Datasets: A New Proposal Applied to the Analysis of Life Satisfaction in Italy

The aim of this work is to propose a tool for measuring a complex concept, and to apply it to big sets of data measured on ordinal and/or dichotomous scales. An important field of application are subjective data, that are often based on opinions or personal evaluations. Many national and international surveys employ this kind of data, measured among thousand of individuals. Thanks to the use of the “average rank” as a synthetic measure of a complex concept, we believe that poset theory could be a very useful approach for dealing with ordinal data avoiding the use of scaling procedures. Because classic poset approaches are at their best when applied to few data at a time, our idea is based on a procedure for sampling units from a big population using a simple criterion to summarize the resulting values appropriately. Applying the central limit theorem enables a comparison of the results obtained from different groups using statistical tests on the means. We used our Height of Groups by Sampling (HOGS) method to compare the average rank among groups that are defined by one or more socio-demographic variables influencing the level of the complex concept we wish to measure. The application of the HOGS procedure to life satisfaction in Italy generated convincing results, revealing significant differences between regions, genders and levels of formal education. We compared the results given by HOGS with the non linear principal component analysis and obtain an easy readable output with convincing precision and accuracy; we are confident that the HOGS procedure can be applied to many other concepts investigated in the social sciences.     

The Role of Internal and External Complexity in Global Factory Performance: An NKC Application

The global factory literature suggests that MNCs can take advantage of global operations by extensively offshoring and outsourcing activities. However, the added difficulty for the lead firm to coordinate the resulting complex structure is often underestimated. Evidence could be found in Boeing's 787 Dreamliner project, in which the external complexity disrupted MNC performance. Motivated by the gap between theory and practice, this study focuses on systems of MNCs connected with each other with supplier-client relationships and/or outsourcing. In particular it investigates the interplay of the internal and external complexity in such systems and how their balance affects the system performance. The study models the internal and external complexity by using the NKC-simulation methodology and adjusting it to the specific MNC context. The NKC methodology is widely used in organization theory to study complex systems. Simulations comparing the performance of MNCs that use outsourcing to different degrees indicate that a balanced level of internal and external complexity is beneficial in the context of global factory.     

FPT-algorithms for some problems related to integer programming

This paper studies a new version of the location problem called the mixed center location problem. Let P be a set of n points in the plane. We first consider the mixed 2-center problem, where one of the centers must be in P, and we solve it in \(O(n^2\log n)\) time. Second, we consider the mixed k-center problem, where m of the centers are in P, and we solve it in \(O(n^{m+O(\sqrt{k-m})})\) time. Motivated by two practical constraints, we propose two variations of the problem. Third, we present a 2-approximation algorithm and three heuristics solving the mixed k-center problem (\(k>2\)).     

Climate change and population migration in Brazil’s Northeast: scenarios for 2025–2050

This research contributes to an understanding of the relationship between climate change, economic impacts and migration. We model the long-term relationship (up to 45 years of projection) between demographic dynamics—particularly migration—driven by changes in the performance of the economy due to climate changes in the Northeast region of Brazil. The region is of particular relevance to the study of climate change impacts given its large human population (28% of Brazil’s population) and high levels of impoverishment, having an extensive semi-dry area which will be severely impacted by growing temperatures. Ultimately, the integrated model generates state- and municipal-level migration scenarios based on climate change impacts on the primary economic sectors and their articulations with other sectors. Results suggest that the predicted climate changes will impact severely the agriculture sector in the region, acting as a potential migration push factor to other regions in the country. Finally, we discuss how the increased vulnerability of some groups, particularly migrants, can be factored into Brazilian public policy and planning.