Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

Alliance behaviour, absorptive capacity and competitive advantage: a comparative study of biopharmaceutical firms in Europe and the US

In this thesis, we explore the relationship between absorptive capacity and alliances, and their influence on firms’ competitive advantage in the US and European biopharmaceutical sectors. The study undertaken in this thesis is based on data from a large-scale international survey of over 2,500 biopharmaceutical firms in the US, the UK, Germany, France and Ireland. The thesis advanced a conceptual framework, which integrated the multi-dimensions of absorptive capacity, exploration-exploitation alliances, and competitive advantage, into a biopharmaceutical firm’s new product development process. The proposed framework is then tested in the empirical analysis, using truncated models to estimate firms’ sales growth, with zero-inflated negative binominal models capturing the number of alliances in which firms engage, and aspects of realised absorptive capacity analysed by ordinal probit models. The empirical results suggest that both skill-based and exploitation-based absorptive capacity play crucial roles in shaping firms’ competitive advantage, while neither exploratory nor exploitation alliances contribute to the improvement in firms’ competitive position. In terms of the interaction between firms’ absorptive capacity and alliance behaviour, the results suggest that engagement with exploratory alliances depends more strongly on firms’ assimilation capability (skills levels and continuity of R&D activities), while participation in exploitation alliances is more conditional on firms’ relevant knowledge monitoring capability. The results highlight the major differences between the determinants of firms’ alliance behaviour, and competitive advantage in the US and Europe – in the US firms’ skill levels prove more significant in determining firms’ engagement with exploratory alliances, whereas in Europe continuity of R&D proves more important. Correspondingly, while US firms’ engagement with exploitation alliances depends on market monitoring capability, that in Europe is more strongly linked to exploitation-based absorptive capacity. In respect of the determinants of firms’ competitive advantage – in Europe, market monitoring capability, engagement with exploitation alliances, and continuous R&D activities, prove more important, while in the US, it is firms’ market characteristics that matter most.

Effects of roadway geometric features on run-off road crashes on the Florida State Highway System

Run-off-road (ROR) crashes have increasingly become a serious concern for transportation officials in the State of Florida. These types of crashes have increased proportionally in recent years statewide and have been the focus of the Florida Department of Transportation. The goal of this research was to develop statistical models that can be used to investigate the possible causal relationships between roadway geometric features and ROR crashes on Florida's rural and urban principal arterials. ^ In this research, Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) Regression models were used to better model the excessive number of roadway segments with no ROR crashes. Since Florida covers a diverse area and since there are sixty-seven counties, it was divided into four geographical regions to minimize possible unobserved heterogeneity. Three years of crash data (2000–2002) encompassing those for principal arterials on the Florida State Highway System were used. Several statistical models based on the ZIP and ZINB regression methods were fitted to predict the expected number of ROR crashes on urban and rural roads for each region. Each region was further divided into urban and rural areas, resulting in a total of eight crash models. A best-fit predictive model was identified for each of these eight models in terms of AIC values. The ZINB regression was found to be appropriate for seven of the eight models and the ZIP regression was found to be more appropriate for the remaining model. To achieve model convergence, some explanatory variables that were not statistically significant were included. Therefore, strong conclusions cannot be derived from some of these models. ^ Given the complex nature of crashes, recommendations for additional research are made. The interaction of weather and human condition would be quite valuable in discerning additional causal relationships for these types of crashes. Additionally, roadside data should be considered and incorporated into future research of ROR crashes. ^

Glycemic control: Risk factors, quality of life, workforce participation, and mortality among US adults with type 2 diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, (1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) .7%), borderline (HbA1c 7-8.9%), and poor (HbA1c .9%) glycemic control and potentially new risk factors (e.g. work characteristics), and (2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and (3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.^

Influence of religious coping on the substance use and HIV risk behaviors of recent Latino immigrants

This study examines the influence of acculturative stress on substance use and HIV risk behaviors among recent Latino immigrants. The central hypothesis of the study is that specific religious coping mechanisms influence the relationship that acculturative stress has on the substance use and HIV-risk behaviors of recent Latino immigrants. Within the Latino culture religiosity is a pervasive force, guiding attitudes, behaviors, and even social interactions. When controlling for education and socioeconomic status, Latinos have been found to use religious coping mechanisms more frequently than their Non-Latino White counterparts. In addition, less acculturated Latinos use religious coping strategies more frequently than those with higher levels of acculturation. Given its prominent role in Latino culture, it appears probable that this mechanism may prove to be influential during difficult life transitions, such as those experienced during the immigration process. This study examines the moderating influence of specific religious coping mechanisms on the relationship between acculturative stress and substance use/HIV risk behaviors of recent Latino immigrants. Analyses for the present study were conducted with wave 2 data from an ongoing longitudinal study investigating associations between pre-immigration factors and health behavior trajectories of recent Latino immigrants. Structural equation and zero-inflated Poisson modeling were implemented to test the specified models and examine the nature of the relationship among the variables. Moderating effects were found for negative religious coping. Higher levels of negative religious coping strengthened an inverse relationship between acculturative stress and substance use. Results also indicated direct relationships between religious coping mechanisms and substance use. External and positive religious coping were inversely related to substance use. Negative religious coping was positively related to substance use. This study aims to contribute knowledge of how religious coping influence's the adaptation process of recent Latino immigrants. Expanding scientific understanding as to the function and effect of these coping mechanisms could lead to enhanced culturally relevant approaches in service delivery among Latino populations. Furthermore this knowledge could inform research about specific cognitions and behaviors that need to be targeted in prevention and treatment programs with this population.

Glycemic Control: Risk Factors, Quality of Life, Workforce Participation, and Mortality Among US Adults with Type 2 Diabetes

Despite research showing the benefits of glycemic control, it remains suboptimal among adults with diabetes in the United States. Possible reasons include unaddressed risk factors as well as lack of awareness of its immediate and long term consequences. The objectives of this study were to, using cross-sectional data, 1) ascertain the association between suboptimal (Hemoglobin A1c (HbA1c) ≥7%), borderline (HbA1c 7-8.9%), and poor (HbA1c ≥9%) glycemic control and potentially new risk factors (e.g. work characteristics), and 2) assess whether aspects of poor health and well-being such as poor health related quality of life (HRQOL), unemployment, and missed-work are associated with glycemic control; and 3) using prospective data, assess the relationship between mortality risk and glycemic control in US adults with type 2 diabetes. Data from the 1988-1994 and 1999-2004 National Health and Nutrition Examination Surveys were used. HbA1c values were used to create dichotomous glycemic control indicators. Binary logistic regression models were used to assess relationships between risk factors, employment status and glycemic control. Multinomial logistic regression analyses were conducted to assess relationships between glycemic control and HRQOL variables. Zero-inflated Poisson regression models were used to assess relationships between missed work days and glycemic control. Cox-proportional hazard models were used to assess effects of glycemic control on mortality risk. Using STATA software, analyses were weighted to account for complex survey design and non-response. Multivariable models adjusted for socio-demographics, body mass index, among other variables. Results revealed that being a farm worker and working over 40 hours/week were risk factors for suboptimal glycemic control. Having greater days of poor mental was associated with suboptimal, borderline, and poor glycemic control. Having greater days of inactivity was associated with poor glycemic control while having greater days of poor physical health was associated with borderline glycemic control. There were no statistically significant relationships between glycemic control, self-reported general health, employment, and missed work. Finally, having an HbA1c value less than 6.5% was protective against mortality. The findings suggest that work-related factors are important in a person’s ability to reach optimal diabetes management levels. Poor glycemic control appears to have significant detrimental effects on HRQOL.

Data, Software, and Advanced Computational Usage of University of Washington Research Leaders

Scientific research is increasingly data-intensive, relying more and more upon advanced computational resources to be able to answer the questions most pressing to our society at large. This report presents findings from a brief descriptive survey sent to a sample of 342 leading researchers at the University of Washington (UW), Seattle, Washington in 2010 and 2011 as the first stage of the larger National Science Foundation project “Interacting with Cyberinfrastructure in the Face of Changing Science.” This survey assesses these researcher’s use of advanced computational resources, data, and software in their research. We present high-level findings that describe UW researchers’: demographics, interdisciplinarity, research groups, data use, software and computational use—including software development and use, data storage and transfer activities, and collaboration tools, and computing resources. These findings offer insights into the state of computational resources in use during this time period as well as offering a look at the data intensiveness of UW researchers.

Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams

Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence–absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.

Is size structure a good measure of future trends of plant populations? an empirical approach using five woody species from the Cerrado (Brazilian savanna)

Size distributions in woody plant populations have been used to assess their regeneration status, assuming that size structures with reverse-J shapes represent stable populations. We present an empirical approach of this issue using five woody species from the Cerrado. Considering count data for all plants of these five species over a 12-year period, we analyzed size distribution by: a) plotting frequency distributions and their adjustment to the negative exponential curve and b) calculating the Gini coefficient. To look for a relationship between size structure and future trends, we considered the size structures from the first census year. We analyzed changes in number over time and performed a simple population viability analysis, which gives the mean population growth rate, its variance and the probability of extinction in a given time period. Frequency distributions and the Gini coefficient were not able to predict future trends in population numbers. We recommend that managers should not use measures of size structure as a basis for management decisions without applying more appropriate demographic studies.

The Edinburgh-Cape Blue Object Survey - I. Description of the survey

The Edinburgh-Cape Blue Object Survey is a major survey to discover blue stellar objects brighter than B similar to 18 in the southern sky. It is planned to cover an area of sky of 10 000 deg(2) with \b\ > 30 degrees and delta < 0 degrees. The blue stellar objects are selected by automatic techniques from U and B pairs of UK Schmidt Telescope plates scanned with the COSMOS measuring machine. Follow-up photometry and spectroscopy are being obtained with the SAAO telescopes to classify objects brighter than B = 16.5. This paper describes the survey, the techniques used to extract the blue stellar objects, the photometric methods and accuracy, the spectroscopic classification, and the limits and completeness of the survey.

Electrical transport properties of CuS single crystals

Electrical resistivity, transverse magnetoresistance and thermoelectric power measurements were performed on CuS high quality single crystals in the range 1.2-300 K and under fields of up to 16 T. The zero field resistivity data are well described below 55 K by a quasi-2D model, consistent with a carrier confinement at lower temperatures, before the transition to the superconducting state. The transverse magnetoresistance develops mainly below 30 K and attains values as large as 470% for a 16 T field at 5 K, this behaviour being ascribed to a band effect mechanism, with a possible magnetic field induced DOS change at the Fermi level. The transverse magnetoresistance shows no signs of saturation, following a power law with field Delta rho/rho(0) proportional to H(1.4), suggesting the existence of open orbits for carriers at the Fermi surface. The thermoelectric power shows an unusual temperature dependence, probably as a result of the complex band structure of CuS.

Double coverage and demand for health care: Evidence from quantile regression

An individual experiences double coverage when he bene ts from more than one health insurance plan at the same time. This paper examines the impact of such supplementary insurance on the demand for health care services. Its novelty is that within the context of count data modelling and without imposing restrictive parametric assumptions, the analysis is carried out for di¤erent points of the conditional distribution, not only for its mean location. Results indicate that moral hazard is present across the whole outcome distribution for both public and private second layers of health insurance coverage but with greater magnitude in the latter group. By looking at di¤erent points we unveil that stronger double coverage e¤ects are smaller for high levels of usage. We use data for Portugal, taking advantage of particular features of the public and private protection schemes on top of the statutory National Health Service. By exploring the last Portuguese Health Survey, we were able to evaluate their impacts on the consumption of doctor visi

Trends in death from circulatory diseases in Brazil between 1979 and 1996

OBJECTIVE: To analyze the trends in mortality due to circulatory diseases in men and women aged > or = 30 years in Brazil from 1979 to 1996. METHODS: We analyzed population count data obtained from the IBGE Foundation and mortality data obtained from the System of Information on Mortality of the DATASUS of the Ministry of Health. RESULTS: Circulatory diseases, ischemic heart disease, and cerebrovascular disease were the major causes of death in men and women in Brazil. The standardized age coefficient for circulatory disease in men aged > or = 30 years ranged from 620 to 506 deaths/100,000 inhabitants and in women from 483 to 383 deaths/100,000 inhabitants for the years 1979 and 1996, respectively. In men, the mean coefficient for the period was 586.25 deaths with a significant trend towards a decrease (P<0.001) and a decline of 8.25 deaths/year. In women, the mean coefficient for the period was 439.58 deaths, a significant trend towards a decrease (P<0.001) and a rate of decline of 7.53 deaths/year. The same significant trend towards a decrease in death (P<0.001) was observed for ischemic heart disease and cerebrovascular disease. Risk of death from these causes was always higher for men of any age group (P<0.001). Cerebrovascular disease was the primary cause of death in women. CONCLUSION: Although circulatory diseases have been the major cause of mortality in men and women in the Brazilian population, with a greater participation by cerebrovascular diseases, a trend towards a decrease in the risk of death from these causes is being observed.

Industrial location at a local level: some comments about the territorial level of the analysis

This paper contributes to the existing literature on industrial location by discussing some issues regarding the territorial levels that have been used in location analysis. We analyse which could be the advantages and disadvantages of performing locational analysis at a different local levels. We use data for new manufacturing firms located at municipality, county and travel to work areas level. We show that location determinants vary according to the territorial level used in the analysis, so we conclude that the level at which we perform the investigation should be carefully selected. Keywords: industrial location, cities, agglomeration economies, count data models.

Agglomeration and location: a nonparametric approach

The aim of this article is to assess the effects of several territorial characteristics, specifically agglomeration economies, on industrial location processes in the Spanish region of Catalonia. Theoretically, the level of agglomeration causes economies which favour the location of new establishments, but an excessive level of agglomeration might cause diseconomies, since congestion effects arise. The empirical evidence on this matter is inconclusive, probably because the models used so far are not suitable enough. We use a more flexible semiparametric specification, which allows us to study the nonlinear relationship between the different types of agglomeration levels and location processes. Our main statistical source is the REIC (Catalan Manufacturing Establishments Register), which has plant-level microdata on location of new industrial establishments. Keywords: agglomeration economies, industrial location, Generalized Additive Models, nonparametric estimation, count data models.